Market Survey Topic Selection and Proposal
Working Principles
First read Sources and Rules to confirm the manuscript basis and rule scope. For deadlines, page counts, weights, anonymity, AI, and submission methods, verify the official documents for the target edition, group, and phase online; if verification fails, mark as pending verification, but do not obstruct research content creation. Do not present manuscript examples as hard competition rules.
Default language is Chinese. First review user's existing materials and current progress; only ask for missing information that would change the plan; for the rest, state assumptions and proceed. Without original data, provide only plans, templates to fill, or clearly identified demonstrations; do not fabricate samples, statistics, interviews, references, or award identities. Deliver according to task depth; partial modifications do not require redoing the entire workflow.
Goal
Transform a hot topic or broad theme into a research project that the team can realistically complete and that supports specific actions. Mainly relies on manuscript chapters 1–3 and sections 8.1–8.2.
Topic Selection Process
- Determine resource boundaries: edition, group, free topic or enterprise assignment, region, timeline, budget, team capabilities, accessible population, and partners. For enterprise assignments, obtain the full assignment text and delivery requirements for the current edition, and decompose item by item; cannot replace mandatory items with free topics. If not yet released, only do general preparation.
- Formulate a problem from contradictions: Write "Who needs to make what decision—what information is missing—what the survey should answer." Policies, news, and industry scale only explain background; they alone cannot prove a specific problem exists. Combine real statistics, literature, observations, or pre-interviews to establish preliminary evidence; mark unconducted interviews as planned.
- Generate comparable candidates: If the user has not specified, usually provide 3–5 directions; each includes object, context, key behavior, research question, decision maker, data channels, unique contribution, and major risks. If the user has already selected a topic, prioritize converging that topic rather than creating an alternative pool.
- Check feasibility before ranking: If the core group cannot be legally accessed, key variables cannot be measured, or the timeline cannot be met, first narrow the scope or replace channels; do not offset infeasibility with "innovation for high scores." After passing feasibility, compare based on decision value, evidence gaps, object clarity, data availability, implementation cost, and ethical conditions; clearly mark self-assigned scores and weights as working tools, not official ratings or award probabilities.
- Ground selected topics in design: Define the target population, surveyable population, inclusion/exclusion criteria, geography, and time; distinguish between potential users, actual users, and repeat purchasers. Explain the sampling frame, channel coverage, proposed sampling method, and extrapolation scope; convenience web samples must not be packaged as random samples. Sample size should be planned based on main indicator precision, key subgroups, design effect, model complexity, and nonresponse; do not uniformly require "at least several hundred."
- Reverse validation: Each research question has variables, items or interview information, data sources, and analysis output; key results have clear users. Remove or downgrade to exploratory questions without matching evidence. First provide a clear topic, then optional rhetorical titles; title scope cannot exceed sample coverage.
Outputs
Provide as needed:
- Candidate comparison table: Topic | Real contradiction and source | Object and scope | Decision use | Data channels | Innovation points | Implementation difficulties | Recommendations.
- Recommended topic and alternatives: Explain tradeoffs, and state what evidence would change the recommendation.
- Proposal argument: Problem background, literature and document matrix, decision problem, core research questions, population boundaries, data and sampling, measurement and methods, feasibility, risks and contingencies, progress milestones.
- Correspondence matrix:
Research question | Concept and operational definition | Variables/items | Data source | Analysis method | Expected output | User. "Expected output" describes the output form, not prewritten significant conclusions. - Pretest tasks: Screening and recruitment feasibility, question comprehension, completion burden, skip patterns, variable distribution, and analysis chain testing; specify criteria for continuing, revising, or narrowing the topic.
Topic Diagnosis
"Development of a national industry" but only surveying students on campus: narrow the topic and extrapolation boundaries. "Preference proportion of one class" has no decision use: introduce truly valuable comparisons or service choices. Distinctive groups require operational definitions and service problems, not just labels. Platform reviews only reflect visible expressions on a specific platform, not all consumers.
Case migration only borrows problem definition and evidence design; do not replicate topics, questionnaires, or models. Award-winning works' frequencies are only for understanding historical characteristics, not to infer which types of topics are more likely to win. Innovation must explain what new understanding is added compared to existing research, and how it will be tested with real evidence.
Completion Criteria
An external reader can answer: Who is being researched, where, what is being studied, why it is worth doing, how data is obtained, and who will use the results. The topic, population, sampling, variables, and recommended audience are consistent. Major resource bottlenecks and alternatives are clearly stated.