Skill UI

This guide provides a framework for generating high-quality, self-explanatory exhibits for empirical academic manuscripts, particularly for fields like Operations Management. It covers creating descriptive statistics, correlation matrices, complex regression/SEM results, interaction plots, and process figures. The focus is on adhering to rigorous standards, ensuring all evidence is readable without accompanying text, and meeting APA style guidelines for publication.

A comprehensive guide for developing rigorous, mechanism-based theoretical arguments for operations management (OM) empirical studies. It teaches how to translate general theories (like RBV or TCE) into specific, testable operational mechanisms, specifying appropriate levels of analysis, mediators, and moderators required for top-tier academic publications.

This guide helps researchers rigorously evaluate whether their operations management or supply chain research topic is suitable for the Journal of Operations Management (JOM). It employs a four-screen test to ensure the topic meets academic criteria: operations must be the core phenomenon, the study must be empirically grounded, it must advance theory, and it must maintain practical relevance for managers.

This skill acts as a comprehensive workflow router for submitting empirical research to the Journal of Operations Management (JOM). It guides researchers through every stage of the manuscript lifecycle, from initial topic selection and theory development to rigorous data analysis, contribution framing, and point-by-point rebuttal. It does not replace specialized skills but diagnoses the current bottleneck and suggests the appropriate next step in the submission process.

This guide provides detailed instructions for authors preparing empirical manuscripts for the Journal of Operations Management (JOM). It emphasizes front-loading the core operations argument, structuring content for a dual audience (academics and practitioners), and adhering to rigorous academic publishing standards, including APA citation and strict text-overlap rules.

This guide details the rigorous requirements for publishing in top-tier quantitative management journals. It specifically targets research outputs that utilize mathematical models, theoretical proofs, and computational algorithms (e.g., operations research, decision theory). It strongly advises against purely empirical surveys or case studies, emphasizing methodological contribution over policy implication.

This comprehensive skill guides researchers through assessing whether their operations and supply chain manuscript meets the rigorous standards of the Journal of Operations Management (JOM). It covers key areas including theoretical framing, empirical rigor (e.g., common-method bias, identification), structural best practices, and common desk-reject triggers, helping authors refine their work for high-impact academic publishing.

A comprehensive guide for authors targeting the Management Science (MS) journal. It helps authors assess if their quantitative work (spanning operations, finance, IS, marketing, etc.) meets the journal's high, interdisciplinary rigor. It acts as a pre-submission checklist, advising on department selection, framing the contribution, and avoiding common desk-reject pitfalls.

This guide helps researchers evaluate if their operations management (OM) paper is suitable for the Manufacturing and Service Operations Management (M&SOM) journal. It provides detailed criteria on scope, required operational insight, method rigor (analytical or empirical), and structural house style, ensuring the core contribution addresses practical operations problems rather than just technical methodology.

A comprehensive guide for authors submitting to top Operations Management journals, such as M&SOM. It teaches how to translate complex analytical or empirical results into explicit, actionable operational contributions. The focus is on making the managerial implication, the core operational decision, and the structured abstract components clearly defined, moving the emphasis away from methodological novelty.

A comprehensive guide detailing the methodological standards for conducting high-impact research in Manufacturing and Service Operations Management. It covers both analytical modeling (proofs, sensitivity) and empirical studies (identification, robustness), emphasizing the necessity of full data and code replicability for published findings.

This guide instructs authors on how to strategically position their research manuscript within the established body of Operations Management (OM) literature. Instead of claiming an unstudied 'void,' the focus is on joining and advancing specific 'operational conversations' within canonical OM streams (e.g., queueing, supply chain, revenue management). It emphasizes differentiating contributions based on novel operational mechanisms or decision levers, ensuring the paper's fit is clear to expert reviewers and editors.