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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Researcher, Academic Writer, Web developer

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Exploring Research Question and Hypothesis Examples: A Comprehensive Guide

This comprehensive guide explores the intricacies of formulating research questions and hypotheses across various academic disciplines. By delving into examples and methodological approaches, the article aims to provide scholars and researchers with the tools necessary to develop robust and effective research frameworks. Understanding and crafting well-formed research questions and hypotheses are pivotal in conducting meaningful research that can significantly contribute to knowledge within a field.

Key Takeaways

• Understand the fundamental differences and connections between research questions and hypotheses.
• Learn how to craft effective and precise research questions that guide the research process.
• Explore various types of hypotheses and methods for testing and refining them.
• Examine practical examples of research questions and hypotheses across multiple disciplines.
• Gain insights into the impact of well-constructed research questions and hypotheses on research outcomes, academic publishing, and grant applications.

Understanding the Fundamentals of Research Questions and Hypotheses

Defining research questions.

Research questions are the backbone of any scholarly inquiry, guiding you through the exploration of your chosen topic. They help you focus your study and determine the direction of your research. A well-crafted research question should be clear, focused, and answerable within the constraints of your study.

Characteristics of a Strong Hypothesis

A strong hypothesis provides a specific, testable prediction about the expected outcomes of your research. It is not merely a guess but is grounded in existing literature and theory. To develop a robust hypothesis, consider the variables involved and ensure that it is feasible to test them within your study's design.

Interrelation Between Research Questions and Hypotheses

Understanding the interrelation between research questions and hypotheses is crucial for structuring your research effectively. Your hypothesis should directly address the gap in the literature highlighted by your research question, providing a clear pathway for investigation. This alignment ensures that your study can contribute valuable insights to your field.

Crafting Effective Research Questions

Identifying the purpose.

To craft an effective research question , you must first identify the purpose of your study. This involves understanding what you aim to discover or elucidate through your research. Ask yourself what the core of your inquiry is and what outcomes you hope to achieve. This clarity will guide your entire research process, ensuring that your question is not only relevant but also deeply rooted in your specific academic or practical goals.

Scope and Limitations

It's crucial to define the scope and limitations of your research early on. This helps in setting realistic boundaries and expectations for your study. Consider factors such as time, resources, and the breadth of the subject area. Narrowing down your focus to a manageable scope can prevent the common pitfall of an overly broad or vague question, which can dilute the impact of your findings.

Formulating Questions that Drive Inquiry

The final step in crafting your research question is formulating it in a way that drives inquiry. This means your question should be clear, concise, and structured to prompt detailed investigation and critical analysis. It should challenge existing knowledge and push the boundaries of what is already known. Utilizing strategies like the Thesis Dialogue Blueprint or the Research Proposal Compass can be instrumental in refining your question to ensure it is both innovative and feasible.

Developing Hypotheses in Research

From research questions to hypotheses.

When you transition from research questions to hypotheses, you are essentially moving from what you want to know to what you predict will happen. This shift involves formulating a specific, testable prediction that directly stems from your initial question. Ensure your hypothesis is directly linked to and derived from your research question to maintain a coherent research strategy.

Types of Hypotheses

There are several types of hypotheses you might encounter, including simple, complex, directional, nondirectional, associative, causal, null, and alternative. Each type serves a different purpose and is chosen based on the specifics of the research question and the nature of the study. For instance, a null hypothesis might be used to test the effectiveness of a new teaching method compared to the standard.

Testing and Refining Hypotheses

Testing your hypothesis is a critical step in the research process. This phase involves collecting data, conducting experiments, or utilizing other research methods to determine the validity of your hypothesis. After testing, you may find that your hypothesis needs refining or even reformation based on the outcomes. This iterative process is essential for narrowing down the most accurate explanation or prediction for your research question.

Examples of Research Questions in Various Disciplines

Humanities and social sciences.

In the realm of Humanities and Social Sciences, research questions often explore cultural, social, historical, or philosophical aspects. How does gender representation in 20th-century American literature reflect broader social changes? This question not only seeks to uncover specific literary trends but also ties them to societal shifts, offering a rich field for analysis.

Natural Sciences

Research questions in the Natural Sciences are typically aimed at understanding natural phenomena or solving specific scientific problems. A common question might be, What are the effects of plastic pollutants on marine biodiversity? This inquiry highlights the environmental concerns and seeks empirical data to understand the impact.

Applied Sciences

In Applied Sciences, the focus is often on improving technology or engineering solutions. A pertinent question could be, How can renewable energy sources be integrated into existing power grids? This question addresses the practical challenges and potential innovations in energy systems, crucial for advancing sustainable technologies.

Analyzing Hypothesis Examples Across Fields

Case studies in psychology.

In psychology, hypotheses often explore the causal relationships between cognitive functions and behaviors. Consider how a hypothesis might predict the impact of stress on memory recall . By examining various case studies, you can see how hypotheses are specifically tailored to address intricate psychological phenomena.

Experimental Research in Biology

Biology experiments frequently test hypotheses about physiological processes or genetic information. For instance, a hypothesis might propose that a specific gene influences plant growth rates. Through rigorous testing, these hypotheses contribute significantly to our understanding of biological systems.

Field Studies in Environmental Science

Field studies in environmental science provide a rich ground for testing hypotheses related to ecosystem dynamics and conservation strategies. A common hypothesis might explore the effects of human activity on biodiversity. These studies often involve complex data collection and analysis, highlighting the interrelation between empirical evidence and theoretical predictions.

Methodological Approaches to Formulating Hypotheses

Quantitative vs. qualitative research.

When you embark on hypothesis formulation, understanding the distinction between quantitative and qualitative research methodologies is crucial. Quantitative research focuses on numerical data and statistical analysis, ideal for hypotheses that require measurable evidence. In contrast, qualitative research delves into thematic and descriptive data, providing depth and context to hypotheses that explore behaviors, perceptions, and experiences.

The Role of Theoretical Frameworks

Theoretical frameworks serve as the backbone for developing robust hypotheses. They provide a structured way to align your hypothesis with existing knowledge. By integrating theories and models relevant to your study, you ensure that your hypothesis has a solid foundation and aligns with established academic thought.

Utilizing Existing Literature to Form Hypotheses

A thorough review of existing literature is indispensable for crafting a well-informed hypothesis. This process not only highlights gaps in current research but also allows you to build on the work of others. By synthesizing findings from previous studies, you can formulate hypotheses that are both innovative and grounded in academic precedent.

Evaluating the Impact of Well-Formed Research Questions and Hypotheses

On research outcomes.

Understanding the impact of well-formed research questions and hypotheses on research outcomes is crucial. Well-crafted questions and hypotheses serve as a framework that guides the entire research process , ensuring that the study remains focused and relevant. They help in defining the scope of the study and in identifying the variables that need to be measured, thus directly influencing the validity and reliability of the research findings.

In Academic Publishing

The role of well-defined research questions and hypotheses extends beyond the research process into the realm of academic publishing. A clear hypothesis provides a strong foundation for the research paper, enhancing its chances of acceptance in prestigious journals. The clarity and direction afforded by a solid hypothesis make the research more appealing to a scholarly audience, potentially increasing citation rates and academic recognition.

In Grant Applications

When applying for research grants, the clarity of your research questions and hypotheses can significantly impact the decision-making process of funding bodies. A well-articulated hypothesis demonstrates a clear vision and a structured approach to addressing a specific issue, which can be crucial in securing funding. Grant reviewers often look for proposals that promise substantial contributions to the field, and a strong hypothesis can be a key factor in showcasing the potential impact of your research.

In our latest article, 'Evaluating the Impact of Well-Formed Research Questions and Hypotheses,' we delve into the crucial role that precise questions and hypotheses play in academic research. Understanding this can significantly enhance your thesis writing process. For a deeper exploration and practical tools to apply these concepts, visit our website and discover how our Thesis Action Plan can transform your academic journey. Don't miss out on our special offers tailored just for you!

In this comprehensive guide, we have explored various examples of research questions and hypotheses, shedding light on their significance and application in academic research. Understanding the distinction between a research question and a hypothesis, as well as knowing how to effectively formulate them, is crucial for conducting methodical and impactful studies. By examining different scenarios and examples, this guide aims to equip researchers with the knowledge to craft well-defined research questions and hypotheses that can drive meaningful investigations and contribute to the broader field of knowledge. As we continue to delve into the intricacies of research design, it is our hope that this guide serves as a valuable resource for both novice and experienced researchers in their scholarly endeavors.

Frequently Asked Questions

What is a research question.

A research question is a clearly defined query that guides a scientific or academic study. It sets the scope and focus of the research by asking about a specific phenomenon or issue.

How does a hypothesis differ from a research question?

A hypothesis is a specific, testable prediction about what will happen in a study based on prior knowledge or theory, while a research question is an open query that guides the direction of the investigation.

What are the characteristics of a strong hypothesis?

A strong hypothesis is clear, testable, based on existing knowledge, and it states an expected relationship between variables.

How can research questions and hypotheses interrelate?

Research questions define the scope of inquiry, while hypotheses provide a specific prediction about the expected outcomes that can be tested through research methods.

What should be considered when formulating a research question?

When formulating a research question, consider clarity, focus, relevance, and the feasibility of answering the question through available research methods.

Why is it important to have a well-formed hypothesis?

A well-formed hypothesis directs the research process, allows for clear testing of assumptions, and helps in drawing meaningful conclusions that can contribute to the body of knowledge.

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

Related Posts

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

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• Formulation of Hypothesis

Children who spend more time playing outside are more likely to be imaginative. What do you think this statement is an example of in terms of scientific research ? If you guessed a hypothesis, then you'd be correct. The formulation of hypotheses is a fundamental step in psychology research.

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• First, we will discuss the importance of hypotheses in research.
• We will then cover formulating hypotheses in research, including the steps in the formulation of hypotheses in research methodology.
• We will provide examples of hypotheses in research throughout the explanation.
• Finally, we will delve into the different types of hypotheses in research.

What is a Hypothesis?

The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research . To be classed as scientific research , it must be observable, valid, reliable and follow a standardised procedure.

One of the important steps in scientific research is to formulate a hypothesis before starting the study procedure.

The hypothesis is a predictive, testable statement predicting the outcome and the results the researcher expects to find.

The hypothesis provides a summary of what direction, if any, is taken to investigate a theory.

In scientific research, there is a criterion that hypotheses need to be met to be regarded as acceptable.

If a hypothesis is disregarded, the research may be rejected by the community of psychology researchers.

Importance of Hypothesis in Research

The purpose of including hypotheses in psychology research is:

• To provide a summary of the research, how it will be investigated, and what is expected to be found.
• To provide an answer to the research question.

When carrying out research, researchers first investigate the research area they are interested in. From this, researchers are required to identify a gap in the literature.

Filling the gap essentially means finding what previous work has not been explained yet, investigated to a sufficient degree, or simply expanding or further investigating a theory if doubt exists.

The researcher then forms a research question that the researcher will attempt to answer in their study.

Remember, the hypothesis is a predictive statement of what is expected to happen when testing the research question.

The hypothesis can be used for later data analysis. This includes inferential tests such as hypothesis testing and identifying if statistical findings are significant.

Steps in the Formulation of Hypothesis in Research Methodology

Researchers must follow certain steps to formulate testable hypotheses when conducting research.

Overall, the researcher has to consider the direction of the research, i.e. will it be looking for a difference caused by independent variables ? Or will it be more concerned with the correlation between variables?

All researchers will likely complete the following.

• Investigating background research in the area of interest.
• Formulating or investigating a theory.
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works.

The above steps are used to formulate testable hypotheses.

The Formulation of Testable Hypotheses

The hypothesis is important in research as it indicates what and how a variable will be investigated.

The hypothesis essentially summarises what and how something will be investigated. This is important as it ensures that the researcher has carefully planned how the research will be done, as the researchers have to follow a set procedure to conduct research.

This is known as the scientific method.

Formulating Hypotheses in Research

When formulating hypotheses, things that researchers should consider are:

Types of Hypotheses in Research

Researchers can propose different types of hypotheses when carrying out research.

The following research scenario will be discussed to show examples of each type of hypothesis that the researchers could use. "A research team was investigating whether memory performance is affected by depression ."

The identified independent variable is the severity of depression scores, and the dependent variable is the scores from a memory performance task.

The null hypothesis predicts that the results will show no or little effect. The null hypothesis is a predictive statement that researchers use when it is thought that the IV will not influence the DV.

In this case, the null hypothesis would be there will be no difference in memory scores on the MMSE test of those who are diagnosed with depression and those who are not.

An alternative hypothesis is a predictive statement used when it is thought that the IV will influence the DV. The alternative hypothesis is also called a non-directional, two-tailed hypothesis, as it predicts the results can go either way, e.g. increase or decrease.

