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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organisation?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

• Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Pritha Bhandari

Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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Data analysis in qualitative research, theertha raj, august 30, 2024.

While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.

This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.

What is qualitative data?

Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video. 

This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.

A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews

Importance of qualitative data

Qualitative data is vital for several reasons:

• It provides a deep, nuanced understanding of complex phenomena.
• It captures the 'why' behind behaviors and opinions.
• It allows for unexpected discoveries and new research directions.
• It puts people's experiences and perspectives at the forefront.
• It enhances quantitative findings with depth and detail.

What is data analysis in qualitative research?

Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.

This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.

How is qualitative data analysis different from quantitative data analysis?

Qualitative and quantitative data analyses differ in several key ways:

• Data type: Qualitative analysis uses non-numerical data (text, images), while quantitative analysis uses numerical data.
• Approach: Qualitative analysis is inductive and exploratory. Quantitative analysis is deductive and confirmatory.
• Sample size: Qualitative studies often use smaller samples. Quantitative studies typically need larger samples for statistical validity.
• Depth vs. breadth: Qualitative analysis provides in-depth insights about a few cases. Quantitative analysis offers broader insights across many cases.
• Subjectivity: Qualitative analysis involves more subjective interpretation. Quantitative analysis aims for objective, statistical measures.

What are the 3 main components of qualitative data analysis?

The three main components of qualitative data analysis are:

• Data reduction: Simplifying and focusing the raw data through coding and categorization.
• Data display: Organizing the reduced data into visual formats like matrices, charts, or networks.
• Conclusion drawing/verification: Interpreting the displayed data and verifying the conclusions.

These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.

How do you write a qualitative analysis?

Step 1: organize your data.

Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.

Immerse yourself in the data. Read through everything multiple times.

Step 2: Code & identify themes

Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.

Pick out direct quotes from your data to illustrate key points.

Step 3: Interpret and reflect

Explain what your results mean in the context of your research and existing literature.

Als discuss, identify and try to eliminate potential biases or limitations in your analysis. 

Summarize main insights and their implications.

What are the 5 qualitative data analysis methods?

Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.

Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.

Grounded Theory Developing theory from data through iterative coding and analysis.

Discourse Analysis Examining language use and meaning in social contexts.

Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.

Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.

What are the 4 data collection methods in qualitative research?

When it comes to collecting qualitative data, researchers primarily rely on four methods.

• Interviews : One-on-one conversations to gather in-depth information.
• Focus Groups : Group discussions to explore collective opinions and experiences.
• Observations : Watching and recording behaviors in natural settings.
• Document Analysis : Examining existing texts, images, or artifacts.

Researchers often use multiple methods to gain a comprehensive understanding of their topic.

How is qualitative data analysis measured?

Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria. 

Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor. 

Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences. 

Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.

Benefits of qualitative data analysis

The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions. 

By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context. 

Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.

Challenges of Qualitative Data Analysis

Researchers face several challenges when conducting qualitative data analysis. 

Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult. 

There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge. 

The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.

Best Software to Analyze Qualitative Data

G2 rating: 4.6/5

Pricing: Starts at $30 monthly.

Looppanel is an AI-powered research assistant and repository platform that can make it 5x faster to get to insights, by automating all the manual, tedious parts of your job. 

Here’s how Looppanel’s features can help with qualitative data analysis:

• Automatic Transcription: Quickly turn speech into accurate text; it works across 8 languages and even heavy accents, with over 90% accuracy.
• AI Note-Taking: The research assistant can join you on calls and take notes, as well as automatically sort your notes based on your interview questions.
• Automatic Tagging: Easily tag and organize your data with free AI tools.
• Insight Generation: Create shareable insights that fit right into your other tools.
• Repository Search: Run Google-like searches within your projects and calls to find a data snippet/quote in seconds
• Smart Summary: Ask the AI a question on your research, and it will give you an answer, using extracts from your data as citations.

Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.

G2 rating: 4.7/5

Pricing: Free version available, with the Plus version costing $20 monthly.

ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:

• Document analysis : It can easily extract and analyze text from various file formats.
• Summarization : GPT can condense lengthy documents into concise summaries.
• Advanced Data Analysis (ADA) : For paid users, Chat-GPT offers quantitative analysis of data documents.
• Sentiment analysis: Although not Chat-GPT’s specialty, it can still perform basic sentiment analysis on text data.

ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.

How to use ChatGPT for qualitative data analysis

ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:

• Use it to summarize long documents or transcripts
• Ask it to identify key themes in your data
• Use it for basic sentiment analysis
• Have it generate potential codes based on your research questions
• Use it to brainstorm interpretations of your findings

G2 rating: 4.7/5 Pricing: Custom

Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.

It’s key data analysis features include:

• Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
• AI-Powered Coding: Uses AI to suggest codes and summarize documents.
• Collaboration Tools: Ideal for teams working on complex research projects.
• Data Visualization: Create network views and other visualizations to showcase relationships in your data.

G2 rating: 4.1/5 Pricing: Custom

NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:

• Data Import and Organization: Easily manage different data types, including text, audio, and video.
• AI-Powered Coding: Speeds up the coding process with machine learning.
• Visualization Tools: Create charts, graphs, and diagrams to represent your findings.
• Collaboration Features: Suitable for team-based research projects.

NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.

Can Excel do qualitative data analysis?

Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.

How do you show qualitative analysis?

Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.

What is the best way to analyze qualitative data?

There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps. 

Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect? 

Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.

In qualitative methods of test analysis, what do test developers do to generate data?

Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.

Which is not a purpose of reflexivity during qualitative data analysis?

Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.

What is a qualitative data analysis example?

A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.

How to analyze qualitative data from a survey?

First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions. 

Which one is better, NVivo or Atlas.ti?

NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can. 

While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.

Do I need to use NVivo for qualitative data analysis?

You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.

Here’s a guide that can help you decide.

How to use NVivo for qualitative data analysis

First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.

How much does NVivo cost?

NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.

What are the four steps of qualitative data analysis?

While qualitative data analysis is often iterative, it generally follows these four main steps:

1. Data Collection: Gathering raw data through interviews, observations, or documents.

2. Data Preparation: Organizing and transcribing the collected data.

3. Data Coding: Identifying and labeling important concepts or themes in the data.

4. Interpretation: Drawing meaning from the coded data and developing insights.

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Home » Qualitative Data – Types, Methods and Examples

Qualitative Data – Types, Methods and Examples

Table of Contents

Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

• Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
• Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
• Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
• Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
• Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
• Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
• Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

• Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
• Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
• Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
• Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
• Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

• Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
• Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
• Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
• Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
• Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
• Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

• Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
• Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
• Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
• Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
• Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
• Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
• Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
• Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
• I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
• Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

• Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
• Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
• Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
• Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
• Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
• Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

• Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
• Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
• Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

• Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
• Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
• Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
• Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
• Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

• Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
• Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
• Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

• Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
• Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
• Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
• Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
• Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
• Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
• Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

• Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
• Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
• Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
• Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
• New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
• Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
• Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

• Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
• Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
• Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
• Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
• Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
• Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
• Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

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The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Qualitative Research: An Overview

• First Online: 24 April 2019

Cite this chapter

• Yanto Chandra 3 &
• Liang Shang 4  

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Qualitative research is one of the most commonly used types of research and methodology in the social sciences. Unfortunately, qualitative research is commonly misunderstood. In this chapter, we describe and explain the misconceptions surrounding qualitative research enterprise, why researchers need to care about when using qualitative research, the characteristics of qualitative research, and review the paradigms in qualitative research.

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Qualitative research is defined as the practice used to study things –– individuals and organizations and their reasons, opinions, and motivations, beliefs in their natural settings. It involves an observer (a researcher) who is located in the field , who transforms the world into a series of representations such as fieldnotes, interviews, conversations, photographs, recordings and memos (Denzin and Lincoln 2011 ). Many researchers employ qualitative research for exploratory purpose while others use it for ‘quasi’ theory testing approach. Qualitative research is a broad umbrella of research methodologies that encompasses grounded theory (Glaser and Strauss 2017 ; Strauss and Corbin 1990 ), case study (Flyvbjerg 2006 ; Yin 2003 ), phenomenology (Sanders 1982 ), discourse analysis (Fairclough 2003 ; Wodak and Meyer 2009 ), ethnography (Geertz 1973 ; Garfinkel 1967 ), and netnography (Kozinets 2002 ), among others. Qualitative research is often synonymous with ‘case study research’ because ‘case study’ primarily uses (but not always) qualitative data.

The quality standards or evaluation criteria of qualitative research comprises: (1) credibility (that a researcher can provide confidence in his/her findings), (2) transferability (that results are more plausible when transported to a highly similar contexts), (3) dependability (that errors have been minimized, proper documentation is provided), and (4) confirmability (that conclusions are internally consistent and supported by data) (see Lincoln and Guba 1985 ).

We classify research into a continuum of theory building — >   theory elaboration — >   theory testing . Theory building is also known as theory exploration. Theory elaboration refers to the use of qualitative data and a method to seek “confirmation” of the relationships among variables or processes or mechanisms of a social reality (Bartunek and Rynes 2015 ).

In the context of qualitative research, theory/ies usually refer(s) to conceptual model(s) or framework(s) that explain the relationships among a set of variables or processes that explain a social phenomenon. Theory or theories could also refer to general ideas or frameworks (e.g., institutional theory, emancipation theory, or identity theory) that are reviewed as background knowledge prior to the commencement of a qualitative research project.

For example, a qualitative research can ask the following question: “How can institutional change succeed in social contexts that are dominated by organized crime?” (Vaccaro and Palazzo 2015 ).

We have witnessed numerous cases in which committed positivist methodologists were asked to review qualitative papers, and they used a survey approach to assess the quality of an interpretivist work. This reviewers’ fallacy is dangerous and hampers the progress of a field of research. Editors must be cognizant of such fallacy and avoid it.

A social enterprises (SE) is an organization that combines social welfare and commercial logics (Doherty et al. 2014 ), or that uses business principles to address social problems (Mair and Marti 2006 ); thus, qualitative research that reports that ‘social impact’ is important for SEs is too descriptive and, arguably, tautological. It is not uncommon to see authors submitting purely descriptive papers to scholarly journals.

Some qualitative researchers have conducted qualitative work using primarily a checklist (ticking the boxes) to show the presence or absence of variables, as if it were a survey-based study. This is utterly inappropriate for a qualitative work. A qualitative work needs to show the richness and depth of qualitative findings. Nevertheless, it is acceptable to use such checklists as supplementary data if a study involves too many informants or variables of interest, or the data is too complex due to its longitudinal nature (e.g., a study that involves 15 cases observed and involving 59 interviews with 33 informants within a 7-year fieldwork used an excel sheet to tabulate the number of events that occurred as supplementary data to the main analysis; see Chandra 2017a , b ).

As mentioned earlier, there are different types of qualitative research. Thus, a qualitative researcher will customize the data collection process to fit the type of research being conducted. For example, for researchers using ethnography, the primary data will be in the form of photos and/or videos and interviews; for those using netnography, the primary data will be internet-based textual data. Interview data is perhaps the most common type of data used across all types of qualitative research designs and is often synonymous with qualitative research.

The purpose of qualitative research is to provide an explanation , not merely a description and certainly not a prediction (which is the realm of quantitative research). However, description is needed to illustrate qualitative data collected, and usually researchers describe their qualitative data by inserting a number of important “informant quotes” in the body of a qualitative research report.

We advise qualitative researchers to adhere to one approach to avoid any epistemological and ontological mismatch that may arise among different camps in qualitative research. For instance, mixing a positivist with a constructivist approach in qualitative research frequently leads to unnecessary criticism and even rejection from journal editors and reviewers; it shows a lack of methodological competence or awareness of one’s epistemological position.

Analytical generalization is not generalization to some defined population that has been sampled, but to a “theory” of the phenomenon being studied, a theory that may have much wider applicability than the particular case studied (Yin 2003 ).

There are different types of contributions. Typically, a researcher is expected to clearly articulate the theoretical contributions for a qualitative work submitted to a scholarly journal. Other types of contributions are practical (or managerial ), common for business/management journals, and policy , common for policy related journals.

There is ongoing debate on whether a template for qualitative research is desirable or necessary, with one camp of scholars (the pluralistic critical realists) that advocates a pluralistic approaches to qualitative research (“qualitative research should not follow a particular template or be prescriptive in its process”) and the other camps are advocating for some form of consensus via the use of particular approaches (e.g., the Eisenhardt or Gioia Approach, etc.). However, as shown in Table 1.1 , even the pluralistic critical realism in itself is a template and advocates an alternative form of consensus through the use of diverse and pluralistic approaches in doing qualitative research.

Alvesson, M., & Kärreman, D. (2007). Constructing mystery: Empirical matters in theory development. Academy of Management Review, 32 (4), 1265–1281.

Article   Google Scholar  

Bartunek, J. M., & Rynes, S. L. (2015). Qualitative research: It just keeps getting more interesting! In Handbook of qualitative organizational research (pp. 41–55). New York: Routledge.

Google Scholar  

Brinkmann, S. (2018). Philosophies of qualitative research . New York: Oxford University Press.

Bucher, S., & Langley, A. (2016). The interplay of reflective and experimental spaces in interrupting and reorienting routine dynamics. Organization Science, 27 (3), 594–613.

Chandra, Y. (2017a). A time-based process model of international entrepreneurial opportunity evaluation. Journal of International Business Studies, 48 (4), 423–451.

Chandra, Y. (2017b). Social entrepreneurship as emancipatory work. Journal of Business Venturing, 32 (6), 657–673.

Corley, K. G., & Gioia, D. A. (2004). Identity ambiguity and change in the wake of a corporate spin-off. Administrative Science Quarterly, 49 (2), 173–208.

Cornelissen, J. P. (2017). Preserving theoretical divergence in management research: Why the explanatory potential of qualitative research should be harnessed rather than suppressed. Journal of Management Studies, 54 (3), 368–383.

Denis, J. L., Lamothe, L., & Langley, A. (2001). The dynamics of collective leadership and strategic change in pluralistic organizations. Academy of Management Journal, 44 (4), 809–837.

Denzin, N. K., & Lincoln, Y. S. (2011). Introduction. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (4th ed.). Thousand Oaks: Sage.

Doherty, B., Haugh, H., & Lyon, F. (2014). Social enterprises as hybrid organizations: A review and research agenda. International Journal of Management Reviews, 16 (4), 417–436.

Dubé, L., & Paré, G. (2003). Rigor in information systems positivist case research: Current practices, trends, and recommendations. MIS Quarterly, 27 (4), 597–636.

Easton, G. (2010). Critical realism in case study research. Industrial Marketing Management, 39 (1), 118–128.

Eisenhardt, K. M. (1989a). Building theories from case study research. Academy of Management Review, 14 (4), 532–550.

Eisenhardt, K. M. (1989b). Making fast strategic decisions in high-velocity environments. Academy of Management Journal, 32 (3), 543–576.

Fairclough, N. (2003). Analysing discourse: Textual analysis for social research . Abingdon: Routledge.

Book   Google Scholar  

Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12 (2), 219–245.

Friese, S. (2011). Using ATLAS.ti for analyzing the financial crisis data [67 paragraphs]. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 12 (1), Art. 39. http://nbn-resolving.de/urn:nbn:de:0114-fqs1101397

Garfinkel, H. (1967). Studies in ethnomethodology . Malden: Blackwell Publishers.

Geertz, C. (1973). Interpretation of cultures . New York: Basic Books.

Gehman, J., Glaser, V. L., Eisenhardt, K. M., Gioia, D., Langley, A., & Corley, K. G. (2017). Finding theory–method fit: A comparison of three qualitative approaches to theory building. Journal of Management Inquiry, 27 , 284–300. in press.

Gioia, D. A. (1992). Pinto fires and personal ethics: A script analysis of missed opportunities. Journal of Business Ethics, 11 (5–6), 379–389.

Gioia, D. A. (2007). Individual epistemology – Interpretive wisdom. In E. H. Kessler & J. R. Bailey (Eds.), The handbook of organizational and managerial wisdom (pp. 277–294). Thousand Oaks: Sage.

Chapter   Google Scholar  

Gioia, D. (2019). If I had a magic wand: Reflections on developing a systematic approach to qualitative research. In B. Boyd, R. Crook, J. Le, & A. Smith (Eds.), Research methodology in strategy and management . https://books.emeraldinsight.com/page/detail/Standing-on-the-Shoulders-of-Giants/?k=9781787563360

Gioia, D. A., & Chittipeddi, K. (1991). Sensemaking and sensegiving in strategic change initiation. Strategic Management Journal, 12 (6), 433–448.

Gioia, D. A., Price, K. N., Hamilton, A. L., & Thomas, J. B. (2010). Forging an identity: An insider-outsider study of processes involved in the formation of organizational identity. Administrative Science Quarterly, 55 (1), 1–46.

Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16 (1), 15–31.

Glaser, B. G., & Strauss, A. L. (2017). Discovery of grounded theory: Strategies for qualitative research . New York: Routledge.

Graebner, M. E., & Eisenhardt, K. M. (2004). The seller’s side of the story: Acquisition as courtship and governance as syndicate in entrepreneurial firms. Administrative Science Quarterly, 49 (3), 366–403.

Grayson, K., & Shulman, D. (2000). Indexicality and the verification function of irreplaceable possessions: A semiotic analysis. Journal of Consumer Research, 27 (1), 17–30.

Hunt, S. D. (1991). Positivism and paradigm dominance in consumer research: Toward critical pluralism and rapprochement. Journal of Consumer Research, 18 (1), 32–44.

King, G., Keohane, R. O., & Verba, S. (1994). Designing social inquiry: Scientific inference in qualitative research . Princeton: Princeton University Press.

Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39 (1), 61–72.

Langley, A. (1988). The roles of formal strategic planning. Long Range Planning, 21 (3), 40–50.

Langley, A., & Abdallah, C. (2011). Templates and turns in qualitative studies of strategy and management. In Building methodological bridges (pp. 201–235). Bingley: Emerald Group Publishing Limited.

Langley, A., Golden-Biddle, K., Reay, T., Denis, J. L., Hébert, Y., Lamothe, L., & Gervais, J. (2012). Identity struggles in merging organizations: Renegotiating the sameness–difference dialectic. The Journal of Applied Behavioral Science, 48 (2), 135–167.

Langley, A. N. N., Smallman, C., Tsoukas, H., & Van de Ven, A. H. (2013). Process studies of change in organization and management: Unveiling temporality, activity, and flow. Academy of Management Journal, 56 (1), 1–13.

Lin, A. C. (1998). Bridging positivist and interpretivist approaches to qualitative methods. Policy Studies Journal, 26 (1), 162–180.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry . Beverly Hills: Sage.

Mair, J., & Marti, I. (2006). Social entrepreneurship research: A source of explanation, prediction, and delight. Journal of World Business, 41 (1), 36–44.

Nag, R., Corley, K. G., & Gioia, D. A. (2007). The intersection of organizational identity, knowledge, and practice: Attempting strategic change via knowledge grafting. Academy of Management Journal, 50 (4), 821–847.

Ozcan, P., & Eisenhardt, K. M. (2009). Origin of alliance portfolios: Entrepreneurs, network strategies, and firm performance. Academy of Management Journal, 52 (2), 246–279.

Prasad, P. (2018). Crafting qualitative research: Beyond positivist traditions . New York: Taylor & Francis.

Pratt, M. G. (2009). From the editors: For the lack of a boilerplate: Tips on writing up (and reviewing) qualitative research. Academy of Management Journal, 52 (5), 856–862.

Ramoglou, S., & Tsang, E. W. (2016). A realist perspective of entrepreneurship: Opportunities as propensities. Academy of Management Review, 41 (3), 410–434.

Sanders, P. (1982). Phenomenology: A new way of viewing organizational research. Academy of Management Review, 7 (3), 353–360.

Sobh, R., & Perry, C. (2006). Research design and data analysis in realism research. European Journal of Marketing, 40 (11/12), 1194–1209.

Stake, R. E. (2010). Qualitative research: Studying how things work . New York: Guilford Press.

Strauss, A., & Corbin, J. M. (1990). Basics of qualitative research: Grounded theory procedures and techniques . Thousand Oaks: Sage.

Vaccaro, A., & Palazzo, G. (2015). Values against violence: Institutional change in societies dominated by organized crime. Academy of Management Journal, 58 (4), 1075–1101.

Weick, K. E. (1989). Theory construction as disciplined imagination. Academy of Management Review, 14 (4), 516–531.

Welch, C. L., Welch, D. E., & Hewerdine, L. (2008). Gender and export behaviour: Evidence from women-owned enterprises. Journal of Business Ethics, 83 (1), 113–126.

Welch, C., Piekkari, R., Plakoyiannaki, E., & Paavilainen-Mäntymäki, E. (2011). Theorising from case studies: Towards a pluralist future for international business research. Journal of International Business Studies, 42 (5), 740–762.

Wodak, R., & Meyer, M. (Eds.). (2009). Methods for critical discourse analysis . London: Sage.

Yin, R. K. (1981). Life histories of innovations: How new practices become routinized. Public Administration Review, 41 , 21–28.

Yin, R. (2003). Case study research: Design and methods . Thousand Oaks: Sage.

Young, R. A., & Collin, A. (2004). Introduction: Constructivism and social constructionism in the career field. Journal of Vocational Behavior, 64 (3), 373–388.

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Chandra, Y., Shang, L. (2019). Qualitative Research: An Overview. In: Qualitative Research Using R: A Systematic Approach. Springer, Singapore. https://doi.org/10.1007/978-981-13-3170-1_1

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What is qualitative data? How to understand, collect, and analyze it

A comprehensive guide to qualitative data, how it differs from quantitative data, and why it's a valuable tool for solving problems.

What is qualitative research?

Importance of qualitative data.

• Differences between qualitative and quantitative data

Characteristics of qualitative data

Types of qualitative data.

• Pros and cons
• Collection methods
• Return to top

Everything that’s done digitally—from surfing the web to conducting a transaction—creates a data trail. And data analysts are constantly exploring and examining that trail, trying to find out ways to use data to make better decisions.

Different types of data define more and more of our interactions online—one of the most common and well-known being qualitative data or data that can be expressed in descriptions and feelings. 

This guide takes a deep look at what qualitative data is, what it can be used for, how it’s collected, and how it’s important to you. 

Key takeaways: 

Qualitative data gives insights into people's thoughts and feelings through detailed descriptions from interviews, observations, and visual materials.

The three main types of qualitative data are binary, nominal, and ordinal.

There are many different types of qualitative data, like data in research, work, and statistics. 

Both qualitative and quantitative research are conducted through surveys and interviews, among other methods. 

What is qualitative data?

Qualitative data is descriptive information that captures observable qualities and characteristics not quantifiable by numbers. It is collected from interviews, focus groups, observations, and documents offering insights into experiences, perceptions, and behaviors.

Qualitative data analysis cannot be counted or measured because it describes the data. It refers to the words or labels used to describe certain characteristics or traits.

This type of data answers the "why" or "how" behind the analysis . It’s often used to conduct open-ended studies, allowing those partaking to show their true feelings and actions without direction.

Think of qualitative data as the type of data you’d get if you were to ask someone why they did something—what was their reasoning? 

Qualitative research not only helps to collect data, it also gives the researcher a chance to understand the trends and meanings of natural actions. 

This type of data research focuses on the qualities of users—the actions behind the numbers. Qualitative research is the descriptive and subjective research that helps bring context to quantitative data. 

It’s flexible and iterative. For example: 

The music had a light tone that filled the kitchen.

Every blue button had white lettering, while the red buttons had yellow. 

The little girl had red hair with a white hat.

Qualitative data is important in determining the frequency of traits or characteristics. 

Understanding your data can help you understand your customers, users, or visitors better. And, when you understand your audience better, you can make them happier.  First-party data , which is collected directly from your own audience, is especially valuable as it provides the most accurate and relevant insights for your specific needs.

Qualitative data helps the market researcher answer questions like what issues or problems they are facing, what motivates them, and what improvements can be made.

Examples of qualitative data

You’ve most likely used qualitative data today. This type of data is found in your everyday work and in statistics all over the web. Here are some examples of qualitative data in descriptions, research, work, and statistics. 

Qualitative data in descriptions

Analysis of qualitative data requires descriptive context in order to support its theories and hypothesis. Here are some core examples of descriptive qualitative data:

The extremely short woman has curly hair and brilliant blue eyes.

A bright white light pierced the small dark space. 

The plump fish jumped out of crystal-clear waters. 

The fluffy brown dog jumped over the tall white fence. 

A soft cloud floated by an otherwise bright blue sky.

Qualitative data in research

Qualitative data research methods allow analysts to use contextual information to create theories and models. These open- and closed-ended questions can be helpful to understand the reasoning behind motivations, frustrations, and actions —in any type of case. 

Some examples of qualitative data collection in research:

What country do you work in? 

What is your most recent job title? 

How do you rank in the search engines? 

How do you rate your purchase: good, bad, or exceptional?

Qualitative data at work

Professionals in various industries use qualitative observations in their work and research. Examples of this type of data in the workforce include:

A manager gives an employee constructive criticism on their skills. "Your efforts are solid and you understand the product knowledge well, just have patience."

