RINSE & RECOVER: The Adoption & Integration of Artificial Intelligence in Service Recovery Among SMEs in the Swedish Retail Industry

Sammanfattning

The development of artificial intelligence is a continuous process and is a research field that that demands further exploration. Scholars have identified several recommendations for further research regarding artificial intelligence, which this thesis aims to contribute to. Service recovery is another research field in need of further research, with identified gaps regarding both conceptual understanding and operational implementation. Thus, this thesis positions itself at the intersection of AI and service recovery research streams and aims to contribute to general, and applicable, knowledge that can be used by organizations in their operations. This thesis explores the adoption and integration of artificial intelligence in service recovery among small- and medium-sized enterprizes in the Swedish retail industry. The purpose is to provide insights regarding the underlying mechanisms and structures that influence AI adoption and integration into service recovery. By applying a developed theoretical framework concerning technology acceptance, as well as organizational and external factors, this thesis provides an understanding of the adoption and integration of artificial intelligence in service recovery. The phenomenon is explored through a qualitative multiple case study, and through the lense of a critical realist research philosophy and abductive approach. The data has been collected through semi-structured interviews with participants from six different organizations. After the interviews were conducted, and the codes and themes had been identified, several interesting findings were identified. The results present AI adoption as an iterative process, containing several steps that organizations move back and forth between. This process is, in turn, directly influenced by both internal and external factors. Further, it has been identified that an interface between humans and AI permeates through each component of the process of adoption and integration of AI in service recovery. The findings of this thesis contribute to existing literature and theories regarding technology acceptance by highlighting an interesting paradoxical relationship between the competitive landscape and the adoption of AI in service recovery. When the competitive landscape is rather inactive, the adoption of AI is more extensive, and the integration is more advanced. When the competitive landscape is rather active, however, the adoption of AI is less extensive, and the integration is to a rather low degree.