Paper Info
Title
Employee Usage Intention of Ubiquitous Learning Technology: An Integrative View of User Perception Regarding Interactivity, Software, and Hardware.
Abstract
Ubiquitous learning (u-learning) has been recognized as an exciting approach to knowledge acquisition and skill development. However, a few studies have examined the factors influencing employees' acceptance of u-learning in a ubiquitous computing environment, and the impact of interaction-oriented learning systems' design factors on usage intention (UI) is rare as well. This paper proposes an integrated model derived from the technology acceptance model (TAM) by focusing on users' perceptions of interactivity (INT), software, and hardware design factors. A questionnaire survey was used, and responses of 368 Chinese knowledge workers as learners in Beijing were analyzed using the partial least square structural equation model. This paper successfully integrated three sub-dimensions-interaction, software, and hardware design-into a learning systems design model from a user perspective. The results reveal that perceived INT, perceived content, and perceived infrastructure significantly influence how employees adopt u-learning. This paper is among the first ones to explore INT as a critical factor influencing employee u-learning. Furthermore, it also explored two attitude dimensions toward "formal" and "informal" u-learning formats and found that these attitudes infl uence UI, and based on that the TAM is extended. This paper contributes to the literature by investigating the factors affecting employee acceptance of u-learning technology. This paper has several implications for both researchers and practitioners of u-learning and in the educational technology context.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2893311
IEEE ACCESS
Keywords
Field
DocType
Ubiquitous learning,educational technology,perceived interactivity,perceived content,perceived infrastructure
Ubiquitous learning,Interactivity,Computer science,Human–computer interaction,Software,Perception,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Weiwei Wu1219.53
Dawei Shang270.73