A judge shares the verdict with the courtroom. "The man was found not guilty and is free to go."

A sales associate collects feedback from customers. "The customer said the check-out button did not work.”

A teacher gives feedback to their student. "I gave you an A on this project because of your dedication and commitment to the cause."

A digital marketer watches a session replay to get an understand of how users use their platform.

Qualitative data in statistics

Qualitative data can provide important statistics about any industry, any group of users, and any products. Here are some examples of qualitative data set collections in statistics:

The age, weight, and height of a group of body types to determine clothing size charts. 

The origin, gender, and location for a census reading.

The name, title, and profession of people attending a conference to aid in follow-up emails.

Difference between qualitative and quantitative data

Qualitative and quantitative data are much different, but bring equal value to any data analysis. When it comes to understanding data research, there are different analysis methods, collection types and uses. 

Here are the differences between qualitative and quantitative data :

Qualitative data is individualized, descriptive, and relating to emotions.

Quantitative data is countable, measurable and relating to numbers.

Qualitative data helps us understand why, or how something occurred behind certain behaviors .

Quantitative data helps us understand how many, how much, or how often something occurred. 

Qualitative data is subjective and personalized.

Quantitative data is fixed and ubiquitous.

Qualitative research methods are conducted through observations or in-depth interviews.

Quantitative research methods are conducted through surveys and factual measuring. 

Qualitative data is analyzed by grouping the data into classifications and topics. 

Quantitative data is analyzed using statistical analysis.

Both provide a ton of value for any data collection and are key to truly understanding trending use cases and patterns in behavior . Dig deeper into quantitative data examples .

The characteristics of qualitative data are vast. There are a few traits that stand out amongst other data that should be understood for successful data analysis. 

Descriptive : describing or classifying in an objective and nonjudgmental way.

Detailed : to give an account in words with full particulars.

Open-ended : having no determined limit or boundary.

Non-numerical : not containing numbers. 

Subjective : based on or influenced by personal feelings, tastes, or opinions.

With qualitative data samples, these traits can help you understand the meaning behind the equation—or for lack of a better term, what’s behind the results. 

As we narrow down the importance of qualitative data, you should understand that there are different data types. Data analysts often categorize qualitative data into three types:

1. Binary data

Binary data is numerically represented by a combination of zeros and ones. Binary data is the only category of data that can be directly understood and executed by a computer.

Data analysts use binary data to create statistical models that predict how often the study subject is likely to be positive or negative, up or down, right or wrong—based on a zero scale.

2. Nominal data

Nominal data , also referred to as “named, labeled data” or “nominal scaled data,” is any type of data used to label something without giving it a numerical value. 

Data analysts use nominal data to determine statistically significant differences between sets of qualitative data. 

For example, a multiple-choice test to profile participants’ skills in a study.

3. Ordinal data

Ordinal data is qualitative data categorized in a particular order or on a ranging scale. When researchers use ordinal data, the order of the qualitative information matters more than the difference between each category. Data analysts might use ordinal data when creating charts, while researchers might use it to classify groups, such as age, gender, or class.

For example, a Net Promoter Score ( NPS ) survey has results that are on a 0-10 satisfaction scale. 

When should you use qualitative research?

One of the important things to learn about qualitative data is when to use it. 

Qualitative data is used when you need to determine the particular trends of traits or characteristics or to form parameters for larger data sets to be observed. Qualitative data provides the means by which analysts can quantify the world around them.

You would use qualitative data to help answer questions like who your customers are, what issues or problems they’re facing, and where they need to focus their attention, so you can better solve those issues.

Qualitative data is widely used to understand language consumers speak—so apply it where necessary. 

Pros and cons of qualitative data

Qualitative data is a detailed, deep understanding of a topic through observing and interviewing a sample of people. There are both benefits and drawbacks to this type of data. 

Pros of qualitative data

Qualitative research is affordable and requires a small sample size.

Qualitative data provides a predictive element and provides specific insight into development.

Qualitative research focuses on the details of personal choice and uses these individual choices as workable data.

Qualitative research works to remove bias from its collected data by using an open-ended response process.

Qualitative data research provides useful content in any thematic analysis.

Cons of qualitative data 

Qualitative data can be time-consuming to collect and can be difficult to scale out to a larger population.

Qualitative research creates subjective information points.

Qualitative research can involve significant levels of repetition and is often difficult to replicate.

Qualitative research relies on the knowledge of the researchers.

Qualitative research does not offer statistical analysis, for that, you have to turn to quantitative data.

Qualitative data collection methods

Here are the main approaches and collection methods of qualitative studies and data: 

1. Interviews

Personal interviews are one of the most commonly used deductive data collection methods for qualitative research, because of its personal approach.

The interview may be informal and unstructured and is often conversational in nature. The interviewer or the researcher collects data directly from the interviewee one-to-one. Mostly the open-ended questions are asked spontaneously, with the interviewer allowing the flow of the interview to dictate the questions and answers.

The point of the interview is to obtain how the interviewee feels about the subject. 

2. Focus groups

Focus groups are held in a discussion-style setting with 6 to 10 people. The moderator is assigned to monitor and dictate the discussion based on focus questions.

Depending on the qualitative data that is needed, the members of the group may have something in common. For example, a researcher conducting a study on dog sled runners understands dogs, sleds, and snow and would have sufficient knowledge of the subject matter.

3. Data records 

Data doesn’t start with your collection, it has most likely been obtained in the past. 

Using already existing reliable data and similar sources of information as the data source is a surefire way to obtain qualitative research. Much like going to a library, you can review books and other reference material to collect relevant data that can be used in the research.

For example, if you were to study the trends of dictionaries, you would want to know the past history of every dictionary made, starting with the very first one. 

4. Observation

Observation is a longstanding qualitative data collection method, where the researcher simply observes behaviors in a participant's natural setting. They keep a keen eye on the participants and take down transcript notes to find out innate responses and reactions without prompting. 

Typically observation is an inductive approach, which is used when a researcher has very little or no idea of the research phenomenon. 

Other documentation methods, such as video recordings, audio recordings, and photo imagery, may be used to obtain qualitative data.

Further reading: Site observations through heatmaps

5. Case studies

Case studies are an intensive analysis of an individual person or community with a stress on developmental factors in relation to the environment. 

In this method, data is gathered by an in-depth analysis and is used to understand both simple and complex subjects. The goal of a case study is to see how using a product or service has positively impacted the subject, showcasing a solution to a problem or the like. 

6. Longitudinal studies

A longitudinal study is where people who share a single characteristic are studied over a period of time. 

This data collection method is performed on the same subject repeatedly over an extended period. It is an observational research method that goes on for a few years and, in some cases, decades. The goal is to find correlations of subjects with common traits.

For example, medical researchers conduct longitudinal studies to ascertain the effects of a drug or the symptoms related.

Qualitative data analysis tools

And, as with anything—you aren’t able to be successful without the right tools. Here are a few qualitative data analysis tools to have in your toolbox: 

MAXQDA —A qualitative and mixed-method data analysis software 

Fullstory —A behavioral data and analysis platform

ATLAS.ti —A powerful qualitative data tool that offers AI-based functions 

Quirkos —Qualitative data analysis software for the simple learner

Dedoose —A project management and analysis tool for collaboration and teamwork

Taguette —A free, open-source, data analysis and organization platform 

MonkeyLearn —AI-powered, qualitative text analysis, and visualization tool 

Qualtrics —Experience management software

Frequently asked questions about qualitative data

Is qualitative data subjective.

Yes, categorical data or qualitative data is information that cannot generally be proven. For instance, the statement “the chair is too small” depends on what it is used for and by whom it is being used.

Who uses qualitative data?

If you’re interested in the following, you should use qualitative data:

Understand emotional connections to your brand

Identify obstacles in any funnel, for example with session replay

Uncover confusion about your messaging

Locate product feature gaps 

Improve usability of your website, app, or experience

Observe how people talk, think, and feel about your brand

Learn how an organization selects vendors and partners

What are the steps for qualitative data?

1. Transcribe your data : Once you’ve collected all the data, you need to transcribe it. The first step in analyzing your data is arranging it systematically. Arranging data means converting all the data into a text format. 

2. Organize your data : Go back to your research objectives and organize the data based on the questions asked. Arrange your research objective in a table, so it appears visually clear. Avoid working with unorganized data, there will be no conclusive results obtained.

3. Categorize and assign the data : The coding process of qualitative data means categorizing and assigning variables, properties, and patterns. Coding is an important step in qualitative data analysis, as you can derive theories from relevant research findings. You can then begin to gain in-depth insight into the data that help make informed decisions.

4. Validate your data : Data validation is a recurring step that should be followed throughout the research process. There are two sides to validating data: the accuracy and reliability of your research methods, which is the extent to which the methods produce accurate data consistently. 

5. Conclude the data analysis : Present your data in a report that shares the method used to conduct the research studies, the outcomes, and the projected hypothesis of your findings in any related areas.

Is qualitative data better than quantitative data?

One is not better than the other, rather they work cohesively to create a better overall data analysis experience. Understanding the importance of both qualitative and quantitative data is going to produce the best possible data content analysis outcome for any study. 

Further reading : Qualitative vs. quantitative data — what's the difference?

Related posts

Learn how to analyze qualitative data. We show examples of how to collect, organize, and analyze qualitative data to gain insights.

Here's how you can quantitatively analyze your qualitative digital experience data to unlock an entirely new workflow.

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

A comprehensive guide to product analysis and analytics platforms, how important they are, and why they’re a valuable asset for your bottom line.

Jordan Morrow shares how AI-driven decision-making can revolutionize your business by harnessing data and enhancing your decision-making processes.

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Home Market Research

Qualitative Data – Definition, Types, Analysis, and Examples

For a market researcher, collecting qualitative data helps answer questions like who their customers are, what issues or problems they are facing, and where they need to focus their attention so problems or issues are resolved. Let’s talk about it.

Content Index

What is qualitative data?

Importance of qualitative data.

• Advantages of qualitative data analysis
• Disadvantages of qualitative data analysis
• Qualitative data analysis methods

Qualitative data analysis approaches

5 steps to qualitative data analysis, qualitative data examples.

• Do you want to create your own survey?n

Qualitative data is defined as data that approximates and characterizes. Qualitative data can be observed and recorded. 

This data type is non-numerical. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups , and similar methods. Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.

Qualitative data is important in determining the particular frequency of traits or characteristics. It allows the statistician or the researchers to form parameters through which larger data sets can be observed. 

It provides how observers can quantify the world around them. Qualitative data is about the emotions or perceptions of people and what they feel. Qualitative analysis is key to getting useful insights from textual data, figuring out its rich context, and finding subtle patterns and themes. 

In qualitative data, these perceptions and emotions are documented. It helps market researchers understand their consumers’ language and solve the research problem effectively and efficiently. 

Advantages of qualitative data

Some advantages of qualitative data are given below:

It helps in-depth analysis

The data collected provide the qualitative researchers with a detailed analysis, like a thematic analysis of subject matters. While collecting it, the researchers tend to probe the participants and can gather ample information by asking the right kind of questions. The data collected is used to conclude a series of questions and answers. 

Understand what customers think

The data helps market researchers understand their customers’ mindsets. Using qualitative data gives businesses an insight into why a customer purchased a product. Understanding customer language helps market research infer the data collected more systematically.

Collected data can also be used to conduct future research. Since the questions asked to collect qualitative data are open-ended questions, respondents are free to express their opinions, leading to more information.

Disadvantages of qualitative data

Some disadvantages of qualitative data are given below:

Time-consuming

 As collecting this data is more time-consuming, fewer people study than collecting quantitative data. Unless time and budget allow, a smaller sample size is included.

Not easy to generalize

Since fewer people are studied, it is difficult to generalize the results of that population.

Dependent on the researcher’s skills

This type of data is collected through one-to-one interviews, observations, focus groups , etc. It relies on the researcher’s skills and experience to collect information from the sample.

It is typically descriptive analysis data and is more difficult to analyze than quantitative data. Now, you have to decide which is the best option for your research project; remember that to obtain and analyze this data, we need a little more time, so you should consider it in your planning.

Learn about: the 12 Best Tools for Researchers

Qualitative data collection methods

Qualitative data collection is exploratory; it involves in-depth analysis and research. Its collection methods mainly focus on gaining insights, reasoning, and motivations; hence, they go deeper into research . Since this data cannot be measured, researchers prefer methods or data collection tools that are structured to a limited extent.

Here are the qualitative data collection methods :

One-to-one interviews

It is one of the most commonly used data collection instruments for qualitative research questions, mainly because of its approach. The interviewer or the researcher collects data directly from the interviewee one-to-one. The interview method may be informal and unstructured – conversational. The open-ended questions are mostly asked spontaneously, with the interviewer letting the interview flow dictate the questions to be asked.

LEARN ABOUT: Best Data Collection Tools

Focus groups

This is done in a group discussion setting. The group is limited to 6-10 people, and a moderator is assigned to moderate the ongoing discussion.

Depending on the data which is sorted, the group members may have something in common. For example, a researcher conducting a study on track runners will choose athletes who are track runners or were track runners and have sufficient knowledge of the subject matter.

Record keeping

This method uses existing reliable documents and similar sources of information as the data source. This data can be used in the new research. It is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can be used in the research.

Process of observation

In this data collection method, the researcher immerses himself/ herself in the setting where his respondents are, keeps a keen eye on the participants, and takes notes. This is known as the process of observation.

Besides taking notes, other documentation methods, such as video and audio recording, photography, and similar methods, can be used.

Longitudinal studies

This data collection method is repeatedly performed on the same data source over an extended period. It is an observational research method that goes on for a few years and sometimes can go on for even decades. Such data collection methods aim to find correlations through empirical studies of subjects with common traits.

Case studies

This method gathers data from an in-depth analysis of case studies . The versatility of this method is demonstrated in how this method can be used to analyze both simple and complex subjects. The strength of this method is how judiciously it uses a combination of one or more qualitative methods to draw inferences.

Learn more: Qualitative Research Methods .

Analyzing qualitative data is vital, as you have spent time and money collecting it. It is essential because you don’t want to find yourself in the dark even after putting in so much effort. However, there are no set ground rules for analyzing data; it all begins with understanding its two main approaches. 

Qualitative data analysis allows researchers to dig deep into research findings and reveal the complex meanings of qualitative data. Two main approaches to qualitative analysis:

Deductive approach

The deductive approach involves analyzing qualitative data based on a structure that the researcher predetermines. A researcher can use the questions as a guide for analyzing the data. This approach is quick and easy and can be used when a researcher has a fair idea about the likely responses that he/she is going to receive from the sample population.

Inductive approach

On the contrary, the inductive approach is not based on a predetermined structure or set ground rules/framework. It is a more time-consuming and thorough approach to the qualitative analysis process. An inductive approach is often used when a researcher has very little or no idea of the research phenomenon. 

Learn more: Data analysis in research .

Whether you want to analyze qualitative data collected through a one-to-one interview or a survey , these simple steps will ensure a robust data analysis .

Step 1: Arrange your data

Once you have collected all the data, it is largely unstructured and sometimes makes no sense when viewed at a glance. Therefore, it is essential that as a researcher, you first need to transcribe the data collected. 

The first step in analyzing your data is arranging it systematically. Arranging data means converting all the data into a text format. You can either export the data into a spreadsheet or manually type in the data, or choose from any of the computer-assisted qualitative data analysis tools.

LEARN ABOUT: Level of Analysis

Step 2: Organize all your data

After transforming and arranging your data, the immediate next step is to organize your data. You may have a large amount of information that still needs to be arranged in an orderly manner. One of the best ways to organize the data is by going back to your research objectives and then organizing the data based on the questions asked. 

Arrange your research objective in a table so it appears visually clear. At all costs, avoid the temptations of working with unorganized data. You will waste time, and no conclusive results will be obtained.

Step 3: Set a code to the data collected

Setting up proper codes for the collected data takes you a step ahead. The coding process is one of the best ways to compress a tremendous amount of information collected. Data coding means categorizing and assigning properties and patterns to the collected data.

Coding is important in this data analysis, as you can derive theories from relevant research findings. After assigning codes to your data, you can build on the patterns to gain in-depth insight into the data that will help make informed decisions.

Step 4: Validate your qualitative data

Validating data is one of the crucial steps of qualitative data analysis for successful research. Since data is quintessential for research, ensuring that the data is not flawed is imperative. Please note that data validation is not just one step in this analysis; this is a recurring step that needs to be followed throughout the research process. There are two sides to validating data:

• Accuracy of your research design or methods.
• Reliability is the extent to which the methods consistently produce accurate data.

Step 5: Concluding the analysis process

It is important to finally conclude your data, which means systematically presenting your data, a report that can be readily used. The report should state the method you used as a researcher to conduct the research studies, the positives and negatives, and the study limitations. In the report, you should also state the suggestions/inferences of your findings and any related areas for future research. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

LEARN ABOUT: Steps in Qualitative Research

Qualitative data is also called categorical data since this data can be grouped according to categories.

For example, think of a student reading a paragraph from a book during class sessions. A teacher listening to the reading gives feedback on how the child reads that paragraph. 

Suppose the teacher gives feedback based on fluency, intonation, word throwing, and pronunciation clarity without giving the child a grade. In that case, this is considered an example of qualitative data.

It’s pretty easy to understand the difference between qualitative and quantitative data. It does not include numbers in its traits definition, whereas quantitative data is all about numbers.

• The cake is orange, blue, and black in color (qualitative).
• Females have brown, black, blonde, and red hair (qualitative).

Quantitative data is any quantifiable information that can be used for mathematical calculation or statistical analysis . This form of data helps in making real-life decisions based on mathematical derivations. Quantitative data is used to answer questions like How many? How often? How much? This data can be validated and verified. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

LEARN ABOUT:   Statistical Analysis Methods

To better understand the concept of qualitative and quantitative data, it’s best to observe examples of particular datasets and how they can be defined. The following are examples of quantitative data .

• There are four cakes and three muffins kept in the basket (quantitative).
• One glass of fizzy drink has 97.5 calories (quantitative).

Do you want to create your own survey?

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Frequently Asking Questions (FAQ)

The ability to identify issues and opportunities from respondents is one of the main characteristics of an effective qualitative research question. of an open-ended nature. Simple to comprehend and absorb, with little need for more explanation.

Validity is the quality of research that relates to how effectively the conclusions acquired from examining the study participants’ data reflect genuine findings among similar individuals outside the study’s population.

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What is qualitative research?

Quantitative vs qualitative research, approaches to qualitative research, qualitative data types and category types, disadvantages of qualitative research, how to use qualitative research to your business’s advantage, 6 steps to conducting good qualitative research, how do you arrange qualitative data for analysis, qualitative data analysis, how qualtrics products can enhance & simplify the qualitative research process, try qualtrics for free, your ultimate guide to qualitative research (with methods and examples).

31 min read You may be already using qualitative research and want to check your understanding, or you may be starting from the beginning. Learn about qualitative research methods and how you can best use them for maximum effect.

Qualitative research is a research method that collects non-numerical data. Typically, it goes beyond the information that quantitative research provides (which we will cover below) because it is used to gain an understanding of underlying reasons, opinions, and motivations.

Qualitative research methods focus on the thoughts, feelings, reasons, motivations, and values of a participant, to understand why people act in the way they do .

In this way, qualitative research can be described as naturalistic research, looking at naturally-occurring social events within natural settings. So, qualitative researchers would describe their part in social research as the ‘vehicle’ for collecting the qualitative research data.

Qualitative researchers discovered this by looking at primary and secondary sources where data is represented in non-numerical form. This can include collecting qualitative research data types like quotes, symbols, images, and written testimonials.

These data types tell qualitative researchers subjective information. While these aren’t facts in themselves, conclusions can be interpreted out of qualitative that can help to provide valuable context.

Because of this, qualitative research is typically viewed as explanatory in nature and is often used in social research, as this gives a window into the behavior and actions of people.

It can be a good research approach for health services research or clinical research projects.

Free eBook: The qualitative research design handbook

In order to compare qualitative and quantitative research methods, let’s explore what quantitative research is first, before exploring how it differs from qualitative research.

Quantitative research

Quantitative research is the research method of collecting quantitative research data – data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed .

Quantitative research methods deal with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data.

Quantitative research data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

The difference between quantitative and qualitative research methodology

While qualitative research is defined as data that supplies non-numerical information, quantitative research focuses on numerical data.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative research methods. If you want to explore ideas, thoughts, and meanings, use qualitative research methods.

Where both qualitative and quantitative methods are not used, qualitative researchers will find that using one without the other leaves you with missing answers.

For example, if a retail company wants to understand whether a new product line of shoes will perform well in the target market:

• Qualitative research methods could be used with a sample of target customers, which would provide subjective reasons why they’d be likely to purchase or not purchase the shoes, while
• Quantitative research methods into the historical customer sales information on shoe-related products would provide insights into the sales performance, and likely future performance of the new product range.

There are five approaches to qualitative research methods:

• Grounded theory: Grounded theory relates to where qualitative researchers come to a stronger hypothesis through induction, all throughout the process of collecting qualitative research data and forming connections. After an initial question to get started, qualitative researchers delve into information that is grouped into ideas or codes, which grow and develop into larger categories, as the qualitative research goes on. At the end of the qualitative research, the researcher may have a completely different hypothesis, based on evidence and inquiry, as well as the initial question.
• Ethnographic research : Ethnographic research is where researchers embed themselves into the environment of the participant or group in order to understand the culture and context of activities and behavior. This is dependent on the involvement of the researcher, and can be subject to researcher interpretation bias and participant observer bias . However, it remains a great way to allow researchers to experience a different ‘world’.
• Action research: With the action research process, both researchers and participants work together to make a change. This can be through taking action, researching and reflecting on the outcomes. Through collaboration, the collective comes to a result, though the way both groups interact and how they affect each other gives insights into their critical thinking skills.
• Phenomenological research: Researchers seek to understand the meaning of an event or behavior phenomenon by describing and interpreting participant’s life experiences. This qualitative research process understands that people create their own structured reality (‘the social construction of reality’), based on their past experiences. So, by viewing the way people intentionally live their lives, we’re able to see the experiential meaning behind why they live as they do.
• Narrative research: Narrative research, or narrative inquiry, is where researchers examine the way stories are told by participants, and how they explain their experiences, as a way of explaining the meaning behind their life choices and events. This qualitative research can arise from using journals, conversational stories, autobiographies or letters, as a few narrative research examples. The narrative is subjective to the participant, so we’re able to understand their views from what they’ve documented/spoken.

Qualitative research methods can use structured research instruments for data collection, like:

Surveys for individual views

A survey is a simple-to-create and easy-to-distribute qualitative research method, which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Qualitative research questions tend to be open questions that ask for more information and provide a text box to allow for unconstrained comments.

Examples include:

• Asking participants to keep a written or a video diary for a period of time to document their feelings and thoughts
• In-Home-Usage tests: Buyers use your product for a period of time and report their experience

Surveys for group consensus (Delphi survey)

A Delphi survey may be used as a way to bring together participants and gain a consensus view over several rounds of questions. It differs from traditional surveys where results go to the researcher only. Instead, results go to participants as well, so they can reflect and consider all responses before another round of questions are submitted.

This can be useful to do as it can help researchers see what variance is among the group of participants and see the process of how consensus was reached.

• Asking participants to act as a fake jury for a trial and revealing parts of the case over several rounds to see how opinions change. At the end, the fake jury must make a unanimous decision about the defendant on trial.
• Asking participants to comment on the versions of a product being developed, as the changes are made and their feedback is taken onboard. At the end, participants must decide whether the product is ready to launch.

Semi-structured interviews

Interviews are a great way to connect with participants, though they require time from the research team to set up and conduct, especially if they’re done face-to-face.

Researchers may also have issues connecting with participants in different geographical regions. The researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

• Conducting a phone interview with participants to run through their feedback on a product. During the conversation, researchers can go ‘off-script’ and ask more probing questions for clarification or build on the insights.

Focus groups

Participants are brought together into a group, where a particular topic is discussed. It is researcher-led and usually occurs in-person in a mutually accessible location, to allow for easy communication between participants in focus groups.

In focus groups , the researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

• Asking participants to do UX tests, which are interface usability tests to show how easily users can complete certain tasks

Direct observation

This is a form of ethnographic research where researchers will observe participants’ behavior in a naturalistic environment. This can be great for understanding the actions in the culture and context of a participant’s setting.

This qualitative research method is prone to researcher bias as it is the researcher that must interpret the actions and reactions of participants. Their findings can be impacted by their own beliefs, values, and inferences.

• Embedding yourself in the location of your buyers to understand how a product would perform against the values and norms of that society

One-to-one interviews

One-to-one interviews are one of the most commonly used data collection instruments for qualitative research questions, mainly because of their approach. The interviewer or the researcher collects data directly from the interviewee one-to-one. The interview method may be informal and unstructured – conversational. The open-ended questions are mostly asked spontaneously, with the interviewer letting the interview flow dictate the questions to be asked.

Record keeping

This method uses existing reliable documents and similar sources of information as the data source. This data can be used in new research. It is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can be used in the research.

