Service Value Created by Customers' Information Exchange About Provider: Agent Based Simulation Approach

Abstract

Service system science emerges as the study of value co-creation phenomenon [8]. At the heart, service is interaction between customer and provider. Both of them are sources of value co-creation according to the Service-dominant logic [10]. Value co-creation model of service innovation [7] claims that co-experience and co-definition of shared internal model is critical for co-creation of service value with customer. Each customer recognized as an active co-creator of knowledge and service value, but how interaction among customers themselves has not been investigated sufficiently. When customers interact with one another and exchange their information about a provider, they may co-create different service value since they contact the provider on the one-to-one basis. For example, customers arrange airline ticket and decide to use an airline by some reasons. Then, customers may contact the airline directly to consider requests and their service. Customers may ask friends and family about the airline also check the airline deeper through SNS or the Internet. Customers try to interact by learning and sharing the internal model about the airline. In this paper, it will be investigated how interaction among customers gives impact on co-creation of service value by adopting agent-based social simulation approach. The assumption of customers is adaptive learning agents. Therefore, customers are classified into three types, that is, (1) type one is customers who communicate the provider directly without communication, (2) type two is customers who contact those face-to-face, like their friends or families, and (3) type three is customers who ask unanimous using the Internet. Adaptive learning model is constructed to describe customer by combine between hypergame models and the genetic algorithm as a methodological basis. Hypergame model adopted because it can explicitly represent internal model (subjective game) for each customer. While Genetic Algorithm is suitable since it provides a method to reorganize knowledge according to the experiences to improve performance [3]. Customers assumed to use learning procedure, by which they select actions, modify and revise current internal model according to their experiences so far. It would be explored under what conditions that interaction on the customers?f side would co-create new value according to the customers?f communication type.