An example of an exploratory attempt to brainstorm on an MBA research theme about consumer trust on AI-enabled financial services in Hong Kong
I am a part-time MBA student currently learning to do my
dissertation project. My research theme is "an assessment of consumer
trust of gen x and gen z (notably on transparency, data privacy and fairness)
in innovative AI-enabled financial services in Hong Kong from the pragmatic
perspective" Could you describe the main research issues, the main public
media-reported issues and 3 main academic theories as related to this research
theme.
Your topic is well
framed for a Hong Kong MBA dissertation: the core issues are trust formation
and trust erosion around AI-enabled financial services, with age-group
differences likely to matter most on transparency, privacy, fairness, and
perceived control. In Hong Kong, public and regulatory discussion is already
centered on model accuracy, data privacy/security, transparency, and
bias/fairness in AI finance.
Main research issues
1.
What “trust” means
in AI finance.
You will need to define consumer trust clearly: trust in the institution, trust
in the AI system, or trust in the outcome of the service. In your topic, trust
likely includes whether users believe the AI is accurate, honest, explainable,
secure, and fair.
2.
Generational
differences.
Gen Z and Gen X may not trust the same cues. Hong Kong evidence suggests Gen Z
tends to demand novelty, product fit, and clear explanations, while Gen X leans
more toward reliability and proven brand reputation. This makes generational
comparison a central research issue rather than a simple control variable.
3.
Transparency and
disclosure.
A major question is whether users want to know when AI is being used, how
decisions are made, and what data is collected. Hong Kong policy discussion
emphasizes disclosure and transparency when AI affects customers or investors,
and recent consumer reporting suggests Hong Kong users want explicit identification
of AI use.
4.
Data privacy and
security.
This is likely one of the strongest trust drivers in financial services because
the context involves sensitive personal and financial data. Hong Kong’s applied
research and policy statements repeatedly identify privacy, security, and
compliance as adoption barriers and governance priorities.
5.
Fairness and bias.
Consumers may worry that AI systems treat people unevenly, discriminate
indirectly, or make opaque decisions. Hong Kong AI governance materials
explicitly frame fairness as avoiding favoritism, discrimination, and harm, so
fairness is not just ethical but also a practical trust issue.
6.
Human oversight
versus automation.
Another issue is whether consumers want AI alone, or AI with human review. Hong
Kong consumer reporting shows many people still prefer access to a human agent
when needed, which suggests that “human-in-the-loop” design may support trust.
Main media-reported issues
·
Synthetic
deception and identity risk.
Media coverage in Hong Kong has highlighted that many consumers cannot tell
AI-generated voices from human staff, raising concerns about impersonation,
fraud, and reputational damage.
·
Low tolerance when
AI underperforms.
Reports suggest Hong Kong consumers are willing to use AI, but they react badly
when service quality falls short, especially in customer service settings.
·
Demand for clear
AI disclosure.
Regional consumer reporting indicates that people want brands to disclose AI
usage, and they see this as important for trust. That issue is especially
relevant in finance, where hidden automation can feel misleading.
·
Privacy and data
use concerns.
Public discussion in Hong Kong repeatedly returns to data privacy, security,
and the use of personal data for model training or personalization. This is
especially sensitive in banking, insurance, and payments.
·
Fairness, bias,
and regulation.
Hong Kong’s AI governance discourse emphasizes fairness, transparency
obligations, and risk-tiered controls, showing that public debate is not only
about innovation but also about preventing discriminatory or harmful outcomes.
Three useful theories
1.
Trust Theory /
Institutional Trust.
This helps explain why consumers rely on financial institutions and AI systems
under uncertainty. It is especially useful for examining perceived competence,
integrity, and benevolence, which map neatly onto your themes of transparency,
privacy, and fairness.
2.
Technology
Acceptance Model or UTAUT.
