本文へ移動
cccskills
無料GitHub で公開

aos-theory-development

Use when building the theoretical engine of an Accounting, Organizations and Society (AOS) manuscript — choosing a social/organizational theory tradition (institutional theory, governmentality, Bourdieu, actor-network theory, practice theory, psychological process theory) and making it generate the analysis rather than decorate it. Builds the lens; it does not collect data (aos-methods) or frame the contribution (aos-contribution-framing).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md6.0 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。

Theoretical Lens & Theorizing (aos-theory-development)

When to trigger

  • Field material is rich but the paper currently "describes what happened at Site X"
  • A theory is cited in the front end yet never reappears in the findings
  • An experiment predicts an effect without specifying the psychological process behind it
  • A reviewer writes "the theoretical framing is underdeveloped" or "the theory does no work"

AOS is theory-first

At AOS the theory is not a motivation ritual — it is the product. Empirics earn publication by extending, complicating, or overturning a way of understanding accounting's social operation. Before drafting anything else, answer: which conversation about how accounting works in organizations and society will this paper change, and through which conceptual apparatus?

The working traditions of the AOS canon

Choose one tradition and inhabit it; AOS referees can tell a user of a theory from a citer of one.

  • Institutional theory. Accounting as a carrier of legitimacy, myth, and isomorphic pressure; loose coupling between formal systems and practice. Entry point: Covaleski & Dirsmith (1986) on budgeting as power and politics. Theorize which institutional logics collide and what the accounting does in the collision.
  • Governmentality (Foucault). Accounting as a technology that renders people and domains calculable and governable — Miller & O'Leary's (1987) "governable person." Theorize the programmes (ideals) and technologies (calculative instruments) and their interplay.
  • Bourdieu. Fields, capital, habitus: accounting as a stake and weapon in struggles over position — used in AOS work on the profession, elites, and expertise. Theorize what forms of capital accounting converts and consecrates.
  • Actor-network theory. Accounting numbers as inscriptions that act at a distance (Robson 1992); expertise fabricated through networks (Chua 1995). Theorize how the inscription travels and enrolls allies, not just that it exists.
  • Structuration / practice theory. Accounting as both medium and outcome of everyday organizational routines — Roberts & Scapens (1985) on accountability systems; Ahrens & Chapman on practice. Theorize the recursive loop between system and situated use.
  • Psychological process theory (experiments). Motivated reasoning, mental models, accountability pressure, affect. Build the Libby-style predictive chain: conceptual cause → operational manipulation → process evidence → operational measure. The process claim, not the main effect, is the theory.

Make the lens generate, not decorate

  • Derive from the tradition what you expect to observe and what would surprise you — surprise is where theorizing starts.
  • For interpretive work, plan the abductive loop: theory guides fieldwork, anomalies push back on theory, iterate until the conceptual claim stabilizes (the discipline Ahrens & Chapman 2006 call positioning data to contribute to theory).
  • For experiments, write the process prediction (mediator, moderator, boundary condition) before the design, so the manipulation isolates the theorized construct.
  • Name the paper's conceptual innovation in one sentence: a new concept, a mechanism, a boundary condition on an existing account, or a demonstrated failure of a received view.

Discipline checks by tradition

  • Interpretive/critical: Can a skeptic trace each conceptual claim to specific field or archival evidence? Does the theory section set up exactly the concepts the findings use — no orphan concepts, no unannounced ones?
  • Experimental: Does theory fix the direction and the condition of the effect? Would a different process theory predict the same pattern (if so, design to discriminate)?
  • Historical: Is the account genealogical (how the present became possible) rather than a chronicle?

Reading depth test

Before committing to a tradition, you should be able to (a) summarize its two or three primary sources from memory, (b) name one AOS paper that used it well and one that used it thinly, and (c) say what the tradition cannot explain. Failing (c) is the tell of a citer rather than a user.

Checklist

  • One tradition chosen and named; its key sources actually read, not name-checked
  • The conceptual innovation is stated in a single sentence
  • Every concept in the framing reappears, working, in the findings
  • (Interpretive) abductive loop planned: theory ↔ anomaly ↔ revision
  • (Experimental) process prediction with mediator/moderator specified pre-design
  • What-would-surprise-us is written down before data collection

Anti-patterns

  • Theory as wallpaper: Bourdieu in the introduction, thick description ever after.
  • Lens mixing: governmentality, institutional logics and ANT stapled together because each explains one finding.
  • Effects without process (experimental): predicting that X moves Y with no account of why in the mind of the decision-maker.
  • Post-hoc lens shopping: choosing whichever theory retroactively "fits" completed fieldwork and presenting it as prospective.

Output format

【Tradition】institutional / governmentality / Bourdieu / ANT / practice / psychological-process
【Conceptual innovation】one sentence ...
【Core concepts】each concept → where it will work in the findings ...
【Expectations & surprises】what the lens predicts; what would count as an anomaly ...
【Process (if experimental)】mediator / moderator / boundary condition ...
【Next step】aos-literature-positioning

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