Exploring the optimal path to online game loyalty: Bayesian networks versus theory-based approaches
- Authors
- Jo N.Y.[Jo N.Y.]; Lee K.C.[Lee K.C.]; Park B.-W.[Park B.-W.]
- Issue Date
- 2011
- Keywords
- Bayesian network; character identification; enjoyment; flow; loneliness; Online games; perceived stress
- Citation
- Communications in Computer and Information Science, v.151 CCIS, no.PART 2, pp.428 - 437
- Indexed
- SCOPUS
- Journal Title
- Communications in Computer and Information Science
- Volume
- 151 CCIS
- Number
- PART 2
- Start Page
- 428
- End Page
- 437
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/71630
- DOI
- 10.1007/978-3-642-20998-7_51
- ISSN
- 1865-0929
- Abstract
- Online games have become a prevailing form of entertainment due to ease in accessing the Internet with tools such as home networks, WiBro, WiFi, and various mobile devices. With the expansion of the online game industry and number of users, researchers have begun to explore the factors affecting user loyalty. Intuitionally, online game playing can be thought of solely as a way to provide pleasure, but the characteristics of online game playing, such as interactions with other users and a real-time approach, introduce the need to explore other psychological factors affecting user loyalty. Thus, this study proposes a new research model consisting of users' perceived loneliness and stress to investigate whether these variables have relationships and mediating roles with enjoyment and flow. To prove the validity of our proposed research model, we first perform an empirical analysis using PLS with 187 valid questionnaire items and find that the proposed research model is statistically significant. Second, we adapt the Bayesian network model to explore new models and compare those with the theory-based SEM. Moreover, we assemble each structure based on best performance to explore an alternative optimized structural model. The empirical results indicate that the theory-based model, which is driven by prior research, performs better than the Bayesian network model. The implications and future research are also discussed. © 2011 Springer-Verlag.
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- Appears in
Collections - Business > Department of Business Administration > 1. Journal Articles
- Business > Global Business Administration > 1. Journal Articles
- Graduate School > Interaction Science > 1. Journal Articles
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