Process of observation

In this data collection method, the researcher immerses themselves in the setting where their respondents are, keeps a keen eye on the participants, and takes notes. This is known as the process of observation.

Besides taking notes, other documentation methods, such as video and audio recording, photography, and similar methods, can be used.

Longitudinal studies

This data collection method is repeatedly performed on the same data source over an extended period. It is an observational research method that goes on for a few years and sometimes can go on for even decades. Such data collection methods aim to find correlations through empirical studies of subjects with common traits.

Case studies

This method gathers data from an in-depth analysis of case studies. The versatility of this method is demonstrated in how this method can be used to analyze both simple and complex subjects. The strength of this method is how judiciously it uses a combination of one or more qualitative methods to draw inferences.

What is data coding in qualitative research?

Data coding in qualitative research involves a systematic process of organizing and interpreting collected data. This process is crucial for identifying patterns and themes within complex data sets. Here’s how it works:

• Data Collection : Initially, researchers gather data through various methods such as interviews, focus groups, and observations. The raw data often includes transcriptions of conversations, notes, or multimedia recordings.
• Initial Coding : Once data is collected, researchers begin the initial coding phase. They break down the data into manageable segments and assign codes—short phrases or words that summarize each piece of information. This step is often referred to as open coding.
• Categorization : Next, researchers categorize the codes into broader themes or concepts. This helps in organizing the data and identifying major patterns. These themes can be linked to theoretical frameworks or emerging patterns from the data itself.
• Review and Refinement : The coding process is iterative, meaning researchers continuously review and refine their codes and categories. They may merge similar codes, adjust categories, or add new codes as deeper understanding develops.
• Thematic Analysis : Finally, researchers perform a thematic analysis to draw meaningful conclusions from the data. They explore how the identified themes relate to the research questions and objectives, providing insights and answering key queries.

Methods and tools for coding

• Manual Coding : Involves using highlighters, sticky notes, and physical organization methods.
• Software Tools : Programs like NVivo, ATLAS.ti, and MAXQDA streamline the coding process, allowing researchers to handle large volumes of data efficiently.

Data coding transforms raw qualitative data into structured information, making it essential for deriving actionable insights and achieving research objectives.

Qualitative research methods often deliver information in the following qualitative research data types:

• Written testimonials

Through contextual analysis of the information, researchers can assign participants to category types:

• Social class
• Political alignment
• Most likely to purchase a product
• Their preferred training learning style

Why is qualitative data important?

Qualitative data plays a pivotal role in understanding the nuances of human behavior and emotions. Unlike quantitative data, which deals with numbers and hard statistics, qualitative data captures the vivid tapestry of opinions, experiences, and motivations.

Understanding emotions and perceptions

One primary reason qualitative data is crucial is its ability to reveal the emotions and perceptions of individuals. This type of data goes beyond mere numbers to provide insights into how people feel and think. For example, understanding consumer sentiments can help businesses tailor their products and services to meet customer needs more effectively.

Rich context and insights

Qualitative analysis dives deep into textual data, uncovering rich context and subtle patterns that might be missed with quantitative methods alone. This kind of data provides comprehensive insights by examining the intricate details of user feedback, interviews, or focus group discussions. For instance, companies like IBM and Nielsen use qualitative data to gain a deeper understanding of market trends and consumer preferences.

Forming research parameters

Researchers use qualitative data to establish parameters for broader studies. By identifying recurring themes and traits, they can design more targeted and effective surveys and experiments. This initial qualitative phase is essential in ensuring that subsequent quantitative research is grounded in real-world observations.

Solving complex problems

In market research, qualitative data is invaluable for solving complex problems. It enables researchers to decode the language of their consumers, identifying pain points and areas for improvement. Brands like Coca-Cola and P&G frequently rely on qualitative insights to refine their marketing strategies and enhance customer satisfaction.

In sum, qualitative data is essential for its ability to capture the depth and complexity of human experiences. It provides the contextual groundwork needed to make informed decisions, understand consumer behavior, and ultimately drive successful outcomes in various fields.

How do you organize qualitative data?

Organizing qualitative data is crucial to extract meaningful insights efficiently. Here’s a step-by-step guide to help you streamline the process:

1. Align with research objectives

Start by revisiting your research objectives. Clarifying the core questions you aim to answer can guide you in structuring your data. Create a table or spreadsheet where these objectives are clearly laid out.

2. Categorize the data

Sort your data based on themes or categories relevant to your research objectives. Use different coding techniques to label each piece of information. Tools like NVivo or Atlas.ti can help in coding and categorizing qualitative data effectively.

3. Use visual aids

Visualizing data can make patterns more apparent. Consider using charts, graphs, or mind maps to represent your categorized data. Applications like Microsoft Excel or Tableau are excellent for creating visual representations.

4. Develop a index system

Create an index system to keep track of where each piece of information fits within your categories. This can be as simple as a detailed index in a Word document or a more complex system within your data analysis software.

5. Summary tables

Develop summary tables that distill large amounts of information into key points. These tables should reflect the core themes and subthemes you’ve identified, making it easier to draw conclusions.

6. Avoid unnecessary data

Don’t fall into the trap of hoarding unorganized or irrelevant information. Regularly review your data to ensure it aligns with your research goals. Trim any redundant or extraneous data to maintain clarity and focus.

By following these steps, you can turn your raw qualitative data into an organized, insightful resource that directly supports your research objectives.

Advantages of qualitative research

• Useful for complex situations: Qualitative research on its own is great when dealing with complex issues, however, providing background context using quantitative facts can give a richer and wider understanding of a topic. In these cases, quantitative research may not be enough.
• A window into the ‘why’: Qualitative research can give you a window into the deeper meaning behind a participant’s answer. It can help you uncover the larger ‘why’ that can’t always be seen by analyzing numerical data.
• Can help improve customer experiences: In service industries where customers are crucial, like in private health services, gaining information about a customer’s experience through health research studies can indicate areas where services can be improved.
• You need to ask the right question: Doing qualitative research may require you to consider what the right question is to uncover the underlying thinking behind a behavior. This may need probing questions to go further, which may suit a focus group or face-to-face interview setting better.
• Results are interpreted: As qualitative research data is written, spoken, and often nuanced, interpreting the data results can be difficult as they come in non-numerical formats. This might make it harder to know if you can accept or reject your hypothesis.
• More bias: There are lower levels of control to qualitative research methods, as they can be subject to biases like confirmation bias, researcher bias, and observation bias. This can have a knock-on effect on the validity and truthfulness of the qualitative research data results.

Qualitative methods help improve your products and marketing in many different ways:

• Understand the emotional connections to your brand
• Identify obstacles to purchase
• Uncover doubts and confusion about your messaging
• Find missing product features
• Improve the usability of your website, app, or chatbot experience
• Learn about how consumers talk about your product
• See how buyers compare your brand to others in the competitive set
• Learn how an organization’s employees evaluate and select vendors

Businesses can benefit from qualitative research by using it to understand the meaning behind data types. There are several steps to this:

• Define your problem or interest area: What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis: Ask yourself what could be the causes for the situation with those qualitative research data types.
• Plan your qualitative research: Use structured qualitative research instruments like surveys, focus groups, or interviews to ask questions that test your hypothesis.
• Data Collection: Collect qualitative research data and understand what your data types are telling you. Once data is collected on different types over long time periods, you can analyze it and give insights into changing attitudes and language patterns.
• Data analysis: Does your information support your hypothesis? (You may need to redo the qualitative research with other variables to see if the results improve)
• Effectively present the qualitative research data: Communicate the results in a clear and concise way to help other people understand the findings.

Transcribing and organizing your qualitative data is crucial for robust analysis. Follow these steps to ensure your data is systematically arranged and ready for interpretation.

1. Transcribe your sata

Converting your gathered information into a textual format is the first step. This involves:

• Listening to audio recordings: Jot down every nuance and detail.
• Reading through notes: Ensure all handwritten or typed notes are coherent and complete.

2. Choose a suitable format

Once transcribed, your data needs to be formatted for ease of analysis. You have several options:

• Spreadsheets: Tools like Microsoft Excel or Google Sheets allow for easy sorting and categorization.
• Specialized software: Consider using computer-assisted qualitative data analysis software (CAQDAS) such as NVivo, ATLAS.ti, or MAXQDA to handle large volumes of data efficiently.

3. Organize by themes

Begin to identify patterns or themes in your data. This method, often called coding, involves:

• Highlighting Key Points: Use different colors or symbols to mark recurring ideas.
• Creating Categories: Group similar themes together to form a coherent structure.

4. Label and store

Finally, label and store your data meticulously to ensure easy retrieval and reference. Label:

• Files and Documents: With clear titles and dates.
• Sections within Documents: With headings and subheadings to distinguish different themes and patterns.

By following these systematic steps, you can convert raw qualitative data into a structured format ready for comprehensive analysis.

Evaluating qualitative research can be tough when there are several analytics platforms to manage and lots of subjective data sources to compare.

Qualtrics provides a number of qualitative research analysis tools, like Text iQ, powered by Qualtrics iQ , provides powerful machine learning and native language processing to help you discover patterns and trends in text.

This also provides you with:

• Sentiment analysis — a technique to help identify the underlying sentiment (say positive, neutral, and/or negative) in qualitative research text responses
• Topic detection/categorisation — this technique is the grouping or bucketing of similar themes that can are relevant for the business & the industry (e.g., ‘Food quality,’ ‘Staff efficiency,’ or ‘Product availability’)

Validating your qualitative data

Validating data is one of the crucial steps of qualitative data analysis for successful research. Since data is quintessential for research, ensuring that the data is not flawed is imperative. Please note that data validation is not just one step in this analysis; it is a recurring step that needs to be followed throughout the research process.

There are two sides to validating data:

• Ensuring that the methods used are designed to produce accurate data.
• The extent to which the methods consistently produce accurate data over time.

Incorporating these validation steps ensures that the qualitative data you gather through tools like Text iQ is both reliable and accurate, providing a solid foundation for your research conclusions.

What are the approaches to qualitative data analysis?

Qualitative data analysis can be tackled using two main approaches: the deductive approach and the inductive approach. Each method offers unique benefits and caters to different research needs.

Deductive approach

The deductive approach involves analyzing qualitative data within a pre-established framework. Typically, researchers use predefined questions to guide their analysis, making it a structured and straightforward process. This method is particularly useful when researchers have a clear hypothesis or a reasonable expectation of the data they will gather.

Advantages :

• Quick and efficient
• Suitable for studies with known variables

Disadvantages :

• Limited flexibility
• May not uncover unexpected insights

Inductive approach

Contrastingly, the inductive approach is characterized by its flexibility and open-ended nature. Rather than starting with a set structure, researchers use this approach to let patterns and themes emerge naturally from the data. This method is time-consuming but thorough, making it ideal for exploratory research where little is known about the phenomenon under study.

• High flexibility
• Uncovers insights that may not be immediately obvious
• Time-intensive
• Requires rigorous interpretation skills

Both approaches have their merits and can be chosen based on the objectives of your research. By understanding the key differences between the deductive and inductive methods, you can select the approach that best suits your analytical needs.

What is the inductive approach to qualitative data analysis?

The inductive approach to qualitative data analysis is a flexible and explorative method. Unlike approaches that follow a fixed framework, the inductive approach builds theories and patterns from the data itself. Here’s a closer look:

• No fixed framework: This method does not rely on predetermined structures or strict guidelines. Instead, it allows patterns and themes to naturally emerge from the data.
• Exploratory nature: Often used when little is known about the research phenomenon, this approach helps researchers unearth new insights without preconceptions.
• Time-consuming but thorough: Due to its comprehensive nature, the inductive approach can be more time-intensive. Researchers meticulously examine data to uncover meaningful connections and build a deep understanding of the subject matter.
• Flexible and adaptive: This approach is particularly useful in dynamic research environments where the subject matter is complex or not well understood.

In essence, the inductive approach is about letting the data lead the research, allowing for the discovery of unexpected insights and a more nuanced understanding of the studied phenomena.

The deductive approach to qualitative data analysis is a method where researchers begin with a predefined structure or framework to guide their examination of data. Essentially, this means they start with specific questions or hypotheses in mind, which helps in directing the analysis process.

Key elements of the deductive approach:

• Researchers have a clear idea of what they are looking for based on prior knowledge or theories.
• This structured framework acts as a guide throughout the analysis.
• Specific questions are developed beforehand.
• These questions help in filtering and categorizing the data effectively.
• The deductive method is typically faster and more straightforward.
• It is particularly useful when researchers anticipate certain types of responses or patterns from their sample population.

In summary, the deductive approach involves using existing theories and structured queries to systematically analyze qualitative data, making the process efficient and focused.

How to conclude the qualitative data analysis process

Concluding your qualitative data analysis involves presenting your findings in a structured report that stakeholders can readily understand and utilize.

Start by describing your methodology . Detail the specific methods you employed during your research, including how you gathered and analyzed data. This helps readers appreciate the rigor of your process.

Next, highlight both the strengths and limitations of your study. Discuss what worked well and areas that posed challenges, providing a balanced view that showcases the robustness of your research while acknowledging potential shortcomings.

Following this, present your key findings and insights . Summarize the main conclusions drawn from your data, ensuring clarity and conciseness. Use bullet points or numbered lists to enhance readability where appropriate.

Moreover, offer suggestions or inferences based on your findings. Identify actionable recommendations or indicate future research areas that emerged from your study.

Finally, emphasize the importance of the synergy between analytics and reporting . Analytics uncover valuable insights, but it’s the reporting that effectively communicates these insights to stakeholders, enabling informed decision-making.

Even in today’s data-obsessed marketplace, qualitative data is valuable – maybe even more so because it helps you establish an authentic human connection to your customers. If qualitative research doesn’t play a role to inform your product and marketing strategy, your decisions aren’t as effective as they could be.

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting qualitative research. From survey creation and data collection to textual analysis and data reporting, it can help all your internal teams gain insights from your subjective and categorical data.

Qualitative methods are catered through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of qualitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Text IQ™ and Driver IQ™ make analyzing subjective and categorical data easy and simple. Choose to highlight key findings based on topic, sentiment, or frequency. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research Article

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study

Roles Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliations Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom, Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom

Roles Data curation, Formal analysis, Validation, Writing – original draft, Writing – review & editing

Affiliation Institute of Population Health, University of Liverpool, Liverpool, United Kingdom

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Institute of Population Health, University of Liverpool, Liverpool, United Kingdom, Directorate of Mental Health and Learning Disabilities, Powys Teaching Health Board, Bronllys, United Kingdom

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Affiliation General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Roles Conceptualization, Supervision, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

Roles Writing – review & editing

Affiliations Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom, Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom

Roles Conceptualization, Writing – review & editing

Affiliation Department of Computer Science, University of Liverpool, Liverpool, United Kingdom

Affiliation Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom

Affiliation NIHR Applied Research Collaboration North West Coast, United Kingdom

Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, School of Computing, University of Leeds, Leeds, United Kingdom

Roles Project administration, Resources

Affiliation Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom

Affiliation Merseycare NHS Foundation Trust, Liverpool, United Kingdom

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing – review & editing

•  [ ... ],

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Centre for Experimental Therapeutics, University of Liverpool, Liverpool, United Kingdom, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom

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• Aseel S. Abuzour, 
• Samantha A. Wilson, 
• Alan A. Woodall, 
• Frances S. Mair, 
• Andrew Clegg, 
• Eduard Shantsila, 
• Mark Gabbay, 
• Michael Abaho, 
• Asra Aslam, 

• Published: August 30, 2024
• https://doi.org/10.1371/journal.pone.0299770
• Reader Comments

Introduction

Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them.

Qualitative focus groups and semi-structured interviews took place between 2022–2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis.

Two key themes limiting the effectiveness of SMRs in clinical practice were identified: ‘Medication Reviews in Practice’ and ‘Medication-related Challenges’. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication.

SMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Citation: Abuzour AS, Wilson SA, Woodall AA, Mair FS, Clegg A, Shantsila E, et al. (2024) A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study. PLoS ONE 19(8): e0299770. https://doi.org/10.1371/journal.pone.0299770

Editor: Kathleen Bennett, Royal College of Surgeons in Ireland, IRELAND

Received: February 16, 2024; Accepted: June 24, 2024; Published: August 30, 2024

Copyright: © 2024 Abuzour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: DynAIRx has been funded by the National Institute for Health and Care Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions (AIM) call (NIHR 203986). MG is partly funded by the NIHR Applied Research Collaboration North West Coast (ARC NWC). AW is partly funded by a Health and Care Research Wales Research Time Award (NHS-RTA-21-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research is supported by the NIHR ARC NWC. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Competing interests: No competing interests

Structured medication reviews (SMRs) were introduced in the United Kingdom (UK) in October 2020 and incorporated into the NHS England Directed Enhanced Service (DES) contract for 2021 [ 1 ]. SMRs represent a National Institute for Health and Care Excellence (NICE)-approved clinical intervention facilitating shared-decision making between clinicians and patients, to inform treatment decisions. The objective is to reduce medication-related harm in patients with complex or problematic polypharmacy [ 1 , 2 ]. While General Practitioners (GPs), pharmacists and advanced nurse practitioners (ANPs) who meet training criteria can conduct SMRs, the prevailing expectation is for clinical pharmacists within Primary Care Networks (PCNs) to assume primary responsibility as a commissioned service [ 3 ]. The varied methods employed by PCNs to proactively identify patients suitable for SMRs, and conduct these reviews, is contingent on available resources and capacity. Anecdotal evidence suggests that PCNs currently use limited digital tools, such as searching electronic health records (EHR) based on the total number of drugs prescribed or disease codes, to identify patients at risk of medication-related harm.

There is sparse empirical evidence reporting on the implementation of SMRs, their impact on patient outcomes, and the challenges faced by healthcare professionals (HCPs) and patients during SMRs [ 4 , 5 ]. This scarcity of evidence is unsurprising given that SMRs were introduced in 2020 amidst the COVID-19 pandemic [ 1 ]. Nonetheless, estimates suggest a percentage reduction in per-patient medicines following an SMR ranging from 2.7% to 9.9%, with up to 19.5% reduction in use for the highest-risk group in care homes [ 6 ].

Patients with complex multimorbidity and polypharmacy, whose medicines have not been optimised are at risk of adverse outcomes and medication-related harm [ 7 ]. The definition of complex multimorbidity is contentious [ 8 ] but here we are referring to patients living with four or more conditions, which is strongly associated with polypharmacy [ 8 , 9 ]. The use of data from EHRs to develop evidence-based digital health tools can be a promising resource to assist HCPs in conducting targeted, efficient and effective SMRs.

The NIHR-funded DynAIRx study (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) aims to develop AI-driven tools that integrate information from electronic health and social care records, clinical guidelines and risk-prediction models in order to support the delivery of SMRs [ 10 ]. The DynAIRx project will produce machine learning models, dashboards, and different tools including Causal Inferencing to provide clinicians and patients with evidence-based information to prioritise patients at most risk of harm and/or patients most likely to benefit from SMRs. Aligned with the NICE multimorbidity guidelines, [ 7 , 11 ] DynAIRx will focus on three patient groups at high-risk of rapidly worsening health from multimorbidity: (a) individuals with mental and physical health co-morbidity, [ 12 – 14 ] in whom the prescribing for mental health improvement can lead to adverse physical health consequences; (b) those with complex multimorbidity (four or more long-term health conditions taking ten or more drugs); [ 9 , 15 ] and (c) older people with frailty who are at high risk of adverse outcomes [ 16 ].

The initial step towards introducing AI-driven prescribing support tools into clinical practice involves understanding the current scope of work, how SMRs are presently undertaken and by whom, the time required in real-world clinical practice to undertake them, and crucially, investigate what determinants act as potential barriers to efficient and effective SMR implementation. The aim of this study was to explore how SMRs are undertaken and what barriers those undertaking them (and receiving them) experience.

Participants and recruitment

This study sought to recruit health care or management professionals working in health care settings (primary care in the community or secondary care in hospital services) across the UK where review of prescription medications is a regular part of the clinical workload. This included those working in General Practice, secondary care hospital services (geriatric medicine, clinical pharmacology, falls clinics, mental health practitioners), clinical commissioning of services or management of clinical services (practice managers), and pharmacists, including PCN pharmacists (those involved in conducting SMRs across several neighbouring GP practices). Patient participants included those with (a) multiple and physical co-morbidities; (b) complex multimorbidity; (c) older people with frailty. Patient and carer representatives of the three key multimorbidity groups outlined above were also invited. This included recruiting adult individuals (over the age of 18) with/or caring for someone with multiple (4 or more) long-term health conditions, co-existing mental and physical health problems, prescribed ≥10 regular medications, frailty.

Purposive sampling identified potential HCP participants that were known to be involved in medicines optimisation services through the researchers own clinical and professional networks. Snowballing (wherein research participants were asked to assist the recruitment by attempting to identify other potential participants) was employed to identify contacts through existing service providers along with advertisement in GP forums and at national events for individuals participating in clinical polypharmacy research [ 17 ]. Purposive sampling of potential patient representatives were identified through advertisement across the NIHR Applied Research Collaboration public advisor networks and through research databases at the researchers host institutions. Potential participants were provided with study information and an invitation to participate. Participants received comprehensive briefings from researchers about the study, and written consent was obtained prior to the focus group or interview participation. Withdrawal of consent was permitted at any stage, even after the focus group or interview.

Ethical approval

The Newcastle North Tyneside Research Ethics Committee (REC reference:22/NE/0088) granted ethical approval for the DynAIRx study.

Data collection

Data collection occurred from November 2022 to November 2023. Focus groups and semi-structured interviews were conducted to gather participant views. Focus groups were utilised for patient participants in order to stimulate discussion of common and shared experiences. Individual interviews supplemented the HCP focus groups in order to ensure as many HCPs could be involved as possible owing to time constraints for some HCPs. Focus group topic guides and interview schedules were developed and refined by the clinical members of the research team (LW, AA, AW, FM, AG) and tailored to HCP and patient groups. The topic guides (see S1 Appendix ) included questions exploring the experience of conducting or receiving SMRs, barriers to undertaking them and opinions on key medication challenges in multimorbidity groups from both the clinician and patient perspective. Sessions occurred in person and online (via Microsoft Teams), lasting from 49 to 109 minutes. Audio recordings underwent verbatim transcription and anonymisation to remove any potentially identifiable information. Each participant was assigned a code, and recordings were subsequently deleted. Data collection and analysis occurred concurrently. The Consolidated Criteria for Reporting Qualitative Research checklist was used to ensure comprehensive reporting of our qualitative study (see S1 Appendix ). No participant withdrew consent for their data to be used in the study.

Data analysis

Transcripts were imported into QSR NVivo 12® and analysed using thematic analysis [ 18 ]. Transcripts were read to familiarise researchers with the data. Inductive reasoning guided the initial coding by AA and SW, who collated and examined codes to identify themes. The multidisciplinary coding team (AA, SW, LW, AW, FM) consisted of clinicians and researchers, and engaged in regular reflexive practices to ensure a rigorous and transparent qualitative study. Recognising the diverse expertise and perspectives within the team, we held regular coding clinics where codes and initial themes were reviewed and discussed. By openly sharing our perspectives and challenging each other’s viewpoints, we aimed to balance our interpretations and avoid overemphasis on any single disciplinary lens. This allowed us to critically examine how our professional backgrounds and assumptions might shape the interpretation of findings. These reflexive sessions were instrumental in identifying and mitigating biases, ensuring that our analysis remained grounded in the participants’ narratives rather than our preconceptions. Themes were defined and supported by quotes. Detailed notes of discussions and developments were kept to ensure analytical rigour and plausibility [ 19 ]. The remaining dataset underwent hybrid inductive and deductive thematic analysis using the inductively defined themes, with codes and themes iteratively revised. Once all team members agreed no new codes or meaning to influence thematic analysis were emerging, we assumed saturation was achieved [ 20 ]. At this point, further recruitment was stopped.

Six focus groups with HCPs (n = 21) and 3 patient focus groups (n = 13) were conducted. A further five semi-structured interviews with HCPs took place (see Table 1 for details).

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https://doi.org/10.1371/journal.pone.0299770.t001

Two overarching themes developed from analysis of the HCP and patient interviews and focus groups, within which a number of sub-themes emerged:

• Limited availability of digital tools to assist in identifying and prioritising patients for a SMR
• Organisational challenges and patient factors affecting patient engagement for a SMR
• Time consuming “detective work”
• SMRs require multiple appointments
• Influence of healthcare context on delivering SMR
• Factors influencing deprescribing discussions
• Poor communication and data sharing between primary and secondary care
• Difficulties managing mental health medication for prescriber and patient
• Challenges around anticholinergic medication optimisation for prescriber

Fig 1 displays each key theme from this study and a detailed list of the barriers to each stage of the SMR process. Supplementary quotes for each theme can be found in S2 Appendix .

https://doi.org/10.1371/journal.pone.0299770.g001

• a. Limited availability of digital tools to assist in identifying and prioritising patients for a SMR

PCN pharmacists reported proactively identifying and prioritising patients to conduct SMRs. Patient identification was based on the criteria outlined by the DESGP contract, which includes patients in care homes (both nursing and residential facilities), individuals with complex multimorbidity and polypharmacy, urgently referred patients, older individuals encountering medication-related harms, and patients recently discharged from hospital. HCPs also referenced using available healthcare management automated search tools within the EHR, including ‘Ardens search’ [ 21 ] and ‘ProActive Register Management (PARM) diabetes’ [ 22 ], to identify pre-determined conditions, prescribing alerts and other variables that form part of the practice workload. They also used targets from the Investment and Impact Fund (IIF) for patient identification (IIF is an incentive scheme where PCNs can improve health and the quality of care for patients with multimorbidity), which participants described as beneficial but a waste of resources in the context of SMRs which should focus on patients with complex multimorbidity and polypharmacy [ 23 ]. Despite this, HCPs noted a limited number of digital tools to assist in identifying and prioritising patients for a SMR.

