A note on research approaches: for MBA students

A note on research approaches: for MBA students

 

Describe the following research approaches in the context of doing MBA dissertation projects:

Category 1 (methods)

1.1. Qualitative research
1.2. Quantitative research

1.3 Mixed methods research

Category 2 (reasoning)
2.1 Inductive approach
2.2 Deductive approach
2.3 Abductive approach

 

Overall distinction

The two categories describe different aspects of an MBA dissertation:

• Category 1—methods: What type of evidence will you collect and analyse?
• Category 2—reasoning: How will you move between theory, data, concepts and explanations?

They are related but not identical. For example, a qualitative study is often inductive, but it can also be deductive or abductive; similarly, quantitative research is usually deductive, but it can involve exploratory and inductive analysis. Research methods should therefore be selected primarily according to the research questions, objectives and practical constraints.

Category 1: Research methods

1.1 Qualitative research

Qualitative research investigates meanings, experiences, perceptions, interpretations, interactions and organisational processes. It normally uses non-numerical data, such as interview transcripts, focus-group discussions, observations, organisational documents, company reports and online material. Its main purpose is to understand how and why a phenomenon occurs rather than to measure its frequency or statistical strength.

In an MBA dissertation, qualitative research is appropriate when the topic concerns:

• Managers’ experiences of implementing digital transformation.
• Employees’ perceptions of leadership or organisational culture.
• How small businesses respond to sustainability pressures.
• Why customers distrust artificial-intelligence-based services.
• How management accountants contribute to strategic decision-making.
• The process through which a housing policy affects service users.

Typical research questions include:

• “How do Hong Kong SMEs implement generative AI in management accounting?”
• “Why do employees resist a new performance-management system?”
• “How do managers interpret sustainability information in strategic decisions?”

Common qualitative methods include:

• Semi-structured interviews.
• Focus groups.
• Case studies.
• Observation.
• Document analysis.
• Thematic, content, narrative or discourse analysis.

A typical qualitative MBA project might involve interviewing 12–20 managers from several SMEs and analysing the transcripts thematically. The researcher may identify themes such as perceived usefulness, lack of skills, data concerns and top-management support.

Strengths

• Provides detailed and context-sensitive understanding.
• Allows participants to explain issues in their own words.
• Is useful for new, sensitive or poorly understood topics.
• Can reveal processes, motivations and unintended consequences.
• Permits refinement of questions during data collection.

Limitations

• Usually involves relatively small, non-probability samples.
• Findings may not be statistically generalisable.
• Data collection and analysis can be time-consuming.
• Researcher interpretation must be carefully justified.
• Access to senior managers or organisations may be difficult.

A qualitative dissertation should explain the sampling logic, interview protocol, data-saturation considerations, coding process, ethical safeguards and procedures used to establish credibility, such as triangulation, member checking or maintaining an audit trail.

1.2 Quantitative research

Quantitative research examines phenomena through numerical measurement and statistical analysis. It is commonly used to describe variables, compare groups, test hypotheses, estimate relationships and assess the possible effects of one variable on another. It is particularly suitable for questions such as how many, how much, how strongly and whether X is associated with Y.

In an MBA dissertation, quantitative research may be used to examine:

• The relationship between leadership style and employee performance.
• The effect of service quality on customer satisfaction.
• Whether digital capability improves SME performance.
• The association between management-accounting practices and business resilience.
• The impact of perceived housing-service quality on resident satisfaction.

A quantitative study generally proceeds through the following sequence:

1.    Review relevant literature and theories.

2.    Define the main concepts and variables.

3.    Develop hypotheses or testable propositions.

4.    Operationalise variables using questionnaire items or secondary indicators.

5.    Collect numerical data.

6.    Analyse the data statistically.

7.    Compare the findings with the hypotheses and prior studies.

For example, a dissertation might propose:

• H1: Perceived service quality has a positive relationship with resident satisfaction.
• H2: Trust strengthens the relationship between service quality and satisfaction.

The researcher could distribute a questionnaire using five-point Likert scales and analyse the responses using descriptive statistics, reliability analysis, correlation, regression, mediation or moderation analysis.

Common quantitative methods include:

• Structured questionnaires.
• Experiments or quasi-experiments.
• Secondary-data analysis.
• Financial-ratio analysis.
• Longitudinal analysis.
• Descriptive and inferential statistics.