These frameworks explain adoption through perceived usefulness, ease of use,
social influence, and facilitating conditions. They are useful because AI
financial services are only trusted if people think they are useful, easy, and
safe enough to use; they also help explain why Gen Z and Gen X may adopt for
different reasons.
3.
Organizational
Justice / Fairness Theory.
This theory is highly relevant because users evaluate whether decisions and
procedures are fair, consistent, and explainable. In AI finance, it helps you
study whether consumers view automated decisions as procedurally fair and
whether they feel treated without bias.
Pragmatic perspective
A pragmatic
perspective fits your dissertation well because it lets you focus on what works
in practice rather than forcing one philosophical position. In your case,
pragmatism supports a mixed understanding of trust: you can study both
measurable patterns, such as survey differences between Gen X and Gen Z, and
deeper explanations, such as why transparency or privacy concerns matter in
context. That makes your theme suitable for a research design that links
real-world problems, consumer experience, and actionable recommendations for
Hong Kong financial firms.
A workable framing
A strong
dissertation framing could be: “How do transparency, data privacy, and fairness
shape Gen X and Gen Z consumer trust in AI-enabled financial services in Hong
Kong, and what practical governance features increase trust?” That framing
stays close to public policy concerns in Hong Kong while giving you a clear
analytical structure.
For the academic theories suggested, pleas provide a few
references on them in Harvard reference format.
Here are key
references for the three main theories, in Harvard style, with a mix of
classic/theory-defining sources and more recent applications to AI, finance, or
privacy:
1. Trust Theory (organizational/consumer
trust)
Mayer, R.C.,
Davis, J.H. and Schoorman, F.D. (1995) An integrative model of organizational
trust. Academy of Management Review, 20(3), pp. 709–734.
https://doi.org/10.5465/amr.1995.9508080335
Gulati, R. and
Singh, H. (2024) Advice by algorithms: dual dimensions of trust in AI financial
service adoption. Journal of Retailing and Consumer Services, 88,
p. 103169. https://doi.org/10.1016/j.jretconser.2024.103169
Farrell, A., et
al. (2016) Trust in artificial intelligence: a comprehensive conceptual
review. Electronic Markets, 32(4), pp. 1–22.
https://doi.org/10.1007/s12525-022-00592-6
2. Technology Acceptance Model (TAM) / UTAUT
Davis, F.D. (1989)
Perceived usefulness, perceived ease of use, and user acceptance of information
technology. MIS Quarterly, 13(3), pp. 319–340.
https://doi.org/10.2307/249008
Venkatesh, V.,
Morris, M.G., Davis, G.B. and Davis, F.D. (2003) User acceptance of information
technology: toward a unified view. MIS Quarterly, 27(3), pp.
425–476. https://doi.org/10.2307/30036540
The impact of AI
on UX in fintech payment systems for Gen Z. (2025) Theseus,
Helsinki. Available at: https://www.theseus.fi/handle/10024/880931
Digital banking
adoption in emerging economies: revisiting TAM. (2025) Quarterly
Journal of Social Sciences, pp. 1–20. Available at:
https://qjss.com.pk/index.php/qjss/article/view/366
3. Organizational Justice / Fairness Theory
(including procedural fairness in privacy)
Colquitt, J.A.
(2001) On the dimensionality of organizational justice: a construct validation
of a measure. Journal of Applied Psychology, 86(3), pp. 386–400.
https://doi.org/10.1037/0021-9010.86.3.386
Culnan, M.J. and
Bies, R.J. (2003) Consumer privacy: balancing economic and justice
considerations. Journal of Social Issues, 59(2), pp. 323–342.
https://doi.org/10.1111/1540-4560.00070
Culnan, M.J. and
Armstrong, P.K. (1999) Information privacy concerns, procedural fairness, and
impersonal trust: an empirical investigation. Organization Science,
10(1), pp. 104–115. https://doi.org/10.1287/orsc.10.1.104
Hong Kong’s policy
statement on responsible application of AI in the financial market. (2024)
Financial Services and the Treasury Bureau, Hong Kong SAR. Available at:
https://gia.info.gov.hk/general/202410/28/P2024102800154_475819_1_1730083937115.pdf
These references
give you:
·
The foundational
trust model (Mayer et al., 1995) and recent AI/finance trust work.
