An example of producing an MBA dissertation proposal: on AI adoption and middle management
I am a part-time MBA student, currently learning to
formulate my dissertation proposal. My research theme is about "the
impacts of AI adoptions on the middle management of the Hong Kong Banking
sector". In addition, I intend to include the following key words in the
formulation of my more concrete research theme: organizational resilience,
employee resilience, managing change. Please propose 2 concrete research themes
for me to consider.
Here are two
focused and researchable dissertation themes that integrate your core topic
with organizational resilience, employee resilience, and managing change in the
Hong Kong banking context:
Theme 1: AI Adoption, Change Management, and
Resilience at Middle Management Level
Proposed title:
“Navigating AI-Driven Transformation: The Role of Middle Management in
Fostering Organizational and Employee Resilience in Hong Kong’s Banking Sector”
Focus:
This theme examines how middle managers act as change agents during AI
adoption, and how their practices influence both organizational resilience
(e.g., adaptability, continuity) and employee resilience (e.g., coping,
learning, psychological adjustment).
Possible angles:
·
How middle
managers interpret and implement AI-driven change initiatives
·
The relationship
between change management strategies and employee resilience
·
Tensions between
top-down AI strategy and middle-level execution
·
The role of
leadership style in building resilient teams during technological disruption
Example research
question:
·
How do middle
managers in Hong Kong banks facilitate employee and organizational resilience
during AI adoption processes?
Theme 2: AI Adoption, Job Redesign, and
Resilience Outcomes
Proposed title:
“AI-Induced Job Redesign and Its Impact on Employee and Organizational
Resilience: Evidence from Middle Management in Hong Kong Banking”
Focus:
This theme centers on how AI reshapes middle management roles (e.g.,
decision-making, monitoring, coordination), and how these changes affect
resilience at both the employee and organizational levels.
Possible angles:
·
Changes in job
scope, autonomy, and skill requirements due to AI
·
Perceived job
insecurity vs. opportunities for upskilling
·
How job redesign
influences adaptive capacity and resilience
·
Middle managers as
both subjects and drivers of resilience under AI transformation
Example research
question:
·
How does AI-driven
job redesign influence the resilience of middle managers and their teams in
Hong Kong banks?
Both themes are
viable, but they differ slightly in emphasis:
·
Theme 1 is
more process-oriented (change management and leadership).
·
Theme 2 is
more structural/role-oriented (job redesign and work
transformation).
Based on the proposed theme 2, please suggest a
dissertation project title to me, together with 4 research objectives and 4
research questions. Also identify 3 related research issues and 3 related
public media-reported issues to me.
Proposed dissertation title
“AI-Driven Job Redesign and Resilience in Hong Kong Banking:
Implications for Middle Management, Employees, and Organizational Adaptation”
Research objectives
·
To examine how AI adoption reshapes the roles,
responsibilities, and skill requirements of middle managers in Hong Kong banks.
·
To assess the impact of AI-driven job redesign on
employee resilience, including adaptability, learning, and psychological coping.
·
To evaluate how changes in middle management roles
contribute to organizational resilience, such as flexibility and continuity
under disruption.
·
To explore how banks manage change during AI adoption,
particularly in supporting middle managers and their teams.
Research questions
·
How does AI adoption transform the job design and role
expectations of middle managers in Hong Kong’s banking sector?
·
In what ways does AI-driven job redesign affect the
resilience of employees managed by middle managers?
·
How do changes in middle management roles influence
organizational resilience during AI implementation?
·
What change management practices are used by banks to
support middle managers and employees in adapting to AI-driven transformation?
Related
research issues (academic focus)
·
Theoretical linkage between job redesign (e.g., job
demands–resources model) and employee resilience in technology-intensive
environments.
·
The evolving strategic role of middle management as both
implementers and interpreters of digital transformation.
·
Tensions between efficiency gains from AI and human
factors such as job insecurity, identity, and workplace well-being.
Public
media–reported issues (Hong Kong / banking context)
·
Concerns over job displacement or restructuring in banks
due to AI and automation (e.g., branch closures, reduced headcount).
·
Strong demand for reskilling and upskilling as banks push
digital transformation and fintech integration.
·
Employee stress and uncertainty linked to rapid
technological change, alongside organizational narratives of “innovation” and
“future readiness.”
Suggest 3 relevant academic theories to me and a few
academic references on these theories to me in Harvard reference format. Also
indicate which theory is related to which research objective study.
Yes — for your
topic, three strong theories are Conservation of Resources (COR) theory, Job
Demands-Resources (JD-R) theory, and Kotter’s change management
theory. Together, they fit your focus on AI-driven job redesign, employee
resilience, organizational resilience, and managing change in Hong Kong
banking.
