Organizational Research Methods最新文献_第6页

Review Research as Scientific Inquiry 评论研究作为科学探究

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-12-26 DOI: 10.1177/10944281221127292

Sven Kunisch, D. Denyer, J. Bartunek, Markus Menz, Laura B. Cardinal

{"title":"Review Research as Scientific Inquiry","authors":"Sven Kunisch, D. Denyer, J. Bartunek, Markus Menz, Laura B. Cardinal","doi":"10.1177/10944281221127292","DOIUrl":"https://doi.org/10.1177/10944281221127292","url":null,"abstract":"This article and the related Feature Topic at Organizational Research Methods upcoming were motivated by the concern that despite the bourgeoning number and diversity of review articles, there was a lack of guidance on how to produce rigorous and impactful literature reviews. In this article, we introduce review research as a class of research inquiries that uses prior research as data sources to develop knowledge contributions for academia, practice and policy. We first trace the evolution of review research both outside of and within management including the articles published in this Feature Topic, and provide a holistic definition of review research. Then, we argue that in the plurality of forms of review research, the alignment of purpose and methods is crucial for high-quality review research. To accomplish this, we discuss several review purposes and criteria for assessing review research's rigor and impact, and discuss how these and the review methods need to be aligned with its purpose. Our paper provides guidance for conducting or evaluating review research and helps establish review research as a credible and legitimate scientific endeavor.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"26 1","pages":"3 - 45"},"PeriodicalIF":9.5,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43364600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

SRM_R: A Web-Based Shiny App for Social Relations Analyses SRM_R:一个基于网络的闪亮的社会关系分析应用程序

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-11-20 DOI: 10.1177/10944281221134104

Man-Nok Wong, D. Kenny, A. Knight

引用次数: 1

Sensitizing Social Interaction with a Mode-Enhanced Transcribing Process 用模式增强转录过程使社会互动敏感化

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-10-31 DOI: 10.1177/10944281221134096

Qian Li

引用次数: 0

Assessment of Path Model Fit: Evidence of Effectiveness and Recommendations for use of the RMSEA-P 路径模型拟合评估:有效性证据和RMSEA-P使用建议

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-10-17 DOI: 10.1177/10944281221124946

L. J. Williams, Aaron R. Williams, Ernest H. O’Boyle

引用次数: 0

Should Moderated Regressions Include or Exclude Quadratic Terms? Present Both! Then Apply Our Linear Algebraic Analysis to Identify the Preferable Specification 适度回归应该包括还是排除二次项？同时出示！然后应用我们的线性代数分析来识别优选规范

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-10-11 DOI: 10.1177/10944281221124945

A. Kalnins

{"title":"Should Moderated Regressions Include or Exclude Quadratic Terms? Present Both! Then Apply Our Linear Algebraic Analysis to Identify the Preferable Specification","authors":"A. Kalnins","doi":"10.1177/10944281221124945","DOIUrl":"https://doi.org/10.1177/10944281221124945","url":null,"abstract":"Organizational research increasingly tests moderated relationships using multiple regression with interaction terms. Most research does so with little concern regarding curvilinear relationships. But methodologists have established that omitting quadratic terms of correlated primary variables may create false interaction positives (type 1 errors). If dependent variables are generated by the canonical process where fully specified regressions satisfy the Gauss-Markov assumptions, including quadratics solves the problem. But our empirical analysis of published organizational research suggests that dependent variables are often generated by processes where, even with quadratics included, regression analyses will remain Gauss-Markov non-compliant. In such cases, our linear algebraic analysis demonstrates that including quadratics—even those motivated by compelling theory—may exacerbate rather than mitigate the incidence of false interaction positives. The interaction coefficient may substantially change its magnitude and even flip sign once quadratics are included, and not necessarily for the better. We encourage researchers to present two full sets of results when testing moderating hypotheses—one with, and one without, quadratic terms. Researchers should then answer five questions developed here in order to determine the preferable set of results.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":" ","pages":""},"PeriodicalIF":9.5,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45785943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring What Matters: Assessing how Executives Reference Firm Performance in Corporate Filings 衡量重要因素：评估高管在公司文件中如何参考公司业绩

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-10-11 DOI: 10.1177/10944281221125160

S. Certo, Chunhu Jeon, Kristen A. Raney, Wookyung Lee

{"title":"Measuring What Matters: Assessing how Executives Reference Firm Performance in Corporate Filings","authors":"S. Certo, Chunhu Jeon, Kristen A. Raney, Wookyung Lee","doi":"10.1177/10944281221125160","DOIUrl":"https://doi.org/10.1177/10944281221125160","url":null,"abstract":"We know very little about the performance measures executives use to make decisions. To fill this void, we investigate the performance variables that executives reference in corporate filings with the SEC. Our analyses suggest that executives refer to monetary variables (i.e., revenue, profit, and cash flow) in over 98% of filings. In contrast, executives refer to the unitless performance measures scaled by size (i.e., return on assets, return on equity), which are favored by organizational scholars, in less than 15% of filings. We find that this preference for unscaled measures remains across market capitalization and actual firm performance. In other words, even observations with the highest levels of ROA and ROE are more likely to include monetary measures as opposed to ratios. In fact, we find that almost every observation that references ratios also includes monetary measures of firm performance. Stated differently, our findings suggest executives use ratios in addition to—and not instead of—monetary measures. We discuss research opportunities for scholars to further align with the practitioner perspective and to revisit conceptualizations of firm performance.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":" ","pages":""},"PeriodicalIF":9.5,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45607947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection 提高文本挖掘中无监督机器学习的可重复性和可问责性：透明性在报告预处理和算法选择中的重要性

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-09-21 DOI: 10.1177/10944281221124947

L. Valtonen, S. Mäkinen, J. Kirjavainen

{"title":"Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection","authors":"L. Valtonen, S. Mäkinen, J. Kirjavainen","doi":"10.1177/10944281221124947","DOIUrl":"https://doi.org/10.1177/10944281221124947","url":null,"abstract":"Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":" ","pages":""},"PeriodicalIF":9.5,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44146497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reviewer Resources: Confirmatory Factor Analysis 评审员资源：验证性因素分析

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-08-31 DOI: 10.1177/10944281221120541

Christopher D. Nye

引用次数: 6

Corrigendum to Using CATA and Machine Learning to Operationalize Old Constructs in New Ways: An Illustration Using U.S. Governors’ COVID-19 Press Briefings 使用CATA和机器学习以新方式实现旧结构的操作:以美国州长COVID-19新闻发布会为例

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2022-08-21 DOI: 10.1177/10944281221114765

J. Marshall, F. Yammarino, S. Parameswaran, M. Cheong

引用次数: 0

Construct Development and Validation in Three Practical Steps: Recommendations for Reviewers, Editors, and Authors* 在三个实际步骤中构建开发和验证:对审稿人，编辑和作者的建议*