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.
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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.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
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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.
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