Organizational Research Methods最新文献_第2页

Modeling Unfolding Response Data Within the Structural Equation Modeling Framework 结构方程建模框架下展开响应数据的建模

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2026-04-08 DOI: 10.1177/10944281261421529

Ringo Moon-ho Ho, Jie Xin Lim, Olexander Chernyshenko

{"title":"Modeling Unfolding Response Data Within the Structural Equation Modeling Framework","authors":"Ringo Moon-ho Ho, Jie Xin Lim, Olexander Chernyshenko","doi":"10.1177/10944281261421529","DOIUrl":"https://doi.org/10.1177/10944281261421529","url":null,"abstract":"Dominance and unfolding response processes describe two ways in which individuals may respond to rating scale items. The dominance process assumes a monotonic relationship between a latent trait and the probability of endorsement and is typically modeled using a linear factor model within structural equation modeling (SEM). In contrast, the unfolding process assumes single-peaked response functions, with endorsement most likely when item and person locations are close on the latent continuum. Fitting unfolding models usually requires specialized software, which limits their integration with SEM. In this article, we proposed the ordered categorical response unfolding model (OCRUM), which can be estimated in Mplus. We illustrated its use with two empirical datasets and found that item and person locations were comparable to those obtained from the generalized graded unfolding model (GGUM). We also conducted Monte Carlo simulations to examine parameter recovery under varying sample sizes, test lengths, and response formats. Finally, we demonstrated that OCRUM can serve as the measurement component of a general structural equation model, enabling dominance and unfolding response processes to be represented within a single SEM framework.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"64 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635727","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

Applying Machine Learning and Natural Language Processing Methods to Support Taxonomy Development and Maintenance 应用机器学习和自然语言处理方法支持分类法的开发和维护

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2026-03-27 DOI: 10.1177/10944281261434975

Nathaniel M. Voss, Jiayi Liu, Saron Demeke, Martin C. Yu, Harrison J. Kell, Brian Prost, Dan J. Putka

{"title":"Applying Machine Learning and Natural Language Processing Methods to Support Taxonomy Development and Maintenance","authors":"Nathaniel M. Voss, Jiayi Liu, Saron Demeke, Martin C. Yu, Harrison J. Kell, Brian Prost, Dan J. Putka","doi":"10.1177/10944281261434975","DOIUrl":"https://doi.org/10.1177/10944281261434975","url":null,"abstract":"Taxonomies provide a systematic way to organize phenomena and have various practical and theoretical benefits for organizational researchers and practitioners. While taxonomy development and maintenance is often a burdensome process (e.g., time-consuming, costly, and prone to judgmental error), advances in natural language processing (NLP) have the potential to streamline this process. In this study, we employed various evaluation metrics (e.g., cosine similarity) to investigate how machine learning (ML) methods and large language models (LLMs) can automate taxonomy development and maintenance. We examined two embedding models, six clustering algorithms, and three generative LLMs (for creating cluster labels) to construct taxonomies and compared their alignment with four established taxonomies (CABIN, IPIP-NEO-120, ATAF, and O*NET). The confirmatory taxonomic method we examined resulted in effective clustering (i.e., similar text statements were consistently grouped), frequently yielded structures similar to the original taxonomies for ATAF, IPIP-NEO-120, and CABIN (with O*NET being more variable), and resulted in extremely efficient taxonomy title generation. These findings can provide researchers with a foundation for how to approach NLP-based taxonomy development and maintenance activities for their own contexts.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"15 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518883","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

Unpacking the Validity of Open-Ended Personality Assessments Using Fine-Tuned Large Language Models 利用微调的大型语言模型揭示开放式人格评估的有效性

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2026-03-11 DOI: 10.1177/10944281251413746

Andrew B. Speer, Angie Y. Delacruz, Takudzwa A. Chawota, James Perrotta, Cort W. Rudolph

