| name | isr-methods |
| description | Use when choosing and stress-testing the research design for an Information Systems Research (ISR) manuscript — matching the genre (behavioral empirical, analytical-economic modeling, design-science, or multimethod) to the question, and ensuring the design can actually support the IS contribution. Designs the study; it does not execute the estimation/derivation (isr-data-analysis) or frame the contribution (isr-contribution-framing). |
Research Design & Method Fit (isr-methods)
When to trigger
- You are unsure which genre best supports your IS claim
- The design may not be able to identify the effect (empirical) or may rest on unjustified assumptions (analytical)
- You are combining methods and need the multimethod logic to hold together
- A reviewer says "the method cannot answer the question" or "the artifact is not evaluated"
Match the genre to the claim
ISR is deliberately pluralistic; no single method is mandated. Choose the genre the claim demands:
| Claim / phenomenon | Genre & design |
|---|
| Causal effect of an IT design/policy on behavior or outcomes | Field/lab experiment, or quasi-experiment with identification |
| How/why IT use is enacted, appropriated, organized | Qualitative / interpretive (interviews, ethnography, case) |
| Equilibrium behavior of platforms, pricing, security, contracts | Analytical economic / game-theoretic model |
| A novel IT artifact that solves a class of problems | Design science — build and rigorous evaluation |
| Value/impact of IT investment at firm/market level | Archival econometrics with a credible identification strategy |
| Mechanism + scope + generalization in one paper | Multimethod (per ISR 36(2) framework) with an explicit integration logic |
Genre-specific design discipline
- Behavioral empirical. Establish construct validity by design (multi-item validated scales, manipulation/attention checks), separate sources/waves to limit common-method bias, justify the sampling frame, and (for experiments) pre-register and power for interactions, not just main effects.
- Analytical modeling. The design is the model: state agents, timing, information structure, and equilibrium concept; defend each assumption; plan the comparative statics and the extensions/robustness that show the result is not an artifact of one assumption. Reserve full proofs for the electronic companion.
- Design science. Specify the artifact, the design objectives, and an evaluation that demonstrates utility (benchmarks, controlled studies, real-world deployment) — a build without evaluation is not a DSR contribution.
- Archival/causal. Name the identification strategy (DiD, IV, RDD, matching) and the threat it addresses; a regression without identification is descriptive.
Sociotechnical level and fit
State the level(s) of analysis and ensure the design observes the level where the mechanism operates (e.g., group-level theory needs group-level variation). Cross-level claims need cross-level data.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. ISR is empirical IS with strong econometric and experimental work; identification (DiD / IV) for observational claims, randomization inference for experiments.
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
Anti-patterns
- Method by convenience: using the data you have rather than the design the claim needs.
- Regression theater: archival regressions presented as causal without identification.
- Build-only DSR: an artifact with no rigorous evaluation.
- Multimethod garnish: a second method bolted on without theoretical integration.
Output format
【Claim】[...]
【Genre & design】experiment / qualitative / analytical / DSR / archival / multimethod
【Identification or assumptions】[...]
【Level(s) observed】[...]
【Validity/robustness plan】[...]
【Page/EC budget】[...]
【Next step】isr-data-analysis
