Lecture note on pragmatism and how to employ it in MBA dissertation projects

Lecture note on pragmatism and how to employ it in MBA dissertation projects

 

I am a part-time MBA student, learning the subject of Research Methods. My understanding is that the research philosophy of pragmatism is highly relevant for doing BA dissertation projects due to the pragmatic and managerial orientation of the MBA program design spirit. Please highlight 3 main ideas of pragmatism and suggest how to employ pragmatism-oriented dissertation investigation tasks with three how-to advices so that the dissertation report is able to create practical (and managerial oriented) values.

You’re right that pragmatism aligns very well with MBA‑style, managerial dissertations. In essence, it lets you focus on “what works” to solve a real business problem, using whatever methods are most useful, and judging your research by its practical consequences rather than abstract philosophical purity.^[1][2][3]

Below are three core ideas of pragmatism, followed by three “how‑to” pieces of advice for designing a pragmatist, value‑creating MBA dissertation.

Three main ideas of pragmatism

1. Focus on practical outcomes and consequences

Pragmatism evaluates knowledge by its usefulness: ideas, theories, and findings matter if they help you act effectively and improve a real situation. Truth is understood in terms of “warranted assertions” – claims whose value is demonstrated through their practical consequences in the world.^[4][1]

For an MBA dissertation, this means:

·       Start from a concrete managerial problem (e.g., low customer retention, poor employee engagement, inefficient logistics).

·       Judge your research design and findings by whether they help managers make better decisions, design interventions, or change policies.

2. “What works” and methodological flexibility

Pragmatism is methodologically pluralist: you choose methods because they help answer your question, not because they fit a rigid paradigm. Pragmatist researchers often combine quantitative and qualitative data, and move between deductive (hypothesis‑testing) and inductive (exploratory) reasoning as needed.^[2][3][5]

Key implications:

·       You can legitimately use mixed methods – surveys, interviews, company data, case studies, experiments – in one coherent design, as long as each part clearly serves the research question.

·       You can draw on both positivist-style measurable relationships and interpretivist-style rich explanations of experience, treating them as complementary lenses on the same managerial issue.^[6][2]

3. Inquiry as action and experience‑based learning

Pragmatism treats research as an action‑oriented inquiry process: you identify a problematic situation, develop possible lines of action, evaluate them by anticipated and observed consequences, and refine your understanding through experience.^[5][7][8]

This leads to:

·       Emphasis on context and lived experience of stakeholders (managers, employees, customers) as a source of knowledge.

·       Viewing your dissertation as part of an ongoing cycle of problem recognition, intervention design, and organisational learning, rather than a purely theoretical exercise.

Three pragmatism‑oriented “how‑to” advices for your dissertation

1. Design the project around a clearly defined managerial problem

Aim: Ensure that every part of the dissertation is anchored in a practical issue and leads to actionable insights.

How‑to:

·       Start and end in practice. Frame your topic explicitly as a problematic situation in a specific organisation or sector (e.g., “How can Company X improve post‑purchase customer engagement to increase repeat purchases?”). Define the current symptoms, stakeholders affected, and business impact.^[7][9]

·       Formulate pragmatist research questions. Write RQs that ask “What works, for whom, and under what conditions?” rather than only “Does X statistically affect Y?”. For instance:

o   RQ1 (quantitative): “What is the relationship between loyalty program participation and repeat purchase frequency among customers of Company X?”

o   RQ2 (qualitative): “How do different customer segments experience and interpret the loyalty program and its perceived value?”

·       Specify intended managerial outputs upfront. In your introduction and methodology chapters, state clearly what decisions your findings are meant to inform (e.g., redesign of loyalty tiers, staff training priorities, segmentation strategy). This aligns your whole project with pragmatist emphasis on consequences and actionable knowledge.^[9][1]

This makes it easy, in the conclusion, to translate findings into concrete recommendations, KPIs, and implementation steps, which examiners in an MBA will view as practical value.

2. Use mixed methods strategically to answer “what works”

Aim: Combine quantitative “pattern‑finding” and qualitative “meaning‑making” methods to build robust, context‑sensitive guidance for managers.

