Predicting Online Consumer Transaction from Big Data: Influential Factors and Strategic Planning

被引:0
|
作者
Cheng, Chiang-Yu [1 ]
Lu, Ming-Ying [1 ]
Tsen, Han-Ping [2 ]
机构
[1] Soochow Univ, Sch Big Data Management, Taipei, Taiwan
[2] Natl Yang Ming Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan
关键词
E-COMMERCE; ANALYTICS; MODEL; OPTIMIZATION; STICKINESS; WEBSITES; TRUST;
D O I
10.1155/2021/8834713
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Online transaction has recently benefited from coronavirus; however, the sales of e-commerce in some areas are substantially on the decline. The current study proposes a theoretically constructed and empirically viable way for predicting the relevant factors that may detract or foster e-commerce success. We apply web analytics (one of the big data techniques) to simultaneously, generalizably, and objectively measure the influential factors of e-commerce success. The findings indicate that (1) pageviews is an important key for consumers to make transactions. (2) Bounce rate of the website should not be a member factor of e-commerce success. (3) Adhesion strategy and repeatability strategy can be used to induce consumer online transaction. Several theoretical contributions and practical implications are also provided.
引用
收藏
页数:9
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