{"title":"Unpacking the Validity of Open-Ended Personality Assessments Using Fine-Tuned Large Language Models","authors":"Andrew B. Speer, Angie Y. Delacruz, Takudzwa A. Chawota, James Perrotta, Cort W. Rudolph","doi":"10.1177/10944281251413746","DOIUrl":"https://doi.org/10.1177/10944281251413746","url":null,"abstract":"Alternative approaches to personality measurement, such as open-ended narrative-based assessments, have potential advantages for organizational research and practice. In this research, we investigate factors that affect valid application of natural language processing (NLP) for scoring open-ended personality assessments and when, how, and why such assessments capture personality-related variance. Using a large sample of responses to open-ended assessments, convergence between NLP scores and self-report target scores increased as the degree of customization and the sophistication of the underlying model increased, with the worst psychometric performance occurring for zero-shot large language model (LLM) scores and the best for fine-tuned LLM scores. However, all scoring methods exhibited evidence of validity. Additionally, when trained to predict direct evaluations of the narrative responses, correlations with target scores were large ( M = .83). NLP scores also exhibited discriminant and criterion-related validity evidence. However, validity was contingent upon the methodological rigor employed in developing writing prompts. Prompts designed to elicit trait-relevant information outperformed generic prompts, and this occurred because trait-specific prompts increased the amount of trait-relevant information (i.e., narrative units), which was associated with enhanced convergence with target scores.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"15 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393412","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

I Gotta Feeling: Advancing Sentiment Analysis in Organizational Science 我有感觉：组织科学中先进的情感分析

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2026-03-02 DOI: 10.1177/10944281251408073

Imran Kadolkar, Divya V Doshi, Scott Tonidandel, Jose M Cortina

{"title":"I Gotta Feeling: Advancing Sentiment Analysis in Organizational Science","authors":"Imran Kadolkar, Divya V Doshi, Scott Tonidandel, Jose M Cortina","doi":"10.1177/10944281251408073","DOIUrl":"https://doi.org/10.1177/10944281251408073","url":null,"abstract":"Sentiment analysis (SA) has grown considerably in organizational science research over the past two decades, particularly in the last few years. While enthusiasm for integrating advanced natural language processing algorithms is encouraging, authors are not reaping the benefits of such tools fully. Our systematic review of SA application in the organizational sciences suggests that authors struggle to appreciate all of the decisions that are inherent to SA, the choices that are available at each decision point, and the consequences of each choice. To address this gap, we use a working example to illustrate four critical decision points authors confront when conducting SA, and the subsequent impact different choices can have on one's conclusion. Decision points include selecting the SA method, computing a sentiment score, preprocessing the data, and using an appropriate level of analysis. We conclude with a framework outlining five dimensions (e.g., accuracy, interpretability, computational cost) to guide the selection of an SA approach based on study goals and needs, along with seven recommendations to authors wishing to apply SA.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"250 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147358803","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

Application of Prototype Analysis to Organizational Research: A Critical Methodological Review 原型分析在组织研究中的应用：批判性方法论回顾

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-12-23 DOI: 10.1177/10944281251399210

Sandra Kiffin-Petersen, Sharon Purchase, Doina Olaru

{"title":"Application of Prototype Analysis to Organizational Research: A Critical Methodological Review","authors":"Sandra Kiffin-Petersen, Sharon Purchase, Doina Olaru","doi":"10.1177/10944281251399210","DOIUrl":"https://doi.org/10.1177/10944281251399210","url":null,"abstract":"Prototypes—internalized knowledge structures of the most typical or characteristic features of a concept—are important because they influence cognitive processing. Yet prototype analysis, the method used to examine prototypes, appears relatively underutilized in organizational research. To introduce prototype analysis to a wider audience of organizational scholars, we conducted a critical methodological literature review following a six-step procedure. Seventy-three prototype analyses published in 35 journals were categorized and their content analyzed. A prototype analysis typically includes a sequence of independent studies conducted over two stages, recently referred to as the standard procedure. Our review makes several contributions, including development of a taxonomy of prototype analysis applications, clarification of the standard procedure of a prototype analysis and possible variations, and suggestions for organizational research. Benefits of undertaking a prototype analysis include improved understanding of abstract workplace concepts that are difficult to measure directly, the ability to compare cross-cultural prototypes, and an approach for investigating the issue of construct redundancy. We conclude with best-practice recommendations, implications for organizational scholarship, methodological limitations, and future research suggestions.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"22 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812772","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

