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

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

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

Swansea University Author: Yogesh Dwivedi

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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...

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Published in: Industrial Marketing Management
ISSN: 0019-8501
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64142
first_indexed 2023-08-26T11:00:22Z
last_indexed 2024-11-25T14:13:31Z
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spelling 2024-09-16T16:22:58.9473961 v2 64142 2023-08-26 Advancing algorithmic bias management capabilities in AI-driven marketing analytics research d154596e71b99ad1285563c8fdd373d7 Yogesh Dwivedi Yogesh Dwivedi true false 2023-08-26 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. Journal Article Industrial Marketing Management 114 243 261 Elsevier BV 0019-8501 Algorithms, Algorithmic bias, AI-driven marketing analytics, Artificial intelligence 1 10 2023 2023-10-01 10.1016/j.indmarman.2023.08.013 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-09-16T16:22:58.9473961 2023-08-26T11:56:46.3413497 Faculty of Humanities and Social Sciences School of Management - Business Management Shahriar Akter 1 Saida Sultana 2 Marcello Mariani 3 Samuel Fosso Wamba 4 Konstantina Spanaki 5 Yogesh Dwivedi 6 64142__28667__4b3a85ef621e4120bde1004614244185.pdf 64142.VOR.pdf 2023-09-29T15:40:53.2817957 Output 1645698 application/pdf Version of Record true This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
spellingShingle Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
Yogesh Dwivedi
title_short Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
title_full Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
title_fullStr Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
title_full_unstemmed Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
title_sort Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Shahriar Akter
Saida Sultana
Marcello Mariani
Samuel Fosso Wamba
Konstantina Spanaki
Yogesh Dwivedi
format Journal article
container_title Industrial Marketing Management
container_volume 114
container_start_page 243
publishDate 2023
institution Swansea University
issn 0019-8501
doi_str_mv 10.1016/j.indmarman.2023.08.013
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2023-10-01T02:43:35Z
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score 11.048042

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