定性比较分析
构造(python库)
奖学金
模糊集
集合(抽象数据类型)
产业与组织心理学
计算机科学
心理学
模糊逻辑
知识管理
管理科学
社会心理学
人工智能
经济
机器学习
程序设计语言
经济增长
作者
Allison S. Gabriel,Joanna Tochman Campbell,Emilija Djurdjevic,Russell E. Johnson,Christopher C. Rosen
标识
DOI:10.1177/1094428117752466
摘要
Person-centered approaches to organizational scholarship can provide critical insights into how sets of related constructs uniquely combine to predict outcomes. Within micro topics, scholars have begun to embrace the use of latent profile analysis (LPA), identifying constellations of constructs related to organizational commitment, turnover intentions, emotional labor, recovery, and well-being, to name a few. Conversely, macro scholars have utilized fuzzy set qualitative comparative analysis (fsQCA) to examine numerous phenomena, such as acquisitions and business strategies, as configurations of explanatory conditions associated with firm-level outcomes. What remains unclear, however, is the extent to which these two approaches deliver similar versus unique insights when applied to the same topic. In this paper, we offer an overview of the ways these two methods converge and diverge, and provide an empirical demonstration by applying both LPA and fsQCA to examine a multidimensional personality construct—core self-evaluations (CSE)—in relation to job satisfaction. In so doing, we offer guidance for scholars who are either choosing between these two methods, or are seeking to use the two methods in a complementary, theory-building manner.
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