Asking for a proposal on ecommerce consumer behavior and HK retailing: for MBA students (information from poe.com)
My question 1: "I am a part-time MBA student, currently working on the formulation of my dissertation proposal. My research theme is on "an assessment of the impact of Information Technology advancement, notably of AI, on the Hong Kong consumer behavior and the e-opportunities of the retailing sector to improve their marketing performance". This is a broad topic. Please suggest 3 academic theories, one about consumer behavior and another one about corporate IT competence, and third one about marketing performance. I intend to conduct a semi-structured interview with 4 of my generation z friends, representing the perspective of consumers, a semi-structured interview of 2 of my friends who have marketing responsibility in companies in the Hong Kong retail sector. I will also carry out a desk research to learn more about my research theme. The overall research philosophy is pragmatism. Please provide a dissertation proposal to me. It should include 4 related research objectives and 2 related research questions. Thanks a lot in advance."
The reply: "Dissertation Proposal
Title
An Assessment of the Impact of Information Technology Advancement, Notably AI, on Hong Kong Consumer Behavior and E-opportunities in the Retailing Sector to Improve Marketing Performance
Introduction
The rapid advancement of Information Technology, particularly Artificial Intelligence (AI), has significantly influenced consumer behavior and marketing strategies in the retail sector. This study aims to explore these impacts within the context of Hong Kong's unique market environment.
Theoretical Framework
1. Consumer Behavior Theory: Theory of Planned Behavior (TPB)
This theory suggests that consumer behavior is driven by intentions, which are influenced by attitudes, subjective norms, and perceived behavioral control. It will help assess how AI influences Gen Z consumers' purchasing decisions in Hong Kong.
2. Corporate IT Competence: Resource-Based View (RBV)
The RBV posits that a firm's competitive advantage stems from its unique resources and capabilities. This theory will guide the analysis of how retail companies leverage IT and AI to enhance their operational efficiency and customer engagement.
3. Marketing Performance Theory: Keller's Brand Equity Model
Keller's model emphasizes the importance of brand equity in driving marketing performance. It will provide a framework for evaluating how AI-driven marketing strategies affect brand perception and customer loyalty in the retail sector.
Research Philosophy
The research will be grounded in a pragmatism philosophy, allowing for a blend of qualitative and quantitative approaches to gather comprehensive insights.
Research Objectives
- To analyze the impact of AI on consumer behavior among Generation Z in Hong Kong.
- To evaluate the corporate IT competencies of retail companies in Hong Kong and their ability to leverage AI for marketing strategies.
- To assess the relationship between AI-driven marketing strategies and marketing performance in the Hong Kong retail sector.
- To identify the e-opportunities presented by AI advancements for enhancing marketing effectiveness in retail.
Research Questions
- How has the advancement of AI influenced the purchasing behavior of Generation Z consumers in Hong Kong?
- What are the key IT competencies that retail companies in Hong Kong need to develop to effectively utilize AI in their marketing strategies?
Methodology
Data Collection
-
Semi-Structured Interviews:
- Participants: 4 Gen Z consumers to understand their behavior and attitudes towards AI in retail.
- Participants: 2 marketing professionals from Hong Kong retail companies to gain insights into corporate strategies.
-
Desk Research:
- Review existing literature, case studies, and industry reports related to IT advancements, consumer behavior, and marketing performance.
Data Analysis
- Qualitative Analysis: Thematic analysis will be conducted on interview transcripts to identify patterns and insights.
- Quantitative Analysis: If applicable, surveys may be developed based on initial findings to gather broader consumer insights.
Expected Contributions
This research aims to contribute to the understanding of how AI technologies shape consumer behavior and marketing practices in the retail sector, providing actionable insights for businesses to enhance their marketing performance.
Conclusion
The proposed study will provide a comprehensive assessment of the interplay between AI, consumer behavior, and marketing strategies in the Hong Kong retail sector, offering valuable insights for both academia and industry practitioners.
Feel free to adjust any sections or details to better fit your vision for your dissertation!"
My question 2: "Could you provide a more concrete plan for the two semi-structured research interview, one for consumers and one for the marketing managers? Also provide a more concrete plan for the desk research. Tks a lot."
The reply: "Detailed Research Plan
Semi-Structured Interviews
1. Consumer Interviews
Objectives
- Understand the impact of AI on consumer behavior among Generation Z.
