Guides the complete workflow for preparing and submitting empirical manuscripts to the Journal of Operations Management, from topic selection and theory building through statistical analysis, APA-compliant writing, exhibit design, and Wiley ReX submission with R&R rebuttals.
Use when articulating the theoretical and practical contribution of a Journal of Operations Management (JOM) manuscript — stating what new OM theory the study delivers, why operations is central to the contribution, and what it means for operations managers, in the intro and discussion.
Use when running and reporting the statistical analysis for a Journal of Operations Management (JOM) empirical manuscript — measurement validity for survey constructs, identification and endogeneity for archival operations data, manipulation checks for behavioral-OM experiments, and robustness. Executes and reports the analysis; it does not design the study (jom-methods) or frame the contribution (jom-contribution-framing).
Use when positioning a Journal of Operations Management (JOM) manuscript within the operations and supply chain management conversation — joining a live OM/SCM debate, distinguishing the paper from analytical OM work, and aligning the framing with the target Department's mission.
Use when choosing and defending the research design for a Journal of Operations Management (JOM) empirical study — matching survey, archival/secondary, field, case, experimental, or intervention-based research to the operations question, anticipating the Empirical Research Methods Department's method check, and keeping the design observation-grounded rather than analytical.
Use when planning and drafting a revision and point-by-point response letter for a Journal of Operations Management (JOM) R&R — prioritizing the Department Editor/Associate Editor's decisive concerns, resolving Empirical Research Methods method-check issues, and documenting new operations analyses across multiple rounds.
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横跨经管社科 · 人文社科 · 自然科学 · 临床医学 · AI 计算机等多个主流学科,为每一本期刊 / 每一个会议单独编码它的投稿工作流。
🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🗺 路线图 · 🌐 English
先看期刊,再进 Pack。点击任意封面即可进入对应的期刊 Skill 包。
🆕 四个最新学科广度合集 —— 工程技术 40 · 农业环境 30 · 临床医学 30 · 英文人文 36;点击封面进入合集页。
其他合集 · 中文体育学 · 12 本 CSSCI 体育学来源刊
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Agent skill stack for submitting to 《经济研究》 (Economic Research Journal), the top economics journal in China. Eighteen skills across the manuscript lifecycle: China-context topic selection, introduction, bilingual literature review, theory & hypotheses, data & sample, modern causal identification (DID / IV / RDD / DML with heterogeneity-robust estimators), mechanism analysis (post-江艇 2022 paradigm), heterogeneity, robustness, three-line tables, policy implications, abstract, house style, reviewer-lens self-audit, reproducibility, submission preflight, and R&R rebuttals. Ships a runnable Stata + Python code library. Bilingual zh-CN / en docs.
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Agent skill stack for manuscripts targeted at the Journal of Financial and Quantitative Analysis (JFQA) — empirical and quantitative financial economics (corporate finance, investments, capital and security markets, financial institutions, and finance-relevant quantitative methods), published by Cambridge University Press for the Michael G. Foster School of Business at the University of Washington. Built around the JFQA realities: submission via Editorial Manager with a text-searchable PDF, a $350 fee (only $275 refunded if not sent to a reviewer), double-anonymous review, a strict one-paragraph / 100-word abstract cap, prescriptive 8.5x11 / 1-inch / 12-pt Times New Roman double-spaced formatting, a one-year resubmission ban for undisclosed prior rejections, and the JFQA Code Sharing Policy with a dedicated JFQA Dataverse at the Harvard Dataverse. Covers topic fit, literature positioning, causal/identification design for finance, robustness, tables and figures, house style, the code/data archive, referee strategy, submission preflight, and R&R rebuttals. Bilingual en / zh-CN docs; Stata / R / Python conventions.
Agent skill stack for manuscripts targeted at Manufacturing & Service Operations Management (M&SOM), the premier operations-management journal published by INFORMS and sponsored by the MSOM Society. Built around M&SOM's defining bar: an operations decision or problem must be central to the contribution, executed with top-tier rigor in either analytical/stochastic modeling (optimization, queueing, game theory, revenue management) or empirical OM/data-driven analytics. Covers OM-centered topic selection, model and theory development, literature positioning, methodology and identification, analysis and replicability, contribution framing, INFORMS-style exhibits and prose, the author-routed Department-Editor ScholarOne submission, double-anonymous review, and R&R rebuttals. Bilingual en / zh-CN docs.
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