A hybrid SEM and ANN approach to predict the individual cloud computing adoption based on the UTAUT2

This study seeks predictors of individual-level adoption of cloud computing (CC) by combining structural equation modeling (SEM) and an artificial neural network (ANN) to analyze data collected by an online survey in South Korea. Application of the Extended Unified Theory of Acceptance and Use of Technology indicates that habit is the strongest determinant of behavioral intent to accept CC in both SEM and ANN analysis, followed by technology factors (performance expectancy, effort expectancy). Social factors (social influence, facilitating conditions) and hedonic motivation are not vital predictors in SEM, whereas they do in ANN. This study offers new empirical evidence for the differences and similarities between the results of SEM and ANN analysis of CC adoption and therefore provides researchers and practitioners with a differentiated and in-depth perspective on understanding adoption of cloud-enabled technology. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.