Applications of Operations Research in Social Commerce

This paper adds to the existing body of knowledge on social commerce by bringing a large and varied body of research together in one structured review. Using a Systematic Literature Review supported by bibliometric analysis and qualitative synthesis, the paper looks at how social commerce research has developed as a whole instead of focusing on one topic at a time. The bibliometric analysis shows that publication output grew over the period studied, that the research is spread across 731 publication sources, and that many papers are written by teams, with over one in four involving international co-authorship. It also shows which countries, authors and keywords are most visible, and that the keywords fall into two broad areas, data science and public health research, with social media and social media analytics at the centre. For researchers, these patterns give a clearer picture of how the field has developed and who is contributing to it. The literature review supports this picture from the qualitative side. It groups earlier work around consumer behaviour, social media analytics, user-generated content, online reviews, electronic word-of-mouth, trust, customer engagement, social influence and influencer marketing, together with artificial intelligence, machine learning and big data analytics. It also shows that studies of this kind tend to remain narrow in focus, dealing mainly with particular aspects of social commerce rather than examining the social-commerce process as a whole, and that only a relatively small body of literature integrates social interactions, commercial activities and technological components within social commerce platforms (Abkenar et al., 2024). Seeing this difference helps researchers place their own work within the existing literature. The findings are also useful for managers, marketers and organisations involved in social commerce. The research does not set out a business strategy, but it gives a broader view of the factors that have been studied and that matter in social commerce. The literature review shows that consumer behaviour, customer engagement, online reviews, user-generated content, social influence and influencer marketing are closely connected with social-media activity and purchasing-related decisions. Reviews, electronic word-of-mouth and other forms of consumer-generated content can influence how consumers evaluate products (Nalluri et al., 2025), and social media analytics has been used to understand customer opinions and to improve marketing strategies (Yadav et al., 2022; Semenda et al., 2024). The keyword analysis points in the same direction, since its largest cluster centres on collecting, processing and analysing big data from online sources for commercial and operational purposes. Seeing how research has developed around these themes can help businesses think about their own social-commerce strategies in a wider context. In particular, the literature review notes that relatively little is known about how trust, social influence, user-generated content, influencers and platform features interact to shape consumer behaviour across different social-commerce environments, which suggests that it may be useful to consider these factors together rather than separately. Managers can therefore use this research as a starting point for understanding the issues that matter, while recognising that it does not test which approaches work best for any particular business. The research also has wider social value because it helps explain what social commerce involves. The literature review suggests that social commerce should be studied as a combination of social interaction and commercial activity rather than simply as another form of online shopping. Consumers do not only look at products; they also share and read information, opinions and experiences produced by other consumers. Online reviews and user-generated content give important information about consumer experiences and purchasing decisions (Jia et al., 2024), and reviews and electronic word-of-mouth can influence how consumers evaluate products (Nalluri et al., 2025). Consumer behaviour in social commerce has also been linked to several interconnected factors, including social influence, online information, reviews, engagement and marketing communication (Arzy et al., 2021). The data analysis adds another view, as its second keyword cluster shows social media being studied as a place where public opinion, community attitudes and human behaviour can be identified. Understanding these relationships between social interaction, user-generated content, social influence and purchasing-related behaviour can contribute to greater awareness of how consumers interact with social-commerce platforms and how information shared by other users can influence consumer decision-making. This research does not suggest that such influence is beneficial or harmful in itself; its value lies in making these interactions easier to understand. The findings may also be relevant to policymakers and to organisations concerned with social-commerce platforms and digital commerce. The literature review identifies trust as one of the factors linked to consumer behaviour, and online reviews and user-generated content as important sources of information for consumers. The keyword analysis also identified a small cluster on digital information integrity, which includes algorithm, disinformation, misinformation, visual analytics and visualization, and a single keyword, risk assessment, which relates to legal, ethical and compliant data science. Although these clusters are small, they show that questions of information integrity and responsible data use appear in the research literature. These points may be useful in discussions about consumer information, consumer protection and the responsible use of digital platforms, although this research does not evaluate any specific law or policy, and its implications are therefore general. During the research process, we also faced practical challenges, particularly in becoming familiar with new research tools. Learning to use Mendeley for managing references and citations, VOSviewer for analysing and visualising keyword relationships and research themes, and R and RStudio for handling and analysing bibliometric information required time and practice. It was also necessary to understand how bibliometric analysis works, to read the different visualisations, and to interpret the information that the tools produced. Dealing with a large amount of academic literature, and keeping the data, literature selection and analysis consistent, were further challenges. However, working through these tasks improved our understanding of research methods and showed us how academic literature can be organised, analysed and presented.

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