Evolution of knowledge sharing behavior in social commerce: An agent-based computational approach

The rapid development of e-commerce has expedited knowledge growth in the e-commerce social community. Knowledge sharing among online users has exhibited a nonlinear dynamic evolution. This paper examines the evolutionary process of knowledge sharing among users of the social commerce; builds an evolutionary game model to depict knowledge sharing phenomenon in the virtual community; and develops a mixed learning algorithm based on individual user's historical game strategy, neighborhood user's strategy, and information noise. We design a computational model based on multi-agent theory and social network, and implement computational experimental system using NetLogo 5.0. We find that the proposed computational-experimental model can help decision makers simulate evolutionary process under various scenarios. The evolutionary game rule and social network structure significantly influence the degree of cooperation and knowledge sharing among users. The greater noise the network information has the less stable the users' behavior will be. One can thus identify an optimal initial cooperation rate to facilitate the system to reach equilibrium state quickly. Our study on the dynamic evolution of knowledge sharing behavior in the social commerce contributes to the theoretical development of literature and provides valuable decision-making support to managers.