digital marketing dissertation report

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Theses/dissertations from 2023 2023.

How Feeling Like a Minority Affects Preferences for Autonomous Digital Interfaces , Ye Seul Kim

Theses/Dissertations from 2022 2022

Spillover Effects in Product Customization , Samuel Babu Sekar

Theses/Dissertations from 2021 2021

External and Internal Factors of Sports Sponsorship Selling Cycles in North American Professional Sports , Dan Kaufmann

Increasing Personal and Product Influence Through Background Auditory and Visual Cues , Zhihao Yu

Theses/Dissertations from 2020 2020

The Effects of Product Recalls on Competitors’ Market Value and Recalling Firm’s Reputation , Dong Liu

Corporate Brand Impact on Sales / Revenue Per Share , Brad A. Puckey

Competition in Upstream Humanitarian Supply Chain: Investigation of Food Banks , Iana Shaheen

Theses/Dissertations from 2019 2019

How Digital versus Non-Digital Modes of Food Ordering Influence Menu Healthfulness Perceptions and Food Choices , Annika Abell

Theses/Dissertations from 2018 2018

The Impact of Digital Marketing Decisions on Market Outcomes in Residential Real Estate , Denise Hunter Gravatt

Theses/Dissertations from 2017 2017

Hate is a Strong Word: The Influence of Hate-Acknowledging Advertising on Brand Outcomes , Lisa Monahan

The Effects of Loneliness on Consumers’ Digital Engagement with Social Media Ads , Yu Qin

Product Shadows and Ad Evaluations , Nazuk Sharma

Theses/Dissertations from 2016 2016

Understanding the Complexity of Product Returns Management: A Complex Adaptive Systems Theory Perspective , Jennifer Anne Espinosa

Branding Implications of Co-Created Social Responsibility , Alexander J. Kull

Theses/Dissertations from 2015 2015

How You Categorize Influences How Helpful You Are: The Effect of Categorization Mindset on Consumers’ Social Decisions , Hsiao-Ching Kuo

Theses/Dissertations from 2014 2014

Taken for Granted or Taken with Gratitude? An Examination of the Differential Effects of Donations of Time and Money on Consumers' Evaluation of Corporate Philanthropy , Ryan Langan

Essays on Consumer's Psychological and Behavioral Responses toward Social Coupons , Chinintorn Nakhata

Muscling Consumers to Optimal Option Differentiation: The Influence of Incidental Muscular Sensations on Option Differentiation , Courtney Szocs

Theses/Dissertations from 2012 2012

Essays on Mental Accounting and Consumers' Decision Making , Ali Besharat

Perceived Firm Transparency: Scale and Model Development , Jennifer Dapko

Theses/Dissertations from 2010 2010

Antecedents and Consequences of Channel Alienation: An Empirical Investigation within Franchised Channels of Distribution , Ivan Lapuka

Theses/Dissertations from 2009 2009

An Empirical Examination of the Dark Side of Relationship Marketing within a Business to Business Context , Brent L. Baker

Developing the Nomological Network of Perceived Corporate Affinity for Technology: A Three Essay Dissertation , David Earl Fleming

Theses/Dissertations from 2008 2008

Self-Directed Learning: Measures and Models for Salesperson Training and Development , Stefanie Leigh Boyer

Emotional Exhaustion and Its Role in Service Sabotage among Boundary Spanners , Diane R. Edmondson

Theses/Dissertations from 2007 2007

Essays on multiple identities and motivated consumption: Exploring the role of identity centrality on self-brand connections , Tracy R. Harmon

The impact of organizational climate variables of perceived organizational support, workplace isolation, and ethical climate on salesperson psychological and behavioral work outcomes , Robert J. Riggle

Theses/Dissertations from 2005 2005

The Effect of Perceived Entitativity on Implicit Image Transfer in Multiple Sponsorships , FrancoÌ?is Anthony Carrillat

Theses/Dissertations from 2004 2004

Relationship Advertising: Investigating the Strategic Appeal of Intimacy (Disclosure) in Services Marketing , Andrea Diahann Gaye Scott

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Digital marketing is the process of marketing the products and services of any individual or business via digital and social media channels to attract internet users. It provides all sizes of businesses with an excellent route to increase money and also helps them to achieve popularity.  

If you are a digital marketing student, you must submit a dissertation on your interests in this area. Writing a dissertation on digital marketing might be difficult, especially if you don't have a clear approach or structure for your work.

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We'll guide you through the process of writing a digital marketing dissertation, starting with the importance of planning your thesis statement.

