RESEARCH PAPERS

Research on Identifying and Rating Perceived Risks of Quantified Self App Users

  • Li Shiyu ,
  • Zhang Xiangxian ,
  • Yan Wei ,
  • Qu Jingye ,
  • Li He
Expand
  • 1 School of Business and Management, Jilin University, Changchun 130012;
    2 School of Information Science and Technology, Northeast Normal University, Changchun 130117;
    3 School of Computer Science and Technology, Beihua University, Jilin 132013;
    4 Library, Beihua University, Jilin 132013
Li Shiyu, doctoral candidate; Zhang Xiangxian, professor, PhD, doctoral supervisor; Yan Wei, PhD, postdoctoral fellow, corresponding author, E-mail: 196277203@qq.com; Qu Jingye, professor, PhD, master supervisor; Li He, professor, PhD, doctoral supervisor.

Received date: 2024-01-09

  Revised date: 2024-05-16

  Online published: 2025-01-15

Supported by

This work is supported by the National Social Science Fund of China project, titled “Research on Users’ Privacy Risks and Privacy Protection in Smart Internet Environment” (Grant No. 20BTQ060).

Abstract

[Purpose/Significance] This study aims to propose a quantitative method for identifying and rating perceived risks of quantitative self App users from the perspective of user perception. It has important theoretical value and practical significance for achieving accurate identification and effective evaluation of perceived risks of quantitative self App users. [Method/Process] This study constructed a risk word list to screen the review texts involving risk content, identified the perceived risks of users using the Bert model and K-Means clustering algorithm, and calculated the risk score and emotional tendency to achieve risk rating. [Result/Conclusion] The study validates the feasibility of quantifying self app user perceived risk and rating methods, analyzes 8 categories of quantified self user perceived risk, and uses a risk matrix to assess their risk levels.

Cite this article

Li Shiyu , Zhang Xiangxian , Yan Wei , Qu Jingye , Li He . Research on Identifying and Rating Perceived Risks of Quantified Self App Users[J]. Library and Information Service, 2025 , 69(1) : 92 -105 . DOI: 10.13266/j.issn.0252-3116.2025.01.009

