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***Apologies for cross-posting 😊***

We welcome you to read the January Issue of ORM that tackles pressing methodological challenges, such as transcribing the rich dynamic interplay in social interactions, assessing your results when investigating moderated relationships, evaluating model fit, transparency in reporting practices of machine learning, using social relations modeling to investigate interpersonal perceptions, and aligning the practitioner and academic perspective regarding decision making based on performance. 

Volume 27 Issue 1, January 2024

Sensitizing Social Interaction with a Mode-Enhanced Transcribing Process

Qian Li

Should Moderated Regressions Include or Exclude Quadratic Terms? Present Both! Then Apply Our Linear Algebraic Analysis to Identify the Preferable Specification

Arturs Kalnins

Assessment of Path Model Fit: Evidence of Effectiveness and Recommendations for use of the RMSEA-P

Larry J. Williams, Aaron R. Williams, and Ernest H. O'Boyle

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

L. Valtonen, Saku J. Mäkinen, and Johanna Kirjavainen

Short Methodological Reports

SRM_R: A Web-Based Shiny App for Social Relations Analyses

Man-Nok Wong, David A. Kenny, and Andrew P. Knight

Measuring What Matters: Assessing How Executives Reference Firm Performance in Corporate Filings

S. Trevis Certo, Chunhu Jeon, Kristen Raney, and Wookyung Lee

Organizational Research Methods (ORM), peer-reviewed and published quarterly, brings relevant methodological developments to a wide range of researchers in organizational and management studies and promotes a more effective understanding of current and new methodologies and their application in organizational settings. 

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Elizabeth Clayton, PhD
Assistant Professor of Management
School of Business
Indiana University Kokomo
Kokomo, IN
elclayto@iu.edu
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