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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
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...
Published in: | Industrial Marketing Management |
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ISSN: | 0019-8501 |
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Elsevier BV
2023
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64142 |
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v2 64142 2023-08-26 Advancing algorithmic bias management capabilities in AI-driven marketing analytics research d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2023-08-26 BBU 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 http://dx.doi.org/10.1016/j.indmarman.2023.08.013 COLLEGE NANME Business COLLEGE CODE BBU Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2023-09-29T15:42:48.4235715 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 0000-0002-5547-9990 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 |
url |
http://dx.doi.org/10.1016/j.indmarman.2023.08.013 |
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 |
2023-10-01T15:42:50Z |
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1778383396653760512 |
score |
11.013015 |