Exploring the potential of Social Commerce for Data-Driven Decisions: perspectives from Information Systems
Posted on: 2025-03-25 12:51:27
On Tuesday, March 25, 2025, a Visiting Lecturer activity was held that discussed interesting topics regarding Social Commerce (S-Commerce) presented by Suaini Binti Sura, Senior Lecturer at Universiti Malaysia Sabah (UMS), in a presentation entitled "from Data to Decision: Mining Social Commerce for IS-Driven Insight."This topic is very relevant for Informatics students, especially those interested in the field of Information Systems (SI), E-commerce, and Data Mining.
The activities are part of the activities of World Class University (WCU) Faculty of Science and Mathematics, Department of Informatics. PIC of this activity is Dinar Mutiara Kusumo Nugraheni, S.T., MInfo Tech (Comp), Ph.D.
The purpose of this visiting lecturer presentation is to provide insight into how technology can be used to explore consumer behavior and assist in data-driven decision making in the world of social commerce.
What is Social Commerce?
Social Commerce is a combination of e-commerce and social media. In a nutshell, social commerce leverages social media platforms to simplify the buying and selling process, where social interaction and User-Generated Content (UGC) have a major influence on purchasing decisions. In social commerce, consumers not only buy products, but also get involved in the community and share their experiences.
The importance of Information Systems in Social Commerce
Information Systems (SI) function to manage data, software, hardware, and processes needed to support decision-making within the organization. In the context of social commerce, information systems play an important role in analyzing consumer behavior, managing interactions, and processing big data generated through social interactions on social media.
Research approach: Eye Tracking and Data Mining/Machine Learning
Dr. Suaini Bint Sura explained two interesting research methods, namely eye tracking and data mining/machine learning (ML), to explore consumer behavior in a social commerce environment.
1. Eye Tracking:
- Eye tracking is a technology used to track a user's eye movements when they interact with visual content. Instagram Facebook using this technology, researchers can identify the areas that attract the most attention of users, for example on platforms such as Facebook or Instagram. This Data helps entrepreneurs to understand consumer preferences and devise more effective marketing strategies.
- in the studies carried out, it was found that there are significant differences in the visual behavior of users among different social platforms. Facebook Instagram and area of interest (AOI) show different patterns of attention.
2. Data Mining and Machine Learning:
- Data Mining and Machine Learning are used to analyze big data generated from user interactions on social media. With sentiment analysis and association rules, researchers can understand consumer reactions to products and services sold on social platforms. For example, analysis of comments, likes, and shares can reveal patterns of user behavior that are useful for increasing engagement and sales conversions.
- for example, research conducted on social media platforms such as Facebook in the context of Health shows how Pattern analysis can help in building models of user engagement.
Difference between E-Commerce and S-Commerce
It is important for Informatics students to understand the fundamental difference between e-commerce and s-commerce:
- E-Commerce: the main focus is efficiency in buying and selling transactions. In e-commerce, interactions with consumers are generally one-way, such as when a consumer searches for a product on a website and makes a purchase.
- Social Commerce: more emphasis on social interaction between consumers and the online community. In s-commerce, customers can interact directly with sellers and other consumers, sharing experiences and product recommendations. This creates a more social and interactive experience compared to traditional e-commerce transactions.
Information Systems success Model (IS Success Model)
In the context of social commerce research, the IS Success Model is applied to assess the extent to which system quality (e.g. website quality), social feature quality, and product quality affect consumer satisfaction and transaction success. The results showed that the quality of the system and social features have a positive impact on the perception of usability and consumer satisfaction, which in turn increases business success.
Challenges and future direction
While social commerce offers many opportunities, there are some challenges that need to be addressed:
- Data quality: the Data used in the analysis is often of varying quality, both in terms of accuracy and completeness.
- Multiplatform data integration: integrating data from multiple social media platforms can be a big challenge, especially in real-time processing.
- Real-time processing scalability: processing data at scale in real time requires robust infrastructure and more advanced technologies.
In the future, AI, AR/VR, and blockchain technologies are expected to play an important role in addressing these challenges, strengthening sentiment analysis, and increasing trust and transparency in social transactions.
For Informatics students, an understanding of social commerce and the application of Information Systems in the digital business world is a much-needed skill. Approaches that combine technologies such as eye tracking and machine learning open up opportunities to explore deeper insights into consumer behavior. In addition, an understanding of the challenges and future direction of social commerce provides an overview of how we can adapt to rapid technological change and create innovative solutions in this field.
So, for those of you who are interested in technology and digital business, knowledge of social commerce and the application of advanced technology in it will open up many career opportunities in the future.
