Exploring the impact of integrated design on employee learning engagement in the ubiquitous learning context: A deep learning-based hybrid multistage approach

Learning engagement has received the attention of academics and practitioners; however, studies on employee learning engagement are limited. Based on an integrated hardware-software-value-design perspective and domain-specific innovativeness theory, we developed and tested a theoretical framework using a novel and hybrid multistage approach combining a partial least squares (PLS) structural equation model (SEM) and artificial neural networks from deep learning. We used multigroup analysis (PLS-MGA-ANN), which examines key integrated design elements and domain-specific innovativeness drivers of employee learning engagement in ubiquitous learning context. According to a sample of learners’ responses, the linear PLS-SEM results demonstrated that (a) integrating design elements, including perceived compatibility, familiarity, value, and user interface design, had a direct impact on domain-specific innovativeness; (b) domain-specific innovativeness had a direct impact on employee learning engagement and played a mediating role in the relationship between integrating design elements and employee learning engagement; and (c) copresence moderated the relationships between domain-specific innovativeness and employee learning engagement. Furthermore, through the evaluation of nonlinear models of the neural network, perceived compatibility and value revealed nonlinear average importance. Practical and theoretical implications are discussed. •Propose a framework on the exploration of employee learning engagement (ELE).•Use a novel and hybrid multistage approach based on statistics and machine learning.•Innovativeness plays the mediating roles between integrates design elements and ELE.•Copresence moderates the relationships between DSI and ELE.•Nonlinear results also show the differentiation average importance on ELE.