Skip to content
cccskills
FreePublished on GitHub

amr-methods

Use when choosing and applying a theory-construction method for an Academy of Management Review (AMR) manuscript — building constructs, defining their domain, specifying relationships, articulating the underlying logic/mechanisms, and setting boundary conditions. This is the THEORY-CONSTRUCTION craft, NOT empirical method; AMR publishes no datasets, no measures, and no statistical tests.

The English summary is being prepared. Showing the original description.

Run score Not tested

See how to install

Files included (1)

  • SKILL.md5.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Theory-Construction Method (amr-methods)

AMR publishes NO empirical data. There are no samples, no measures, no estimation, and no results. "Method" here means the craft of building theory: how you construct constructs, derive relationships, ground mechanisms, and bound the theory. If your project needs data to make its point, it belongs at AMJ / ASQ / SMJ.

When to trigger

  • Constructs and propositions exist but the way they were built feels ad hoc
  • A reviewer would ask "where did this construct's domain come from?"
  • The theory needs a more disciplined construction approach than intuition
  • You need to choose among theory-building styles for your puzzle

Choosing a theory-construction approach

ApproachWhen it fitsCore moves
Conceptual elaborationExtending a known theory to a new domainCarry over the core logic; re-specify constructs for the new setting; add boundary conditions
Analogical / metaphorical transferA construct from another field illuminates a management phenomenonMap source → target carefully; theorize where the analogy holds and breaks
Typology constructionThe phenomenon has distinct, theoretically meaningful typesDerive dimensions from theory (not data); ensure types are mutually exclusive and jointly exhaustive; theorize transitions (e.g., Oliver's strategic-responses typology, AMR 1991, DOI 10.5465/amr.1991.4279002)
Process theorizingThe phenomenon is a sequence/becoming, not a variance relationshipSpecify stages, triggers, feedback loops, and timing; theorize the engine that drives movement
Construct re-specificationAn existing construct conflates distinct phenomenaSplit or merge; re-define the domain; re-derive downstream relationships
Cross-level theorizingThe action spans individual ↔ collectiveSpecify emergence (bottom-up) and top-down effects; avoid level confusion

Building each component well

  • Constructs: a good construct is necessary (the theory cannot be stated without it), distinct (not a synonym), and has a clear domain and scope conditions (Suddaby's construct-clarity criteria, AMR 2010, DOI 10.5465/amr.2010.0419). Define what it excludes, not only what it includes. Construct domain is the conceptual analog of avoiding a contaminated sample — but done in argument, not data cleaning.
  • Relationships: derive, don't assert. Each link should follow from the constructs and an explicit mechanism. Specify form (causal / recursive / moderating / mediating / constitutive).
  • Mechanisms: name the engine — the why that makes theory explanatory rather than descriptive (Whetten, AMR 1989, DOI 10.5465/amr.1989.4308371). Prefer mechanisms with micro-foundations — actors doing things for stated reasons — over black-box "is associated with."
  • Boundary conditions: state the contextual, temporal, and level limits. Strong theory specifies its own scope; this is contribution, not hedging.
  • Falsifiability: design propositions to be refutable in principle (AMR's scope demands "testable knowledge-based claims"). This is the AMR analog of identification rigor at AMJ — credibility rests on tight, falsifiable logic, not on a clean estimate.

Checklist

  • A named construction approach (or explicit hybrid) is used, not intuition alone
  • Each construct is necessary, distinct, and domain-bounded
  • Each relationship is derived from constructs + mechanism, not asserted
  • Mechanisms are specified with micro-foundations where possible
  • Typologies (if used) are mutually exclusive and jointly exhaustive, derived from theory
  • Process theories specify stages, triggers, feedback, and timing
  • Boundary conditions are stated as part of the theory
  • No empirical apparatus has crept in (no samples, measures, tests)

Anti-patterns

  • Treating "method" as data collection — there is none at AMR
  • A typology whose dimensions came from convenience, not theory
  • A process "model" that is really a static box-and-arrow diagram with no engine
  • Borrowing a construct from another field without theorizing where the analogy fails
  • Cross-level claims that confuse the level of theory with the level of the mechanism
  • Asserting relationships and back-filling a mechanism afterward

Output format

【Construction approach】elaboration / analogy / typology / process / re-specification / cross-level
【Constructs built】[name + how its domain was derived]
【Mechanism basis】micro-foundations / structural / cognitive / ...
【Boundary conditions】contextual / temporal / level
【Empirical apparatus present?】must be: none
【Next step】amr-data-analysis (stress-test the logic)

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

See all skills by brycewang-stanford

Report a problem with this skill