The Applications of Analytics in Social Commerce: A Bibliometric Analysis

This study intends to be a useful piece of work for faculty members, researchers and students of operations research, and for anyone who wants to understand how analytics is used in social commerce. It shows that research in this field has grown quickly, from about 10 articles in 2016 to 82 articles in 2026. Most of this research focuses on social media platforms such as TikTok, Instagram and Facebook, influencer marketing and customer engagement. Analytics tools such as artificial intelligence, chatbots, big data and natural language processing (NLP) are still at the edges of the field. As more people discover, discuss and buy products on social media, businesses are collecting more data than ever on what customers like, share and buy. More research is needed on how this data can be used, especially through AI, chatbots and NLP, so that businesses can make better decisions and customers can trust the platforms they buy from. Therefore, there is a huge scope for further research in this field. The main limitation of this study is that it uses only one database, Elsevier’s Scopus, and one search term, “Social Commerce AND Analytics”, which limits the number of documents that could be analysed. Conference papers and non-English articles were also left out, and the data for 2026 covers only part of the year.

1.           Abhishek, S., & Srivastava, M. (2021). Mapping the influence of influencer marketing: A bibliometric analysis. Marketing Intelligence & Planning, 39(7), 979–1003. https://doi.org/10.1108/MIP-03-2021-0085   2.           Ahmad, A., Ghani, N. A., & Hamid, S. (2025). Examining the predictors of consumer trust and social commerce engagement: A systematic literature review. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), Article 247. https://doi.org/10.3390/jtaer20030247   3.           Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48, 79–95. https://doi.org/10.1007/s11747-019-00695-1   4.           Aria, M., & Cuccurullo, C. (2016). bibliometrix: Comprehensive science mapping analysis [Computer software]. The Comprehensive R Archive Network. https://doi.org/10.32614/cran.package.bibliometrix   5.           Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007   6.           Aria, M., & Cuccurullo, C. (2026). Science mapping analysis: A primer with Biblioshiny. McGraw-Hill.   7.           Busalim, A. H., & Hussin, A. R. C. (2016). Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management, 36(6), 1075–1088. https://doi.org/10.1016/j.ijinfomgt.2016.06.009   8.           Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing, 39(8), 1529–1562. https://doi.org/10.1002/mar.21670 9.           Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002   10.        Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070