| In recent China, mobile voice communications has been growing rapidly with the development of mobile communication technology (i.e.3G). And mobile communications have be a major revenue source for mobile service providers. However, for reasons of market saturation and severe competition, the average revenue per user (ARPU) of mobile service providers has decreased radically in recent years.With the development of the next-generation network NGN, internet protocol (IP), mobile value-added services with more diversity and personality could be promoted with high level of service penetration, and would be the next source of growth for mobile service providers.However, mobile value-added industry in China has not yet realized the adequacy revenue from these value-added services. As compared to the great quantity of mobile phone users, the number of consumers adopting mobile value-added services in China is far from adequate. In addition, the whole mobile value-added industry has been developing slowly. Thus, it is believed that there is still growth room for mobile value-added services in recent China. The main reason for the slow development of mobile value-added services is the lack of coordination between participants in value chain. The objective of this research is to provide a global perspective to study the collaboration mechanism of mobile value chain.To minimize the marketing cost and maximize user adoption behavior, The optimization model discribing mobile bank user adoption behavior decision problem, with constraint of current state of influencing factors is developed。 We use adoption drivers as an example with questionnaire designed to obtain data from customers, which is then statistically analyzed using SPSS.Based on the objective function obtain by using BP neural network,and dynamic evolution of the linkages among adoption drivers driven by QSIM algorithm,qualitative modeling and simulation is created to study the optimal drivers combination.. Model is validated by oscillation-equilibrium phenomenon, which contributes some supports for real-time optimization decision of mobile bank business promotion. The results also show that except standard habit, corporate reputation and perceived risk have significant effect on user acceptance, though the impact of perceived risk has decreased.Besides match the strategic factors of its own, one service provider is more necessary to make its value-added strategy match the strategic factors of other entities in the value-added chain. Therefore, cross-enteiprise is available for service provider to enable the value chain obtains an equilibrium state. However, the information in management field is always ambiguous, incomplete or qualitative. The conventional quantitative method is unavailable to deal with the optimal problem, especially the strategy problem. Thus, a new paradigm for optimal problem with incomplete information is necessary to be developed.To explore how to allocate the limited resource to each strategic factor, an optimization model for value-added strategy alignment with incomplete information is developed. A combination of qualitative simulation and empirical study is able to serve as a solution to the optimization problem. Firstly, we use value-added strategy alignment as an example with questionnaire designed to obtain data from managers and scholars in mobile value-added industry, which is then statistically analyzed using ANP to examine the interactions between adoption drivers. Secondly, a qualitative simulation method is introduced to drive the evolution of the interactions between drivers. For more accuracy, the empirical relations obtained from empirical study is utilized to represent the casualty graph in traditional qualitative simulation models, where the casualty weights are denoted as the path coefficients between strategic factors in empirical study. Thirdly, according to the empirical relationships, an optimization model is established to examine the value-added strategy alignment in mobile value chain. The combination of empirical relationships and qualitative simulation enable a solution to the optimization problem. To validate the paradigm proposed, a qualitative simulation system is developed on the platform MATLAB7.0. It is found that the result is consistent with common sense in real management scenery, and the framework is able to contribute some supports to real-time optimization decision in mobile banking marketing. In practice, the identification of optimal combination of change directions can serve as the development priorities in strategic factors, and is likely to influence resource allocation in future value-added strategy alignment.We proposed a new paradigm to investigate the collaborative mechanism in mobile value chain with uncertain demand at different stages of industry life cycle. An integrated paradigm is proposed based on multi-agent simulation, including a bass diffusion model, and two games models among service providers, content providers, content and service provider, mobile operators and users.(1) To address the real-time demand, an extended Bass diffusion model is proposed based on system dynamics theory.(2) Driven by the market demand, a cooperative game model is built to examine partner selection of mobile operators.(3) To describe evolution of collaboration in service providers alliance, evolution game theory was introduced. To validate the framework, a muti-agent prototype system based on Anylogic is developed. The simulation experiments show that this system is available to enable appropriate decision-making to improve collaboration efficiency. By analyzing the impact of factors on industry revenue at different stages of life cycle, the accompanying measures are well understood. It is found that:(1) service provider scale, operators scale and strength, and the proportion of content and service provider in provider alliance is positively associated with total industry revenue;(2) Mass media is helpful to promote mobile commerce throughout the industry life cycle except the decline stage.(3) The significant effect of penalty mechanism on the value chain is limited to the maturity stage.(4) The industry life cycle will be prolonged by means of constraint measures... |
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