AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships
Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals a...
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| Format: | Article |
| Language: | English |
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UUM Press
2025-07-01
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| Series: | Journal of ICT |
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| Online Access: | https://www.e-journal.uum.edu.my/index.php/jict/article/view/28313 |
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| author | Juhaida Abu Bakar Chong Kar Min Mohd Zulhisham Mohd Radzi Fauziah Baharom Yuhanis Yusof Mohamed Ali Saip Ruziana Muhamad Rasli Muhammad Amirul Ariff Zulkifli Nuratikah Jamaludin Mustafa Ali Abuzaraida |
| author_facet | Juhaida Abu Bakar Chong Kar Min Mohd Zulhisham Mohd Radzi Fauziah Baharom Yuhanis Yusof Mohamed Ali Saip Ruziana Muhamad Rasli Muhammad Amirul Ariff Zulkifli Nuratikah Jamaludin Mustafa Ali Abuzaraida |
| author_sort | Juhaida Abu Bakar |
| collection | DOAJ |
| description |
Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.
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| format | Article |
| id | doaj-art-495c6908e3684f3ebc11853e4023fc96 |
| institution | Kabale University |
| issn | 1675-414X 2180-3862 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | UUM Press |
| record_format | Article |
| series | Journal of ICT |
| spelling | doaj-art-495c6908e3684f3ebc11853e4023fc962025-08-20T03:42:34ZengUUM PressJournal of ICT1675-414X2180-38622025-07-0124310.32890/jict2025.24.3.1AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce RelationshipsJuhaida Abu Bakar0Chong Kar Min1Mohd Zulhisham Mohd Radzi2Fauziah Baharom3Yuhanis Yusof4Mohamed Ali Saip5Ruziana Muhamad Rasli6Muhammad Amirul Ariff Zulkifli7Nuratikah Jamaludin8Mustafa Ali Abuzaraida9Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaData Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaFaculty of Electrical Engineering & Technology, Universiti Malaysia PerlisData Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaData Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaData Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaSchool of Multimedia Technology & Communication, Universiti Utara Malaysia, MalaysiaData Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaData Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, MalaysiaDepartment of Computer Science, Faculty of Information Technology, Misurata University, Libya Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries. https://www.e-journal.uum.edu.my/index.php/jict/article/view/28313Social network analysisAI-driven market analysisinfluencer identificationweighted degree centralitybusiness decision-making |
| spellingShingle | Juhaida Abu Bakar Chong Kar Min Mohd Zulhisham Mohd Radzi Fauziah Baharom Yuhanis Yusof Mohamed Ali Saip Ruziana Muhamad Rasli Muhammad Amirul Ariff Zulkifli Nuratikah Jamaludin Mustafa Ali Abuzaraida AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships Journal of ICT Social network analysis AI-driven market analysis influencer identification weighted degree centrality business decision-making |
| title | AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships |
| title_full | AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships |
| title_fullStr | AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships |
| title_full_unstemmed | AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships |
| title_short | AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships |
| title_sort | ai driven influencer and market analysis a social network approach to measure e commerce relationships |
| topic | Social network analysis AI-driven market analysis influencer identification weighted degree centrality business decision-making |
| url | https://www.e-journal.uum.edu.my/index.php/jict/article/view/28313 |
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