IB Business Management IA Ideas — Research Questions

This post is about choosing and framing the question — the part students get stuck on. For how to structure the report, meet the criteria and avoid moderator traps, read the companion IB Business Management IA guide.

SL and HL differ in the length and scope of the task and the depth of analysis expected — always check the current subject guide and your teacher's brief for word counts and requirements. The full process, criteria and pitfalls are covered in the IA guide; this article stays on the question itself.

• Focused — one decision, one business, one issue. Narrow beats broad every time.
• Forward-looking — it asks what the business should do next, not what it has done. Good stems: "Should…", "To what extent would…", "How could…".
• A specific, real business — named, and ideally local or accessible so you can collect primary data.
• Tool-friendly — the question naturally invites the toolkit (SWOT, break-even, ratios, marketing mix, investment appraisal, motivation theory), so analysis is applied, not bolted on.
• Genuinely open — the decision is undecided, so a recommendation is meaningful. If the answer is obvious, there is nothing to evaluate.

A quick test: can you argue both sides and end with a recommendation the business could actually act on? If yes, the question works.

• Go local and accessible. A café, gym, salon, family firm or school enterprise will usually give you an interview or let you survey customers.
• Check you can get primary data. One manager interview plus a customer or staff survey is often enough — but line it up before you finalise the question.
• Be careful with huge multinationals. You cannot interview the CEO of Apple, and their scale makes a single decision hard to research. Big brands work only if you narrow to one local branch or one documented decision.
• Sort ethics early. Get consent for surveys and interviews, and anonymise the business if needed, following your school's guidance.

Human resources

• To what extent would introducing a new incentive scheme improve staff motivation at [local restaurant]? — motivation theory (Herzberg, Maslow, Taylor), financial vs non-financial rewards.
• How could [growing startup] restructure its teams to cope with expansion? — organisational structure, spans of control, chains of command.
• Should [business] introduce remote or hybrid working for its office staff? — motivation, productivity, organisational culture, cost.
• To what extent would improved staff training reduce turnover at [retailer]? — HR planning, recruitment/retention, labour turnover analysis.

Finance and accounts

• How could [local café/shop] improve its cash flow? — cash-flow forecast, working capital, ratio analysis (current, acid-test).
• Should [business] invest in [new equipment / larger premises]? — investment appraisal (payback, ARR, NPV), break-even analysis.
• To what extent is [SME] financially healthy enough to expand? — profitability and liquidity ratios, final accounts.
• Should [business] change its pricing to improve profitability? — break-even, contribution, cost classification.

Marketing

• Should [business] launch [a new product / an online store]? — marketing mix (4Ps/7Ps), market research, Ansoff matrix.
• How could [business] improve its social-media marketing? — digital marketing, marketing mix (promotion), target-market segmentation.
• To what extent would rebranding help [business] reach a younger market? — market segmentation, positioning, marketing objectives.
• Should [local business] expand its product range? — product life cycle, Boston matrix, Ansoff.

Operations

• Should [manufacturer] adopt lean production / JIT? — operations methods, stock control, capacity utilisation.
• Should [business] invest in new technology to speed up production? — operations methods, productivity, investment appraisal.
• Should [business] relocate or expand to [new location]? — location factors, capacity, break-even.
• To what extent would improving quality management raise customer satisfaction at [service business]? — quality methods (TQM, quality assurance), operations objectives.

• Primary research is data you collect: a manager interview about the decision, a customer survey on demand for a new product, staff questionnaires on motivation. This is where local, accessible businesses pay off.
• Secondary research is existing data: company accounts, industry statistics, competitor pricing, news and market reports.

A well-framed forward-looking question demands primary data — "Should X launch this?" almost forces a survey or interview. A backward-looking, descriptive question rarely does, which is one reason it scores poorly.

• Backward-looking. "How successful has [business] been over the last five years?" You can only describe the past, not evaluate a decision. Reframe to "Should [business] do X next?"
• Too broad. "How can [business] be more profitable?" invites everything and analyses nothing. Narrow to one lever — one product, one cost, one location.
• No data access. Picking a business you cannot interview or survey caps your primary research and your marks.
• Descriptive, not analytical. If the question can be answered by listing facts, there is nothing to weigh. A good RQ has two credible sides and needs a judgment.
• Answer already obvious. If the recommendation is a foregone conclusion, evaluation is impossible. The decision must be genuinely open.

1. Start from a decision, not a topic. "Marketing at [café]" is a topic. "Should [café] launch a loyalty app?" is a decision.
2. Name the business and the choice. Specificity forces focus.
3. Pick a forward-looking stem — "Should…", "To what extent would…", "How could…".
4. Check the tools fit. List the two or three tools the question invites. If none fit cleanly, adjust the question.
5. Confirm data access. Can you get the primary data this question needs? If not, change the business or the decision.
6. Pressure-test both sides. If you can argue for and against and reach a recommendation, you have a strong RQ.

What makes a good Business Management IA research question?

It is focused, forward-looking, and about a specific real business you can gather data on. It asks what the business should do next ("Should…", "To what extent…", "How could…"), invites relevant tools, and concerns a decision that is genuinely open — so you can analyse both sides and reach a supported recommendation.

Can I use a big company like Apple for my IA?

Usually not well. You cannot get primary data from a multinational, and their scale makes any decision hard to research meaningfully. If you want a large brand, narrow to one local branch or a single, well-documented decision — but a smaller, accessible business almost always makes a stronger IA because you can interview and survey.

Does my research question have to be forward-looking?

Effectively, yes. Backward-looking questions ("How successful has X been?") only let you describe the past, which caps your analysis and evaluation marks. Forward-looking questions about a future decision are what let you apply tools and justify a recommendation.

What business tools should I use?

Only the ones the question actually needs — applied, not defined. Common fits: SWOT and Ansoff for strategy, break-even and investment appraisal and ratios for finance, the marketing mix and market research for marketing, and motivation theory for HR. Two or three well-applied tools beat a long list of shallow ones.

How do I get primary research for my IA?

Line up access before finalising the question. A manager interview about the decision plus a short customer or staff survey is often enough. Choose a local or accessible business, get consent, and anonymise if needed following your school's ethics guidance.