Use when deciding which asq-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an Administrative Science Quarterly (ASQ) manuscript. Routes — it does not replace — the specialized skills.
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This is the router. It does not replace any specialized skill; it tells you which asq- skill to use at your current stage*.
Default assumption: unless the user says otherwise, treat the target as Administrative Science Quarterly (ASQ) — a top, theory-driven journal in organization theory and the sociology of organizations, founded in 1956 by James D. Thompson (first editor 1956–1973; author of Organizations in Action) and published by SAGE in association with the Samuel Curtis Johnson Graduate School of Management at Cornell University. Its motto: "Dedicated to advancing the understanding of administration through empirical investigation and theoretical analysis." ASQ's hallmark is a deep, often surprising theoretical insight about organizations, delivered with superb craft. It publishes both rigorous quantitative work and rich qualitative/inductive studies (grounded-theory, ethnographic, historical) — method follows the theoretical question. The current Editor is Beth Bechky (UC Davis; term began July 1, 2025, succeeding Christine Beckman and András Tilcsik), an ethnographer of work and occupations — a signal of ASQ's openness to inductive fieldwork. ASQ has no submission fee, is exceptionally selective (reported acceptance ~8%), uses APA references (since January 2025), and is distinctive among management journals for its standing book review section.
| Current symptom | Next skill |
|---|---|
| Idea is interesting but not yet surprising; unsure it fits ASQ | asq-topic-selection |
| Have a phenomenon but no theoretical engine / mechanism | asq-theory-development |
| Lit review reads like a list; not in dialogue with org-theory traditions | asq-literature-positioning |
| Unsure whether design should be qualitative or quantitative; rigor gaps | asq-methods |
| Have data but coding/analysis is opaque; no data-to-theory link | asq-data-analysis |
| Can't state the contribution as a "so-what" for organization theory | asq-contribution-framing |
| Tables/figures are dense or do not show the data structure (qual or quant) | asq-tables-figures |
| Prose is flat, jargon-heavy, or "variance-speak" where process is needed | asq-writing-style |
| Preparing to submit; need a preflight checklist | asq-submission |
| Want to understand ASQ's developmental review before/after submitting | asq-review-process |
| Received an R&R and need to write the response | asq-rebuttal |
asq-topic-selection — lock the puzzle and the surprising organizational insightasq-theory-development — build the theoretical engine (process or variance) before data workasq-literature-positioning — situate in organization-theory conversationsasq-methods — choose and justify design (qualitative or quantitative); set the rigor barasq-data-analysis — execute analysis and the data-to-theory linkasq-contribution-framing — sharpen the theoretical "so what" once results existasq-tables-figures — finalize data tables, data-to-theory tables, models, figuresasq-writing-style — full-manuscript prose and craft polishasq-submission — pre-submission preflightasq-review-process — understand the developmental, multi-round processasq-rebuttal — after the R&R decision
asq-writing-styleandasq-tables-figuresare late-stage polish — do not run them while the theoretical contribution is still unsettled. At ASQ, craft cannot rescue a thin idea.
asq-topic-selectionasq-theory-developmentasq-literature-positioningasq-theory-development then asq-contribution-framingasq-data-analysisasq-contribution-framingasq-submissionasq-review-process then asq-rebuttalA pure conceptual paper fits an AMR-style stack better. A hypothesis-testing study aimed purely at firm-performance outcomes with no organizational-theory surprise may belong at SMJ — at ASQ a thin contribution dressed in a strong method will not survive screening, however clean the identification.
When a draft is thrashing, score each row 0–2 (0 = broken, 1 = shaky, 2 = solid). The lowest row is your next skill — at ASQ a low "surprise" or "so-what" score outranks any polished exhibit. Craft never rescues a thin idea here.
ASQ BOTTLENECK TRIAGE score(0-2) route if lowest
surprise / puzzle ............. [ ] asq-topic-selection
theoretical engine (mechanism) [ ] asq-theory-development
dialogue w/ org-theory canon .. [ ] asq-literature-positioning
design fits the question ...... [ ] asq-methods
data -> theory link is visible [ ] asq-data-analysis
"so what" for organization theory[ ] asq-contribution-framing
exhibits show the data structure [ ] asq-tables-figures
prose is process-, not variance- [ ] asq-writing-style
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RULE: fix the lowest theory row before touching any craft row.
qualitative & quantitative are equally first-class — method follows the question.
asq-theory-development and rush to data — at ASQ the contribution is theoretical, not methodological.asq-tables-figures polish exhibits before the contribution is settled.asq-rebuttal draft a response letter before the manuscript itself has been revised.No reviews yet. Tell others what you thought.
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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.
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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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