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

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

Research output: Contribution to journalArticlepeer-review

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Abstract

Purpose
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.

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, identifying the main trends in the literature and suggesting directions for future research.

Practical implications
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.

Originality/value
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.
Original languageEnglish
Pages (from-to)2467-2497
JournalIndustrial Management & Data Systems
Volume121
Issue number12
Early online date13 Aug 2021
DOIs
Publication statusE-pub ahead of print - 13 Aug 2021

Bibliographical note

Publisher Copyright:
© 2021, Emerald Publishing Limited.

Keywords

  • Artificial intelligence
  • Business-to-business marketing
  • Systematic literature review
  • Bibliometric analysis
  • Content analysis

ASJC Scopus subject areas

  • Industrial relations
  • Management Information Systems
  • Industrial and Manufacturing Engineering
  • Computer Science Applications
  • Strategy and Management

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