Organizational Research Methods最新文献

A Machine Learning Toolkit for Selecting Studies and Topics in Systematic Literature Reviews 在系统文献综述中选择研究和主题的机器学习工具包

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-05-26 DOI: 10.1177/10944281251341571

Andrea Simonetti, Michele Tumminello, Pasquale Massimo Picone, Anna Minà

{"title":"A Machine Learning Toolkit for Selecting Studies and Topics in Systematic Literature Reviews","authors":"Andrea Simonetti, Michele Tumminello, Pasquale Massimo Picone, Anna Minà","doi":"10.1177/10944281251341571","DOIUrl":"https://doi.org/10.1177/10944281251341571","url":null,"abstract":"Scholars conduct systematic literature reviews to summarize knowledge and identify gaps in understanding. Machine learning can assist researchers in carrying out these studies. This paper introduces a machine learning toolkit that employs Network Analysis and Natural Language Processing methods to extract textual features and categorize academic papers. The toolkit comprises two algorithms that enable researchers to: (a) select relevant studies for a given theme; and (b) identify the main topics within that theme. We demonstrate the effectiveness of our toolkit by analyzing three streams of literature: cobranding, coopetition, and the psychological resilience of entrepreneurs. By comparing the results obtained through our toolkit with previously published literature reviews, we highlight its advantages in enhancing transparency, coherence, and comprehensiveness in literature reviews. We also provide quantitative evidence about the toolkit's efficacy in addressing the challenges inherent in conducting a literature review, as compared with state-of-the-art Natural Language Processing methods. Finally, we discuss the critical role of researchers in implementing and overseeing a literature review aided by our toolkit.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"51 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145566","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 Coreference Resolution to Mitigate Measurement Error in Text Analysis 利用共参考分辨率减轻文本分析中的测量误差

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-05-21 DOI: 10.1177/10944281251334777

Farhan Iqbal, Michael D. Pfarrer

引用次数: 0

Enhancing Theorization Using Artificial Intelligence: Leveraging Large Language Models for Qualitative Analysis of Online Data 利用人工智能增强理论化：利用大型语言模型对在线数据进行定性分析

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-05-21 DOI: 10.1177/10944281251339144

Diana Garcia Quevedo, Anna Glaser, Caroline Verzat

{"title":"Enhancing Theorization Using Artificial Intelligence: Leveraging Large Language Models for Qualitative Analysis of Online Data","authors":"Diana Garcia Quevedo, Anna Glaser, Caroline Verzat","doi":"10.1177/10944281251339144","DOIUrl":"https://doi.org/10.1177/10944281251339144","url":null,"abstract":"Online data are constantly growing, providing a wide range of opportunities to explore social phenomena. Large Language Models (LLMs) capture the inherent structure, contextual meaning, and nuance of human language and are the base for state-of-the-art Natural Language Processing (NLP) algorithms. In this article, we describe a method to assist qualitative researchers in the theorization process by efficiently exploring and selecting the most relevant information from a large online dataset. Using LLM-based NLP algorithms, qualitative researchers can efficiently analyze large amounts of online data while still maintaining deep contact with the data and preserving the richness of qualitative analysis. We illustrate the usefulness of our method by examining 5,516 social media posts from 18 entrepreneurs pursuing an environmental mission (ecopreneurs) to analyze their impression management tactics. By helping researchers to explore and select online data efficiently, our method enhances their analytical capabilities, leads to new insights, and ensures precision in counting and classification, thus strengthening the theorization process. We argue that LLMs push researchers to rethink research methods as the distinction between qualitative and quantitative approaches becomes blurred.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"16 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113628","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

Efficient Processing of Long Sequence Text Data in Transformer: An Examination of Five Different Approaches 变压器中长序列文本数据的有效处理：五种不同方法的检验

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-03-18 DOI: 10.1177/10944281251326062

Zihao Jia, Philseok Lee

{"title":"Efficient Processing of Long Sequence Text Data in Transformer: An Examination of Five Different Approaches","authors":"Zihao Jia, Philseok Lee","doi":"10.1177/10944281251326062","DOIUrl":"https://doi.org/10.1177/10944281251326062","url":null,"abstract":"The advent of machine learning and artificial intelligence has profoundly transformed organizational research, especially with the growing application of natural language processing (NLP). Despite these advances, managing long-sequence text input data remains a persistent and significant challenge in NLP analysis within organizational studies. This study introduces five different approaches for handling long sequence text data: term frequency-inverse document frequency with a random forest algorithm (TF-IDF-RF), Longformer, GPT-4o, truncation with averaged scores and our proposed construct-relevant text-selection approach. We also present analytical strategies for each approach and evaluate their effectiveness by comparing the psychometric properties of the predicted scores. Among them, GPT-4o, the truncation with averaged scores, and the proposed text-selection approach generally demonstrate slightly superior psychometric properties compared to TF-IDF-RF and Longformer. However, no single approach consistently outperforms the others across all psychometric criteria. The discussion explores the practical considerations, limitations, and potential directions for future research on these methods, enriching the dialogue on effective long-sequence text management in NLP-driven organizational research.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"22 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653938","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

What Are Mechanisms? Ways of Conceptualizing and Studying Causal Mechanisms 什么是机制？因果机制的概念化与研究方法

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-03-14 DOI: 10.1177/10944281251318727

Joep P. Cornelissen, Mirjam Werner

引用次数: 0

Shedding Light on the Black Box: Integrating Prediction Models and Explainability Using Explainable Machine Learning 揭示黑箱：使用可解释机器学习整合预测模型和可解释性

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-03-13 DOI: 10.1177/10944281251323248

Yucheng Zhang, Yuyan Zheng, Dan Wang, Xiaowei Gu, Michael J. Zyphur, Lin Xiao, Shudi Liao, Yangyang Deng

