图书馆杂志

图书馆杂志 ›› 2024, Vol. 43 ›› Issue (394): 96-108.

• 信息管理 • 上一篇    下一篇

个性化算法推荐服务用户采纳意愿影响因素: 元分析研究

施雨1 茆意宏1 张贵香2 (1 南京农业大学信息管理学院 2 中国人民大学信息资源管理学院)   

  • 出版日期:2024-02-15 发布日期:2024-03-05
  • 作者简介:施 雨 南京农业大学信息管理学院,博士研究生。研究方向:信息服务与信息行为。作者贡献:论文研究设计、数据收集与分析、论文撰写与修改。E-mail: shiyu@stu.njau.edu.cn 江苏南京 210031 茆意宏 南京农业大学信息管理学院,教授,博士生导师。研究方向:信息服务、信息行为。作者贡献: 论文思路指导、论文修改。 江苏南京 210031 张贵香 中国人民大学信息管理学院,博士研究生。研究方向:元数据、信息组织。作者贡献:数据收集与分析。 北京 100872

A Meta-Analysis Research on Influencing Factors of Users’ Adoption Intention of Personalized Algorithm Recommendation Services

Shi Yu1, Mao Yihong1, Zhang Guixiang2 (1 College of Information Management, Nanjing Agricultural University; 2 School of Information Resource Management, Renmin University of China)   

  • Online:2024-02-15 Published:2024-03-05
  • About author:Shi Yu1, Mao Yihong1, Zhang Guixiang2 (1 College of Information Management, Nanjing Agricultural University; 2 School of Information Resource Management, Renmin University of China)

摘要: 数智时代,个性化算法推荐服务迅速发展。关于个性化算法推荐服务用户采纳意愿影响因素的实证研究结论存在不一致的现象,为明确关键影响因素,对该主题实证研究成果进行梳理与再分析,为后续相关研究提供借鉴。本文运用元分析方法,从国内外聚焦个性化算法推荐服务用户采纳意愿的51 篇实证研究成果中识别出14 个关键影响因素,其中信任、态度、感知愉悦性、交互质量、推荐信息质量等因素对用户采纳意愿作用程度较强。研究时间、社会环境和应用平台类型会对相关变量与个性化算法推荐服务用户采纳意愿之间的关系产生调节作用。

Abstract: In the era of digital intelligence, personalized algorithm recommendation services are developing rapidly. There are inconsistencies in the findings of empirical studies on the factors influencing the adoption intention of users of personalized algorithmic recommendation services. In order to clarify the key influencing factors, the empirical research results on this topic were sorted out and re-analyzed to provide reference for subsequent research. This paper used the meta-analysis method to identify 14 key factors from 51 domestic and foreign empirical studies focusing on the adoption intention of users of personalized algorithm recommendation services. Among these factors, trust, attitude, perceived pleasure, interaction quality, recommendation information quality and other factors played a strong role in user’s adoption intention. Research time, social environment and application platform type moderated the relationship between the relevant variables and the adoption intention of users of personalized algorithm recommendation services.