图书情报工作 ›› 2023, Vol. 67 ›› Issue (2): 14-22.DOI: 10.13266/j.issn.0252-3116.2023.02.002

• 专题:基于用户算法素养的算法治理研究 • 上一篇    下一篇

PADM视角下算法推荐服务风险的用户应对行为研究

邓胜利, 段文豪, 夏苏迪   

  1. 武汉大学信息管理学院 武汉 430072
  • 收稿日期:2022-08-31 修回日期:2022-12-07 出版日期:2023-02-09 发布日期:2023-02-09
  • 通讯作者: 夏苏迪,博士研究生,通信作者,E-mail:sandy_xia@whu.edu.cn。
  • 作者简介:邓胜利,教授,博士,博士生导师;段文豪,硕士研究生。
  • 基金资助:
    本文系国家自然科学基金项目“信息生态链视角下在线知识社区用户贡献行为评价及预测研究”(项目编号:71974149)研究成果之一。

Research on the Coping Behavior of Users in the Face of Algorithm Recommendation Service Risks Based on PADM Theory

Deng Shengli, Duan Wenhao, Xia Sudi   

  1. School of Information Management, Wuhan University, Wuhan 430072
  • Received:2022-08-31 Revised:2022-12-07 Online:2023-02-09 Published:2023-02-09

摘要: [目的/意义] 算法推荐服务衍生的风险给人们的日常生活造成重大影响,厘清算法推荐服务风险的产生机制以及用户应对算法推荐服务风险的行为特征有助于进一步完善算法推荐服务体系。[方法/过程] 运用扎根理论,采用半结构化的访谈方式,一对一深度访谈30名网络用户,结合防护性行为模型和风险应对行为理论,探索算法推荐服务风险中用户的应对机制、影响因素以及决策路径。[结果/结论] 研究发现风险属性、传播渠道、平台特征和用户行为特征对算法推荐服务风险的传播产生影响;在风险传播的基础上,用户形成风险感知、利益相关者感知和防护性行为感知;用户在感知的基础上形成态度和效果评估,并以此为依据制定决策,采取不同的应对行为。最后从算法素养、风险参与和个体学习3个层次提出建议,以应对算法推荐服务风险。

关键词: 算法推荐服务, 防护性行为模型, 风险应对, 扎根理论

Abstract: [Purpose/Significance] The risks derived from algorithmic recommendation services have a significant impact on people's daily lives. Clarifying the generation mechanism of algorithmic recommendation service risks and the behavior characteristics of users' response to algorithmic recommendation service risks will help to further improve the algorithmic recommendation service system.[Method/Process] Based on the grounded theory and semi-structured interview, this study conducted in-depth one-on-one interviews with 30 Internet users. Combined with the protective action decision model and coping behavior theory, this study explored the users' coping mechanism, influence factors and decision paths of algorithmic recommendation service risks.[Result/Conclusion] The study finds that risk attributes, communication channels, platform characteristics and users' behavior characteristics will have an impact on the spread of algorithm recommendation service risks. Based on the cognition of risk communication, users will form the perception of risk, stakeholder and protective behavior. Then, users will form attitudes and effect evaluation on the basis of perception, and make decisions and adopt different coping behaviors based on this. Ultimately, this study puts forward suggestions from the three levels of algorithm literacy, risk participation and individual learning to deal with the risk of algorithm recommendation service.

Key words: algorithm recommendation service, the protective action decision model, risk coping, grounded theory

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