The example in this scenario is there will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

The directional alternative hypothesis states how the IV will influence the DV, identifying a specific direction, such as if there will be an increase or decrease in the observed results.

The example in this scenario is people with low depressive scores will perform better in the memory performance task than people who score higher in depressive symptoms.

Example Hypothesis in Research

To summarise, let's look at an example of a straightforward hypothesis that indicates the relationship between two variables: the independent and the dependent.

If you stay up late, you will feel tired the following day; the more caffeine you drink, the harder you find it to fall asleep, or the more sunlight plants get, the taller they will grow.

Formulation of Hypothesis - Key Takeaways

• The current community of psychologists believe that the best approach to understanding behaviour is to conduct scientific research. One of the important steps in scientific research is to create a hypothesis.
• The hypothesis is a predictive, testable statement concerning the outcome/results that the researcher expects to find.
• Hypotheses are needed in research to provide a summary of what the research is, how to investigate a theory and what is expected to be found, and to provide an answer to the research question so that the hypothesis can be used for later data analysis.
• There are requirements for the formulation of testable hypotheses. The hypotheses should identify and operationalise the IV and DV. In addition, they should describe the nature of the relationship between the IV and DV.
• There are different types of hypotheses: Null hypothesis, Alternative hypothesis (this is also known as the non-directional, two-tailed hypothesis), and Directional hypothesis (this is also known as the one-tailed hypothesis).

Flashcards in Formulation of Hypothesis 18

What type of hypothesis matches the following definition. A predictive statement that researchers use when it is thought that the IV will not influence the DV.

Null hypothesis 

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV. But, the hypothesis does not state how the IV will influence the DV. 

Alternative hypothesis 

What type of hypothesis matches the following definition. A hypothesis that states that the IV will influence the DV, and states how it will influence the DV. 

Directional, alternative hypothesis 

Which type of hypothesis is also known as a two-tailed hypothesis? 

What type of hypothesis is the following example. There will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What type of hypothesis is the following example. There will be an observed difference in scores from a memory performance task between people with high- or low-depressive scores.

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Frequently Asked Questions about Formulation of Hypothesis

What are the 3 types of hypotheses?

The three types of hypotheses are:

• Null hypothesis 
• Alternative hypothesis 
• Directional/non-directional hypothesis 

What is an example of a hypothesis in psychology?

An example of a null hypothesis in psychology is, there will be no observed difference in scores from a memory performance task between people with high- or low-depressive scores.

What are the steps in formulating a hypothesis?

All researchers will likely complete the following

• Investigating background research in the area of interest 
• Formulating or investigating a theory 
• Identify how the theory will be tested and what the researcher expects to find based on relevant, previously published scientific works 

What is formulation of hypothesis in research? 

The formulation of a hypothesis in research is when the researcher formulates a predictive statement of what is expected to happen when testing the research question based on background research.

How to formulate  null and alternative hypothesis?

When formulating a null hypothesis the researcher would state a prediction that they expect to see no difference in the dependent variable when the independent variable changes or is manipulated. Whereas, when using an alternative hypothesis then it would be predicted that there will be a change in the dependent variable. The researcher can state in which direction they expect the results to go. 

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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Definition of a Hypothesis

What it is and how it's used in sociology

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

• Null Hypothesis Examples
• Difference Between Independent and Dependent Variables
• Examples of Independent and Dependent Variables
• What Is a Hypothesis? (Science)
• What Are the Elements of a Good Hypothesis?
• Understanding Path Analysis
• What It Means When a Variable Is Spurious
• What 'Fail to Reject' Means in a Hypothesis Test
• How Intervening Variables Work in Sociology
• Null Hypothesis Definition and Examples
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• Null Hypothesis and Alternative Hypothesis
• Six Steps of the Scientific Method
• What Are Examples of a Hypothesis?
• Scientific Method Flow Chart

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A concise guide to reproducible research using secondary data

Chapter 2 formulating a hypothesis.

“There is no single best way to develop a research idea.” ( Pischke 2012 )

2.1 How do you develop a research question and formulate a hypothesis?

You decide to undertake a scientific project. Where do you start? First, you need to find a research question that interests you and formulate a hypothesis. We will introduce some key terminology, steps you can take, and examples how to develop research questions. Note that .

What if someone assigns a topic to me? For students attending undergraduate and graduate courses that often pick topics from a list, all of these steps are equally important and necessary. You still need to formulate a research question and a hypothesis. And it is important to clarify the relevance of your topic for yourself.

When thinking about a research question, you need to identify a topic that is:

• Relevant , important in the world and interesting to you as a researcher: Does working on the topic excites you? You will spend many hours thinking about it and working on it. Therefore, it should be interesting and engaging enough for you to motivate your continued work on this topic.
• Specific : not too broad and not too narrow
• Feasible to research within a given time frame: Is it possible to answer the research question based on your time budget, data and additional resources.

How do you find a topic or develop a feasible research idea in the first place? Finding an idea is not difficult, the critical part is to find a good idea. How do you do that? There is no one specific way how one gets an idea, rather there is a myriad of ways how people come up with potential ideas (for example, as stated by Varian ( 2016 ) ).

You can find inspiration by

• Looking at insights from the world around you: your own life and experiences, observe the behavior of people around you
• Talking to people around you, experts, other students, family members
• Talking to individuals outside your field (non-economists)
• Talking to professionals working in the area you are interested in (you may use social media and professional platforms like LinkedIN or Twitter to make contact)
• Reading journal articles from other non-economic social sciences and the medical literature
• What are the issues being discussed?
• How do these issues affect people’s lives?

In addition you could

• Go to virtual and in-person seminars, for example, the Essen Health Economics Seminar
• Look at abstracts of scientific articles and working papers
• Look at the literature in a specific field you are interested in, for example, screening complete issues of journals or editorials about certain research advancements. By reading this literature you might come up with the idea on how to extend and refine previous research.

Once you identified a research question that is of interest to you, you need to define a hypothesis.

2.2 What is a hypothesis?

A hypothesis is a statement that introduces your research question and suggests the results you might find. It is an educated guess. You start by posing an economic question and formulate a hypothesis about this question. Then you test it with your data and empirical analysis and either accept or reject the hypothesis. It constitutes the main basis of your scientific investigation and you should be careful when creating it.

2.2.1 Develop a hypothesis

Before you formulate your hypothesis, read up on the topic of interest. This should provide you with sufficient information to narrow down your research question. Once you find your question you need to develop a hypothesis, which contains a statement of your expectations regarding your research question’s results. You propose to prove your hypothesis with your research by testing the relationship between two variables of interest. Thus, a hypothesis should be testable with the data at hand. There are two types of hypotheses: alternative or null. Null states that there is no effect. Alternative states that there is an effect.

There is an alternative view on this that suggests one should not look at the literature too early on in the idea-generating process to not be influenced and shaped by someone else’s ideas ( Varian 2016 ) . According to this view you can spend some time (i.e. a few weeks) trying to develop your own original idea. Even if you end up with an idea that has already been pursued by someone else, this will still provide you with good practice in developing publishable ideas. After you have developed an idea and made sure that it was not yet investigated in the literature, you can start conducting a systematic literature review. By doing this, you can find some other interesting insights from the work of others that you can synthesize in your own work to produce something novel and original.

2.2.2 Identify relevant literature

For your research project you will need to identify and collect previous relevant literature. It should involve a thorough search of the keywords in relevant databases and journals. Place emphasis on articles from high-ranking journals with significant numbers of citations. This will give you an indication of the most influential and important work in the field. Once you identify and collect the relevant literature for your topic, you will need to critically synthesize it in your literature review.

When you perform your literature review, consider theories that may inform your research question. For example, when studying physician behavior you may consider principal-agent theory.

2.2.3 Research question or literature review: the chicken or the egg problem?

Whether you start reading the literature first or by developing an idea may depend on your level (graduate student, early career researcher) and other goals. However, thinking freely about what you like to investigate first may help to critically develop a feasible and interesting research question.

We highlight an example how to start with investigating the real world and subsequently posing a research question ( “How to Write a Strong Hypothesis Steps and Examples ” 2019 ; “Developing Strong Research Questions Criteria and Examples ” 2019 ; Schilbach 2019 ) . For example, based on your observation you notice that people spend extensive amount of time looking at their smartphones. Maybe even you yourself engage in the same behavior. In addition, you read a BBC News article Social media damages teenagers’ mental health, report says .

(#fig:social_media)Social media and mental health

Source: BBC

You decide to translate this article and your observations into a research question : How does social media use affect mental health? Before you formulate your hypothesis, read up on the topic of interest. Read economic, medical and other social science literature on the topic. There is likely to be a vast amount of literature from non-economic fields that are doing research on your topic of interest, for example, psychology or neuroscience. Familiarize yourself with it and master it. Do not get distracted by different scientific methodologies and techniques that might seem not up-to-par to the economic studies (small sample sizes, endogeneity, uncovering association rather than causation, etc.), but rather focus on suggestions of potential mechanisms.

A hypothesis is then your research question distilled into a one sentence statement, which presents your expectations regarding the results. You propose to prove your hypothesis by testing the relationship between two variables of interest with the data at hand. There are two types of hypotheses: alternative or null. The null hypothesis states that there is no effect. The alternative hypothesis states that there is an effect.

A hypothesis related to the above-stated research question could be: The increased use of social media among teenagers leads to (is associated with) worse mental health outcomes, i.e. increased incidence of depression, eating disorders, worse well-being and lower self-esteem. It suggests a direction of a relationship that you expect to find that is guided by your observations and existing evidence. It is testable with scientific research methods by using statistical analysis of the relevant data.

Your hypothesis suggests a relationship between two variables: social media use (your independent variable \(X\) ) and mental health (dependent variable \(Y\) ). It could be framed in terms of correlation (is associated with) or causation (leads to). This should be reflected in the choice of scientific investigation you decide to undertake.

The null hypothesis is: There is no relationship between social media use among teenagers and their mental health .

2.3 Resources box

2.3.1 how to develop strong research questions.

• The form of the research process
• Varian, H. R. (2016). How to build an economic model in your spare time. The American Economist, 61(1), 81-90.

2.3.2 Identify relevant literature from major general interest and field literature

To identify the relevant literature you can

• use academic search engines such as Google Scholar, Web of Science, EconLit, PubMed.
• search working paper series such as the National Bureau of Economic Research , NetEc or IZA
• search more general resource sites such as Resources for Economists
• go to the library/use library database

2.3.3 Assess the quality of a journal article

Several rankings may help to assess the quality of research you consider

• Journals of general interest and by field in economics and management - For German-speaking countries, consider the VWL / BWL Handelsblatt Ranking for economics and management - The German Association of Management Scholars provides an expert-based ranking VHB JourQual 3.0, Teilranking Management im Gesundheitswesen - Web of Science Impact Factors - Scimago
• Health Economics, Health Services and Health Care Managment Research: Health Economics Journals List
• Be aware that like in any other domain there are predatory publishing practices .

Use tools to investigate how a journal article is connected to other works

• Citationgecko
• Connected papers
• scite_ – a tool to get a first impression whether a study is disputed or academic consensus

2.3.4 Organize your literature

• Zotero (free of charge)
• Mendeley (free of charge)
• EndNote (potentially free of charge via your university)
• Citavi (potentially free of charge via your university)
• BibTEX if you work with TEX
• Excel spread sheet

2.4 Checklist to get started with formulating your hypothesis

• Find an interesting and relevant research topic, if not assigned
• Try to suck up all information you can easily obtain from various sources within and outside academic literature
• Formulate one compelling research question
• Find the best available empirical and theoretical evidence that is related to your research question
• Formulate a hypothesis
• Check whether data are available for analysis
• Challenge your idea with your fellows or senior researchers

2.5 Example: Hellerstein ( 1998 )

As an illustration of the research process of formulating a hypothesis, designing a study, running a study, collecting and analyzing the data and, finally, reporting the study, we provide an example by replicating Judith K. Hellerstein’s paper “The Importance of the Physician in the Generic versus Trade-Name Prescription Decision” that was published in 1998 in the RAND Journal of Economics.

Hellerstein’s 1998 paper has impacted discussion about behavioral factors of physician decisions and pharmaceutical markets over two decades. The study received 448 citations on Google Scholar since 1998 by 27/03/2022, including recent mentions in top field journals such as Journal of Public Economics (2021) , Journal of Health Economics (2019) , and Health Economics (2019) .

Figure 2.1: Connected graph of Hellerstein ( 1998 ) , February 2022

Figure 2.1 shows a connected graph of prior and derivative works related to the study.

The work has impacted the literature researching the role of physician behavior and its influence on access, adoption and diffusion of health services, moral hazard and incentives in prescription and treatment decisions and the influence of different payment schemes, and a vast body of literature studying the pharmaceutical market.

The research that has been influenced by Hellerstein includes evidence on:

• generic drug entries and market efficiency
• the effectiveness of pharmaceutical promotion
• the effectiveness of price regulations
• the role of patents and dynamics of market segmentation

At the end of each chapter, we demonstrate insights into this study that we replicate.

