Lecture note on the literature review tasks for writing an MBA dissertation report chapter 2 (literature review): on positivism

Lecture note on the literature review tasks for writing an MBA dissertation report chapter 2 (literature review)

 

In the context of doing an MBA dissertation project, based on positivism, recommend 4 specific literature review tasks to perform on writing the dissertation report chapter 2 (literature review).

For a positivist MBA dissertation, Chapter 2 should do more than describe previous studies: it should build a deductive, evidence-based path from established theory to measurable variables and testable hypotheses. Positivism typically emphasizes observable measures, a priori hypotheses, quantitative testing, and potentially generalisable explanatory relationships.

1. Systematically map empirical findings

Create a structured evidence table of prior quantitative empirical studies relevant to your research question.

For each study, record:

• Author, year, country/industry, and unit of analysis
• Sample size and sampling method
• Independent, dependent, mediating, moderating, and control variables
• Research design and statistical method
• Main statistical findings: direction, significance, and—where available—effect size
• Limitations relevant to your proposed study

Then write the review thematically, not author-by-author. For example, group evidence under headings such as “Effect of social-media engagement on brand loyalty” or “The mediating role of perceived value.”

This task enables you to identify whether the literature indicates a consistent positive, negative, null, or context-dependent relationship.

2. Define and delimit every construct

For each central variable, compare scholarly definitions and arrive at a precise conceptual definition for your study.

You should:

• Identify competing definitions of the construct.
• State its dimensions, if it is multidimensional.
• Distinguish it from closely related constructs.
• Specify the appropriate level of analysis: individual consumer, employee, firm, store, or industry.
• Justify the definition you adopt, using authoritative and frequently cited sources.

A positivist design requires conceptual variables to be translated into observable indicators. Construct-review work should therefore connect the theoretical definition to a measurement model—for instance, whether “customer loyalty” is measured as a single construct or through attitudinal and behavioural dimensions. Reviews of measurement practice commonly examine both definitions and existing scales because conceptual and operational clarity are inseparable.

3. Critically evaluate measurement instruments

Locate and compare established scales or objective indicators used to measure each construct. This is especially important for survey-based MBA projects.

Build a measurement table containing:

• Original scale source and later validation studies
• Number of items and response format, such as a five- or seven-point Likert scale
• Dimensions and sample items
• Reported reliability, such as Cronbach’s alpha
• Evidence of convergent, discriminant, and content validity
• Prior use in a comparable country, culture, sector, or respondent group
• Any planned adaptation, translation, or contextual change

Prefer validated instruments over creating untested questions. If you modify a scale—for example, adapting a Western retail scale for Hong Kong online shoppers—explain why, retain the construct’s meaning, and plan to test reliability and validity in Chapter 4. Sound construct development involves defining the construct, choosing matching operationalisations, and empirically assessing construct validity.

4. Synthesize theory into a conceptual framework and hypotheses

Use the reviewed theory and empirical evidence to produce a visual conceptual framework, then formulate directional hypotheses.

For each proposed relationship:

1.    Introduce the relevant theory.

2.    Explain the causal logic linking the variables.

3.    Evaluate supporting and contradictory empirical findings.

4.    Explain any gap—such as an under-researched Hong Kong context, online-sales setting, population, mechanism, or boundary condition.

5.    State a specific, measurable hypothesis.

For example:

Drawing on perceived-value theory, higher perceived value should increase customers’ intention to repurchase. Prior survey studies generally report a positive association, but evidence among Hong Kong small online retail customers remains limited. Therefore, H1: Perceived value has a positive effect on repurchase intention among customers of Hong Kong online retail outlets.

Your framework should clearly show the expected direction of each association, including mediators or moderators where justified. This follows positivism’s hypothetico-deductive logic: define variables, operationalise them, and test hypotheses derived before data collection. Systematic management-review guidance likewise stresses defining the theoretical framework and constructs early, and using a pre-specified coding scheme when the review supports a deductive study.

Suggested Chapter 2 structure

1.    Introduction to the chapter

2.    Core theory or theories

3.    Definitions and dimensions of constructs

4.    Review of empirical evidence for each proposed relationship

5.    Measurement approaches used in prior research

6.    Research gap and contextual justification

7.    Conceptual framework and hypotheses

8.    Chapter summary

This structure ensures that Chapter 2 directly supports a quantitative methodology chapter, survey instrument, and statistical hypothesis tests rather than becoming a descriptive catalogue of articles.