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Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.Design/methodology/approachA conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.FindingsThis paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.Originality/valueThis study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Business and Industrial Marketing Emerald Publishing

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

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References (133)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0885-8624
eISSN
0885-8624
DOI
10.1108/jbim-09-2020-0448
Publisher site
See Article on Publisher Site

Abstract

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.Design/methodology/approachA conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.FindingsThis paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.Originality/valueThis study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

Journal

Journal of Business and Industrial Marketing – Emerald Publishing

Published: Apr 15, 2022

Keywords: Artificial intelligence; Business-to-business marketing; Information processing theory; Organizational learning theory; Conceptual

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