International Journal of Electronic Commerce Studies

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ISSN : 20739729
Total articles ≅ 116
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Yann-Jy Yang, Jiann-Chyau Hwang, MetaEdge corporation
International Journal of Electronic Commerce Studies, Volume 11, pp 1-12; doi:10.7903/ijecs.1931

The publisher has not yet granted permission to display this abstract.
Putu Wuri Handayani, I Made Eka Ariantana, Ave Adriana Pinem
International Journal of Electronic Commerce Studies, Volume 11, pp 13-32; doi:10.7903/ijecs.1721

Tuan-Vinh Le, Chih-Chin Liang
International Journal of Electronic Commerce Studies, Volume 11, pp 33-44; doi:10.7903/ijecs.1645

Preeti Thakur, Anupriya Kaur
International Journal of Electronic Commerce Studies, Volume 11, pp 45-74; doi:10.7903/ijecs.1818

Jong-Min Kim, Xingyao Xiao, Iksuk K Im
International Journal of Electronic Commerce Studies, Volume 11, pp 75-92; doi:10.7903/ijecs.1731

Mathupayas Thongmak
International Journal of Electronic Commerce Studies, Volume 10, pp 141-174; doi:10.7903/ijecs.1602

Abstract:
Social commerce is e-commerce empowered by social media. Good content strategy that takes into account what customers value is important to success, but very little is known about proper content types. The goal of this study was thus to understand the role of post quantity and content types in customer engagement on Facebook pages using content analysis to collect qualitative data and quantitative data. Content types were categorized into two categories and nine sub-categories according to the 4Ps theory along with uses and gratifications theory. More than 1,500 posts from 183 brand pages were analyzed to examine the relationships between overall posts per page in a week, the page’s engagement, and popularity. The results represent the first attempts to explore content types with various established theories. The findings of this study could reflect the post types of brand pages by industries and guide brand page administrators toward effective content strategy.To cite this document: Mathupayas Thongmak, "Do We Know What Contents Work for Social Commerce? A Case of Customer Engagement in Facebook Brand Pages", International Journal of Electronic Commerce Studies, Vol.10, No.2, pp.141-174, 2019.Permanent link to this document:http://dx.doi.org/10.7903/ijecs.1602
Chien-Wen Chen
International Journal of Electronic Commerce Studies, Volume 10, pp 203-238; doi:10.7903/ijecs.1729

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Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam, Asif Karim
International Journal of Electronic Commerce Studies, Volume 10, pp 175-202; doi:10.7903/ijecs.1732

Abstract:
Fraudulent credit card transactions are on the rise and have become a significantly problematic issue for financial intuitions and individuals. Various methods have already been implemented to handle the issue, but the embezzlers have always managed to employ innovative tactics to circumvent a number of security measures and execute the fraudulent transactions. Thus, instead of a rule-based system, an intelligent and adaptable machine learning based algorithm should be an answer to tackle such sophisticated digital theft. The presented framework uses k-NN for classification and utilises Principal Component Analysis (PCA) for raw data transformation. Neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance-based feature selection method was employed. The proposed process performed well by having a precision and F-Score of 98.32% and 97.44% respectively for k-NN and 100% and 98.24% respectively for Time subset when using the misclassified instances. This work also demonstrates a larger and clearer classification breakdown, which aids in achieving higher precision rate and improved recall rate. In a view to accomplish such high accuracy, the original datum was transformed using Principal Component Analysis (PCA), neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance based feature selection method was employed. The proposed process performed well when using the misclassified instances in the test dataset used in the previous work, while demonstrating a larger and clearer classification breakdown.To cite this document: Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam, and Asif karim, "Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns", International Journal of Electronic Commerce Studies, Vol.10, No.2, pp.175-202, 2019.Permanent link to this document:http://dx.doi.org/10.7903/ijecs.1732
Kuo-Sui Lin
International Journal of Electronic Commerce Studies, Volume 10, pp 89-112; doi:10.7903/ijecs.1536

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