From jmis-skills
Selects and defends JMIS-compatible research designs—IT-value econometrics, behavioral experiments, analytical models, or design-science artifacts—matching method to causal claim and page budget.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jmis-skills:jmis-methodsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- You have a mechanism or propositions but no defensible way to test/evaluate them
JMIS is methodologically broad but the design must earn the causal/economic verb in the claim.
| Style | Typical designs | The design must establish |
|---|---|---|
| IT business value / firm | Panel econometrics, natural experiment, DiD, IV, matching | Credible identification of IT's causal value against endogenous IT investment |
| Platform / e-commerce | Quasi-experiments on platform shocks, structural demand, field experiments | The network/two-sided mechanism, controlling for selection on platform data |
| Behavioral IS | Lab/online/field experiment, multi-wave survey, panel | Internal + construct validity and procedural remedies for common-method bias |
| Economics of IS | Analytical model; empirical test of a model's prediction | A coherent model with stated assumptions, or a test that maps to the prediction |
| Design-science / data-science | Build-and-evaluate of an IT/ML artifact | Novelty and managerial utility vs. credible baselines — not "it ran" |
IT investment and platform participation are chosen, not random. Anchor identification in a real source of exogenous variation — a policy change, a staggered system rollout, a platform redesign, a security breach, a pricing shock — and pre-commit the comparison and the assumptions you will defend. With staggered adoption, plan a modern estimator (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille) rather than naive TWFE, and design the event-study leads up front. Endogeneity that is only "controlled for" with covariates will draw reviewer fire.
Build procedural separations against common-method bias — temporal/source/psychological separation, validated and pretested scales, attention and manipulation checks — because statistical fixes (e.g., a marker variable) alone will not convince reviewers later. For experiments, make the IT manipulation realistic and the estimand explicit; report power.
A JMIS artifact paper lives on managerial utility, not algorithmic novelty alone. Decide before building how you will demonstrate utility: held-out benchmarks against credible (not strawman) baselines, a controlled experiment or A/B field deployment, simulation, or expert evaluation — each tied to the artifact's design rationale and to a real managerial decision. State the problem's relevance and the evaluation criteria so reviewers judge rigor and relevance.
The complete manuscript is capped at ≤50 pages (12pt, double-spaced). Online appendixes are permitted, but the main paper must be self-contained and the core claims established in the body — do not design a study whose key evidence only fits by exporting it. Survey instruments go as separate anonymized attachments. (检索于 2026-06;以官网为准.)
A team wants to claim that an ERP go-live raised plant productivity. A cross-section of ERP-adopters vs. non-adopters cannot carry that claim — adopters differ systematically (larger, better-managed firms self-select). The JMIS design uses the staggered go-live timing across plants as the variation, estimates with Callaway–Sant'Anna (not naive TWFE), pre-specifies the event-study window, and checks that pre-trends are flat before go-live. The identifying assumption — that go-live timing is not driven by anticipated productivity shocks — is argued from the institutional rollout schedule and falsified with a placebo on plants whose go-live slipped. That is the difference between "ERP correlates with productivity" and "ERP go-live raised productivity by X%."
Platform and marketplace data — a JMIS staple — carry built-in threats that the design must anticipate, not patch later. Participation and intensity are endogenous (sellers/buyers self-select into features); two-sidedness means a shock to one side feeds back to the other, so a naive one-side regression is mis-specified; ranking/recommendation systems create feedback loops where the outcome you measure was partly caused by the system you study; and platform redesigns are often rolled out non-randomly. Build the identification around a genuine source of exogenous variation (a staged redesign, an exogenous policy or pricing change, a randomized experiment the platform ran) and state explicitly how you handle cross-side feedback and algorithmic confounding. Reviewers on platform papers will probe exactly these points.
JMIS reviewers reward a method chosen because the claim and phenomenon demand it, and they penalize method for method's sake. A flashy deep-learning model where a transparent regression would answer the managerial question better is a liability, not an asset; conversely, a simple OLS where the design clearly calls for a quasi-experiment will not carry a causal claim. State, in the design section, why this method is the right tool for this claim — what it identifies or evaluates that a simpler or fancier alternative would not. Where you combine methods (e.g., an experiment to establish a mechanism plus field data for external validity), say what each leg buys you. The method should read as the inevitable consequence of the question, not a showcase.
【Style & design】firm econometrics / platform quasi-exp / survey-experiment / analytical / build-and-evaluate
【Identification or evaluation】source of variation OR evaluation plan + credible baselines
【Validity threats handled】endogeneity / CMB / construct validity / external validity
【Fits ≤50pp?】yes / trim
【Next step】jmis-data-analysis
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin jmis-skillsMatches research design to MIS Quarterly manuscript tradition: behavioral, economics-of-IS, design science, or qualitative. Plans evaluation strategies and guards against validity threats.
Guides selection and defense of research design for JAIS manuscripts, matching method to claim under methodological pluralism. Covers behavioral, economics-of-IS, design-science, qualitative, and review traditions.
Guides selection and stress-testing of an Information Systems Research (ISR) manuscript's research design, matching empirical, analytical, design-science, or multimethod genres to the research question and ensuring the design supports the intended contribution.