“I’ll be honest that we’ve not really had any tools that have been developed specific for supporting SMR.” (Pharmacist 2, Interview)

Since the introduction of EHRs in the NHS, HCPs are expected to assign ‘SNOMED codes’ to document patients with specific diagnostic, symptom or treatment codes in a logical hierarchical manner to specify clinical information [ 24 ]. These codes facilitate searches for specific medical conditions, symptoms and treatments within the GP EHR, facilitating the identification of individuals requiring an SMR. Pharmacists noted that EHR searches do not adequately consider the complexity of patients, making it challenging to stratify those that would benefit most from an SMR. Additionally, participants highlighted a lack of sensitivity and specificity in the current search mechanisms (meaning the searches either do not identify all the intended patients or identify too many).

“ The actual indicator that my team has been focusing on is supposed to be the ones where patients are prone to medication errors … but when I actually look at the patients, I haven’t got a clue why the actual computer system has decided that most of the time.” (Participant 1, Pharmacist FG1)

HCPs were concerned that the identification of patients who would benefit from an SMR could exceed the clinical capacity of the staff available to meet the need. They felt that any digital tool used to prioritise patients’ needs should match the clinical capacity of the practice.

“ The tools have to be a bit cleverer … We can generate a list of patients today … PCNs at the moment essentially do that, but what you have to do is almost the list that’s generated to the capacity … People would not switch it on if they felt that it could generate lots of patients you would not then see.” (Policy-maker, Interview)

• b. Organisational challenges and patient factors affecting patient engagement for a SMR

GPs or secondary care clinicians (excluding clinical pharmacologists) often conducted opportunistic medication reviews, compared to the proactive SMRs conducted by PCN pharmacists. In alignment with the DES requirements, HCPs described how the task of conducting an SMR was contingent upon organisational contracts, practice size, and staff availability. The presence of a PCN pharmacist for SMRs facilitated streamlined tasks, enabling GPs to focus on patients with more complex medical profiles. GPs voiced concerns about burnout in areas where demand for SMR exceeded the clinical capacity to undertake them. This challenge was particularly pronounced in regions of lower socioeconomic status where patients often presented with complex multimorbidity and polypharmacy at a younger age, especially those with co-morbid mental and physical health problems. Moreover, respondents felt that patients residing in deprived areas were less likely to attend scheduled SMRs, compelling GPs to resort to opportunistic reviews. This highlights potential inequity in access to SMRs and overall health surveillance.

“In the poorer area of the practice there’s no clinical pharmacist, that’s all done opportunistically, if done at all, by the GP partners there. I think that there’s a couple that are approaching burn out, if not complete burn out and the practice is almost run by locums. So, when I’m going in there, it’s quite tough and I will often see medications that are inappropriately prescribed, polypharmacy, several of the same drugs, and I will opportunistically undertake a structured medical review.” (Participant 3, GP FG1)

• c. Time consuming “detective work”

Whether HCPs identified patients proactively or opportunistically, the preparation time for a medication review ranged from 10 minutes to 1 hour. Several factors influenced this preparation time, including the availability of information, case complexity, barriers to accessing information, information density, and time constraints. The challenge in finding and collating information within the patient’s clinical records constituted a significant portion of the preparation time. For instance, discharge letters from hospitals are often located as attachments within the patient record, requiring HCPs to locate and read the letter. These necessary preparatory activities take away from the face-to-face time available with the patient.

“ Probably double the amount of prep time than it was actually with the patient. I mean, granted we did spend a while with the patient because we both like to talk, and the patient certainly did, but I think, and that’s the problem, isn’t it? You get the best information out of your patient when you let them talk and you let them tell you lots of things that you wouldn’t normally ask, but you haven’t got the time to do that so it’s tricky isn’t it to find the balance. But the biggest thing with the prep time was getting the information.” (Participant 3, Pharmacist FG1)

HCPs also conveyed frustration regarding the substantial time required to determine the original indication for a particular prescription and the ongoing necessity for it, even during major transition periods such as a patient’s admission to a care home.

“ We don’t get enough actual structured reviews, so they’ll be getting put on medication, people in care homes, and then left on those medicines. There’s no recognition of the changes. As you move in a care home, you’re generally more frailer, your renal function, haematic function might not be as great and, you know, you’re not moving as much, so your need for some medicines might not be as great as it was when the medicine was first started. ” (Policy-maker, Interview) “So along with what you said about deprescribing Selective Serotonin Reuptake Inhibitors (SSRIs) , especially , you know they’ve been on them for 4–5 years and they are adamant they don’t want to reduce them or stop them or have any sort of conversation about it , yeah , they’re quite challenging I think . Also , I think sometimes you can’t quite work out what medications people have been on . I mean if we talk about SSRIs they may have tried multiple different ones in the past and sometimes it’s difficult to work out what they’ve been on without having to go through the long , long list searching all the different medications that are SSRIs that they’ve tried . It would be so helpful if you know it could just bring up yeah been used before , and then know which one you could try…knowing that you want to try a different one . ” (Participant 6, GP FG1)

Although it is possible within the EHR system to link the prescription of an individual drug to its clinical indication, anecdotal evidence suggests this is time consuming and therefore may not be done in clinical practice.. As such, indications for prescribed medicines are recorded in the free text for the consultation which can easily become obscured over time within the extensive information contained in the clinical record. Examining the clinical free text for this information was emphasised as a challenge in efficiently conducting SMRs.

“ Although in my letters I would clearly state to the GP why I am prescribing the second line antipsychotic just so that people know, but over time that tends to get lost, the rationale for that prescribing tends to get lost and before you know you leave post, somebody else comes and begins to increase that second antipsychotic you know, so that becomes a problem. ” (Participant 1, Psychiatrist FG)

Moreover, existing EHRs are not adept at presenting patient histories in a manner conducive to HCPs pinpointing areas for potential deprescribing. This deficiency in the system leads to a cumulative high pill burden for patients, as illustrated in the quote below.

“ At the age of [ 18 – 20 ], I was diagnosed with bipolar. I am now [71–74] and I have lived for that period of time on medication, a lot of medication actually … I counted the number of tablets and my boxes on my bedside the other day and there was 13 different tablets, so that is what I am being prescribed by my GP. ” (Patient 1, Mental and physical co-morbidities FG)

Patients also expressed uncertainty about the initial reasons for starting medications. Patients reported receiving medications for many years and being unsure whether the medication was still necessary.

“ She is also on a daily injection of adult growth hormone which another consultant put her on at the time and she has been having them for probably 10 to 15 years, and no-one seems to know now who initially prescribed it and who is in charge of that. I am concerned, does she really need them? She is having them every day … Initially it was an asset to go with the immunodeficiency but now I don’t really know. ” (Patient 5, Mental and physical co-morbidities FG)

• d. SMRs require multiple appointments

SMRs typically lasted a minimum of 30 minutes, often extending beyond this duration. The variability in duration was contingent upon the patient’s complexity and the focused nature of the review. Allowing adequate time to address broader health concerns was deemed crucial, enabling the identification of potential issues requiring deeper exploration by the clinician.

HCPs acknowledged that SMRs were not a singular event, and patients might necessitate multiple appointments for a comprehensive review. Consequently, EHR systems were recognised as needing functionality to alert HCPs to schedule additional appointments after the initial SMR, emphasising the iterative and ongoing nature of medication reviews.

“ The first time I see patients, you want almost a bit of a holistic conversation, but actually when you start making interventions you go with what matters most to the patient or where the biggest risk is and you then table the others … You can imagine that being 2 or 3 hours in 4 different appointments before you get to the bottom of where you want to be … I think we had to contact on average about 2 to 3 times per patient, but there were more complex patients as well … I don’t think you can stop medicines or optimise medicines without seeing that patient again as least once. ” (Policy-maker interview)

Patients expressed a desire to be involved in the decision-making during reviews and valued the opportunity to discuss issues such as how medications fit into their routines and other resources that may be available to them.

"I’ve got a series of chronic things, take a load of pills and they’re each for separate things, and I have been concerned for years whether there’s any interaction with them, between them. And also they make me feel tired all the time and perhaps there are some of them where I could actually get rid of them." (Patient 1, Older people with frailty FG)

• e. Influence of healthcare context on delivering SMR

The duration of conducting a SMR was also contingent on the specific healthcare setting in which it took place. One pharmacist highlighted that SMRs conducted in care homes lacked a strict time limit and were oriented towards achieving specific outcomes, such as the number of medicines deprescribed in particular patient groups. This reflected the contextual variability in the conduct and objectives of SMRs, emphasising the need for flexibility in the approach based on the healthcare environment and patient population.

“ We were just told take whatever time you need but because we were not measured on the quantity, we were measured on the quality, and we were recorded the number of medicines basically stopped and in particular groups actually so, and then that would have gone on the report because that was the way of showing what we were doing and the basic value for money I guess .” (Participant 1, Pharmacist FG1)

The emphasis placed on a medication list varied depending on the reviewer and the healthcare setting. A pharmacist working in secondary care articulated a tendency to allocate less attention to certain medicines in a hospital setting, prioritising focus on medications more likely to cause harm. This perspective highlights the nuanced approach that different HCPs may adopt based on their expertise and the specific context in which they operate.

“ At the moment, the bisphosphonate would be something that I’m less concerned about it a very high acuity environment, that’s the thing that I’m probably going to, maybe if I get a chance, write in the discharge summary for the GP to check up on whether or not that’s still appropriate. Whereas I’m chasing those big harms .” (Participant 2, Pharmacist FG1)

• f. Factors influencing deprescribing discussions

Discussions around deprescribing between HCPs and patients were reported to be influenced by several factors. These included the specific type of medicine to be deprescribed, the patient’s willingness to discontinue the medication, sociodemographic location, availability of additional health services in the area, whether the medication was initially prescribed in primary or secondary care and the existence of pre-established stopping criteria for certain medications (e.g., bisphosphonates for more than 5 years). Additionally, HCPs and patients acknowledged a degree of reluctance to engage in deprescribing due to perceived potential risks associated with the cessation of certain medications. These multifaceted factors contribute to the complexity and individualised nature of deprescribing discussions within the healthcare context.

“I find it really, really difficult because all of the guidelines will say, oh, you should have this patient on statins, etc., and you think I really probably shouldn’t they’re 95, but having stopped them in the past then a patient unfortunately ends up with a stroke, they go to hospital, the hospital tells them it’s because their GP stopped their statin and puts them back on .” (Participant 2, GP FG2) “ The antidepressant one is interesting . So , I did work for 9 years in a really deprived area . . . SSRIs for a long time and they were pretty reluctant to come off it but usually willing to accept if it didn’t work out just put them back on it . It just meant in a much more middle-class area there’s the opposite where they’re all desperate to come off it and probably coming off it far too soon . So , I don’t think it needs to be doctor-led , it seems to be more about their external pressures and there’s been a lot of areas done in deprived communities with link workers and social prescribers and I think if you’re going to look at polypharmacy in these sort of groups then that’s probably the way to do it stopping all their meds .” (Participant 4, GP FG1)

In one case, a pharmacist highlighted the challenges associated with decision-making when optimising medicines for complex, younger patients, emphasising the impact of side-effects on their quality of life. Equally, the importance of considering quality of life in frail older adults with polypharmacy was acknowledged, although perceived as less complicated than in younger adults.

"I know well that’s it isn’t it, it’s not necessarily that they’re a complex medicine it’s that there is evidence to say that this can prolong your life but it’s causing them that much upset, so it’s, for me it’s not necessarily the, I can’t think of any particular group of drug its more the younger you get, you know 50 is very young and that you have got a lot of life left to live so that’s when it becomes more of a clinical decision for me that as a pharmacist I don’t feel like I would be able to make" (Participant 1, Pharmacist FG1)

• 2. Medication-related Challenges

Potential for medication-related harm identified by our key stakeholder groups included issues with specific medicines, conditions, and risky medication combinations; mental health medications; prescriptions from specialists; anticholinergic medicines; difficulties in determining prescription timelines to assist in decision-making; challenges with younger complex patients; and siloed care.

• a. Poor communication & data sharing between primary and secondary care

HCPs identified the challenge of extracting information from hospital discharge letters as a key source of frustration. Patients, in particular, assumed that EHRs seamlessly connected primary and secondary care, and in some cases, their community pharmacy. This assumption left patients bewildered and, in certain cases, reliant on the HCPs knowing the complete narrative behind their health records. The disjointed communication and misconceptions surrounding record integration emphasised the need for improved interoperability to enhance the continuum of care.

“ We will be waiting a week for an outpatient letter to come through and it’s really confusing, stop this, change that, increase the dose here, and you’re kind of stuck in the middle. So sometimes the patient will have left that meeting there and it will be ‘like go and see your GP and they will do this bit’, well [that’s] not happening until I’ve got that letter. There is that real mismatch of communication. ” (Participant 1, GP FG2) “ When the repeat prescription came through , despite the fact that the surgery had received the discharge letter , everything was all wrong , and this is just one of those things that happens . So , you get a review and it is whether that data from that review and the story behind it and who it goes back to and whether it is acted on , I think that is important .” (Patient 3, Complex multimorbidity FG)

Communication gaps between GPs, specialist clinicians, and patients were evident due to varying expectations. GPs expressed challenges in managing specialist medications with patients, including concerns about patients’ ability to self-manage their medicines. These challenges highlighted aspects of fragmented care between primary care and specialist clinicians. HCPs also cited difficulties and reluctance in communicating and potentially engaging in conflict with specialist doctors. Participants described specialist doctors as lacking a holistic view when prescribing for patients, favouring certain medicines, and having limited knowledge in drug interactions.

“I think multidisciplinary is key, however when I notice that I liaise with specialists, depending on who I speak to, their drug is the most important and trying to get a consensus about what is best for the patient is obviously one of the challenges like that’s how we got in this situation to begin with you know, so I do find that’s one of the challenges. so, I think you need someone who is you know, who is a specialist but has also got a sort of holistic view of patient care as well which doesn’t always happen in secondary care, but sometimes does.” (Participant 3, Polypharmacy FG)

Participants noted that central nervous system medicines had complicated medication regimens and hence required more coordinated care and responsibility between the specialist prescriber and GP. Addressing these challenges calls for enhanced collaboration, knowledge exchange, and a holistic approach to patient care between primary care and specialist clinicians.

“ I find it, with the pain management clinic, they stop medication, give you a list of all these other tablets you need to start to see how things go and then sort of leave you to it, discharge the patient in your hands and expect you to sort of manage it all. And the same thing is with migraine and headaches from neurology. That’s just a minefield … I think when you’re in specialty, you feel that you can give any sort of long protracted complicated regime and the patient is just going to manage it because that’s the only medication that you think that they’re on. So yeah, they can be quite difficult .” (Participant 5, GP FG1) “ He [GP] says that we can’t actually change any medication to do with your bipolar , that has got to be done by your psychiatrists … I don’t think they would change anything to do with psychiatry .” (Patient 1, Mental and physical co-morbidities FG)

• b. Difficulties managing mental health medication

Mental health medication and management emerged as a consistent sub-theme across key HCP stakeholder groups, irrespective of their professional background. Both doctors and pharmacists described difficulties in monitoring and adjusting psychiatric medicines, including uncertainties about how to address specific issues related to psychiatric medicines. Participants expressed a sense of being ‘out of their depth’, particularly concerning antipsychotic medicines. They conveyed a lack of confidence in assessing the risks and benefits of antipsychotic prescribing, feeling deskilled in this specific area of medication management, and finding it challenging to safely challenge prescribers. This sense of unease prompted participants to seek ways of contacting the mental health team, only to encounter additional hurdles, such as difficulties in locating relevant information within patient records to facilitate multidisciplinary coordinated patient care.

“ But the other one is someone with very complex psychiatric problems, still maybe under the mental health team, and I haven’t got really access to the details apart from maybe I’ve got, you know, some of the other diagnoses. But if I think maybe one of those drugs is potentially causing more harm than could then I’m not clear how then to action that and who to speak to and who were they actually seeing ” (Participant 1, Polypharmacy FG)

For example, a clinical pharmacologist explained that evaluating the success of managing antipsychotics is not as straightforward as assessing physical health conditions. This complexity may contribute to the observed lack of confidence among HCPs when it comes to deprescribing psychiatric medicines. The nuanced nature of mental health outcomes, compared to more tangible markers of success in physical health, adds an additional layer of intricacy to the decision-making process in psychiatry. This includes the complexity of managing mental health medication.

“ The biggest challenge group that I think we face in a deprived area is the patients who are on long term opioid medication, long term neuropathic meds, they’ve probably got a diagnosis of fibromyalgia, they’ve probably got personality disorder plus / minus mental health problems. And the issues that we have is that they’ve almost been sequentially added medication on because GPs don’t really often know what to do with them unless you have a special interest in that field like I do. And when they go and see pharmacists, they are very challenging to pharmacists and pharmacists don’t have the clinical knowledge to be able to sift through what can often be quite dramatic presentations. ” (Participant 1, GP FG1)

• c. Challenges around anticholinergic medicines

Anticholinergic medicines, which inhibit the neurotransmitter acetylcholine involved in numerous physiological functions, has been associated with adverse outcomes such as cognitive decline and falls, particularly when multiple anticholinergic medicines are used concurrently (termed anticholinergic burden) [ 25 , 26 ]. GPs, clinical pharmacologists, and pharmacists described the importance of reviewing and deprescribing anticholinergic medicines where possible. However, the process of calculating anticholinergic burden (ACB) in frail, older adults is time consuming, primarily due to the absence of automated calculators embedded within the EHR system.

Doctors and pharmacists expressed frustrations around the re-prescribing of anticholinergics after deprescribing them. They attributed the persistence of high ACB to limitations in prescribing guidelines and a scarcity of alternative options to replace anticholinergic drugs. These challenges highlighted the need for tools within EHR systems to facilitate efficient assessment of ACB, alongside a broader exploration of prescribing guidelines and alternatives to enhance deprescribing practices.

“ One of the things that I often see in general practice is that there’s lots of anticholinergics, usually amitriptyline because it’s kind of given out for other reasons for what it’s licensed for. So, sleep is probably the most common thing that I see it used for, or avoiding long term opioids in chronic arthritic pain, and often that’s because we have other options for them but we’re not allowed to prescribe them. So, melatonin is probably the most common thing that we could put them on which has a lot better safety profile, but we are just completely discouraged from prescribing it. And likewise access to other interventions that would help arthritic pain rather than putting them on NSAIDS which obviously carry risk or opioids which aren’t overly effective outside the acute pain window. It’s often the lack of other stuff that raises all of the anticholinergic burden. ” (Participant 1, GP FG1)

Participants welcomed any digital tool that could streamline routine work processes, including information retrieval, automated dose calculations, and assessing the risk of developing diseases to optimise medicines during a SMR in a patient-centred manner, with the goal of enhancing efficiency in the medication optimisation process.

Medication reviews by HCPs can take significant preparation, and are time consuming, primarily due to the need to gather and understand patient information and to develop an understanding of a patient’s medical history and social circumstances. In addition, currently, there is no easy way to identify from the EHR which patients are at greatest risk of medication-related harm and those most likely to benefit from an SMR. The EHR systems used in primary care contain enormous volumes of information which becomes particularly challenging and time-consuming to navigate for complex individuals living with multiple long-term conditions and taking many medications. The way that information is organised in the system leads to a large proportion of time spent linking medications to their original indication and examining the patient journey. This time could be better spent discussing shared decisions with the patient. The EHR has not evolved in line with increasing patient complexity. The findings of this report emphasise the need for enhanced functionalities in EHRs to support effective medication management in the context of deprescribing discussions where a nuanced understanding of a patient’s medication history is crucial.

Our study has highlighted the challenges facing those undertaking SMRs in more socioeconomically disadvantaged areas, where people experience multimorbidity (and co-existent polypharmacy) 10–15 years earlier than their affluent peers [ 27 , 28 ]. These populations have complex healthcare needs at a younger age, the care of which falls to the already over-stretched GPs. Areas with greater socioeconomic disadvantage often have lower health literacy, resulting from a combination of lower educational attainment, economic barriers like the need to prioritise food and heating over health seeking, and psychosocial stressors affecting decision-making relating to health [ 29 , 30 ]. Health literacy applies not only to the patient but to the clinician who may also be unaware of the psychosocioeconomic situation of the patient, leading to a communication gap when discussing the risk and benefit of medicines to reach a shared-decision [ 31 – 33 ]. Accordingly, complex conversations involving numeracy calculations of risk may take longer and require repetition, but may also be of less priority for the patient and/or carer than other more immediate life concerns.

As preparation time is repeatedly cited as a barrier to effective SMR, a potential solution that would support SMRs in those with lower health literacy should include any digital intervention that saves on preparation time. This would enable more time for the clinician to engage with the patient and discuss complexities around risk and benefit, which would go some way to addressing the existing health disparity that affects those experiencing socioeconomic disadvantage. For HCPs working in areas of socioeconomic deprivation, lack of HCP capacity alongside patients declining SMR invitations were cited as barriers to undertaking SMRs. Moreover, HCPs described the usefulness of a system to identify availability of different health services in surrounding areas (e.g. weight management service) [ 27 , 28 ]. A recent study co-produced SMR resources to empower patients in their healthcare and support them in making the most out of their SMR. This included producing resources in a number of different languages including audio recorded resources for patients with visual impairment [ 34 , 35 ]. Embedded links to resources for HCPs to provide to patients before and/or after an SMR can be one potential way to utilise digital health and empower patients to reduce inequity in access to healthcare.

Our study also highlighted medication-related challenges such as difficulties managing mental health, specialist and anticholinergic medications. HCPs reported that a lack of alternatives to medication for symptom management hampered their ability to optimise some of the more potentially harmful medication classes such as opioids, anti-depressants, anticholinergics and gabapentinoids. Non-pharmacological alternatives, where appropriate, such as counselling need to be readily and equitably accessible for this approach to be considered a reliable option [ 36 ]. Mental health medication management stood out as a consistent challenge. HCPs in our study, regardless of their professional background, expressed difficulty in monitoring and adjusting psychiatric drugs. There was also a general lack of confidence and skill when it came to monitoring and adjusting antipsychotic medications, with the measurement of success in managing these medications being ambiguous. This is consistent with previous studies that note GPs lack of confidence in managing patients with serious mental health illness [ 12 , 37 ]. One recent study reported that less than half of GP trainees in England and Wales have trained in a mental health setting between 2013 and 2015 [ 38 ]. In addition to the need for HCPs in primary care to become trained to address issues related to psychiatric medicines, EHRs must include basic information about the indication for the prescribed psychiatric medicine and the appropriate mental health team contact details for GPs to be able to address these issues. This would have the potential to enable multidisciplinary coordination of care with mental health patients.

Another challenging group of medicines was those with anticholinergic effects. This drug class was also a recurring issue among GPs, clinical pharmacologists, and pharmacists. HCPs found it time consuming to calculate the ACB in frail, older adults. Although there are several ACB scales available that have been developed and validated, participants stated that automated calculators to calculate ACB are not easily accessible or embedded into EHRs. In addition, there is considerable variability between anticholinergic scales making it difficult to ascertain which scale to use to calculate ACB [ 39 ]. As such, taking the time out to include every medicine a patient is taking to calculate their ACB is time consuming, reducing opportunities for potential deprescribing discussions with patients.

Limitations

This study was conducted in the UK, which provides universal access to healthcare. However, findings from our study may also be applicable internationally to other health systems which operate a universal social insurance model where there is a primary care and secondary care gatekeeping model, including the need for HCP coordination [ 40 , 41 ] or deprescribing challenges [ 42 , 43 ]. This study is part of a larger qualitative study examining both barriers to SMRs and potential digital solutions, including AI-assisted approaches. As such, the HCP participants likely included a number of clinicians with a particular interest in digital-driven solutions in healthcare. We sought to include a wide variety of HCPs from different practice backgrounds in order to mitigate this. In addition, some of the focus groups contained unexpected small numbers of clinicians at one time, owing to the competing demands on clinician time. However, the data collected were rich and contributed significantly to achieving thematic saturation. The insights gained from these discussions were consistent with those from larger groups, reinforcing the validity of our findings.