2.5.1 Context of the study - escalating health expenditures

In the United States, the total prescription drug expenditure in 2020 marked about 358.7 billion US Dollars ( Statista n.d. ) . The prescription of generic drugs in comparison to more expensive brand-name versions is an option in reducing the total health care expenditure. Generic drugs are bioequivalent in the active ingredients and can serve as a channel to contain prescription expenditure ( Kesselheim 2008 ) as generic drugs are between 20 and 90% cheaper than their trade-name alternatives ( Dunne et al. 2013 ) .

2.5.2 Research question - How does a patient’s insurance status influence the physician’s choice between generic compared to brand-name drugs?

Physicians are faced with a multitude of medication options, including the choice between generic and trade-name drugs. Physicians ideally act as agents for their patients to identify the best available treatment option based on their needs. Choosing the best treatment entails cost of coordination and cognition. The prescription of generic drugs may serve as an example to what extent physicians customize treatments according to patients’ needs with regards to cost. From an economic point of view we may expect that once a generic drug is available, a perfectly rational agent (i.e. physician) would prescribe a generic drug instead of the trade-name version if therapeutically identical ( Dranove 1989 ) . This leads to the following research question: “Do physicians vary their prescription decisions on a patient-by-patient basis or do they systematically prescribe the same version, trade-name or generic, to all patients?” .

The 1998 Hellerstein’s study examines two hypotheses:

• The physician prescribing choice influences the selection of a generic over a brand-name drug
• The patient’s insurance status influences the physician’s choice between generic and brand-name drugs.

For the purpose of this example and in the replication exercise we focus on the second aspect.

2.5.3 Hypothesis

The paper formulates the following hypothesis:

Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals (moral hazard in insurance)

Hellerstein ( 1998 ) discusses that, based on insurance status, some patients may demand certain care more than others. If, for example, the prescription drug is reimbursed by the patient’s health insurance, this may cause overconsumption. This behavior can potentially differ by the patient’s insurance scheme. A patient that has no insurance and, thus, does not get any reimbursement for prescription drugs, might have a higher incentive to demand cheaper generic drugs ( Danzon and Furukawa 2011 ) than a patient with insurance that covers prescription drugs, either generic or trade-name. Given that the United States have different insurance schemes with varying prescription drug coverage, it is of interest to investigate the role of a patient’s insurance status in the physician’s choice between generic compared to brand-name drugs.

Hellerstein ( 1998 ) considers a patient’s insurance status as a matter of dividing the study population in groups for which the choice between generic and brand-name drugs differs. She suggests that There is a relationship between the prescription of a generic drug and insurance status of a patient. ( Hellerstein 1998 ) .

Providing answers to a research question requires formulating and testing a hypothesis. Based on logic, theory or previous research, a hypothesis proposes an expected relationship within the given data. According to her research question, Hellerstein hypothesizes that: Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals.

Specifically, she writes “if there is moral hazard in insurance when it comes to physician prescription behavior, there will be differences in the propensity of physicians to prescribe low-cost generic drugs, and these differences will be (partially) a function of the insurance held by the patient. In particular, if moral hazard exists, patients with extensive insurance coverage for prescription drugs (like those on Medicaid in 1989) should receive prescriptions written for generic drugs less frequently than patients with no prescription drug coverage.” ( Hellerstein 1998, 113 )

Based on Hellerstein’s considerations, we expect the effect of the insurance status on whether a patient receives a generic to be different from zero. To obtain a testable null hypothesis, we reformulate this relationship so that we reject the hypothesis if our expectations are correct. This means, if we expect to see an effect of insurance on prescriptions of generics, our null hypothesis is that insurance status has no effect on the outcome (prescription of generic drugs). No moral hazard arises from having obtained insurance.

Research Problem

Ai generator.

A research problem is a specific issue or gap in knowledge that a researcher aims to address through systematic investigation. It forms the foundation of a study, guiding the research question, research design , and potential outcomes. Identifying a clear research problem is crucial as it often emerges from existing literature, theoretical frameworks, and practical considerations. In a student case study , the research question and hypothesis stem from the identified research problem.

What is a Research Problem?

A research problem is a specific issue, difficulty, contradiction, or gap in knowledge that a researcher aims to address through systematic investigation. It forms the basis of a study, guiding the research question, research design, and the formulation of a hypothesis.

Examples of Research Problem

• Impact of Social Media on Adolescent Mental Health : Investigating how social media usage affects the mental health and well-being of teenagers.
• Climate Change and Agricultural Productivity : Examining the effects of climate change on crop yields and farming practices.
• Online Learning and Student Engagement : Assessing the effectiveness of online learning platforms in maintaining student engagement and academic performance.
• Healthcare Access in Rural Areas : Exploring the barriers to healthcare access in rural communities and potential solutions.
• Workplace Diversity and Employee Performance : Analyzing how workplace diversity influences team dynamics and employee productivity.
• Renewable Energy Adoption : Studying the factors that influence the adoption of renewable energy sources in urban versus rural areas.
• AI in Healthcare Diagnostics : Evaluating the accuracy and reliability of artificial intelligence in medical diagnostics.
• Gender Disparities in STEM Education : Investigating the causes and consequences of gender disparities in STEM education and careers.
• Urbanization and Housing Affordability : Exploring the impact of rapid urbanization on housing affordability and availability in major cities.
• Public Transportation Efficiency : Assessing the efficiency and effectiveness of public transportation systems in reducing urban traffic congestion.

Research Problem Examples for Students

• The Impact of Homework on Academic Achievement in High School Students
• The Relationship Between Sleep Patterns and Academic Performance in College Students
• The Effects of Extracurricular Activities on Social Skills Development
• Influence of Parental Involvement on Students’ Attitudes Toward Learning
• The Role of Technology in Enhancing Classroom Learning
• Factors Contributing to Student Anxiety During Exams
• The Effectiveness of Peer Tutoring in Improving Reading Skills
• Challenges Faced by International Students in Adapting to New Educational Systems
• Impact of Nutrition on Concentration and Academic Performance
• The Role of Socioeconomic Status in Access to Higher Education Opportunities

Research Problems Examples in Education

• Effect of Class Size on Student Learning Outcomes
• Impact of Technology Integration in Classroom Instruction
• Influence of Teacher Professional Development on Student Achievement
• Challenges in Implementing Inclusive Education for Students with Disabilities
• Effectiveness of Bilingual Education Programs on Language Proficiency
• Role of Parental Involvement in Enhancing Academic Performance
• Impact of School Leadership on Teacher Retention and Job Satisfaction
• Assessment of Remote Learning Efficacy During the COVID-19 Pandemic
• Barriers to STEM Education Participation Among Female Students
• Effect of Socioeconomic Status on Access to Quality Education

Research Problems Examples in Business

• Impact of Employee Engagement on Productivity and Retention
• Effectiveness of Social Media Marketing Strategies on Consumer Behavior
• Challenges in Implementing Sustainable Business Practices
• Influence of Leadership Styles on Organizational Performance
• Role of Corporate Culture in Driving Innovation
• Impact of Remote Work on Team Collaboration and Communication
• Strategies for Managing Supply Chain Disruptions
• Effect of Customer Feedback on Product Development
• Challenges in Expanding into International Markets
• Influence of Brand Loyalty on Customer Retention

Basic Research Problem Examples

• Effect of Sleep on Cognitive Function
• Impact of Exercise on Mental Health
• Influence of Diet on Academic Performance
• Role of Social Support in Stress Management
• Impact of Screen Time on Children’s Behavior
• Effects of Pollution on Public Health
• Influence of Music on Mood and Productivity
• Role of Genetics in Disease Susceptibility
• Impact of Advertising on Consumer Choices
• Effects of Climate Change on Local Wildlife

Research Problem in Research Methodology

A research problem in research methodology refers to an issue or gap in the process of conducting research that requires a solution. Examples include:

• Validity and Reliability of Measurement Tools : Ensuring that instruments used for data collection consistently produce accurate results.
• Selection of Appropriate Sampling Techniques : Determining the best sampling method to ensure the sample represents the population accurately.
• Bias in Data Collection and Analysis : Identifying and minimizing biases that can affect the validity of research findings.
• Ethical Considerations in Research : Addressing ethical issues related to participant consent, confidentiality, and data protection.
• Generalizability of Research Findings : Ensuring that research results are applicable to broader populations beyond the study sample.
• Mixed Methods Research Design : Effectively integrating qualitative and quantitative approaches in a single study.
• Data Interpretation and Reporting : Developing accurate and unbiased interpretations and reports of research findings.
• Longitudinal Study Challenges : Managing the complexities of conducting studies over extended periods.
• Control of Extraneous Variables : Identifying and controlling variables that can affect the dependent variable outside the study’s primary focus.
• Developing Theoretical Frameworks : Constructing robust frameworks that guide the research process and support hypothesis development.

Characteristics of a Research Problem

• Clarity : The research problem should be clearly defined, unambiguous, and understandable to all stakeholders.
• Specificity : It should be specific and narrow enough to be addressed comprehensively within the scope of the research.
• Relevance : The problem should be significant and relevant to the field of study, contributing to the advancement of knowledge or practice.
• Feasibility : It should be practical and manageable, considering the resources, time, and capabilities available to the researcher.
• Novelty : The research problem should address an original question or gap in the existing literature, providing new insights or perspectives.
• Researchability : The problem should be researchable using scientific methods, including data collection, analysis, and interpretation.
• Ethical Considerations : The research problem should be ethically sound, ensuring no harm to participants or the environment.
• Alignment with Objectives : The problem should align with the research objectives and goals, guiding the direction and purpose of the study.
• Measurability : It should be possible to measure and evaluate the outcomes related to the problem using appropriate metrics and methodologies.
• Contextualization : The problem should be placed within a broader context, considering theoretical frameworks, existing literature, and practical applications.

Types of Research Problems

• Aim: To describe the characteristics of a specific phenomenon or population.
• Example: “What are the key features of successful online education programs?”
• Aim: To compare two or more groups, variables, or phenomena.
• Example: “How does employee satisfaction differ between remote and on-site workers?”
• Aim: To determine cause-and-effect relationships between variables.
• Example: “What is the impact of leadership style on employee productivity?”
• Aim: To examine the relationship between two or more variables.
• Example: “What is the relationship between social media usage and self-esteem among teenagers?”
• Aim: To explore a new or under-researched area where little information is available.
• Example: “What are the emerging trends in consumer behavior post-pandemic?”
• Aim: To solve a specific, practical problem faced by an organization or society.
• Example: “How can small businesses improve their cybersecurity measures?”
• Aim: To expand existing theories or develop new theoretical frameworks.
• Example: “How can existing theories of motivation be integrated to better understand employee behavior?”
• Aim: To evaluate the effects of policies or suggest improvements.
• Example: “What are the effects of the new minimum wage laws on small businesses?”
• Aim: To investigate ethical issues within a field or practice.
• Example: “What are the ethical implications of AI in decision-making processes?”
• Aim: To address issues that span multiple disciplines or fields of study.
• Example: “How can principles of environmental science and economics be combined to develop sustainable business practices?”

How to Define a Research Problem

Defining a research problem involves several key steps that help in identifying and articulating a specific issue that needs investigation. Here’s a structured approach:

• Choose a general area of interest or field relevant to your expertise or curiosity. This can be broad initially and will be narrowed down through the next steps.
• Review existing research to understand what has already been studied. This helps in identifying gaps, inconsistencies, or areas that need further exploration.
• Based on your literature review, refine your broad topic to a more specific issue or aspect that has not been adequately addressed.
• Ensure the problem is significant and relevant to the field. It should address a real-world issue or theoretical gap that contributes to advancing knowledge or solving practical problems.
• Clearly articulate the problem in a concise and precise manner. This statement should explain what the problem is, why it is important, and how it impacts the field.
• Develop specific research questions that your study will answer. These questions should be directly related to your problem statement and guide the direction of your research.
• Establish clear research objectives that outline what you aim to achieve. Formulate hypotheses if applicable, which are testable predictions related to your research questions.
• Consider the resources, time, and scope of your study. Ensure that the research problem you have defined is feasible to investigate within the constraints you have.
• Discuss your defined research problem with peers, mentors, or experts in the field. Feedback can help refine and improve your problem statement.

Importance of Research Problem

The research problem is crucial as it forms the foundation of any research study, guiding the direction and focus of the investigation. It helps in:

• Defining Objectives : Clarifies the purpose and objectives of the research, ensuring the study remains focused and relevant.
• Guiding Research Design : Determines the methodology and approach, including data collection and analysis techniques.
• Identifying Significance : Highlights the importance and relevance of the study, demonstrating its potential impact on the field.
• Focusing Efforts : Helps researchers concentrate their efforts on addressing specific issues, leading to more precise and meaningful results.
• Resource Allocation : Assists in the efficient allocation of resources, including time, funding, and manpower, by prioritizing critical aspects of the research.