- Explore attitudes towards AI-driven retail experiences.
Participants
- 4 Gen Z consumers, aged 18-25, living in Hong Kong.
Interview Structure
-
Introduction (5 minutes)
- Brief overview of the study and its purpose.
- Obtain consent for recording and participation.
-
Background Questions (10 minutes)
- Age, occupation, education.
- Shopping habits (online vs. offline).
-
Main Questions (25 minutes)
- How do you perceive AI technologies (e.g., chatbots, recommendations) in your shopping experience?
- Can you describe a recent shopping experience where AI played a role?
- How do you feel AI impacts your decision-making process when purchasing?
- What are your concerns about privacy and data usage related to AI in retail?
- In what ways do you think AI could enhance your shopping experience further?
-
Conclusion (5 minutes)
- Thank participants and ask if they have any final thoughts or questions.
2. Marketing Manager Interviews
Objectives
- Assess the corporate IT competencies needed for leveraging AI.
- Understand the implementation of AI strategies in marketing.
Participants
- 2 marketing managers from retail companies in Hong Kong.
Interview Structure
-
Introduction (5 minutes)
- Brief overview of the study and its purpose.
- Obtain consent for recording and participation.
-
Background Questions (10 minutes)
- Role and responsibilities in the company.
- Company size and market position.
-
Main Questions (25 minutes)
- How has your company integrated AI into its marketing strategy?
- What specific AI tools or technologies are being used, and for what purposes?
- What challenges have you faced in implementing AI in your marketing efforts?
- How do you measure the effectiveness of AI-driven marketing initiatives?
- What skills or IT competencies do you believe are essential for your team to successfully leverage AI?
-
Conclusion (5 minutes)
- Thank participants and ask if they have any final thoughts or questions.
Desk Research Plan
Objectives
- Gather existing literature on AI impacts on consumer behavior and marketing performance.
- Analyze case studies and reports relevant to the retail sector in Hong Kong.
Steps
-
Literature Review (2-3 weeks)
- Search academic databases (e.g., Google Scholar, JSTOR) for articles on AI in retail, consumer behavior theories, and marketing performance metrics.
- Identify key themes, theories, and findings relevant to the research questions.
-
Industry Reports and Case Studies (1-2 weeks)
- Collect reports from industry organizations (e.g., Hong Kong Retail Management Association) on the state of AI in retail.
- Review case studies of retail companies that successfully implemented AI strategies.
-
Market Analysis (1-2 weeks)
- Analyze market trends and consumer reports to understand the broader context of retail in Hong Kong.
- Look for statistical data on consumer behavior changes due to AI.
-
Synthesis of Findings (1 week)
- Compile insights from literature, case studies, and market reports.
- Create a summary that highlights the current landscape of AI in retail, consumer perceptions, and marketing effectiveness.
Expected Outcomes
- A comprehensive background that informs the semi-structured interviews.
- A solid foundation for analyzing interview data and drawing conclusions about the research questions.
This detailed plan should help guide your research effectively! Feel free to modify any parts as needed."
My question 3: "Could you comment on the originality, academic and practical values of the dissertation project?"
The reply: "Originality, Academic, and Practical Values of the Dissertation Project
Originality
-
Focus on Hong Kong Market:
- This study targets the unique retail environment of Hong Kong, which has distinct consumer behaviors and market dynamics compared to other regions. This localized focus adds originality to the research.
-
Integration of AI and Consumer Behavior:
- While existing literature discusses AI broadly, this dissertation specifically examines its impact on Generation Z consumer behavior in retail settings. This demographic's perspectives are often underrepresented in academic research.
-
Multi-Faceted Approach:
- The combination of semi-structured interviews, desk research, and the application of established theories (TPB, RBV, Keller's Model) offers a comprehensive view that is less common in current studies.
Academic Value
-
Contribution to Consumer Behavior Literature:
- The study will enhance understanding of how AI influences consumer decision-making, particularly among younger generations. It will provide empirical data that could be used to further explore this area in future research.
-
Insights into Corporate IT Competence:
- By evaluating the necessary IT competencies for effective AI integration, this research contributes to the literature on corporate strategy and technology management, offering a framework for future studies.
