Assessing the Implementation of AI Integrated CRM System for B2C Relationship Management: Integrating Contingency Theory and Dynamic Capability View Theory

Abstract

Customer relationship management (CRM) is a strategic approach to manage an organization’s interaction with current and potential customers. Artificial Intelligence (AI) can analyze huge volume of data without human intervention. The integration of AI with existing legacy CRM system in the business to customer (B2C) relationship makes sense given the massive potential for growth of AI integrated CRM system. Failure to plan AI-CRM technology implementation in an organization could lead some to success and others to failure. The Contingency theory states that it is not possible for organizations to take decisions without a contingency plan and the optimal course of action depends on the internal and external circumstances. The Dynamic Capability View theory emphasizes the organizational ability to react adequately in a timely manner to any external changes and combines multiple capabilities of the organization, including organizational CRM and AI capabilities. Against this background, the purpose of this study is to examine the success and failure of implementation of AI integrated CRM system in an organization from B2C perspective using Contingency theory and Dynamic Capability View theory. The study finds that information quality, system fit, and organizational fit significantly and positively impact the implementation of AI-CRM for B2C relationship management. Also, there is a moderating impact of technology turbulence on both acceptance and failure of AI-CRM capability in the organization.