2007 Annual International CHRIE Conference & Exposition 353
Figure 2. The final model Cognitive Dimension
Relational Dimension
Propensity
to share
Low Self-Efficacy
& High Outcome
Expectations
Intention to participate and share 0.264 ** 0.180** 0.441** -0.25 7** R²=0.61 0.048 0.223 0.014 Chi-square
408.3
df
236
Normed
Chi-Square
1.73
GFI
0.84
CFI
0.90
RMSEA
0.06
Fit Indices (** indicates a significance at
α
= .05)
Therefore, to understand knowledge sharing in an online environment, one must look into the structure of
the social grouping surrounding an individual. While developing knowledge sharing systems, it is important to
encourage and enhance manager’s identification with the group at the early stage and establish reward and
recognition mechanisms to entice active participation. These practices can be very relevant to this study context
wherein the online community would be built for an existing trade association with international members dispersed
geographically and reward/recognition structures can be easily established such as recognition of monthly “Online
Experts” with annual conference fee discounts. The results of this study also emphasize the importance role
organizations can play in encouraging both intra- and inter- organizational knowledge sharing.
The Hypothesis 3 (path coefficient = 0.441) was supported to indicate that the propensity to share
knowledge was also found to have a significantly positive impact on knowledge sharing intention. One aspect of this
dimension is the distinction between organization-specific information and personal expertise. Consistent with
Constant
et al ’s (1996) findings, results show that individuals who believe that certain forms of information as
belonging to the organization are not comfortable sharing them outside of the organization whereas they view
personal expertise as their own property and are more willing to share it with members in the online community.
Significant benefits can be accrued if organizations can carefully frame their member’s views about different forms
of knowledge and attitude towards sharing (Jarvenpaa & Staples, 2000), which is directly linked to the success of
various knowledge sharing systems initiatives. Finally, the authors found a significant support for low self-efficacy
and high outcome expectations (path coefficient = -0.257) that impair online knowledge-sharing intention. Online
communities of practice are fundamentally technology-based and the comfort level with usage of such technical
tools seems to be a good predictor of knowledge sharing. If individual members feel that such systems are too
complex to use, then their active participation is negatively affected. Proper education/training and supporting
functionalities should be in place to enable effective use of technology tools for facilitating knowledge sharing
activities. Prior research (Wasko & Faraj, 2000) showed that knowledge sharing in online communities of practice is
facilitated by a sense of reciprocity and fairness in knowledge exchanges. This bears on an individual’s perception
that knowledge sharing is being rewarded through two-way communications. Online knowledge-based system
developers should always provide open online “channels” (e.g., asynchronized messaging boards or synchronized