Individual knowledge sharing behavior in dynamic virtual communities: the perspectives of network effects and status competition
Pi and Cai Frontiers of Business Research in China
Individual knowledge sharing behavior in dynamic virtual communities: the perspectives of network effects and status competition
Shenglei Pi 1
Weining Cai 0
0 Great Wall Strategy Consultants , Beijing 100101 , China
1 School of Management, Guagnzhou University , Guangzhou, Guangdong 510410 , China
Purpose: While most literature concerning knowledge sharing examines it as an organizational method for innovation and value creation, this paper considers online knowledge sharing as an individual behavior decision embedded in a virtual community. We attempt to explore which sharing behavior can help individual participants gain a better position in an online community, improving social status, reputation, and other social networking interests. Design/methodology/approach: We collected and measured the knowledge sharing activities and discussion from a Chinese online expertise knowledge network in Business Management Consulting. We tested the mediating effects of the sharing behavior of the major members of the online knowledge network on members' status (network centrality) in different time units (days). Findings: In a dynamic virtual community, the direct result of knowledge sharing behavior is reflected in the individual status position (the degree of node centrality). At the same time, individual knowledge sharing behavior has an “inertia effect”: individual prior status (the degree of node centrality) affects current knowledge sharing behavior, while current knowledge sharing behavior affects current status in the knowledge network, forming an inertial circuit between personal behavior and network status. Originality/value: We expound the theory of individual knowledge sharing in the context of an inter-person dynamic virtual community; we provide action “strategies” for individual knowledge sharing behavior choice, for better understanding the nature of individual knowledge behavior, and we also propose and test the “inertia effect” of knowledge sharing behavior and the knowledge network, and demonstrate the theory of network effects from an individual perspective.
Dynamic virtual community; Individual knowledge sharing; Network effect; Competition
Introduction
Knowledge sharing was initiated as a method of knowledge management
(Kang, et al.,
2017)
and organizational learning
(Almeida and Soares, 2014)
, especially in the context of
innovative process management
(Zhou and Li, 2012)
, or supply chain management
(Cai, et al., 2013)
. Most literature on knowledge sharing aims to examine whether
knowledge sharing can improve organizational performance in innovation and learning
and if so, how
(Ritala, et al., 2015)
. While the development of social network technology
(Jiang, 2015)
and the emergence of the sharing economy
(Wang et al., 2011)
in recent
years might make knowledge sharing a common and crucial method for individuals in
individual learning, social networking, and even career improvement (Chen and Hung,
2010). Especially in online knowledge networks, knowledge sharing is mainly for catching
“eyeballs” and turning it into actual economic and social benefits
(Qiu and Wang, 2011)
.
Individual knowledge sharing actions are not just for knowledge learning, but also for
inter-personal competition, cooperation, self-reputation, or even free-rider experiences
(Liao et al., 2013)
. Therefore revealing the mechanism of individual knowledge sharing
behavior is not only important for knowledge management and organizational learning in
a sharing economy but also helps to understand and establish knowledge sharing networks
while encouraging participants to share. Moreover, most scholars examine knowledge
sharing in a static context or knowledge network
(Saifi et al., 2016; Reinholt and Foss,
2011)
, with only a few building dynamic models of knowledge sharing from the perspective
of network effects
(Gong, 2011; Butts, 2011)
.
Individual knowledge sharing in the sharing economy has turned out to be more
likely an individual behavior, rather than just an organizational decision. Also, the
decision to engage in knowledge sharing behavior is operating under the dynamic
structure of the entire knowledge network. This study aims to explore the mechanism
between the character of individual online knowledge sharing behavior and individual
network status, to help individual participants gain more social and economic benefits
by choosing effective sharing behavior. According to the theory of network effects,
one can have more network connections with fewer costs when one gains critical
mass
(Economides and Himmelberg, 1995)
. Though network effect theory indicates
that the scale of participants will help the entire network gain alternative value and
improve performance
(Arroyo, 2007)
, individual choice and decisions taken under the
network effect are still a blind spot. More connections bring more social capital and
other benefits for the i (...truncated)