Understanding Relative Differences with Magnitude-Based Hypotheses: A Methodological Conceptualization and Data Illustration 理解以震级为基础的假设的相对差异：方法概念化和数据说明

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-10-07 DOI: 10.1177/10944281251377139

Dane P. Blevins, David J. Skandera, Roberto Ragozzino

引用次数: 0

Generative Artificial Intelligence in Qualitative Data Analysis: Analyzing—Or Just Chatting? 定性数据分析中的生成式人工智能：分析还是聊天？

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-09-30 DOI: 10.1177/10944281251377154

Duc Cuong Nguyen, Catherine Welch

引用次数: 0

Unleashing the Creative Potential of Research Tensions: Toward a Paradox Approach to Methods 释放研究紧张的创造潜力：走向方法的悖论方法

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-07-08 DOI: 10.1177/10944281251346804

Stephanie Schrage, Constantine Andriopoulos, Marianne W. Lewis, Wendy K. Smith

引用次数: 0

The Journey of Forced Choice Measurement Over 80 Years: Past, Present, and Future 80多年的强迫选择测量之旅：过去、现在和未来

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-07-07 DOI: 10.1177/10944281251350687

Philseok Lee, Mina Son, Steven Zhou, Sean Joo, Zihao Jia, Virginia Cheng

{"title":"The Journey of Forced Choice Measurement Over 80 Years: Past, Present, and Future","authors":"Philseok Lee, Mina Son, Steven Zhou, Sean Joo, Zihao Jia, Virginia Cheng","doi":"10.1177/10944281251350687","DOIUrl":"https://doi.org/10.1177/10944281251350687","url":null,"abstract":"Over the past two decades, forced-choice (FC) measures have received considerable attention from researchers and practitioners in industrial and organizational psychology. Despite the growing body of research on FC measures, there has not yet been a comprehensive review synthesizing the diverse lines of research. This article bridges this gap by presenting a systematic review of post-2000 literature on FC measures, addressing ten critical questions, including: 1) validity evidence, 2) faking resistance, 3) FC IRT models, 4) FC test design, 5) FC measure development, 6) test-taker reactions and response processes, 7) measurement and predictive bias, 8) reliability, 9) computerized adaptive testing, and 10) random responding. The review adopts a historical perspective, tracing the development of FC measures and highlighting key empirical findings, methodological advances, current trends, and future directions. By synthesizing a substantial body of evidence across multiple research streams, this article serves as a valuable resource, providing insights into the psychometric properties, theoretical underpinnings, and practical applications of FC measures in organizational contexts such as personnel selection, development, and assessment.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"109 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578319","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

Using Markov Chains to Detect Careless Responding in Survey Research 利用马尔可夫链检测调查研究中的粗心响应

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-06-24 DOI: 10.1177/10944281251334778

Torsten Biemann, Irmela F. Koch-Bayram, Madleen Meier-Barthold, Herman Aguinis

{"title":"Using Markov Chains to Detect Careless Responding in Survey Research","authors":"Torsten Biemann, Irmela F. Koch-Bayram, Madleen Meier-Barthold, Herman Aguinis","doi":"10.1177/10944281251334778","DOIUrl":"https://doi.org/10.1177/10944281251334778","url":null,"abstract":"Careless responses by survey participants threaten data quality and lead to misleading substantive conclusions that result in theory and practice derailments. Prior research developed valuable precautionary and post-hoc approaches to detect certain types of careless responding. However, existing approaches fail to detect certain repeated response patterns, such as diagonal-lining and alternating responses. Moreover, some existing approaches risk falsely flagging careful response patterns. To address these challenges, we developed a methodological advancement based on first-order Markov chains called Lazy Respondents (Laz.R) that relies on predicting careless responses based on prior responses. We analyzed two large datasets and conducted an experimental study to compare careless responding indices to Laz.R and provide evidence that its use improves validity. To facilitate the use of Laz.R, we describe a procedure for establishing sample-specific cutoff values for careless respondents using the “kneedle algorithm” and make an R Shiny application available to produce all calculations. We expect that using Laz.R in combination with other approaches will help mitigate the threat of careless responses and improve the accuracy of substantive conclusions in future research.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"235 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479220","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