FAQ’s

Why is defining a research problem important.

Defining a research problem is crucial because it guides the research process, helps focus on specific objectives, and determines the direction of the study.

How do you identify a research problem?

Identify a research problem by reviewing existing literature, considering real-world issues, discussing with experts, and reflecting on personal experiences and observations.

What is the difference between a research problem and a research question?

A research problem identifies the issue to be addressed, while a research question is a specific query the research aims to answer.

Can a research problem change during the study?

Yes, a research problem can evolve as new data and insights emerge, requiring refinement or redefinition to better align with findings.

How do you formulate a research problem?

Formulate a research problem by clearly stating the issue, outlining its significance, and specifying the context and scope of the problem.

What is the role of literature review in identifying a research problem?

A literature review helps identify gaps, inconsistencies, and unresolved issues in existing research, which can guide the formulation of a research problem.

How does a research problem impact the research design?

The research problem shapes the research design by determining the methodology, data collection techniques, and analysis strategies needed to address the issue.

What are common sources of research problems?

Common sources include academic literature, practical experiences, societal issues, technological advancements, and gaps identified in previous research.

How specific should a research problem be?

A research problem should be specific enough to guide focused research but broad enough to allow comprehensive investigation and meaningful results.

How do research objectives relate to the research problem?

Research objectives are specific goals derived from the research problem, detailing what the study aims to achieve and how it plans to address the problem.

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• Published: 24 June 2024

The impact of digital transformation on the quality and safety level of agricultural exports: evidence from Chinese listed companies

• Yuchen Liu   ORCID: orcid.org/0009-0000-2336-8092 1 ,
• Yinguo Dong 1 &
• Weiwen Qian 2  

Humanities and Social Sciences Communications volume  11 , Article number:  817 ( 2024 ) Cite this article

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• Business and management

Enhancing the quality and safety of exported agricultural products and improving export competitiveness is the key to establishing enhanced competitive advantages in agricultural products, developing a trade powerhouse and realising high-quality development of agriculture. This paper uses the data of Chinese listed companies and Chinese Customs from 2007 to 2016 to discuss the effect and mechanism of digital transformation of enterprises on the quality and safety level of export agricultural products by using the staggered differential method. The study shows that (1) Enterprise digital transformation effectively improves the quality and safety of exported agricultural products, and this result holds after endogeneity, placebo and multiple robustness tests; (2) Heterogeneity analyses reveal that the quality and safety effect of enterprise digital transformation is greater for exporting to developed countries’ markets, non-state-owned enterprises and enterprises in the eastern region, in addition to bulk agricultural products and consumer-oriented agricultural products; (3) Mechanism analyses shows that enterprise digital transformation raises the quality and safety of exported agricultural products through technological innovation, product tracing, information sharing and quality assurance effects.

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Introduction.

To comprehensively improve the level of product quality and promote the development of a strong trading nation, in 2023, the Chinese State Council issued the Outline for the Construction of a Strong Quality Country, declaring the need for China to shift from promoting development to improving quality and efficiency, raising the quality of exported products and the value of exported units and realising product quality upgrading Footnote 1 . Agriculture is the foundation of the country, quality safety is the basis of improving the quality of agricultural products, and vigorously improving agricultural products’ quality is essential for advancing the country’s high-quality development. China has become more involved in raising the incomes of the country’s 600 million farmers. However, issues related to sanitary and phytosanitary (SPS) measures which are frequently encountered in exports such as excessive pesticide residues, preservatives, microbial contamination and metallic foreign objects have become critical factors hindering Chinese agricultural product exports; thus, agricultural products are caught in the quality upgrading dilemma (Liu and Dong 2021 ). Statistics released by the Korea Food and Drug Administration on irregularities in food products imported in South Korea show that 75 cases arose from Chinese agri-food products from April to June 2023, indicating a 23% increase compared with the same period in 2022 Footnote 2 . In the first half of 2023, the US Food and Drug Administration identified 975 batches of products from China, of which 418 cases were agri-food products, representing an annual increase of 13.9%, accounting for 42.87% of the notified products from China Footnote 3 . Consequently, to promote the high-quality development of China’s agricultural trade, it is essential to transition exported agricultural products from quantity to quality and urgently improve the quality and safety of Chinese exported agricultural products. In November 2021, the Ministry of Commerce issued the 14th Five-Year Plan for the High-Quality Development of Foreign Trade, which listed ‘digital trade’ as a key project of foreign trade and proposed to advance digital empowerment, accelerate digital transformation, promote the in-depth fusion of digital technology and trade development and continuously strengthen the engine of foreign trade development Footnote 4 . Based on the digital transformation of promote the development of export trade, is undoubtedly the important way to realising the strategic goal of trade power.

Enterprise digitalisation refers to the process of enterprises’ industrial upgrading and transformation using emerging technologies (Zhang et al. 2021 ). As the main body of the agricultural industry chain, digital transformation has altered the value creation and value capture of the agricultural industry chain and transformed the agricultural enterprise model (Yi et al. 2021 ). Central Document No. 1 of 2023 noted that the implementation of in-depth digital village development should be conducted, promoting research and development (R&D) and digital application scenarios Footnote 5 . The Party Central Committee clearly attaches considerable importance to digital economy development as an important aspect of high-quality agricultural development through the deep integration of the digital economy in rural industry for prosperity, agricultural modernisation and development to provide inexhaustible water and sustained momentum. The scale of the agricultural digital economy is expected to reach 1.26 trillion yuan by 2025, 7.8 trillion yuan by 2035 and 24 trillion yuan by 2050 Footnote 6 . From a theoretical perspective, enterprises’ digital transformation can improve the control and supervision of production processes through intelligent monitoring and optimising the digital management of the supply chain to improve the standardisation and safety of agricultural production (Wu and Yao 2023 ). Furthermore, digitalising data and information can improve information transmission and sharing efficiency to promote the continuous exchange of information between enterprises and consumers and improve the safety and credibility of agricultural products (Li et al. 2023 ). As primary participants in market economic activities and the source of vitality for economic and social development, can agricultural enterprises improve the quality and safety of exported products and address the quality and safety problems associated with exported agricultural products through digital transformation? If so, what are the mechanisms of action? Is there heterogeneity in the impact of digital transformation among agricultural enterprises? It is of great theoretical value and practical significance to answer the above questions from the micro level. In theory, it enriches and expands the theoretical framework of the impact of digital transformation on the export of enterprises, and lays the foundation for the high-quality development of China’s foreign trade by evaluating the digitalisation of enterprises. At the same time, it helps to deeply understand the new driving mechanism for upgrading the quality and safety level of enterprises’ export agricultural products in the digital era, and also provides strong empirical evidence for helping Chinese foreign trade enterprises to ride the ‘digital revolution’ and achieve high-quality export development.

The remainder of this paper is structured as follows. Section “Literature review” presents the literature review. Sections “Theoretical research and hypothesis formulation” conducts theoretical and mechanism analyses. Section “Model setting, variable construction and data sources” describes the empirical model, data and estimation strategy. Section “Empirical results and analysis” details the results. Section “Mechanism of action test” presents the mechanism testing and Section “Conclusions and policy implications” concludes.

Literature review

Three strands of literature are closely related to this study. The first strand of literature has examined the quality of agricultural exports. Studies on product quality were first proposed by Linder ( 1961 ), who argued that the level of per capita income has a direct impact on trade development and that income levels are highly correlated with national product quality requirements. Melitz ( 2003 ) argued against the assumption of homogeneity among production enterprises, proposing a novel trade theory, and research began to really consider the heterogeneity of enterprises’ product quality. Regarding the measurement of agricultural product quality, the most common models have included unit value (Schott 2004 ), ex-post backcasting (Khandelwal et al. 2013 ; Shi 2014 ) and nested logit (Dong and Huang 2016 ) methods. Regarding the influencing factors of the quality of exported agricultural products, some studies have found that trade measures such as the positive list system (Chen and Xu 2017 ), SPS measures in importing countries (Dong and Liu 2019 ), maximum residue limitation standards (Jiang and Yao 2019 ) and the development of digital finance (Li and Wang 2024 ) have compelled the quality upgrade of China’s exported agricultural products.

The second category is research on enterprise digitalisation, which focuses on its connotations, measurement and related economic effects. Studies have found that digital transformation refers to the comprehensive transformation and optimisation of business models, operating processes, value creation and delivery methods by organisations or enterprises using digital technologies and information technology to enhance their competitiveness, innovation and sustainable development (Vial, 2019 ; Verhoef et al. 2019 ). Digitalisation measurement has included three aspects of investment, application and business transformation, using annual reports of Chinese listed companies in different sample years and examining machine learning word frequency statistics to measure the digital transformation of Chinese enterprises (Liu, 2020 ; Du et al. 2022 ). In terms of the economic effects of digitisation, scholars have focused on the impact of digitalisation on enterprises’ total factor productivity, innovation, international trade, input-output efficiency and specialised division of labour. For example, Zhao et al. ( 2021 ) studied that digital transformation can promote total factor productivity by improving innovation capacity, optimising human capital structure, and reducing costs. Chaney ( 2014 ) suggested that the widespread use of information technology (ICT) can promote export growth by reducing information search and distribution costs. Loebbecke and Picot ( 2015 ) found that digital transformation can reduce the cost of effective information acquisition, optimise the enterprise’s R&D model and improve the efficiency of innovation investment. Yuan et al. ( 2021 ) argued that the digital transformation of enterprises has significantly improved the specialisation level of listed enterprises in China. Liu et al. ( 2021 ) found that there is a inverted U-shaped relationship between enterprise digital investment and efficiency.

The third strand of literature has examined the impact of firms’ digital transformation on export trade. Freund and Weinhold ( 2004 ) first suggested that adopting digital technologies removes information barriers between trading parties, widening the network of trade links between countries and expanding trade flow and scope. It has also been argued that the digital skill factor is increasingly replacing the labour factor as the main driver of firms’ production and exports (Acemoglu and Restrepo 2020 ). Qi and Cai ( 2020 ) found that digital transformation can expand exports, reduce entry costs and increase the number of exports, product variety and trading partners. Meijers ( 2014 ) argued that firms’ digital innovation in an industry improves the added value of products and facilitates advancement to the middle and high end of the global value chain. Nambisan et al. ( 2017 ) found that enterprises can realise rapid iterative upgrading of export products digital transformation, expediently adjust the range of export products and export high-quality products that are adapted to international market demands. For the study of agricultural trade, Liu and Gao ( 2022 ) used a vector auto-regressive model, revealing a stable dynamic relationship between the digital economy and the total number of imported and exported agricultural products. The authors also demonstrated that different agricultural products have different characteristics and the digital economy can explain the total import and export amount of animal products, grains and fruits to a greater extent. Ma and Guo ( 2023 ) found that the digital economy can expand the scale of agricultural exports and increase the technical complexity of agricultural exports.

Examining previous literature reveals that researchers have paid limited attention to the impact of enterprises’ digital transformation on agricultural product exports, and even less research has analysed the impact mechanism of digital transformation on the quality and safety of exported agricultural products according to the characteristics of agricultural products. Although Du et al. ( 2022 ) and Hong et al. ( 2022 ) both emphasized the positive impact of digital transformation on the quality of export products of enterprises, they failed to analyse the impact of digital transformation on the quality and safety of export agricultural products from the perspective of quality and safety based on the characteristics of agricultural products. Compared with industrial products, the biggest challenge facing the quality and safety of exported agricultural products is food safety and quality, which refers to ensuring that the whole process of agricultural products, from production, to processing to export, meets the quality and safety standards of importing countries and keeping them fresh and safe during transport, storage and sale. In the context of strongly advocating the empowerment of traditional agriculture with digital technology and comprehensively promoting the digital transformation of agriculture, it is essential to leverage enterprise digital transformation to address the challenges of ensuring food safety and the quality of exported agricultural products. This study uses the data of Chinese listed companies and Chinese Customs data from 2007 to 2016 to examine the of digital transformation intensity of listed companies exporting agricultural products using Python crawler technology and adopts a staggered difference-in-differences (DID) model to explore the effect and mechanism of the influence of enterprise digital transformation on the quality and safety of exported agricultural products.

