A note on research philosophy and its relatedness to research objectives and questions: for MBA students

A note on research philosophy and its relatedness to research objectives and questions: for MBA students 

In the context of doing MBA dissertation projects, please explain how the research philosophy chosen could affect the formulation of the project's research objectives and research questions. Try to illustrate your ideas with reference to the MBA research theme of "A study of the impacts of AI employment on the organizational climate in the Hong Kong banking sector".

The research philosophy you choose acts as the foundational lens that determines what counts as valid knowledge, which directly shapes whether your objectives seek measurable facts or subjective meanings, and whether your questions are hypothesis-testing or exploratory.

Below is an explanation using your MBA theme: “A study of the impacts of AI employment on the organizational climate in the Hong Kong banking sector.”

Key Differences in How Philosophy Shapes Objectives & Questions

Dimension	Positivism (Objective, quantitative)	Interpretivism (Subjective, qualitative)	Pragmatism (Practical, mixed-methods)
View of reality	Single, measurable reality independent of observers	Multiple realities constructed through human experience	Reality is what works; focus on practical outcomes
Research aim	Test hypotheses, identify causal laws, generalize	Understand meanings, interpretations, context-specific insights	Solve a practical problem with actionable recommendations
Typical objective	“To quantify the relationship between AI adoption levels and employee trust scores in HK banks”	“To explore how bank employees in HK interpret and experience organizational climate changes due to AI”	“To identify both measurable impacts and employee perceptions of AI to recommend change management strategies”
Typical question	“What is the statistical effect of AI employment on organizational climate scores?” “Does AI adoption significantly reduce perceived job security?”	“How do bank employees in HK make sense of AI’s presence in their workplace?” “What meanings do staff attach to changes in team dynamics after AI introduction?”	“What are the measurable and perceived impacts of AI on organizational climate?” “What practical interventions can HK banks implement to manage AI-related climate shifts?”
Methodology link	Large-N surveys, standardized climate scales, regression analysis	In-depth interviews, focus groups, thematic analysis	Mixed methods: surveys + interviews, sequential or concurrent

How Philosophy Drives the Formulation Process

1.    Positivism → Deductive, hypothesis-driven

·        You start with theory (e.g., technology acceptance model, job displacement theory).

·        Objectives become: measure variables, test relationships.

·        Questions are closed, often starting with “To what extent…”, “Does…”, “What is the correlation…”.

·        Example objective for your theme:

“To measure the impact of AI-driven automation on employee trust and collaboration scores in 5 major Hong Kong banks.”

2.    Interpretivism → Inductive, meaning-driven

·        You start with people’s lived experiences.

·        Objectives become: understand perceptions, contexts, narratives.

·        Questions are open, often starting with “How…”, “What experiences…”, “In what ways…”.

·        Example objective for your theme:

“To understand how frontline banking staff in Hong Kong experience and interpret changes in organizational climate following AI integration.”

3.    Pragmatism → Problem-driven, mixed

·        You start with the practical problem: HR managers need actionable guidance.

·        Objectives combine measurement and understanding.

·        Questions include both “What is the effect…” and “How do people experience…”.

·        Example objective for your theme:

“To assess both the statistical impact and the subjective experience of AI employment on organizational climate, and to develop practical change management recommendations for HK banks.”

Why This Matters for an MBA Dissertation

·        Alignment: Your philosophy must align with your research questions, objectives, methodology, and data analysis. Mismatch (e.g., interpretivist questions with purely positivist methods) undermines credibility.

·        MBA relevance: MBA dissertations often favor pragmatism because they aim to produce actionable business recommendations, not just theoretical generalizations.

·        Hong Kong banking context: Given the sector’s rapid AI adoption and culturally nuanced workplace dynamics, an interpretivist or pragmatic approach may yield richer insights into how AI reshapes team dynamics, trust, and morale beyond what survey scores alone can show.

In short, your research philosophy determines whether your dissertation will measure AI’s impact statistically, interpret employees’ lived experiences, or solve a practical management problem using both—directly shaping every objective and question you write.

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