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

Abstract

Purpose The 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/approach Sample 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. Findings In 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/value It 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.

The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at Universitas Indonesia

“…Several studies have used ECM to measure the continuance use of the e-learning system (Cheng, 2021;Cheng & Yuen, 2019;Chow & Shi, 2014;Ibrahim & Hidayatur-Rehman, 2021;Islam, 2011;Lee, 2010;Safsouf et al, 2020;Saputra et al, 2020;Suzianti & Paramadini, 2021;Yeung & Jordan, 2006). Lee (2010) combines ECM with TPB and TAM.…”

Data replication techniques in the Internet of Things: a systematic literature review

“…(2) Another significant concern to be extensively explored is how many acceptable new replicas can be generated in the IoT and cloud to satisfy a fair device availability criterion (Cheng, 2021).…”

Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

“…Some of the IoT applications (like the Internet of Vehicles (IoV) that use sensors, actuator and cameras) transfer their data to Roadside Units (RSUs) for processing [15,24,26]. Offloading task to remote processing node especially with a large data size increases the delay [13,43,52,54,55]. In order to reduce the delay incurred as result of offloading large data size from IoT to either fog or cloud, there is need for data reduction method to downsize the amount of data to be offloaded.…”