We'll discuss a thesis statement, why it's important, and how you can create an effective one for your digital marketing dissertation. By following these guidelines, you'll be well on your way to producing a high-quality dissertation demonstrating your expertise in the field. 

Visit our digital marketing dissertation examples below:

Example: 1   Impact of Digital Marketing in Generating High Customer Base

Example: 2   Impact of Social Media Marketing on Customer Buying Behaviour Note : Below is a complete guide with some more examples and tips for you to ace the skill of dissertation writing in economics.

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What is Digital Marketing Dissertation?  

A digital marketing dissertation is an academic analysis report that focuses on the field. A dissertation on digital marketing involves conducting an initial analysis to answer a research question. It may include a literature review, data analysis , case studies, and other research methods to support the writer's argument or analysis.

The topic of a digital marketing dissertation can vary widely. It may include social media marketing , search engine optimisation, mobile marketing, content marketing, email marketing, and other digital marketing strategies and tactics.

The purpose of the dissertation is to demonstrate the student's critical thinking and ability. The end result is a contribution to digital marketing, providing insights, recommendations or original discoveries that can further advance the industry.

Examples of digital marketing dissertations might assist you in determining the best match for your thesis for your degree. We provide examples of bachelor's, master's, and doctoral dissertations to help you select the ideal one. 

Bachelor Digital Marketing Dissertation Ideas

• The Impact of Social Media Marketing on Brand Awareness: A Case Study
• The Use of Email Marketing in the Travel Industry: A Case Study of Customer Engagement and Satisfaction
• The Impact of Mobile Marketing on Consumer Behaviour: A Comparative Analysis
• The Role of Content Marketing in B2B Lead Generation: A Case Study of a Tech Company
• The Impact of Influencer Marketing on Consumer Trust: A Study of the Beauty Industry
• The Use of Search Engine Optimisation (SEO) in Small Business Marketing: A Comparative Study of Local Businesses 

Master Digital Marketing Dissertation Examples 

• The Effectiveness of Social Media Advertising: A Study of Consumer Behaviour on Facebook and Instagram
• The Role of Personalisation in Email Marketing: A Study of Customer Engagement and Loyalty
• The Impact of Video Marketing on Brand Awareness: A Comparative Analysis of YouTube and TikTok
• The Use of Chatbots in Customer Service: A Study of Customer Satisfaction and Loyalty
• The Impact of Augmented Reality (AR) on Consumer Engagement: A Study of the Retail Industry
• The Use of Data Analytics in Marketing Strategy: A Study of Big Data and Machine Learning 

Doctoral Digital Marketing Dissertation Examples 

• The Impact of Artificial Intelligence (AI) on Digital Marketing Strategy: A Study of Machine Learning and Predictive Analytics
• The Use of Virtual Reality (VR) in Digital Advertising: A Study of Consumer Engagement and Brand Recall
• The Role of Customer Relationship Management (CRM) in E-commerce: A Study of Personalisation and Loyalty Programs
• The Impact of User-Generated Content (UGC) on Brand Equity: A Study of Social Media Platforms
• The Use of Blockchain Technology in Digital Marketing: A Study of Security and Trust
• The Impact of Omnichannel Marketing on Customer Experience: A Study of Retail and E-commerce Industries 

Why is a thesis important to your digital marketing dissertation?

A digital marketing thesis requires extensive research, analysis, and critical thinking. A strong thesis statement is one of the most crucial elements of a successful research paper. 

It is the foundation of your dissertation, providing a roadmap for your research and guiding your writing and analysis.

Let's get into the detail of why the thesis statement is significant.

Guides Your Research

A clear and specific thesis statement provides a clear point of view of your research and helps you stay focused on your main research objectives. It provides a framework for your literature review and empirical studies relevant to your research. Also, a clear statement helps you to identify the most appropriate research methodology for your study. 

Define the Scope of Your Study

Defining the scope of your study is a critical aspect of writing any dissertation, and your thesis statement plays an essential role in this process. Your thesis statement should clearly define the boundaries of your study. This includes the specific topics you will explore, the research questions you aim to answer, and the research methods you will use.

Demonstrating Your Contribution

A thesis statement plays a critical role in demonstrating your contribution to the field. Your thesis statement should clearly articulate your original contribution to knowledge or practice in the area of digital marketing. It is essential because a dissertation is not just a summary of existing research; it should contribute new knowledge or insights to the field.

Sets the Tone for Your Dissertation

The tone of your dissertation should be professional, academic , and objective. Your thesis statement should reflect this tone and be written clearly and concisely, avoiding subjective or emotional language. It sets the stage for the rest of your dissertation. This is by providing the reader with a roadmap of what to expect and your research.

