Rasoolimanesh, S.M., Ringle, C.M., Sarstedt, M. & Olya, H. (2021) International Journal of Contemporary Hospitality Management [Core Economics, Q2]

Publicat: 18 Mai 2021

Rasoolimanesh, S.M., Ringle, C.M., Sarstedt, M. & Olya, H. (2021) The combined use of symmetric and asymmetric approaches: partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis. International Journal of Contemporary Hospitality Management, 33 (5), 1571-1592.

DOI: https://doi.org/10.1108/IJCHM-10-2020-1164

✓ Publisher: Emerald
✓ Web of Science Core Collection: Social Sciences Citation Index
✓ Categories: Hospitality, leisure, sport & tourism; Management
✓ Article Influence Score (AIS): 1.289 (2021) / Q1 in Hospitality, leisure, sport & tourism; Q2 in Management

Abstract: 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. 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. 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. 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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