Visualising the Knowledge Domain of Artificial Intelligence in Marketing: A Bibliometric Analysis
As the number of research outputs in the field of AI in Marketing increased greatly in the past 20 years, a systematic review of the literature and its developmental process is essential to provide a consolidated view of this area. This study conducted a
- PDF / 2,748,829 Bytes
- 11 Pages / 439.37 x 666.142 pts Page_size
- 60 Downloads / 179 Views
Faculty of Management, Law and Social Sciences, University of Bradford, Bradford, UK {e.ismagilova,n.p.rana}@bradford.ac.uk 2 Emerging Markets Research Centre, School of Management, Swansea University, Swansea, UK [email protected]
Abstract. As the number of research outputs in the field of AI in Marketing increased greatly in the past 20 years, a systematic review of the literature and its developmental process is essential to provide a consolidated view of this area. This study conducted a bibliometric analysis for the knowledge domain of AI in Marketing by using 617 research outputs from the Web of Science database from 1992 to 2020. Knowledge maps of AI in marketing research were visualised by employing CiteSpace software. Keywords: Artificial intelligence
Marketing Bibliometric analysis
1 Introduction With the rapid development of technologies, it is predicted that Artificial intelligence (AI) will significantly change traditional marketing including marketing strategies, business models, sales processes, and customer service options [1]. AI is defined as “the ability of a machine to learn from experience, adjust to new inputs and perform human-like tasks” [2]. Due to the relevance of the application of AI in marketing for a broad group of stakeholders and the benefits and challenges connected with its implementation, adoption, and use, the field has been attracting high attention from researchers and practitioners. The previous studies investigated the application of AI in the context of sales forecasting [3], recommendation systems [4], customer classification [5], profit maximization [6], retail store scheme [7, 8], and design of the marketing campaign [9], to name a few. A number of studies conducted a review of the literature in the field of AI [1, 10, 11]. However, limited research has been done using bibliometric analysis. It is argued that a thorough analysis and review of the key topics can offer researchers a consolidated view on this area [12, 13]. Thus, the current study aims to provide in-depth analysis with a bibliometric method of accumulated studies on AI and Marketing. To conduct the bibliometric analysis, the CiteSpace software was used to visualize and analyse trends and patterns in the scientific literature. © IFIP International Federation for Information Processing 2020 Published by Springer Nature Switzerland AG 2020 S. K. Sharma et al. (Eds.): TDIT 2020, IFIP AICT 617, pp. 43–53, 2020. https://doi.org/10.1007/978-3-030-64849-7_5
44
E. Ismagiloiva et al.
The rest of the paper is organised as follows. First, the research design section provides the details of the data collected and software used. Next, a statistical analysis of data is presented, followed by hotspot analysis. Finally, the paper is concluded in Sect. 5.
2 Methodology 2.1
Source of Data
This study used data from the Web of Science databases. Web of science was chosen because of its wide coverage of publications on overall academic fields and includes all bibliographic information (e.g. authors, citations, journals) f
Data Loading...