·
The original TAM
(Davis, 1989), UTAUT (Venkatesh et al., 2003), and recent applications to
fintech/Gen Z.
·
Core
organizational justice/fairness work plus privacy-specific fairness and trust
(Culnan & Bies; Culnan & Armstrong), and Hong Kong AI policy context
for your setting.
As my additional preference is on the research issues of
(1) data privacy and security and (2) human oversight vs automation) and that
my favored primary research methods to use are (1) focus group with 6
participants, two being Gen X financial services consumers and two being Gen Z
financial services consumers, one being an IT manager with a financial service
firm in Hong Kong and one being a university lecturer on IT AND (2) an opinion
questionnaire survey to my friends. Also bear in mind that this is a part-time
MBA dissertation project. Could you suggest a dissertation proposal to me,
notably with 4 research objectives and 4 research questions.
Below is a focused, MBA‑appropriate
dissertation proposal tailored to your preferences: two priority research
issues (data privacy & security; human oversight vs automation), a
pragmatic mixed‑methods design, one small focus group (6 participants: 2 Gen X
consumers, 2 Gen Z consumers, 1 IT manager from a Hong Kong financial firm, 1
IT lecturer), and an opinion questionnaire survey to your friends. It includes
four research objectives and four research questions.
Dissertation proposal (part‑time MBA)
Working title
An assessment of consumer trust of Gen X and Gen Z in innovative AI‑enabled
financial services in Hong Kong from a pragmatic perspective: the roles of data
privacy & security and human oversight versus automation
Research theme and context
AI‑enabled financial services in Hong Kong are expanding rapidly, but adoption
is constrained by concerns over data privacy, security, transparency, and
fairness. Consumers' trust is critical for adoption, yet Gen X and Gen Z may
differ in what drives trust. Hong Kong's AI policy and industry reports
emphasize privacy/security, fairness, and transparency as core governance
principles.
Research problem
Despite
growth in AI applications in finance, there is limited empirical evidence on
how Gen X and Gen Z consumers in Hong Kong trust AI‑enabled financial services,
especially regarding:
·
Data privacy and security (how
data is collected, used, protected), and
·
Human oversight vs
automation (degree of human review, ability to reach a human
agent, accountability).
This
gap is important for managers designing AI services and governance practices
that build trust across generations.
Research philosophy
Pragmatism:
A pragmatic worldview is well‑suited to mixed‑methods research and applied
managerial problems. It focuses on “what works” to solve real‑world problems,
allowing you to combine qualitative insights (focus group) with quantitative
patterns (survey) to inform practical recommendations.
Theoretical framework
You
will draw on:
1. Mayer et al.'s integrative model of organizational trust (competence,
integrity, benevolence) to frame trust in AI/financial institutions.
2. Technology Acceptance Model (TAM) / UTAUT (perceived
usefulness, ease of use, trust, security, privacy risk) to explain adoption and
trust in AI financial services.
3. Organizational justice / fairness theory (procedural,
distributive, interactional justice) to examine fairness, transparency, and
accountability in AI decisions.
These
theories align with your focus on transparency, privacy, security, fairness,
and human oversight.
Research objectives
1. RO1: To examine how Gen X and Gen Z consumers in Hong Kong
perceive data privacy and security risks in AI‑enabled financial services and
how these perceptions affect their trust.
2. RO2: To explore how preferences for human oversight versus
full automation shape consumer trust in AI‑enabled financial services for Gen X
and Gen Z.
3. RO3: To compare differences between Gen X and Gen Z in the
relative importance of privacy/security, human oversight, transparency, and
fairness for trust in AI financial services.
