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 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).
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。
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
まだレビューはありません。使ってみた感想をお寄せください。
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。