The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Investigating the determinants and age and gender differences in the acceptance of mobile learning
Abstract
With the proliferation of mobile computing technology, mobile learning (m‐learning) will play a vital role in the rapidly growing electronic learning market. M‐learning is the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices. However, acceptance of m‐learning by individuals is critical to the successful implementation of m‐learning systems. Thus, there is a need to research the factors that affect user intention to use m‐learning. Based on the unified theory of acceptance and use of technology (UTAUT), which integrates elements across eight models of information technology use, this study was to investigate the determinants of m‐learning acceptance and to discover if there exist either age or gender differences in the acceptance of m‐learning, or both. Data collected from 330 respondents in Taiwan were tested against the research model using the structural equation modelling approach. The results indicate that performance expectancy, effort expectancy, social influence, perceived playfulness, and self‐management of learning were all significant determinants of behavioural intention to use m‐learning. We also found that age differences moderate the effects of effort expectancy and social influence on m‐learning use intention, and that gender differences moderate the effects of social influence and self‐management of learning on m‐learning use intention. These findings provide several important implications for m‐learning acceptance, in terms of both research and practice.
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Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664
Extrinsic and Intrinsic Motivation to Use Computers in the Workplace 1
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1992.tb00945.x · Cited by 6,231
Agarwal, R., & Karahanna, E. (2000) · MIS Quarterly · doi:10.2307/3250951 · Cited by 4,726
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
A Model of the Antecedents of Perceived Ease of Use: Development and Test
Venkatesh, V., & Davis, F. D. (1996) · Decision Sciences · doi:10.1111/j.1540-5915.1996.tb01822.x · Cited by 3,802
Agarwal, R., & Prasad, J. (1998) · Information Systems Research · doi:10.1287/isre.9.2.204 · Cited by 3,575
Assessing IT Usage: The Role of Prior Experience
Taylor, S., & Todd, P. (1995) · MIS Quarterly · doi:10.2307/249633 · Cited by 2,891
AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE
MORRIS, M. G., & VENKATESH, V. (2000) · Personnel Psychology · doi:10.1111/j.1744-6570.2000.tb00206.x · Cited by 1,723 · cited 4×
“Based on the UTAUT and previous literature (Morris & Venkatesh, 2000; Venkatesh & Morris, 2000), gender and age are theorised to play a moderating role on the influence of performance expectancy on behavioural intention.”
BackgroundMethodResultInfluentialEmpirical Evaluation of the Revised Technology Acceptance Model
Szajna, B. (1996) · Management Science · doi:10.1287/mnsc.42.1.85 · Cited by 1,656
Digital game-based learning: Towards an experiential gaming model
Kiili, K. (2005) · The Internet and Higher Education · doi:10.1016/j.iheduc.2004.12.001 · Cited by 1,457
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.2000.2896 · Cited by 1,237
Extending the technology acceptance model: the influence of perceived user resources
Mathieson, K., Peacock, E., & Chin, W. W. (2001) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/506724.506730 · Cited by 1,099
Critical success factors for e-learning acceptance: Confirmatory factor models
Selim, H. M. (2007) · Computers & Education · doi:10.1016/j.compedu.2005.09.004 · Cited by 1,055
Cited by
The unified theory of acceptance and use of technology (UTAUT): a literature review
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015) · Journal of Enterprise Information Management · doi:10.1108/jeim-09-2014-0088 · Cited by 1,339
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.04.015 · Cited by 1,057
Review of trends from mobile learning studies: A meta-analysis
Wu, W.-H., Jim Wu, Y.-C., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.03.016 · Cited by 946
Demographic differences in perceived benefits from gamification
Koivisto, J., & Hamari, J. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.03.007 · Cited by 841
Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM
Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.06.004 · Cited by 808
Metadata
- Authors
- Wang, Y., Wu, M., & Wang, H.
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2008-12-23
- Volume
- 40
- Issue
- 1
- Pages
- 92-118
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W2157446202
- Cited by
- 1,218
- References
- 20
- FWCI
- 48.70
- Type
- journal article
- Language
- en
- Open access
- closed
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