Compared with the existing literature, the marginal contributions of this study are reflected in the following three aspects. (1) In terms of research perspective, this study constructs an indicator system for the quality and safety level of agricultural products from four dimensions of quality tracing, information communication, quality control, and risk prevention and focuses for the first time on the impact of enterprises’ digital transformation on the quality and safety of exported agricultural products from the perspective of food safety, expanding the research scope of the economic effects of enterprises’ digital transformation and exploring the issue of its intrinsic impact mechanisms, laying the foundation for assessing the impact of enterprises’ digitalisation on the high-quality development of China’s foreign trade. (2) In terms of research content, this paper enriches and expands the theoretical framework of the impact of digital transformation on the export of enterprises, introduces digital transformation into the heterogeneous trade model of enterprises, and discusses the impact and specific mechanism of digital transformation on the quality and safety level of export agricultural products based on the general equilibrium perspective and combined with the characteristics of agricultural products. (3) In terms of research data and modelling methodology, this study combines data from Chinese listed companies with Chinese Customs data, uses crawler technology to quantify the intensity of the digital transformation of listed companies exporting agricultural products in five dimensions: digital technology application, digital information system, digital intelligent management, digital marketing model, digital efficiency enhancement and explores the impact effect of enterprise digital transformation on the quality and safety of exported agricultural products and the mechanisms of impact based on a staggered DID model, providing micro-level evidence regarding enterprises’ digital transformation.

Theoretical research and hypothesis formulation

Theoretical models.

Based on the heterogeneous trade model proposed by Melitz ( 2003 ) and Antoniades ( 2015 ), this paper incorporates the factors of digital transformation into an open economic framework, comprehensively considering the personalised needs of consumers and the cost characteristics of manufacturers. By solving for the maximisation of consumer utility and enterprise profit, the equilibrium of enterprise quality investment is obtained, and the impact of digital transformation on the quality and safety level of exported agricultural products is theoretically discussed.

Assume that firms in the country \(i\) export products to the country \(j\) where \(i,j\in 1,\mathrm{..}.N\) , the country \(j\) has \({L}_{j}\) consumers who consume the product set \({\varOmega }_{j}\) and that the utility function of the consumers is of the Dixit–Stiglitz form, which can be expressed as follows:

In Eq. ( 1 ), \(\sigma > 1\) denotes the elasticity of substitution between different commodities. \({q}_{ij}(\omega )\) is the quality of the product \(\omega\) , and \({x}_{ij}(\omega )\) represents the demand of the country \(j\) for the product \(\omega\) in the country \(i\) , which can be expressed as follows:

Equation ( 2 ) represents the optimal demand of consumers in the country \(j\) for the product \(\omega\) in the country \(i\) . \({p}_{ij}(\omega )\) is the price of the product \(\omega\) , \({P}_{j}(\omega )=\{{{\int }_{\omega \in {\varOmega }_{j}}[{p}_{ij}(\omega )/{q}_{ij}(\omega )]}^{1-\sigma }]d\omega {\}}^{\frac{1-\sigma }{\sigma }}\) is the total price index of all products consumed in the country \(j\) , and \({E}_{j}\) is the total expenditure on these products in the country \(j\) . As the price of a product falls or the quality improves, consumer demand increases.

Enterprises

Assuming that the firm is in a monopolistically competitive market, the firm faces two types of fixed costs, namely fixed export costs \({f}_{ij}\) (excluding trade variable costs \({\tau }_{ij}\) ) and fixed production costs \({f}_{d}{q}_{ij}^{\beta }\) . \({f}_{d}\) denotes the fixed cost of production in the absence of quality adjustment. \(\beta > 0\) denotes a measure of the elasticity of fixed production costs with respect to the quality of the product, which usually consists of fixed capital inputs that include the firm’s R&D or production equipment inputs. Since the digital transformation of a firm reduces the cost of search and the cost of information exchange, \({\tau }_{ij}=\alpha f(\cdot ){e}^{-dig}\) , \({\tau }_{ij}^{\text{'}}=-dig\ast \alpha f(\cdot ){e}^{-dig-1} < 0\) . Assuming that the unit cost of a firm’s quality inputs is \({\mu }_{i}\) , its relationship with digital transformation can be expressed as \({\mu }_{i}(dig)\) , \({\mu }_{i}^{\text{'}}(dig) < 0\) . Here, \({\mu }_{i}\) is also influenced by other factors of production such as labour and capital. Using \(c(\cdot )\) as a measure of the unit cost of other influences on the quality inputs of the firm, the cost of quality due to digital transformation is denoted as: \({\mu }_{i}=\frac{c(\cdot )}{{e}^{dig}}\) .

Define \({\theta }_{L}\) as the productivity of a firm’s labour force, and the relationship between the productivity of a firm’s labour force and the digital transformation of a firm as \({\theta }_{L}(dig)\) , where \({\theta }_{L}^{\text{'}}(dig) > 0\) . Assuming that each labour force has increased its productivity by acquiring better technology ( \(\xi\) ), it follows:

Assuming that quality is positively related to the marginal cost of production, the total factor productivity (TFP) function per unit of firm in the country \(i\) can be defined as:

In the above equation, \(\varphi ({\theta }_{k},{\theta }_{L})\) increases as \({\theta }_{k}\) and \({\theta }_{L}\) increase. Therefore, the marginal cost of production of a product exported from the country \(i\) to the country \(j\) should be \({\mu }_{i}{\tau }_{ij}{q}_{ij}^{\alpha }/\varphi ({\theta }_{k},{\theta }_{L})\) . Where \(\alpha \in (0,1)\) represents the elasticity of marginal cost with respect to product quality.

Balanced quality inputs from enterprises

Combining the consumer utility function and the firm’s production function, the firm’s profit from exports from the country \(i\) to the country \(j\) should be:

For the first order condition, this yields:

From Eqs. ( 6 ) and ( 7 ), the optimal quantity decision made by the firm can be determined by the following conditions:

In Eq. ( 8 ), \(\beta -(1-\alpha )(\sigma -1) > 0\) , holding all other factors constant, the firm’s optimal quality increases as the cost of trade \({\tau }_{ij}\) decreases ( \(1-\alpha < 0\) ). Combined with the conditions of \({\tau }_{ij}^{\text{'}} < 0\) , the hypothesis can be obtained:

Hypothesis 1: Digital transformation of enterprises improves the quality and safety of exported agricultural products.

Enterprise digital transformation raises the quality and safety of exported agricultural products through technological innovation, product tracing, information sharing and quality assurance effects (See Fig. 1 ).

The mechanism of the digital transformation of enterprises and the quality and safety of exported agricultural products.

Technological innovation effect

Innovation is a powerful tool to strengthen enterprises’ competitive advantage and the primary driving force for incentivising enterprises to enter the global market and advance export quality upgrading (Carboni and Medda 2020 ). First, the digital transformation of enterprises brings digital production factors to agricultural trade. Digital production factors such as the Internet of Things, big data analytics and artificial intelligence have improved the efficiency of agricultural production and management, increased the transparency of the market, and promoted cross-border cooperation and innovation, creating good conditions for the sustainable development of agricultural trade and the income growth of farmers (Wen and Chen 2020 ). Second, digital transformation transforms agricultural production. The addition of digital production factors change the practices of traditional agricultural production, which are predominantly guided by human experience and promotes the transformation of crude and non-standard traditional agricultural practices to standardised and accurate agricultural production. These benefits advance the improvement of agricultural production efficiency and the quality and safety of exported agricultural products (Sun et al. 2023 ). Second, digitalisation enables technical information sharing. Digital technologies such as the internet promote users’ access to explicit and implicit knowledge and technology sharing (Grant et al. 2010 ), and enterprises can employ digital technologies to access and learn to navigate new agricultural technology resources such as Good Agricultural Practices audit implementation, enhanced planting and cultivation practices and external knowledge on the rational use of pesticides and chemical fertilisers and introduction to new technology and promote the continuous optimisation and innovation of agricultural products, promoting continuous quality improvement of exported agricultural products. Therefore, this study proposes the following:

Hypothesis 2: Digital transformation improves the quality and safety of exported agricultural products through technological innovation effect.

Product traceability effect

First, digitalisation enables the traceability of agricultural products’ entire production chain. By using digital technology, enterprises can establish a complete archive and information database of the production process, recording key data and information regarding all aspects of agricultural products’ planting, breeding, production, processing, packaging and transport. This information can help enterprises trace the source, production conditions, direction of flow and other important information of agricultural products to strengthen quality control and ensure quality and safety (Tan et al. 2015 ). Agricultural export enterprises face risks such as freshness and obstruction of traffic and logistics in transport, and digital transformation can improve agricultural export enterprises’ information circulation efficiency, enable enterprises to obtain timely and effective market information and logistics information more expediently, facilitate efficient communication between upstream farmers and downstream enterprises, conduct risk prediction and prevention and reduce supply chain risk (Song et al. 2023 ). These benefits subsequently ensure enterprises’ production efficiency and improve the quality and safety of export products. Second, digitalisation enables rapid recall and location of agricultural products’ problems. When quality and safety problems arise for exported agricultural products, enterprises can rapidly locate and investigate the root causes of food safety problems using efficient digital traceability system, expediently implement corrective measures, implement precise recall or treatment measures, reduce the quantity and scope of affected agricultural products and protect the rights and interests of consumers (Rauniyar et al. 2023 ), which leads to the hypothesis:

Hypothesis 3: Digital transformation improves the quality and safety of exported agricultural products through product traceability effect.

Information sharing effect

Through digital transformation, companies can share more information with consumers regarding product origins, quality and production processes, increasing product transparency and traceability, reducing food safety issues and promoting the production and export of high-quality agricultural products. First, digital traceability information sharing can be implemented as enterprises can establish a supply chain system with higher transparency and traceability using digital technology (Zhang and Gu 2023 ). By scanning the QR code on the product packaging or using mobile phone apps and other digitally enabled techniques, consumers can obtain information on the entire trajectory of agricultural products’ planting, breeding, production and processing in addition to quality test results, including data on pesticide residues, heavy metal content, nutrient content and other relevant considerations. This transparency effectively alleviates the information asymmetry between producers and consumers and increases consumers’ trust in product quality and safety (Cuesta et al. 2013 ). Companies can also employ big data analytic to monitor and analyse key indicators in the production process in addition to market and consumer behaviour data. Using this information, enterprises can proactively establish early warning systems to early detection systems for potential food safety issues and implement appropriate measures to intervene and improve the quality of exported agricultural products. Second, digital transformation enables companies to expediently collect, analyse and respond to consumer feedback and monitoring information (Zhang et al. 2023 ). Consumers can share opinions and suggestions on the quality of exported agricultural products and food safety through social media, online surveys, evaluation platforms and other channels. Enterprises can use this information to quickly identify and address potential problems and apply measures to improve the quality of exported agricultural products and production processes. Therefore, we propose the following hypothesis:

Hypothesis 4: Digital transformation improves the quality and safety of exported agricultural products through information sharing effect.

Quality assurance effect

Enterprises can use digital technology to establish a digital product certification system to manage and verify agricultural products’ quality certification and labelling information more efficiently and accurately, enhance market trust and reduce the occurrence of food safety incidents (Wang et al. 2023 ). First, quality assurance systems ensure the authenticity and integrity of agricultural products’ certification information. Compared with paper certifications, electronic certification uses technical means such as encryption algorithms and digital signatures, reducing the risk of tampering and facilitating traceability and verification (Dogui and Ivanov 2022 ) and guaranteeing the quality and safety of exported agricultural products. Second, enterprises can present quality certification and labelling information for consumers on digital platforms, demonstrate product quality through visual elements that confirm safety information in the form of certificates and certification marks and externalise intrinsic quality information of the products, transforming the attributes of agricultural products from trusted goods to searched goods (Hong and Cho 2011 ). Consumers in importing countries can verify the certification status of the products online, providing real-time information regarding the products’ characteristics and quality, which improves the level of food safety compliance and enhances the credibility and competitiveness of the exported agricultural products. Therefore, this study proposes the final hypothesis:

Hypothesis 5: Digital transformation improves the quality and safety of exported agricultural products through quality assurance effect.

Model setting, variable construction and data sources

To quantify the impact of agricultural export enterprises’ digital transformation on the quality of exported agricultural products, based on the theoretical analysis above, this study adopts the two-way fixed effect model and uses the staggered DID model for empirical testing (Nunn and Qian 2011 ). The basic idea for this approach is that because enterprises have undergone digital transformation at different points in time and to varying degrees, ordinary DID models cannot measure the change and degree of digital transformation. This study subsequently adopts a staggered DID model for testing, setting enterprises with no digital transformation as the control group and enterprises with any degree of digital transformation as the experimental group. In the sample observation period, most enterprises’ degree of digital transformation has undergone a shift from 0 to non-0, in alignment with the design of the intensity variable in the staggered DID, which provides a better quasi-natural experimental environment for this study, and the specific model is constructed as follows:

Where \(f\) , \(k\) , \(j\) and \(t\) denote the enterprise, product, destination country and year respectively. The explanatory variable \(qua\_sa{f}_{fkjt}\) is the quality and safety of agricultural products exported by the enterprise, the core explanatory variable \(digita{l}_{ft}\) is the degree of digital transformation of the enterprise \(f\) in the year \(t\) , \(pos{t}_{ft}\) indicates whether the enterprise has undergone digital transformation in the year \(t\) , if yes, then it takes 1, otherwise it takes 0; \(Contro{l}_{kjt}\) represents the control variables. This paper also controls the two-dimensional combination of the enterprise-product fixed effect \({\delta }_{ik}\) , the two-dimensional combination of the destination country-product fixed effect \({\delta }_{jk}\) , and the firm-destination country fixed effects \({\delta }_{jk}\) , and further controls for product-year fixed effects \({\delta }_{kt}\) , so as to control for all individual effects related to firms, products and destinations that do not change over time; \({\varepsilon }_{fkjt}\) is the error term, and \(\beta\) is the core estimation parameter representing the net effect of the impact of firms’ digital transformation on the quality and safety of China’s exported agricultural products.

