Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions
Runyue Hana, Hugo K.S. Lama, Yuanzhu Zhana, Yichuan Wangb, Yogesh Kumar Dwivedic, Kim Hua Tand
- aManagement School, University of Liverpool, Liverpool, UK
- bThe University of Sheffield, Sheffield, UK
- cSwansea University, Swansea, UK
- dThe University of Nottingham, Nottingham, UK
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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.
TL;DRThe research represents one of the first large-scale reviews of relevant literature on AI in B2B marketing by obtaining and comparing the most influential works based on a series of analyses, and identifying five domains of research into how AI can be used for facilitating B 2B marketing innovation. (Semantic Scholar)