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NX9624 Management Enquiry: how do you plan a 40-credit capstone?

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NX9624 is a 40-credit final-year module at Newcastle Business School, Northumbria University, which makes it worth twice a standard module and roughly a third of your final year.

NX9624 is a 40-credit final-year module at Newcastle Business School, Northumbria University, which makes it worth twice a standard module and roughly a third of your final year. It is a capstone, and it is unusual in one important way: it requires you to go out and talk to a real practitioner, then collect and analyse live data from real people. That is not a library project. It means ethics, access and timing become part of your grade long before your writing does. What follows deals with the planning decisions that quietly determine how the whole year goes.

Author: MAAS Editorial Team · Reviewed by a Senior Research Methods mentor (PhD, Management)
Last updated: 2026-07-29
Category: writing-tips


What is NX9624 Management Enquiry about?

Direct answer: NX9624 is a Year Three module worth 40 credits. Northumbria lists it as core on some Business Management routes and as optional on others, so check the module list for your own programme. It is a capstone that has you undertake a substantial individual project demonstrating authentic engagement with professionals in your discipline. The learning approach is experiential and problem-based, focused on discovering and questioning key practice-based issues, and it explicitly develops transferable skills including time management, project management and communication.

Evidence: The word "capstone" is doing real work here. A capstone is designed to make you draw together what the degree has taught you and apply it to a problem nobody has pre-packaged for you. The module description pairs that with "authentic engagement with professionals", which means the problem has to come from practice rather than from a textbook, and someone in the field has to actually be involved.

Example: A Vietnamese student at Northumbria told her MAAS mentor she had picked a topic from a lecture she enjoyed. Her mentor asked which practitioner she would interview about it and what live data she could realistically gather. Neither question had an answer. The topic was academically interesting and operationally impossible, and she found that out in week two rather than week ten.


How is NX9624 assessed?

Direct answer: In two parts. Part A is worth 35% and involves interviewing a manager or professional to explore a key issue in your discipline, alongside a critical examination of the relevant literature. Part B is worth 65% and requires authentic engagement with the problem you identified: collecting and analysing live data using ethically considered research methods, then reflecting on your findings and providing clear, prioritised, well-justified, practical and actionable recommendations for change. Confirm exact word counts and deadlines on your module page.

Evidence: Read that list of adjectives attached to the recommendations again: prioritised, well-justified, practical, actionable. That is a marking rubric written as a sentence. Each word is a separate test. Prioritised means ranked, not listed. Well-justified means traced to your data. Practical means the organisation could actually do it. Actionable means someone could start on Monday.

Example: A student's final recommendations read: improve communication, invest in training, enhance culture. His mentor applied the four adjectives one at a time. They were not prioritised, not traced to any finding, not costed against the organisation's constraints, and not actionable by any named role. Rewriting three vague aspirations into two specific, ranked, evidenced actions lifted the section from the bottom band to a strong one.


How do Part A and Part B fit together?

Direct answer: Part A finds and frames the problem; Part B investigates it and recommends change. The interview in Part A is not a warm-up exercise, it is how you discover a problem worth 65% of the module. Choose that interview badly and everything downstream is built on sand.

Evidence: The module is problem-based and experiential, and Part B refers to "the identified problem", meaning the problem identified in Part A. The two parts are sequentially dependent by design. This is why the weighting is not an accurate guide to where your effort should go early: Part A is 35% of the marks but close to 100% of the risk.

Example: A student interviewed a family friend who managed a small retail store, because access was easy. The issue that emerged was staff scheduling, and he could not gather live data on it without the owner's cooperation, which was never granted. His mentor's advice for next time was blunt: before you choose your interviewee, ask whether their organisation would let you collect data there too.


What counts as live data, and how do you get it ethically?

Direct answer: Live data means data you generate yourself from real participants during the project, typically interviews, a short survey, or structured observation, rather than data someone else already published. Ethically considered means you have thought about consent, anonymity, storage, and the participant's right to withdraw, and that you have followed your programme's ethics approval process before you collect anything. Approval takes time; build it into your plan rather than discovering it in week six.

Evidence: The module specifies collecting and analysing live data using ethically considered research methods, which makes research ethics part of the assessed methodology rather than an administrative hurdle. A project whose data was gathered before approval, or without recorded consent, has a methodological defect that no amount of good analysis repairs.

Example: A student planned to record interviews on her phone and transcribe them later. Her mentor asked where the recordings would be stored and what she had told participants about it. She had not addressed either. Adding a one-page consent sheet and a clear statement on storage and deletion took her an afternoon, and it later formed part of her methodology section rather than a gap in it.


How should you structure the project?

Direct answer: Work backwards from Part B's deliverable, the prioritised recommendations, because everything must feed it. A workable shape: Part A opens with the practice issue as your interviewee described it, then sets it against what the literature already says, and closes by naming the specific question your data will answer. Part B then covers method and ethics, findings from your live data, analysis of what those findings mean against the literature, and finally the recommendations, ranked with justification. Give the analysis and recommendation sections the space they deserve; they carry Part B's weight.

