Efficiency and Effectiveness of C2C Interactions and Mutual Learning for Value Co-Creation: Agent-Based Simulation Approach

  •  Santi Novani    
  •  Kyoichi Kijima    


The active role of customers and their experience to create the value in service system have been recognized in
the literature. So far, the focus of value co-creation only for business-business (B2B) or business-customers
(B2C), meanwhile the attention to the customer-to-customer (C2C) is not so much. Recently, the attention to
C2C interaction is increasingly viewed by marketer and researcher. However, the existing research of C2C is on
the conceptual and empirical model, therefore it may not describe C2C interaction dynamically. In this research,
we show and develop a model of customers’ interaction by using agent-based simulation to capture the dynamic
of interaction. The research aim is to explore how effective and efficient C2C by engaging customers’
experience in value co-creation. Furthermore, we investigate the needs of mutual learning by using agent based
simulation. The previous research is focus on two types of C2C interaction, i.e., face-to-face and social media
interaction. In the present research, we show more general the needs of human interaction by engaging
customers’ experience which compare between ‘customer-to-customer (C2C) interaction’ and ‘no interaction’
(i.e., customers interact with the provider directly without interaction with other customers). By conducting
agent based simulation, we investigate how C2C interaction is effective which measured by learning
performance and how efficient which measured by learning efficiency. On the other hand, we conduct
sensitivity analysis to investigate the needs of mutual learning between customers and provider in value
co-creation. In our simulation result, it shows customer-to-customer (C2C) interaction gives significant
influence than ’no interaction’. Moreover, we have already shown the needs mutual learning between customers
and provider from the simulation results.

This work is licensed under a Creative Commons Attribution 4.0 License.