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.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the theoretical puzzle is the bottleneck for an Academy of Management Review (AMR) manuscript — deciding whether an idea is theory-worthy and AMR-fit before any theorizing begins. Frames the puzzle; it does NOT build constructs (that is amr-theory-development) or design any empirical study (AMR has none).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
AMR wants a theoretical puzzle, not a topic. A topic is "leadership and remote work." A puzzle is "existing leadership theory predicts X, yet under condition C we should see not-X — and no current theory resolves the contradiction." Run every idea through:
amr-literature-positioning.AMR's own scope names four legitimate paths for a topic: (a) develop new theory; (b) significantly challenge or clarify existing theory; (c) synthesize recent advances into fresh theory; (d) identify and delineate a novel theoretical problem. If your idea fits none of these, it is likely not AMR. Exemplars to emulate: Oliver's "Strategic Responses to Institutional Processes" (AMR 1991, DOI 10.5465/amr.1991.4279002) challenged institutional theory's conformity assumption by theorizing agency; Dyer & Singh's "The Relational View" (AMR 1998, DOI 10.5465/amr.1998.1255632) synthesized a new locus of advantage. Neither used data.
| Puzzle type | Looks like |
|---|---|
| Theoretical contradiction | Two established theories make opposite predictions in the same setting |
| Boundary failure | An accepted theory breaks down at a condition no one has theorized |
| Construct ambiguity | A widely used construct conflates two distinct phenomena that should be separated |
| Missing mechanism | An empirical regularity is robust but the why has never been theorized |
| Phenomenon outrunning theory | A new organizational form / practice that current categories cannot capture |
| Reconceptualization | An old construct, re-specified, dissolves a long-standing debate |
If any line is blank, the idea is not yet AMR-ready.
The most common AMR desk reject is "this is an empirical paper"; the next is "incremental theory." Screen for both here, before investing in
amr-theory-development.
【Phenomenon】one sentence
【Theoretical tension】what existing theory predicts vs. what should hold
【Theory at stake】the conversation to challenge & extend
【Theoretical question】one sentence
【So-what for theory】what scholars can newly explain
【AMR fit】yes / borderline (route empirical work elsewhere) / no
【Next step】amr-literature-positioning
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概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。