-
Marketing Performance Analysis:
- The application of Keller’s Brand Equity Model to assess AI-driven marketing strategies will provide new insights into the relationship between technology and brand management, enriching the field of marketing studies.
Practical Value
-
Guidance for Retailers:
- The findings will offer actionable insights for retailers in Hong Kong on how to effectively leverage AI to enhance consumer engagement and improve marketing performance.
-
Strategic Recommendations:
- The research will identify key IT competencies and strategies that marketing managers need to develop, supporting companies in their digital transformation efforts.
-
Consumer-Centric Marketing Approaches:
- The study will help retailers understand Gen Z’s preferences and behaviors, allowing them to tailor their marketing strategies more effectively and improve customer satisfaction.
Conclusion
Overall, this dissertation project is poised to make significant contributions to both academic literature and practical applications in the retail sector. Its originality, coupled with the relevance of its findings, positions it as a valuable resource for scholars and industry practitioners alike."
My question 4: "Please indicate which research method is related to the study of which research objective and research question in this case. Thanks."
The reply: "Here's a breakdown of the research methods related to each research objective and research question:
Research Objectives and Corresponding Methods
-
Objective 1: To analyze the impact of AI on consumer behavior among Generation Z in Hong Kong.
- Method: Semi-structured interviews with 4 Gen Z consumers.
-
Objective 2: To evaluate the corporate IT competencies of retail companies in Hong Kong and their ability to leverage AI for marketing strategies.
- Method: Semi-structured interviews with 2 marketing managers from retail companies.
-
Objective 3: To assess the relationship between AI-driven marketing strategies and marketing performance in the Hong Kong retail sector.
- Method: Desk research analyzing existing literature, case studies, and market reports.
-
Objective 4: To identify the e-opportunities presented by AI advancements for enhancing marketing effectiveness in retail.
- Method: Desk research focusing on industry reports and market analysis.
Research Questions and Corresponding Methods
-
Research Question 1: How has the advancement of AI influenced the purchasing behavior of Generation Z consumers in Hong Kong?
- Related Objective: Objective 1
- Method: Semi-structured interviews with Gen Z consumers.
-
Research Question 2: What are the key IT competencies that retail companies in Hong Kong need to develop to effectively utilize AI in their marketing strategies?
- Related Objective: Objective 2
- Method: Semi-structured interviews with marketing managers.
This framework aligns each research method with its respective objective and question, ensuring a coherent approach to the study."
My question 5: "Please comment on the strengths and weaknesses of the research methods and research philosophy employed in this study, given that this is a 6-month project on a part-time basis. Thanks."
The reply: "Strengths and Weaknesses of Research Methods and Philosophy
Research Methods
Strengths
-
Semi-Structured Interviews:
- Flexibility: Allows for in-depth exploration of participants' thoughts and experiences, leading to rich qualitative data.
- Targeted Insights: Tailored questions can probe specific areas of interest, facilitating a deeper understanding of consumer behavior and corporate strategies.
-
Desk Research:
- Cost-Effective: Utilizes existing literature and data, minimizing the need for extensive primary data collection.
- Broad Context: Provides a comprehensive overview of the current state of AI in retail, supporting findings from interviews with relevant background information.
Weaknesses
-
Semi-Structured Interviews:
- Time-Consuming: Conducting and transcribing interviews can be labor-intensive, which may be a constraint within a 6-month timeframe.
- Potential Bias: Responses may be influenced by the interviewer's framing of questions or the participants’ desire to provide socially acceptable answers.
-
Desk Research:
- Limited Specificity: Existing literature may not fully address the specific context of Hong Kong's retail sector or the nuances of Gen Z consumer behavior.
- Data Quality Variability: The quality and relevance of secondary data can vary, potentially affecting the robustness of the analysis.
Research Philosophy: Pragmatism
Strengths
-
Practical Approach:
- Pragmatism emphasizes practical implications, making the research relevant and applicable to real-world scenarios in the retail sector.
-
Combination of Methods:
- This philosophy supports the use of both qualitative and quantitative methods, allowing for a comprehensive understanding of the research questions.
Weaknesses
-
Potential Lack of Depth:
- While pragmatism promotes practicality, it may lead to a focus on immediate solutions rather than deeper theoretical insights, which could limit academic contributions.
