本文へ移動
cccskills
無料GitHub で公開

amj-topic-selection

Use when scoping or sanity-checking a research question for an Academy of Management Journal (AMJ) manuscript — fit, theory-drivenness, and feasibility. Decides whether the idea can earn a theoretical contribution; it does not build the theory itself (use amj-theory-development) or position the literature (amj-literature-positioning).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.1 KB

SKILL.md(原文)

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。

Topic Selection & AMJ Fit (amj-topic-selection)

When to trigger

  • You have a dataset, a context, or a phenomenon, but no sharp research question
  • You are unsure whether the idea belongs at AMJ vs. its AOM sister AMR (theory-only, no data), or at non-AOM venues ASQ (Cornell/SAGE) or SMJ (Wiley/SMS), or a field journal
  • The question feels like "no one has studied X here" rather than "theory predicts something surprising"
  • You want to pressure-test fit before investing months in data collection

AMJ fit test (all four must pass)

AMJ's mission is "to publish empirical research that tests, extends, or builds management theory and contributes to management practice," and it judges every paper on three criteria: a strong empirical contribution, a meaningful theoretical contribution, and practical relevance. The fit test below operationalizes those criteria.

  1. Phenomenon-grounded but theory-driven. AMJ wants real organizational phenomena (micro OB, macro/strategy, HR, entrepreneurship, organizational theory) examined through a theoretical lens. A purely descriptive or purely methodological paper does not fit.
  2. A theoretical mechanism, not just an empirical association. Ask: why would A affect B, and when? If you can only say "they are correlated," you have a topic, not yet an AMJ paper.
  3. Original data and a defensible design are feasible. AMJ is empirical and welcomes all empirical methods — qualitative, quantitative, field, laboratory, meta-analytic, and mixed. If you cannot get suitable data of any of these kinds, it is an AMR idea (theory-only, no data), not an AMJ idea.
  4. Surprise or tension. The strongest AMJ questions sit on a tension between theories, a counterintuitive boundary condition, or an unresolved debate — not a confirmation of the obvious.

Choosing the divisional home

AMJ routes manuscripts to divisional action editors. Knowing your home sharpens framing:

If your core construct is...Likely division / framing
Individuals/teams (motivation, leadership, emotion)Micro / OB
Firms/industries (strategy, performance, M&A)Macro / strategy
Selection, training, turnover, careersHR
New ventures, founders, opportunityEntrepreneurship
Institutions, identity, sensemaking, fieldsOrganization theory / OMT

Frame the question in the vocabulary of its home division and cite that division's recent AMJ papers.

From phenomenon to question

  • State the phenomenon in one sentence (what happens in the world).
  • Name the focal theory you will use as a lens.
  • Write the research question as a why/when statement, not a whether statement.
  • Identify the surprising tension: what does naive intuition or a rival theory predict instead?
  • Draft a one-line "potential theoretical contribution" claim and sanity-check it: would a reviewer learn something new about the theory?

Checklist

  • Question is framed as why/when, with an explicit mechanism, not just whether A predicts B
  • There is a tension, puzzle, or boundary condition (not a confirmation of the obvious)
  • The phenomenon is a genuine organizational/management phenomenon, not a methods demo
  • Suitable original data and a credible design are feasible (else it is an AMR idea)
  • Target division identified; 3–5 recent AMJ papers in that division read and notable
  • One-line theoretical-contribution claim drafted and judged non-trivial

Anti-patterns

  • Gap-spotting: "No study has examined X in context Y." A gap is not a contribution; AMJ wants a theoretical reason the gap matters.
  • Borrowed phenomenon, no theory: an interesting dataset with no theoretical question behind it.
  • Wrong venue: a purely conceptual idea (belongs at AMR) or a pure methods/measurement paper.
  • Over-scoping: three theories and ten constructs in one paper — pick the one tension you can defend.
  • Context as the only novelty: "same model, new country/industry" without a theoretical reason the context changes the mechanism.

Output format

【Phenomenon】one sentence (what happens in the world)
【Focal theory / lens】...
【Research question】why/when ...
【Tension / surprise】rival prediction or puzzle ...
【Candidate theoretical contribution】one line ...
【Division / framing home】micro / macro / HR / entrepreneurship / OMT
【AMJ fit verdict】fit / borderline / better at AMR/ASQ/SMJ/field
【Next step】amj-theory-development (build mechanism + hypotheses)

レビュー

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。

同じリポジトリのスキル

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

brycewang-stanford のスキルをすべて見る

このスキルの問題を報告する