A systemic picture of research project tasks and associated questions: a note for MBA students

A systemic picture of research project tasks and associated questions: a note for MBA students

Suggested readings for task questions

Task question a: on three types of sub-project backgrounds

Task question b: on researcher positionality

Task question c: on research philosophy.

Task question d:

Task question e: on research objectives and research questions

Task question f:

Task question g:

Task question h:

Task question i:

Task question j: on research ethics.

Task question k: on literature review tasks

Examining research ethics considerations of a study on marketing creativity: for MBA students

Examining research ethics considerations of a study on marketing creativity: for MBA students

The notes examines the article of: Pagani, M. and Wind, Y. (2025) "Unlocking marketing creativity using artificial intelligence"  Journal of Interactive Marketing 60(1): 1-24.

What are (1) the research theme, (2) the research objectives, and the research questions of the article?

The article's research focus and questions are as follows:

1.    Research Theme: The central theme is exploring how artificial intelligence (AI), including both nongenerative and generative AI, enhances marketing creativity by examining the dynamic interaction and synergy between computational (AI-driven) and human creative processes within marketing contexts.

2.    Research Objectives:

• To investigate how creative individuals, particularly artists, utilize AI in their creative work and how AI affects their creative experiences and processes.
• To assess and categorize a large dataset of AI systems and models regarding their creative capabilities and contributions.
• To develop and validate a conceptual framework that guides marketing executives in leveraging AI for enhancing marketing creativity.
• To bridge the research gap on AI’s impact on marketing creativity by providing empirical evidence and practical insights.

3.    Research Questions: While not explicitly listed as numbered research questions, the study implicitly addresses:

• How do creative individuals (especially artists) integrate and interact with AI technologies in their creative processes?
• How do nongenerative and generative AI systems differ in their applications and impact on creativity in marketing?
• What are the levels and clusters of creativity exhibited by existing AI systems and models evaluated through extensive datasets?
• How can AI-enabled creativity be conceptualized and strategically applied in marketing to inspire agile methods, augment human creativity, and foster unconventional thinking?
• What are the implications, challenges, and opportunities of integrating AI within marketing creativity and interactive marketing strategies?

These elements form the foundation of the study's design and guide the two empirical studies, framework development, and validation efforts.

 

Briefly describe the research methodology as well as the main research methods (both primary and secondary) of this article.

The article employs a conceptually driven and empirically derived research methodology structured into four main stages, combining both qualitative and quantitative approaches:

1.    Research Methodology:

• The study uses an exploratory, mixed-methods design, integrating qualitative research (interviews, open-ended questionnaires, phenomenological analysis) with large-scale quantitative content analysis of AI datasets.
• The methodology follows McCracken’s (1988) approach to structure the research process with iterative phases of data collection, analysis, framework development, and validation.

2.    Primary Research Methods:

• Study 1 (Demand Side): Exploratory personal interviews and open-ended questionnaires with 37 multidisciplinary artists (43% using generative AI) to understand how they use AI in creative processes. Three additional interviews with chief marketing officers validated these insights within marketing contexts.
• Qualitative data analysis used a phenomenological approach with independent coders to extract themes, structural topic modeling (STM) for thematic identification from text data, and Linguistic Inquiry and Word Count (LIWC2015) software for linguistic-emotional-cognitive analysis. Speak AI software assessed user sentiment.

3.    Secondary Research Methods:

• Study 2 (Supply Side): Content analysis of a large longitudinal dataset of 1,036 AI systems (2015–2021) and 241,292 AI models (2022–2024) categorized into 35 tasks to assess AI systems’ and models’ creativity in marketing applications.
• A follow-up detailed analysis of a sample of 350 AI models was conducted for direct evaluation of novelty and usefulness.
• Interviews with AI system providers offered insights into design intent and marketing relevance.

4.    Framework Development and Validation:

• The insights from both studies fed into a conceptual framework for AI-enabled creativity.
• This framework was validated in three workshops and nine in-depth interviews involving senior marketing leaders and scholars to ensure practical applicability.

Overall, the research combines rich qualitative insights from creatives and marketing managers with extensive quantitative data analysis of AI systems to holistically understand AI's role in marketing creativity.

 

Discuss the main research ethics considerations of (1) the research objectives chosen, (2) the research methodology and the research methods involved.

The research ethics considerations in the article span three main areas: the research objectives, the methodology, and the research methods employed.

