---

name: jms-methods description: Use when the research design is the bottleneck for a Journal of Management Studies (JMS) manuscript — matching design (qualitative case/ethnography, process/longitudinal, survey, archival, experiment, multi-method) to the theoretical question, with qualitative rigor treated as first-class. Designs the study; it does not run the estimation or trustworthiness checks (jms-data-analysis).

Research Design & Methods (jms-methods)

When to trigger

• The design may not match the theory's level, timing, or causal claim
• A qualitative study's case selection, saturation, or analytic procedure is under-specified
• Quantitative data are single-source, single-wave, self-reported (common-method bias risk)
• The theory is causal but the design is cross-sectional/correlational
• A reviewer says "the design cannot test/show this" or "the method is not rigorous enough"

Match the design to the question — pluralism with rigor

JMS welcomes all rigorous designs and is, distinctively, a friendly home for qualitative and process work — but rigor must clear a top-tier bar regardless of method. Choose the design the question demands:

Theoretical claim / question	Design that earns it
How/why a phenomenon emerges or works	Inductive multi-case (Eisenhardt) or ethnography
How something unfolds over time	Process / longitudinal (temporal bracketing, visual mapping)
Causal effect of a manipulable cause	Experiment (lab / field / online) or natural experiment
Whether & how much, with generalisation	Survey (multi-wave) or panel archival
Cross-level mechanism (firm → individual)	Multilevel / nested design (HLM-appropriate)
Contested, novel, or richly contextual	Multi-method (e.g., qual study 1 + quant study 2)

Designing qualitative rigor (first-class at JMS)

• Case/site selection is theoretical, not convenient: state the sampling logic (extreme, polar, theoretically replicating). Justify the number of cases and why they let the theory travel.
• Data sources triangulated: interviews + archives + observation; report counts (informants, hours, documents) and the period.
• Analytic procedure stated: which approach (Gioia, Eisenhardt cross-case, grounded theory, narrative/temporal bracketing) and how codes became constructs.
• Trustworthiness in the qualitative idiom: member checking, an audit trail, negative-case analysis, inter-coder agreement where appropriate — not p-values.

Designing against the threats JMS reviewers cite (quantitative)

• Common-method bias: separate sources / temporal separation across waves; objective or archival outcomes where possible. Procedural design beats a post-hoc Harman test (the Podsakoff guidance is standard).
• Endogeneity (archival/survey): anticipate omitted variables, reverse causality, selection; plan an identification strategy (panel FE, DiD, IV/2SLS, natural experiment, matching) and state each one's assumptions.
• Measurement: validated multi-item scales; pilot new measures; plan a CFA; state the level each construct is measured at and justify any aggregation (ICC, r_wg).
• Power & sampling: justify the frame, response rate, and power — interactions need more power than main effects.

Level-of-analysis discipline

State the level for theory, measurement, and analysis, and keep them aligned. If theory is at the firm level but data are individual, justify aggregation; for cross-level effects, model the nesting — do not run OLS on nested data.

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. JMS mixes qualitative and quantitative management research; the chain below is for the quantitative-empirical lane.

• detect_design → recommend → fit with as_handle=true → audit_result to enumerate the checks the design owes.
• Panel / staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition
  • honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD: rdrobust + mccrary_test.
• Experiments: randomization-based inference and romano_wolf for the many-outcome family-wise correction reviewers expect.

Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Checklist

• Design can actually answer the question (causal claims have causal leverage)
• Qualitative: theoretical case selection, triangulated sources with counts, named analytic procedure, trustworthiness checks
• Quantitative: CMB addressed by design; endogeneity strategy specified; validated/piloted measures; CFA planned
• Level of analysis aligned across theory, measurement, analysis; aggregation justified
• Sampling frame, N, and power (incl. interactions) justified
• Where useful, a second study triangulates the mechanism

Anti-patterns

• Qualitative-by-default vagueness: "we did a case study" with no selection logic or analytic procedure
• Cross-sectional causal claims: "X causes Y" from one-wave correlational data
• CMB as afterthought: relying on a single Harman test instead of designed separation
• Ignored endogeneity: an obviously endogenous regressor with no identification strategy
• Mismatched levels: theorising at the firm level, testing disaggregated individual data via OLS
• Method theatre: a fashionable estimator or qualitative label not justified by the question

Output format

【Design】qual multi-case / ethnography / process / experiment / survey / panel-archival / multi-method
【Question-design fit】can the design answer each claim? notes …
【Qualitative rigor】(if qual) case selection · sources+counts · analytic procedure · trustworthiness
【CMB / endogeneity】(if quant) procedural remedy · identification strategy
【Measures】validated? new (piloted)? CFA planned?
【Levels】theory / measurement / analysis aligned? aggregation justified?
【Next step】jms-data-analysis