Partial Least Squares Structural Equation Modeling in Service Research

Synthèse

Service research is characterized by complex theoretical frameworks involving latent variables (constructs), complex structural relationships, including higher-order models, and a growing emphasis on prediction. Under these conditions, partial least squares structural equation modeling (PLS-SEM) has gained increasing prominence as a methodological approach well suited to provide answers to research questions in the service domain. As a result, PLS-SEM has become widely adopted across a broad spectrum of applications in the service domain.

These applications span technology-enabled services—such as consumers' responses to service robots, smart voice assistants, AI-based services, and chatbots—as well as market-related phenomena, including ownership perceptions in the sharing economy. Moreover, PLS-SEM has been extensively used to examine employee- and organization-related issues, such as frontline employee characteristics and servitization, service failure and recovery, employee–AI collaboration, customer experience management, value co-creation, and leadership styles.

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Thèmes proposés

• Methodological developments in PLS-SEM with direct relevance for service research
• Scale development and diagnosing common method variance in PLS-SEM
• Differences in model development from explanatory versus predictive perspectives
• Explanatory versus predictive model evaluation and reporting
• Novel metrics and guidelines for goodness-of-fit assessment and predictive power assessment
• Endogeneity issues and remedies in PLS-SEM
• Observed heterogeneity (e.g., multigroup analysis, moderation, conditional mediation) and unobserved heterogeneity (e.g., segmentation) in PLS-SEM
• Applications and extensions of necessary condition analysis in PLS-SEM
• Multimethod SEM involving PLS-SEM
• Empirical studies on contemporary service research topics (e.g., technology-enabled services, transformative service research, customer experience) employing recent advances in PLS-SEM
• Demonstrations of best practices in the application, reporting, and interpretation of PLS-SEM results
• Extensions of PLS-SEM research designs
• Integration of PLS-SEM with complementary analytical approaches