The combined use of symmetric and asymmetric approaches: partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis

定性比较分析 结构方程建模 构造(python库) 偏最小二乘回归 数学 计算机科学 验证性因素分析 计量经济学 管理科学 潜变量 人工智能 机器学习 经济 程序设计语言
作者
S. Mostafa Rasoolimanesh,Christian M. Ringle,Marko Sarstedt,Hossein Olya
出处
期刊:International Journal of Contemporary Hospitality Management [Emerald Publishing Limited]
卷期号:33 (5): 1571-1592 被引量:385
标识
DOI:10.1108/ijchm-10-2020-1164
摘要

Purpose This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field. Design/methodology/approach This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct. Findings This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions. Originality/value The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.
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