The role of recommender systems in fostering consumers' long-term platform engagement

被引:8
|
作者
Maslowska, Ewa [1 ]
Malthouse, Edward C. [2 ,3 ]
Hollebeek, Linda D. [4 ,5 ,6 ]
机构
[1] Univ Illinois, Dept Advertising, Champaign, IL USA
[2] Northwestern Univ, Dept Integrated Mkt Commun, Evanston, IL 60208 USA
[3] Northwestern Univ, Dept Ind Engn & Management Sci, Evanston, IL 60208 USA
[4] IPAG Business Sch, Dept Mkt & Commun, Paris, France
[5] Vilnius Univ, Dept Mkt, Vilnius, Lithuania
[6] Tallinn Univ Technol, Dept Business Adm, Tallinn, Estonia
关键词
Consumer engagement; Recommender systems; Service interactions; Service communication; CUSTOMER ENGAGEMENT; DYNAMICS; BEHAVIOR; CREATION;
D O I
10.1108/JOSM-12-2021-0487
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Purpose Recommender systems (RS) are designed to communicate with users and drive consumers' engagement with the platform. However, little is known about the strength of this relationship and how RS can create stronger consumer engagement (CE) with the platform brand. Addressing this gap, this paper examines the role of RS in converting consumers' short-term engagement with the RS to their longer-term platform engagement. Design/methodology/approach To explore these issues, the authors review key literature in the areas of CE and RS, from which they develop a conceptual framework. Findings The proposed framework suggests RS design as an important precursor to consumers' RS use, which is expected to affect their platform engagement/disengagement, in turn impacting the firm's long-term outcomes. The authors also identify key managerial tactics, strategies and challenges to aid the conversion of consumers' RS to CE. Research limitations/implications This research raises pertinent implications for research on the RS/CE interface, as synthesized in a proposed research agenda. Practical implications Based on the attained insight, authors outline implications for managing, facilitating and leveraging the proposed RS to CE conversion process. Correspondingly, authors argue that, to optimize RS effectiveness, RS designers should understand the nature of CE. Originality/value By exploring the effect of consumers' RS on their longer-term CE with the platform, the analyses offer pioneering managerial insight into RS effectiveness from a CE perspective.
引用
收藏
页码:721 / 732
页数:12
相关论文
共 50 条
  • [21] The Role of Housing in Sustainable European Long-Term Care Systems
    Rogelj, Valerija
    Bogataj, David
    Bogataj, Marija
    Campuzano-Bolarin, Francisco
    Drobez, Eneja
    SUSTAINABILITY, 2023, 15 (04)
  • [22] The role of an IoT Platform in the Design of Real-time Recommender Systems
    Cha, Sangwhan
    Ruiz, Marta Padilla
    Wachowicz, Monica
    Loc Hoang Tran
    Cao, Hung
    Maduako, Ikechukwu
    2016 IEEE 3RD WORLD FORUM ON INTERNET OF THINGS (WF-IOT), 2016, : 448 - 453
  • [23] Are Consumers Vulnerable to Low Knowledge of Long-Term Care?
    Matzek, Amanda
    Stum, Marlene
    FAMILY & CONSUMER SCIENCES RESEARCH JOURNAL, 2010, 38 (04): : 420 - 434
  • [24] Long-term lessons learned from shopping with consumers
    Otnes, C
    Lowry, TM
    Nelson, M
    ADVANCES IN CONSUMER RESEARCH, VOL 26, 1999, 26 : 176 - 176
  • [25] Predicting Long-Term Engagement in mHealth Apps:ComparativeStudy of Engagement Indices
    Tak, Yae Won
    Lee, Jong Won
    Kim, Junetae
    Lee, Yura
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2024, 26
  • [26] Positive aspects of caregiving in incident and long-term caregivers: Role of social engagement and distress
    Liu, Chelsea
    Marino, Victoria R.
    Howard, Virginia J.
    Haley, William E.
    Roth, David L.
    AGING & MENTAL HEALTH, 2023, 27 (01) : 87 - 93
  • [27] POSITIVE ASPECTS OF CAREGIVING IN INCIDENT AND LONG-TERM CAREGIVERS: ROLE OF SOCIAL ENGAGEMENT AND DISTRESS
    Liu, Chelsea
    Marino, Victoria
    Howard, Virginia
    Haley, William
    Roth, David
    INNOVATION IN AGING, 2021, 5 : 252 - 253
  • [28] Recommender systems for learning: Building user and expert models through long-term observation of application use
    Linton, F
    Schaefer, HP
    USER MODELING AND USER-ADAPTED INTERACTION, 2000, 10 (2-3) : 181 - 207
  • [29] Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use
    Frank Linton
    Hans-Peter Schaefer
    User Modeling and User-Adapted Interaction, 2000, 10 : 181 - 208
  • [30] Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems
    Zhang, Qihua
    Liu, Junning
    Dai, Yuzhuo
    Qi, Yiyan
    Yuan, Yifan
    Zheng, Kunlun
    Huang, Fan
    Tan, Xianfeng
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 4510 - 4520