A note on MBA dissertation report writing styles

 A note on MBA dissertation report writing styles

On 4 main characteristics of dissertation writing style with regard to each of the following adopted research philosophies for an MBA dissertation project: (1) positivism, (2) interpretivism, (3) pragmatism, and (4) critical realism.

Here are four main dissertation-writing characteristics for each research philosophy in an MBA project, focused on style, tone, and argumentation.

1. Positivism

·        Writing is impersonal and objective, with limited use of first person.

·        Sentences are usually formal, concise, and structured around variables, hypotheses, and measurable relationships.

·        The tone emphasizes neutrality, reliability, validity, and statistical evidence.

·        Argumentation is deductive: theory, hypotheses, data, results, and generalizable conclusions.

2. Interpretivism

·        Writing is more reflexive and often uses a more personal scholarly voice.

·        The style is descriptive and context-rich, aiming to capture meaning, experience, and participant perspectives.

·        The tone accepts subjectivity and often explains the researcher’s position and influence.

·        Argumentation is interpretive and thematic, moving from data patterns to nuanced understandings.

3. Pragmatism

·        Writing is flexible and problem-focused, not tightly bound to one fixed style.

·        The dissertation often blends quantitative reporting with qualitative explanation where useful.

·        The tone is practical, accessible, and oriented toward action or decision-making.

·        Argumentation centers on what works best to answer the research question and solve the business problem.

4. Critical realism

·        Writing is analytically layered, linking observed events to deeper structures or mechanisms.

·        The style is explanatory rather than merely descriptive, often moving between evidence and interpretation.

·        The tone is balanced: it recognizes a real world exists, but also that knowledge is fallible and partial.

·        Argumentation often uses abduction or retroduction, asking not just what happened but why it happened.

Simple distinction

A useful shorthand is: positivism writes to measure, interpretivism writes to understand, pragmatism writes to solve, and critical realism writes to explain.

 

On 3 illustrative examples in an MBA dissertation report writing for each of these four cases (i.e. based on positivism, interpretivism, pragmatism and critical realism).

MBA dissertation writing varies by research philosophy, with examples reflecting distinct styles in reporting findings, discussions, or conclusions. Here are three illustrative excerpts for each philosophy, tailored to a hypothetical MBA study on "employee motivation in Hong Kong tech firms."

Positivism Examples

Positivist writing prioritizes objectivity, quantifiable data, and hypothesis testing.

1.    Hypothesis results: "Regression analysis revealed a significant positive correlation between financial incentives and productivity (β = 0.42, p < 0.01), supporting H1. The R² value of 0.35 indicates that 35% of variance in output is explained by incentive structures."

2.    Data interpretation: "ANOVA results (F(3, 156) = 12.47, p < 0.001) confirm that training hours significantly predict motivation scores across firms, with no interaction effects observed."

3.    Conclusion phrasing: "The findings generalize to similar high-tech sectors, underscoring the reliability of incentive-based models for performance enhancement."

Interpretivism Examples

Interpretivist writing emphasizes subjective meanings, thick descriptions, and researcher reflexivity.

1.    Thematic finding: "Participants described motivation as 'a sense of belonging amid chaos,' revealing how flexible work arrangements foster emotional ties in Hong Kong's fast-paced tech culture."

2.    Participant voice: "As one manager reflected, 'It's not the bonus; it's knowing my ideas matter'—a recurring theme across interviews that highlights relational dynamics over metrics."

3.    Reflexive discussion: "My own background in Hong Kong business shaped my interpretation of these narratives, prompting deeper empathy for cultural nuances in motivation."

Pragmatism Examples

Pragmatist writing is practical, mixed-method, and solution-oriented.

1.    Mixed evidence: "Survey data showed a 22% motivation lift from incentives (quantitative), while focus groups stressed team autonomy as equally vital (qualitative)—both informing a hybrid policy recommendation."

2.    Actionable insight: "What works best? Combining bonuses with collaborative tools, as validated by pilot tests yielding 15% higher retention rates."