Measurement and description of variables

Explained variable.

The explanatory variable is the quality and safety level of exported agricultural products ( \(qua\,{\_}\,sa{f}_{fkjt}\) ). Since the concept of ‘Food Safety’ was put forward by the Food and Agriculture Organisation of the United Nations in 1974, food safety can be divided into two levels: Food security and Food safety. Food security refers to the availability of adequate food at the global, national and regional levels, and household levels (Pinstrup-Andersen 2009 ). Food Safety, mainly from the perspective of food hygiene and safety, requires that food should avoid the threat of food-borne diseases in the production process (Kirch 2008 ). In demonstrating the practicability and feasibility of systematic evaluation of food and feed safety, experts from the European Food Safety Authority (EFSA) believe that food safety is a multi-faceted concept that needs to be comprehensively considered from the four perspectives of human health, plant health, animal welfare and environment (Aiassaa et al. 2015 ). Food safety is a macro concept involving many factors. At present, food safety is defined through the three dimensions of quantity safety, quality safety and sustainability, and an indicator system for evaluating food safety is built on this basis, which has gained wide consensus. For example, the food safety evaluation index system built by the Economist takes quantitative safety, quality safety and sustainability as three first-level indicators.

The quality and safety of agricultural products covers all the quality attributes of agricultural products and highlights the safety attributes, highlighting the overall quality safety concept of agricultural products management. The quality and safety information of agricultural products is the effective information that can reflect the quality characteristics of agricultural products such as the safety of agricultural products, packaging of agricultural products and production process of agricultural products. Therefore, this paper uses content analysis to define and measure the quality and safety information disclosure of sample enterprises, in accordance with the provisions of the national food safety standard ‘General Hygiene Standards for Food Production’ (GB 14881-2013), referring to the social responsibility index system in Guide 3.0 for Food Enterprises issued by the Chinese Academy of Social Sciences, and with reference to the research of Alix-Garcia et al. ( 2013 ), Sumner and Ross ( 2002 ), Chen ( 2016 ) and Cheng et al. ( 2019 ). Based on the perspectives of food quality and safety assurance, food quality and safety information disclosure and customer responsibility, the words ‘traceability’, ‘product quality’, ‘certification’, ‘risk management’ and ‘food safety’ were retrieved from the annual report, internal control self-evaluation report and social responsibility report of the sample enterprises, from the four dimensions of quality traceability, information communication, quality control and risk prevention. A total of 18 indicators were used to measure the quality and safety level of the sample enterprises (see Table 1 ). Score item by item according to the actual disclosure situation of the quality safety level of export agricultural products of the enterprise, and assign 1 value to each disclosure content, that is, if the sample enterprise discloses one of the indicators, assign 1 value, otherwise, 0 value, and summarise the score value of the quality safety level of export agricultural products of the enterprise. As enterprises attach different importance to core and peripheral products, enterprises will tilt internal resources and management focus to core products, adjust product mix and improve the quality of core products (Sun et al. 2022 ), including more and better production factors and a larger share of R&D investment, so as to improve the quality of core products. Therefore, this paper takes the export value as the weight, and refines the quality and safety level of export agricultural products from the enterprise level to the product level.

Policy variable

The Policy variable is enterprise digitalisation transformation index ( \(digita{l}_{ft}\) ). This study uses the Python crawler function to identify keyword word frequencies in the annual reports of listed companies exporting agricultural products, constructs a thesaurus and quantifies the word frequencies to determine listed companies’ degree of digital transformation (see Table 2 ). This measurement method makes up for the insufficiency of dummy variables used in previous studies, quantifies the differences in digital transformation intensity and also establishes a suitable data environment for conducting a quasi-natural experiment with staggered DID (Yuan et al. 2021 ). Based on the above analyses, we use the text analysis method to delineate indicators of enterprises’ degree of digital transformation. Specifically, in the initial step, we first screen keywords indicating digital transformation from policy documents on advancing the digital economy released by the state, digitalisation themes and literature related to agricultural digitalisation. According to the research intent of this study, words related to food safety, agricultural production and agricultural trade are then selected from the keywords of digital transformation. In the second step, we supplemented the keyword thesaurus by examining agriculture- and digitalisation-related words that appear more frequently in the annual reports of listed companies. In the third step, we referenced the 2021 Research Report on the Digital Transformation of Central Enterprises, the 2022 Research Report on the Digital Transformation of Chinese Private Enterprises and the 2022 Report on the Development of China’s Digital Countryside, dividing the keywords into five dimensions according to ‘digital technology application’, ‘digital information system’, ‘digital intelligent management’, ‘digital marketing model’ and ‘digital efficiency improvement’. In the fourth step, using the keyword thesaurus formed in the above steps, we count the frequency of words involving the above keywords in the annual reports of listed companies exporting agricultural products and take the logarithm of the frequency of the words to establish an indicator of enterprises’ degree of digital transformation ( \(digita{l}_{ft}\) ), where a larger the indicator value indicates a higher degree of enterprise digital transformation.

Control variables

This paper also controls for other variables that affect the quality and safety of exported agricultural products, of which \(SP{S}_{kjt}\) is the importing country’s SPS measure, measured by the number of notifications made by the importing country to the HS 2-digit code level in the period of \(t-1\) ; \(ope{n}_{jt}\) is the importing country’s level of openness to the outside world, which is expressed by the importing country’s total imports and exports in terms of the share of its GDP, and is used to measure the relevance of the importing country to the outside economy; \(pgd{p}_{jt}\) is the importing country’s level of per capita income, which measures \(exchang{e}_{jt}\) is the exchange rate of RMB, which is converted using the US dollar as an intermediary measure to control the impact of trade costs; tariffs of importing countries’ products ( \(tarif{f}_{jt}\) ) are expressed as tariff rates corresponding to HS6-coded products, which are used to control the impact of tariff barriers, and missing values are replaced by tariff rates of HS4- or HS2-coded industries; geographic distance ( \(distanc{e}_{jt}\) ) is measured as the geographic distance between the capitals of China and the importing countries.

Data description and descriptive statistics

The data used in the empirical research of this study are obtained from the China Customs Database, the Cathay Pacific Financial and Economic Database (CSMAR), the RESSET Financial Research Database and listed companies’ annual financial reports. Considering that all listed companies began to implement the new accounting standard system on 1 January 2007, and some indicators are only counted from 2007 onwards and currently available Chinese Customs data cover 2000–2016, to ensure the consistency of our data indicator measurements, this study uses data from 2007 to 2016 for the study. After matching the above data, we cleaned the data as follows. (1) Excluding financial, ST and *ST enterprises, and retaining only A-share listed companies. (2) Excluding data with missing values for key indicators such as total assets, revenue and number of employees or data that do not comply with accounting rules. Among the control variables, the data for importing countries’ per capita GDP, population size, degree of openness to the outside world, product tariffs and exchange rates are obtained from the World Bank database, geographic distance data are obtained from CEPII-GeoDist database and SPS measures data are obtained from the World Trade Organisation’s notification system for SPS measures. Descriptive statistics of the variables are detailed in Table 3 .

Empirical results and analysis

Typical facts and a priori judgements.

In order to reflect more intuitively the changes in the quality of exported agricultural products in the experimental and control groups over the sample period, this paper uses curves to portray the trends in the average quality index of exported agricultural products in the experimental and control groups, respectively, as shown in Fig. 2 . China’s substantial policies regarding the development of digital transformation began in 2013, and the creation of new types of digital economy businesses mainly occurred after this (Ma et al. 2015 ; Du and Zhang 2021 ). As can be seen from Fig. 2 , before 2013, there were fluctuations in the quality index of exported agricultural products in the experimental group and the control group, and after 2013 the quality index of exported agricultural products in the experimental group and the control group generally showed an upward trend, and for the experimental group, the trend of growth in the quality of the export was significantly stronger than that of the control group. Among them, the experimental group’s export agricultural product quality index increased more after 2013, indicating that the quality of export agricultural products was affected by factors such as digital transformation and changes in the international trade environment. As an ex ante test, Fig. 2 reflects from the side that the difference in the change in the quality of exported agricultural products between the experimental group and the control group is correlated with the digital transformation of enterprises, which provides an a priori judgement for this paper’s empirical research using the staggered double-difference model.

Trends in the quality and safety level of exported agricultural products.

Parallel trend test

The DID model requires that the data satisfy the parallel trend assumption that prior to firms’ digital transformation, digitally transformed (treat group) and non-digitally transformed (control group) firms essentially maintained the same trend in terms of changes in export quality. Under this assumption, changes that occur in exported agricultural products’ quality after firms’ digital transformation can then be considered as the effect of policy intervention. This study references Beck et al. ( 2010 ), examining the dynamic changes in the quality of exported agricultural products before and after enterprises’ digital transformation. If the quality of exported agricultural products did not improve significantly before the digital transformation of enterprises, but improved significantly after the transformation, this indicates that this improvement is indeed attributable to digital transformation, and the conclusions drawn from the baseline regression are plausible. Considering the limitation of data length, this study selects four years prior to mutual recognition and three years following mutual recognition to conduct the dynamic trend test, establishing the fixed effect model shown below:

In Eq. ( 10 ), \(n=t-year\) and \(year\) denote the year of the enterprise’s digital transformation shock, and \({D}_{fn}\) is a dummy variable; if the enterprise \(f\) is a digitally transformed enterprise and the year is \(year\) from the year of the transformation shock, \({D}_{fn}\) is set to take the value of ‘1’, otherwise it is ‘0’. Here, the time interval before and after the transformation impact is narrowed to the first 4 and the last 3 periods Footnote 7 , so that \({D}_{in}\) is a set of variables including \([{D}_{i(-4)},{D}_{i(-3)},\mathrm{..}.,{D}_{i(0)},\mathrm{..}.{D}_{i(3)}]\) . The remaining variables in Eq. ( 10 ) have the same symbolic meaning as in Eq. ( 9 ). The parallel trend test focuses on the changes in a series of coefficients \({\xi }_{n}\) .

Based on the size and significance of the economic effect in each period in Fig. 3 , the positive impact effect in each period after digital transformation is greater, changing from an insignificant effect to a significant effect, confirming that before digital transformation, no significant difference is evident between the transformed and non-transformed enterprises in the quality of exported agricultural products. In contrast, after the transformation shock, the quality of the exported agricultural products of the transformed enterprises compared to the non-transformed enterprises significantly improved, indicating the effectiveness of digital transformation. In terms of the trend of change in the effect of digital transformation, the positive impact effect increasingly rises, which lasts until the third period after the digital transformation, indicating that enterprises’ digital transformation has a medium- to long-term effect in promoting the quality of exported agricultural products.

Dynamic effects of quality and safety of exported agricultural products.

Estimated results of the benchmark regression

Considering that the occurrence of ‘zero trade flow’ prevails in reality due to excessive trade costs, and that the trade impact identification model of enterprise digital transformation includes fixed effects at country, enterprise, product and time levels, we reference Correia et al. ( 2020 ), testing the impact of enterprises’ digital transformation on the quality of exported agricultural products using Poisson pseudo-maximum likelihood method and Stata software. The regression results are presented in Table 4 .

Examining the baseline regression analyses in Table 4 , the coefficients of digital in columns (1)–(3) are significant and positive after the inclusion of control variables and fixed effects variables, indicating that enterprises’ digital transformation significantly improves exported agricultural products’ overall quality and alleviates the food safety concerns of exported agricultural products, which improves the quality of China’s exported agricultural products, supporting Hypothesis 1. For example, Yantai Shuangta Foods Co., Ltd, which is a leading manufacturer of Longkou vermicelli, focuses on the digital economy and uses enterprise big data, establishing an information technology software system, information technology hardware and the fusion of digitalisation and business operations to develop an internal data source for the enterprise. Through digital empowerment, the company realises the fusion of digital and production management, successfully making the ‘green factory’ list, which is a national green food manufacturing benchmark for enterprises. From 2023 January to October, Yantai Shuangta Foods exported 820 million yuan in product export value, representing an average annual growth rate of 5% Footnote 8 .

