## Quality Bar

- [ ] The empirical design matches the business theory claim, construct definitions, level of analysis, and data-generating process.
- [ ] Quantitative studies document construct validity, source data, sample filters, model fit, fixed effects, clustering, robustness, and inference limits.
- [ ] Qualitative or mixed-method studies document theoretical sampling, coding reliability, triangulation, negative cases, evidence chain, and transparency appendix.
- [ ] Survey or perceptual measures address common-method bias, scale validity, respondent level, aggregation logic, and nonresponse risk.
- [ ] Strategy, organization, marketing, operations, and entrepreneurship claims separate mechanism evidence from managerial implication.
- [ ] Every coefficient, quote, event, or case comparison is tied to a theory contribution rather than only a business phenomenon.
- [ ] Unit, construct, and claim level are aligned across hypotheses, data, models, and interpretation.
- [ ] Rival explanations and boundary conditions are tested, triangulated, or explicitly scoped.
- [ ] Doctoral-level journal claims include enough transparency for reviewers to trace evidence to constructs.

## Common Pitfalls

| Pitfall | Impact | Fix |
|---------|--------|-----|
| Treating a business case as theory | Produces a consulting-style paper without publishable contribution | Name the literature conversation, mechanism, and boundary conditions |
| Level-of-analysis mismatch | Individual, team, firm, platform, or market claims are unsupported | Align constructs, data, and inference level |
| Convenience sampling | Reviewers doubt setting and contribution | Justify theoretical sampling or sample construction |
| Quote dumping | Qualitative evidence looks illustrative rather than analytic | Link quotes, codes, themes, and theoretical claims |
| Common-method bias ignored | Survey associations may reflect measurement artifact | Add temporal separation, source separation, marker variables, or scope claims |
| Construct validity assumed | Named constructs drift from measures | Provide scale logic, coding evidence, validation, or alternative measures |
| Managerial implications before theory | Weakens academic contribution | Establish theory contribution before implications |
| Model fit omitted | Quantitative evidence does not support construct relationships | Report fit, diagnostics, robustness, and rival model checks |