Digital Marketing Dissertation Example: A Case Study 

Topic: The Role of Customer Relationship Management (CRM) in E-commerce: A Study of Personalisation and Loyalty Programs

This study examines the role of Customer Relationship Management (CRM) in e-commerce, specifically focusing on the impact of personalisation and loyalty programs on customer loyalty. With increasing competition in the e-commerce industry, businesses must maintain strong relationships with their customers. This study uses the quantitative approach, which involves data collection from online surveys conducted on a sample of e-commerce customers. The study reveals that personalisation and loyalty programs have a positive impact on customer loyalty in e-commerce. Furthermore, the study also highlights the importance of effectively implementing CRM strategies in e-commerce businesses to achieve a competitive advantage. This study contributes to the existing literature on CRM in e-commerce. It provides practical implications for e-commerce businesses to enhance their customer relationships and loyalty.

Literature Review 

Customer Relationship Management (CRM) is an important research topic in digital marketing, particularly in e-commerce. Personalisation and loyalty programs are two key CRM components that impact customer loyalty in e-commerce. Personalisation can increase customer engagement and loyalty, while loyalty programs incentivise repeat purchases and reward loyal customers. Research suggests that CRM, particularly personalisation and loyalty programs, can significantly impact customer loyalty in e-commerce. But e-commerce businesses must implement effective CRM strategies that balance personalisation with privacy concerns and design loyalty programs that are relevant and meaningful to their customers.

Methodology 

The research methodology for this study on the role of customer relationship management (CRM) in e-commerce, specifically focusing on personalisation and loyalty programs, is a quantitative approach. Data collection involves an online survey distributed to a sample of e-commerce customers who have made at least one purchase from the selected e-commerce websites. The sample size for this study consists of 500 e-commerce customers who have made at least one purchase from the selected e-commerce websites. Data analysis examines the relationship between personalisation, loyalty programs, and customer loyalty. Ethical considerations include obtaining informed consent from the participants, ensuring confidentiality and anonymity of the participants, and adhering to ethical guidelines for research involving human subjects. Limitations of this study include convenience sampling, which may not represent the entire population of e-commerce customers, and the potential for response bias.

This study finds that personalisation and loyalty programs positively impact customer loyalty in e-commerce. These strategies enhance customer satisfaction, increase customer loyalty, and improve business performance. However, this study has limitations, such as convenience sampling and response bias. Future research should consider using a more representative sample and exploring additional CRM strategies.

Conclusion  

This guide provides valuable insight into planning a strong thesis statement for a digital marketing dissertation. The thesis statement is essential to a dissertation as it guides the research, sets the tone, and demonstrates the study's contribution. By following these steps, you can create a clear and concise thesis statement highlighting the research question and the key objectives of the dissertation. Additionally, this guide emphasises the importance of conducting a thorough literature review, defining the scope of the study, and selecting an appropriate research methodology. In addition, this guide includes examples of bachelor's, master's, and PhD digital marketing dissertations that can serve as a helpful reference for you.

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Dissertation The Impact of Digital Marketing on Consumer Buying Behavior

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Digital Marketing as a Tool to Promote Rural Tourism Ventures: The Case of Casa da Lagoa

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• Elsa Esteves   ORCID: orcid.org/0000-0002-8463-9842 8 &
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Rural tourism emerges as a unique tourism product, capable of attracting the attention of tourists to rural areas whose tourist potential is considerable, but which is sometimes not well used. For this reason, companies operating in rural tourism must adopt specific measures to be able to promote their product/service, and the tourist destination itself, being able to use the various digital marketing tools for the same purpose. In this sense, the main objective of this paper is to analyse the presence of Casa da Lagoa, Portugal, in the different social media, through an exploratory analysis of its website and a quantitative analysis of the other social media used by the company.

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Funding and Acknowledgements

The authors acknowledge the financial support of CiTUR, R&D unit funded by the FCT—Portuguese Foundation for the Development of Science and Technology, Ministry of Science, Technology and Higher Education.

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Ribeiro, M., Esteves, E., Morais, E.P. (2024). Digital Marketing as a Tool to Promote Rural Tourism Ventures: The Case of Casa da Lagoa. In: Reis, J.L., Zelený, J., Gavurová, B., Santos, J.P.M.d. (eds) Marketing and Smart Technologies. ICMarkTech 2023. Smart Innovation, Systems and Technologies, vol 386. Springer, Singapore. https://doi.org/10.1007/978-981-97-1552-7_49

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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