Can tasks and learning be balanced? A dual-pathway model of cloud-based e-learning continuance intention and performance outcomes

Abstract

PurposeThe purpose of this paper is to examine the roles of task-technology fit (TTF), learning-technology fit (LTF) and cognitive absorption (CA) in determining medical professionals’ cloud-based electronic learning (e-learning) system continuance intention and performance outcomes and evaluate whether medical professionals’ perceived impact on learning can affect their perceived impact on tasks within medical institutions.Design/methodology/approachSample data for this study were collected from medical professionals at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 373 (62.2%) usable questionnaires were analyzed using structural equation modeling in this study.FindingsIn this study, medical professionals’ perceived TTF and LTF as antecedents to their cloud-based e-learning continuance intention and performance outcomes were validated, and medical professionals’ perceived impact on learning had a positive effect on their perceived impact on tasks. Synthetically speaking, this study’s results strongly support the research model with all hypothesized links being significant.Originality/valueIt is particularly worth mentioning that this study introduces a new construct, “LTF,” to conceptualize, define and measure it, and further contributes to the application of capturing both expectation–confirmation model and CA (i.e. an intrinsic motivator) for completely explaining medical professionals’ perceived TTF and LTF as external variables to their cloud-based e-learning continuance intention and performance outcomes.