Comparing technology acceptance of AR-based and 3D map-based mobile library applications: a multigroup SEM analysis

ABSTRACT

This study integrated augmented reality (AR) and three-dimensional (3D) maps into mobile learning for library use instruction. To maximize the benefits of mobile library applications (MLAs), their technology acceptance was examined. Because of the e-learning context, the General Extended Technology Acceptance Model for E-Learning (GETAMEL) developed by Abdullah and Ward ([2016]. Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256) was used. A total of 450 university students participated in this study. Structural equation modeling was conducted to identify factors influencing MLA adoption, to test the measurement invariance of the GETAMEL questionnaire and to compare the structural coefficients of GETAMEL between the AR and 3D map groups. The results showed that subjective norms, enjoyment, computer-related experience, self-efficacy, perceived ease of use, and perceived usefulness are essential for designing MLA. Moreover, the measures of the questionnaire had the same meanings across the two MLAs, and the impacts of perceived usefulness and perceived ease of use on behavioral intention differed significantly across the two groups, suggesting that AR-based MLA suits people who are not familiar with libraries, whereas 3D map-based MLA suits people who want to find research materials immediately.

KEYWORDS:

• Augmented reality
• 3D map
• mobile library application
• technology acceptance
• SEM

Acknowledgements

This research was supported by the Higher Education Sprout Project of the National Yang Ming Chiao Tung University (NYCU) and the Ministry of Education (MOE), Taiwan, as well as the Ministry of Science and Technology (MOST) in Taiwan through Grant numbers MOST 110-2511-HA49-009-MY2, MOST 107-2628-H-009-004-MY3 and MOST 105-2511-S-009-013-MY5. We would like to thank National Taiwan University Library for supporting our research by providing the AR-based library and the 360cam tours. We would also like to thank Yu-Ching Hsu and Jerry Jiun-Yu Wu for their valuable suggestions in data analysis. Finally, we would like to thank NYCU's ILTM (Interactive Learning Technology and Motivation, see: http://ILTM.nctu.edu.tw) lab members and the students for helping conduct the experiment, as well as the reviewers who provided valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Higher Education Sprout Project of the National Yang Ming Chiao Tung University (NYCU) and the Ministry of Education (MOE), Taiwan, as well as the Ministry of Science and Technology (MOST) in Taiwan through Grant numbers MOST 110-2511-HA49-009-MY2, MOST 107-2628-H-009-004-MY3, and MOST 105-2511-S-009-013-MY5.

Notes on contributors

Yiming Liu

Yiming Liu is currently a Ph.D. candidate in the Faculty of Education at the University of Hong Kong. His research interests include learning analytics, educational data mining, game-based assessment, collaborative problem solving, and self-regulated learning.

Jerry Chih-Yuan Sun

Jerry Chih-Yuan Sun is a Distinguished Professor in the Institute of Education at National Yang Ming Chiao Tung University (NYCU), where he leads the Interactive Learning Technology and Motivation Lab (see http://ILTM.nctu.edu.tw). His research focuses on assessing innovative feedback technologies and their effects on students’ learning and motivation.

Ssu-Kuang Chen

Ssu-Kuang Chen is a post-doctoral researcher in the Institute of Education at National Yang Ming Chiao Tung University (NYCU). Her recent research focused on the applications of advanced statistical techniques (e.g. multi-level structural equation modeling and latent class analysis) in educational psychology.