doi:10.1016/j.ijhcs.2006.09.003
Copyright © 2006 Elsevier Ltd All rights reserved.
Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations
Meng-Hsiang Hsua,
,
, Teresa L. Jub, Chia-Hui Yenc and Chun-Ming Changa
aDepartment of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan
bDepartment of Information Management, Shu-Te University, Kaohsiung, Taiwan
cDepartment of International Business Management, Wufeng Institute of Technology, Chiayi, Taiwan
Received 28 January 2005;
revised 11 September 2006;
accepted 23 September 2006.
Communicated by P. Zhang.
Available online 13 November 2006.
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Abstract
There has been a growing interest in examining the factors that support or hinder one's knowledge sharing behavior in the virtual communities. However, still very few studies examined them from both personal and environmental perspectives. In order to explore the knowledge sharing behaviors within the virtual communities of professional societies, this study proposed a social cognitive theory (SCT)-based model that includes knowledge sharing self-efficacy and outcome expectations for personal influences, and multi-dimensional trusts for environmental influences. The proposed research model was then evaluated with structural equation modeling, and confirmatory factor analysis was also applied to test if the empirical data conform to the proposed model.
Keywords: Knowledge sharing behavior; Trust; Self-efficacy; Social cognitive theory; Virtual communities
Fig. 2. SEM analysis of research model.
Table 1.
Trust type and measures in previous research

Table 2.
Sample demographics of this study

Table 3.
Summary of measurement scales

Table 4.
Correlations and AVE

Legend: ET= Economy-based trust; IT=Information-based trust; DT=Identification-based trust; EP=Personal outcome expectations; EC=Community-related outcome expectations; SE=Knowledge sharing self-efficacy; BE=Knowledge sharing behavior; CR=Composite reliability
AVE is average variance extracted (i.e., proportion of variance in construct that is not due to measurement error). For discriminant validity, AVE should be larger than squared correlation between any pair of constructs.
Table 5.
Model fit index summary
