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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
| Approach | When it fits | Core moves |
|---|---|---|
| Conceptual elaboration | Extending a known theory to a new domain | Carry over the core logic; re-specify constructs for the new setting; add boundary conditions |
| Analogical / metaphorical transfer | A construct from another field illuminates a management phenomenon | Map source → target carefully; theorize where the analogy holds and breaks |
| Typology construction | The phenomenon has distinct, theoretically meaningful types | Derive 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 theorizing | The phenomenon is a sequence/becoming, not a variance relationship | Specify stages, triggers, feedback loops, and timing; theorize the engine that drives movement |
| Construct re-specification | An existing construct conflates distinct phenomena | Split or merge; re-define the domain; re-derive downstream relationships |
| Cross-level theorizing | The action spans individual ↔ collective | Specify emergence (bottom-up) and top-down effects; avoid level confusion |
【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)
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.