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Cited by 255

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Understanding Behavioral Intention to Participate in Virtual Communities

Lin, Hsiu-Fen·2006·CyberPsychology & Behavior

Abstract

Virtual communities are formed on the Internet and are expected to serve the needs of members for communication, information, and entertainment. Online businesses should consider virtual communities as a new market place since their members are current or future customers. Thus, there is a need to understand the determinants of member intentions to participate in virtual communities. Based on the extended theory of planned behavior (TPB), this study develop a research model to identify the attitudinal, social, and perceived behavioral control factors that would influence members intentions to participate in virtual communities. Specifically, the research model decomposes the attitude component into perceived usefulness, perceived ease of use and perceived trust, and the perceived behavioral control component into Internet self-efficacy and facilitating conditions. Based on a survey of 165 community members, this study uses structural equation modeling (SEM) approach to investigate the research model. The results indicate that attitude and perceived behavioral control significantly influence member behavioral intentions, while subjective norms do not. Finally, this study discusses the implications of these findings and offer directions for future research.

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Metadata

Authors
Lin, H.-F.
Venue
CyberPsychology & Behavior
Published
2006-10
Volume
9
Issue
5
Pages
540-547
ISSN
  • 1094-9313 (ISSN-L)
  • 1557-8364
OpenAlex
W2041967743
Cited by
255
References
8
FWCI
14.96
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.