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 turning results into an explicit theoretical contribution for an Academy of Management Journal (AMJ) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution and implications; it does not build the original theory (amj-theory-development) or run the analysis (amj-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。
AMJ evaluates every paper on three criteria: a strong empirical contribution, a meaningful theoretical contribution, and practical relevance. The empirical contribution must be paired with a theoretical one: what do we now understand about the theory that we did not before? — and the discussion must also speak to why it matters for management practice. A clear, defensible theoretical answer is essential; strong results with no theoretical advance read as a technical report, not an AMJ paper. AMJ's From the Editors tradition frames this as the "so what?" and "who cares?" tests.
| Contribution type | What it does |
|---|---|
| Reveal a new mechanism | Explains why an established effect occurs |
| Establish a boundary | Shows when an effect holds, weakens, or reverses |
| Adjudicate competing theories | Shows which of two rival predictions holds, and why |
| Integrate / bridge | Connects two literatures into a more general account |
| Challenge an assumption | Overturns a taken-for-granted premise (problematization) |
| Introduce/refine a construct | Conceptualizes and validates a new construct that earns its keep |
State explicitly which type(s) you claim; do not gesture vaguely at "contributing to the literature."
A strong AMJ discussion typically:
Match the discussion to the front end: the contributions you claim here must be the ones you promised in the introduction. A reviewer will check that the intro's promise and the discussion's delivery line up.
Draft 2–4 explicit contribution sentences and place a version in the introduction and the discussion. Each should be of the form: "We contribute to [literature] by showing [mechanism/boundary/integration], which advances [theory] because [reason]."
【Contribution type(s)】mechanism / boundary / adjudication / integration / assumption / construct
【Contribution sentences】1–4, intro + discussion aligned
【Theoretical implications】per literature: what changes and why ...
【Practical implications】specific, grounded ...
【Limitations → boundaries】...
【Future research】concrete, mechanism-tied ...
【Intro↔discussion consistency】yes/no
【Next step】amj-tables-figures, then amj-writing-style
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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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