Conclusions

There are few useful digital tools that can identify patients that would benefit most from an SMR or monitor the effects of medication optimisation when medicines are altered. Our findings showed that significant time is needed to prepare and conduct a SMR, with complex patients sometimes needing multiple appointments to enable a comprehensive review. The DynAIRx project will use findings from this study to address the barriers of conducting an SMR by producing dashboards and visualisations to summarise the patient’s medical journey; develop digital tools to prioritise patients that would benefit most from an SMR; and identify optimal interventions for specific multimorbidity and polypharmacy patient groups.

Supporting information

S1 appendix. topic guides for interviews and focus groups and coreq checklist..

https://doi.org/10.1371/journal.pone.0299770.s001

S2 Appendix. Table containing additional explanatory quotes relating to subthemes.

https://doi.org/10.1371/journal.pone.0299770.s002

• 1. NHS. Network Contract Directed Enhanced Service. Structured medication reviews and medicines optimisation: guidance. In: NHS, editor. 2020.
• 2. NHS England. Network contract DES guidance for 2024/25: Part A ‐ Clinical and support services (Section 8). In: NHS England, editor. 2024.
• 3. NHS. Network Contract Directed Enhanced Service. Contract specification 2021/22 ‐ PCN Requirements and Entitlements. 2021.
• View Article
• PubMed/NCBI
• Google Scholar
• 7. NICE. Multimorbidity and polypharmacy 2017 [Available from: https://www.nice.org.uk/advice/ktt18 ].
• 11. NICE. Multimorbidity: clinical assessment and management: NICE,; 2016 [Available from: https://www.nice.org.uk/guidance/ng56 ].
• 20. Pope C, Mays N. Qualitative research in health care. Fourth edition ed. Hoboken: Wiley-Blackwell; 2020.
• 21. Ardens Healthcare Informatics. The leaders in providing EMIS Web & SystmOne templates & resources [Available from: https://www.ardens.org.uk/ ].
• 22. PARM Diabetes. A health management tool for people with diabetes co-created by Lilly and NHS Devon CCG [Available from: https://parmdiabetes.co.uk/ ].
• 23. NHS. Network Contract Directed Enhanced Service. Investment and Impact Fund 2020/21: guidance. 2020.
• 24. NHS Digital. SNOMED CT 2023 [Available from: https://digital.nhs.uk/services/terminology-and-classifications/snomed-ct ].
• 32. Citizens Advice. A very general practice. How much time do GPs spend on issues other than health?: Citizens Advice,; 2015 [Available from: https://www.citizensadvice.org.uk/Global/CitizensAdvice/Public%20services%20publications/CitizensAdvice_AVeryGeneralPractice_May2015.pdf ].
• 35. Health Innovation Network. Resources to support patients having a Structured Medication Review [Available from: https://thehealthinnovationnetwork.co.uk/programmes/medicines/polypharmacy/patient-information/ ].
• 38. MIND. Better equipped, better care. Improving mental health training for GPs and practice nurses 2016 [cited 2023 December 11]. Available from: https://www.mind.org.uk/media-a/4501/find-the-words-report-better-equipped-better-care.pdf .

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8 best qualitative research tools.

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Qualitative research plays a vital role in understanding human experiences, motivations, and behaviors. Researchers often face the challenge of analyzing complex data gathered through interviews and focus groups. To navigate this intricacy, selecting the right qualitative tools is essential. This section explores the top qualitative tools available, designed to simplify the analysis process and enhance research quality.

With an array of options, these tools cater to varying expertise levels, ensuring accessibility for both seasoned researchers and newcomers. By harnessing these top qualitative tools, you can effectively convert rich qualitative data into meaningful insights, guiding critical decisions in your projects. As we delve into our top picks, you will discover how they can transform your research experience.

Why Qualitative Research Matters

Qualitative research plays a crucial role in understanding users' experiences, motivations, and behaviors. It sheds light on the nuances that quantitative data might overlook, providing rich insights that can drive meaningful change. When organizations aim to improve products, services, or customer interactions, qualitative research tools become invaluable. They enable researchers to unearth deep sentiments and perspectives that help in crafting solutions tailored to real needs.

These tools can reveal patterns and shared themes that inform strategic decisions. For instance, by analyzing verbatim responses, researchers can identify key insights that resonate across different participants. This understanding goes beyond surface-level statistics, facilitating a more profound comprehension of user needs. Embracing qualitative research empowers teams to ask the right questions and engage with customers on a deeper level, ultimately enhancing the effectiveness of their strategies. Understanding these aspects is essential for anyone looking to utilize top qualitative tools effectively.

Key Features of Top Qualitative Tools

Top Qualitative Tools share several key features that enhance their effectiveness for researchers. Ease of use is crucial; tools should simplify complex processes, making them accessible to users without extensive research experience. User-friendly interfaces enable quick learning and seamless integration into various projects. Furthermore, robust data analysis capabilities allow researchers to extract meaningful insights effortlessly, breaking down intricate interview transcripts and qualitative data effectively.

Another vital feature is customization. The best tools can adapt to unique research needs, allowing users to tailor their analysis workflows. Added functionalities such as tagging, sorting, and interactive visualizations enhance understanding of collected data. Collaborative features also stand out, enabling teams to work together efficiently and share valuable insights. In sum, Top Qualitative Tools provide comprehensive solutions that transform raw data into actionable insights, empowering researchers to make informed decisions based on nuanced interpretations.

Top Qualitative Tools for Effective Research

Understanding the top qualitative tools for effective research can significantly enhance your insights and findings. These tools allow researchers to collect, analyze, and interpret qualitative data, making the process more structured and insightful. From interview analysis to survey response evaluation, these tools cater to various research needs.

Key tools in this domain include transcription software, qualitative data analysis platforms, and user feedback systems. For instance, transcription software simplifies converting spoken words into written text, enabling more focused analysis. Qualitative data analysis tools provide robust frameworks for identifying patterns, themes, and insights from collected information. Additionally, user feedback systems facilitate direct engagement with participants, ensuring that the insights gained are relevant and actionable. Together, these top qualitative tools empower researchers to derive meaningful conclusions, ultimately driving informed decision-making.

Software Solutions for Qualitative Data Analysis

Qualitative data analysis is essential for understanding complex human behaviors and emotions. Utilizing software solutions can significantly enhance this process, making it efficient and systematic. The top qualitative tools are designed to simplify the transcription, analysis, and reporting of qualitative data, which often involves text, audio, and video formats. These tools help researchers focus on deriving meaningful insights instead of spending excessive time on tedious manual tasks.

When selecting software solutions for qualitative data analysis, consider key features such as automation, ease of use, and collaboration capabilities. Here are some crucial aspects to evaluate:

Automation : Look for tools that offer automated transcription and analysis to save time and reduce errors.

Collaboration : Opt for software that facilitates teamwork, allowing multiple researchers to work on the same project seamlessly.

Data Visualization : Choose tools that provide visualization options to make insights more digestible.

Integration : Ensure that the software can integrate with other research tools or databases.

Support and Training : Good software comes with responsive customer support and educational resources.

Choosing the right software can lead to more accurate insights and a streamlined research process, ultimately enhancing the quality of qualitative analysis.

NVivo: An In-Depth Look

NVivo stands out among the top qualitative tools by providing researchers with a user-friendly and efficient platform for data analysis. Its innovative features allow users to quickly organize, manage, and analyze qualitative data, transforming complex datasets into actionable insights. One notable aspect is its ability to adapt to various project needs. Researchers can utilize ready-made templates tailored for market research, employee engagement, or product development.

Additionally, NVivo enhances the analysis process through AI integrations. This capability enables users to pose intricate questions and receive clear, data-driven responses in moments. With its visual dashboards, researchers can easily track their findings, facilitating a deeper understanding of the data at hand. Overall, NVivo is not only a powerful tool for qualitative research but also an essential asset for those seeking to derive meaningful insights from their data efficiently.

Atlas.ti: Simplifying Complex Data

The tool excels in simplifying complex data, making it easier for researchers to analyze. By integrating various features, it allows users to create robust datasets while highlighting relevant insights. Researchers can delve into multi-project queries to extract information across numerous datasets. This capability is especially beneficial in qualitative research, as it provides a holistic view of varied data sources.

Moreover, visual experiences like journey maps enhance data interpretation by transforming abstract concepts into tangible representations. Users can draft recommendations based on concrete data, guiding decision-making processes in fields such as banking and customer experience. With its user-friendly approach, the tool stands out as one of the top qualitative tools for researchers aiming to uncover meaningful insights efficiently. Streamlining the analysis process ultimately empowers researchers to focus on their core objectives while navigating the complexities of qualitative data.

Online Tools for Qualitative Surveys

Online tools for qualitative surveys have revolutionized the research process, enabling researchers to gather insights efficiently. These tools facilitate the collection and analysis of qualitative data, allowing for a deeper understanding of participants' perspectives. Throughout this journey, the challenge lies in selecting the right technology that meets the needs of your study while ensuring ease of use for all participants.

Several key online tools can enhance your qualitative research efforts. Firstly, engaging survey platforms offer intuitive designs that make it simple for respondents to share their thoughts. Secondly, transcription services convert verbal interviews into text quickly, saving valuable time. Additionally, visualization tools help researchers to interpret and present their findings in a compelling manner. Each of these solutions aims to streamline the process and improve data quality, making your research more effective. Remember, selecting the right tool can significantly impact the quality of insights you gain.

SurveyMonkey: Powering Research with Ease

When it comes to qualitative research, efficient data collection and analysis are crucial. This platform excels in powering research with ease, allowing users to create insightful surveys quickly. With user-friendly features, researchers can effortlessly design tailored surveys to capture rich qualitative data. Through engaging formats, such as open-ended questions, the platform encourages participants to share their thoughts and experiences in greater detail.

Additionally, the platform offers a powerful AI-driven analysis tool that transforms raw data into valuable insights. By using customizable templates, researchers can streamline their projects for various purposes like market analysis or employee engagement. This capability ensures that researchers can extract actionable insights faster, aiding in decision-making processes. With these robust features, the platform stands out among the top qualitative tools, enabling better understanding and more informed strategies.

Google Forms: A Free and Flexible Tool

Google Forms is a robust option among the top qualitative tools available today. Its user-friendly interface is designed to cater to researchers of all experience levels. With this tool, you can easily create surveys, collect data, and analyze responses without needing advanced technical skills. This flexibility makes it an ideal platform for various research needs, from academic studies to user feedback initiatives.

Here are some key features that enhance Google Forms' appeal:

Customization : Users can incorporate various question types, including multiple choice, text responses, and checkboxes to tailor surveys to specific research goals.

Real-Time Collaboration : Teams can collaborate on forms simultaneously, allowing seamless communication and faster data collection.

Accessibility : Since it is cloud-based, researchers can access their forms and data from any device, enhancing workflow efficiency.

Automatic Data Collection : Responses are automatically gathered and can be visualized in charts for easy analysis, facilitating quick insights.

These features underscore why Google Forms remains a go-to tool for qualitative research efforts.

Tools for Ethnographic Research

Ethnographic research often requires specific tools that facilitate in-depth understanding of cultural and social dynamics. These tools gather qualitative data through direct observation, interviews, and participatory methods. Among the top qualitative tools available, researchers can utilize field notes, audio recordings, and video documentation. Each of these tools plays a significant role in capturing the nuances of human experience.

Field notes allow researchers to document their observations in real-time, offering insights that might not be captured through formal interviews. Audio recordings of conversations can provide deeper context, enabling researchers to analyze tone and emotional cues. Video documentation captures visual interactions and settings, further enriching the understanding of a subject's environment. Employing these tools in ethnographic research ensures a comprehensive approach to data collection, ultimately leading to richer insights and more reliable conclusions.

Dedoose: Multi-Method Research Simplified

Dedoose simplifies multi-method research by seamlessly integrating qualitative and quantitative data analysis tools. Researchers can explore and synthesize complex information without the hassle of traditional methods. This tool is particularly useful for projects that involve multiple interviews, allowing users to manage and analyze data effectively. By offering features like the research matrix, it facilitates the comparison of themes across various transcripts, leading to richer insights.

Developers designed this software to enhance collaboration and streamline workflows. Users can easily mix standard spreadsheet tasks with advanced AI functions, making it a standout choice among top qualitative tools. As a result, researchers can extract specific insights and identify trends efficiently. This capabilities ultimately empower teams to make data-driven decisions that are informed by clear, actionable findings, ensuring a more structured approach to qualitative research.

MAXQDA: Bridging Quantitative and Qualitative Data

MAXQDA plays a vital role in bridging the gap between qualitative and quantitative data, acting as a robust research tool. In the realm of qualitative research, it supports users by analyzing text, audio, and video data, thus enriching the understanding of complex insights. This dual approach allows researchers to complement numerical data with rich narratives, providing a more holistic view of their studies.

One of the standout features of MAXQDA is its ability to automate insights and streamline reporting workflows. By minimizing the need for manual data transcriptions and analyses, it significantly enhances efficiency while maintaining high-quality outcomes. The integration of AI technologies ensures a reduction in bias, prompting better decision-making based on reliable data. Ultimately, tools like this exemplify the future of qualitative research, allowing for deeper insights that greatly inform strategies and outcomes.

Real-Time Collaboration Tools

In today’s rapidly evolving work environment, real-time collaboration tools have become essential for qualitative research teams. These tools allow multiple team members to contribute ideas and insights simultaneously, fostering creativity and enhancing productivity. Tools like shared digital whiteboards and brainstorming platforms enable researchers to organize thoughts visually, ensuring that no critical perspective is overlooked.

Moreover, real-time collaboration enhances communication among team members, providing instant feedback and support. Using chat features or video conferencing, teams can discuss findings and refine strategies immediately. This synergy is crucial when adapting to emerging trends or shifts in data. Ultimately, incorporating real-time collaboration into qualitative research processes streamlines workflows and produces richer, more nuanced insights. By utilizing the best qualitative tools available, research teams can transform their approach to gathering and analyzing information effectively.

Microsoft OneNote: Collaborative Note-Taking

Microsoft OneNote is a powerful tool for collaborative note-taking, making it a top contender among qualitative research tools. It enables users to gather and organize ideas from various discussions in a structured manner. By storing conversations, summaries, and highlights, users can easily revisit key insights that emerge during collaborative sessions. This capability encourages seamless communication among team members while allowing everyone to contribute their thoughts and perspectives.

Moreover, OneNote supports various media formats, allowing users to incorporate images, links, and documents. This versatility enhances the richness of data collected, encouraging dynamic interactions. The tool also enables project integration, simplifying the process of linking notes to specific tasks or objectives. Its user-friendly interface streamlines collaboration, ensuring that teams can work together efficiently, making it an invaluable asset in qualitative research.

Trello: Organizing Research Processes

Trello excels at organizing research processes by providing a dynamic platform for project management. It allows teams to create boards that can categorize various qualitative research tasks, making collaboration straightforward and effective. For researchers, maintaining clarity amidst complex data collection and analysis is essential, and this tool facilitates that by enabling users to establish individual cards for each aspect of their research.

Each card can include descriptions, checklists, and attachments, ensuring that every detail is covered. Additionally, Trello’s commenting feature allows team members to communicate directly on respective tasks, thus reducing miscommunication. By visually representing tasks and their progress, Trello enhances the overall workflow. This level of organization is vital for researchers striving to achieve quality insights while managing time efficiently. Ultimately, utilizing such a tool is crucial for anyone looking to streamline their qualitative research efforts.

Conclusion: Choosing the Right Top Qualitative Tools

In conclusion, selecting the right top qualitative tools can significantly enhance your research efforts. The ideal tools should be user-friendly while offering powerful analytics capabilities. This balance allows researchers, regardless of their experience level, to draw valuable insights from their qualitative data.

Consider your specific needs when evaluating these tools. Features such as ease of use, scalability, and integration with existing systems can impact your choice. Ultimately, the right tool will empower you to gather rich, meaningful data, enabling informed decision-making and deeper understanding of your target audience.

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

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• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

Abdi S, de Witte L, Hawley M (2020) Emerging technologies with potential care and support applications for older people: review of gray literature. JMIR Aging 3(2):e17286. https://doi.org/10.2196/17286

Article   PubMed   PubMed Central   Google Scholar  

Achuthan K, Nair VK, Kowalski R, Ramanathan S, Raman R (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput Human Behav 140:107566. https://doi.org/10.1016/j.chb.2022.107566

Article   Google Scholar  

Ahmad A, Mozelius P (2022) Human-Computer Interaction for Older Adults: a Literature Review on Technology Acceptance of eHealth Systems. J Eng Res Sci 1(4):119–126. https://doi.org/10.55708/js0104014

Ale Ebrahim N, Salehi H, Embi MA, Habibi F, Gholizadeh H, Motahar SM (2014) Visibility and citation impact. Int Educ Stud 7(4):120–125. https://doi.org/10.5539/ies.v7n4p120

Amin MS, Johnson VL, Prybutok V, Koh CE (2024) An investigation into factors affecting the willingness to disclose personal health information when using AI-enabled caregiver robots. Ind Manag Data Syst 124(4):1677–1699. https://doi.org/10.1108/IMDS-09-2023-0608

Baer NR, Vietzke J, Schenk L (2022) Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: a systematic mixed studies review. PLoS One 17(12):e0278879. https://doi.org/10.1371/journal.pone.0278879

Barnard Y, Bradley MD, Hodgson F, Lloyd AD (2013) Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Comput Human Behav 29(4):1715–1724. https://doi.org/10.1016/j.chb.2013.02.006

Berkowsky RW, Sharit J, Czaja SJ (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 3(1):igy002. https://doi.org/10.1093/geroni/igy002

Braun MT (2013) Obstacles to social networking website use among older adults. Comput Human Behav 29(3):673–680. https://doi.org/10.1016/j.chb.2012.12.004

Article   MathSciNet   Google Scholar  

Campo-Prieto P, Rodríguez-Fuentes G, Cancela-Carral JM (2021) Immersive virtual reality exergame promotes the practice of physical activity in older people: An opportunity during COVID-19. Multimodal Technol Interact 5(9):52. https://doi.org/10.3390/mti5090052

Chen C (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317

Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813

Article   PubMed   Google Scholar  

Chen C, Leydesdorff L (2014) Patterns of connections and movements in dual‐map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol 65(2):334–351. https://doi.org/10.1002/asi.22968

Chen J, Wang C, Tang Y (2022) Knowledge mapping of volunteer motivation: A bibliometric analysis and cross-cultural comparative study. Front Psychol 13:883150. https://doi.org/10.3389/fpsyg.2022.883150

Chen JY, Liu YD, Dai J, Wang CL (2023) Development and status of moral education research: Visual analysis based on knowledge graph. Front Psychol 13:1079955. https://doi.org/10.3389/fpsyg.2022.1079955

Chen K, Chan AH (2011) A review of technology acceptance by older adults. Gerontechnology 10(1):1–12. https://doi.org/10.4017/gt.2011.10.01.006.00

Chen K, Chan AH (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855

Chen K, Zhang Y, Fu X (2019) International research collaboration: An emerging domain of innovation studies? Res Policy 48(1):149–168. https://doi.org/10.1016/j.respol.2018.08.005

Chen X, Hu Z, Wang C (2024) Empowering education development through AIGC: A systematic literature review. Educ Inf Technol 1–53. https://doi.org/10.1007/s10639-024-12549-7

Chen Y, Chen CM, Liu ZY, Hu ZG, Wang XW (2015) The methodology function of CiteSpace mapping knowledge domains. Stud Sci Sci 33(2):242–253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009

Codfrey GS, Baharum A, Zain NHM, Omar M, Deris FD (2022) User Experience in Product Design and Development: Perspectives and Strategies. Math Stat Eng Appl 71(2):257–262. https://doi.org/10.17762/msea.v71i2.83

Dai J, Zhang X, Wang CL (2024) A meta-analysis of learners’ continuance intention toward online education platforms. Educ Inf Technol 1–36. https://doi.org/10.1007/s10639-024-12654-7

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

Delmastro F, Dolciotti C, Palumbo F, Magrini M, Di Martino F, La Rosa D, Barcaro U (2018) Long-term care: how to improve the quality of life with mobile and e-health services. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 12–19. IEEE. https://doi.org/10.1109/WiMOB.2018.8589157

Dupuis K, Tsotsos LE (2018) Technology for remote health monitoring in an older population: a role for mobile devices. Multimodal Technol Interact 2(3):43. https://doi.org/10.3390/mti2030043

Ferguson C, Hickman LD, Turkmani S, Breen P, Gargiulo G, Inglis SC (2021) Wearables only work on patients that wear them”: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies. Cardiovasc Digit Health J 2(2):137–147. https://doi.org/10.1016/j.cvdhj.2021.02.001

Fisk AD, Czaja SJ, Rogers WA, Charness N, Sharit J (2020) Designing for older adults: Principles and creative human factors approaches. CRC Press. https://doi.org/10.1201/9781420080681

Friesen S, Brémault-Phillips S, Rudrum L, Rogers LG (2016) Environmental design that supports healthy aging: Evaluating a new supportive living facility. J Hous Elderly 30(1):18–34. https://doi.org/10.1080/02763893.2015.1129380

Garcia Reyes EP, Kelly R, Buchanan G, Waycott J (2023) Understanding Older Adults’ Experiences With Technologies for Health Self-management: Interview Study. JMIR Aging 6:e43197. https://doi.org/10.2196/43197

Geng Z, Wang J, Liu J, Miao J (2024) Bibliometric analysis of the development, current status, and trends in adult degenerative scoliosis research: A systematic review from 1998 to 2023. J Pain Res 17:153–169. https://doi.org/10.2147/JPR.S437575

González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594. https://doi.org/10.1080/03601277.2011.595314

Guner H, Acarturk C (2020) The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19(2):311–330. https://doi.org/10.1007/s10209-018-0642-4

Halim I, Saptari A, Perumal PA, Abdullah Z, Abdullah S, Muhammad MN (2022) A Review on Usability and User Experience of Assistive Social Robots for Older Persons. Int J Integr Eng 14(6):102–124. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8566

He Y, He Q, Liu Q (2022) Technology acceptance in socially assistive robots: Scoping review of models, measurement, and influencing factors. J Healthc Eng 2022(1):6334732. https://doi.org/10.1155/2022/6334732

Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2:361–375. https://doi.org/10.1007/s12369-010-0068-5

Ho A (2020) Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr 20(1):358. https://doi.org/10.1186/s12877-020-01764-9

Hoque R, Sorwar G (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 101:75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002

Hota PK, Subramanian B, Narayanamurthy G (2020) Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J Bus Ethics 166(1):89–114. https://doi.org/10.1007/s10551-019-04129-4

Huang R, Yan P, Yang X (2021) Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry. IET Collab Intell Manuf 3(3):243–251. https://doi.org/10.1049/cim2.12024

Article   ADS   Google Scholar  

Jing Y, Wang C, Chen Y, Wang H, Yu T, Shadiev R (2023) Bibliometric mapping techniques in educational technology research: A systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6

Jing YH, Wang CL, Chen ZY, Shen SS, Shadiev R (2024a) A Bibliometric Analysis of Studies on Technology-Supported Learning Environments: Hotopics and Frontier Evolution. J Comput Assist Learn 1–16. https://doi.org/10.1111/jcal.12934

Jing YH, Wang HM, Chen XJ, Wang CL (2024b) What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit Soc Sci Commun 11:319. https://doi.org/10.1057/s41599-024-02751-w

Kamrani P, Dorsch I, Stock WG (2021) Do researchers know what the h-index is? And how do they estimate its importance? Scientometrics 126(7):5489–5508. https://doi.org/10.1007/s11192-021-03968-1

Kim HS, Lee KH, Kim H, Kim JH (2014) Using mobile phones in healthcare management for the elderly. Maturitas 79(4):381–388. https://doi.org/10.1016/j.maturitas.2014.08.013

Article   MathSciNet   PubMed   Google Scholar  

Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 91–101. https://doi.org/10.1145/775047.775061

Kruse C, Fohn J, Wilson N, Patlan EN, Zipp S, Mileski M (2020) Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: systematic review. JMIR Med Inform 8(8):e20359. https://doi.org/10.2196/20359

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40. https://doi.org/10.1007/s10660-021-09464-1

Kwiek M (2021) What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Stud High Educ 46(12):2629–2649. https://doi.org/10.1080/03075079.2020.1749254

Lee C, Coughlin JF (2015) PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J Prod Innov Manag 32(5):747–759. https://doi.org/10.1111/jpim.12176

Lee CH, Wang C, Fan X, Li F, Chen CH (2023) Artificial intelligence-enabled digital transformation in elderly healthcare field: scoping review. Adv Eng Inform 55:101874. https://doi.org/10.1016/j.aei.2023.101874

Leydesdorff L, Rafols I (2012) Interactive overlays: A new method for generating global journal maps from Web-of-Science data. J Informetr 6(2):318–332. https://doi.org/10.1016/j.joi.2011.11.003

Li J, Ma Q, Chan AH, Man S (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006

