On the Performance of Artificial Intelligence Empowerment on Consumer Behavior

Chengcheng Ji (), Yushen Zhang (), Zhitao Zhu (), Xiangyu Li (), Hongyan Lv () and Chunhong Yuan ()
Additional contact information
Chengcheng Ji: Beijing University of Civil Engineering and Architecture, Department of Urban Economics and Management
Yushen Zhang: Beijing University of Civil Engineering and Architecture, School of architecture and urban planning
Zhitao Zhu: Beijing University of Posts and Telecommunications
Xiangyu Li: Shanghai Jiao Tong University, Department of Electronic Engineering
Hongyan Lv: Southeast University, School of Cyber Science and Engineering
Chunhong Yuan: Kazan (Volga Region) Federal University, Department of Pre-Engineering

A chapter in Proceedings of the 2025 5th International Conference on Informatization Economic Development and Management (IEDM 2025), 2025, pp 108-117 from Springer

Abstract: Abstract In recent years, the rapid advancement of artificial intelligence (AI) technology has provided substantial impetus for the innovation and optimization of personalized recommendation systems. However, research on the correlation between personalized recommender systems and consumer behavior remains relatively limited, with a lack of systematic theoretical exploration and empirical analysis. This study employs a questionnaire survey method to collect detailed data samples and innovatively integrates heat map analysis technology to conduct a comprehensive investigation into the relationship between personalized recommendation systems and consumer behavior evaluation variables. The study focuses on elucidating the mechanisms through which personalized recommender systems influence consumer preferences, purchase decisions, and usage satisfaction, aiming to uncover their multi-level and multi-dimensional impact pathways. The data analysis reveals that personalized recommendation systems significantly affect multiple key dimensions of consumer behavior, demonstrating a high degree of relevance and interactivity. These findings provide crucial theoretical support for a deeper understanding of the operational principles and optimization strategies of personalized recommendation systems, while also offering a new practical perspective for interdisciplinary research at the intersection of AI technology and consumer behavior, thereby holding significant academic and practical value.

Keywords: Artificial intelligence; Personalized recommendations; Consumer behavior; E-Commerce (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-724-3_12

Ordering information: This item can be ordered from
http://www.springer.com/9789464637243

DOI: 10.2991/978-94-6463-724-3_12

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().