Mobile Commerce Risk Factor Analysis And Risk Avoidance Strategy

As the development and popularization of the mobile terminal equipment, various mobile service began to come into people's life, including mobile payment, mobile marketing, mobile banking and mobile office automation, But when facing with the constant changing of business environment, the development and management of every new mobile service is accompanied with a series of risk problems, such as the value chain cooperation, market promotion cost and forecasting the future profit. The current literatures have studied some aspects of mobile commerce risk. However, still we could find the lack of systematic analysis, evaluation and simulation framework for mobile commerce risk management.Based on the literature review of mobile commerce risk analysis, this study conducted a systematic analysis of all kinds of mobile commerce risk factors, with the application of the fuzzy reasoning method, real option theory, supply chain risk management theory and portfolio theory in mobile commerce fields, introducing new tools and methods for the mobile commerce risk assessment and avoidance, achieved the interdisciplinary intersect research for mobile commerce. The main work and highlights of this research can be listed as follows:First, this paper used the Delphi method to examine the top 10 risk factors of mobile commerce, which can be categorized into three dimensions of macro-environment risk, industrial risk and operational risk according to the SCP model. The dimensions of macro-environment risk are economic risk, political risk, technological risk and social & cultural risk; The dimension of industrial risk includes competition with other MNOs, cooperation with SPs and cooperation with manufacturers; The operational risk dimension includes cost management risk, consolidation risk and market risk. Then we calculated the risk exposure and risk matrix for each risk factor, comparing with Ernst & Young global telecommunication risk report.Based on the survey results of risk factors, using fuzzy cognitive influence diagram, this paper constructed a correlation analysis for mobile commerce risk factors. The risk control tactics using the combination of short-term price strategy, short-term promotion strategy, network construction strategy and long-term price strategy. Using the fuzzy cognitive influence diagram expert experience are extracted and simulation analysis are conducted, from this four types of typical risk control strategy are concluded to make a balance between short-term profit, short-term risk, long-term profit and the long-term risk.According to the portfolio theory, this paper categorized the mobile service into four types. According to whether it is diversifiable the mobile commerce risk could be separated into system risk and idiosyncratic risk, we analyzed the features of different mobile service from consumer adoption, pricing, the cost composition and life-cycle characteristic. Then we proposed a mobile service portfolio optimization model based on the risk diversification methodology, thus the best hedging of risk factors can be realized, the curves of profit under the different risk tolerance for the mobile service combination then can be drawn, providing the theoretical basis for the mobile network operators and service providers to achieve a better mobile service portfolio.For the specific risk avoidance strategies in the mobile commerce value chain, this paper achieved pareto improvement by the dynamic sharing of mobile commerce risk using real option theory. With the real option contract, the mobile network operators could purchase the option of a mobile service from service providers, thus the dynamic adjustment of returns and risk sharing can be achieved according to market changes in the life cycle of mobile service. So it can effectively reduce the value chain cooperation risk, market risk and cost management risk for both the mobile network operators and service providers. Finally, the risk avoidance effect is proved through mobile TV numerical example and sensitivity analysis is conducted.