Why Experience Matters to Privacy: How Context-Based Experience Moderates Consumer Privacy Expectations for Mobile Applications

被引:29
|
作者
Martin, Kirsten [1 ]
Shilton, Katie [2 ]
机构
[1] George Washington Univ, Sch Business, Strateg Management & Publ Policy, Washington, DC 20052 USA
[2] Univ Maryland, Coll Informat Studies, 4121H Hornbake Bldg,South Wing, College Pk, MD USA
基金
美国国家科学基金会;
关键词
INFORMATION PRIVACY; FACTORIAL VIGNETTE; ONLINE; SECURITY; INTERNET; TRUST;
D O I
10.1002/asi.23500
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Two dominant theoretical models for privacyindividual privacy preferences and context-dependent definitions of privacyare often studied separately in information systems research. This paper unites these theories by examining how individual privacy preferences impact context-dependent privacy expectations. The paper theorizes that experience provides a bridge between individuals' general privacy attitudes and nuanced contextual factors. This leads to the hypothesis that, when making judgments about privacy expectations, individuals with less experience in a context rely more on individual preferences such as their generalized privacy beliefs, whereas individuals with more experience in a context are influenced by contextual factors and norms. To test this hypothesis, 1,925 American users of mobile applications made judgments about whether varied real-world scenarios involving data collection and use met their privacy expectations. Analysis of the data suggests that experience using mobile applications did moderate the effect of individual preferences and contextual factors on privacy judgments. Experience changed the equation respondents used to assess whether data collection and use scenarios met their privacy expectations. Discovering the bridge between 2 dominant theoretical models enables future privacy research to consider both personal and contextual variables by taking differences in experience into account.
引用
收藏
页码:1871 / 1882
页数:12
相关论文
共 9 条
  • [1] Towards awareness of privacy and quality of context in context-based access control for ubiquitous applications
    Filho, José Bringel
    Martin, Hervé
    Journal of Digital Information Management, 2009, 7 (04): : 219 - 226
  • [2] Context-Based Privacy Protection for Location-Based Mobile Services using Pseudonyms
    Zeiss, Joachim
    Jorns, Oliver
    2008 NINTH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT WORKSHOPS, 2008, : 73 - 79
  • [3] Embedding Privacy First Human Centric in User Experience for Mobile Applications
    Phokela, Kanchanjot Kaur
    Singi, Kapil
    Kaulgud, Vikrant
    PROCEEDINGS OF THE 17TH INNOVATIONS IN SOFTWARE ENGINEERING CONFERENCE, ISEC 2024, 2024,
  • [4] Role based privacy applied to context-aware mobile applications
    Häkkilä, J
    Känsälä, I
    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7, 2004, : 5467 - 5472
  • [5] Context-based service adaptation platform: Improving the user experience towards mobile location services
    Schou, Saowanee
    2008 THE INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, 2008, : 123 - +
  • [6] A data security and privacy scheme for user quality of experience in a Mobile Edge Computing-based network
    Sindjoung, Miguel Landry Foko
    Velempini, Mthulisi
    Djamegni, Clementin Tayou
    ARRAY, 2023, 19
  • [7] E-commerce consumer privacy protection and immersive business experience simulation based on intrusion detection algorithms
    Zhuang, Sumei
    ENTERTAINMENT COMPUTING, 2024, 51
  • [8] Detecting Covid-19 relevant situations using Privacy-by-Design based Mobile Experience Sampling
    Restel, Hannes
    Lukau, Eridy
    Sterl, Sebastian
    Gerhold, Lars
    Proceedings of the International ISCRAM Conference, 2022, 2022-May : 506 - 527
  • [9] Method Based on Context-Information to Improve User Experience on Mobile Web-Based Applications
    Espada, Jordan Pascual
    Garcia-Diaz, Vicente
    Gonzalez Crespo, Ruben
    Motenegro Marin, Carlos Enrique
    Sanjuan Martinez, Oscar
    Pelayo Garcia-Bustelo, B. Cristina
    Cueva Lovelle, Juan Manuel
    ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2012, 2012, 7637 : 732 - 741