Chatbots in the frontline: drivers of acceptance

Abstract

Purpose

By extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots.

Design/methodology/approach

By using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.

Findings

Findings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.

Practical implications

The study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.

Originality/value

This is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.

Citation

Aslam, W., Ahmed Siddiqui, D., Arif, I. and Farhat, K. (2023), "Chatbots in the frontline: drivers of acceptance", Kybernetes, Vol. 52 No. 9, pp. 3781-3810. https://doi.org/10.1108/K-11-2021-1119