-
Complex Integration:
- Balancing qualitative and quantitative approaches can be challenging, especially within a limited timeframe, potentially complicating data analysis and synthesis.
Conclusion
Overall, the chosen research methods and philosophy align well with the study's objectives and time constraints. However, careful management of time and resources will be crucial to mitigate the weaknesses identified, ensuring that the project remains feasible and impactful within the 6-month timeframe."
My question 6: "Are there any research ethics issues that I need to pay attention to for doing this dissertation project?"
The reply: "Yes, there are several important research ethics issues to consider for your dissertation project. Here are the key areas to focus on:
1. Informed Consent
- Explanation: Ensure that all participants understand the purpose of the research, what their participation entails, and any potential risks.
- Action: Provide a consent form detailing these aspects and obtain written consent before conducting interviews.
2. Confidentiality and Anonymity
- Explanation: Protect participants' identities and personal information throughout the research process.
- Action: Use pseudonyms or codes for participants in your analysis, and securely store any data collected to prevent unauthorized access.
3. Right to Withdraw
- Explanation: Participants should have the right to withdraw from the study at any time without any consequences.
- Action: Clearly communicate this right to participants at the beginning of the interviews.
4. Data Security
- Explanation: Ensure that all data collected is securely stored and protected against breaches.
- Action: Use password-protected files and limit access to your research data to authorized individuals only.
5. Ethical Treatment of Participants
- Explanation: Treat all participants with respect and ensure their well-being throughout the research process.
- Action: Be sensitive to participants’ feelings and reactions during interviews, and provide support if any discomfort arises.
6. Avoiding Bias
- Explanation: Maintain objectivity and avoid leading questions that may influence participants’ responses.
- Action: Carefully design interview questions to be neutral and open-ended.
7. Reporting Findings
- Explanation: Ensure that findings are reported honestly and accurately, without fabrication or manipulation.
- Action: Present data and insights transparently, and acknowledge any limitations in your study.
Conclusion
Addressing these ethical considerations will help ensure the integrity of your research and the welfare of your participants. It’s important to consult your institution's ethical guidelines and obtain any necessary approvals before proceeding with data collection."
My question 7: "For the research interview with marketing managers, could you suggest a 4-item coding scheme, mainly based on relevant theories, for each of these questions for deductive qualitative data analysis purpose?
• How has your company integrated AI into its marketing strategy?
• What specific AI tools or technologies are being used, and for what purposes?
• What challenges have you faced in implementing AI in your marketing efforts?
• How do you measure the effectiveness of AI-driven marketing initiatives?
• What skills or IT competencies do you believe are essential for your team to successfully leverage AI?"
The reply: "Here’s a 4-item coding scheme for each interview question based on relevant theories:
Coding Scheme for Marketing Manager Interviews
Question 1: How has your company integrated AI into its marketing strategy?
-
Integration Level (Based on Resource-Based View)
- Core Competency
- Complementary Capability
- Experimental Use
- Not Integrated
-
Strategic Alignment (Based on Strategic Fit Theory)
- High Alignment
- Moderate Alignment
- Low Alignment
- Misalignment
-
Consumer Engagement (Based on Theory of Planned Behavior)
- Active Engagement
- Passive Engagement
- Minimal Engagement
- No Engagement
-
Innovation Focus (Based on Innovation Diffusion Theory)
- Disruptive Innovation
- Incremental Innovation
- No Innovation
- Uncertain
Question 2: What specific AI tools or technologies are being used, and for what purposes?
-
Tool Type (Based on Technology Acceptance Model)
- User-Friendly Tools
- Analytical Tools
- Automation Tools
- Emerging Technologies
-
Purpose of Use (Based on Keller's Brand Equity Model)
- Brand Awareness
- Customer Experience
- Sales Optimization
- Market Insight
-
User Adoption Level (Based on Adoption Theory)
- High Adoption
- Moderate Adoption
- Low Adoption
- No Adoption
-
Integration Complexity (Based on Complexity Theory)
- Low Complexity
- Moderate Complexity
- High Complexity
- Not Applicable
Question 3: What challenges have you faced in implementing AI in your marketing efforts?
