Abstract
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.
Item Type: | Journal article |
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Keywords: | Partial least squares structural equation modeling (PLS-SEM); Predictive power; Qualitative comparative analysis (QCA); Fuzzy-set qualitative comparative analysis (fsQCA); Prediction oriented methods; Symmetric and asymmetric techniques |
Faculties: | Munich School of Management > Institute for Marketing |
Subjects: | 300 Social sciences > 330 Economics |
ISSN: | 0959-6119 |
Language: | English |
Item ID: | 96064 |
Date Deposited: | 03. May 2023, 07:29 |
Last Modified: | 03. May 2023, 07:29 |