References

[1] ETKIN J. The hidden cost of personal quantification[J]. Journal of consumer research, 2016, 42(6): 967-984.
[2] 朱庆华, 徐孝婷, 赵宇翔, 等. 基于移动经验取样法的量化自我参与流程及内在机理研究[J]. 情报学报, 2022, 41(3): 217-228. (ZHU Q H, XU X T, ZHAO Y X, et al. Research on participation process and underlying mechanism of quantified self based on mobile experience sampling method[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(3): 217-228.)
[3] 徐孝婷, 朱庆华, 杨梦晴, 等. 面向个人健康信息管理的量化自我持续参与动机研究[J]. 情报学报, 2022, 41(3): 229-243. (XU X T, ZHU Q H, YANG M Q, et al. Sustained participation motivation of quantified self for personal health information management[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(3): 229-243.)
[4] 张茜, 谢卫红. 量化自我的隐私披露行为: 内涵特征、理论框架与研究展望[J]. 情报杂志, 2022, 41(9): 112-120. (ZHANG Q, XIE W H. Quantified self and privacy disclosure behavior: connotative features, theoretical framework, and research prospects[J]. Journal of intelligence, 2022, 41(9): 112-120.)
[5] 沈睿. App用户隐私政策阅读意愿的影响研究[D]. 上海: 华东师范大学, 2022. (SHEN R. Study on the influence of app users’ willingness to read privacy policy [D]. Shanghai: East China Normal University, 2022.)
[6] 张茜, 谢卫红, 王忠. 应对理论视角下量化自我的隐私悖论研究[J]. 情报杂志, 2023, 42(5): 175-183, 199. (ZHANG Q, XIE W H, WANG Z. Research on privacy paradox in quantified self from the perspective of coping theory[J]. Journal of intelligence, 2023, 42(5): 175-183, 199.)
[7] SWAN M. The quantified self: fundamental disruption in big data science and biological discovery[J]. Big data, 2013, 1(2): 85.
[8] GAO Y, LI H, LUO Y. An empirical study of wearable technology acceptance in healthcare[J]. Industrial management & data systems, 2015, 115(9): 1704-1723.
[9] 李宝库, 卢文君. 基于技术接受模型的用户量化自我持续参与意愿研究[J]. 情报探索, 2021(2): 1-7. (LI B K, LU W J. Research on users’ quantitative self sustained participation willingness based on technology acceptance model[J]. Information research, 2021(2): 1-7.)
[10] 胡德华, 张彦斐. 量化自我研究[J]. 图书馆论坛, 2018, 38(2): 1-7. (HU D H, ZHANG Y F. research on quantified self[J]. Library tribune, 2018, 38(2): 1-7.)
[11] BAUER R A. Consumer behavior as risk taking, dynamic marketing for a changing World[C]//Proceedings of the 43rd conference of the American Marketing Association. Chicago: American Marketing Association, 1960: 389-398.
[12] JACOBY J, KAPLAN L B. The components of perceived risk[C]//Proceedings of the third annual conference of Association for Consumer Research. Chicago: Association for Consumer Research, 1972: 382-393
[13] JARVENPAA S L, TODD P A. Consumer reactions to electronic shopping on the World Wide Web[J]. International journal of electronic commerce, 1996, 1(2): 59-88.
[14] CASES A S. Perceived risk and risk-reduction strategies in Internet shopping[J]. The international review of retail, distribution and consumer research, 2002, 12(4): 375-394.
[15] HASSAN A M, KUNZ M B, PEARSON A W, et al. Conceptualization and measurement of perceived risk in online shopping[J]. Marketing management journal, 2006, 16(1): 138-147.
[16] 张一涵, 袁勤俭, 沈洪洲. 感知风险理论及其在信息系统研究领域的应用与展望[J]. 现代情报, 2022, 42(5): 149-159. (ZHANG Y H, YUAN Q J, SHEN H Z. Perceived risk theory and its application and prospect in the field of information system research[J]. Journal of modern information, 2022, 42(5): 149-159.)
[17] MARTIN S S, CAMARERO C. How perceived risk affects online buying[J]. Online information review, 2009, 33(4) :629-654.
[18] MIN J. Personal information concerns and provision in social network sites: Interplay between secure preservation and true presentation[J]. Journal of the Association for Information Science and Technology, 2016, 67(1): 26-42.
[19] 金艳飞. 社交电商信息内容对消费者感知风险的影响研究[D]. 杭州: 杭州师范大学, 2023. (JIN Y F. Research on the impact of social e-commerce information content on consumers’ perceived risk [D]. Hangzhou: Hangzhou Normal University, 2023.)
[20] 姚丽娜, 冯叶彤. 生鲜水产品负面在线评论对消费者购买意愿的影响——以京东生鲜为例[J]. 中国渔业经济, 2023, 41(2): 73-81. (YAO L N, FENG Y T. The impact of negative online reviews of fresh aquatic products on consumer purchase intention—taking JD fresh as an example. Chinese fisheries economics, 2023, 41(2): 73-81)
[21] ZHOU S. LIU Y. Effects of perceived privacy risk and disclosure benefits on the online privacy protection behaviors among Chinese teens[J]. Sustainability, 2023, 15(2): 1657.
[22] ROHDEN S F, ZEFERINO D G. Recommendation agents: an analysis of consumers’ risk perceptions toward artificial intelligence[J]. Electronic commerce research, 2022, 23: 2035-2050.
