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 deciding which amr-* sub-skill to invoke next, or when sequencing a theory-building manuscript from theoretical-puzzle framing through developmental-review revision for an Academy of Management Review (AMR) submission. Routes — does not replace — the specialized skills.
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。
This is the router. It does not replace any specialized skill — it tells you which amr- skill to use at the current stage* of an Academy of Management Review manuscript.
Default assumption: unless the user says otherwise, treat the target as AMR — the Academy of Management's premier theory-development journal. AMR publishes conceptual articles that build new theory; per its scope, submissions "must extend theory in ways that develop testable knowledge-based claims." It contains no datasets, no hypothesis tests, no results section — empirical hypothesis-testing is the lane of its sibling AMJ. The deliverable is a genuinely new theoretical contribution: new constructs, a new process model, or a reconceptualization — developed with rigorous logic, explicit assumptions, propositions, and boundary conditions. If the project has data and tests, it belongs at AMJ / ASQ / SMJ, not AMR.
The AMR contribution bar is set by two AMR-published editorials worth keeping in view at every stage: Whetten's "What Constitutes a Theoretical Contribution?" (1989, DOI 10.5465/amr.1989.4308371) — the What/How/Why/Who-Where-When rubric — and Suddaby's "Editor's Comments: Construct Clarity in Theories of Management and Organization" (2010, DOI 10.5465/amr.2010.0419). Pure-theory exemplars built by argument alone: Oliver, "Strategic Responses to Institutional Processes" (1991, DOI 10.5465/amr.1991.4279002); Dyer & Singh, "The Relational View" (1998, DOI 10.5465/amr.1998.1255632). References follow APA-style (AOM house style).
| Current symptom | Next skill |
|---|---|
| Idea is vague; not sure there is a real theoretical puzzle | amr-topic-selection |
| Have a puzzle but constructs/relationships/logic are not built | amr-theory-development |
| Unsure which conversation to enter or what to "challenge" | amr-literature-positioning |
| Propositions exist but the construction method feels thin | amr-methods |
| Propositions stated but underlying logical argument is missing | amr-data-analysis |
| Cannot articulate what is NEW vs. prior theory | amr-contribution-framing |
| Box-and-arrow figure has no mechanism / typology not earning its keep | amr-tables-figures |
| Prose reads like a literature review, not an argument | amr-writing-style |
| Ready to submit; need the ScholarOne preflight | amr-submission |
| Want to understand AMR's developmental, multi-round review | amr-review-process |
| Received an R&R; need to write the response document | amr-rebuttal |
amr-topic-selection — lock the theoretical puzzle (the "why doesn't existing theory explain this?")amr-literature-positioning — identify the conversation to challenge and extendamr-theory-development — build constructs, relationships, propositions, boundary conditionsamr-methods — theory-construction craft: construct domains, mechanisms, assumptionsamr-data-analysis — argument development: logic checks, counterfactuals, alternative explanationsamr-contribution-framing — differentiate the new theory from prior workamr-tables-figures — finalize the conceptual figure / typology / propositions tableamr-writing-style — AOM house style; argument-driven prose (polish)amr-submission — ScholarOne preflightamr-review-process — understand the developmental review you are about to enteramr-rebuttal — after the R&R
amr-writing-styleis a late-stage polish. Do not polish prose before the theory's logic stands up (amr-data-analysis) and the contribution is differentiated (amr-contribution-framing).
amr-topic-selectionamr-literature-positioningamr-data-analysisamr-theory-development then amr-data-analysisamr-contribution-framingamr-tables-figuresamr-writing-styleamr-submissionamr-review-process then amr-rebuttalIf the manuscript has data, measures, and statistical tests, an empirical-management stack (AMJ / ASQ / SMJ) fits better. The core split:
A common failure is sending an AMR draft that is really an under-powered empirical paper, or an AMJ draft whose "theory" is a literature summary. Pick the right stack early.
Keep one honest line per stage. AMR reviewers judge the theory, so the ledger tracks
argument-state, not word count. Mark a stage DONE only when its gate is truly cleared.
AMR THEORY-BUILDING LEDGER — manuscript: ____________________
[ ] puzzle why does existing theory FAIL to explain X? ......... route: amr-topic-selection
[ ] conversation whose theory am I extending / challenging? ........... route: amr-literature-positioning
[ ] constructs each construct: name + domain + What/How/Why (Whetten) route: amr-theory-development
[ ] propositions P1..Pn stated; each has an explicit causal logic ..... route: amr-data-analysis
[ ] boundaries Who/Where/When conditions named for the theory ....... route: amr-theory-development
[ ] contribution one sentence: "New vs. prior theory is ____" ......... route: amr-contribution-framing
[ ] figure box-and-arrow shows a MECHANISM, not a taxonomy ...... route: amr-tables-figures
[ ] prose reads as argument, not literature review ............. route: amr-writing-style
[ ] preflight 200-word abstract, APA refs, ScholarOne fields ....... route: amr-submission
GATE: no data, no hypothesis tests, no results section — if present, this is an AMJ paper.
amr-literature-positioning and jump to building — reviewers first ask whose conversation you are in.amr-tables-figures pretty up a model before the propositions and mechanisms exist.amr-rebuttal draft a response before the theory itself has actually been revised.Submission specifics change. The source map now verifies the current editor, ScholarOne portal, no-fee policy, AOM style mechanics, 200-word abstract limit, and AI-disclosure policy; keep checking the official AMR / AOM author pages for manuscript length, portal prompts, and any newly published acceptance-rate or reviewer-count figures.
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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。