Jais Data Analysis

Data Analysis & Evidence (jais-data-analysis)

When to trigger

• Data are collected (or the artifact built) and it is time to estimate, evaluate, and report
• A reviewer probes measurement validity, identification, artifact utility, or replicability
• You must prepare the correlation/covariance matrix plus descriptives JAIS requires for quantitative studies
• Effects are reported as stars with no magnitude or theoretical meaning

Analyze in the currency of your tradition

JAIS's pluralism means there is no single mandated estimator; the standard is the rigor norm of your tradition, reported transparently enough for a developmental Senior Editor to interrogate. Pick the row.

Behavioral IS: defend measurement, then satisfy the JAIS matrix rule

Report the measurement model first: reliabilities, AVE, and discriminant validity. PLS-SEM suits predictive/formative models; covariance-based SEM suits theory-testing with reflective constructs — justify the choice. Address common-method bias beyond a single-factor (Harman) test — a marker variable, an unmeasured method factor, or showing interactions survive. Then report structural paths with effect sizes, not just significance. Crucially, JAIS requires you to "provide a full correlation matrix or covariation matrix as a part of articles (appendix)" for SEM studies, plus descriptives — prepare this now, not at proof stage (检索于 2026-06；以官网为准).

Economics of IS: make the causal claim earn its keep

Lead with the identification logic, then stress-test it: alternative specifications, placebo and event-study plots, sensitivity to the key assumption, and clustering matched to the data structure. With staggered timing, use a modern estimator and show flat pre-trends. Report magnitudes and their economic meaning, not just stars.

Report robustness as a defense of the theory, not a ritual

A robustness battery at JAIS should be legible as protecting the theoretical claim, not as a checklist. For each check, state the threat it neutralizes: a placebo test guards against spurious timing, an alternative specification guards against functional-form dependence, a sensitivity analysis bounds the unobserved-confounding concern. Listing checks without naming the threat each addresses is a recurring pushback — and at a theory-forward journal, an unmotivated robustness section signals that the author is not sure which threat actually endangers the contribution.

Design science: evaluate the artifact, not just the math

Benchmark against the baselines a skeptic would name, run ablations to show which design principles matter, and connect each result back to a design proposition. Where feasible, evaluate in a realistic field setting. Utility for a real problem is the contribution.

Qualitative: make the path from data to theory traceable

Show the coding structure and an audit trail so a reader can follow how raw material became constructs. Representative quotations and negative cases build trustworthiness; the analytic narrative, not a coefficient, carries the claim.

Tie every result back to the theory

JAIS is theory-forward, so analysis that floats free of the theoretical argument reads as dredging. After each estimate, evaluation, or coded theme, state explicitly which hypothesis, proposition, or construct it bears on and what it implies for the mechanism. A results section that marches through coefficients without re-connecting to the theory invites the "strong finding, thin contribution" critique — the most common JAIS pushback. The discipline is to report the number and immediately say what the field now knows because of it.

Prepare the JAIS data materials

JAIS policy requires authors to make datasets "available on request for checking by senior editors or reviewers after care has been taken to anonymize the data," and for quantitative studies to provide "the co-variance or correlation matrix plus descriptives." If you reuse a dataset, you must justify it for an alternative theoretical purpose or a new methodological approach. Assemble these now and keep them anonymized for double-blind review.