Special Session: Relationship Intelligence: Affordance of AI in Practice: An Abstract

Abstract

Customer relationship marketing (CRM) is a rapidly advancing area of marketing. CRM practitioners face challenges such as efficient management of enormous amount of data generated by customer interactions plus extracting, analyzing and interpreting this data to give true value to the information received (Bradlow et al. 2017). A further challenge is the centralization of data from interactions on digital interfaces, social networks and Internet of Things (Culotta et al. 2015), in CRM to provide value for the business objectives. Whilst the application of artificial intelligence (AI) may seem like a radical innovation, most eminent predictive techniques- Neural networks (NN) and Classification and Regression trees (CART) have their roots in AI. Indeed, automated notifications and auto-responders are early examples of automation in customer relationship management. Of late, these automated forms of interactivity are becoming increasingly sophisticated with the rapid advancement in the AI technologies. Interestingly authors Kumar et al. (2016) point to an considerable increase of customer relationships and interactions with the businesses through automated means in near future, particularly as the main objective of AI as a science is to create intelligent machines to do things that otherwise would require intelligence if done by humans (Boden 1977). The quantity of academic works examining the impact of AI on CRM remains limited reflecting the nascence of the topic. However there remains a gap for work that examines the perceptions, understanding and attitudes of the business professionals who will be adopting and implementing AI in their business practices. In this paper we use Affordance Theory as a lens with which to identify the affordances and constraints that AI offers business practitioners. Drawing on the work of Nagy and Neff (2015) we utilize the concept of the “imagined affordance” to test a model of AI acceptance that draws user perception, attitudes and expectation. Our work develops the role of imagination and vision in technology acceptance research and provides an alternative approach to the dominant by rational, conscious and process-orientated perspectives (Nagy and Neff 2015).