"Find a flight for me, Oscar!" Motivational customer experiences with chatbots. Issue 11 (4th August 2021)

• Record Type: Journal Article
• Authors: Jiménez-Barreto, Jano; Rubio, Natalia; Molinillo, Sebastian
• Is Part Of: International journal of contemporary hospitality management. Volume 33:Issue 11(2021)
• Page Start: 3860
• Page End: 3882
• Publication Date: 2021-08-04
• Subjects: Artificial intelligence -- Customer experience -- Chatbot -- Hybrid experiential model; Hospitality industry -- Management -- Periodicals; 647.94068
• DOI: 10.1108/IJCHM-10-2020-1244
• Languages: English
• ISSNs: 0959-6119

Abstract

Abstract : Purpose: Drawing on the self-determination theory, the assemblage theory and customer experience literature, this paper aims to develop a framework to understand motivational customer experiences with chatbots. Design/methodology/approach: This paper uses a multimethod approach to examine the interaction between individuals and airlines' chatbots. Three components of self-determined interaction with the chatbot (competence, autonomy and relatedness) and five components of the customer–chatbot experience (sensory, intellectual, affective, behavioral and social) are analyzed qualitatively and quantitatively. Findings: The findings confirm the direct influence of self-determined interaction on customer experience and the direct effects of these two constructs on participants' attitudes toward and satisfaction with the chatbot. The model also supports the mediating roles of customer experience and attitude toward the chatbot. Practical implications: This paper offers managers a broad understanding of individuals' interactions with chatbots through three elements: motivation to use chatbots, experiential responses and individuals' valuation of whether the interactions have amplified (or limited) the outcomes obtained from the experience. Originality/value: This paper contributes to the hospitality and tourism literature with a hybrid approach that reflects on current theoretical developments regarding human- and interaction-centric interpretations of customer experience with chatbots.