The coefficients of the control variables are in line with expectations, with positive coefficients for the variables of GDP per capita in the importing country and the degree of openness to the outside world, indicating that a high level of economic level in the importing country and a high degree of openness to the outside world can help to improve the quality and safety of China’s agricultural exports. The negative coefficients on the variables of tariffs on products from importing countries, RMB exchange rate, and geographical distance indicate that high tariffs in importing countries, RMB appreciation, and China’s distance from importing countries hinder the quality upgrading of China’s exported agricultural products.The coefficient of the SPS on the upgrading of agricultural products is uncertain, possibly because the effect of SPS measures on quality upgrading depends on the magnitude of the cost of compliance and the cost of market shifting (Liu and Dong 2021 ).

Robustness tests

This study conducts five robustness tests to ensure the accuracy of the baseline regression results.

Dependent variable replacement

In this paper, the quality of the current period is worse than that of the previous period to represent the quality upgrade ( \(qualit{y}_{fjkt}^{\text{'}}\) ). As for the measurement of product quality, according to the research of Khandelwal et al. ( 2013 ) and Shi ( 2014 ), Eq. ( 11 ) is regression:The results presented in column (1) of Table 5 are basically the same as those of the benchmark regression, validating that the benchmark regression results are robust.

Where \({q}_{fjkt}\) and \({p}_{fjkt}\) are the number of products exported by the firm and the price of the exported products, \({\sigma }_{k}\) is the elasticity of substitution of the product \(k\) , \({\delta }_{k}\) and \({\delta }_{jt}\) are the product fixed effects, time fixed effects of the importing country, and \({\varepsilon }_{fjkt}\) is the residual component. Using the sample data of price and quantity, OLS regression of the above equations gives the quality of the product being estimated, which is expressed in the form:

The final expression for product quality can be obtained by normalising the results of Eq. ( 12 ),

Where \(maxqualit{y}_{kt}\) and \(minqualit{y}_{kt}\) denote the maximum and minimum values of the quality of the product \(k\) exported to all destination countries in the year \(t\) , respectively, and \(qualit{y}_{fjkt}^{\text{'}}\) is the quality of the firm \(f\) exporting the product \(k\) to the country \(j\) in the year \(t\) .

Sample shrinkage and truncation treatment

To effectively avoid the impact of outliers on the estimation results, this study references Crinò and Ogliari ( 2015 ), conducting bilateral shrinking and bilateral truncation for the sample (i.e. all the results in the 1% and 5% quartiles are directly excluded as outliers, and the re-estimating Eq. ( 9 )). Combined with the results in columns (2) and (3) of Table 5 , digital is basically consistent with the regression results in Table 3 in terms of coefficient size, sign and significance, further verifying our benchmark results.

Overcoming sample selection bias

We next apply the Heckman two-step approach to overcome sample selection bias. We use whether firms had exporting behaviour in the previous period as the exclusion variable (Chatterjee et al. 2013 ) and the test results are reported in column (4) of Table 6 , with significant coefficients on the inverse Mills ratio, indicating that firms’ digital transformation still significantly improves the quality upgrade of exports.

Digital transformation shock time selection

In order to test the effectiveness of digital transformation time point selection, on the one hand, the digital transformation time point is set as two years before the digital transformation time, one year before and one year after the digital transformation time for testing. The results of columns (1)–(3) in Table 6 show that the two years before the digital transformation time and one year before the digital transformation time have no significant impact on the quality and safety level of export agricultural products. One year after the digital transformation, the quality and safety level of export agricultural products had a positive promoting effect.

Placebo test

To test whether the effects of digital transformation derived above are potentially driven by unobservant factors at the country-product-year level, we next conduct a placebo test by randomly assigning mutually recognised products (Cai et al. 2016 ). Firms are randomly selected as the treatment group and assumed to have undergone digital transformation, while others are non-digitally transformed firms, establishing ‘pseudo’ treatment and control groups. In this paper, the quality upgrading of China’s exported agricultural products is regressed 1000 times as an explanatory variable. The estimated coefficients of digital in column (4) of Table 6 are insignificant, once again confirming that the baseline regression results are robust.

Endogeneity test

Considering that firms exporting high-quality products can be considered to have particular incentives to take the initiative to implement digital transformation, this creates a potential two-way causation problem. To address the potential reverse causation problem, this study uses a lagged period of digital transformation data, which is based on the fact that since enterprises’ digital transformation is a continuous process, the degree of digital transformation in the previous year is the basis for the digital transformation of the current year, and at the same time, a certain time lag effect of the impact of digital transformation on exports is expected. Therefore, the degree of digital transformation in the previous year should have an impact on exports in the current year, but the enterprise’s exports in the current year will not impact the digital transformation of the previous year. The results of the test are as shown in column (1) of Table 7 .

To address the possible omitted variables problem, this paper uses the instrumental variable two-stage least squares (IV-2SLS) method for testing. First, referencing Du et al. ( 2022 ), we use the density of long-distance fibre-optic cable lines in the province where the listed company is located as an IV for enterprise digital transformation (i.e. instrumental variable = long-distance fibre-optic cable lines in the province where the listed company is located/area of the host province and city). First, Internet access and continuously updated data are the crucial components of enterprises’ digital transformation, and long-distance cable lines are important infrastructure for data transmission, where denser long-distance cable lines in a province indicate better the digital infrastructure in the province and a higher degree of satisfaction of the external conditions for enterprise digital transformation. Therefore, the degree of enterprise digital transformation is highly correlated with the density of long-distance cable lines in the province where the company is located. Second, the density of long-distance cable lines is a function of the area of the province where the listed company is located, and the density of long-distance cable lines is controlled by the four major network operators in China, meaning that enterprises cannot change or control the density of long-distance cable lines according to their own needs. Thus, the density of long-distance cable lines cannot impact the export scale or product quality of the enterprises in this province, which meets the conditions for the use of IVs. The results of the test are presented in column (2) of Table 7 .

Second, as listed companies are distributed in various cities, and even in the same province, and each city has differences in development, we reference Huang et al. ( 2019 ) and use the number of post offices per million population in each city in 1984 as an IV for firms’ digital transformation, and further introduce city fixed effects. Historically, post and telecommunications have been important means of communication, and the number of post and telecommunications in history is expected to affect the local acceptance of information technology, with an impact on the application and promotion of information technology in the local area. Therefore, a certain degree of correlation is assumed between the number of post and telecommunications in a city and firms’ digital transformation. Furthermore, post and telecommunications are social and public service facilities focusing on the provision of communications for the general public; therefore it does not have an impact on enterprises’ export scale and product quality, satisfying the conditions for the use of instrumental variables. Since the 1984 post and telecommunications data are cross-sectional data, while the data used in this study are panel data, we reference Zhao et al. ( 2020 ), using and the cross-multiplier terms of the number of post and telecommunications per million people in each city in 1984 and the number of people who have accessed the internet nationwide in the previous year as the IV data for enterprises’ digital transformation. The results of the test are shown in column (3) of Table 7 , revealing that the impact of firms’ digital transformation on the quality of exported agricultural products remains significantly positive, indicating that the estimation results are still robust after addressing endogeneity problems caused by reverse causation and omitted variables. In addition, in the estimation results of IV method, the p -values of Kleibergen-Paap rk LM statistics are all 0, rejecting the original hypothesis that IVs are not identifiable at the 1% level. In addition, the Kleibergen-Paap rk Wald F-values are all greater than the 10% critical value of 16.38; thus, the original hypothesis of weak IVs is rejected, indicating valid IVs.

Heterogeneity test

Heterogeneity in the level of export destination countries’ economic development.

The impact of enterprises’ digital transformation on upgrading the quality of exported agricultural products may also vary depending on export destination countries’ economic development. We examine the impact of firms’ digital transformation on the quality of agricultural exports from developed and developing countries separately according to the World Bank’s classification of developed and developing countries. Columns (1) and (2) of Table 8 reveal that the impact of firms’ digital transformation on the quality of agricultural products exported to developed and developing countries is positive and has a greater impact on exports to developed countries than developing countries. The possible rationale for this outcome is that in countries with a high level of economic development, consumers’ shopping habits and behavioural patterns tend to be more online, which provides a new sales channel for digitally transformed firms exporting agricultural products, through which they can directly reach consumers, reduce sales costs and improve transparency and transaction efficiency. This means more sales channels and higher sales efficiency for digitally transformed enterprises, which improves the quality of exported agricultural products. In addition, consumers in developed countries usually have more disposable income to spend on high-quality agricultural products and are more concerned about the quality, safety and nutritional value of the food, agricultural production methods and the impact on the environment and animal welfare.

Heterogeneity of exporting firms

At the level of export enterprises, the regions where export enterprises are located and enterprise ownership are important factors that can affect enterprises’ digital transformation of upgrade the quality of exported agricultural products. Among them, for the region where the export enterprises are located ( \(area\) ), considering the differences in the digital development of enterprises in different regions, this study divides the sample into eastern, central and western regions according to the region where the enterprises are located to conduct regressions. For enterprise ownership ( \(ownership\) ), this study divides the enterprises into state-owned and non-state-owned samples for regression according to the nature of the actual controller of the enterprise. The results in columns (3)–(5) of Table 8 show that the coefficient of enterprise digital transformation on the quality of exported agricultural products in the eastern region is significantly positive, while the regression results for enterprises in central and western regions are not significant. The possible reasons for this are that with the higher level of economic development in the eastern region, which generally has a leading role in the development of high-end digital industries, with more complete information infrastructure and more advanced technology, enterprises can obtain more opportunities for digital development, and also have access to better external agricultural resources and technical support, while the economic development of the central and western regions is relatively slow, with a relative lack of digital talent, technology and agricultural resources; thus, enterprises’ ability to obtain more digital development opportunities are relatively scarce, resulting in differences in enterprise development. The results in columns (6) and (7) of Table 8 show that the digital transformation of both state-owned and non-state-owned enterprises has a significant upgrading effect on the quality of exported agricultural products; however, the quality upgrading effect is greater for non-state-owned enterprises. The management, operation mechanism and corporate culture of state-owned enterprises are more inclined towards maintaining operational stability and security, and decisions to conduct digital agricultural production and activities will be relatively cautious and conservative; thus, the degree of digital transformation is lower. In addition, because the main body of agricultural exports are from state-owned enterprises, which have relative advantages in policy support, government subsidies, credit financing and other support, the agricultural exports of state-owned enterprises are subject to less competitive pressure (Shen et al. 2012 ). This will lead to the lack of intrinsic incentives for state-owned enterprises to innovate in agriculture, affecting improvement in the quality of exported agricultural products.

Heterogeneity of agricultural product types

We next examine the differences in the impact of digital transformation of enterprises on the quality of exported agricultural products are examined based on types of exported agricultural products. First, we classify exported products into bulk, intermediate, consumer-oriented and other related agricultural products Footnote 9 . Second, in terms of export product quality, we calculate the average product quality of each firm during the sample period, classifying the top one-third of products with the highest product quality in each HS 2-digit code as high-quality products, and the rest as medium- and low-quality products in a sub-sample regression.

The results in columns (1) and (2) of Table 9 show that firms’ digital transformation enhances the quality of low- and medium-quality agricultural products more than that of high-quality agricultural products. There is more room for improvement of lower quality products, and firms’ digital transformation will promote them more; thus, digital transformation is more likely to affect low- and medium-quality agricultural products. The regression results in columns (3)–(6) of Table 9 show that the effect of digital transformation on the quality of exported bulk and consumer-oriented agricultural products is significantly positive, while that on intermediate and other related agricultural products is not significant. The possible reason for this is that bulk and consumer-oriented agricultural products have high standards and requirements in all aspects of the production process, processing and packaging and the application of digital technology can improve the quality and safety performance of these products, obtaining a higher market value. For intermediate and other related agricultural products, the impact of digital transformation is relatively small because the quality and safety performance of these products are relatively low, and the application of digital technology has limited effect on improvement. In addition, the market competitiveness of these products primarily depends on market demand and price factors, and the application of digital technology has limited impact on market demand and price.

Mechanism of action test

Model setting.

Our findings demonstrate that digital transformation of firms facilitates the quality upgrade of exported agricultural products. The question that arises is through what mechanism does this process occur? This paper draws on the research of Jiang ( 2022 ) to further investigate whether enterprise digital transformation will contribute to the quality upgrading of export agricultural products through the product traceability effect, technological innovation effect, information sharing effect and quality assurance effect, and the model is constructed as follows:

In Eq. ( 14 ), \({T}_{fkjt}\) represents the proxy variables for the technological innovation effect ( \(tech\,{\_}\,in{n}_{fkjt}\) ), product traceability effect ( \(pro\,{\_}\,trac{e}_{fkjt}\) ), information sharing effect ( \(inf\,{\_}\,shar{e}_{fkjt}\) ) and quality assurance effect ( \(qua\,{\_}\,as{s}_{fkjt}\) ), respectively, and the rest of the variables are consistent with the benchmark regression, with the coefficient \({\beta }^{\text{'}}\) being the core coefficient of interest in this paper.

