图书情报工作

图书情报工作 >

2023 , Vol. 67 >Issue 21: 100 - 110

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.21.010

情报研究

算法感知视角下跨平台信息推荐用户接受意愿研究

  • 毕达天 ,
  • 张雪 ,
  • 米艳霖 ,
  • 孔婧媛
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  • 吉林大学商学与管理学院 长春 130012
毕达天,教授,博士,E-mail:bdt@jlu.edu.cn;张雪,博士研究生;米艳霖,硕士研究生;孔婧媛,博士研究生

收稿日期: 2023-05-18

  修回日期: 2023-07-21

  网络出版日期: 2023-11-27

基金资助

本文系国家社会科学基金项目“基于用户跨社交媒体的信息行为偏好特征挖掘与推荐研究”(项目编号:21BTQ059)研究成果之一

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Research on Users’ Willingness to Accept Cross-platform Information Recommendation from the Perspective of Algorithm Perception

  • Bi Datian ,
  • Zhang Xue ,
  • Mi Yanlin ,
  • Kong Jingyuan
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  • School of Business and Management, Jilin University

Received date: 2023-05-18

  Revised date: 2023-07-21

  Online published: 2023-11-27

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摘要

[目的/意义] 旨在从算法感知的视角出发,探明用户对跨平台信息推荐接受意愿的成因与作用机制,为解决用户与智能算法交互领域的问题提供理论参考。[方法/过程] 从感知公平性、感知可问责性和感知透明度3方面阐释用户对跨平台信息推荐的算法感知内涵,基于启发式—系统式模型框架,探究不同维度的算法感知对用户接受意愿的差异化影响及其作用机制,采用结构方程建模进行实证研究。[结果/结论] 感知公平性、感知可问责性和感知透明度可以显著降低用户的隐私关注,进而提升用户对跨平台信息推荐的接受意愿,感知可问责性对隐私关注产生的负向影响最大;感知公平性和感知可问责性还可以增强用户的社会临场感,社会临场感对用户的推荐接受意愿具有正向影响。根据研究结论,为在线平台企业更好地实施跨平台信息推荐策略,以及互联网信息管理机构进一步完善推荐算法治理模式提供对策与建议。

关键词： 跨平台推荐; 算法感知; 隐私关注; 社会临场感; 启发式—系统式模型

本文引用格式

毕达天 , 张雪 , 米艳霖 , 孔婧媛 . 算法感知视角下跨平台信息推荐用户接受意愿研究[J]. 图书情报工作, 2023 , 67(21) : 100 -110 . DOI: 10.13266/j.issn.0252-3116.2023.21.010

Abstract

[Purpose/Rignificance] This paper aims to explore the causes and mechanisms of users’ willingness to accept cross-platform information recommendation from the perspective of algorithmic perception, providing theoretical reference for resolving issues in the field of interaction between users and intelligent algorithms.[Method/Process] The connotation of users’ perception of cross-platform information recommendation algorithm is explained from three aspects: perceived fairness, perceived accountability, and perceived transparency. Based on the heuristic-systematic model framework, it explores the differential impacts and mechanisms of algorithm perception on recommendation acceptance intention in different dimensions, and employing structural equation modeling for empirical research. [Result/Conclusion] Perceived fairness, perceived accountability and perceived transparency can significantly reduce users’ privacy concerns, and thus improve users’ willingness to accept cross-platform information recommendation. Perceived accountability has the greatest negative impact on privacy concerns. Perceived fairness and perceived accountability can also enhance users’ social presence, which has a positive impact on users’ willingness to accept recommendations. According to the research conclusions, this paper provides countermeasures and suggestions for online platform enterprises to better implement cross-platform information recommendation strategies, and for Internet information management institutions to further improve the governance models of recommendation algorithms.

Key words： cross-platform recommendation; algorithm perception; privacy concerns; social presence; heuristic-systematic model

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