Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Abstract

Mobile application innovation diffusion can be used to enhance the reputation and competitiveness of Internet enterprises. However, few works have explored the process of mobile application innovation diffusion from the individual perspective. Therefore, this paper employs multi-agent methods to simulate the innovation diffusion of mobile applications from the perspective of social networks. Specifically, we combine psychology, sociology, game theory and network effect theory to model user behaviors of adoption and rejection decisions for mobile applications. The multi-agent model was built in Anylogic 8 to simulate the communication and interaction between individual users. Then, this paper discusses the evolution of decision-making of social network user groups with different network structures and network effects. We also investigate the impact of different firms' promotion on innovation diffusion. Our findings suggest firms could make better strategies and achieve better diffusion effects from mobile applications. IntroductionNowadays, market competition in mobile applications is increasingly fierce. The success of the mobile application in market promotion not only helps Internet enterprises to seize the market and create business value, but also allows firms establish a good reputation and gain a competitive advantage [1][2][3].The innovation diffusion of mobile applications always occurs in user groups. Since users can communicate and interact with others, most of the diffusion of mobile applications is based on each user in a social network [4,5]. Numerous studies indicate that the development of mobile internet and technology social networks, significantly reducing the cost of communication between users, makes communication and interaction very convenient [6,7]. As various mobile applications allow users to share happiness and dissatisfaction with others on the Internet, users' decisions on mobile applications are also influenced by social relations and social network structure [8,9]. In addition, research evidence also supports the fact that there are network effects in the diffusion of mobile applications; that is, the utility of mobile applications to users will increase with an increase in the number of applications [10]. Specifically, network effects can be divided into global and local network effects. Global network effect means that, when more users adopt innovative technology in the global environment, the value of mobile applications to users will be higher. While the local network effect refers to the effect of related users on the utility value, which emphasizing the interaction between users. The decision-making of users will largely depend on the decision-making of their surroundings. As the number of users adopting mobile applications increases, their neighbors also would like to adopt the same applications. Then, the value and utility of the mobile applications to the users will also increase, which ultimately Sustainability 2020, 12, 2884 2 of 17 affects user decision-making. In additio...

Analyzing the Coevolution of Mobile Application Diffusion and Social Network: A Multi-Agent Model

“…The focus of the developed model is the evolution of social networks. At the micro-level, the mechanism of the user’s adoption or rejection of mobile applications is the same as that of Zhou et al [ 7 ], including the mobile application hypothesis, the conceptual model for individual decisions by a mobile application user, and the design of the mobile application user decision and interaction process. Next, we introduce the hypothesis of social network evolution and the design of social network evolution rules.…”

“…The detailed descriptions and specific meanings of b , c , f , m , k , U i , U j and other parameters in the model are the same as Zhou et al’s [ 7 ].…”

“…The parameters’ settings have a very important impact on the accuracy of the simulation results. Thus, the parameters’ settings of our work referred to Zhou’s [ 7 ] and Laciana’s studies [ 40 ]. The number of initial users was n = 100, and 10% of initial users were seed users.…”

“…Zhou et al [ 7 ] constructed a simulation model based on the static social network environment and compared mobile application diffusion results under different social network structures and network effects. However, the dynamic changes in network structures were not considered.…”

Influence of farming household interactions on rural homestead withdrawal compensation from the perspective of social network

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