The Application of Operations Research in Social Commerce

This study mapped the development of social commerce analytics research through a systematic and bibliometric review of 415 documents published between 2016 and 2026. In line with the research objectives, annual output grew from 17 publications in 2016 to 66 in 2026, at an annual growth rate of 14.53% (R² = 0.7954). IEEE Access, India, China and the USA emerged as the leading source and contributing countries, supported by a 33.98% rate of international co-authorship, while the conceptual structure linked social media and commerce with big data, machine learning and predictive analysis. Thematic evolution moved from social media and e-commerce foundations before 2020, to data analytics and machine learning during the COVID-19 period, and to predictive analytics, artificial intelligence and strategic decision-making after 2022. Academically, the study provides a reproducible map of the field and shows a shift from descriptive to predictive, decision-oriented analytics, which offers a base for applying OR techniques to social commerce problems. Managerially, e-commerce platforms, retailers, marketers and small businesses selling through social channels can use these findings to prioritise sentiment analysis, machine learning and predictive analytics. Socially, the growing contribution of emerging economies and the widening focus on customer engagement and sustainability point to benefits beyond individual firms, provided that data is used responsibly. The study was limited by time and data availability, as it relied on a single database (Scopus), English-language publications and one search string, and it does not assess the quality of individual studies. These gaps can be addressed in future research by extending the dataset to additional databases and applying OR techniques, such as forecasting and optimisation, to specific social commerce decisions.

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