How‑to:

·       Start from the research question, not the paradigm. For each sub‑question, ask: “What evidence will most convincingly help a manager decide what to do?” If the answer involves measuring effects, use quantitative methods; if it involves understanding why stakeholders behave or feel a certain way, use qualitative methods.^[2][5]

·       Design complementary data streams. For example, in an MBA dissertation on employee engagement:

o   Quantitative: An online survey measuring engagement scores, job characteristics, and performance metrics.

o   Qualitative: Semi‑structured interviews or focus groups exploring how employees interpret engagement initiatives, leadership behaviour, and organisational culture.
The survey shows which factors correlate with engagement; the interviews explain how and why those factors matter in your specific context.^[1][6]

·       Integrate findings around managerial decisions. In the discussion chapter, don’t treat survey and interview findings as separate silos. Use a pragmatist “integration” logic:

o   Use quantitative results to identify priority levers (e.g., supervisor support and autonomy strongly predict engagement).

o   Use qualitative insights to design feasible interventions (e.g., micro‑practices managers can adopt, how autonomy can be increased without hurting compliance, how employees perceive fairness).
Make explicit how each integrated insight translates into a decision rule, policy change, process redesign, or management practice.^[9][1][2]

This is exactly the kind of “what works” mixed‑methods reasoning that pragmatism is known for, and it naturally produces managerial value.

3. Build a clear “impact pathway” from findings to change in practice

Aim: Ensure your dissertation doesn’t stop at “interesting findings”, but demonstrates how those findings can generate real organisational improvements.

How‑to:

·       Map the problem–evidence–action chain. In your discussion or implications chapter, use a simple structure for each key finding:

a.     Problem statement (e.g., low repeat purchase rate among a specific customer segment).

b.    Evidence summary (e.g., data show clear link between perceived value of after‑sales service and repeat purchase; interviews reveal customers feel ignored post‑purchase).

c.     Proposed actionable intervention (e.g., design a segmented after‑sales follow‑up protocol, with specific contact frequency and content, integrated into CRM).

d.    Expected consequences and KPIs (e.g., target % increase in repeat purchases; customer satisfaction scores).^[7][9]

·       Co‑design or validate with practitioners where possible. If feasible, involve managers or stakeholders in reviewing your proposed interventions (through expert interviews, workshops, or feedback sessions). This is very much in line with pragmatism’s emphasis on inquiry embedded in practice and co‑creation with practitioners.^[5][9]

·       Reflect on implementation constraints and learning cycles. Pragmatism emphasises that knowledge is fallible and always open to refinement based on experience. In your conclusion, explicitly discuss:^[4][9]

a.     What organisational constraints might limit implementation (budget, culture, technology).

b.    How managers could pilot your recommendations, monitor outcomes, and iteratively adjust (e.g., A/B testing, phased roll‑out, feedback loops).
By showing this “learning cycle”, you position your dissertation as a starting point for ongoing organisational inquiry, not a static blueprint.

Putting it together in your MBA dissertation

If you frame your methodology chapter explicitly in pragmatist terms, you might emphasise:

·       Ontology/Epistemology (briefly): You treat reality as experienced in practice and knowable through its observed and felt consequences; you value both numerical patterns and subjective experiences as legitimate evidence for managerial decisions.^[2][5]

·       Axiology: Your research is explicitly value‑oriented, aiming to improve organisational outcomes and stakeholder experiences, not just to advance theory.^[3][9]

·       Methodological stance: You adopt a flexible, mixed‑methods design driven by the research question and the need to produce actionable, context‑specific insights.

With this framing, your dissertation will clearly embody the “spirit” of an MBA: solving real managerial problems, using whatever methods work best, and translating evidence into practical recommendations that your examiners can see as genuine, pragmatic value.