Description of variables

Technological Innovation Effect ( \(tech\,{\_}\,in{n}_{fkjt}\) ). In this paper, we use the research and development (R&D) investment intensity (RD) of enterprises, i.e., the logarithm of the R&D investment of enterprises in the current year, to measure as a proxy variable for enterprise technological innovation, and take the natural logarithm after adding 1 to it. At the same time, the improvement of innovation level as well as technology introduction will lead to technological progress, so this paper refers to Sheng and Mao ( 2017 ), and also uses the technological complexity of export products as a proxy variable for enterprise innovation to further explore the mediating effect.

Product traceability effect ( \(pro\,{\_}\,trac{e}_{fkjt}\) ). In this paper, the statistics of the electronic certification mark displayed on the official website of the enterprise and Wechat public number are carried out, including the electronic certification certificate of agricultural products, the green certification of agricultural products, the organic certification, the geographical indication certification and other picture information, and the number of pictures, videos, and two-dimensional code information that provide the electronic certificate certification are cumulatively summed up, and the product traceability index of the enterprise is obtained in the end.

Information sharing effect ( \(inf\,{\_}\,shar{e}_{fkjt}\) ). The opening of the official website of the enterprise can facilitate consumers to understand the production process of the enterprise, and understand the relevant raw material procurement, agricultural production and processing information of the enterprise in a more graphic manner, and the establishment of the enterprise’s applet is a reflection of the enterprise’s willingness to communicate with consumers and the degree of information sharing. Therefore, this paper measures the information sharing effect through the opening of enterprise homepage and applets.

Quality assurance effect ( \(qua\,{\_}\,as{s}_{fkjt}\) ). An enterprise’s product quality assurance capability can be measured by establishing a sound quality management system, setting up product files, actively participating in certification assessment, and utilising technological means (Guo and Xiao, 2022 ). Therefore, this paper applies whether the enterprise obtains quality management certifications such as ISO9001, ISO22000, HACCP, and product certification information such as QS and CCC to measure the product traceability effect, and if it is, then it takes 1 and sums up to obtain a proxy variable for the quality assurance effect.

Mediating mechanism test

The test results for technological innovation and product traceability effects of enterprise digital transformation are presented in Table 10 . In terms of the technological innovation effect, the impact of enterprise digital transformation on R&D investment intensity is significantly positive, and enterprise digital transformation promotes innovation, which subsequently promotes upgrading the quality of exported agricultural products, supporting Hypothesis 2. Digital transformation facilitates enterprises’ acquisition of new agricultural technologies and enhances coordination and resource sharing in all aspects of agricultural production, which strengthens enterprises’ innovation and ultimately improves the quality of exported agricultural products. For example, Fuling Squash constantly pushes forward and focuses on the entire industry chain of squash, opening up the data flow of green beetroot planting and acquisition, salt vegetable block processing and sales and squash marketing, among other activities, promoting quality improvement through technological and product innovation. The export volume of Fuling Squash is expected to reach 100000 tonnes, with an output value of more than 1.5 billion yuan by 2027 Footnote 10 .

The effect of enterprise digital transformation on product traceability is significantly positive, supporting Hypothesis 3. The focus of agri-food enterprises is how to form a closed loop of the entire chain of quality management; for example, New Hope Dairy was the first in the industry to engage in digital transformation and upgrade, developing the digital quality management tool Fresh Source and the digital supply chain system Shipping Lychee, to trace the source of products, and launching the digital supply chain system Litchi, as the first in the industry. The company also launched the digital marketing tool Fresh Go, and the Lighthouse Factory, making food production more transparent, intelligent, efficient and flexible, to achieve industry chain visualisation, transparency and product traceability, effectively guaranteeing the quality and safety of its dairy products Footnote 11 .

The impact of enterprise digital transformation on information sharing is significantly positive, indicating that enterprise digital transformation promotes export quality upgrading by improving information sharing capacity, which supports Hypothesis 4. Digital transformation enhances information sharing in agricultural production and export links, which improves the quality and international competitiveness of agricultural products. For example, Shandong Dong’a Gum Co. comprehensively combined 5G convergence application areas and built a new retail platform for customers, an ‘internal marketisation’ platform for employees and a ‘creativity platform’ for social participation, launching the Freshly Made Ready-to-Eat customisation service, with real-time production after customers have placed orders by means of 5G. Through 5G transmission, big data and cloud computing, Shandong Dong’a Gum Co. fulfills real-time production arrangements after customers place orders and interacts with customers in the process, which has led to a significant increase in online sales Footnote 12 . Dong’a products have passed all kinds of national sampling and flying inspections with high pass rates, and exports to Japan, passing the most stringent quality inspection by the Ministry of Health, Labour and Welfare of Japan, with 842 testing items, including pesticide residues, veterinary drug residues and heavy metals and bacteria, all of which are ‘zero detectable’ Footnote 13 .

In terms of quality assurance effects, Hypothesis 5 is supported by the assumption that firms’ digital transformation improves product certification and the quality of exported agricultural products. Through digital transformation, achieving quality certification becomes more efficient, accurate and reliable, which improves the quality and competitiveness of exported agricultural products. To provide the market and consumers with genuine Korla Scented Pears, Xinjiang Korla Scented Pear Co., Ltd. certified one million cases of Korla Scented Pears sold on its e-commerce platform with Chinese Inspection and Quarantine Agency (CIQ) traceability certification, with affixed CIQ traceability labels. Scanning the two-dimensional CIQ traceability code label provides origin information for Korla balsam pears, along with planting base, soil testing, product, quality testing, certification, manufacturers and dealers’ information Footnote 14 .

Conclusions and policy implications

Enhancing the quality of exported agricultural products and increasing trade added value is the key to establishing a new competitive advantage in exported agricultural products, building a trade powerhouse and achieving high-quality agricultural development. In this paper, based on the theoretical analysis of the mechanism of the impact of enterprise digital transformation on the quality and safety level of export agricultural products, using the data of Chinese listed companies and China Customs data from 2007 to 2016, with the help of Python crawler technology to portray the intensity of digital transformation of listed companies exporting agricultural products, and using the interleaved double difference method to explore the impact effect and mechanism of enterprise digital transformation on the quality and safety level of export agricultural products quality upgrading influence effect and mechanism. The study shows that (1) Enterprise digital transformation effectively improves the quality and safety of exported agricultural products, and this result holds after endogeneity, placebo and multiple robustness tests; (2) Heterogeneity analyses reveal that the quality and safety effect of enterprise digital transformation is greater for exporting to developed countries’ markets, non-state-owned enterprises and enterprises in the eastern region, in addition to bulk agricultural products and consumer-oriented agricultural products; (3) Mechanism analyses shows that enterprise digital transformation raises the quality and safety of exported agricultural products through technological innovation, product tracing, information sharing and quality assurance effects.

To further enhance the role of digital transformation in promoting the quality upgrading of enterprises exporting agricultural products, this study proposes three relevant policy recommendations.

First, under the trend of a new round of scientific and technological revolution and industrial transformation, China should accelerate the deep integration of digital technology and foreign trade entity enterprises, further increase the support for digital transformation of foreign trade enterprises, actively guide and help enterprises to achieve digital transformation, and break through the dilemma of ‘do not want to transform’, ‘cannot transform’ and ‘will not transform’. On the one hand, it is necessary to strengthen the construction of digital infrastructure, accelerate the construction of information network infrastructure, strengthen the support capacity of public services, and lay a solid foundation for the digital transformation of enterprises. On the other hand, it is necessary to increase the financial and financial support for enterprises’ digital transformation, realise the optimisation and upgrading of traditional production technologies, organisational processes and management methods, and improve the quality and safety level of enterprises’ export agricultural products.

Second, in the process of promoting the digital transformation of enterprises and the formulation of relevant policies, we should adhere to local conditions, policies based on enterprises, and step by step. For some enterprises with difficulties in transformation, the transformation threshold should be lowered, and appropriate support measures should be taken to lay a solid foundation for digital transformation of enterprises and provide more powerful support. At the same time, it is necessary to continue to consolidate the achievements of digital transformation in the eastern region, increase support for digital transformation in the central and western regions, and narrow the digital divide between regions.

Third, enterprise digital transformation is an important approach for addressing the problem of exported agricultural products’ quality and safety. Therefore, the government should issue relevant regulations to clarify the obligations and approaches for relevant enterprises to implement digital traceability of exported agricultural product quality and safety, implementing the requirements and functional settings of a safety traceability system for exported agricultural products and enhancing the capacity of intelligent supervision of agricultural product quality and safety to compel Chinese agricultural industries to upgrade the quality and safety of exported agricultural products.

Data availability

The data that support the findings of this study are available from the Experimental Teaching Centre for Intelligent Business of East China University of Science and Technology, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from authors upon reasonable request and with permission of the Experimental Teaching Centre for Intelligent Business of East China University of Science and Technology.

See the Outline for Building a Quality Country issued by the CPC Central Committee and The State Council, http://www.gov.cn/zhengce/2023-02/06/content_5740407.htm?share_token=B4AF8828-EB72-4ED4-8773-839D5EC45F63&tt_from=weixin_moments&utm_medium=toutiao_ios&utm_campaign=client_share&wxshare_count=1 .

Data source: Food violations in the second quarter of 2023, http://qingdao.customs.gov.cn/beijing_customs/ztzl1/jgjmzl/gzld43/5173307/index.html .

See the Obstruction of China’s exports of consumer goods, agricultural food and medical devices to Europe and the United States in the first half of 2023, http://shanghai.customs.gov.cn/beijing_customs/ztzl1/jgjmzl/gzld43/5282758/index.html .

See the Ministry of Commerce issued the 14th Five-Year Plan for High-quality Development of Foreign Trade, http://www.mofcom.gov.cn/article/xwfb/xwrcxw/202111/20211103220185.shtml?ivk_sa=1023197a .

See the Implementation Opinions of the Ministry of Agriculture and Rural Affairs on Implementing the Key Work Deployment of the CPC Central Committee and The State Council for Comprehensively Promoting Rural Revitalisation in 2023, http://www.gov.cn/zhengce/zhengceku/2023-02/22/content_5742671.html .

See ‘Cyberspace Administration of China releases Digital China Development Report ( 2020 )’, http://www.cac.gov.cn/2021–06/28/c_1626464503226700.htm?from=timeline .

In the parallel trend test, in order to avoid multi-collinearity, this paper deleted \({D}_{i}(-1)\) , that is, the first phase before the impact of digital transformation.

See ‘Twin Towers Food: Carrying the centurial Dream of Longkou Fans’, https://nongye.yantai.gov.cn/art/2023/11/20/art_20522_2919458.html .

According to the USDA Global Agricultural Trade System Online BICO classification standard for agricultural products, agricultural products can be classified into bulk agricultural products, intermediate agricultural products, consumer oriented agricultural products and other related agricultural products (mainly covering aquatic products).

Chongqing Fuling: To promote the production and output value of pickled mustard industry to continue to develop overseas markets, https://www.farmer.com.cn/2023/02/21/99921810.html .

New Hope Dairy Co., LTD. – Platform control digital traceability full link pain relief, https://www.cqn.com.cn/pp/content/2023-10/23/content_8991432.html .

5G new infrastructure enables digital marketing transformation, broadens application scenarios, and promotes industrial development, http://www.cac.gov.cn/2021-01/15/c_1612283173042149.html .

Dong ’e ejiao products were successfully selected into the ‘Good product Shandong’ brand, https://www.cqn.com.cn/zgzlb/content/2022-03/15/content_8795246.html .

Traceability has my taste and you – 1 million boxes of ‘Korla fragrant pear’ obtained ‘Traceability’ ID card, https://xj.ccic.com/index.php/article/908/1155.html .

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Acknowledgements

Funding for this research was provided by National Natural Science Foundation of China (Grant No. 71673087) and Ministry of Education of Humanities and Social Science of China (Grant No. 23YJA790017).

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School of Business, East China University of Science and Technology, Shanghai, China

Yuchen Liu & Yinguo Dong

Business School, University of Shanghai for Science and Technology, Shanghai, China

Weiwen Qian

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Yuchen Liu performed the writing—original draft preparation, the writing— review & editing, methodology, data analysis and software; Yinguo Dong carried out the conceptualisation, supervision, and funding acquisition; Weiwen Qian contributed to the writing— review & editing. All authors have revised the paper for important intellectual content and have read and agreed to the present version of the paper.

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Liu, Y., Dong, Y. & Qian, W. The impact of digital transformation on the quality and safety level of agricultural exports: evidence from Chinese listed companies. Humanit Soc Sci Commun 11 , 817 (2024). https://doi.org/10.1057/s41599-024-03321-w

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Received : 14 February 2024

Accepted : 10 June 2024

Published : 24 June 2024

DOI : https://doi.org/10.1057/s41599-024-03321-w

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