Thursday, 16 April 2026

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.

Tuesday, 14 April 2026

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:


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.”


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.


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.”


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.


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.


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.


 

Monday, 13 April 2026

An example of method-decomposing on “understanding experience” in phenomenology

An example of method-decomposing on “understanding experience” in phenomenology


On 6 ideas in phenomenology on the topic of "understanding experience" and describe 1 main claim on this topic in terms of Toulmin's model of argument.

    Phenomenology offers several core ideas about how we understand experience. Here are six key ideas, followed by a Toulmin‑style articulation of one main claim.


1. Experience as lived and first‑person

Phenomenology treats experience as “lived” (Erlebnis) from the first‑person perspective, focusing on what it is like to undergo particular experiences rather than reducing them to third‑person mechanisms such as brain states or behavioral outputs.

2. Intentionality of consciousness

Consciousness is always “of” something: every experience is intentionally directed toward an object (perceiving, remembering, fearing, desiring). Understanding experience thus means clarifying the structures of intentionality and how objects show up within conscious acts.

3. Bracketing or “phenomenological reduction”

Phenomenologists suspend (bracket) assumptions about the external, causal reality of objects (epoché) in order to describe how they appear in experience. This step aims to preserve the purity of subjective appearance rather than explaining experience away scientifically.

4. Lifeworld and pre‑reflective structures

The “lifeworld” (Lebenswelt) refers to the pre‑theoretical, everyday world in which we already act and understand, prior to explicit philosophical or scientific theorizing. Phenomenological understanding of experience therefore includes uncovering these background structures that make reflection possible.

5. Embodied and situated experience

Phenomenology emphasizes that experience is not that of a disembodied mind; it is embodied (e.g., through bodily perception and affordances) and situated in a social‑historical context. This leads to an understanding of experience as already interwoven with social practices, language, and culture.

6. Hermeneutic dimension of understanding

Hermeneutic phenomenology (e.g., Heidegger, Gadamer) stresses that understanding experience is interpretive: we never meet phenomena “raw” but always through prior meanings, traditions, and practical engagements. Understanding experience thus becomes a dialogical reconstruction of meaning‑structures in the lifeworld.


A main claim in Toulmin’s model:

Claim (C):
Phenomenological description is the most appropriate method for understanding the meaning of human experience, because it preserves the first‑person givenness of experience without reducing it to causal or objective explanations.

In Toulmin’s terms:

·        Grounds (G):
Phenomenology provides detailed, first‑person descriptions of how objects, emotions, and situations appear in lived experience (e.g., as in Husserlian analyses of perception, memory, or imaginative acts).

·        Warrant (W):
If a method stays close to the way phenomena are given in consciousness, then it yields a more adequate understanding of human experience than methods that explain experience away via external causes.

·        Backing (B):
Empirical qualitative research using phenomenology (e.g., in health professions education) shows that such descriptions are systematically useful for learning from others’ experiences and for reconstructing their lifeworlds.

·        Qualification (Q):
This claim holds primarily for understanding the meaning and structure of experience; it does not replace causal or explanatory accounts in the natural sciences.

·        Rebuttal/Reservation (R):
Critics may argue that first‑person descriptions are too subjective or unverifiable, but phenomenologists respond that intersubjective validation comes through shared lifeworld structures and careful methodological reflection.

Thus, through Toulmin’s model, phenomenology stakes a claim that understanding experience requires descriptive, first‑person‑oriented methods that leave the intentional and lifeworld structures of experience intact.


Please illustrate the ideas a bit more in the case of "understanding gen z customers' experience of happiness when visiting the Ocean Park of Hong Kong.

    Phenomenology can help you understand how Gen Z customers experience happiness at Ocean Park Hong Kong by focusing on how this happiness shows up for them, rather than just measuring satisfaction scores or social‑media metrics. Here are the same six ideas, now illustrated in this concrete case.


