Evaluating the Effectiveness of Recommendation Engines on Customer Experience Across Product Categories

被引:0
|
作者
Sasanuma, Katsunobu [1 ]
Yang, Gyung Yeol [1 ]
机构
[1] Nagoya Univ Commerce & Business, Nisshin, Japan
关键词
AI-Powered Tools; Correlation Analysis; Customer Journey; Online Marketing; Recommendation Engine; Similarity Analysis; ENGAGEMENT; CHOICE; AI;
D O I
10.4018/IJTHI.345928
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Artificial intelligence (AI)-powered tools such as recommendation engines are widely used in online marketing and e-commerce; however, online retailers often deploy these tools without understanding which human factors play a role in which products and at which stage of the customer journey. Understanding the interaction between AI-powered tools and humans can help practitioners create more effective online marketing platforms and improve human interaction with e-commerce tools. This paper examines customers' reliance on recommendation engines when purchasing fashion goods, electronics, and media content such as video and music. This paper also discusses the potential for improvement in recommendation engines in online marketing and e-commerce.
引用
收藏
页数:22
相关论文
共 50 条
  • [31] OVERLAP OF OPINION LEADERSHIP ACROSS CONSUMER PRODUCT CATEGORIES
    KING, CW
    SUMMERS, JO
    JOURNAL OF MARKETING RESEARCH, 1970, 7 (01) : 43 - 50
  • [32] Evaluating the effectiveness of Web search engines on results diversification
    Wu, Shengli
    Zhang, Zhongmin
    Xu, Chunlin
    INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL, 2019, 24 (01):
  • [33] Effects of customer experience across service types, customer types and time
    Roy, Subhadip
    JOURNAL OF SERVICES MARKETING, 2018, 32 (04) : 400 - 413
  • [34] Research on the Effect of the Recommendation System on Customer Online Shopping Experience
    Liu, J.
    Hu, G. Z.
    Yu, Y.
    Yi, W. J.
    Zuo, L. L.
    2017 14TH INTERNATIONAL CONFERENCE ON SERVICES SYSTEMS AND SERVICES MANAGEMENT (ICSSSM), 2017,
  • [35] Product hierarchy-based customer profilesfor electronic commerce recommendation
    Niu, L
    Yan, XW
    Zhang, CQ
    Zhang, SC
    2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 1075 - 1080
  • [36] Applied Big Data Analysis to Build Customer Product Recommendation Model
    Lin, Rong-Ho.
    Chuang, Wei-Wei
    Chuang, Chun-Ling
    Chang, Wan-Sin
    SUSTAINABILITY, 2021, 13 (09)
  • [37] THE EFFECTIVENESS OF ONLINE VIDEO ADS ON CUSTOMER EXPERIENCE
    Ljubojevic, Milos
    Vaskovic, Vojkan
    Vaskovic, Jelena
    Stankovic, Srecko
    METALURGIA INTERNATIONAL, 2012, 17 (12): : 111 - 116
  • [38] Effectiveness of product recommendation framing on online retail platforms
    Zhang, Junhui
    Balaji, M. S.
    Luo, Jun
    Jha, Subhash
    JOURNAL OF BUSINESS RESEARCH, 2022, 153 : 185 - 197
  • [39] Evaluating Categories From Experience: The Simple Averaging Heuristic
    Woiczyk, Thomas K. A.
    Le Mens, Gael
    JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY, 2021, 121 (04) : 747 - 773
  • [40] Consumer experience and consideration sets for brands and product categories
    Johnson, MD
    Lehmann, DR
    ADVANCES IN CONSUMER RESEARCH, VOL XXIV, 1997, 24 : 295 - 300