** a collection of lecture notes on research methods

Lecture note on research philosophies and the practical value of MBA dissertation projects

Lecture note on research philosophies and the practical value of MBA dissertation projects

 

In the context of doing part-time MBA dissertation projects, which primarily is pragmatic and managerial in orientation, I am told that an MBA dissertation project is expected to create some practical value (e.g. knowledge with actionable and managerial value). Thus, I can easily understand why the research philosophy of pragmatism is relevant for this kind of project. Kindly suggest to me 2 ways on treating dissertation research findings based on (1) positivism and (2) interpretivism so as to gain some practical value. To clarify the discussion, consider to use the example of the crude MBA research theme of "understanding and enhancing visitors' tour experience in Ocean Park of Hong Kong during the public holidays".

For a pragmatic, managerial MBA project, you can treat findings in two different but complementary ways: as measurable patterns and causal relationships (positivism), and as situated meanings and narratives (interpretivism). Both can be turned into practical value for Ocean Park’s management if you are explicit about how each type of finding translates into concrete decisions and interventions.^[1][2]

Below are two clear ways of “treating” your findings to gain practical value, using your theme of “understanding and enhancing visitors’ tour experience in Ocean Park of Hong Kong during public holidays.”

1. Positivist treatment: patterns → rules → interventions

Under positivism, you treat your findings as objective, generalizable relationships between variables that can guide managerial rules, KPIs, and operational decisions.^[1]

Step A: Frame findings as measurable relationships

Example types of findings in a positivist Ocean Park study:

·       Crowding and satisfaction
“Perceived crowding score” is negatively associated with overall satisfaction and intention to revisit, especially during 2–4pm peak hours on public holidays.

·       Queue time and spending
Average queue time for headline attractions (e.g. major rides) shows a threshold effect: beyond 45 minutes, reported intention to purchase F&B and souvenirs drops significantly.

·       Staff interaction quality and Net Promoter Score (NPS)
A 1‑point increase in “staff helpfulness” rating corresponds to a measurable increase in NPS and likelihood to recommend Ocean Park to friends.

You present these as statistical patterns (correlations, regressions, differences between groups) with statements like “Higher perceived crowding is associated with lower satisfaction” rather than rich stories.^[3][1]

Step B: Translate relationships into actionable management rules

You then treat the findings as design rules and control levers:

1.    Set operational thresholds and KPIs

o   Use findings on crowding and queue time to set KPIs such as:
“During public holidays, 80% of visitors should experience queues under 40 minutes for headline attractions.”

o   Monitor these via real‑time operations dashboards and adjust staffing or ride scheduling when thresholds are exceeded.

2.    Optimize resource allocation and scheduling

o   If data show specific zones (e.g. marine exhibits) are under‑utilized while ride areas are congested, re‑allocate entertainment shows and characters to congested zones to distribute flows.

o   Use time‑of‑day patterns to schedule more staff at guest‑services points and popular rides during peak hours, based on empirical relationships between staffing, crowding, and satisfaction.^[4][5][6]

3.    Design experiments for continuous improvement

o   Treat your findings as hypotheses for A/B tests:
For instance, “Implementing a virtual queue system for two rides should increase overall satisfaction scores by X and increase secondary spending by Y.”

o   Run controlled trials over several public holidays; measure the change in key metrics (satisfaction, NPS, per‑capita spending) to confirm or refine the rules.

4.    Prioritize investments and justify business cases

o   If your data show that reducing queue time at Ride A has a much larger effect on satisfaction than at Ride B, you can argue for capex investment (e.g. new loading platform, virtual queue tech) at Ride A first.

o   Put numbers into a simple business case: “A projected 0.3‑point increase in average satisfaction yields Z% increase in revisit intention; with an average ticket price of HKD…, this supports an expected revenue uplift of ….”

In short: Under positivism, you treat findings as evidence‑based rules about “what works” in a generalizable way, and convert them into KPIs, process changes, and structured experiments that operational managers can implement.

2. Interpretivist treatment: meanings → narratives → design principles

Under interpretivism, you treat findings as context‑dependent meanings and lived experiences that help managers understand “what it feels like” to be a visitor and how different segments construct their holiday experience.^[2][7][1]

Step A: Frame findings as themes and stories

You would typically collect qualitative data (e.g. semi‑structured interviews, on‑site ethnographic observation, visitor diaries) and code them into themes:

Example interpretivist findings at Ocean Park:

·       Theme: “Holiday family ritual”
Visitors describe Ocean Park as part of a recurring family ritual (“We come here every Lunar New Year”), emphasizing togetherness, photo‑taking, and shared memories more than rides per se.