Strengths

• Enables numerical comparison between respondents, organisations or periods.
• Can test relationships derived from theory.
• May produce findings that are generalisable when sampling is appropriate.
• Provides transparent and replicable analytical procedures.
• Is useful for measuring attitudes, behaviours and organisational outcomes.

Limitations

• Complex ideas may be reduced to numerical indicators.
• A questionnaire may not explain why respondents hold particular views.
• Poorly designed measures can weaken validity and reliability.
• Large samples may be difficult for MBA students to obtain.
• Statistical association does not automatically establish causation.

A strong quantitative dissertation must justify the population, sampling method, sample size, measurement scales, questionnaire design, reliability and validity procedures, statistical techniques and treatment of missing or abnormal data.

1.3 Mixed-methods research

Mixed-methods research intentionally combines quantitative and qualitative evidence within one dissertation. The purpose is not merely to use two types of data, but to integrate them so that the combined study answers the research problem more effectively than either method alone. For example, a survey may identify a pattern, while interviews explain the reasons behind it.

A mixed-methods MBA dissertation might investigate employee acceptance of AI:

• Quantitative phase: Survey employees to measure perceived usefulness, trust, anxiety and intention to use AI.
• Qualitative phase: Interview selected employees to understand why some groups are enthusiastic while others are resistant.
• Integration: Compare the statistical patterns with the interview themes and develop a more complete explanation.

Common mixed-methods designs include:

Design	Sequence	Suitable purpose
Explanatory sequential	Quantitative → qualitative	Explain unexpected or important survey findings
Exploratory sequential	Qualitative → quantitative	Develop concepts or questionnaire measures, then test them
Convergent	Quantitative and qualitative at roughly the same time	Compare or combine two perspectives on the same issue
Embedded	One method is dominant and the other is included within it	Add supporting evidence to a main study

For example, an explanatory sequential study could first discover that digitalisation improves performance only in some SMEs. Follow-up interviews could then explore whether management support, employee skills or organisational culture explains this difference.

Strengths

• Combines breadth from quantitative data with depth from qualitative data.
• Enables triangulation and comparison of findings.
• Can explain statistical patterns and test qualitative insights.
• Is useful for complex management problems involving both measurable outcomes and human experiences.

Limitations

• Requires more time, skills and resources than a single-method study.
• The two components may become disconnected.
• Sampling, analysis and integration are more demanding.
• The dissertation may become too broad for an MBA timetable.
• The researcher must justify why both methods are necessary.

A mixed-methods dissertation should specify the priority of each method, the sequence of the phases, how participants or cases are selected, how each dataset is analysed and, most importantly, how the findings are integrated.

Category 2: Research reasoning

2.1 Inductive approach

Inductive reasoning moves from specific observations to broader patterns, concepts, propositions or tentative explanations. The researcher begins with data rather than a fully specified theory and develops an interpretation from recurring themes or relationships. The conclusion is plausible and evidence-supported, but it is not logically guaranteed.

The logic can be represented as:

Observations→Patterns→Concepts→Tentative explanation

In an MBA dissertation, an inductive approach is appropriate when:

• The topic is relatively new or under-researched.
• Existing theories do not adequately explain the context.
• The objective is to explore experiences or processes.
• The researcher wants to develop a conceptual framework from field evidence.

Example:

The researcher interviews managers in Hong Kong SMEs about their use of generative AI. Repeated references to cost pressure, employee experimentation, weak governance and client expectations lead to a conceptual explanation of how SMEs gradually institutionalise AI.

The researcher does not begin by testing a fixed hypothesis. Instead, concepts are developed through coding, comparison and interpretation. Grounded theory is a particularly systematic inductive strategy, although ordinary qualitative thematic analysis can also have an inductive orientation.

Induction does not mean that the researcher has no prior ideas. A literature review, professional experience and existing concepts will usually influence the research. The important point is that the final themes or explanation are developed substantially from the evidence rather than imposed entirely in advance.

2.2 Deductive approach

Deductive reasoning begins with an existing theory, conceptual model or general proposition and derives specific hypotheses or expectations that can be examined using data. It is often described as a theory-first approach.