Evidence: The module asks you to reflect on findings and then provide recommendations, which places reflection between analysis and recommendation as a distinct step. That step is where you decide which findings actually matter enough to act on, and skipping it is why weak recommendations feel disconnected from the data that preceded them.

Example: A student presented six findings and then six recommendations, one per finding, in the same order. Her mentor asked which two mattered most to the organisation and why. Once she ranked them by impact and feasibility, two recommendations carried the report and four became secondary observations. The word "prioritised" in the brief had been asking for exactly that.


What are the most common mistakes in a capstone like this?

Direct answer: Most of what goes wrong here is a planning failure rather than a writing failure.

Mistake Why it costs marks The fix
Choosing a topic before checking access Part B needs live data from somewhere Confirm access before you commit to the problem
Treating ethics approval as paperwork The brief names ethically considered methods Start approval early; write ethics into methodology
Interviewing whoever is easiest to reach Part A frames the whole project Choose for relevance and for data access
Recommendations as a wish list Four adjectives in the brief say otherwise Rank them, cost them, name who acts
Findings reported, not analysed Reflection is a named step Say what each finding means, then what follows
Leaving Part B until Part A is marked The parts are sequential but the clock is not Begin data planning while Part A is in progress

Evidence: A 40-credit module compresses a research process into one academic year alongside other modules, and the steps that depend on other people, access, approval, participant availability, are the only ones you cannot fix by working harder. Those are the steps that need float in the plan.

Example: A student budgeted two weeks for interviews and got three of his eight participants in that window because it fell over a holiday period. His mentor's lesson for the next student was to schedule participant-dependent work first and writing second, since writing can compress and people cannot.


How do you analyse qualitative data without overcomplicating it?

Direct answer: For most undergraduate projects at this level, thematic analysis is the appropriate and defensible choice. It means working systematically through your transcripts, coding what is there, grouping codes into themes, and checking those themes against the full dataset rather than against your expectations. What matters for marks is that you describe the process you actually followed, including how you handled data that did not fit.

Evidence: Braun and Clarke (2006) set out thematic analysis as an accessible and theoretically flexible approach, provided clear guidelines for conducting it rigorously, and identified the common pitfalls, most notably presenting a collection of extracts as though it were an analysis. Brinkmann and Kvale (2014) similarly treat interviewing as a craft with epistemological and ethical commitments rather than as a neutral data-collection technique.

Example: A student's findings section consisted of eight quotations with a sentence of paraphrase after each. Her mentor asked what the pattern across them was and whether any participant had said the opposite. Identifying one dissenting account and explaining it turned a quotation list into an analysis, and it was the dissenting case that made the eventual recommendation credible.


Frequently asked questions

Why is NX9624 worth 40 credits?
Because it is a capstone that asks you to run a full enquiry, from framing a practice problem through primary data collection to recommendations. It carries double the weight of a standard module and correspondingly more of your final-year mark.

Do I need a large sample?
No. At undergraduate level a small, well-justified sample analysed carefully is expected and appropriate. What is assessed is whether your method suits your question and whether you are honest about what your data can and cannot show.

What if my interviewee's organisation will not let me collect data?
Then you need a different problem or a different site, and it is much cheaper to discover that in Part A than in Part B. This is precisely why access should be confirmed before the problem is fixed.

Can I study the organisation where I work or have worked?
Often yes, and it can help with access, but check your programme's ethics guidance carefully. Existing relationships raise consent and confidentiality questions that your methodology will need to address openly.

Can MAAS help me with NX9624?
MAAS mentors work with you across the phased research process: testing whether a problem is researchable, preparing interview questions, planning for ethics approval, and pressure-testing whether your recommendations meet all four of the brief's adjectives. You remain the researcher and the author; a mentor pressure-tests feasibility while changing course is still cheap.


Test feasibility before you fall in love with a topic

The capstones that go well rarely stand out for with the most ambitious question. What they share is that they where access was confirmed before the topic was fixed, ethics approval started early, and the recommendations were ranked and evidenced rather than listed. If you have an idea but no clear route to data, a MAAS mentor can work through the feasibility with you before you commit.

Talk to a MAAS mentor about your module



References

  • Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  • Brinkmann, S. & Kvale, S. (2014). InterViews: Learning the craft of qualitative research interviewing (3rd ed.). SAGE.
  • Gibbs, G. (1988). Learning by doing: A guide to teaching and learning methods. Further Education Unit.
  • Saunders, M. Lewis, P. & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.

Tools & resources


This article is part of the MAAS Journal series for Vietnamese international students. MAAS Academic Mentoring is an advisory partner; we coach students through the phased research process with developmental feedback from PhD-level mentors. We do not write or submit work on a student's behalf.

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