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

Lujie Chen, Mengqi Jiang, Fu Jia*, Guoquan Liu

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

48 Citations (Scopus)

Abstract

Purpose: 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/approach: A 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. Findings: This 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/value: This 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.

Original languageEnglish
Pages (from-to)1025-1044
Number of pages20
JournalJournal of Business and Industrial Marketing
Volume37
Issue number5
DOIs
Publication statusPublished - 15 Apr 2022

Keywords

  • Artificial intelligence
  • Business-to-business marketing
  • Conceptual
  • Information processing theory
  • Organizational learning theory

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