The logic can be represented as:

Theory→Hypotheses→Data collection→Testing

A deductive MBA dissertation may proceed as follows:

1.    Select a theory, such as the Technology–Organisation–Environment framework.

2.    Identify relevant constructs, such as technological readiness, organisational support and competitive pressure.

3.    Develop hypotheses about their relationships with AI adoption.

4.    Design a questionnaire to measure the constructs.

5.    Test the hypotheses using statistical analysis.

6.    Determine whether the results support or fail to support the theoretical expectations.

Example hypotheses could include:

• Technological readiness is positively associated with AI adoption.
• Top-management support is positively associated with AI adoption.
• Perceived implementation risk is negatively associated with AI adoption.

Deduction is especially common in quantitative research because variables can be operationalised and hypotheses can be statistically tested. However, it can also be used in qualitative research—for example, when interview questions and coding categories are derived from an existing theory.

A hypothesis that is not supported does not necessarily mean that the dissertation has failed. It may indicate that the theory does not apply fully to the selected population, that contextual factors matter, or that the measures require improvement.

2.3 Abductive approach

Abductive reasoning moves back and forth between empirical evidence and theoretical explanations in order to develop the most plausible interpretation of a surprising, incomplete or contradictory finding. It is often called inference to the best explanation.

The logic can be represented as:

Initial theory↔Empirical evidence↔Revised explanation

An abductive dissertation does not follow a completely linear “theory first” or “data first” process. Instead, the researcher may:

1.    Begin with an initial literature-based framework.

2.    Collect preliminary data.

3.    Notice findings that the framework does not explain.

4.    Return to the literature to identify alternative concepts.

5.    Collect or analyse further evidence.

6.    Refine the explanation.

7.    Use the revised framework to interpret the complete dataset.

Example:

A survey suggests that employees with high perceived usefulness of AI nevertheless show low intention to use it. Interviews reveal that employees are worried that AI adoption will lead to job displacement. The researcher returns to the literature, introduces perceived employment threat as an additional concept, and develops a revised explanation involving both usefulness and insecurity.

Abduction is particularly suitable for:

• Case-study research.
• Organisational problem-solving.
• Exploratory mixed-methods designs.
• Research involving unexpected findings.
• Studies where existing theories are useful but incomplete.
• Practical MBA projects seeking to explain a business problem rather than merely test or generate theory.

Abduction should not be used simply as a vague label for “using both theory and data.” The dissertation should explain what triggered the iterative movement, how alternative explanations were considered and why the final explanation is more convincing than competing interpretations.

How the categories fit together

The following combinations are common, but they are not compulsory:

Methods category	Common reasoning approach	MBA dissertation example
Qualitative	Inductive	Develop themes from interviews about managers’ sustainability practices
Qualitative	Deductive	Use institutional theory to analyse interview data
Qualitative	Abductive	Revise an explanation after an unexpected case-study finding
Quantitative	Deductive	Test hypotheses about service quality and customer satisfaction
Quantitative	Inductive	Explore previously unknown statistical patterns in secondary data
Mixed methods	Abductive or combined inductive–deductive	Use interviews to develop survey measures and then test them

The key distinction is:

• Qualitative, quantitative and mixed methods describe the nature and combination of the data and analytical procedures.
• Inductive, deductive and abductive approaches describe the logic by which the researcher connects theory and evidence.
• Research design describes the overall structure, such as a case study, survey, experiment or sequential mixed-methods design.
• Methods are the specific techniques, such as interviews, questionnaires, document analysis, regression and thematic coding.

Practical selection guide

Choose a qualitative and mainly inductive approach when your central question is about meanings, experiences, processes or reasons and the topic requires detailed contextual understanding.

Choose a quantitative and mainly deductive approach when you have clearly defined variables, an established theoretical framework, measurable constructs and a question involving relationships, differences or effects.

Choose a mixed-methods and often abductive approach when the problem has both measurable and interpretive dimensions—for example, when you need to identify the scale of a problem and explain the organisational reasons behind it.

For a manageable MBA dissertation, the best approach is usually the one that creates a clear alignment between:

Research question→objectives→theory→data→analysis→conclusion

A method should not be selected merely because it appears more sophisticated. A focused qualitative case study can be stronger than an over-ambitious mixed-methods project, and a carefully designed questionnaire can be more appropriate than interviews when the dissertation is specifically testing relationships between measurable constructs.

*** Also study article review, focusing on research approaches: doc 1; doc 2.