No Cover Image

Journal article 368 views 113 downloads

Advancing algorithmic bias management capabilities in AI-driven marketing analytics research

Shahriar Akter, Saida Sultana, Marcello Mariani, Samuel Fosso Wamba, Konstantina Spanaki, Yogesh Dwivedi Orcid Logo

Industrial Marketing Management, Volume: 114, Pages: 243 - 261

Swansea University Author: Yogesh Dwivedi Orcid Logo

  • 64142.VOR.pdf

    PDF | Version of Record

    This is an open access article under the CC BY license.

    Download (1.57MB)

Check full text

DOI (Published version): 10.1016/j.indmarman.2023.08.013

Abstract

Algorithms in the age of artificial intelligence (AI) constantly transform customer behaviour, marketing programs, and marketing strategies in industrial markets. However, algorithms often fail to perform as expected due to various data, model, and market biases. Motivated by this challenge, this st...

Full description

Published in: Industrial Marketing Management
ISSN: 0019-8501
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64142
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Algorithms in the age of artificial intelligence (AI) constantly transform customer behaviour, marketing programs, and marketing strategies in industrial markets. However, algorithms often fail to perform as expected due to various data, model, and market biases. Motivated by this challenge, this study presents a framework of algorithmic bias management capabilities for industrial markets that contribute to customer equity in terms of value, brand and relationship equity. Drawing on the dynamic capability theory, this study fills this gap by conducting a literature review, thematic analysis, and two rounds of surveys (n=200 analytics professionals and n=200 business customers) in the financial service industry in Australia. The findings show that algorithmic bias management capability consists of three primary dimensions (data, model, and deployment capabilities) and nine subdimensions. These findings have important implications for scholars and managers interested in developing algorithmic bias management capabilities to influence customer equity in industrial markets.
Keywords: Algorithms, Algorithmic bias, AI-driven marketing analytics, Artificial intelligence
College: Faculty of Humanities and Social Sciences
Funders: Swansea University
Start Page: 243
End Page: 261

Similar Items