-
Type of Challenge (Based on Change Management Theory)
- Technical Challenges
- Organizational Resistance
- Skill Gaps
- Budget Constraints
-
Impact Severity (Based on Impact Assessment Framework)
- Major Impact
- Moderate Impact
- Minor Impact
- No Impact
-
Resource Availability (Based on Resource Dependency Theory)
- Adequate Resources
- Limited Resources
- Critical Shortage
- Uncertain
-
Change Management Needs (Based on Kotter's 8-Step Change Model)
- Comprehensive Strategy Needed
- Minor Adjustments Needed
- No Strategy Needed
- Uncertain
Question 4: How do you measure the effectiveness of AI-driven marketing initiatives?
-
Measurement Metrics (Based on Marketing Performance Model)
- Quantitative Metrics
- Qualitative Metrics
- Mixed Metrics
- No Defined Metrics
-
Frequency of Evaluation (Based on Continuous Improvement Theory)
- Regularly Scheduled
- Periodically
- Ad-hoc
- Not Evaluated
-
Data Sources (Based on Data-Driven Decision Making)
- Internal Data
- Customer Feedback
- Market Research
- Competitor Analysis
-
Feedback Loop (Based on Learning Organization Theory)
- Established Feedback Loop
- Informal Feedback Process
- No Feedback Mechanism
- Uncertain
Question 5: What skills or IT competencies do you believe are essential for your team to successfully leverage AI?
-
Technical Skills (Based on Competency Framework)
- Data Analysis
- AI/ML Knowledge
- Software Development
- Digital Marketing Skills
-
Soft Skills (Based on Emotional Intelligence Theory)
- Communication
- Adaptability
- Collaboration
- Problem-Solving
-
Training Needs (Based on Adult Learning Theory)
- Comprehensive Training
- Ongoing Development
- Minimal Training
- No Training Needed
-
Team Structure (Based on Team Dynamics Theory)
- Cross-Functional Teams
- Specialized Roles
- Generalist Teams
- Uncertain Structure
This coding scheme aligns with relevant theories, facilitating a more structured and theoretical analysis of the qualitative data collected from marketing managers."
My question 8: "For the research interview with consumers, could you suggest a 4-item coding scheme, mainly based on relevant theories, for each of these questions for deductive qualitative data analysis purpose?
The reply: "Here’s a 4-item coding scheme for each interview question directed at consumers, based on relevant theories for deductive qualitative data analysis:
Coding Scheme for Consumer Interviews
Question 1: How do you perceive AI technologies (e.g., chatbots, recommendations) in your shopping experience?
-
Perceived Usefulness (Based on Technology Acceptance Model)
- Highly Useful
- Moderately Useful
- Slightly Useful
- Not Useful
-
Ease of Use (Based on Technology Acceptance Model)
- Very Easy
- Somewhat Easy
- Somewhat Difficult
- Very Difficult
-
Engagement Level (Based on Theory of Planned Behavior)
- Actively Engaged
- Passively Engaged
- Not Engaged
- Uncertain
-
Emotional Response (Based on Affect Theory)
- Positive Emotions
- Neutral Emotions
- Negative Emotions
- Mixed Emotions
Question 2: Can you describe a recent shopping experience where AI played a role?
-
Type of AI Used (Based on Innovation Diffusion Theory)
- Chatbots
- Product Recommendations
- Virtual Assistants
- Other (Specify)
-
Experience Outcome (Based on Customer Experience Theory)
- Positive Outcome
- Neutral Outcome
- Negative Outcome
- Uncertain Outcome
-
Satisfaction Level (Based on Expectation Confirmation Theory)
- Highly Satisfied
- Satisfied
- Dissatisfied
- Very Dissatisfied
-
Influence on Purchase (Based on Consumer Decision-Making Process)
- Strong Influence
- Moderate Influence
- Minimal Influence
- No Influence
Question 3: How do you feel AI impacts your decision-making process when purchasing?
-
Information Availability (Based on Information Processing Theory)
- Abundant Information
- Adequate Information
- Limited Information
- No Information
-
Confidence Level (Based on Decision Theory)
- High Confidence
- Moderate Confidence
- Low Confidence
- No Confidence
-
Speed of Decision (Based on Decision-Making Speed Theory)
- Much Faster
- Somewhat Faster
- No Change
- Slower
-
Consideration of Alternatives (Based on Compensatory Decision-Making Model)
- More Alternatives Considered
- Same Number of Alternatives
- Fewer Alternatives Considered
- No Change
Question 4: What are your concerns about privacy and data usage related to AI in retail?
