A note on the MBA dissertation project literature review tasks of comparison, synthesis and framework-building: on staff reduction

A note on the MBA dissertation project literature review tasks of comparison, synthesis and framework-building: on staff reduction

 

I am a part-time MBA student, currently learning to do dissertation project. I have a query about the literature review task. Specifically, please suggest 4 ideas on each of the following literature review tasks: 1. how to compare and contrast 2 to 4 relevant academic theories 2. how to conduct literature synthesis arising from the review of a few relevant academic theories 3. how to construct a theoretical framework from the review of a few relevant academic theories. It would be useful to also clarify your ideas a bit more with reference to the MBA research theme of "to investigate how to effectively and ethically carry out staff reduction tactics, notably on the gen y employees, arising from the introduction of AI applications in the financial services sector in Hong Kong"

Yes — for an MBA dissertation literature review, you can treat those three tasks as different levels of analysis: comparison, synthesis, and framework-building. For your AI-and-staff-reduction topic in Hong Kong financial services, I suggest using theories that let you connect technology adoption, employee reactions, ethics, and organizational decision-making.

1. Comparing and contrasting theories

1.    Compare theories by their core unit of explanation.
Ask whether each theory explains individual attitudes, managerial decisions, organizational processes, or ethical outcomes. For your topic, for example, one theory may explain why Gen Y employees react negatively to AI-led downsizing, while another explains why firms justify the tactic as efficiency-driven. This gives you a clean basis for contrast rather than listing theories one by one.

2.    Compare theories by assumptions about people and organizations.
Some theories assume employees are rational responders to incentives; others assume they interpret fairness, identity, or trust. In your topic, this helps you contrast a “productivity/efficiency” view of staff reduction with a “justice/psychological impact” view.

3.    Compare theories by what they predict in the same situation.
Use the same Hong Kong AI adoption context and ask: what would each theory predict about morale, turnover intention, resistance, or ethical acceptability? If one theory predicts acceptance because of perceived organizational efficiency, while another predicts resistance because of perceived breach of fairness, you have a strong comparison.

4.    Compare theories by their explanatory limits.
Identify what each theory cannot explain well. For example, one theory may explain technology acceptance but not moral legitimacy; another may explain ethics but not operational implementation. This is especially useful in your topic because AI-driven staff reduction is both a technological and ethical issue.

2. Synthesizing theories

1.    Synthesize by finding shared themes across theories.
Look for overlap, such as all theories pointing to the importance of perceived fairness, transparency, or managerial communication. In your topic, you could synthesize theories into a common argument that successful AI-related staff reduction depends not only on cost logic but also on employee trust and ethical process.

2.    Synthesize by building a layered explanation.
Put the theories into levels: one theory explains organizational drivers, another explains employee reactions, and a third explains ethical evaluation. For example, AI adoption may create restructuring pressure, which then triggers employee perceptions of insecurity, which in turn shapes judgments about fairness and legitimacy.

3.    Synthesize by identifying tensions and resolving them.
Literature reviews should highlight agreement and disagreement, not just summarize sources. In your case, the tension may be between efficiency-centered theories and people-centered/ethical theories; your synthesis can argue that both are necessary to understand “effective and ethical” staff reduction.

4.    Synthesize by converting separate theories into themes or propositions.
Rather than discussing each theory separately, group them into themes such as “technology pressure,” “employee vulnerability,” “perceived justice,” and “ethical governance.” This makes the review read like an integrated argument and helps you move toward a dissertation question.

3. Building a theoretical framework

1.    Build the framework as a cause-and-effect chain.
Start with the independent driver: AI introduction in financial services. Then trace effects through organizational restructuring, staff reduction decisions, employee perceptions, and outcomes such as commitment, resistance, or ethical acceptance. This works well if your dissertation wants to explain both effectiveness and ethics.

2.    Build the framework as a set of interacting constructs.
Show how AI adoption, management strategy, communication quality, perceived fairness, and employee outcomes relate to one another. For your topic, the framework could show that AI does not directly “cause” acceptable staff reduction; rather, the outcome depends on how management handles the process.

3.    Build the framework around mediators and moderators.
A useful dissertation-style framework often includes mediating variables like perceived organizational justice or trust, and moderating variables like employee age cohort, job type, or Hong Kong’s financial-sector norms. In your topic, Gen Y status may moderate how strongly staff reduction is experienced as threatening or unfair.

4.    Build the framework from a gap in the literature.
End the review by showing what existing theories miss, then position your framework as the bridge. For example, prior studies may discuss AI adoption, downsizing, or ethics separately, but not together in the Hong Kong financial-services setting with a focus on Gen Y employees. That gap justifies your framework and your research questions.

A useful way to organize the review

For your topic, a practical sequence is:

1.    explain the AI-driven business context;

2.    review 2 to 4 theories;

3.    compare them on assumptions, scope, and limits;

4.    synthesize them into 3 to 5 themes;

5.    convert those themes into a framework with variables or propositions.
That structure matches common dissertation guidance on classifying literature by theory, comparing studies, and identifying unresolved issues.

Suggested theory types

For your specific theme, the most relevant theory mix would usually include:

·        one theory about technology adoption or organizational change;

·        one theory about employee attitudes or reactions;

·        one theory about justice, ethics, or legitimacy;

·        optionally one theory about strategic HR or downsizing decisions.

That combination gives you enough breadth to discuss why the firm adopts AI, how staff reduction unfolds, and whether the process is perceived as effective and ethical.

 

** the note is supported by perplexity.ai

** Also study the note on the steps to conduct literature review and theory/method-decomposing.

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