{"title":"Shedding Light on the Black Box: Integrating Prediction Models and Explainability Using Explainable Machine Learning","authors":"Yucheng Zhang, Yuyan Zheng, Dan Wang, Xiaowei Gu, Michael J. Zyphur, Lin Xiao, Shudi Liao, Yangyang Deng","doi":"10.1177/10944281251323248","DOIUrl":"https://doi.org/10.1177/10944281251323248","url":null,"abstract":"In contemporary organizational research, when dealing with large heterogeneous datasets and complex relationships, statistical modeling focused on developing substantive explanations typically results in low predictive accuracy. In contrast, machine learning (ML) exhibits remarkable strength for prediction, but suffers from an unexplainable analytical process and output—thus ML is often known as a “black box” approach. The recent development of explainable machine learning (XML) integrates high predictive accuracy with explainability, which combines the advantages inherent in both statistical modeling and ML paradigms. This paper compares XML with statistical modeling and the traditional ML approaches, focusing on an advanced application of XML known as evolving fuzzy system (EFS), which enhances model transparency by clarifying the unique contribution of each modeled predictor. In an illustrative study, we demonstrate two EFS-based XML models and conduct comparative analyses among XML, ML, and statistical models with a commonly-used database in organizational research. Our study offers a thorough description of analysis procedures for implementing XML in organizational research, along with best-practice recommendations for each step as well as Python code to aid future research using XML. Finally, we discuss the benefits of XML for organizational research and its potential development.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"14 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618540","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

Feature Topic for ORM: Advanced Analytic Approaches to Theorize From Qualitative Research ORM专题：从定性研究中推导出理论的高级分析方法

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-01-31 DOI: 10.1177/10944281251314059

Tine Köhler, Anne Smith, Thomas Greckhamer, Jane Lê

引用次数: 0

Surveying the Upper Echelons: An Update to Cycyota and Harrison (2006) on Top Manager Response Rates and Recommendations for the Future 调查高层：对Cycyota和Harrison（2006）关于高层管理者回应率和未来建议的更新

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2025-01-13 DOI: 10.1177/10944281241310574

Cameron J. Borgholthaus, Alaric Bourgoin, Peter D. Harms, Joshua V. White, Tyler N. A. Fezzey

引用次数: 0

Manipulation in Organizational Research: On Executing and Interpreting Designs from Treatments to Primes 组织研究中的操纵：关于执行和解释从治疗到标本的设计

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2024-12-17 DOI: 10.1177/10944281241300952

Kira F. Schabram, Christopher G. Myers, Ashley E. Hardin

{"title":"Manipulation in Organizational Research: On Executing and Interpreting Designs from Treatments to Primes","authors":"Kira F. Schabram, Christopher G. Myers, Ashley E. Hardin","doi":"10.1177/10944281241300952","DOIUrl":"https://doi.org/10.1177/10944281241300952","url":null,"abstract":"While other applied sciences systematically distinguish between manipulation designs, organizational research does not. Herein, we disentangle distinct applications that differ in how the manipulation is deployed, analyzed, and interpreted in support of hypotheses. First, we define two archetypes: treatments, experimental designs that expose participants to different levels/types of a manipulation of theoretical interest, and primes, manipulations that are not of theoretical interest but generate variance in a state that is. We position these and creative derivations (e.g., interventions and invariant prompts) as specialized tools in our methodological kit. Second, we review 450 manipulations published in leading organizational journals to identify each type's prevalence and application in our field. From this we derive our guiding thesis that while treatments offer unique advantages (foremost establishing causality), they are not always possible, nor the best fit for a research question; in these cases, a non-causal but accurate test of theory, such as a prime design, may prove superior to a causal but inaccurate test. We conclude by outlining best practices for selection, execution, and evaluation by researchers, reviewers, and readers.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"86 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841975","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 Personality When Stakes Are High: Are Graded Paired Comparisons a More Reliable Alternative to Traditional Forced-Choice Methods? 高风险下的人格测量：分级配对比较法是传统强迫选择法的更可靠替代方法吗？

IF 9.5 2区管理学

Organizational Research Methods Pub Date : 2024-12-13 DOI: 10.1177/10944281241279790

Harriet Lingel, Paul-Christian Bürkner, Klaus G. Melchers, Niklas Schulte

{"title":"Measuring Personality When Stakes Are High: Are Graded Paired Comparisons a More Reliable Alternative to Traditional Forced-Choice Methods?","authors":"Harriet Lingel, Paul-Christian Bürkner, Klaus G. Melchers, Niklas Schulte","doi":"10.1177/10944281241279790","DOIUrl":"https://doi.org/10.1177/10944281241279790","url":null,"abstract":"In graded paired comparisons (GPCs), two items are compared using a multipoint rating scale. GPCs are expected to reduce faking compared with Likert-type scales and to produce more reliable, less ipsative trait scores than traditional binary forced-choice formats. To investigate the statistical properties of GPCs, we simulated 960 conditions in which we varied six independent factors and additionally implemented conditions with algorithmically optimized item combinations. Using Thurstonian IRT models, good reliabilities and low ipsativity of trait score estimates were achieved for questionnaires with 50% unequally keyed item pairs or equally keyed item pairs with an optimized combination of loadings. However, in conditions with 20% unequally keyed item pairs and equally keyed conditions without optimization, reliabilities were lower with evidence of ipsativity. Overall, more response categories led to higher reliabilities and nearly fully normative trait scores. In an empirical example, we demonstrate the identified mechanisms under both honest and faking conditions and study the effects of social desirability matching on reliability. In sum, our studies inform about the psychometric properties of GPCs under different conditions and make specific recommendations for improving these properties.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"29 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820668","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