Chatbot integration for online shopping: a bibliometric review and future research agenda

Abstract

Purpose

This study aims to conduct a comprehensive analysis of the current body of existing literature on chatbots and online shopping. Additionally, this study identifies and emphasize the future research agenda and emerging trends within this domain.

Design/methodology/approach

A thorough investigation was conducted on a set of 147 publications sourced from the Scopus database spanning the years 2016 to 2023 by using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. The analysis included bibliometric techniques through VOSviewer, including science mapping and performance analysis of the literature under investigation.

Findings

The findings of the study indicate a systematic impression of prevailing scientific research on integration of Chatbot in online shopping. A majority of publications were contributed by developing countries specifically Asian regions. There has been a notable rise in research collaborations over the course of time. Further, themes were identified through keyword co-occurrence for exploration of future trends in the domain.

Practical implications

This study identifies and analyzes the patterns in the existing literature on chatbot and online shopping, with the objective of enhancing e-retailers comprehension of this particular topic area. The research findings hold significance for both researchers and organizations in their efforts to enhance strategy design.

Originality/value

This study uses bibliometric analysis to examine the literature on chatbots and online shopping, aiming to develop a systematic comprehension of the research field. This study makes a valuable contribution to the current scholarly discourse and provides support for future scholars in their investigations.

Citation

Sharma, N., Arora, M., Tandon, U. and Mittal, A. (2024), "Chatbot integration for online shopping: a bibliometric review and future research agenda", Information Discovery and Delivery, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IDD-11-2023-0132