The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Understanding the behavioural intention to play online games: An extension of the theory of planned behaviour
Abstract
Purpose: The purpose of this paper is to investigate whether flow experience, perceived enjoyment, and interaction affect people's behavioural intention to play online games and whether gender, age and prior experience have moderating effects on online game acceptance. Design/methodology/approach: This study extends the theory of planned behaviour (TPB) with flow experience, perceived enjoyment, and interaction to propose a theoretical model to explain and predict people's behavioural intention to play online games. This model is examined through an empirical study involving 458 participants using structural equation modelling techniques. In addition, a competing model based on the technology acceptance model (TAM) is proposed to evaluate whether TPB is more suitable than TAM to explain the use of online games. The two action‐theoretical models are compared in terms of their predictive power and their practical utility. Findings: Although both models explain the players' intention to play online games very well, the extended TPB model provides a better fit and explanatory power. Notably, this study finds that flow experience is a more important factor than perceived enjoyment in influencing customer acceptance of online games. Further analysis reveals that gender is a key moderator of online game acceptance. Practical implications: Online game developers need to search for flow experience building strategies that might assist in engaging players. This study suggests that game developers should consider focusing more on establishing the interactions between players (social interaction) and online games (human‐computer interaction) in their marketing strategies. Originality/value: This study is significant for two reasons. First, it synthesises the theory of planned behaviour with psychological and interaction factors and, second, it presents a blueprint for an entertainment‐oriented technology acceptance model.
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Citations
Cites
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
From Intentions to Actions: A Theory of Planned Behavior
Ajzen, I. (1985) · Action Control · doi:10.1007/978-3-642-69746-3_2 · Cited by 19,057
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
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 · cited 4×
“From a motivation perspective people make an effort to use information technology for both intrinsic and extrinsic reasons (Davis et al., 1992).”
BackgroundInfluentialToward a hierarchical model of intrinsic and extrinsic motivation
Vallerand, R. J. (1997) · Advances in experimental social psychology · doi:10.1016/s0065-2601(08)60019-2 · Cited by 3,198
Assessing IT Usage: The Role of Prior Experience
Taylor, S., & Todd, P. (1995) · MIS Quarterly · doi:10.2307/249633 · Cited by 2,891
Sweetser (2005) · doi:10.1145/1077246.1077253 · Cited by 2,124
Optimal experience in work and leisure.
Csikszentmihalyi, M., & LeFevre, J. (1989) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.56.5.815 · Cited by 1,943
Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005) · Information & Management · doi:10.1016/j.im.2003.10.007 · Cited by 1,184
Self-citationPredicting behavioral intention of choosing a travel destination
Lam, T., & Hsu, C. H. C. (2006) · Tourism Management · doi:10.1016/j.tourman.2005.02.003 · Cited by 1,111 · cited 3×
“Moreover, TAM does not include the influence of social and control factors on behaviour, and such factors have been found to significantly influence online usage behaviour (Bosnjak et al., 2006; Chen et al., 2007; Hsu and Chiu, 2004; Hung and Chang, 2005; Lam and Hsu, 2006; Liao et al., 2007; Lin, 2007).”
Background
Cited by
What catalyses mobile apps usage intention: An empirical analysis
Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Wei, J. (2015) · Industrial Management & Data Systems · doi:10.1108/imds-01-2015-0028 · Cited by 383
Why do people play games? A meta-analysis
Hamari, J., & Keronen, L. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.01.006 · Cited by 368
Adoption of digital payment systems in the era of demonetization in India: An empirical study
Sivathanu, B. (2019) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-07-2017-0033 · Cited by 339
Li, K. (2023) · Sustainability · doi:10.3390/su15065221 · Cited by 157
The influence of buyers’ time orientation on online shopping behavior: a typology.
Xu-Priour, D. L., Cliquet, G., & Palmer, A. (2017) · International Journal of Electronic Commerce · doi:10.1080/10864415.2016.1319206 · Cited by 34
“To better measure attitude toward the behavior vis-à-vis Internet technology and e-shopping adoption, new specific beliefs have been incorporated into an extended TPB: perceived risk [24], trust, perceived self-efficacy [92], flow experience, perceived enjoyment, and interaction [70].”
Method
Metadata
- Authors
- Lee, M.
- Venue
- Online Information Review
- Publisher
- Emerald
- Published
- 2009-09-25
- Volume
- 33
- Issue
- 5
- Pages
- 849-872
- ISSN
- 1468-4527 (ISSN-L)
- OpenAlex
- W1545605750
- Cited by
- 230
- References
- 11
- FWCI
- 10.82
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
