A Study of Data Science in the Clothing Retail Industry: Qualitative Approach

DATA SCIENCE IN CLOTHING RETAI L 1 A Study of Data Science in the Clothing Retail Industry: Qualitative Approach Doctoral Project Presented to the Faculty School of Business and Management California Southern University in partial fulfillment of the requirements for the degree of DOCTOR OF BUSINESS ADMINISTRATION by Shek Chi Eric Chan Date of Defense October 21, 2025 Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 2 Copyright Release Agreement Many DBA doctoral candidates decide to copyright their projects. This is a good idea if follow-up research is anticipated or if a truly innovative concept is developed in the project. The University retains the right to use Doctoral Projects for academic purposes such as displaying them in a library that is open for public review, making them available for review by other doctoral candidates of this institution, and providing copies for review by educational or professional licensing and accrediting agencies. In the event the doctoral candidate chooses to copyright the Doctoral Project; the University still retains its right to use the Doctoral Project for educational purposes as described. To document the doctoral candidate’s agreement with this condition, the doctoral candidate is to sign and date the following statement and return to the Committee Chair with a copy attached to the final version of the project submitted for the course. ________________________________________________________________________ To: School of Business and Management From: Shek Chi Eric Chan, Doctoral Candidate Subject: Copyright Agreement Release Date: October 2025 I, Shek Chi Eric Chan, Doctoral Candidate, do hereby grant California Southern University permission to use my Doctoral Project for educational purposes as described in this memorandum. _______________________________________ October 21, 2025 Shek Chi Eric Chan, Doctoral Candidate Date © 2025 Chan Shek Chi Eric Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 3 CALIFORNIA SOUTHERN UNIVERSITY APPROVAL We, the undersigned, certify we have read this Doctoral Project and approve it as adequate in scope and quality for the degree of Doctor of Business Administration. Doctoral Candidate: Chan Shek Chi Eric Title of Doctoral Project: A Study of Data Science in The Clothing Retail Industry: Qualitative Approach Doctoral Project Committee: George Singleton, DBA Signed: ________________________________________________ Project Chair Date Christi Sanders Via, DBA Signed: ________________________________________________ _________ Committee Member Date Kerri Wood, PhD Signed: ________________________________________________ _________ Committee Member Date Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 October 27, 2025 October 27, 2025 October 28, 2025 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 4 DEDICATION This dissertation is dedicated to all the dreamers and innovators who strive to make the world a better place. May this work serve as a reminder that perseverance and passion can turn even the smallest ideas into meaningful contributions. Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 5 ACKNOWLEDGMENTS I would like to express my gratitude to my chair, Dr. George Singleton, for his exceptional guidance, unwavering support, and commitment to my success throughout the doctoral project journey. His insight and encouragement have been instrumental in shaping my work. I also extend my sincere thanks to my doctoral project committee members, Dr. Christi Sanders Via and Dr. Kerri Wood; their invaluable feedback and expertise have greatly enriched my research and helped me navigate the complexities of this project. Additionally, I would like to thank all the participants who generously shared their time and insights for this research survey. I am also grateful for the staff and faculty at California Southern University, whose resources and support provided a solid foundation for my work. Their encouragement and assistance have been invaluable in enriching my academic experience. Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 6 ABSTRACT This qualitative study investigated data science implementation in the clothing retail industry of Hong Kong and Macau, addressing the disconnect between abundant consumer data and effective targeted marketing. Drawing from Technology Acceptance Model frameworks, the research examined perceptions, challenges, and opportunities through interviews with 24 participants, including retail owners, executives, and customers. Thematic analysis revealed several key findings: retailers experience significant technical integration challenges and organizational resistance during implementation, while also reporting substantial benefits, including revenue increases and improved forecast accuracy when successful; customers value personalization conveniences while expressing privacy concerns, taking active measures to protect their data; and regional cultural and linguistic factors significantly influence implementation approaches. The study identified critical barriers including clothing-specific data complexities, real-time processing limitations, and predictive modeling challenges in trend forecasting. Future opportunities include emotional response monitoring, dynamic pricing optimization, and culturally adapted recommendation systems. The findings suggest retailers should adopt phased implementation approaches, prioritize cross-channel data integration, develop style-evolution algorithms, and implement transparent opt-in systems for data collection. This research contributes to understanding technology adoption in specialized retail environments and provides a framework for evaluating data science implementations that balance technical capabilities with organizational readiness and cultural contexts. Keywords: data science, clothing retail, targeted marketing, personalization, technology adoption Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 7 TABLE OF CONTENTS ACKNOWLEDGMENTS .........................................................................................................5 ABSTRACT ...............................................................................................................................6 CHAPTER ONE OVERVIEW OF THE STUDY.....................................................................9 Background of the Problem ...........................................................................................9 Statement of the Problem .............................................................................................11 Purpose of the Study ....................................................................................................11 Theoretical Framework ................................................................................................14 Significance of the Study .............................................................................................15 Assumptions, Limitations and Delimitations ...............................................................16 Definitions and Key Terms ..........................................................................................20 Organization and Summary .........................................................................................21 CHAPTER TWO REVIEW OF RELATED LITERATURE ..................................................23 Overview of Data Science ...........................................................................................24 Review of Methodological Issue ..................................................................................25 Research Designs ........................................................................................................25 Methodological Issues and Complications ..................................................................27 The Role of Data Science in Different Fields ..............................................................28 Healthcare ....................................................................................................................28 Supply Chain Management ..........................................................................................30 Finance .........................................................................................................................32 Marketing .....................................................................................................................34 Human Resources.........................................................................................................34 Operations Management ..............................................................................................36 Big Data Analytics Frameworks, Techniques, and Tools............................................37 Framework ...................................................................................................................37 Techniques and Tools ..................................................................................................38 Challenges ....................................................................................................................38 Data Quality .................................................................................................................39 Data Privacy and Security ............................................................................................39 Scalability .....................................................................................................................40 Data Integration ............................................................................................................40 Impact of Data Science on Innovation and Performance .............................................41 Innovation ....................................................................................................................41 Performance .................................................................................................................42 Predictive Analytics and Machine Learning ................................................................43 Predictive Analytics .....................................................................................................43 Machine Learning ........................................................................................................44 Challenges and Future Research Directions ................................................................45 Scarcity of Skilled Data Scientists ...............................................................................45 Scaling Efficiency for Large Datasets..........................................................................46 Ethical and Responsible Use of Artificial Intelligence ................................................46 Effectiveness and Scalability of Data Analysis Approaches .......................................47 Legal and Ethical Concerns in Data-Driven Research .................................................47 Data-Driven Marketing and Customer Segmentation ..................................................49 Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 8 Theoretical Framework ................................................................................................50 Summary ......................................................................................................................51 CHAPTER THREE METHODOLOGY .................................................................................52 Research Method .........................................................................................................53 Participants ...................................................................................................................54 Instruments ...................................................................................................................57 Data Collection ............................................................................................................58 Data Analysis ...............................................................................................................61 Ethical Assurances .......................................................................................................62 Summary ......................................................................................................................62 CHAPTER FOUR RESULTS .................................................................................................64 Participants ...................................................................................................................64 Results Research Question One ...................................................................................66 Results Research Question Two ..................................................................................71 Results Research Question Three ................................................................................76 Results Research Question Four ..................................................................................81 Summary ......................................................................................................................87 CHAPTER FIVE