Mapping the Intellectual Structure of Customer Loyalty and Customer Relationship: A Bibliometric Analysis

Abstract- Customer loyalty and customer relationship management (CRM) have become important research areas as businesses increasingly focus on customer retention and long-term relationship development. This study aims to map the intellectual structure and research trends concerning customer loyalty and customer relationships through a bibliometric analysis. Data were collected from the Scopus database covering the period 2021–2026 and analyzed using VOSviewer to examine publication trends, keyword co-occurrence, research themes, and emerging topics. The findings indicate that customer loyalty is closely associated with customer satisfaction, customer engagement, brand loyalty, and customer experience. Recent research also highlights the growing role of artificial intelligence, digital engagement, predictive analytics, and personalized services in strengthening customer relationships and improving loyalty. These developments demonstrate a shift toward technology-driven and data-informed customer management strategies. This study contributes to the literature by providing an overview of the intellectual landscape, identifying emerging research directions, and offering insights for future investigations into the relationship between CRM practices and customer loyalty in an increasingly digital business environment.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-14
DOI
https://doi.org/10.5281/zenodo.23373040
Primary Topic
Customer Service Quality and Loyalty
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mapping the Intellectual Structure of Customer Loyalty and Customer Relationship: A Bibliometric Analysis

Bawono Abyan Putra, Michael Dermawansyah
Zenodo (CERN European Organization for Nuclear Research)
Customer Service Quality and Loyalty
article

Mapping the Intellectual Structure of Customer Loyalty and Customer Relationship: A Bibliometric Analysis

Bawono Abyan Putra, Michael Dermawansyah
article en

Abstract

Abstract- Customer loyalty and customer relationship management (CRM) have become important research areas as businesses increasingly focus on customer retention and long-term relationship development. This study aims to map the intellectual structure and research trends concerning customer loyalty and customer relationships through a bibliometric analysis. Data were collected from the Scopus database covering the period 2021–2026 and analyzed using VOSviewer to examine publication trends, keyword co-occurrence, research themes, and emerging topics. The findings indicate that customer loyalty is closely associated with customer satisfaction, customer engagement, brand loyalty, and customer experience. Recent research also highlights the growing role of artificial intelligence, digital engagement, predictive analytics, and personalized services in strengthening customer relationships and improving loyalty. These developments demonstrate a shift toward technology-driven and data-informed customer management strategies. This study contributes to the literature by providing an overview of the intellectual landscape, identifying emerging research directions, and offering insights for future investigations into the relationship between CRM practices and customer loyalty in an increasingly digital business environment.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 7%
Customer Service Quality and Loyalty
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.