-
Privacy Concerns (Based on Privacy Calculus Theory)
- High Concerns
- Moderate Concerns
- Low Concerns
- No Concerns
-
Data Usage Awareness (Based on Consumer Awareness Theory)
- Highly Aware
- Somewhat Aware
- Unaware
- Uncertain
-
Trust in Retailers (Based on Trust Theory)
- High Trust
- Moderate Trust
- Low Trust
- No Trust
-
Impact on Shopping Behavior (Based on Behavioral Intention Theory)
- Significant Impact
- Some Impact
- Minimal Impact
- No Impact
Question 5: In what ways do you think AI could enhance your shopping experience further?
-
Desired Features (Based on User Experience Design Theory)
- Personalized Recommendations
- Enhanced Customer Service
- Faster Checkout Processes
- Improved Product Information
-
Value Addition (Based on Value-Added Services Theory)
- High Value
- Moderate Value
- Low Value
- No Value
-
Innovation Desire (Based on Innovation Adoption Theory)
- Strong Desire for Innovation
- Moderate Desire for Innovation
- Low Desire for Innovation
- No Desire
-
Impact on Loyalty (Based on Customer Loyalty Theory)
- Strong Impact on Loyalty
- Moderate Impact on Loyalty
- Minimal Impact on Loyalty
- No Impact on Loyalty
This coding scheme provides a theoretical framework for analyzing qualitative data collected from consumers, facilitating structured insights into their perceptions and experiences with AI in retail."
My question 9: "Could you present the following information in more essay form, rather than point form:
Research Question 1: How has the advancement of AI influenced the purchasing behavior of Generation Z consumers in Hong Kong?
Research Question 2: What are the key IT competencies that retail companies in Hong Kong need to develop to effectively utilize AI in their marketing strategies?
The reply: "The research project aims to explore the impact of AI on various aspects of consumer behavior and marketing strategies within Hong Kong's retail sector, particularly focusing on Generation Z consumers. To achieve this, four key research objectives have been identified, each accompanied by specific methods for data collection.
The first objective is to analyze the impact of AI on consumer behavior among Generation Z in Hong Kong. To address this objective, semi-structured interviews will be conducted with four Gen Z consumers. This qualitative method allows for rich, detailed insights into how AI technologies influence their purchasing decisions and overall shopping experiences. By engaging directly with these consumers, the research seeks to uncover nuanced perspectives that may not be captured through quantitative approaches.
The second objective focuses on evaluating the corporate IT competencies of retail companies in Hong Kong and their ability to leverage AI for marketing strategies. This objective will be explored through semi-structured interviews with two marketing managers from retail companies. This method provides an opportunity to gain in-depth information about the specific skills and resources necessary for effective AI integration in marketing efforts, as well as the challenges these companies face in utilizing these technologies.
The third objective involves assessing the relationship between AI-driven marketing strategies and marketing performance in the Hong Kong retail sector. For this, desk research will be utilized, analyzing existing literature, case studies, and market reports. This approach allows for a comprehensive understanding of how AI impacts marketing effectiveness by synthesizing a wide range of data and insights from various sources.
The fourth objective aims to identify the e-opportunities presented by AI advancements for enhancing marketing effectiveness in retail. Similarly, desk research will be employed, focusing on industry reports and market analyses. This method enables the exploration of current trends and potential future developments in AI that could benefit retail marketing strategies.
In addition to these objectives, two key research questions guide the inquiry. The first question seeks to understand how the advancement of AI has influenced the purchasing behavior of Generation Z consumers in Hong Kong. This question is directly related to the first objective and will be addressed through the semi-structured interviews with Gen Z consumers.
The second research question aims to uncover the key IT competencies that retail companies in Hong Kong need to develop to effectively utilize AI in their marketing strategies. This question is aligned with the second objective and will also be explored through semi-structured interviews with marketing managers.
By integrating these objectives and questions with appropriate research methods, the study is positioned to provide valuable insights into the interplay between AI technologies and consumer behavior within the context of Hong Kong's retail landscape."
** also see the related exploratory study on perplexity.ai.
A collection of blog notes on using chatgpt for research purpose.
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