Behavior Analysis Based on Bigdata: Evidence from Live Streaming and College Students' Online Purchase Behavior
- DOI
- 10.2991/978-94-6463-298-9_54How to use a DOI?
- Keywords
- Bigdata; Consumer Behavior; Online Purchasing
- Abstract
Contemporarily, bigdata technique has been widely implemented in various fields. Based on the background of bigdata, this study briefly describes the application and commonly used models of big data analysis in consumer behavior. With the help of the TAM model and the SOR model, the situations for using data analysis in consumer behavior are specifically analyzed: webcast delivery of goods and college students' buying habits on the internet. According to the model, the impact mechanism is summarized and analyzed, and then relevant suggestions are put forward based on the model analysis results. It is concluded that perceived usefulness, perceived ease of use, user's willingness to interact with information and immersive experience have an important impact on consumer purchase behavior. For students at colleges, they increased focus on quality of product and the advertising interaction, which have the greatest impact on final purchase behavior. Finally, the phenomenon of "big data killing familiarity" and suggestions for improvement are mentioned.
- 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 - Fangyu Zhu PY - 2023 DA - 2023/11/30 TI - Behavior Analysis Based on Bigdata: Evidence from Live Streaming and College Students' Online Purchase Behavior BT - Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023) PB - Atlantis Press SP - 497 EP - 506 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-298-9_54 DO - 10.2991/978-94-6463-298-9_54 ID - Zhu2023 ER -