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Algorithmic bias in machine learning-based marketing models

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

Journal of Business Research, Volume: 144, Pages: 201 - 216

Swansea University Author: Yogesh Dwivedi Orcid Logo

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DOI (Published version): 10.1016/j.jbusres.2022.01.083

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 alg...

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Published in: Journal of Business Research
ISSN: 0148-2963
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59260
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first_indexed 2022-01-28T08:26:33Z
last_indexed 2022-06-25T03:15:39Z
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spelling 2022-06-24T14:56:09.0593944 v2 59260 2022-01-28 Algorithmic bias in machine learning-based marketing models d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2022-01-28 BBU 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. Journal Article Journal of Business Research 144 201 216 Elsevier BV 0148-2963 Algorithmic bias; Machine learning; Marketing models; Data bias; Design bias; Socio-cultural bias; Microfoundations; Dynamic managerial capability 1 5 2022 2022-05-01 10.1016/j.jbusres.2022.01.083 COLLEGE NANME Business COLLEGE CODE BBU Swansea University SU Library paid the OA fee (TA Institutional Deal) 2022-06-24T14:56:09.0593944 2022-01-28T08:20:50.8312560 Faculty of Humanities and Social Sciences School of Management - Business Management Shahriar Akter 1 Yogesh Dwivedi 0000-0002-5547-9990 2 Shahriar Sajib 3 Kumar Biswas 4 Ruwan J. Bandara 5 Katina Michael 6 59260__24046__80a16971304f44119984f2a764ee867e.pdf 59260.VOR.pdf 2022-05-09T17:27:47.6338735 Output 993529 application/pdf Version of Record true This is an open access article under the CC BY-NC-ND license. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Algorithmic bias in machine learning-based marketing models
spellingShingle Algorithmic bias in machine learning-based marketing models
Yogesh Dwivedi
title_short Algorithmic bias in machine learning-based marketing models
title_full Algorithmic bias in machine learning-based marketing models
title_fullStr Algorithmic bias in machine learning-based marketing models
title_full_unstemmed Algorithmic bias in machine learning-based marketing models
title_sort Algorithmic bias in machine learning-based marketing models
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Shahriar Akter
Yogesh Dwivedi
Shahriar Sajib
Kumar Biswas
Ruwan J. Bandara
Katina Michael
format Journal article
container_title Journal of Business Research
container_volume 144
container_start_page 201
publishDate 2022
institution Swansea University
issn 0148-2963
doi_str_mv 10.1016/j.jbusres.2022.01.083
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 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.
published_date 2022-05-01T04:16:26Z
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score 11.012678

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