Article   ADS   PubMed   Google Scholar  

Li X, Zhou D (2020) Product design requirement information visualization approach for intelligent manufacturing services. China Mech Eng 31(07):871, http://www.cmemo.org.cn/EN/Y2020/V31/I07/871

Google Scholar  

Lin Y, Yu Z (2024a) An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behav Inf Technol 1–25. https://doi.org/10.1080/0144929X.2024.2332458

Lin Y, Yu Z (2024b) A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interact Technol Smart Educ 21(2):189–213. https://doi.org/10.1108/ITSE-12-2022-0165

Liu L, Duffy VG (2023) Exploring the future development of Artificial Intelligence (AI) applications in chatbots: a bibliometric analysis. Int J Soc Robot 15(5):703–716. https://doi.org/10.1007/s12369-022-00956-0

Liu R, Li X, Chu J (2022) Evolution of applied variables in the research on technology acceptance of the elderly. In: International Conference on Human-Computer Interaction, Cham: Springer International Publishing, pp 500–520. https://doi.org/10.1007/978-3-031-05581-23_5

Luijkx K, Peek S, Wouters E (2015) “Grandma, you should do it—It’s cool” Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health 12(12):15470–15485. https://doi.org/10.3390/ijerph121214999

Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Bier N (2018) Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 23(2):838–847. https://doi.org/10.1109/JBHI.2018.2834317

López-Robles JR, Otegi-Olaso JR, Porto Gomez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H (2018) Bibliometric network analysis to identify the intellectual structure and evolution of the big data research field. In: International Conference on Intelligent Data Engineering and Automated Learning, Cham: Springer International Publishing, pp 113–120. https://doi.org/10.1007/978-3-030-03496-2_13

Ma Q, Chan AH, Chen K (2016) Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 54:62–71. https://doi.org/10.1016/j.apergo.2015.11.015

Ma Q, Chan AHS, Teh PL (2021) Insights into Older Adults’ Technology Acceptance through Meta-Analysis. Int J Hum-Comput Interact 37(11):1049–1062. https://doi.org/10.1080/10447318.2020.1865005

Macedo IM (2017) Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput Human Behav 75:935–948. https://doi.org/10.1016/j.chb.2017.06.013

Maidhof C, Offermann J, Ziefle M (2023) Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed. Front Public Health 11:1186944. https://doi.org/10.3389/fpubh.2023.1186944

Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ (2017) Smart homes for elderly healthcare—Recent advances and research challenges. Sensors 17(11):2496. https://doi.org/10.3390/s17112496

Article   ADS   PubMed   PubMed Central   Google Scholar  

Mhlanga D (2023) Artificial Intelligence in elderly care: Navigating ethical and responsible AI adoption for seniors. Available at SSRN 4675564. 4675564 min) Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 58(1):102428. https://doi.org/10.1016/j.ipm.2020.102428

Mitzner TL, Boron JB, Fausset CB, Adams AE, Charness N, Czaja SJ, Sharit J (2010) Older adults talk technology: Technology usage and attitudes. Comput Human Behav 26(6):1710–1721. https://doi.org/10.1016/j.chb.2010.06.020

Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA (2019) Technology adoption by older adults: Findings from the PRISM trial. Gerontologist 59(1):34–44. https://doi.org/10.1093/geront/gny113

Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228. https://doi.org/10.1007/s11192-015-1765-5

Mostaghel R (2016) Innovation and technology for the elderly: Systematic literature review. J Bus Res 69(11):4896–4900. https://doi.org/10.1016/j.jbusres.2016.04.049

Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J (2023) Acceptance and privacy perceptions toward video-based active and assisted living technologies: Scoping review. J Med Internet Res 25:e45297. https://doi.org/10.2196/45297

Naseri RNN, Azis SN, Abas N (2023) A Review of Technology Acceptance and Adoption Models in Consumer Study. FIRM J Manage Stud 8(2):188–199. https://doi.org/10.33021/firm.v8i2.4536

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of Simulation & Gaming to the literature, 1970–2019: A bibliometric review. Simul Gaming 51(6):744–769. https://doi.org/10.1177/1046878120941569

Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL (2022) Remote healthcare for elderly people using wearables: A review. Biosensors 12(2):73. https://doi.org/10.3390/bios12020073

Pan S, Jordan-Marsh M (2010) Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Human Behav 26(5):1111–1119. https://doi.org/10.1016/j.chb.2010.03.015

Pan X, Yan E, Cui M, Hua W (2018) Examining the usage, citation, and diffusion patterns of bibliometric map software: A comparative study of three tools. J Informetr 12(2):481–493. https://doi.org/10.1016/j.joi.2018.03.005

Park JS, Kim NR, Han EJ (2018) Analysis of trends in science and technology using keyword network analysis. J Korea Ind Inf Syst Res 23(2):63–73. https://doi.org/10.9723/jksiis.2018.23.2.063

Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, Van Der Voort CS, Aarts S, Wouters EJ (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2):226–237. https://doi.org/10.1159/000430949

Peek ST, Luijkx KG, Vrijhoef HJ, Nieboer ME, Aarts S, van der Voort CS, Wouters EJ (2017) Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatr 17:1–18. https://doi.org/10.1186/s12877-017-0582-5

Peek ST, Wouters EJ, Van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ (2014) Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 83(4):235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004

Peek STM, Luijkx KG, Vrijhoef HJM, Nieboer ME, Aarts S, Van Der Voort CS, Wouters EJM (2019) Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatr 19:1–13. https://doi.org/10.1186/s12877-019-1241-9

Perez AJ, Siddiqui F, Zeadally S, Lane D (2023) A review of IoT systems to enable independence for the elderly and disabled individuals. Internet Things 21:100653. https://doi.org/10.1016/j.iot.2022.100653

Piau A, Wild K, Mattek N, Kaye J (2019) Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 21(8):e12785. https://doi.org/10.2196/12785

Pirzada P, Wilde A, Doherty GH, Harris-Birtill D (2022) Ethics and acceptance of smart homes for older adults. Inform Health Soc Care 47(1):10–37. https://doi.org/10.1080/17538157.2021.1923500

Pranckutė R (2021) Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(1):12. https://doi.org/10.3390/publications9010012

Qian K, Zhang Z, Yamamoto Y, Schuller BW (2021) Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Process Mag 38(4):78–88. https://doi.org/10.1109/MSP.2021.3057298

Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B-Condens Matter Complex Syst 4(2):131–134. https://doi.org/10.1007/s100510050359

Sayago S (ed.) (2019) Perspectives on human-computer interaction research with older people. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-06076-3

Schomakers EM, Ziefle M (2023) Privacy vs. security: trade-offs in the acceptance of smart technologies for aging-in-place. Int J Hum Comput Interact 39(5):1043–1058. https://doi.org/10.1080/10447318.2022.2078463

Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J (2023) Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging 6(1):e44564. https://doi.org/10.2196/44564

Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K (2021) Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res 23(11):e26522. https://doi.org/10.2196/26522

Seuwou P, Banissi E, Ubakanma G (2016) User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In: Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11:230-251. Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19

Shiau WL, Wang X, Zheng F (2023) What are the trend and core knowledge of information security? A citation and co-citation analysis. Inf Manag 60(3):103774. https://doi.org/10.1016/j.im.2023.103774

Sinha S, Verma A, Tiwari P (2021) Technology: Saving and enriching life during COVID-19. Front Psychol 12:647681. https://doi.org/10.3389/fpsyg.2021.647681

Soar J (2010) The potential of information and communication technologies to support ageing and independent living. Ann Telecommun 65:479–483. https://doi.org/10.1007/s12243-010-0167-1

Strotmann A, Zhao D (2012) Author name disambiguation: What difference does it make in author‐based citation analysis? J Am Soc Inf Sci Technol 63(9):1820–1833. https://doi.org/10.1002/asi.22695

Talukder MS, Sorwar G, Bao Y, Ahmed JU, Palash MAS (2020) Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol Forecast Soc Change 150:119793. https://doi.org/10.1016/j.techfore.2019.119793

Taskin Z, Al U (2019) Natural language processing applications in library and information science. Online Inf Rev 43(4):676–690. https://doi.org/10.1108/oir-07-2018-0217

Touqeer H, Zaman S, Amin R, Hussain M, Al-Turjman F, Bilal M (2021) Smart home security: challenges, issues and solutions at different IoT layers. J Supercomput 77(12):14053–14089. https://doi.org/10.1007/s11227-021-03825-1

United Nations Department of Economic and Social Affairs (2023) World population ageing 2023: Highlights. https://www.un.org/zh/193220

Valk CAL, Lu Y, Randriambelonoro M, Jessen J (2018) Designing for technology acceptance of wearable and mobile technologies for senior citizen users. In: 21st DMI: Academic Design Management Conference (ADMC 2018), Design Management Institute, pp 1361–1373. https://www.dmi.org/page/ADMC2018

Van Eck N, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3

Vancea M, Solé-Casals J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging Dis 7(4):526. https://doi.org/10.14336/AD.2015.1214

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Wagner N, Hassanein K, Head M (2010) Computer use by older adults: A multi-disciplinary review. Comput Human Behav 26(5):870–882. https://doi.org/10.1016/j.chb.2010.03.029

Wahlroos N, Narsakka N, Stolt M, Suhonen R (2023) Physical environment maintaining independence and self-management of older people in long-term care settings—An integrative literature review. J Aging Environ 37(3):295–313. https://doi.org/10.1080/26892618.2022.2092927

Wang CL, Chen XJ, Yu T, Liu YD, Jing YH (2024a) Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-02717-y

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023a) Understanding the Continuance Intention of College Students Toward New E-learning Spaces Based on an Integrated Model of the TAM and TTF. Int J Hum-comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609

Wang CL, Wang HM, Li YY, Dai J, Gu XQ, Yu T (2024b) Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. Int J Hum-comput Int 1–23. https://doi.org/10.1080/10447318.2024.2383033

Wang J, Zhao W, Zhang Z, Liu X, Xie T, Wang L, Zhang Y (2024c) A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater 36(15):2308915. https://doi.org/10.1002/adma.202308915

Wang J, Chen Y, Huo S, Mai L, Jia F (2023b) Research hotspots and trends of social robot interaction design: A bibliometric analysis. Sensors 23(23):9369. https://doi.org/10.3390/s23239369

Wang KH, Chen G, Chen HG (2017) A model of technology adoption by older adults. Soc Behav Personal 45(4):563–572. https://doi.org/10.2224/sbp.5778

Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C (2019) Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare 7(2):60. https://doi.org/10.3390/healthcare7020060

Wang Z, Liu D, Sun Y, Pang X, Sun P, Lin F, Ren K (2022) A survey on IoT-enabled home automation systems: Attacks and defenses. IEEE Commun Surv Tutor 24(4):2292–2328. https://doi.org/10.1109/COMST.2022.3201557

Wilkowska W, Offermann J, Spinsante S, Poli A, Ziefle M (2022) Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies. PloS One 17(7):e0269642. https://doi.org/10.1371/journal.pone.0269642

Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F (2021) Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21:1–12. https://doi.org/10.1186/s12889-021-11623-w

Xia YQ, Deng YL, Tao XY, Zhang SN, Wang CL (2024) Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanit Soc Sci Commun 11:266. https://doi.org/10.1057/s41599-024-02718-x

Xie H, Zhang Y, Duan K (2020) Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat Int 95:102100. https://doi.org/10.1016/j.habitatint.2019.10210

Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change 170:120896. https://doi.org/10.1016/j.techfore.2021.120896

Yap YY, Tan SH, Choon SW (2022) Elderly’s intention to use technologies: a systematic literature review. Heliyon 8(1). https://doi.org/10.1016/j.heliyon.2022.e08765

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.1057/s41599-023-01904-7

Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: A systematic review. Int J Med Inform 94:112–116. https://doi.org/10.1016/j.ijmedinf.2016.07.004

Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826. https://doi.org/10.1016/j.eswa.2021.115826

Zhao Y, Li J (2024) Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Sci Rep 14(1):9254. https://doi.org/10.1038/s41598-024-60067-w

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

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• Ugochukwu Simeon Asogwa,
• Hannah Ifedapo Maiyekogbon,
• Margaret Offoboche Agada-Mba,
• Oluwaseyi John Jemisenia

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Background: In the dynamic field of qualitative research, a contentious issue persists: Is digital software a more effective tool for research analysis than the manual method? To shed light on this debate, we undertook quasi-experimental research, focusing on our study's unique contribution to exploring the capabilities of both methods in analysing health datasets. Objective: Our study aims to compare the effectiveness of qualitative analysis between researchers who are proficient in digital software and those skilled in the manual method. We seek to understand which method is more effective in data analysis. Methodology: We employed a quasi-experimental design and a purposive sampling approach to select our study participants. These participants (n=150) were then divided into two groups: those proficient in digital software and those skilled in the manual method. We then conducted an intervention in which participants analysed a qualitative dataset using their preferred method. The data collected was then analysed using quantitative measures, such as percentages, central tendency measures, and independent samples t-tests. Results: The t-test result showed that statistically significant differences exist between the two groups across all indicators (all Ps<.0001). Specific observation of the mean scores revealed that for perceived efficiency (M=3.50 [SD=0.55]), productivity (M=3.40 [SD=0.60]), collaboration (M=3.55 [SD=0.50]), identification of complex themes (M=3.60 [0.45]), and visualisation techniques(M=3.60 [SD=0.45]), participants who used digital software scored higher than those who used manual method of data analysis. However, for perceived depth of analysis (M=3.50 [SD=0.55]), coding flexibility(M=3.45 [SD=0.50]), reflective quality(M=3.60 [SD=0.50]) and integration of contextual knowledge(M=3.55 [SD=0.45]), participants in the manual method group scored higher compared to those in the digital software group Contribution: This study adds to burgeoning and existing knowledge on the need for a complementary approach to adopting and using digital tools and manual methods in conducting qualitative data analysis. Although using both methods can offer many benefits, it is crucial to use the advantages of one method to address the drawbacks of the other where possible. While these benefits should be observed when combining both methods, the challenges of both methods must be acknowledged. Conclusion: This study emphasises the complementary advantages of digital and manual qualitative data analysis methods. Recommendation: A well-rounded strategy that uses the benefits of both approaches is advised to provide thorough and complex qualitative research results.

• Qualitative data analysis
• Digital software
• Manual methods
• Quasi-experimental research

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• Open access
• Published: 02 September 2024

Implementation of health-promoting retail initiatives in the Healthier Choices in Supermarkets Study—qualitative perspectives from a feasibility study

• Katrine Sidenius Duus   ORCID: orcid.org/0000-0002-1630-3132 1 ,
• Tine Tjørnhøj-Thomsen   ORCID: orcid.org/0000-0003-3621-6682 1 &
• Rikke Fredenslund Krølner   ORCID: orcid.org/0000-0002-4928-4310 1  

BMC Medicine volume  22 , Article number:  349 ( 2024 ) Cite this article

Metrics details

Improving food environments like supermarkets has the potential to affect customers’ health positively. Scholars suggest researchers and retailers collaborate closely on implementing and testing such health-promoting interventions, but knowledge of the implementation of such interventions is limited. We explore the implementation of four health-promoting food retail initiatives selected and developed by a partnership between a research institution, a large retail group, and a non-governmental organisation.

The four initiatives included downsizing of bags for pick’n’ mix sweets and soda bottles at the check-out registers, shelf tags promoting healthier breakfast cereal options, and replacing a complimentary bun with a banana offered to children. The initiatives were implemented for 6 weeks (or longer if the store manager allowed it) in one store in Copenhagen, Denmark. Data were collected through observations, informal interviews with customers, and semi-structured interviews with retailers. We conducted a thematic analysis of transcripts and field notes inspired by process evaluation concepts and included quantitative summaries of selected data.

Two out of four initiatives were not implemented as intended. The implementation was delayed due to delivery issues, which also resulted in soda bottles not being downsized as intended. The maintenance of the shelf tags decreased over time. Retailers expressed different levels of acceptability towards the initiatives, with a preference for the complimentary banana for children. This was also the only initiative noticed by customers with both positive and negative responses. Barriers and facilitators of implementation fell into three themes: Health is not the number one priority, general capacity of retailers, and influence of customers and other stakeholders on store operation.

Conclusions

The retailers’ interests, priorities, and general capacity influenced the initiative implementation. Retailers’ acceptability of the initiatives was mixed despite their involvement in the pre-intervention phase. Our study also suggests that customer responses towards health-promoting initiatives, as well as cooperation with suppliers and manufacturers in the development phase, may be determining to successful implementation. Future studies should explore strategies to facilitate implementation, which can be applied prior to and during the intervention.

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What we eat affects our health and well-being [ 1 ]. Diet is associated with obesity, cancers [ 2 ], and mental well-being [ 3 ], and a healthy diet has been associated with lower all-cause mortality [ 4 ]. One important factor in improving diet is to create a food environment that supports a healthy diet [ 5 , 6 ]. In modern societies, such as Denmark, supermarkets are the main source of food [ 7 ]. Supermarkets therefore hold a significant influence on what food we buy and potentially also eat [ 7 , 8 , 9 ]. Studies report associations between the concentration of supermarkets and overweight and obesity in the neighbourhood [ 10 ] and between the healthfulness of supermarkets and people’s diets [ 11 , 12 ]. Moreover, unhealthy food and beverage products are promoted more often than healthy products and beverages in, for example, supermarkets [ 9 , 13 , 14 ]. This indicates a need to explore how and if it is possible to implement health promotion initiatives in supermarkets and whether customers respond to such initiatives as intended.

Studies show that health-promoting interventions in supermarkets can affect customers to purchase more healthy products [ 7 , 9 , 15 , 16 , 17 ]. Reviews and a meta-analysis have concluded that the most effective initiative in supermarket settings is price changes—the evidence points to the positive effects of reduced prices to increase the purchase of healthier products, especially fruit and vegetables [ 7 , 17 ]. Even though price reductions seem to be effective, they seem more challenging to implement due to retailers’ drive for profit and low preference for financing such price cuts [ 7 , 18 ]. There is some evidence that nudges in terms of product information and positioning, as well as altering the number of available products, can impact what products are being purchased [ 15 , 16 ]. However, the quality of this evidence is low. Overall, most of the studies that have explored the effect of interventions in supermarkets have been conducted in the USA and other high-income countries [ 15 , 16 ], in controlled settings, or applied a weak study design, such as non-randomised studies [ 16 , 17 ]. To our knowledge, only a few studies have been conducted in Denmark [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. These studies represent different designs and types of interventions: reformulation of private-label products to reduce calorie content [ 24 ], informational claims to promote low-salt foods [ 23 ], nudges via signs to promote sales of fruit and vegetables [ 22 ], positioning (shelf-space management) of dairy products [ 20 ], replacement of sugar confectionery with fruit and healthy snacks at the checkout [ 19 ], discount on fruit and vegetables combined with space management [ 25 ] and structural changes in supermarkets and education of supermarket employees as part of a multicomponent intervention [ 21 ] (the three latter studies are reporting from the same project). All but one study [ 23 ] found an effect of the applied intervention strategies, although mostly small or modest. This calls for more studies in real-life settings and investigations of why some interventions have the desired effect while others do not. Lack of effect may be explained by 1) customers not noticing or finding the initiatives relevant [ 19 , 23 ], 2) customers buying other products instead of or additionally to promoted intervention products [ 20 , 24 ], 3) the shelf organising effect [ 20 ], or 4) theory fail in regards to customer behaviour [ 22 ].

Several studies have explored facilitators and barriers to the implementation of health-promoting interventions in supermarkets. Reviews show that implementation is supported if the retailer is receptive to innovation, feels responsible for community health, and receives financial support or subsidies [ 26 ]. Furthermore, implementation is supported if the intervention provides the retailers with knowledge of health promotion and business skills [ 26 , 27 ]. Other facilitators include compatibility with context and customers’ needs, positive customer responses to the initiative, the prospect of improved public image, establishment of partnerships, low retailer effort requirements, and increased profit or sales [ 26 , 27 ]. Health-promoting interventions in supermarkets are hindered by high customer demand for unhealthy products and lower demand for healthy products, constraints of store infrastructure, challenges in product supply, high staff turnover, and lack of time [ 26 , 27 ]. Other barriers are doubt regarding changing customers’ behaviour, poor communication between collaborators [ 26 ], high running costs, and risk of spoilage [ 26 , 27 ].

Middle et al. [ 26 ] conclude that the underlying mechanism of barriers and facilitators of implementation is the (mis)alignment of retailers’ and intervention researchers’ interests. The authors, therefore, suggest a close collaboration between intervention researchers and retailers to work towards an alignment of interests and resolving or avoiding misalignment, which is supported by Gupta et al. [ 27 ]. However, knowledge of how such collaborative efforts affect the implementation of healthy food retail interventions is warranted.

The aim of this study is to explore the implementation, acceptability, and feasibility of four different health-promoting food retail initiatives to increase customers’ purchase of healthy food and beverages, which were selected and developed together with food retailers: 1) Promotion of healthier breakfast cereals and products using shelf tags, 2) downsizing of sodas sold at the checkout desks, 3) downsizing of bags for the pick’n’ mix sweets, 4) replacement of a complimentary bun for children with a banana. The study has three research objectives:

To document the implementation and sustainment of the initiatives over time

To explore the retailers’ and customers’ responses to and acceptability of the initiatives

To investigate barriers and facilitators of implementation and sustainment of the initiatives.

Setting and the initiatives

This study was conducted in Denmark during 2020 and 2021, 2 years that involved two major societal events, first the coronavirus disease pandemic and later the start of the Russia-Ukraine war. Both events heavily influenced the circumstances of everyday life including opportunities for conducting research and running businesses. The specific influences on this study will be unfolded later in the findings and discussion sections.

In this study, we collaborated with the retailer Salling Group, which holds 34.2% of the market share of grocery retailers in Denmark [ 28 ]. Salling Group is owned by the Salling Foundations and has no shareholders—all profits go to reinvestment in the business and donations to sports (amateur and professional), charity, education, and research. Salling Group owns three national supermarket chains: føtex, Netto and Bilka, alongside other businesses. For the feasibility test, we collaborated with føtex, which owns over 100 stores all over Denmark, including 23 stores called føtex food. føtex (except føtex food) offers both groceries and many different non-food products (e.g. textiles, cosmetics, toys, electronics, and home accessories).

The initiatives were selected and developed by a partnership, including a group of researchers at the National Institute of Public Health, University of Southern Denmark, consultants from the Danish Cancer Society, and employees at the Corporate Social Responsibility (CSR) department in Salling Group, the marketing department at føtex, and two store managers (hereafter referred collectively to as ‘the retailers’) over approximately 2 years. The process involved in-person meetings, desk research (the use of existing material [ 29 ]), visits to the test store, and a prototype test of three suggested initiatives. The researchers initiated the collaboration and were responsible for designing the research study and data collection and analyses. The retailers hosted the site of the feasibility test, contributed to the selection and development of initiatives and co-managed the practical part of the study. The Danish Cancer Society was recruited by the research project to develop the initiatives. A detailed description of the collaboration and development process is reported elsewhere (Duus et al.  unpublished ).

The feasibility test ended up including four initiatives: 1) Promotion of healthier breakfast cereals and products using shelf tags, 2) downsizing of soda sold at the checkout desks, 3) downsizing of bags for the pick’n’ mix sweets, 4) replacement of a complimentary bun for children with a banana (suggested by the retailers). The initiatives were based on a compromise between the willingness of the retailers and the interest and ideas of the remaining partners rather than on what the literature suggests are the most effective strategies (Duus et al.  unpublished ). Detailed descriptions of the initiatives and the rationale behind them are found in Table 1 .

The prototype test showed that 1) It was important to have a sign informing the customers about the initiative that offered a free banana to children instead of the usual free bun to create a better understanding of the changed offer; 2) Promotional shelf tags needed weekly maintenance as some would fall off; 3) It was difficult to sustain an initiative promoting ready-to-serve salads and ready-to-cook vegetables next to different fresh meats, as it met resistance among the staff due to being an additional task and led to more product waste (Customers did not expect to find these products next to the meat and therefore might not notice them). The learnings from the prototype test led to modifications of the implementation plan and the discard of the latter initiative. The prototype test also made us aware of how quickly the selection of food offered and the layout of the store changed over time, which the researcher, therefore, paid extra attention to during subsequent data collection. Moreover, the researcher made sure to update the list of products that should have a shelf tag a few weeks before the implementation to include new products offered.

The føtex marketing department developed a script to inform the staff at the test store about the feasibility test, explaining and showing each initiative and the aim of the study overall. This was sent to the store manager after being reviewed by the researchers. The store manager was responsible for informing all relevant staff about the implementation and maintenance of the initiatives. The føtex marketing department also made sure to inform the relevant suppliers. Employees at the test store and brand staff from a brewery (who stock the coolers at the check-out desks) implemented the initiatives in the store. The research group did not correct or maintain the initiatives in the store after they were launched; however, the researchers monitored it and reported back to the retailers, either at meetings or by email.

Overall study design

The four initiatives were implemented in the test store for 6 weeks (or longer if the store manager allowed it) starting in September 2021. A føtex store in central Copenhagen (the capital city of Denmark) was chosen as the test store. This decision was made for pragmatic reasons, as the research institute is based in Copenhagen, and based on Salling Group’s decision as it offered their new store layout, which all stores were in the process of being converted to (it was the same store as where the prototype test was conducted).

