Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions



Han, Runyue, Lam, Hugo KS ORCID: 0000-0002-4674-6145, Zhan, Yuanzhu ORCID: 0000-0002-8585-8828, Wang, Yichuan, Dwivedi, Yogesh K and Tan, Kim Hua
(2021) Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 121 (12). pp. 2467-2497.

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Abstract

<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>Although the value of artificial intelligence (AI) has been acknowledged by companies, the literature shows challenges concerning AI-enabled business-to-business (B2B) marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>Applying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><jats:p>Apart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identifying the main trends in the literature and suggesting directions for future research.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Practical implications</jats:title><jats:p>Through the five identified domains, practitioners can assess their current use of AI and identify their future needs in the relevant domains in order to make appropriate decisions on how to invest in AI. Thus, the research enables companies to realise their digital marketing innovation strategies through AI.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title><jats:p>The research represents one of the first large-scale reviews of relevant literature on AI in B2B marketing by (1) obtaining and comparing the most influential works based on a series of analyses; (2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation and (3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field.</jats:p></jats:sec>

Item Type: Article
Uncontrolled Keywords: Artificial intelligence, Business-to-business marketing, Systematic literature review, Bibliometric analysis, Content analysis
Divisions: Faculty of Humanities and Social Sciences > School of Management
Depositing User: Symplectic Admin
Date Deposited: 03 Aug 2021 10:04
Last Modified: 21 Feb 2023 18:19
DOI: 10.1108/IMDS-05-2021-0300
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3132249