Research on Reconstruction of New Retail Triangle Driven by AI
Authors
Yiyue Li1, Keyan Miao2, *, Xinyu Jiang3, Zuoning Zhang4
1College of Foreign Language, Northeastern University, Shenyang, China
2School of Management, Nanjing University of Posts and Telecommunications, Nanjing, China
3College of Business Administration, JiMei University, Xiamen, China
4College of Tourism and Service Management, Nankai University, Tianjin, China
*Corresponding author.
Email: miao_1412@foxmail.com
Corresponding Author
Keyan Miao
Available Online 2 December 2022.
- DOI
- 10.2991/978-94-6463-010-7_40How to use a DOI?
- Keywords
- New retail; AI; Reconstruction; New Retail Triangle; Freshippo
- Abstract
With the continuous application of AI technology, the business model of retail industry is also developing rapidly. Through the case study of Freshippo and the literature review by CiteSpace, this article aims to explore the new retail Triangle business model. The study helps us identify the evolution mechanism of the new retail triangle through EGM analysis method and AHP data analysis method. Besides, this paper sums up the most key factor of the new retail triangle, and helps reconstruct it.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yiyue Li AU - Keyan Miao AU - Xinyu Jiang AU - Zuoning Zhang PY - 2022 DA - 2022/12/02 TI - Research on Reconstruction of New Retail Triangle Driven by AI BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 382 EP - 392 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_40 DO - 10.2991/978-94-6463-010-7_40 ID - Li2022 ER -