DISCUSSION OF THE FINDINGS ..........................................................89 Discussion of Findings .................................................................................................89 Implications for Professional Practice .......................................................................126 Recommendations for Research ................................................................................128 Conclusion .................................................................................................................130 REFERENCES ......................................................................................................................132 APPENDIX A: Interview Questions .....................................................................................143 APPENDIX B: Initial Invitation Letter .................................................................................146 APPENDIX C: Consent Letter ..............................................................................................147 APPENDIX D: IRB approval letter .......................................................................................150 APPENDIX E: Site Permission Approval Letter...................................................................151 APPENDIX F: Expert Panel Review of Interview Questions ...............................................152 APPENDIX G: CITI Certificate ............................................................................................155 APPENDIX H: Approved Academic Review 1 ....................................................................156 APPENDIX I: Participant Profiles and Participation in Research Question .........................157 APPENDIX J: Codes and Corresponding Themes ................................................................158 Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 9 CHAPTER ONE OVERVIEW OF THE STUDY Chapter One introduces the study's central topic and methods to give a complete overview of the research. Problems in using consumer data for targeted advertising are the main focus of the study in the retail sector. According to Dekimpe (2020), despite having access to a wealth of consumer data, many retailers still fail to implement meaningful and tailored promotions, resulting in customer dissatisfaction and reduced chances of potential customers making purchases. Extensive research in this area has revealed a significant gap between consumer expectations and the actual offerings of stores (Elfeky & Elbyaly, 2021). Consumers expect personalized experiences and relevant promotions, and stores often fail to meet these expectations. To understand the real-world difficulties and effective tactics of retail marketing, the qualitative approach entails looking at case studies and talking to professionals in the field. Background of the Problem The retail industry, characterized by its energetic nature, faces many challenges, and one critical issue relates to the adequacy of focus on promotion endeavors (Dekimpe, 2020). Despite the endless availability of client information, numerous retailers battle to convey personalized and significant sales notices to their clients. Inquiries have demonstrated that more than 50 percent of clients consider insignificant and unseemly promotions as a source of irritation (Accenture, 2017). This issue has far-reaching results, affecting retailers, marketers, and customers. Statistics and information validate and support the presence of this issue. According to some studies, 67 percent of consumers expect a personalized experience, and only 23 percent of retailers successfully deliver targeted promotions (McKinsey, 2021). With the appearance of Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 16 markеting (Elfeky & Elbyaly, 2021). Thе study's importancе еncompassеd qualitativе aspеcts, thoroughly comprеhеnding stakеholdеrs' pеrspеctivеs and actions in this sеtting. The study's thorough analysis of thе clothing rеtail sеctor providеd specific information to thе еxisting body of litеraturе. This offеrs prеcisе insights that can bе usеd to guidе futurе rеsеarch and practical applications in thе broadеr fiеld of tеchnology adoption and targеtеd markеting in thе businеss world. Assumptions, Limitations and Delimitations of the Study Recognizing the assumptions, limits, and delimitations that shape the study's scope and interpretative lens was essential when doing research. This section describes these important points, elucidating the study's guiding principles, the parameters of research, and the limits of examination. The goal in tackling these aspects was to provide a clear and practical background for the study so that its results and ramifications may be better understood. Assumptions Creswell and Poth (2023) and Adom et al. (2018) pointed out that the research approach in this study is heavily dependent on several assumptions. There is an element of risk or uncertainty in the study due to these assumptions, which are fundamental but not yet proven. The first presumption was that the data on customer behavior and preferences collected by the apparel retail industry was reliable and typical of the market as a whole. The study's understanding of marketing inefficiencies and the promise of data science and machine learning was greatly influenced by this premise, which is crucial. Another assumption was that participants such as marketers, customers, and experts in the field, in interviews and focus groups, would be forthright and objective when sharing their thoughts and experiences. To get an accurate understanding of the possibilities and threats Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 17 associated with using data science and machine learning in targeted marketing, this assumption was critical. The study also assumed that data science and machine learning algorithms and technologies were mature enough to improve apparel retail marketing techniques. To investigate the uses and advantages of various technologies, it was essential to make this assumption. Several measures were implemented to manage or lessen the impact of these assumptions. Customer data was cross-checked with numerous sources wherever feasible to ensure accuracy and representation. To mitigate the possibility of bias in qualitative replies, participants were carefully chosen from varied backgrounds, and their responses were critically examined to identify any trends or disparities. Finally, a realistic picture of the possibilities and constraints of the present technical instruments was ensured by reviewing their capabilities through literature reviews and expert contacts. By testing these hypotheses, the aim was to offer a fair and accurate assessment of data science and machine learning's function in the apparel retail industry. Limitations According to Creswell (2018), limitations in research refer to shortcomings, conditions, or influences that cannot be controlled and that place restrictions on methodology or the generalizability of findings. Factors beyond control cause several constraints to the study of data science and machine learning integration in the marketing strategies of clothing retail. The possible inaccessibility of confidential information held by apparel retail corporations was a major restriction. The amount and variety of data that were analyzed could be limited since many companies protect their customers' data and internal strategies. This was lessened by forming agreements with retail firms that are open to sharing data or by supplementing primary data with publicly available data and secondary sources. Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 18 Data science and machine learning technologies are developing at a rapid pace, which is another constraint. The current methods and algorithms may soon be obsolete, which might affect the study's usefulness and relevance in the future. To keep this under control, the study centered on general tactics and concepts instead of a particular technology, so the results were applicable even as those technologies evolved. In addition to its qualitative design, the study's results demonstrated potential applicability to a broader population. Although qualitative research has inherent limitations in terms of generalizability, these drawbacks are effectively addressed by the rich and in-depth insights that such research provides. While qualitative data is valuable for specifics, it does not always have the scope to draw broad conclusions. To tackle this, a varied sample was sought that encompasses a range of demographics, firm sizes, and geographic locations to gather viewpoints from all corners of the market. Last, because qualitative data is subjective, the study can have drawbacks. Accomplished analysts analyzed the data, which reduced individual biases and increased the dependability of the conclusions. This ensured objectivity. By taking these steps, the study's validity and trustworthiness may be improved while also addressing its shortcomings. Delimitations According to Creswell (2018), delimitations in research refer to the specific parameters or boundaries established to help narrow the scope of the study. The decisions made and the study's design largely determined the limits in terms of scope and focus management. Though they constrained the study in certain respects, these delimitations were necessary for keeping the research strategy clear, manageable, and focused. Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 19 First, the apparel retail industry in certain regions is the exclusive focus of the study. This choice was to provide a more thorough and contextual knowledge of the industry in particular regions, but it restricted the relevance of the results to other regions. To counteract this, the study offered a more holistic view by discussing how the results might be similar to or different from those in other settings. Also, the study examined how regional characteristics such as consumer behavior, market maturity, and technological infrastructure influenced data science adoption in clothing retail operations. Second, the study's target demographics were specific to the apparel retail sector, including merchants, marketers, and shoppers. This narrowing of attention permitted a deeper dive into the lived realities and worldviews of these communities. However, this also meant that suppliers or regulatory agencies would not be included in the feedback, two additional groups whose opinions can be important. This restriction was acknowledged by the study, and ways in which future research might address these other perspectives were proposed. Selecting qualitative research methodologies as the primary approach was another restriction. The capacity to generalize findings across the whole sector was limited by this technique, even if it supplied rich, comprehensive data. This was addressed by the research by recommending quantitative investigations for more generalizable results and by precisely defining the circumstances in which the results could be most useful. Finally, all the research was conducted in accordance with the state of the art and current market trends. The study recognized that these variables can change and that researchers may have to adjust their methods in light of new discoveries. As data that is pertinent to the present but may require re-evaluation in the future, the research focused on the current state of technology and market dynamics. Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 20 Definitions and Key Terms Algorithmic Bias Algorithmic Bias refers to systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others (Mišić & Perakis, 2020). Data Mining Data Mining is the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the web, and other information repositories or data that are streamed into the system dynamically (Mišić & Perakis, 2020). Machine Learning Machine Learning is a subset of artificial intelligence involving the study and construction of algorithms that can learn from and make predictions or decisions based on data. These algorithms operate by building a model from sample inputs and making data-driven predictions or decisions, rather than following only explicitly programmed instructions (Elfeky and Elbyaly, 2021). Omnichannel Marketing Omnichannel Marketing refers to the multichannel sales approach that provides the customer with an integrated shopping experience. The customer can be shopping online from a desktop or mobile device, by telephone, or in a brick-and-mortar store, and the experience would be seamless (Wang et al., 2023). Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 21 Personalization Personalization in marketing is the practice of using algorithms and data to deliver individualized messages and product offerings to current or prospective customers, enhancing the customer experience and increasing the effectiveness of marketing efforts (Accenture, 2017). Targeted Marketing Targeted Marketing is a marketing strategy that identifies and caters to specific demographic segments within a larger market, using tailored messages or products that appeal to these specific segments (Dekimpe, 2020). Organization and Summary Chapter One aimed to provide readers with a comprehensive overview of the research by introducing the central topic. The study primarily focused on the challenges associated with utilizing consumer data for targeted advertising within the clothing retail sector in Hong Kong and Macau. Despite having abundant access to consumer data, numerous retailers struggle to implement effective and personalized promotional strategies. This chapter also delved into the rapidly evolving landscape of data-driven marketing in the retail industry, with a particular emphasis on the clothing sector. Chapter One highlighted the growing importance of leveraging big data and advanced analytics to gain insights into consumer behavior and preferences. Chapter Two offers an in-depth examination of existing literature on the application of data science and machine learning in the clothing retail sector, particularly in the context of targeted marketing. The research aimed to contextualize the study within the broader academic discourse, highlighting pivotal theories, past research findings, and the evolution of marketing strategies in response to technological advancements. Chapter Three contains the research design and methodology of the study. The research described the qualitative approach, including the Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 22 selection criteria for participants, the methods for data collection, such as interviews and focus groups, and the procedures for data analysis. The chapter also discussed the ethical considerations and steps taken to ensure the integrity and validity of the research. Chapter Four presents the findings from the data collected. It includes a thematic analysis of the interviews and focus groups, highlighting key trends, patterns, and insights regarding the use of data science and machine learning in the clothing retail sector. This chapter aimed to shed light on the practical implications of these technologies in marketing and the perceived barriers and opportunities as voiced by industry experts and consumers. The final chapter synthesizes the findings and offers conclusions. Chapter Five discussed the implications of the study for retailers, marketers, and policymakers. Chapter Five also provided recommendations based on the research findings, suggesting strategies for more effective use of data science in marketing. The research concluded with suggestions for future research, acknowledging the limitations of the current study and proposing areas for further investigation. Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 23 CHAPTER TWO REVIEW OF RELATED LITERATURE Data science has come into the limelight since 2014 because of its capacity to extract meaning from very large and complex information. Companies and organizations across many sectors now rely heavily on data scientists as a result of the increasing availability of data and the advent of new technology (Ahmed & Yaqoob, 2019). Data science is best described and understood as an inexorable progression, with many different applications that have affected many different processes throughout the globe. Researchers and academics examined how data science may be articulated and applied to particular fields, including business, entertainment, technology, and innovation. Data science is a relatively recent subject that grew out of using computers, statistics, and mathematics, according to research by Priestley and McGrath (2019). Data science is a very young field of study, and as such, there is significant development across all of the distinguishing features that define the field. According to further study insights provided by Raban and Gordon (2020), the unification of the notion of data science, which cuts across multiple areas of specialty, is gaining momentum as a consequence of the continued increase in scientific research. In the realm of business and industry, in particular, the idea of data science has cutting-edge applications. Data science products are described as important assets by Medeiros et al. (2020) for businesses because they provide access to vast stores of data that have been collected from many competing sources and packaged in several ways for ease of consumption. Having access to data and data analytics enables firms to extract useful insights and develop a sophisticated, data-driven decision-making architecture that may aid them in interpreting the competitive and dynamic parts of the business environment (Vicario & Coleman, 2019). The purpose of this literature review was to provide readers with an overview of data Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 PREVIEW DATA SCIENCE IN CLOTHING RETAIL 24 science studies, focusing on the most pivotal concepts, procedures, and applications of the field. The advantages and disadvantages of data science applications were analyzed, and avenues for research expansion were proposed. Overview of Data Science Data scientists used an assortment of scientific methods, procedures, algorithms, and systems to extract useful information from a variety of data types, including structured and unstructured data (Ahmed & Yaqoob, 2019). To find hidden relationships, trends, and patterns in massive datasets, data scientists used techniques including statistical analysis, machine learning, data mining, and predictive analytics (Olson, 2017). The effects of data science on the healthcare industry are discussed by Singh (2018). By studying large and diverse datasets, the healthcare sector may gain insights into illness patterns, patient behavior, and therapeutic results. Singh (2018) likewise highlighted the role data science plays in improving the accuracy and efficacy of medical diagnoses and treatments. Data science has emerged as a significant tool in the field of supply chain management, helping to improve operational efficiency and decision-making (Olson, 2017). Supply chain operations data, such as inventory management, shipping, and warehousing records, may help businesses improve their efficiency and productivity. Wang et al. (2017) provided a comprehensive review of the current status of big data analytics in supply chains. It was clear from the results that data science can help firms improve supply chain transparency, which in turn can save costs and improve services. The authors also highlighted the need for data governance and data security to ensure the correct and ethical use of data in logistics management. Data science has numerous potential applications, as seen by the wide range of published works that explore this topic. Modern data analytics techniques allow businesses to Docusign Envelope ID: 957EE667-5B1C-4659-9E84-4DB5BFF1DD44 Reproduced with permission of copyright owner. Further reproduction prohibited without permission. PREVIEW