Special knowledge sharing incentive mechanism for two clients with complementary knowledge: A principal-agent perspective☆
Introduction
In knowledge economy era, it is difficult for single firm to complete a large project, which usually requires complex, diverse and different special knowledge. In order to successfully complete a large and complex project, the firm outsources this project to a virtual alliance with different clients. Different clients engage to do different parts of this project. Different clients’ knowledge structures are different, because different clients master special knowledge of different areas. Consequently, knowledge of different clients is usually complementary. As pointed out in Wang (2002), knowledge complementary is one of the reason for benefits increasing. So many researchers and practitioners concern the effects of knowledge complementarity on benefits of enterprises, see Fan and Lu, 2008, Xu and Zhao, 2010, Marianna et al., 2010. Many firms also aware that they must encourage different clients to share complementary special knowledge in order to obtain more benefits. Therefore, how to encourage different clients to share complementary special knowledge is one of the key issues faced by firms who want to raise their benefits.
There have existed many literatures on special knowledge sharing. Most researchers focused on effects of information technology on special knowledge sharing, see Shan and Dorothy, 2003, Huysman and Wulf, 2006 and their references. However, Andreas, 2005, Alexandre, 2008, Juan et al., 2009 showed that special knowledge sharing did not occur automatically and there were many inherent obstacles for knowledge sharing. Because clients usually viewed special knowledge as one of main factors for obtaining benefits, clients are not reluctant to share special knowledge. These obstacles prevent clients from sharing special knowledge consciously and actively. And information technology cannot assure that special knowledge sharing occurs automatically and clients generate motivation for special knowledge-sharing. Therefore, it is necessary to give clients some incentives, in order to encourage clients to share special knowledge voluntarily. More and more researchers and practitioners are aware of effects of incentives of non-IT factors on special knowledge sharing. As shown in economics, incentives can drive clients to share knowledge.
Some researchers have begun to study incentive mechanism for external clients special knowledge sharing. Zhang and Tan (2003) studied sub-game perfect Nash equilibrium in client knowledge sharing process under the complete information by using dynamic game analysis. Carl and Patrick (2008) identified the relationship between subsidiary bonus pay based on multinational corporation performance and knowledge sharing between different units of the multinational. Wei and Ju (2009) established the incentive mechanism models of knowledge sharing when enterprise-type customer are risk-neutral and risk-averse under the symmetric information and the asymmetric information. Benn, Kenneth, Paul, and Robert (2009) investigated the impact of formal and informal socialization mechanisms on the level of knowledge sharing within inter-organizational product development projects and the subsequent effect on buyer firm performance. Li and Jhang-Li (2010) applied game theory to analyze the incentives of knowledge-sharing activities in various types of communities of practice, characterized by individual profiles and decision structures. The literatures above studied knowledge-sharing incentives from perspective of knowledge-sharing environment, knowledge-sharing bodies and knowledge-sharing methods. Moreover, in above literatures, knowledge are viewed as a general integral concept. However, knowledge has inherent properties, e.g. knowledge complementary. These literatures do not study knowledge sharing from perspective of the special knowledge’ inherent properties, for example, knowledge complementary. Incentive mechanism for complementary special knowledge sharing is different from incentive mechanism for general and integral special knowledge-sharing in above literatures. Therefore, it is necessary to study incentive mechanism for special knowledge-sharing, from perspective of knowledge inherent properties.
Based on limitations above, some researchers begin to focus on incentive mechanism for special knowledge-sharing, from perspective of knowledge complementary. But so far, there is less attention paid on special knowledge-sharing from perspective of knowledge complementary. We have only found two literatures. Bandyopadhyay and Pathak (2007) studied the knowledge sharing between employees of the host firm and the outsourcing firm from the perspective of knowledge complementarity degree. Song, Li, and Xu (2008) discussed the sharing of mutually-complementary knowledge resources across organizations in the competitive strategic alliance. The literatures above qualitatively analyzed incentive mechanism for complementary knowledge sharing, but they did not quantitatively analyze knowledge complementary effects.
In sum, differences between this article’s research and previous research are as follows. This article does not take knowledge as general and integral concept. Instead, according to knowledge complementary, this article will introduce knowledge complementary effects into incentive model for special knowledge-sharing. Then this article will design optimal incentive mechanism for complementary special knowledge-sharing. Thereby optimal incentive mechanism for knowledge-sharing derived in this article are more suitable for clients with different special knowledge structures and levels who work together and coordinately.
The rest of this article is as follows. Section 2 analyzes and defines the content of knowledge complementary and knowledge sharing. Section 3 develops a two-client (agent) incentive model in which both clients take unobservable actions and have complementary special knowledge. Then optimal incentive mechanism and its properties are further derived. Section 4 presents concluding comments.