4. RO4: To develop practical recommendations for financial
service firms in Hong Kong on designing AI services and governance (e.g.,
disclosure, human‑in‑the‑loop, security measures) that enhance consumer trust
across generations.
Research questions
1. RQ1: How do Gen X and Gen Z consumers in Hong Kong perceive
data privacy and security risks in AI‑enabled financial services, and how do
these perceptions influence their trust?
2. RQ2: How do preferences for human oversight versus
automation affect consumer trust in AI‑enabled financial services for Gen X and
Gen Z?
3. RQ3: In what ways do Gen X and Gen Z differ in the relative
importance of privacy/security, human oversight, transparency, and fairness for
trust in AI financial services?
4. RQ4: What practical governance and design practices can Hong
Kong financial service firms adopt to strengthen consumer trust in AI‑enabled
services across Gen X and Gen Z?
Methodology
(pragmatic mixed‑methods)
Overall design
·
Qualitative strand:
One semi‑structured focus group (6 participants) to explore in depth how
different stakeholders understand trust, privacy/security, and human oversight
in AI finance, and to generate rich insights and hypotheses.
·
Quantitative strand:
An opinion questionnaire survey distributed to your friends to measure
attitudes, perceived risks, and trust levels related to AI financial services,
and to compare Gen X vs Gen Z at a basic level.
·
Integration:
Use focus group findings to inform survey items and interpretation; use survey
results to check whether qualitative themes appear more broadly. This
triangulation supports a pragmatic, problem‑centered approach.
Participants and sampling
Focus group (n = 6)
·
2 Gen X consumers of financial services (born
~1965–1980), Hong Kong residents
·
2 Gen Z consumers of financial services (born
~1997–2012), Hong Kong residents
·
1 IT manager from a Hong Kong financial service firm
·
1 university lecturer specializing in IT (possibly with
interest in AI/finance)
Sampling:
purposive, based on generation, role, and relevance to AI/finance.
Survey
·
Convenience sample of your friends (likely mixed
generations, including Gen X and Gen Z).
·
You will record basic demographics (generation, age,
gender, use of AI financial services, experience level).
·
You will acknowledge limitations (non‑probability sample,
potential bias) and frame findings as exploratory, not generalizable to the
whole population.
Data collection instruments
Focus group guide (semi‑structured, ~90 minutes)
Key topics:
·
Experiences with AI‑enabled financial services (chatbots,
robo‑advisors, AI‑driven credit decisions, fraud detection).
·
Perceptions of data privacy and security:
·
What data is collected?
·
How is it used?
·
Concerns about breaches, misuse, surveillance.
·
Human oversight vs automation:
·
Preferences for human review of AI decisions.
·
Expectations of access to a human agent when things go
wrong.
·
Views on accountability and explainability.
·
Transparency and fairness:
·
What information do they want about AI use?
·
What feels fair or unfair in automated decisions?
·
Generational differences:
·
How do Gen X and Gen Z views differ?
·
What builds or erodes trust for each group?
Survey questionnaire (online, ~15–20 minutes)
Sections:
1. Demographics: generation, age, gender, occupation, financial service usage.
2. AI financial service experience: types used, frequency, confidence.
3. Data privacy & security perceptions (Likert scale items, e.g., 1–5):
· “I am concerned that AI financial services collect too much personal data.”
· “I worry about data breaches in AI‑enabled financial services.”
· “I trust Hong Kong financial firms to protect my data in AI systems.”
4. Human oversight vs automation:
· “I prefer AI decisions in finance to be reviewed by a human before being final.”
· “I want the option to speak to a human when using AI financial services.”
· “Fully automated financial advice gives me less trust than human advisors.”
5. Transparency & fairness:
· “I want clear disclosure when AI is used in financial decisions.”
· “I am concerned that AI may treat people unfairly.”