1.    Research Objectives:

• The study aims to explore how AI enhances creativity in marketing by examining human-AI interaction and AI’s creative capabilities. Ethically, this objective addresses the important contemporary issue of AI’s role in creative industries and marketing, a domain with significant social impact.
• The focus on understanding AI’s influence on human creativity respects the agency of individuals and seeks to improve marketing creativity without promoting AI as a replacement but rather as a complement to human creativity, which carefully navigates concerns about job displacement and autonomy.
• The objective to propose a responsible and strategic framework for AI use acknowledges potential ethical challenges (trust, bias, privacy) and advocates for cautious adoption, thus reflecting an ethical posture towards AI deployment in marketing.

2.    Research Methodology:

• The mixed methods design integrates qualitative and quantitative data with a phenomenological approach emphasizing participants’ lived experiences. This respects the subjective perspectives and voices of creatives, ensuring their experiences are authentically represented, a vital ethical consideration when researching human creativity and technology use.
• Validation through workshops with diverse stakeholders (academic scholars, marketing executives) adds transparency and rigor, reinforcing ethical standards in research reliability and applicability.
• Use of secondary large datasets on AI systems and models does not raise direct ethical concerns regarding human subjects, but the study is mindful of biases and explainability issues inherent in the AI technologies analyzed, indicating ethical sensitivity to technological impact.

3.    Research Methods:

• Primary data collection includes in-depth interviews and open-ended questionnaires with artists and marketing managers. Ethical considerations include informed consent, confidentiality, and respectful engagement with participants, particularly as some questions delve into personal creative processes and professional practices.
• The selection of participants from an international artist community and marketing leaders reflects ethical inclusiveness and diversity.
• The qualitative analysis employed independent coders and rigorous audit trails to ensure validity and reduce bias, illustrating ethical research conduct in data analysis.
• Use of automated tools such as STM and LIWC for text analysis, and sentiment analysis software, is ethically appropriate since these tools are applied to anonymized textual data, minimizing privacy risks.
• The study acknowledges the ethical challenges with AI in creative processes, including trust, explainability, bias, and data privacy risks, thus demonstrating reflexivity and ethical awareness for future AI implementation in marketing.

In summary, the research carefully considers ethical implications by choosing socially relevant objectives, applying rigorous and transparent methodologies, ensuring participant respect and data integrity, and by explicitly recognizing ethical challenges associated with AI in marketing creativity.

Is the consideration of "informed consent" handled well in this study?

The provided excerpts from the study do not explicitly detail the procedures or protocols regarding informed consent for participants involved in the research, such as the 37 artists and the marketing managers interviewed. However, some indirect evidence suggests ethical attention to participant involvement:

• The study describes conducting exploratory personal interviews and administering open-ended questionnaires to artists, with participation being voluntary (artists "agreed to participate") and involving detailed personal insights into their creative practices with AI.
• The researchers emphasize the use of rigorous qualitative analysis with independent coders and maintaining an audit trail for transparency and reliability, indicating careful handling of data and ethical research standards.
• The involvement of international participants from diverse cultural backgrounds implies an awareness of ethical inclusiveness and careful participant engagement.
• The study also conducted validation workshops and further interviews with senior marketing leaders, suggesting collaboration with professionals likely under formal organizational research protocols.

Despite these indicators of ethical rigor, there is no explicit mention in the excerpts of formal informed consent procedures, such as:

• How participants were informed about the purpose, risks, and benefits of the research,
• How confidentiality and anonymity were assured,
• Whether participants had the right to withdraw,
• How data was stored and used.

Given that the article was published in a reputable academic journal, it is likely that standard ethical procedures including informed consent were followed, but this is not documented in the excerpts provided.

In conclusion, while the study reflects careful and respectful participant engagement and rigorous methodology implying ethical conduct, explicit handling of informed consent is not detailed in the available text. Therefore, the consideration of informed consent appears to be adequately handled from an implicit ethical standpoint but is not clearly documented or discussed in the excerpts.

 

** the exercise is supported by chatpdf.com

A collection of blog notes on using chatgpt for research purpose.

A note on reviewing the literature review task on an article about customer delight and AI-driven services

A note on reviewing the literature review task on an article about customer delight and AI-driven services

The article reviewed is: Grappi, S. et al. (2026) "Customer delight in AI-driven services" Journal of Business Research 203 January. 115808.

What is the overall research theme of this article?

The overall research theme of this article is the investigation of customer delight in the context of AI-driven services. It focuses on understanding how Artificial Intelligence (AI) technologies in service interactions evoke emotional reactions, specifically delight, and the strategic implications of these emotional responses for marketers. The study aims to reconceptualize customer delight within AI service environments, develop a scale to measure it, and explore its effects on key marketing outcomes such as customer loyalty and positive word of mouth. Furthermore, it addresses how different aspects of delight vary across AI service types and provides managerial insights on designing AI services that maximize customer delight. This theme integrates concepts from AI, service marketing, customer emotions, and consumer behavior, highlighting the transformative role of AI in service industries.

