Combination of Empirical Study with Qualitative Simulation for Optimization Problem in Mobile Banking Adoption

To explore the minimization of the marketing cost and the maximization of the user perceived utility, an optimization model for mobile banking adoption with incomplete information is developed. A combination of qualitative simulation and empirical study can serve as a solution to the optimization problem. Firstly, we use mobile banking system as an example with questionnaire designed to obtain data from customers, which is then statistically analyzed using SPSS to examine the interactions among adoption drivers. Secondly, a qualitative simulation method is introduced to drive the evolution of the interactions among these adoption drivers. Thirdly, according to the empirical relations, an optimization model is established, and the objective functions are examined by the BP neural network. Then, to examine the feasibility of the framework, a prototype system based on MATLAB is implemented. It is found that the results are consistent with common sense (oscillation-equilibrium theory), and the framework is able to contribute to real-time optimization decision supports in the mobile banking marketing. In practice, the identification of the optimal combination of change directions can serve as the development priorities in adoption drivers, and is likely to influence resource allocation in the future mobile banking development.