6. Trust in AI financial services (outcome variable):
· “I trust AI‑enabled financial services in Hong Kong.”
· “I would recommend AI financial services to friends/family.”
7. Generational comparison items: optional open‑ended question on what would make them trust AI finance more.
You
will pilot the survey with 2–3 people to check clarity.
Data analysis
Focus group
·
Transcribe (or use detailed notes) and code responses
thematically.
·
Identify themes around:
·
Privacy & security concerns
·
Human oversight preferences
·
Transparency and fairness
·
Generational differences
·
Use simple matrices to compare Gen X vs Gen Z views.
Survey
·
Basic descriptive statistics (means, frequencies) for all
items.
·
Compare Gen X vs Gen Z on key constructs using simple
group comparisons (e.g., mean differences).
·
Optionally, simple correlation or regression to see how
privacy/security concerns and human oversight preferences relate to trust.
·
Present results in tables and brief narratives.
Integration
·
Use focus group insights to explain survey patterns
(e.g., why Gen Z rates privacy concerns higher/lower).
·
Highlight convergences and divergences between
qualitative and quantitative findings.
Expected contributions
Managerial/practical
contributions
·
Clear guidance for Hong Kong financial firms on:
·
How to design privacy‑by‑default and security measures to
build trust.
·
How to balance automation with human oversight and human‑agent
access.
·
What transparency and fairness features matter most to
Gen X and Gen Z.
·
Practical recommendations for AI governance aligned with
Hong Kong's responsible AI principles (fairness, privacy & security,
transparency).
Academic contributions
·
Empirical evidence on generational differences in trust
in AI finance in Hong Kong.
·
Application of trust theory, TAM/UTAUT, and organizational
justice to a new context (AI‑enabled financial services).
·
A pragmatic mixed‑methods approach that can be extended
in future research with larger samples.
Ethical considerations
·
Obtain informed consent from all focus group participants
and survey respondents.
·
Ensure anonymity and confidentiality (no names in
analysis; use pseudonyms for focus group quotes).
·
State that participation is voluntary and that
respondents can withdraw at any time.
·
For the survey to friends, explicitly state that:
·
It is for academic research.
·
Data will be used only for the dissertation.
·
Results will be reported in aggregate.
·
Obtain any required ethics approval from your MBA program
before data collection.
Limitations
·
Small focus group:
6 participants limits depth and generalizability; findings are exploratory and
illustrative rather than definitive.
·
Convenience survey:
Friends are not a random sample; results may be biased and not representative
of all Hong Kong consumers. Frame findings as preliminary and use them to
generate insights, not strong general claims.
·
Self‑report data:
Attitudes may not match actual behavior.
·
Single city context:
Results apply to Hong Kong and may not generalize to other regions.
You
will address these by:
·
Being transparent about limitations.
·
Using triangulation (focus group + survey) to strengthen
confidence in key themes.
·
Positioning the study as an applied, managerial
investigation rather than a large‑scale epidemiological study.
Proposed chapter structure
1. Introduction
· Background, problem statement, research objectives and questions, significance, scope.
2. Literature review
· Consumer trust in AI; generational differences (Gen X vs Gen Z); privacy/security; human oversight vs automation; transparency and fairness.
· Theoretical framework: trust theory, TAM/UTAUT, organizational justice.
· Hong Kong context: AI in finance, regulatory and policy environment.
3. Methodology
· Research philosophy (pragmatism), design, participants, instruments, procedures, data analysis, ethics, limitations.
4. Findings
· Focus group results (themes, quotes, generational comparisons).
· Survey results (descriptive statistics, Gen X vs Gen Z comparisons).
· Integrated discussion of findings.
5. Discussion
· Interpretation in relation to theory and literature.
· Managerial implications for Hong Kong financial firms.
· Recommendations for AI governance and service design.
6. Conclusion
· Summary of key findings.
· Contributions.
· Limitations and suggestions for future research.
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