Specifying, estimating and validating consumer eSports engagement composite model: a composite confirmatory approach

Purpose: The present study aims to specify, estimate, and validate the composite formative model of consumer eSports engagement (CeSE) through utilizing the composite confirmatory analysis (CCA) methodological approach. Design/methodology/approach: To validate the composite model of CeSE, we collected the data using the Mturk online tool from eSports gamers located in European countries. The partial least squares based structural equation modelling (PLS-SEM) approach was employed using SmartPLS to conduct CCA analyses. Findings: Using CCA, the authors first evaluated the reflective constructs (three first-order dimensions comprising cognitive, affective, and activation) and found reliable and valid reflective constructs. The authors then proceeded to assess the composite formative model of CeSE at higher-order level and resulted that CeSE is a valid composite model. Besides, the nomological network of CeSE with technology acceptance variables (e.g. behavioral intention and usage behavioral were estimated and found significant results). Originality/value: This study contributes to consumer engagement literature in many directions. First, this study applies consumer engagement scale in the eSports context (i.e. eSports game brands). Second, this study notably specifies, estimates, and validates CeSE as composite model comprising cognitive, affective, and activation factors as first-order reflective constructs and CeSE as second/higher-order formative construct. Third, this study applies CCA guidelines to establish the CeSE as a composite formative measurement model and make significant methodology contribution. Fourth, this study extends the TAM model via studying CeSE as an important determinant of behavioral intention to play eSports games, which in turn impacts on eSports gaming/usage behavior.