 

 What are the main research objectives and research questions of this article?

The main research objectives and research questions of the article "Customer delight in AI-driven services" are as follows:

1.    To reconceptualize the construct of customer delight specifically in the context of AI-driven service interactions, recognizing the unique emotional reactions customers experience when interacting with AI services.

2.    To identify and unveil the key elements that constitute customer delight in AI service interactions.

3.    To develop a novel, useful scale to measure customer delight in AI-driven services.

4.    To test the effects of customer delight in AI services on important marketing outcomes such as continued use intention, positive word of mouth, and tolerance to service failure.

5.    To explore how different aspects of customer delight vary in relevance across different types of AI services.

6.    To provide practical suggestions for managers and practitioners on how to design and deliver AI services that effectively delight customers, offering a roadmap for success in the evolving landscape of AI service applications.

These objectives reflect a multimethod research approach drawing on prior frameworks in AI service, customer delight, and emotion research.

In summary, the research questions revolve around:

• What elements define customer delight in AI-driven services?
• How can customer delight be measured reliably in this context?
• What impact does customer delight have on marketing-related outcomes?
• Which elements are most relevant for different AI service types?
• How can businesses create delightful AI service experiences?

 

Does the literature review of the article involve the following review tasks:

1.    Defining, evaluating and extending the relevant academic ideas.

2.    Updating the readers on the latest academic development as related to the article's research theme.

3.    Examining the research issues as related to: (1) the relevant theories, perspectives and concepts and (2) the relevant research methods employed to study the related research topics and themes.

4.    Synthesize the relevant academic ideas to come up with a more comprehensive research framework or theories.

5.    Discuss how some of the relevant theories and concepts can be employed to inform the research methodology (and research methods) for this study.

    Based on the content and structure presented in the article "Customer delight in AI-driven services," the literature review involves the following review tasks:

1.    Defining, evaluating, and extending the relevant academic ideas: The article clearly discusses the construct of customer delight in service contexts, particularly within AI-driven services. It draws on previous frameworks and seminal literature about AI services, customer delight, and emotions, aiming to reconceptualize customer delight in AI service interactions. This indicates defining and extending relevant academic ideas.

2.    Updating the readers on the latest academic developments related to the article's theme: The article references current developments in AI applications in services, such as IKEA's adoption of AI tools and cites recent market data and literature (e.g., Huang and Rust, 2021; Statista, 2024), indicating that it provides an updated view of the landscape relevant to AI service and customer delight.

3.    Examining research issues related to relevant theories, perspectives, concepts, and research methods: The article applies a multimethod research approach to study customer delight in AI-driven services, reflecting on the emotional components, service literature, and AI's integration with services. The methodology section (Studies 2–6, Table 2) details various research methods like surveys, scale development, cross-country validation, and MTMM analyses, indicating examination of both concepts and methods.

4.    Synthesizing relevant academic ideas into a comprehensive research framework or theories: The goal of building a new scale and testing its effects on marketing outcomes, alongside conceptual work reconceptualizing customer delight in AI contexts, suggests a synthesis of prior ideas into a novel framework that better captures customer delight in AI service interactions.

5.    Discussing how relevant theories and concepts inform the research methodology and methods: The article describes drawing on theories related to emotions, AI service, and customer delight to guide the research design, scale development, and validation processes. The multimethod approach and detailed explanation of survey procedures illustrate how theory informed the study's methodology.

Therefore, the literature review in this article appears to comprehensively address all five review tasks you listed.

 

Identify 3 main research ethics considerations that need to be dealt with for conducting its research methodology.

Regarding the research methodology used in the article "Customer delight in AI-driven services," three main research ethics considerations that need to be addressed are:

1.    Informed Consent and Participant Awareness: The studies relied on online surveys where participants were asked to recall and describe their experiences with AI-powered services. Ensuring that participants are fully informed about the study’s purpose, what participation involves, and their rights (including the option to withdraw at any time) is critical for ethical research conduct.

2.    Data Privacy and Confidentiality: Collecting personal or demographic data necessitates strict measures to protect participants' privacy and confidentiality. Given the use of platforms like Prolific and Qualtrics, it is essential to secure data storage and anonymize responses to prevent unauthorized access or linkage of data to identifiable individuals, consistent with ethical standards.

3.    Avoidance of Harm and Psychological Distress: Participants were asked to recall potentially emotional experiences involving AI services. Although recalling such experiences is generally low risk, researchers should ensure that the process does not cause undue distress or discomfort, especially when discussing possibly embarrassing or negative service encounters.

These ethics considerations align with standard practices in survey-based consumer research and are particularly pertinent in research involving human participants and technological interactions.

** the exercise is supported by chatpdf.com

A collection of blog notes on using chatgpt for research purpose.