Theory 1: Conservation of Resources theory
COR theory
explains stress and adaptation through the protection, loss, and replenishment
of valued resources, which makes it highly relevant to AI-related job
disruption and resilience. In your study, it is especially useful for
understanding how middle managers and employees experience resource loss, such
as autonomy, confidence, status, or time, and how they recover through new
resources like skills, support, and learning.
Best fit with your
research objectives:
·
Objective 1:
AI-driven job redesign of middle managers.
·
Objective 2:
Impact on employee resilience.
·
Objective 3:
Impact on organizational resilience.
It is the strongest fit for explaining how people protect and rebuild resources
during AI adoption.
Harvard
references:
Hobfoll, S.E. (1989) ‘Conservation of resources: A new attempt at
conceptualizing stress’, American Psychologist, 44(3), pp. 513–524.
Hobfoll, S.E. (2001) ‘The influence of culture, community, and the nested-self
in the stress process: Advancing conservation of resources theory’, Applied
Psychology, 50(3), pp. 337–421.
Hobfoll, S.E., Halbesleben, J., Neveu, J.-P. and Westman, M. (2018) ‘Conservation
of resources in the organizational context: The reality of resources and their
consequences’, Annual Review of Organizational Psychology and
Organizational Behavior, 5, pp. 103–128.
Theory 2: Job Demands-Resources theory
JD-R theory argues
that job demands produce strain while job resources promote motivation and
engagement, which makes it very suitable for studying AI-driven job redesign in
banking. It helps you examine whether AI increases demands such as
technostress, role ambiguity, workload, and monitoring pressure, while also
creating resources such as efficiency, decision support, and learning
opportunities.
Best fit with your
research objectives:
·
Objective 1:
AI-driven job redesign.
·
Objective 2:
Employee resilience.
·
Objective 4:
Managing change.
This is the best theory for linking AI implementation to employee well-being,
adaptation, and resilience outcomes.
Harvard
references:
Demerouti, E., Bakker, A.B., Nachreiner, F. and Schaufeli, W.B. (2001) ‘The job
demands-resources model of burnout’, Journal of Applied Psychology,
86(3), pp. 499–512.
Bakker, A.B. and Demerouti, E. (2007) ‘The job demands-resources model: State
of the art’, Journal of Managerial Psychology, 22(3), pp. 309–328.
Bakker, A.B. and Demerouti, E. (2017) ‘Job demands-resources theory: Taking
stock and looking forward’, Journal of Occupational Health Psychology,
22(3), pp. 273–285.
Theory 3: Kotter’s change management theory
Kotter’s framework
is useful because AI adoption in banks is not only a technical shift but also a
change process that must be led, communicated, and institutionalized. It is
especially relevant for understanding how middle managers translate AI strategy
into practice, mobilize teams, and help staff adapt to new roles and workflows.
Best fit with your
research objectives:
·
Objective 3:
Organizational resilience.
·
Objective 4:
Managing change.
It is the strongest fit for studying how banks manage transformation and reduce
resistance during AI adoption.
Harvard
references:
Kotter, J.P. (1996) Leading Change. Boston, MA: Harvard Business
School Press.
Kotter, J.P. (2012) Leading Change. Boston, MA: Harvard Business
Review Press.
Kotter, J.P. (1995) ‘Leading change: Why transformation efforts fail’, Harvard
Business Review, 73(2), pp. 59–67.
Theory-objective fit
|
Research
objective |
Best
matching theory |
Why
it fits |
|
AI-driven job redesign of
middle managers |
JD-R theory |
Explains how AI changes job
demands and resources . |
|
Employee resilience |
COR theory |
Explains resource loss,
recovery, and resilience building . |
|
Organizational resilience |
COR theory / Kotter |
COR explains capability
renewal; Kotter explains organizational change execution . |
|
Managing change |
Kotter’s theory |
Directly addresses how to
lead and institutionalize change . |
Practical recommendation
For your
dissertation, the cleanest combination is: COR theory as the main lens, JD-R
theory as the employee-level explanatory model, and Kotter’s theory
as the change-management lens. This gives you a coherent structure: AI
creates demands and resources, people lose or gain resources, and banks must
manage the transformation effectively.
A strong way to
position your study is to say that AI-driven job redesign affects
middle managers through changing job demands/resources, which in turn shapes
employee resilience and organizational resilience during change.
*** also study the note of the theory-decomposing on the conservation of resource theory.
e-resource support: https://www.perplexity.ai/
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