ORGANIZATIONAL RESEARCH METHODS组织研究方法
ORGANIZATIONAL RESEARCH METHODS(英文缩写 ORGAN RES METHODS),ISSN 1094-4281,eISSN 1552-7425,中文译名:组织研究方法 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 8.247 | Q1 |
| 2022 | 9.500 | Q1 |
| 2023 | 8.900 | Q1 |
| 2024 | 7.600 | Q1 |
| 2025 | 8.700 | Q1 |
ORGANIZATIONAL RESEARCH METHODS 最新收录文献
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1. Comparative Configurational Process Analysis: A New Set-Theoretic Technique for Longitudinal Case Analysis.
PMID:日期:2024-06-18In the past 20 years, researchers have significantly advanced various management fields by examining organizational phenomena through a configurational lens, including competitive strategies, corporate governance mechanisms, and innovation systems. Qualitative comparative analysis (QCA) has emerged as a primary method for empirically investigating organizational configurations. However, QCA has traditionally struggled to capture the temporal aspects of configurational phenomena. In this paper, we present configurational comparative process analysis (CPA), which merges QCA with sequence analysis. We introduce the concept of configurational themes-recognizable temporal patterns of recurring combinations of explanatory conditions-to identify and track the temporal dynamics among these phenomena. We also outline configurational matching-a method for empirically identifying these themes by distinguishing theme-defining from theme-supporting conditions. CPA allows researchers to explore the temporal dynamics of configurational phenomena, such as their stability, emergence, and decline at critical junctures. We illustrate the application of CPA through a study of shareholder value orientation and discuss its potential for addressing key questions in management research.
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2. From Ties to Events in the Analysis of Interorganizational Exchange Relations.
2. 从纽带关系到事件：组织间交换关系分析中的转换PMID:日期:2022-03-16Relational event models expand the analytical possibilities of existing statistical models for interorganizational networks by: (i) making efficient use of information contained in the sequential ordering of observed events connecting sending and receiving units; (ii) accounting for the intensity of the relation between exchange partners, and (iii) distinguishing between short- and long-term network effects. We introduce a recently developed relational event model (REM) for the analysis of continuously observed interorganizational exchange relations. The combination of efficient sampling algorithms and sender-based stratification makes the models that we present particularly useful for the analysis of very large samples of relational event data generated by interaction among heterogeneous actors. We demonstrate the empirical value of event-oriented network models in two different settings for interorganizational exchange relations-that is, high-frequency overnight transactions among European banks and patient-sharing relations within a community of Italian hospitals. We focus on patterns of direct and generalized reciprocity while accounting for more complex forms of dependence present in the data. Empirical results suggest that distinguishing between degree- and intensity-based network effects, and between short- and long-term effects is crucial to our understanding of the dynamics of interorganizational dependence and exchange relations. We discuss the general implications of these results for the analysis of social interaction data routinely collected in organizational research to examine the evolutionary dynamics of social networks within and between organizations.
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3. Text Mining in Organizational Research.
3. 组织研究中的文本挖掘PMID:日期:2017-08-10Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.
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4. Text Classification for Organizational Researchers: A Tutorial.
PMID:日期:2017-07-12Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output.
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5. Overview of MEG.
5. MEG概述PMID:日期:2016-11-09Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.
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6. The Psychometric Latent Agreement Model (PLAM) for Discrete Latent Variables Measured by Multiple Items.
6. 用于多题项测量的离散潜变量的心理测量潜一致性模型（PLAM）PMID:日期:2011-01The Psychometric Latent Agreement Model (PLAM) is proposed for estimating the subpopulation membership of individuals (e.g., satisfactory performers vs. unsatisfactory performers) at discrete levels of multiple latent trait variables. A binary latent Type variable is introduced to take account of the possibility that, for a given set of observed variables, the latent group memberships of some individuals are indeterminate. The latent Type variable allows for separating individuals who can reliably be assigned to satisfactory versus unsatisfactory performers classes from those individuals whose ratings do not contain the necessary information to make the class assignment possible for a particular set of rating items. Agreements among discrete latent trait variables are also estimated. The PLAM was illustrated with two examples using real data on behavioral rating measures. One example involved ratings of two behavioral constructs by a single rater type, whereas the other involved ratings of one construct by three rater types. Implications were presented for using behavioral ratings to determine the subpopulation membership, such as qualified versus unqualified groupings in hiring decisions and pass versus fail groupings in performance evaluations.