Predicting the Drivers of Cloud Computing Adoption by using Structural Equation Modeling and Artificial Neural Network

Abstract

This study explores the predictors of individual-level adoption of cloud computing (CC) by using both an artificial neural network (ANN) and the structural equation modeling (SEM), which has been the standard tool to evaluate this process. This study uses data collected by an online survey in South Korea. Application of the extended Unified Theory of Acceptance and Use of Technology shows that the ANN is a better option than the SEM to identify determinants of CC adoption and can capture nonlinear relationships, which SEM cannot. The SEM results indicate that only performance expectancy, effort expectancy and habit are significant predictors of CC adoption. However, when the importance is normalized by using a multilayer perceptron, the ANN analysis shows that all variables (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit) are significant, although to different degrees. This study provides researchers and practitioners with a differentiated and extended perspective on understanding adoption of cloud-enabled technology.