We designed a qualitative study involving participant observations and interviews to evaluate the feasibility of the initiatives. The methods were designed to explore the partnership and collaboration (the aim of another publication [Duus et al. Unpublished ]), as well as the implementation of the initiatives [ 30 ]. In the design of this study, we were inspired by McGill et al.'s (2020) two-phase framework of qualitative process evaluation from a complex systems perspective. This framework suggests an evaluation that looks at changes over time, starting with phase 1, the static system description and hypothesis generation about how the system might change when the intervention is introduced, followed by phase 2, an adaptive evaluation approach to the system undergoing change which follows emerging findings [ 31 ].

Data collection

In-store observations.

During October and November 2020, we mapped the store layout and customer flow in the test store as part of the static system description. Over 3 weeks, three research assistants performed 12 participant observations of 1005 min in total. The observations followed an observation guide which covered 1) the physical setting (e.g. the layout, placement of products, signs, and pictures); 2) the people (e.g. who are the customers? Are people shopping alone or together with others? How do they move around the store? What are the staff doing?) and 3) short interviews with customers (if possible) about their shopping at the particular store, and their thoughts about the layout of the store. The research teams’ access to the store was approved by the store manager, and research assistants wore a key chain with a sign showing their name and affiliation during the observations. During this data collection period, it was made mandatory to use face masks in supermarkets due to the coronavirus disease pandemic. As the implementation was delayed to approximately 1 year after this static description was completed, one participant observation in the test store was performed at the end of August 2021, just before initiative implementation, to document any major changes in the store layout and selection. Key lessons from these observations about the test supermarket and customers’ behaviour in the store included knowledge on 1) the route around the store, 2) the different times spent at the store, 3) interactions with objects (e.g. products and phones), 4) interactions with children, 5) behaviour of the staff, and 6) sensory impression (Additional file 1). These lessons informed our following data generation and assisted in contextualising our analysis.

The first author monitored the implementation process through participant observations of status meetings ( n  = 2) and correspondence via email and phone with the store manager and the contact person at føtex. In-store participant observations were conducted during and after the feasibility test period, September 2021–May 2022 ( n  = 25 ~ 1795 min in total; see Additional file 2). These observations focused on documenting the presence of the initiatives as well as customers’ and staff’s responses to the initiatives. Access to the store was once again approved by the store manager, and the researcher wore a key chain. During the participant observations in-store, we conducted informal interviews with customers (see Additional file 2 for examples of questions), which lasted a maximum of 5 min each. The first author would approach people and ask if they were interested in answering a brief question. She introduced herself by her first name, where she worked and explained she was doing a research project about shopping patterns. The participant observations were documented by taking notes and photos. Handwritten notes were digitalised and written down at the first chance after leaving the store.

Qualitative interviews

Between November 2021 and February 2023, the first author conducted four semi-structured interviews with retailers ( n  = 3) who had been involved in the study (Table 2 ) to explore their views on the initiatives and the implementation process. Interview guides were used in all interviews alongside different prompts (e.g. timelines and documents). Interview guides were tailored to each participant’s specific role and involvement in the development and implementation of the initiatives. Besides questions related to the initiatives and the implementation effort, the guides included questions about the informants’ background and motivation for the project (personally and professionally), their view on their role and scope for action (individually and organisationally) and their perception of the collaboration with the other organisations. After the participants’ consent was given verbally right before the interview, the interviews were recorded and later transcribed verbatim.

To explore the level of implementation (research objective I), all field notes and photos taken during and after the feasibility test were reviewed to assess whether the initiatives were present and to what degree (e.g. x out of x possible tags).

To explore the perception of the initiatives among employees and customers (research objective II) and identify barriers and facilitators for implementing the initiatives (research objective III), we followed a thematic analysis inspired by Braun and Clarke [ 32 ]. Firstly, field notes and interview transcripts were read thoroughly and openly coded, by writing keywords in the margin of the material, with a focus on the two research objectives. After initial coding, the codes were summarised into broader themes, by writing them into a document with short descriptions and revised according to data excerpts and the full empirical material. The themes drew on the process evaluation concepts: acceptability, responsiveness [ 30 ], motivation, general capacity to implement [ 33 ] and commercial viability [ 34 ]. Lastly, the themes were named, and the final analysis was written up.

We have structured the presentation of study findings as follows: Firstly, we present the implementation of the initiatives overall. Secondly, we present the implementation of each initiative, customers’ responses to them, and the retailers’ perspectives. Lastly, we present the overall facilitators and barriers to the implementation of the initiatives.

Implementation of the initiatives

The implementation of the initiatives was challenged. Firstly, we found that not all the preparations for the implementation were finished in time for the scheduled day. On the scheduled day, the retailer decided to push back the implementation by 1 week. The main reasons were that there had been some misunderstandings around the ordering of the smaller sodas. It was informed that the smaller soda would be a 330 ml can instead of the 375 ml bottle at the price of DKK 10.00 (~ 1.3 euros). The 500 ml bottle usually sold at the coolers cost DKK 16.00 (~ 2.2 euros). The Danish Cancer Society and the research group had two concerns about this: 1) the use of a can instead of a bottle would make the interpretation of the results very difficult, as the bottle and the can have two different functions to the customer—with the can, the product would be consumed all at once, whereas the bottle with the screw lid could be saved for later after it had been open; 2) the price was too low—the price per litre would be lower on the smaller sodas than it had been on those replaced. No changes were made despite these concerns.

Secondly, just days before the implementation, the retailers informed the other partners that they would stick with cans for the test of smaller-sized sodas and that they would now be 250 ml. They acknowledged that both the size and the packing were not optimal but that the optimal 375 ml in a bottle was just not possible. Additionally, they informed the researchers that they could no longer find the new bags produced for the pick’n’mix sweet display.

These challenges led to a delay of the implementation of the initiatives by 1 week, but also a staggered implementation, where the initiatives were implemented when ready (the soda initiative 2 weeks later and the bags for pick’n’ mix sweets 8 weeks later). The retailers agreed to push back the end day correspondingly, upholding the 6 weeks of implementation. Table 3 shows an overview of the implementation of the four initiatives according to the day and week of the feasibility test period.

Smaller product sizes of sodas at the checkout desk

As seen from Table  3 , we did observe the implementation of a smaller product size of the targeted sodas in all coolers, besides the one at the bakery, in the week leading up to the agreed date. We hereafter observed a full implementation of 250 ml cans during the first 2 weeks of implementation. During the third week and the beginning of the fourth week, we observed a mix of 250 and 330 ml cans or only 330 ml cans. The store manager explained that this was probably due to non-delivering from the supplier. At the end of the fourth week and for the last 2 weeks, we observed a full implementation of 250 ml cans. As the targeted size of the initiative was a 375 ml bottle, the initiative was not implemented as intended. After the 6-week feasibility test period, we observed that the smaller 250 ml cans were available in all coolers for at least eight more weeks. As expected, the presentation of the coolers fluctuated over the period. On days of stocking (Monday, Wednesday, and Friday), the coolers would look neat and full, while they would appear more empty or messy on other days.

Customer responsiveness

We observed very few customers who bought any products from the coolers, and we did not get to talk to any customers about the initiative. However, the observations in the store showed no distinct change in customers’ behaviour around the coolers nor expressions of discontent or excitement with the initiative. In an interview with the store manager, he explained that he believed customers had not noticed the change.

Retailer perspectives

The store manager was positive about the initiative, but from his perspective, the decision to implement it should be made at the procurement level and by the suppliers. However, he did have an opinion on how to implement it. The price needed to be fair according to the product it replaced. Moreover, he drew attention to the fact that it was the supplier’s personnel who stocked the products rather than his own. The store manager was, therefore, not surprised that the employees at the store had little to say about the initiative. føtex’s representative (B) was also positive about the initiative and expressed in the interview that the chain would be willing to implement it—if they found it to be the ‘right thing’ to do. However, the representative also emphasised the importance of agreeing with the suppliers, which is a time-consuming process and ‘not done in just six months’.

Shelf tags for breakfast cereal products

From the first day of the implementation, some tags were missing, and one tag was consistently misplaced (Table  3 ). During the first 3 weeks, 10% ( n  = 3) of the tags were missing. This portion progressively increased to 23% until the end of the fifth week. In the sixth week, the portion decreased at first to 16% but decreased again and ended at 26%. In the weeks after the implementation period, the tags stayed present but slowly came off. Approximately 6 months later, three (10%) of the tags were still present. We observed throughout the feasibility test that the presentation of the area varied, which is to be expected in a busy supermarket. At times, the area looked messy; boxes would block access to some products, products would be sold out, some would change packaging, and new products would be introduced to the selection.

When we asked customers about the tags, we learned that they had been unaware of them and that some believed that it was not something they would use—some did not know the meaning of the labels on the tags, while others did not find the labels relevant for them.

[The tags] don’t matter. My wife is pretty health conscious, so we don’t use those, let alone know with such a thing as breakfast cereal. (Male customer)

From our observations of the behaviour of the customers in the breakfast products and cereals department, we find two interesting groups: Those who shop alone and those who shop together with others (primarily children). These groups seem to practice different behaviours.

Among those who do their grocery shopping by themselves, we find two subgroups: 1) those who have planned or know exactly what they want to buy, and 2) those who decide at the store. For the first sub-group, we observed that some showed this by practising a behaviour where they would walk quickly and purposefully towards the shelves and quickly pick up a product. Others would look determined to find a specific product, as the fieldnote excerpt illustrates:

A woman stands looking at the muesli. She first grabs an orange bag on the bottom shelf, then a more yellow one next door and puts the first one back on the shelf. She inspects the bag she took. She starts to look around the shelves more and reaches for a bag that has a pinker look on the top shelf. She puts it back and reaches into the space next to it, where there are a few bags at the very back, but she has difficulty reaching them. A man comes by, notices the woman, and offers to help her. The woman indicates a yes, and the man reaches up and grabs a bag ‘that's the one!’ says the woman as the man hands her the bag.

Another example was a man who kept looking back and forth between some muesli and granola products and his phone before he eventually chose a product. It is unknown whether the man was looking at a specific note, a text request from his family, or a picture on his phone, yet what was on his phone seemed to determine the product he bought. Overall, this group seemed very unlikely to be influenced by the tags, as they had made their choice already before they entered the store.

For the second sub-group, those who seemed to make their decision in the store, we observed that some would just stop and glance at the products without choosing one before moving on with their shopping. Others would look more randomly at the selection than those described above, walk back and forth in the aisle, compare different products and read the info on the back of the products.

For those who shopped together with others (most often children), we observed that when adults shopped with children, the choices of the child and the choices of the adult often conflicted. In one example of a child and a woman who looked at breakfast cereal products, the child was initially allowed to pick a product and asked for different chocolate variants, which all featured cartoon figures; however, the woman rejected all of the child’s choices. In the interaction, the child was met with demands from the woman regarding the attributes of the products: they could not contain chocolate or sugar. In the end, it was the woman who chose a product based on her experience of the child’s preferences and her criteria. In similar situations, we did observe an attempt at compromising between the adult’s and the child’s criteria, which was explained by this woman:

I ask them [woman and boy aged about 10] what they look for when choosing breakfast cereals. The woman looks at the boy and says, ‘Well, what are we looking for?’. The boy does not answer but looks at her and me and smiles. The woman herself replies, ‘Something we can agree on. Something he likes but is not too unhealthy, either’. I ask her what she considers unhealthy. She waffles for a bit and then replies, ‘Yes, but he wants that Lions cereal, for example, and I don’t want him to have that. So something that’s not de facto sweets’. She takes the box of granola that they have chosen [Paulún's blueberry/lemon granola] out of the basket, looks at it and says, ‘So we chose this one. There's probably also a lot of fructose and caramelised stuff in it, but yeah.’

This illustrates the high impact children had on the choices of breakfast products, but also how the parents tried to control and negotiate the final choice.

Retailer perspective

The store manager had little faith in the effectiveness of the shelf tags:

The thing about tagging cereals, I don't think that makes the slightest difference. The reason why I’m sceptical in that regard is that it’s a mixture of what I do on a daily basis. It’s especially the behavioural patterns of our customers, but also how I act as a customer myself to a degree. I don't think shelf tags with the whole grain label or anything like that; in my experience it hasn’t changed things much. (Store manager)

His view on the effect of the initiative was in line with our observations of the customers in the store. Furthermore, the store manager explained that it was difficult to maintain the initiative, as it was not part of the employees’ daily routine. This was also the argument of why the tags lingered after the test period—it was just not part of the usual protocol either to hang them up or take them down. This perspective was shared by the føtex representative (B), who also highlighted the cost of this maintenance.

Contrary to the store managers’ sceptics, the føtex representative (B) was more positive about the initiative:

I think it’s a good initiative. We work a lot with tags and labels in general. [...] I think making it transparent to the consumer is really interesting because there’s nothing wrong with buying a box of Nesquick cereal every once in a while. At least we should not claim it’s the wrong thing to do. But you just have to be clear about what you’re buying, and I think those labels help with that. (føtex representative (B))

She explained that the initiative was highly compatible with their usual strategies. However, she also explained in the interview that a barrier to using shelf tags to promote the buying of certain products was that the chain was trying to reduce the printed material they used in their stores as part of their CSR strategy and to reduce costs.

Replacement of the complimentary bun for children with a banana

The complimentary banana was fully implemented in the feasibility test period except for 1 day of observation, where the signs were not visible (Table  3 ). The initiative also remained available and present by the sign for at least 10 weeks after the implementation period. Furthermore, the store manager informed the researcher that they would continue to provide bananas for customers requesting this as an act of customer service. From the observations, we do find that the presentation of the initiative changed throughout the period. At first, the bananas were placed in a cardboard box on the display counter, which was later replaced with a nicer-looking basket. The number of bananas and their colour also fluctuated during the different days, which would be expected due to the delivery of the bananas and how often they are restocked. However, compared to the buns, we never observed that the bananas were not available, making it a reliable offer no matter the time of the day.

We observed two ways (1 and 2) that the complimentary offer for children was brought up: 1) A customer would ask for the ‘bun for children’, or 2) the staff would offer the complimentary banana to buying customers. In the first way 1), we saw two responses from the staff (a and b) and the customers (i and ii): (a) The customer would be offered the bun with no mention of the banana, or (b) the staff would inform the customer that they no longer offered buns but that they offered a banana instead. The customers had two primary responses to this message: (i) The customer rejected the offer and decided to buy a bun or another item instead. The child was often included in this decision. (ii) The customer accepted the offer and received the banana. In some cases, the child did not accept the offer and the customer compensated for this response by buying a bun or another product for the child. In the second way 2), in which the staff offered the banana spontaneously, the customers almost always reacted positively and accepted the offer.

The following excerpt illustrates why some customers rejected the offer:

A woman with a child of about 1-year-old in a stroller walks up to the bakery and asks for a children's bun. The child has already noticed the buns from the moment they arrive and sits, pointing at the buns through the glass window and babbling. The shop assistant says that there are no children's buns but bananas and points to the sign. The woman replies, ‘I’d like to buy a bun, then’. The assistant takes the bun and enters it into the till, while the woman says, ‘Bananas are so messy’. The assistant smiles and says, ‘Well yeah, I'll pass that on’. The woman replies, ‘It's just that the banana is rather a bother, and the assistant replies, ‘But I think we’ll be offering [the buns] again eventually’.

Thus, adults rejected the offer because eating a banana was a messier process than eating a bun. During meetings and interviews, the retailer also highlighted this as the main reason for rejections of the offer, especially among those with younger children. Another reason for rejection was that the parents did not appreciate the offer nor perceived a need to offer their children a banana instead of a bun.

This initiative was the most successful and interesting one in the eyes of the store manager.

I’d like to highlight the banana for kids, which is clearly the initiative I found most customers were pleased with. (Store manager)

Many customers responded positively to the new offer, which was emphasised as a marker of success. It was also the reason why the initiative continued after the 6-week period, and the store manager explained that they would continue to give bananas to those who asked for them.

The following excerpt illustrates what the bun meant to føtex and the chain’s relationship with its customers.

The children's bun has been around for donkey’s years, and it’s become ingrained in parents and kids alike that you can get them in føtex. So, we’re quite interested in learning how many people would actually, if presented with the alternative, choose something else, like, for example, the banana. I’m quite surprised by that – we can't track it, unfortunately – but off the top of my head, up to 40 to 50 percent actually choose the banana. I find that very interesting. (føtex representative (B))

Thus, it came as a surprise that the initiative was so well received. However, despite the positive experiences with the initiative, the retailers also commented on the cost. They highlighted that the banana was more expensive than the bun, and if it should be an option offered in all stores, then it would have to be prioritised at the executive level as an additional expenditure. In this case, the banana would only be an alternative to the bun and not a replacement. This was rationalised by the retailers’ attitude of not making choices on behalf of the customers.

Smaller bags for pick’n’ mix sweets

This initiative was not implemented until 8 weeks after the initial implementation date. It was fully implemented for five out of the six weeks; during the third week, we observed that the old, larger bags had been hung in front of the new smaller bags. At 2 weeks and four and a half months after the feasibility test, the smaller bags could still be found behind the larger bags—however, it is unlikely that these would have been used, as the obvious choice would have been the bag at the very front. As described for the other areas, this also fluctuated in its presentation and stocking.

We did not get any direct reactions from customers on the smaller bag. However, our observations showed that different strategies were used to decide the amount of candy among customers who bought pick’n’mix sweets. Some showed signs of visually assessing the amount of sweets in the bag, which were the customers we would expect to influence. We often observed this strategy among adults with children, where it was the adult who would visually assess the amount and communicate to the child when they had picked enough.

Those with very young children would walk alongside the child and select the sweets for them, and some adults would encourage the choice of the child by pointing out different variants and commenting on the appearance of the sweets.

Other strategies were to mix according to a pre-defined number of pieces or volume:

A boy of about 10 and a girl of about 8 come over and mix sweets. They repeatedly weigh the bag while doing so. A woman comes over, and the girl says, ‘Hello mummy!’ The woman says, ‘Don’t forget to weigh it’. She then grabs a bag herself and begins to mix sweets. The boy asks the girl, ‘Did you weigh it?’. The girl walks over to the scales and says, ‘I think I’ve got enough’. However, she does not close the bag, and she begins to walk around somewhat restlessly, then says, ‘I don’t know what to pick. I’m still [a few] grammes short’.

An interesting aspect of the situation above is that the girl expressed that she was satisfied with what she had chosen, but she felt that she had to meet the prespecified weight and, therefore, tried to find more sweets to put in her bag. Such strategies undermine the mechanism which the initiative was trying to influence.

Overall, the retailers were positive about this initiative. The føtex representative (B) highlighted that this initiative was interesting as it was a stealth initiative, compared to the initiatives with the sodas, and would change the behaviour of the customers without them noticing. In her opinion, this was not a problem, as people paid per gram.

The store manager had a clear demand for the implementation; it should be easy for both the staff and customers to use. This perspective was backed up by a føtex representative (B) who said:

If there’s something that doesn’t work for us, it’s... if it doesn’t work for our customers, that’s what we need to solve first. (føtex representative (B))

This shows how one success criterion of the retailers is customer satisfaction, which we elaborate on later (See: Influence of customers and other stakeholders on store operation).

The initiative was very delayed, and one reason was that it was challenging to create a new bag that would work in the store. This resulted in the order of many different bags in large quantities due to the agreements with the suppliers, which had been very costly for the retailer.

The føtex representative (B) also reflected on what the potential evidence of an effect would mean to the retailer:

Then we’ll have to wait and see if people buy fewer sweets. And of course, this is something that we must take into account because it’s no secret that part of being a responsible business is to make a profit. And if we sell fewer sweets, then we make less money. (føtex representative (B))

This shows how health and financial profit were seen as opposites and how the success of the initiative would not necessarily lead to it being viewed favourably, as it would negatively affect their profit. Any implementation in the chain would, therefore, have to be a strategic decision.

Facilitators and barriers

In the sections above, we have focused on the four specific initiatives. In the following, we will present analytical findings that go across the initiatives and elucidate what facilitated and hampered the implementation of the initiatives overall. We have organised our findings under three headings: Health is not the number one priority; General capacity of the retailer; and Influence of customers and other stakeholders on store operation.

Health is not the number one priority

In this section, we present the retailers’ motivation for and interest in engaging in the project and working with health and health promotion and what drives and/or curbs this motivation. In our understanding of motivation, we draw on Scaccia et al. [ 33 ] and view motivation as incentives and disincentives that contribute to the desirability of using an initiative focusing on health.

We find that the retailers expressed motivation for working with health and health promotion, which at first seemed to be based on interest. The retailer representatives explained how they personally were interested in health and wanted to learn more, but also that the organisation had an interest in health, especially among children and young people, and wanted to contribute to health-preventing activities, for example, by financially supporting local sports clubs. According to one retailer representative, this was because physical activity and healthy eating promote happier customers, as well as happy employees. The argument points to retailers’ focus on customer satisfaction (see: Influence of customers and other stakeholders on store operation). The focus on the customers relates to another factor of motivation: Working with health was also seen as a relative advantage in that customers increasingly demand healthier products and alternatives. Lastly, we found that the motivation for working with health was a feeling of obligation due to the view of having a social responsibility:

I would say, in purely business and commercial terms, we are, indeed, a commercial business that was created to make money. There’s no ignoring that (laughs). So, of course, this is our main KPI [key performance indicator]. But that being said, we also agree that we have a social responsibility because we are as big as we are. We make a lot of foodstuffs available to the Danes, as do many of our colleagues in our industry, so there is no doubt that we have a role to play in terms of what we make available. (føtex representative (A))

According to the excerpt, this obligation was rooted in the size of the organisation and, thereby, the major influence on people’s selection of food products. However, the excerpt also highlighted that health was not their first priority, which was profit. This point has been repeatedly mentioned among retailers, which reinforces its validity; they were a business and had to gain profit to keep running their operation, which presented limits for what could be implemented. The store manager even expressed how he perceived the running of a supermarket and promotion of public health as incompatible goals and something he had never seen an example of in a real-life supermarket.

However, from the interviews with the retailers and our fieldwork, it seemed that this was not completely black and white, as the retailers were willing to give up their profit in some cases. An example is the hiding of tobacco products in all Salling Groups’ supermarket chains, which they voluntarily implemented in 2018, which led to a significant decrease in profit from tobacco products.

After all, the Salling Group pioneered this with tobacco products. I'm proud of that, but I also think it’s the right thing to do. My personal opinion is that it was the absolutely correct move they chose to make, by making it harder to market a product that is obviously bad for my health. We’re not there with pick‘n’mix sweets just yet, in that we would claim they’re bad for your health, but the mindset in terms of; that is, upholding the mindset when it comes to cigarettes is something that we, as an industry, can easily support in close cooperation with, among others, yourselves [researchers] and the industry. (Store manager)

Risk seemed to be the driver. If the retailer was convinced that the risk was real or big enough, then they were willing to give up some of their profits because it was the ‘right thing to do’, and they would have the courage and power to do so. It was mentioned by all three informants that they did not believe in bans, limitations or hiding of products, as this interfered with the customer’s freedom of choice. This viewpoint was a barrier to the implementation of all initiatives that used strategies that would minimise or reduce the availability of a product. Yet, as with the tobacco products, we found other examples where this restriction of choice was justified by the retailer. One example was that the føtex chain only sold organic bananas. From a sign in the store, this was because:

‘we want to avoid the spray agent chlorpyrifos. Among other things, it is suspected of harming the development of children and foetuses. We can’t live with that suspicion and therefore you can only buy organic bananas in the future’

As with the cigarettes, the argument here was the health risks. In the interview with the store manager about restricting choices, animal welfare and political reasons (e.g. Russia’s warfare against Ukraine) were mentioned as other arguments for doing so.

So, despite an immediate motivation for working with health, the retailer also expressed how other interests and priorities may hinder and set aside the work with health.

General capacity of the retailer

This section presents our findings relating to the general capacity of the retailer in the form of resources, organisational size, and culture. General capacity is understood as the readiness or ability to implement any new initiative [ 33 ].

Through the interviews with the føtex representative (B) and working together with the retailer during the project, we have found that the retailer seemed to be used to and willing to implement new initiatives. In this current study, they accounted for all expenses related to the development of materials for the test and were also willing to risk some of their profit for a short period of time. The føtex representative (B) highlighted this high level of available resources several times in the interview:

I have some leverage, so when we do something, we don’t do it by halves. What I find most motivating, and I can say that with complete peace of mind, is that if the Salling Group says they’re going to do something, or if føtex says they’re going to do something or says they want to win this particular battle, then we win it, and then we do it to the full. [...] So when we say, for example, with this health project, that ‘we want to work with health,’ then we do want to work with health, and we’re going to make a difference in health, too. (føtex representative (B))

In this excerpt, she expressed that the mere size of the company allowed them to push any agenda if they wanted to. However, this also underlines that this capacity is dependent on the retailer’s willingness, a willingness that was not in favour of many of the initiatives that the researcher, based on the literature, thought would have the greatest effect.

