Customer purchase prediction in B2C e-business: A systematic review and future research agenda

被引:2
|
作者
Chen, Shuixia [1 ,2 ]
Xu, Zeshui [2 ]
Xu, Duo [3 ]
Gou, Xunjie [2 ]
机构
[1] Macau Univ Sci & Technol, Sch Business, Dept Decis Sci, Ave Wai Long, Taipa, Macau, Peoples R China
[2] Sichuan Univ, Business Sch, Chengdu 610064, Peoples R China
[3] Southeast Univ, Sch Econ & Management, Nanjing 211189, Peoples R China
关键词
Customer purchase prediction; Machine learning; Purchase feature analysis; Systematic review; BIBLIOMETRIC ANALYSIS; BEHAVIOR PREDICTION; STATED INTENTIONS; CONVERSION; MODEL; PATTERNS; LOYALTY; CHOICE; TIME;
D O I
10.1016/j.eswa.2024.124261
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Customer purchase prediction is increasingly recognized as a crucial marketing strategy in B2C e-business, promising enhanced business profitability and customer satisfaction. Although this topic has attracted substantial interest from both academics and practitioners, there remains a scarcity of comprehensive systematic reviews and clearly defined future research agendas. To fill the research gap, this study utilizes bibliometric and thematic analyses to examine 98 articles on customer purchase prediction in B2C e-business published from 1995 to 2023. The bibliometric analysis reveals a rapid evolution and growing focus within the field, and the thematic analysis uncovers the research focus of existing studies, identifying key thematic clusters. Furthermore, this study discusses key takeaways and their implications, providing valuable insights to guide further research in this area. This study contributes to highlighting the development of the existing research, identifying research focuses, presenting major thematic clusters with detailed content analysis, and clarifying potential knowledge areas for future researchers. These insights, along with a summary of dominant methodologies, equip practitioners and scholars with essential knowledge to further explore customer purchase prediction in B2C e-business.
引用
收藏
页数:19
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