Past, present and future of research in relationship marketing - a machine learning perspective

Abstract

Purpose

This paper aims to take stock of research done in the domain of relationship marketing (RM). Additionally, this article aims to identify the potential areas of future research.

Design/methodology/approach

The authors have used machine learning-based structural topic modelling using R-software to analyse the dataset of 1,905 RM articles published between 1978 and 2020.

Findings

Structural topic modeling (STM) analysis led to identifying 14 topics, out of which 7 (viz. customer loyalty, customer relationship management systems, interfirm and network relationships, relationship selling, services and relationship management, consumer brand relationships and relationship marketing research) have shown a rising trend. The study also proposes a taxonomical framework to summarize RM research.

Originality/value

This is the first comprehensive review of RM research spanning over more than four decades. The study’s insights would benefit future scholars of this field to plan/execute their research for greater publication success. Additionally, managers could use the practical implications for achieving better RM outcomes.

Citation

Das, K., Mungra, Y., Sharma, A. and Kumar, S. (2022), "Past, present and future of research in relationship marketing - a machine learning perspective", Marketing Intelligence & Planning, Vol. 40 No. 6, pp. 693-709. https://doi.org/10.1108/MIP-11-2021-0393