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Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

Brendan James Keegan, Denis Dennehy Orcid Logo, Peter Naudé

Information Systems Frontiers

Swansea University Author: Denis Dennehy Orcid Logo

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DOI (Published version): 10.1007/s10796-022-10294-1

Abstract

Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical a...

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Published in: Information Systems Frontiers
ISSN: 1387-3326 1572-9419
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59600
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first_indexed 2022-03-14T14:05:16Z
last_indexed 2023-01-11T14:41:00Z
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spelling v2 59600 2022-03-14 Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective ba782cbe94139075e5418dc9274e8304 0000-0001-9931-762X Denis Dennehy Denis Dennehy true false 2022-03-14 BBU Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences. Journal Article Information Systems Frontiers 0 Springer Science and Business Media LLC 1387-3326 1572-9419 Artificial Intelligence; Activity Theory, B2B Marketing 26 5 2022 2022-05-26 10.1007/s10796-022-10294-1 COLLEGE NANME Business COLLEGE CODE BBU Swansea University Open Access funding provided by the IReL Consortium 2023-09-20T11:01:22.3208145 2022-03-14T13:54:40.4417576 Faculty of Humanities and Social Sciences School of Management - Business Management Brendan James Keegan 1 Denis Dennehy 0000-0001-9931-762X 2 Peter Naudé 3 59600__24262__75cad4a180b74f8a857a2c0ef66209d6.pdf 59600.pdf 2022-06-09T12:04:14.8196255 Output 1231795 application/pdf Version of Record true © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
spellingShingle Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
Denis Dennehy
title_short Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
title_full Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
title_fullStr Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
title_full_unstemmed Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
title_sort Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective
author_id_str_mv ba782cbe94139075e5418dc9274e8304
author_id_fullname_str_mv ba782cbe94139075e5418dc9274e8304_***_Denis Dennehy
author Denis Dennehy
author2 Brendan James Keegan
Denis Dennehy
Peter Naudé
format Journal article
container_title Information Systems Frontiers
container_volume 0
publishDate 2022
institution Swansea University
issn 1387-3326
1572-9419
doi_str_mv 10.1007/s10796-022-10294-1
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
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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 Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.
published_date 2022-05-26T11:01:19Z
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score 10.999161

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