Even though the size of the company came with many available resources, the retailer also explained how the size of the company had worked against the project in several ways:

What I think made it difficult for us to get through with some of these things let's just take the sodas, in that case, we have a private label collaborator who has production facilities, and when they press the ‘Salling sodas’ button, it doesn't just spew out a few thousand bottles, but millions. So saying ‘can't we just try to reduce the size and give it a try.’ It's a giant setup, so it’s not possible to do that at a whim. You’d need to get a whole or half chain on board that can help sell such volumes because otherwise, the costs would go through the roof. (føtex representative (A))

What this excerpt explains is that even changes that appeared small would take tremendous effort and be very costly, due to the size of the organisation.

Another challenge of the implementation was embedded in the retailers’ organisational culture. Føtex representative (B) explained in the interview that conflicting goals between employees made it difficult and time-consuming when implementing new initiatives. Another barrier to implementing the initiatives was high staff turnover at the retailer. In an interview with a føtex representative, she explained that people often shifted around different positions in the organisation, which ended in the project falling between two stools, leading to misunderstandings of agreements and changes in attitudes towards the initiatives.

In summary, we find that the retailers could, in some respects, have a strong general capacity to implement new initiatives by having available resources and being used to implement new initiatives. Regardless, this study shows that this was not utilised due to a lack of willingness. Moreover, we find that the size and organisational culture of the retailer hampered the implementation of the initiatives.

Influence of customers and other stakeholders on store operation

The last section reports on the influence of customers on the retailer’s willingness to implement the initiatives, and the influence of other stakeholders, especially producers, on what can be implemented.

We found that the customer’s reactions and attitudes were determining to the retailer when implementing any new initiative, as indicated in the sections above. According to the retailer, the customer was the focus when designing the layout of the store:

We are in very close dialogue with our clients, we do quantitative surveys and we do focus groups, we do in-depth interviews. And in that context, we're trying to understand, when you're shopping, how do you go about it. Is it easy for you to find the items you are looking for? And based on the responses, we try to adapt our stores to make things easy for our customers. (føtex representative (A))

The same representative also mentioned that she thought it would have been a strength of the project to have conducted interviews with the customers as a part of the development process, emphasising the weight they put on the customer’s attitudes. The retailers highlighted the importance of customer satisfaction and convenience in their shopping experience as a barrier to implementing certain initiatives, such as changing the placement of products. However, these same factors have also proven to be facilitators for other initiatives, such as the tags for breakfast products and the complimentary banana for children, as demonstrated above.

Another important stakeholder for the supermarkets was the suppliers of their products. Others were government actors (e.g. the Danish Veterinary and Food Administration). For both downsizing initiatives, the suppliers of the products (sodas and bags for sweets) were key to the success of their implementation. In an interview with the store manager, he explained the huge role some of these suppliers have in the daily operation of the store and the chain.

After all, we’ve got a chain agreement that our head office has made with the breweries. I don’t get to decide which items are in our refrigerators. [...] The tricky thing is that we’re not only dealing with føtex or the Salling Group. We also have to do with some other, equally large companies that are also just coming in. Plus, I have people here X times a week to service their particular area. [...] [Another thing] that proved tricky, as far as I recall, was that the alternatives offered, people felt strongly about those because the breweries made some strategic choices, and because of those, some of the items that we might be able to stock, they didn't want to sell those. (Store manager)

This excerpt illustrates how suppliers like the breweries, as shown earlier, influenced the implementation and affected the decisions made by the retailer.

This section indicates that even though the retailer is convinced that a given initiative would be interesting to implement in their supermarket, the suppliers often must agree as well, and finally, the customers must also welcome it.

In this study, we have explored the implementation, acceptability, and feasibility of four different health-promoting food retail initiatives aimed at customers in a real-life supermarket setting, using different qualitative methods. We found that (i) Two initiatives (downsizing of bags for the pick’n’ mix sweets and the complimentary banana for children) were implemented to a high degree, yet delivery issues caused delays according to the planned date, especially for the bags. The downsizing of soda bottles was not implemented as intended; the size and packaging deviated from the original plan due to delivery failure. Moreover, the implementation decreased over the feasibility test for the initiative with shelf tags, as it took more continuous maintenance. For all initiatives, we found that they lingered after the feasibility test; however, only the banana for children was somewhat sustained for a period to accommodate customer demand. (ii) The retailers expressed different levels of acceptability towards the initiatives, and different representatives sometimes also showed different levels of acceptability towards the same initiative, such as the tags on the breakfast products. The most well-received initiative was the banana for children, which is somewhat unsurprising, as it was the retailers themselves that suggested including this initiative. Additionally, the positive response from the customers that they got supported the retailers’ positive attitude towards the initiative. We also found that many customers responded well to this initiative; however, we also observed a group that did not accept the initiative and preferred the bun over the banana. For the remaining initiatives, customers did not seem to notice them. Yet, we did observe customer behaviours that would probably work against the suggested mechanisms of some of the initiatives. (iii) In general, we describe three themes of barriers and facilitators that influence the implementation and possible sustainment of the initiatives: Health is not the number one priority, General capacity of the retailer, and Influence of customers and other stakeholders on store operation. Firstly, we found the retailers were motivated to work with health, both from a personal and professional perspective. The motivation was rooted in a feeling of social responsibility as well as health initiatives being viewed as a relative advantage, due to demand and making customers happier. Still, other priorities, such as profit and maintaining customers’ ‘free choice’, challenged the motivation to implement such initiatives. Secondly, the retailer showed a high level of available resources, which supported their general capacity to implement the initiatives; however, the large size of the organisation and its culture proved to be barriers to the implementation. Lastly, the analysis showed that the influence of both customers and other stakeholders was crucial to the implementation, both in terms of what is possible and what the retailers would be interested in and prioritise.

Our findings are similar to those of others [ 26 , 35 ]. Winkler et al. [ 35 ] found that even though supermarket actors found health-promoting initiatives meaningful to engage in, their engagement was challenged by a business mindset, practical routines, and structural requirements. Thus, despite the involvement of retailers in the development, selection and implementation of the initiatives, studies suggest that healthy food retail initiatives still encounter some fundamental barriers towards the implementation, such as the economical aspect or the view on customers’ free choice. However, our results also indicate that it might be possible to persuade food retailers to remove products or restrict choices if the evidence or arguments of it being the right thing to do are sufficiently strong, as with organic bananas or tobacco products. This has also been the case of another retailer in Denmark, which has decided that all their stores should be tobacco and nicotine-free by the end of 2028 to reduce the number of smokers [ 36 ]. Another solution is to identify win–win initiatives, as the complimentary banana for children was somewhat an example of (if we consider the banana as a healthier alternative) and which other studies have found as well [ 35 , 37 ].

Even though the four initiatives were implemented (yet two not as intended) in this study, and we found them to be somewhat acceptable to the retailers, we must still highlight that these initiatives represent a very small portion of the initiatives first suggested and entail several compromises from what the researchers had initially planned (Duus et al. Unpublished ). Moreover, the customer’s responses to the initiatives were mixed, and in some cases, their behaviour indicated that the initiatives would have little effect. Compared with studies testing similar initiatives, we find that 1) Shelf tags alone were found unlikely to change food purchases [ 38 ] and are likely to contribute to disparities in food purchases as not all customers know nutrition labels or have the literacy to read and understand them [ 39 ]. 2) Smaller bags for pick’n’ mix sweets could be successfully implemented and, based on results from another study, might be able to decrease the volume of sweets sold [ 40 ]. Moreover, others have also shown that customers are willing to buy smaller product options [ 41 ]. Taken together, this suggests that voluntary engagement with researchers might not suffice to make changes that would improve the supermarket environment as opted for to support population health. This view has also been suggested by Winkler et al. [ 35 ], and in the Lancet series on commercial determinants of health, an even more critical perspective on engagements with commercial actors as food retailers is presented [ 42 , 43 ]. Here they warn against how commercial actors use partnerships with researchers, among others, as a tool to improve their reputation and credibility [ 42 ].

In our collaborative process with the retailer, we experienced many challenges. We did not accomplish aligning retailers’ and researchers’ interests as scholars have suggested being the prerequisite of implementing healthy food retail interventions in supermarkets [ 26 , 27 ]. This underlines the importance of the pre-intervention phase, as described by Hawe, Shiell, and Riley [ 44 ], which is fundamental to a successful implementation. During the pre-intervention phase, the establishment of relationships between different people or agencies often occurs, and these relationships may play a crucial role in the implementation and the explanation of why some work and others do not [ 44 ]. In line with this, another study has suggested exploring what implementation strategies might promote the uptake of evidence-based interventions among food retailers [ 45 ]. They found that contrary to many other studies, the intervention in their study was compatible with the interest of the store managers to which it was presented—these store managers had a strong feeling of social responsibility towards the communities they operated in [ 45 ].

Strength and limitations

The investigation of the feasibility test was strengthened by using different methods, process evaluation concepts, and a broad view including both the delivery and presentation of the initiatives as well as customer and retailer perspectives. We primarily got the retailer perspective from a strategic level, yet we had planned on conducting focus group interviews with staff at the test store to get perspectives from an operational level on the initiatives and the implementation process. However, no staff wanted to participate in an interview. The store manager explained that this probably was due to three things: 1) They had no interest in the study, or they were tired of the study, 2) the recruitment was done too late (approximately 2 months after the feasibility test period), and 3) the staff was overworked as a result of understaffing due to the coronavirus disease pandemic. Future studies aim also to analyse sales data in order to evaluate whether any changes in sales of the products we intervened on occurred. However, with the available data, we will not be able to analyse whether the initiatives change people’s eating patterns or whether they influence people differently in terms of their socioeconomic factors or other characteristics.

A thorough needs assessment [ 46 ] among supermarket customers to test the initiative’s assumptions and their food purchase patterns would have strengthened the study. However, this was not possible within the timeframe and funding scheme, so the development drew primarily on existing knowledge and the experience of the retailer and the Danish Cancer Society. Furthermore, the store visits conducted in the store during the development of the initiative also provided a few customer perspectives, which led to the exclusion of some ideas (Duus et al.  unpublished ).

Furthermore, we learned two methodological lessons from the in-store observations: 1) All observers were met by the feeling of being ‘in the way’ and a need to be in almost constant movement to not interfere with the order in the store. The observers were met with a feeling of self-awareness and a need to legitimise their presence at the store by wearing a sticker on their shirts saying ‘visitor’ or their university identification card. These feelings were amplified by the governmental advice of social distancing and the requirement to wear face masks in grocery stores, introduced during the period of observations. 2) Concerning this, the observers also found it challenging to approach customers for the short interviews due to the feeling of invading people’s private space, hence only five were conducted. This was especially challenging when wearing face masks, as it was impossible to produce and read non-verbal signals (e.g. smiles), and difficult to hear what people were saying.

Implications for future studies and practice

This study presents an investigation of the implementation of healthy food retail initiatives for supermarkets that have been developed and selected together with retailers as suggested by the literature. It suggests that the implementation of such initiatives is possible and—to some degree—high. Yet, the quality of the initiatives was rather low, and some were not implemented as intended. Moreover, we still present some of the same barriers and limitations as former studies that have not implemented collaborative strategies in the pre-intervention phase. Some of this may be due to challenges such as a high staff turnover at the retailer and a lack of a shared understanding, as shown in another study (Duus et al. unpublished ). Future studies must explore this further.

Lessons for future studies are to identify initiatives that customers appreciate, as this is important to retailers. Underlining a needs assessment as an important first step in intervention development [ 30 , 46 ]. Furthermore, future studies should involve a broader range of stakeholders, including manufacturers and suppliers, in the development of the initiatives, as they have significant power over what can be implemented. Future studies would also benefit from identifying and testing implementation strategies that can facilitate the implementation of this type of intervention in this setting.

We performed a qualitative investigation of the implementation, acceptability, and feasibility of four different healthy food retail initiatives aimed at customers in a real-life supermarket setting, which had been developed and selected together with retailers. Only two of the four initiatives were implemented as intended, and the perspectives of retailers and customers were mixed or unclear. Altogether, the study highlights the challenges of implementing healthy retail food initiatives despite early involvement of retailers in the selection and design of those initiatives. Adding to the challenges of implementation, the initiatives also represent a compromise between the interests of the researcher and the retailers and do not represent what the literature suggests as the most effective strategies. A compromise made to uphold the partnership and complete the funded research project. Future studies should further examine the impact and pitfalls of including retailers (or other commercial actors) in the development and selection of healthy food retail initiatives and try to identify successful implementation strategies facilitating implementation.

Availability of data and materials

The data generated and analysed during the current study are not publicly available due to their sensitive and confidential nature but are available from the corresponding author upon reasonable request.

Abbreviations

Corporate Social Responsibility

Key Performance Indicator

Healthy diet. WHO. 2020. https://www.who.int/news-room/fact-sheets/detail/healthy-diet . Accessed 20 July 2023.

Greenwald P, Clifford CK, Milner JA. Diet and cancer prevention. Eur J Cancer. 2001May 1;37(8):948–65.

Article   PubMed   CAS   Google Scholar  

Firth J, Gangwisch JE, Borsini A, Wootton RE, Mayer EA. Food and mood: how do diet and nutrition affect mental wellbeing? BMJ. 2020Jun;29(369): m2382.

Article   Google Scholar  

English LK, Ard JD, Bailey RL, Bates M, Bazzano LA, Boushey CJ, et al. Evaluation of Dietary Patterns and All-Cause Mortality. JAMA Netw Open. 2021Aug 31;4(8): e2122277.

Article   PubMed   PubMed Central   Google Scholar  

Swinburn B, Caterson I, Seidell J, James W. Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr. 2004Feb;7(1a):123–46.

Brug J. Determinants of healthy eating: motivation, abilities and environmental opportunities. Fam Pract. 2008 Dec 1;25(suppl_1):i50–5.

Adam A, Jensen JD. What is the effectiveness of obesity related interventions at retail grocery stores and supermarkets? —a systematic review. BMC Public Health. 2016Dec;16(1):1247.

Ball K, Timperio AF, Crawford DA. Understanding environmental influences on nutrition and physical activity behaviors: where should we look and what should we count? Int J Behav Nutr Phys Act. 2006Sep 26;3(1):33.

Sonntag D, Schneider S, Mdege N, Ali S, Schmidt B. Beyond Food Promotion: A Systematic Review on the Influence of the Food Industry on Obesity-Related Dietary Behaviour among Children. Nutrients. 2015;7(10):8565–76.

Viola D, Arno PS, Maroko AR, Schechter CB, Sohler N, Rundle A, et al. Overweight and obesity: Can we reconcile evidence about supermarkets and fast food retailers for public health policy? J Public Health Policy. 2013Aug;34(3):424–38.

Black C, Moon G, Baird J. Dietary inequalities: What is the evidence for the effect of the neighbourhood food environment? Health Place. 2014May;27:229–42.

Article   PubMed   Google Scholar  

Vogel C, Ntani G, Inskip H, Barker M, Cummins S, Cooper C, et al. Education and the Relationship Between Supermarket Environment and Diet. Am J Prev Med. 2016Aug;51(2):e27-34.

Chandon P, Wansink B. Does food marketing need to make us fat? A review and solutions Nutr Rev. 2012;70(10):571–93.

PubMed   Google Scholar  

Bennett R, Zorbas C, Huse O, Peeters A, Cameron AJ, Sacks G, et al. Prevalence of healthy and unhealthy food and beverage price promotions and their potential influence on shopper purchasing behaviour: A systematic review of the literature. Obes Rev. 2020 Jan;21(1).

Harbers MC, Beulens JWJ, Rutters F, de Boer F, Gillebaart M, Sluijs I, et al. The effects of nudges on purchases, food choice, and energy intake or content of purchases in real-life food purchasing environments: a systematic review and evidence synthesis. Nutr J. 2020Dec;19(1):103.

Hollands GJ, Carter P, Anwer S, King SE, Jebb SA, Ogilvie D, et al. Altering the availability or proximity of food, alcohol, and tobacco products to change their selection and consumption. Cochrane Public Health Group, editor. Cochrane Database Syst Rev. 2019 Sep 4.

Slapø H, Schjøll A, Strømgren B, Sandaker I, Lekhal S. Efficiency of In-Store Interventions to Impact Customers to Purchase Healthier Food and Beverage Products in Real-Life Grocery Stores: A Systematic Review and Meta-Analysis. Foods. 2021May;10(5):922.

Gravlee CC, Boston PQ, Mitchell MM, Schultz AF, Betterley C. Food store owners’ and managers’ perspectives on the food environment: an exploratory mixed-methods study. BMC Public Health. 2014Dec;14(1):1031.

Winkler LL, Christensen U, Glümer C, Bloch P, Mikkelsen BE, Wansink B, et al. Substituting sugar confectionery with fruit and healthy snacks at checkout – a win-win strategy for consumers and food stores? a study on consumer attitudes and sales effects of a healthy supermarket intervention. BMC Public Health. 2016Nov 22;16(1):1184.

Adam A, Jensen JD, Sommer I, Hansen GL. Does shelf space management intervention have an effect on calorie turnover at supermarkets? J Retail Consum Serv. 2017Jan;1(34):311–8.

Toft U, Buch-Andersen T, Bloch P, Reinbach HC, Jensen BB, Mikkelsen BE, et al. A Community-Based, Participatory, Multi-Component Intervention Increased Sales of Healthy Foods in Local Supermarkets—The Health and Local Community Project (SoL). Int J Environ Res Public Health. 2023Jan;20(3):2478.

Bauer JM, Aarestrup SC, Hansen PG, Reisch LA. Nudging more sustainable grocery purchases: Behavioural innovations in a supermarket setting. Technol Forecast Soc Change. 2022Jun;1(179): 121605.

Denver S, Christensen T, Nordström J. Consumer preferences for low-salt foods: a Danish case study based on a comprehensive supermarket intervention. Public Health Nutr. 2021;24(12):3956–65.

Jensen JD, Sommer I. Reducing calorie sales from supermarkets – ‘silent’ reformulation of retailer-brand food products. Int J Behav Nutr Phys Act. 2017Aug 23;14(1):104.

Toft U, Winkler LL, Mikkelsen BE, Bloch P, Glümer C. Discounts on fruit and vegetables combined with a space management intervention increased sales in supermarkets. Eur J Clin Nutr. 2017Apr;71(4):476–80.

Middel CNH, Schuitmaker-Warnaar TJ, Mackenbach JD, Broerse JEW. Systematic review: a systems innovation perspective on barriers and facilitators for the implementation of healthy food-store interventions. Int J Behav Nutr Phys Act. 2019Dec;16(1):108.

Gupta A, Alston L, Needham C, Robinson E, Marshall J, Boelsen-Robinson T, et al. Factors Influencing Implementation, Sustainability and Scalability of Healthy Food Retail Interventions: A Systematic Review of Reviews. Nutrients. 2022Jan;14(2):294.

Denmark: market share of grocery retailers 2020. https://www.statista.com/statistics/565747/market-share-of-selected-grocery-retailers-in-denmark/ . Accessed 19 July 2023.

Moore N, editor. Desk research. In: How to Do Research: The Practical Guide to Designing and Managing Research Projects. Facet; 2006. p. 106–11.

Schultz Petersen K, Maindal HT, Ledderer L, Overgaard C. Komplekse interventioner: Udvikling, test, evaluering og implementering. Aalborg Universitetsforlag; 2022.

McGill E, Marks D, Er V, Penney T, Petticrew M, Egan M. Qualitative process evaluation from a complex systems perspective: A systematic review and framework for public health evaluators. PLoS Med. 2020Nov 2;17(11): e1003368.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006Jan;3(2):77–101.

Scaccia JP, Cook BS, Lamont A, Wandersman A, Castellow J, Katz J, et al. A practical implementation science heuristic for organizational readiness: R = MC2. J Community Psychol. 2015Apr;43(4):484–501.

Blake MR, Backholer K, Lancsar E, Boelsen-Robinson T, Mah C, Brimblecombe J, et al. Investigating business outcomes of healthy food retail strategies: A systematic scoping review. Obes Rev. 2019Oct;20(10):1384–99.

Winkler LL, Toft U, Glümer C, Bloch P, Buch-Andersen T, Christensen U. Involving supermarkets in health promotion interventions in the Danish Project SoL. A practice-oriented qualitative study on the engagement of supermarket staff and managers. BMC Public Health. 2023 Apr 18;23(1):706.

Lidl Danmark. https://om.lidl.dk/ansvarlighed/vi-fremmer-sundheden/udfasning-af-tobak . Accessed 7 March 2024.

Blake MR, Sacks G, Zorbas C, Marshall J, Orellana L, Brown AK, et al. The ‘Eat Well @ IGA’ healthy supermarket randomised controlled trial: process evaluation. Int J Behav Nutr Phys Act. 2021Dec;18(1):36.

Vandevijvere S, Berger N. The impact of shelf tags with Nutri-Score on consumer purchases: a difference-in-difference analysis of a natural experiment in supermarkets of a major retailer in Belgium. Int J Behav Nutr Phys Act. 2021Nov 18;18(1):150.

Robertson A, Lobstein T, Knai C. Obesity and socio-economic groups in Europe: Evidence review and implications for action. 2007.

Mørck CJ. Nyt forsøg afslører: Bland selv-posens størrelse gør en stor forskel. 2024. https://www.cancer.dk/nyheder-og-fortaellinger/2024/nyt-forsoeg-afsloerer-bland-selv-posens-stoerrelse-goer-en-stor-forskel/ . Accessed 4 July 2024.

Vandenbroele J, Slabbinck H, Van Kerckhove A, Vermeir I. Curbing portion size effects by adding smaller portions at the point of purchase. Food Qual Prefer. 2018Mar;1(64):82–7.

Gilmore AB, Fabbri A, Baum F, Bertscher A, Bondy K, Chang HJ, et al. Defining and conceptualising the commercial determinants of health. Lancet. 2023Apr;401(10383):1194–213.

Lacy-Nichols J, Nandi S, Mialon M, McCambridge J, Lee K, Jones A, et al. Conceptualising commercial entities in public health: beyond unhealthy commodities and transnational corporations. Lancet. 2023Apr;401(10383):1214–28.

Hawe P, Shiell A, Riley T. Theorising Interventions as Events in Systems. Am J Community Psychol. 2009;43(3–4):267–76.

Brimblecombe J, Miles B, Chappell E, De Silva K, Ferguson M, Mah C, et al. Implementation of a food retail intervention to reduce purchase of unhealthy food and beverages in remote Australia: mixed-method evaluation using the consolidated framework for implementation research. Int J Behav Nutr Phys Act. 2023Feb 17;20(1):20.

Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021Sep;30(374): n2061.

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Acknowledgements

We want to thank all the participating retail group and supermarket staff members involved in this project and the implementation process. We appreciate the time and effort you have dedicated to this project and your openness. Furthermore, we want to acknowledge the customers who took the time to share their opinions with us during their daily grocery shopping.

We acknowledge Johanne Aviaja Rosing, Louise Ayoe Sparvath Brautsch, and Carl Johannes Middelboe for their assistance in conducting the pre- and post-intervention observations.

Open access funding provided by University of Southern Denmark This study is funded by the Danish Cancer Society, grant no.: R274-A16920. The first author (Katrine Sidenius Duus) has also received a Faculty Scholarship from the Faculty of Health Sciences at the University of Southern Denmark to support the completion of her PhD thesis, which this study is part of.

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KSD, RFK, and TTT contributed to the funding acquisition, study conception and design. Data generation and analyses were performed by KSD. The first draft of the manuscript was written by KSD. RFK and TTT commented on previous versions of the manuscript and contributed in writing the final manuscript. KSD wrote up the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Katrine Sidenius Duus .

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This study has been approved by SDU Research & Innovation Organization (notification no. 11.136). All informants who participated in interviews received written and verbal information about the aim, that participation was voluntary and that their information would be used for research purposes only and treated with confidentiality. By participating, consent for their data to be used for research was given. Data from the observation and documents were handled confidentially and with caution to protect sensitive information that could identify individuals.

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Duus, K.S., Tjørnhøj-Thomsen, T. & Krølner, R.F. Implementation of health-promoting retail initiatives in the Healthier Choices in Supermarkets Study—qualitative perspectives from a feasibility study. BMC Med 22 , 349 (2024). https://doi.org/10.1186/s12916-024-03561-2

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Received : 24 May 2024

Accepted : 14 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1186/s12916-024-03561-2

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Abstract: Concern has recently been expressed by HCI researchers as to the inappropriate treatment of qualitative studies through a positivistic mode of evaluation that places emphasis on metrics and measurement. This contrasts with the nature of qualitative research, which privileges interpretation and understanding over quantification. This paper explains the difference between positivism and interpretivism, the limits of quantification in human science, the distinctive contribution of qualitative research, and how quality assurance might be provided for in the absence of numbers via five basic criteria that reviewers may use to evaluate qualitative studies on their own terms.

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