University students' attitudes towards ubiquitous library-supported learning: an empirical investigation in the context of the Line@Library. Issue 1 (6th May 2021)

• Record Type: Journal Article
• Authors: Tu, Yun-Fang; Hwang, Gwo-Jen; Chen, Joyce Chao-Chen; Lai, Chiulin
• Is Part Of: Electronic library. Volume 39:Issue 1(2021)
• Page Start: 186
• Page End: 207
• Publication Date: 2021-05-06
• Subjects: Attitude surveys -- Attitudes towards mobile learning -- Mobile libraries -- Mobile learning -- Mobile services -- Task-technology fit -- Social media; Digital libraries -- Periodicals; Libraries -- Automation -- Periodicals; 025.00285
• DOI: 10.1108/EL-03-2020-0076
• Languages: English
• ISSNs: 0264-0473

Abstract

Abstract : Purpose: This study aims to investigate the influences of task-technology fit on university students' attitudes towards ubiquitous library-supported learning when they use a mobile library app, Line@Library. Design/methodology/approach: In this study, structural equation modelling to examine 158 valid questionnaires are used. The study aims to examine the effects of task-technology fit (TTF) on university students' attitudes towards mobile learning (AML) when using Line@Library. Findings: The results show that task-technology fit is an important role that influences the students' attitudes towards mobile learning. The factor "technology characteristics" is considered when the students attempted to use the mobile app to solve problems or complete tasks. This study also found that the students responded with positive perceptions of the task-technology fit and had positive perceptions of its ease of use. Furthermore, usefulness, ease of use and affection of AML were found to be the most influential predictors of mobile library adoption intention. Originality/value: From the perspective of learners, this study investigates the relationships of the combination of social media and a mobile library between TTF and AML. This study further found that not only ease of use, usefulness and affection but also task-technology fit can be a predictor that influences students' attitudes towards mobile learning.