3.    Problem-solving close: "This pragmatic approach equips managers with flexible strategies to boost motivation, regardless of philosophical purity."

Critical Realism Examples

Critical realist writing layers surface events with underlying mechanisms and generative structures.

1.    Mechanism explanation: "While surveys captured a 28% motivation drop post-restructuring (empirical domain), this reflects deeper power imbalances in hierarchical firm structures (real domain)."

2.    Retroduction: "Low engagement isn't mere 'burnout' but stems from unobservable mechanisms like eroded trust, activated by rapid tech-sector changes."   [Retroduction is a logical mode of inference, often associated with, or considered a type of, abduction, that involves inferring the most likely explanation or underlying causal mechanism for an observed phenomenon]

3.    Stratified analysis: "Observed behaviors (actual domain) arise from generative mechanisms of neoliberal pressures, partially masked by official policies."

Quick Style Guide

Philosophy	Key Phrase Style	Evidence Focus
Positivism	Precise stats, neutral tone	Numbers, tests
Interpretivism	Quotes, personal context	Stories, themes
Pragmatism	Practical blends, recommendations	Mixed, applicable
Critical Realism	Layered causes, "why" probes	Structures, hidden

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

A note on using a company’s own research report findings in an MBA dissertation project

A note on using a company’s own research report findings in an MBA dissertation project

With regard to doing a part-time MBA dissertation report, how can the dissertation student make use of a company's own study (e.g. customer opinion questionnaire survey with statistical analysis findings) in the dissertation report without making dishonest academic behavior (e.g. claiming that the customer survey and its analysis is done by the student)? For instance, how to report this kind of company report findings in the dissertation report?

    You should treat the company’s survey and analysis as secondary/company-provided evidence, not as your own primary data collection, and say so explicitly in the dissertation. In practice, that means you describe the report, cite it properly, and explain how you used its findings rather than implying you conducted the survey yourself.

How to describe it

In the methodology or data section, write something like:

·        “This dissertation uses a company-internal customer survey report as secondary data.”

·        “The questionnaire was designed, administered, and statistically analysed by the company; the present study draws on the reported findings for further academic interpretation.”

·        “The author did not collect the raw data and did not independently verify the statistical procedures unless stated otherwise.”

That wording makes ownership of the work clear and avoids misrepresentation.

How to write the analysis

You can still discuss the findings in your dissertation, but frame them as reported results from the company study. For example:

·        “According to the company’s customer opinion survey, 72% of respondents rated service quality as satisfactory.”

·        “The company report found a significant association between delivery speed and customer satisfaction.”

·        “These findings suggest that operational performance may be linked to customer loyalty, although the original data and full statistical outputs were not available to the author.”

This is acceptable because you are analysing the company’s report, not claiming the underlying survey was your own.

Example wording

A safe paragraph might read:

“Secondary data were obtained from an internal company customer survey report. The survey questionnaire, sampling procedure, and statistical analysis were conducted by the company’s research team. The dissertation uses the published summary findings as documentary evidence to support the case study analysis, while acknowledging that the raw data were not independently collected by the author.”

That wording is transparent and academically defensible.

Referencing the report

If the company report is unpublished or internal, cite it as an internal report in the reference list, following your school’s style guide. A common APA-style format is: Company Name. (Year). Title of report. Internal report, unpublished. If the report is accessible only within the company, state that clearly in the bibliography or appendix notes.

Good practice

·        State in an ethics or limitations section that access to raw data was restricted.

·        Distinguish between “company-reported findings” and your own interpretation.

·        Do not reproduce tables or claims as though you generated them.

·        If you quote exact figures, note that they come from the company report, not your own fieldwork.

This approach is consistent with guidance on using secondary/company data in research and with general expectations of transparency in academic-company data use.

 

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

A note on qualitative memoing for MBA students

A note on qualitative memoing for MBA students

 

On 6 main ideas of qualitative memoing in qualitative research.