·       Theme: “Being trapped in queues”
Some visitors narrate feeling “trapped” or “wasting my holiday” when queueing, describing frustration, boredom, and guilt about children getting tired.

·       Theme: “Hong Kong identity and pride”
Local visitors talk about Ocean Park as “our park,” contrasting it with overseas parks; they describe pride in local marine conservation and local food options.

·       Theme: “Micro‑moments of magic”
Especially for first‑time visitors, small interactions (a staff member helping with photos, a surprise character encounter) are described as “the most memorable part” of the day.

These are not just variables but interpretations of what the experience means to different segments.^[7][3]

Step B: Translate meanings into design and communication principles

You then treat the findings as design principles and narrative guidelines for managers:

1.    Experience design principles

o   From “holiday family ritual” and “micro‑moments of magic,” derive principles such as:
“Design the park day as a sequence of memorable family micro‑moments, not just rides.”

o   Concrete actions: more photo spots with good backdrops; staff trained to offer proactive family assistance (e.g. volunteer to take family photos, give small souvenirs to children during peak queuing times).

2.    Queue experience re‑framing

o   Instead of only reducing queue length (positivist focus), interpretivist insights suggest re‑framing the meaning of queuing:

§  Introduce interactive queue entertainment (mini‑shows, AR games, educational marine content).

§  Provide “parent‑friendly” features (seating, fans, shade, child play corners) so parents do not feel guilty or stressed.

o   The aim is to shift the narrative from “wasting time” to “part of the shared adventure.”

3.    Segment‑specific storytelling and communication

o   For local Hong Kong families, emphasize “our park,” conservation stories, and local culture in communication materials and on‑site signage.

o   For Mainland or overseas visitors, highlight discovery, novelty, and “must‑do” experiences, connecting their own holiday motives and expectations.

o   These narrative strategies are grounded in how different segments interpret their visit (“family ritual,” “status trip,” “children’s reward,” etc.).

4.    Staff training and scripts based on visitor meanings

o   Use typical visitor stories in staff training:

§  Show how a small act (e.g. helping a lost guest) becomes the highlight of someone’s day.

§  Discuss how certain phrases or attitudes can either affirm visitors’ holiday narratives (“You’ve picked the perfect day for your family adventure!”) or undermine them (“It’s always crowded, what do you expect?”).

o   Develop interaction scripts that reinforce positive narratives (family bonding, pride, discovery) and de‑escalate negative ones (feeling trapped, disrespected).

5.    Reflexive policy and inclusion

o   Interpretivist work also encourages managers to reflect on whose experience is being privileged.

o   For example, stories from visitors with disabilities or elderly grandparents may reveal that the park design implicitly centers able‑bodied younger adults; this can guide inclusive design changes (wheelchair‑friendly routes, resting points, clearer signage).

In short: Under interpretivism, you treat findings as rich accounts of how visitors make sense of their day, and convert these into experience design principles, narrative strategies, training content, and inclusion policies.

Putting both approaches to practical use

For a pragmatic MBA dissertation, you can explicitly show how both treatments create value:

·       In your positivist chapters, emphasize:

o   Clear variables (crowding, queue time, satisfaction, spending).

o   Statistical relationships.

o   KPIs, resource allocation decisions, and experiment plans derived from those relationships.

·       In your interpretivist or qualitative chapters, emphasize:

o   Visitor meanings and narratives (how different segments experience and interpret Ocean Park).

o   Design and communication principles.

o   Staff training, inclusive policies, and experience re‑framing grounded in those narratives.

A very nice, pragmatic position for your dissertation is to say:

“Positivist analysis tells Ocean Park management where and when experiences are weak and which operational levers matter most; interpretivist analysis tells them why visitors feel as they do and how interventions can be designed to align with visitors’ own meanings and holiday narratives.”

** a collection of lecture notes on research methods