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

Han, Runyue; Lam, Hugo K.S.; Zhan, Yuanzhu; Wang, Yichuan; Dwivedi, Yogesh K.; Tan, Kim Hua

Abstract

Purpose-Although the value of AI has been acknowledged by companies, the literature shows challenges concerning AI-enabled 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. Design/methodology/approach-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. Findings-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, identified the main trends in the literature, and suggest directions for future research. Practical implications-Through our identified five domains, practitioners can assess their current use of AI ability in terms of their conceptualisation capability, technological applications, and identify their future needs in the relevant domains in order to make appropriate decisions on whether to invest in AI. Thus, the research outcomes can help companies to realise their digital marketing innovation strategy through AI. Originality/value-While more and more studies acknowledge the potential value of AI in B2B marketing, few attempts have been made to synthesise the literature. The results from the study can contribute 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.

• Journal Article Type: Article
• Acceptance Date: Jul 25, 2021
• Online Publication Date: Aug 13, 2021
• Publication Date: Nov 10, 2021
• Deposit Date: Feb 11, 2022
• Publicly Available Date: Feb 11, 2022
• Journal: Industrial Management & Data Systems
• Print ISSN: 0263-5577
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• Volume: 121
• Issue: 12
• Pages: 2467-2497
• DOI: https://doi.org/10.1108/imds-05-2021-0300
• Keywords: Industrial and Manufacturing Engineering; Strategy and Management; Computer Science Applications; Industrial relations; Management Information Systems
• Public URL: https://nottingham-repository.worktribe.com/output/6610800
• Publisher URL: https://www.emerald.com/insight/content/doi/10.1108/IMDS-05-2021-0300/full/html