Qualitative memoing is best understood as a running analytic practice that helps researchers think with the data rather than just about it. Here are 6 main ideas:

1.    Reflexivity — memos help you examine your own assumptions, biases, and role in shaping interpretation.

2.    Analytic thinking — they capture your emerging interpretations, hunches, and questions during coding and analysis.

3.    Constant comparison — memos support comparing new data with earlier data, codes, and categories to refine ideas.

4.    Theory building — they help turn observations into concepts, patterns, and eventually theoretical arguments.

5.    Decision trail — memos record methodological choices and analytical steps, creating an audit trail of how conclusions were reached.

6.    Communication and continuity — they preserve insights over time and make it easier to share and revisit analysis across the project.

 

To illustrate your ideas further with the MBA research theme of "to evaluate the worries of job insecurity with regard to the impact of AI on the project management employees in ABC telecom company in Hong Kong based the staff interview transcripts and structured observation at the company's office".

Here’s how those six ideas of qualitative memoing can be illustrated concretely within your MBA research theme:

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1. Reflexivity

In memos you might write reflections such as:

·        “Am I over‑interpreting job‑insecurity talk because I am worried about AI in my own sector?”

·        “My background in project management makes me more sensitive to how AI tools are framed as ‘efficiency promoters’ rather than ‘replacement tools’.”

This helps you track how your role as an academic and your own anxieties shape readings of expressions like “AI will take our jobs” or “we’ll be monitored more closely.”

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2. Analytic thinking

After each interview or observation session, you could write a short memo that:

·        Notes recurring phrases such as “we feel like we’re constantly under surveillance with AI‑driven dashboards,” or “AI‑based tools are forcing us to change how we plan.”

·        Jots down early hunches, e.g., “Job‑insecurity appears stronger among long‑tenured project managers than among younger staff,” or “‘AI monitoring’ is more worrying than ‘AI as a tool’.”

These memos push you from description to explanation.

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3. Constant comparison

In a memo you might compare:

·        How junior project officers versus senior project managers describe AI‑related changes.

·        Whether interview statements about workload (“AI‑based tracking makes me work harder”) align with what you see in structured observation (e.g., frequent checking of dashboards, overtime, or visible stress).

For example, a memo might say: “Interviewee A feared AI would replace routine tasks, whereas Interviewee B explicitly linked AI to performance pressure and fear of contract non‑renewal, which matches my observation of tighter reporting cycles.”

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4. Theory building

Memos can help you build concepts like:

·        AI‑induced surveillance stress: the idea that AI‑driven performance tracking heightens perceived job insecurity.

·        Technological boundary‑spanning work: the shift where project managers increasingly need to manage both AI tools and human teams.

You might write a memo that links interview quotations (“we’re always on the radar now”) with observation notes (e.g., managers checking dashboards before team meetings) to begin formulating a tentative conceptual framework.

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5. Decision trail

In your memos you can document:

·        How you decided which themes to label “job insecurity” versus “adaptation” or “skill development.”

·        Why you combined certain interview quotes with specific observation episodes when constructing a case‑study section.

For example: “I decided to treat ‘fear of being replaced by AI‑based scheduling tools’ as a core indicator of job‑insecurity, distinct from ‘fear of being left behind by new tools’ (which I coded as skill‑gap anxiety).” This creates an audit trail for your analytic choices.

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6. Communication and continuity

Across time, memos help you:

·        Keep consistent focus on the core question: “How AI‑related changes affect job‑insecurity among project‑management staff.”

·        Revisit earlier interviews confidently when you collect follow‑up data or feedback, because the memo states: “This interview supports the earlier pattern of fear around AI‑based monitoring, but contradicts the assumption that younger staff are less anxious.”

For supervisors or examiners, these memos can later be excerpted or summarized to show how your analysis evolved from interview transcripts and structured observation into a coherent MBA‑level discussion.

Relevant links: on ALRA 2026.

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