人工智能在电子商务领域应用的研究图景与热点分析——基于国际核心文献的计量分析
Research Landscape and Hotspot Analysis of Artificial Intelligence Applications in E-Commerce: A Bibliometric Study Based on International Core Literature
DOI: 10.12677/ecl.2026.15101146, PDF,    科研立项经费支持
作者: 彭健坤, 陈江毅:南京邮电大学教育科学与技术学院,江苏 南京
关键词: 人工智能;电子商务;文献计量分析;知识图谱;Artificial Intelligence; E-Commerce; Bibliometric Analysis; Knowledge Mapping
摘要: 随着人工智能技术深度嵌入电子商务价值链各环节,系统梳理该领域的知识结构与演进趋势具有重要的理论价值。本文基于Web of Science核心合集收录的2016~2025年间593篇相关文献,运用VOSviewer软件进行文献计量与可视化分析,剖析人工智能在电子商务领域的发文特征、研究合作以及热点主题,进而呈现出该领域的研究图景。未来研究应在技术效能、交互范式、文化情境与价值边界四个维度实现突破,推动电商AI研究超越单纯的商业效率逻辑,真正迈向兼顾生态效益与社会福祉的综合价值新高度。
Abstract: As artificial intelligence becomes deeply embedded across all segments of the e-commerce value chain, systematically mapping the knowledge structure and evolutionary trends in this domain holds significant theoretical value. Drawing on 593 relevant publications from the Web of Science Core Collection spanning the period 2016~2025, this study employs VOSviewer to conduct a bibliometric and visualization analysis, examining the publication characteristics, research collaborations, and key thematic areas of AI in e-commerce, thereby delineating the research landscape of the field. Future research should pursue breakthroughs across four dimensions—technical efficacy, interaction paradigms, cultural contexts, and value boundaries—so as to steer e-commerce AI research beyond a narrow logic of commercial efficiency and toward a new level of integrated value that reconciles ecological sustainability with social well-being.
文章引用:彭健坤, 陈江毅. 人工智能在电子商务领域应用的研究图景与热点分析——基于国际核心文献的计量分析[J]. 电子商务评论, 2026, 15(10): 506-515. https://doi.org/10.12677/ecl.2026.15101146

参考文献

[1] Dat Van, N. (2025) Bibliometric Examination of Artificial Intelligence within the Framework of E-Commerce Technology from 1996 to 2024. Journal of Logistics, Informatics and Service Science, 12, 138-150.
https://www.aasmr.org/liss/Vol.12/No.2/Vol.12.No.2.09.pdf
[2] Adam, M., Wessel, M. and Benlian, A. (2020) AI-Based Chatbots in Customer Service and Their Effects on User Compliance. Electronic Markets, 31, 427-445.
https://doi.org/10.1007/s12525-020-00414-7
[3] He, X. and Liu, Y. (2024) Knowledge Evolutionary Process of Artificial Intelligence in E-Commerce: Main Path Analysis and Science Mapping Analysis. Expert Systems with Applications, 238, Article ID: 121801.
https://doi.org/10.1016/j.eswa.2023.121801
[4] Wang, S. and Zhang, H. (2025) Generative Artificial Intelligence and Internationalization Green Innovation: Roles of Supply Chain Innovations and AI Regulation for SMEs. Technology in Society, 82, Article ID: 102898.
https://doi.org/10.1016/j.techsoc.2025.102898
[5] Cabrera-Sánchez, J., Ramos-de-Luna, I., Carvajal-Trujillo, E. and Villarejo-Ramos, Á.F. (2020) Online Recommendation Systems: Factors Influencing Use in E-Commerce. Sustainability, 12, Article No. 8888.
https://doi.org/10.3390/su12218888
[6] Tang, Y.M., Chau, K.Y., Lau, Y. and Zheng, Z. (2023) Data-Intensive Inventory Forecasting with Artificial Intelligence Models for Cross-Border E-Commerce Service Automation. Applied Sciences, 13, Article No. 3051.
https://doi.org/10.3390/app13053051
[7] Mah, P.M., Skalna, I. and Pelech-Pilichowski, T. (2025) AI-Driven Anomaly Detection in E-Commerce Services: A Deep Learning and NLP Approach to the Isolation Forest Algorithm Trees. Journal of Theoretical and Applied Electronic Commerce Research, 20, Article No. 214.
https://doi.org/10.3390/jtaer20030214
[8] Pizzi, G., Vannucci, V., Mazzoli, V. and Donvito, R. (2023) I, Chatbot! The Impact of Anthropomorphism and Gaze Direction on Willingness to Disclose Personal Information and Behavioral Intentions. Psychology & Marketing, 40, 1372-1387.
https://doi.org/10.1002/mar.21813
[9] Asante, I.O., Jiang, Y., Hossin, A.M., et al. (2023) Optimization of Consumer Engagement with Artificial Intelligence Elements on Electronic Commerce Platforms. Journal of Electronic Commerce Research, 24, 7-28.
[10] Adam, M., Wessel, M. and Benlian, A. (2021) AI-Based Chatbots in Customer Service and Their Effects on User Compliance. Electronic Markets, 31, 427-445.
https://doi.org/10.1007/s12525-020-00414-7
[11] Trivedi, S.K., Patra, P., Srivastava, P.R., Zhang, J.Z. and Zheng, L.J. (2022) What Prompts Consumers to Purchase Online? A Machine Learning Approach. Electronic Commerce Research, 24, 2953-2989.
https://doi.org/10.1007/s10660-022-09624-x
[12] Kautish, P. and Khare, A. (2022) Investigating the Moderating Role of AI-Enabled Services on Flow and Awe Experience. International Journal of Information Management, 66, Article ID: 102519.
https://doi.org/10.1016/j.ijinfomgt.2022.102519
[13] Zhang, Q. and Xiong, Y. (2024) Harnessing AI Potential in E-Commerce: Improving User Engagement and Sales through Deep Learning-Based Product Recommendations. Current Psychology, 43, 30379-30401.
https://doi.org/10.1007/s12144-024-06649-3
[14] Davoodi, L. and Mezei, J. (2024) A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce. Applied Sciences, 14, Article No. 10069.
https://doi.org/10.3390/app142110069
[15] Wasilewski, A., Chawla, Y. and Pralat, E. (2025) Enhanced E-Commerce Personalization through AI-Powered Content Generation Tools. IEEE Access, 13, 48083-48095.
https://doi.org/10.1109/access.2025.3550956