Algorithmic bias in machine learning-based marketing models

Shahriar Akter, Yogesh K. Dwivedi, Shahriar Sajib, Kumar Biswas, Ruwan J. Bandara, Katina Michael

Research output: Contribution to journalArticlepeer-review

Abstract

This article introduces algorithmic bias in machine learning (ML) based marketing models. Although the dramatic growth of algorithmic decision making continues to gain momentum in marketing, research in this stream is still inadequate despite the devastating, asymmetric and oppressive impacts of algorithmic bias on various customer groups. To fill this void, this study presents a framework identifying the sources of algorithmic bias in marketing, drawing on the microfoundations of dynamic capability. Using a systematic literature review and in-depth interviews of ML professionals, the findings of the study show three primary dimensions (i.e., design bias, contextual bias and application bias) and ten corresponding subdimensions (model, data, method, cultural, social, personal, product, price, place and promotion). Synthesizing diverse perspectives using both theories and practices, we propose a framework to build a dynamic algorithm management capability to tackle algorithmic bias in ML-based marketing decision making.

Original languageEnglish (US)
Pages (from-to)201-216
Number of pages16
JournalJournal of Business Research
Volume144
DOIs
StatePublished - May 2022

Keywords

  • Algorithmic bias
  • Data bias
  • Design bias
  • Dynamic managerial capability
  • Machine learning
  • Marketing models
  • Microfoundations
  • Socio-cultural bias

ASJC Scopus subject areas

  • Marketing

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