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 articulating and differentiating the theoretical contribution of an Academy of Management Review (AMR) manuscript — showing precisely what is NEW versus prior theory and why it matters. Frames the contribution; it does NOT build the theory (amr-theory-development) or check its internal logic (amr-data-analysis).
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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 seeks "novel, insightful, and carefully crafted conceptual articles that challenge conventional wisdom." Whetten's classic AMR editorial, "What Constitutes a Theoretical Contribution?" (1989, DOI 10.5465/amr.1989.4308371), is the rubric: a complete contribution specifies the What (constructs), How (relationships), Why (the underlying logic), and Who/Where/When (boundary conditions). The Why is the centerpiece — it is what separates theory from description. AMR's scope frames the four legitimate contribution paths: develop new theory; significantly challenge or clarify existing theory; synthesize advances into fresh theory; or identify and delineate a novel theoretical problem.
A contribution claim that cannot be stated as "Before this paper, theory could not X; after it, theory can X" is not yet a contribution.
The single most common AMR rejection reason is "contribution not differentiated from prior work." Defend against it explicitly:
| Contribution type | Strength at AMR |
|---|---|
| New theory / reconceptualization that reorients a conversation | Strongest |
| New construct + mechanism that explains a persistent puzzle | Strong |
| Bridging two conversations to generate new propositions | Strong |
| Bounding an existing theory in a newly theorized way | Solid |
| Adding a contingency/moderator to existing theory | Usually too thin for AMR alone |
| Reviewing/organizing a literature | Not a theoretical contribution |
Dyer & Singh's "The Relational View" (AMR 1998, DOI 10.5465/amr.1998.1255632) states its "before → after" crisply: before, competitive advantage was theorized at the firm or industry level; after, relational rents can accrue to dyads and networks, with four specified sources. That is new theoretical machinery, differentiated from the nearest prior theories — not a relabel and not an added moderator.
A complete AMR contribution closes with implications:
【What's new】the theoretical machinery that did not exist before
【Before → after】theory could not X; now it can X
【Closest prior theory】name + the specific difference
【Contribution type】reconceptualization / new construct+mechanism / bridge / bounding
【Implications】theory / future research / practice
【Over-claim risk】none / trim to: ...
【Next step】amr-tables-figures
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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.
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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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