Section snippets
Special knowledge complementary and sharing
In practice, any people cannot master all knowledge in the world, but only can master some parts/segments of knowledge. When many objects complete a large and complex project together, interface between technician and technician/manger occurs frequently. Each people has own special knowledge different from other people. In order to raise output and benefits, technicians need communicate and collaborate with other technicians/managers, and need share their special knowledge and integrate all
Incentive model and optimal incentive mechanism
Consider a problem that how to design an optimal incentive mechanism when the firm outsources a large project to two clients. The two clients’ special knowledge has complementary effects. In order to gain output of special knowledge complementary effects, firm encourages both clients to share special knowledge. There are asymmetry information between the firm and two clients. Under asymmetric information, each client knows its own specific effort level, but firm can not directly and fully
Numerical analysis
In this subsection, we explain the effects of risk-averseness on optimal incentive coefficient by numerical experiment.
Fig. 1 presents relationship between optimal incentive coefficient risk-averse degree. From Fig. 1, we can found out the following results:
- •
The two sub-figures in Fig. 1 are the same because knowledge complementary effects’ coefficient μ1, μ2 in β∗ has the symmetrical form.
- •
When clients are risk neutral (ρ = 0), optimal incentive coefficient is constant no matter size of risks (σ =
Conclusions
In this paper, complementary effects are introduced into incentive mechanism for special knowledge sharing. Base on knowledge complementary effects and principal-agent theory, incentive models for special knowledge-sharing are established when both clients are risk-neutral and risk-averse under the asymmetric information. Further, this article designs optimal incentive mechanisms for different clients’ special knowledge-sharing under conditions of asymmetric information. In the process of
References (20)
- et al.
Knowledge sharing in communities of practice: A game theoretic analysis
European Journal of Operational Research
(2010) - et al.
Knowledge sharing and cooperation in outsourcing projects-a game theoretic analysis
Decision Support Systems
(2007) Enhancing employee tendencies to share knowledge-case studies of nine companies in Taiwan
International Journal of Information Management
(2006)Knowledge representation, knowledge complementary and game theoretical equilibria of intellectual property rights
Economic Research Journal
(2002)- et al.
Study on super-marginal model of complementary knowledge in industrial cluster
Science and Technology Management Research
(2008) - et al.
The effects of knowledge base complementary on technology alliance formation and partner selection
Science of Science and Management of S.& T.
(2010) - et al.
Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions
Strategic Management Journal
(2010) - et al.
Bridging communities of practice with information technology in pursuit of global knowledge sharing
Journal of Strategic Information Systems
(2003) - et al.
IT to support knowledge sharing in communities, towards a social capital analysis
Journal of Information and Technology
(2006) Three-dozen knowledge-sharing barriers managers must consider
Journal of Knowledge Management
(2005)
Cited by (20)
Exploring the microscopic mechanism of credit repair knowledge dissemination: A complex network-based approach
2024, Expert Systems with ApplicationsExamining the influence of knowledge spillover on partner selection in knowledge Alliances: The role of benefit distribution
2023, Computers and Industrial EngineeringThe incentive mechanism in knowledge alliance: based on the input-output of knowledge
2022, Journal of Innovation and KnowledgeCitation Excerpt :Alliance is a carrier of knowledge as it provides a platform for firms to outsource knowledge as well as gain knowledge from outside. Many scholars consider different parts of knowledge to be complementary and expected to increase the benefits of enterprises (Wang & Shao, 2012; Makri, Hitt, & Lane, 2010). Meanwhile, strategic alliances can effectively promote organizational performance (Bhattacharyya, 2018).
Tacit knowledge sharing in IT R&D teams: Nonlinear evolutionary theoretical perspective
2020, Information and ManagementCitation Excerpt :This framework is represented by individual profiles and decision structures. Wang and Shao [42] adopted a principal–agent method to examine the incentive mechanism in special knowledge sharing by considering knowledge-inherent characters. Yang and Wu [23], Wang et al. [16], and Jiang et al. [58] used an agent-based modeling method to explore the evolution of knowledge sharing behavior.
A game theoretic analysis of knowledge sharing behavior of academics: Bi-level programming application
2019, Computers and Industrial EngineeringCitation Excerpt :However, an FH can change the Nash equilibrium of the knowledge sharing among FMs through interventions such as rewarding and providing technological facilities (e.g., Tan, 2015). Some previous studies have developed principal-agent models to offer suggestions about designing a reward system for knowledge sharing behavior (e.g., Wang & Shao, 2012). However, a lack of principal-agent analysis in the area of knowledge sharing among academics is felt.
The impact of knowledge complementarities on supply chain performance through knowledge exchange
2015, Expert Systems with ApplicationsCitation Excerpt :In order to balance future supply and demand, firms need to plan future activities in key functional areas such as raw material procurement, production, and shipping and delivery (Huang, Stewart, & Chen, 2010). In this planning process, the focal firm’s knowledge needs to be complemented by the partner’s knowledge (Wang & Shao, 2012). For example, effective production planning for a supplier requires knowledge about ultimate markets from its buyers.