1. Experience as lived and first‑person

Phenomenology would ask: What is it like for a Gen Z visitor to feel happiness while walking through Ocean Park?
You would not just count “smiles” or likes; you would attend to descriptions of sensations (e.g., the thrill of the roller‑coaster, the relief of shade after waiting in line, the warmth of laughing with friends), emotional shifts, and how time feels “slowed down” or “flying by.”


2. Intentionality of consciousness

A Gen Z visitor’s happiness is always happiness about something:

·        it might be directed at the pandas, the ocean tunnel, the view from the cable car, or the moment when a friend records a TikTok reaction.
Phenomenology would map how these objects appear in different acts: perceptual joy when seeing animals up close, anticipatory joy before a ride, or social joy when sharing the moment in a group selfie.


3. Phenomenological reduction (bracketing)

To understand their happiness, you would suspend assumptions such as:

·        “They’re only here for Instagram,” or “Happiness here is just about rides and prices.”
Instead, you would ask participants to describe exactly what made them feel happy at specific moments, and then analyze how Ocean Park “shows up” in those descriptions—without reducing it to “marketing success” or “ticket sales.”


4. Lifeworld and background meanings

Gen Z visitors bring with them a lifeworld shaped by digital culture, K‑pop, school stress, and social anxiety.
Their happiness might be meaningful partly because Ocean Park offers an escape from school pressure, a chance to perform “coolness” on camera, or a rare in‑person bonding with friends who usually interact only online. Phenomenology would uncover how these background concerns shape what counts as “happy” there.


5. Embodied and situated experience

Their happiness is not purely “mental”; it is embodied (e.g., butterflies in the stomach on a ride, tired feet after long walks, the coolness of the air‑conditioned aquarium) and situated (e.g., differences between weekdays vs weekends, peak hours vs quiet mornings, family vs friend groups).
Phenomenology would attend to such bodily and spatial cues as part of what makes an experience “happy” or “overwhelming” rather than treating happiness as a detached attitude.


6. Hermeneutic (interpretive) understanding

Their happiness is also already interpreted through prior meanings: what they’ve seen on Instagram, travel vlogs, or school friends’ stories.
A phenomenological‑hermeneutic interview might unfold dialogically, asking:

·        “When that moment felt happy, what did it remind you of?”

·        “How did this compare to trips to Disneyland or local theme parks?”
Here, understanding becomes reconstructing how Gen Z makes sense of Ocean Park happiness within their own interpretive world.


Toulmin‑style main claim (adapted to this case)

Claim (C):
To understand Gen Z customers’ happiness at Ocean Park Hong Kong, managers should rely on phenomenological descriptions of lived experience rather than on purely quantitative satisfaction data.

·        Grounds (G):
Gen Z visitors describe happiness in terms of specific moments (e.g., “seeing the panda yawn,” “laughing with friends in the queue,” “the view from the top”), which cannot be fully captured by Likert‑scale ratings.

·        Warrant (W):
Only methods that stay close to the first‑person givenness of experience (phenomenological interviews, experience‑mapping) can reveal how happiness is structured by intentionality, embodiment, and social context.

·        Backing (B):
Qualitative phenomenological studies in tourism and health‑care show that such descriptions help practitioners reconstruct patients’ or visitors’ lifeworlds in a way that informs empathetic design and service improvements.

·        Qualification (Q):
This holds for understanding the meaning and structure of happiness; it does not replace econometric or marketing research on pricing or operational efficiency.

·        Rebuttal/Reservation (R):
Some managers may object that “stories” are too soft or subjective, but phenomenologists argue that careful, systematic description—backed by inter‑rater checks and thematic analysis—can yield robust, actionable insights into Gen Z experience.

In short, a phenomenological approach to “Gen Z happiness at Ocean Park” means treating each visit as a whole lived situation, not just a set of data points, and letting Gen Z’s own descriptions show you what happiness does and means in that specific context.



Relevant links: on ALRA 2026.

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