Towards Responsible Predictive Loyalty: A Bibliometric and Conceptual Mapping Study on AI and Ethics in Marketing
The optimization of customer loyalty strategies depends heavily on artificial intelligence (AI) through predictive models which forecast customer churn and engagement patterns. The data-driven tools are transforming modern marketing practices [1 ; 2]. The implementation of automation systems creates essential ethical issues regarding transparency and algorithmic accountability and stakeholder respect [3 ; 4] which creates conflicts between performance optimization and stakeholder respect. This paper addresses the challenges by conducting a bibliometric literature review about AI and predictive loyalty and marketing ethics. Our request was:(( TITLE-ABS-KEY ( «ARTIFICIAL INTELLIGENCE» OR «AI» OR «MACHINE LEARNING» OR «PREDICTIVE ANALYTICS» ) AND TITLE-ABS-KEY ( «CUSTOMER LOYALTY» OR «CHURN» OR «RETENTION» OR «CUSTOMER RELATIONSHIP» ) AND TITLE-ABS-KEY ( «ETHIC*» OR «TRANSPARENCY» OR «ACCOUNTABILITY» OR «PRIVACY» OR «TRUST» ) ) AND ( LIMIT-TO ( SRCTYPE , «J» ) ) AND ( LIMIT-TO ( PUBSTAGE , «FINAL» ) ) AND ( LIMIT-TO ( DOCTYPE , «AR» ) ) AND ( LIMIT-TO ( SUBJAREA , «BUSI» ) OR LIMIT-TO ( SUBJAREA , «SOCI» ) OR LIMIT-TO ( SUBJAREA , «DECI» ) OR LIMIT-TO ( SUBJAREA , «ECON» ) ) AND ( LIMIT-TO ( LANGUAGE , «ENGLISH» ) )The research aims to track existing scholarly trends while discovering essential conceptual groupings and new academic patterns related to responsible predictive loyalty. The research draws its data from 82 Scopus-indexed articles which VOSviewer analyses [5]. The research employs three methods to analyse data: keyword co-occurrence analysis and co-citation mapping and network clustering. The initial research results show that the study explores personalization [6], trust in automated systems [7] and data governance. However, few works adopt a systemic ethical approach to predictive loyalty practices. The review demonstrates a significant gap in existing research which requires a critical framework to evaluate AI applications by their efficiency and their social responsibility and transparency and sustainable customer relationship aspects.
Paper
Full text
Towards Responsible Predictive Loyalty: A Bibliometric and Conceptual Mapping Study on AI and Ethics in Marketing
Semantic Scholar · 2025
Abstract
The optimization of customer loyalty strategies depends heavily on artificial intelligence (AI) through predictive models which forecast customer churn and engagement patterns. The data-driven tools are transforming modern marketing practices [1 ; 2]. The implementation of automation systems creates essential ethical issues regarding transparency and algorithmic accountability and stakeholder respect [3 ; 4] which creates conflicts between performance optimization and stakeholder respect. This paper addresses the challenges by conducting a bibliometric literature review about AI and predictive loyalty and marketing ethics. Our request was:(( TITLE-ABS-KEY ( «ARTIFICIAL INTELLIGENCE» OR «AI» OR «MACHINE LEARNING» OR «PREDICTIVE ANALYTICS» ) AND TITLE-ABS-KEY ( «CUSTOMER LOYALTY» OR «CHURN» OR «RETENTION» OR «CUSTOMER RELATIONSHIP» ) AND TITLE-ABS-KEY ( «ETHIC*» OR «TRANSPARENCY» OR «ACCOUNTABILITY» OR «PRIVACY» OR «TRUST» ) ) AND ( LIMIT-TO ( SRCTYPE , «J» ) ) AND ( LIMIT-TO ( PUBSTAGE , «FINAL» ) ) AND ( LIMIT-TO ( DOCTYPE , «AR» ) ) AND ( LIMIT-TO ( SUBJAREA , «BUSI» ) OR LIMIT-TO ( SUBJAREA , «SOCI» ) OR LIMIT-TO ( SUBJAREA , «DECI» ) OR LIMIT-TO ( SUBJAREA , «ECON» ) ) AND ( LIMIT-TO ( LANGUAGE , «ENGLISH» ) )The research aims to track existing scholarly trends while discovering essential conceptual groupings and new academic patterns related to responsible predictive loyalty. The research draws its data from 82 Scopus-indexed articles which VOSviewer analyses [5]. The research employs three methods to analyse data: keyword co-occurrence analysis and co-citation mapping and network clustering. The initial research results show that the study explores personalization [6], trust in automated systems [7] and data governance. However, few works adopt a systemic ethical approach to predictive loyalty practices. The review demonstrates a significant gap in existing research which requires a critical framework to evaluate AI applications by their efficiency and their social responsibility and transparency and sustainable customer relationship aspects.