IIFT International Business and Management
Review Journal
issue front

Anjali Chopra1, K. S. Ranjani2 and Soujanya Narsipur3

First Published 24 Jul 2023. https://doi.org/10.1177/jiift.221150355
Article Information Volume 1, Issue 1 June 2023
Corresponding Author:

Anjali Chopra, Department of Marketing and International Business, K J Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai, Maharashtra 400077, India.
Email: anjali.chopra@somaiya.edu

1Department of Marketing and International Business, K J Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai, Maharashtra, India

2Faculty of Finance, National Institute of Industrial Engineering, Mumbai, Maharashtra, India

3K J Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai, Maharashtra, India

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.


The purpose of this article is to investigate the antecedents of satisfaction of consumers with chatbots using service quality (SERVQUAL) in the South Asian context. Customer satisfaction is one of the critical attitudinal variables that may influence customer behaviour and lead to greater customer loyalty. Automated query resolutions facilitated through AI chatbots are used to enhance SERVQUAL and customer satisfaction. However, it has been observed that inability to resolve queries satisfactorily results in frustration and abandonment of the channel. To address this service deficiency, the proposed study uses the SERVQUAL scale to identify antecedents of customer satisfaction. A total of 355 usable responses were collected from consumers who used chatbot services across different sectors. Confirmatory factor analysis and structural equation modelling (AMOS 26) were employed to test the proposed model. Tangibles, assurance and efficiency were significant in explaining customer satisfaction while responsiveness and privacy were insignificant. Assurance was significant and caused dissatisfaction when perceived as absent; tangibles were significant but were no longer a differentiator; efficiency was perceived as significant and can be critical in building assurance and accelerating adoption. The findings from this study provide beneficial insights to businesses who handle large volumes of service requests for instance, digital banking applications, travel sites, e-commerce sites and educational institutions. Businesses need to focus on critical service dimensions like assurance and efficiency rather than improving the conversational ability of chatbots. This article addresses an important paradox namely poor satisfaction with chatbot services despite demand for technology-enabled services. Theoretically speaking, easy-to-use and easy-to-access technologies should result in higher engagement among consumers. However, the rapid deployment of chatbots by businesses is met with high dropout in chatbot service channel. Technology adoption has been studied widely, but this study addresses the larger issue of what could enable better customer satisfaction in an easy-to-use technology.


AI chatbots, customer satisfaction, service quality, service delivery


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