[23] JOINSON A N, REIPS U D, BUCHANAN T, et al. Privacy, trust, and self-disclosure online[J]. Human computer interaction, 2010, 25(1): 1-24.
[24] 孙霄凌, 程阳, 朱庆华. 社会化搜索中用户隐私披露行为意向的影响因素研究[J]. 情报杂志, 2017, 36(10): 172-179, 201. (SUN X L, CHENG Y, ZHU Q H. Exploring the factors of social search user’s privacy disclosure intention[J]. Journal of intelligence, 2017, 36(10): 172-179, 201.)
[25] LI Y, KOBSA A. Context and privacy concerns in friend request decisions[J]. Journal of the Association for Information Science and Technology, 2020, 71(6): 632-643.
[26] 朱侯, 张明鑫. 移动APP用户隐私信息设置行为影响因素及其组态效应研究[J]. 情报科学, 2021, 39(7): 54-62. (ZHU H, ZHANG M X. Study on the influencing factors and configurational effects of mobile APP users’ privacy information settings behavior[J]. Information science, 2021, 39(7): 54-62.)
[27] CHEN R. Living a private life in public social networks: an exploration of member self-disclosure[J]. Decision support systems, 2013, 55(3): 661-668.
[28] 陈渝, 尹依. 移动短视频APP用户信息分享行为实证研究——感知风险的调节效应[J]. 重庆理工大学学报(社会科学), 2023, 37(8): 82-94. (CHEN Y, YIN Y. Empirical study on information sharing behavior of mobile short video app users: the moderating effect of perceived risk [J]. Journal of Chongqing University of Technology (social science), 2023, 37(8): 82-94.)
[29] 张继东, 蔡雪. 基于用户行为感知的移动社交网络信息服务持续使用意愿研究[J]. 现代情报, 2019, 39(1): 70-77. (ZHANG J D, CAI X. Research on continuance usage intention of mobile social network information service based on user behavior perception[J]. Journal of modern information, 2019, 39(1): 70-77.)
[30] AMIRTHA R, SIVAKUMAR V J, HWANG Y. Influence of perceived risk dimensions on e-shopping behavioural intention among women: a family life cycle stage perspective[J]. Journal of theoretical and applied electronic commerce research, 2020, 16(3): 320-355.
[31] 张茜, 谢卫红, 王永健, 等. 量化自我隐私顾虑的前因组态对隐私披露意愿的影响研究——权力-责任均衡视角[J/OL]. 情报杂志, 1-9[2024-07-28]. http://kns.cnki.net/kcms/detail/61.1167.G3.20240411.0835.006.html. (ZHANG Q, XIE W H, WANG Y J, et al. Study on the impact of the antecedent configuration of quantified self-privacy concerns on the willingness to disclose privacy: a perspective of power-responsibility equilibrium [J/OL]. Journal of intelligence, 1-9[2024-07-28]. http://kns.cnki.net/kcms/detail/61.1167.G3.20240411.0835.006.html.)
[32] 俞艺涵, 付钰, 吴晓平. 基于Shannon信息熵与BP神经网络的隐私数据度量与分级模型[J]. 通信学报, 2018, 39(12): 10-17. (YU Y H, FU Y, WU X P. Metric and classification model for privacy data based n Shannon information entropy and BP neural network [J]. Journal of communications, 2018, 39(12): 10-17.)
[33] 严海伦. 基于知识图谱的网络敏感文本分级方法研究[D]. 武汉: 华中科技大学, 2022. (YAN, H L. Research on Grading Method of Online Sensitive Texts Based on Knowledge Graph [D]. Wuhan: Huazhong University of Science and Technology, 2022.)
[34] 李瀛, 王冠楠. 网络新闻敏感信息识别与风险分级方法研究[J]. 情报理论与实践, 2022, 45(4): 105-112. (LI Y, WANG G N. Research on identification and risk grading method of network news sensitive information [J]. Information studies: theory & application, 2022, 45(4): 105-112.)
[35] 董士豪, 郑朗, 王特. 基于知识图谱技术的上市企业产业链风险预测[J]. 网络安全与数据治理, 2023, 42(9): 21-28. (DONG S H, ZHENG L, WANG T. Risk prediction of listed companies’ industrial chain based on knowledge graph technology[J]. Cyber security and data governance, 2023, 42(9): 21-28.)
[36] ZHANG J, ZHENG W, WANG S. The study of the effect of online review on purchase behavior: comparing the two research methods[J]. International journal of crowd science, 2020, 4(1): 73-86.
[37] GARVEY P R, LANSDOWNE Z F. Risk matrix: an approach for identifying, assessing, and ranking program risks[J]. Air Force J. Logist, 1998, 22: 18-21.
[38] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]//Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis: Association for Computational Linguistics, 2019: 4171-4186.
[39] MACQUEEN J. Some methods for classification and analysis of multivariate observations[C]//Proceedings of the fifth Berkeley symposium on mathmatical statistics and probability. Oakland: University of California Press, 1967: 281-297.
[40] CLORE G, SCHWARZ N, CONWAY M. Affective causes and consequences of social information processing[J]. Handbook of social cognition, 1994, 1: 323-417.
[41] 张涛. 移动商务用户隐私信息披露风险因素及风险评估方法研究[D]. 昆明: 云南财经大学, 2021. (ZHANG T. Research on risk factors and risk assessment methods of user privacy disclosure in mobile commerce [D]. Kunming: Yunnan University of Finance and Economics, 2021.)
Outlines

/