skip to main content
research-article

How Privacy Concerns, Trust and Risk Beliefs, and Privacy Literacy Influence Users' Intentions to Use Privacy-Enhancing Technologies: The Case of Tor

Published:21 January 2020Publication History
Skip Abstract Section

Abstract

Due to an increasing collection of personal data by internet companies and several data breaches, research related to privacy gained importance in the last years in the information systems domain. Privacy concerns can strongly influence users' decision to use a service. The Internet Users Information Privacy Concerns (IUIPC) construct is one operationalization to measure the impact of privacy concerns on the use of technologies. However, when applied to a privacy enhancing technology (PET) such as an anonymization service, the original rationales do not hold anymore. In particular, an inverted impact of trusting and risk beliefs on behavioral intentions can be expected. We show that the IUIPC model needs to be adapted for the case of PETs. In addition, we extend the original causal model by including trusting beliefs in the anonymization service itself as well as a measure for privacy literacy. A survey among 124 users of the anonymization service Tor shows that trust in Tor has a statistically significant effect on the actual use behavior of the PET. In addition, the results indicate that privacy literacy has a negative impact on trusting beliefs in general and a positive effect on trust in Tor.

References

  1. Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339--370.Google ScholarGoogle ScholarCross RefCross Ref
  2. Bartsch, M., & Dienlin, T. (2016). Control your Facebook: An analysis of online privacy literacy. Computers in Human Behavior, 56, 147--154. https://doi.org/10.1016/j.chb.2015.11.022Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bédard, M. (2016). The underestimated economic benefits of the internet. In Regulation series, The Montreal Economic Institute.Google ScholarGoogle Scholar
  4. Benenson, Z., Girard, A., & Krontiris, I. (2015). User acceptance factors for anonymous credentials: An empirical investigation. 14th Annual Workshop on the Economics of Information Security (WEIS), 1--33.Google ScholarGoogle Scholar
  5. Blome, C., & Paulraj, A. (2013). Ethical climate and purchasing social responsibility: A benevolence focus. Journal of Business Ethics, 116(3), 567--585. https://doi.org/10.1007/s10551-012--1481--5Google ScholarGoogle ScholarCross RefCross Ref
  6. Borking, J. J., & Raab, C. (2001). Laws, pets and other technologies for privacy protection. Journal of Information, Law and Technology, 1, 1--14.Google ScholarGoogle Scholar
  7. Brecht, F., Fabian, B., Kunz, S., & Mueller, S. (2011). Are you willing to wait longer for internet privacy? In ECIS 2011 Proceedings. Retrieved from http://aisel.aisnet.org/ecis2011/236Google ScholarGoogle Scholar
  8. Brecht, F., Fabian, B., Kunz, S., & Müller, S. (2012). Communication anonymizers: Personality, internet privacy literacy and their influence on technology acceptance. In ECIS 2012 Proceedings (pp. 1--13). Retrieved from http://aisel.aisnet.org/ecis2012/214Google ScholarGoogle Scholar
  9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. HillsDale, NJ.Google ScholarGoogle Scholar
  10. David, E. E., & Fano, R. M. (1965). Some thoughts about the social implications of accessible computing. In Proceedings 1965 Fall Joint Computer Conference.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Dienlin, T., & Trepte, S. (2015). Is the privacy paradox a relic of the past? An in-depth analysis of privacy attitudes and privacy behaviors. European Journal of Social Psychology, 45(3), 285--297. https://doi.org/10.1002/ejsp.2049Google ScholarGoogle ScholarCross RefCross Ref
  12. Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61--80. https://doi.org/10.1287/isre.1060.0080Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. https://doi.org/10.2307/2065853Google ScholarGoogle Scholar
  14. Hair, J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications.Google ScholarGoogle Scholar
  15. Hair, J., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139--152. https://doi.org/10.2753/MTP1069--6679190202Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Harborth, D., Cai, X., & Pape, S. (2019). Why do people pay for privacy-enhancing technologies? The case of Tor and JonDonym. In G. Dhillon, F. Karlsson, K. Hedström, & A. Zúquete (Eds.), ICT Systems Security and Privacy Protection. SEC 2019. IFIP Advances in Information and Communication Technology, 56, 253--267, Springer, Cham. https://doi.org/https://doi.org/10.1007/978--3-030--22312-0_18Google ScholarGoogle Scholar
  17. Harborth, D., & Pape, S. (2018a). Examining technology use factors of privacy-enhancing technologies: The role of perceived anonymity and trust. In Twenty-fourth Americas Conference on Information Systems. New Orleans, USA.Google ScholarGoogle Scholar
  18. Harborth, D., & Pape, S. (2018b). JonDonym users' information privacy concerns. In L. Janczewski & M. Kuty?owski (Eds.), ICT systems security and privacy protection. SEC 2018. IFIP Advances in Information and Communication Technology, 52, 170--184. Poznan, Poland: Springer, Cham. https://doi.org/https://doi.org/10.1007/978--3--319--99828--2_13Google ScholarGoogle Scholar
  19. Harborth, D., & Pape, S. (2019). How privacy concerns and trust and risk beliefs influence users' intentions to use privacy-enhancing technologies - The case of Tor. In Hawaii International Conference on System Sciences (HICSS) Proceedings (pp. 4851--4860). Hawaii, US.Google ScholarGoogle ScholarCross RefCross Ref
  20. Heales, J., Cockcroft, S., & Trieu, V.-H. (2017). The influence of privacy, trust, and national culture on internet transactions. In G. Meiselwitz (Ed.), Social computing and social media. Human Behavior (pp. 159--176). Springer International Publishing.Google ScholarGoogle Scholar
  21. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115--135. https://doi.org/10.1007/s11747-014-0403--8Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Hoofnagle, C. J., King, J., Li, S., & Turow, J. (2010). How different are young adults from older adults when it comes to information privacy attitudes and policies? https://repository.upenn.edu/asc_papers/399. https://doi.org/10.2139/ssrn.1589864Google ScholarGoogle Scholar
  23. Joeckel, S., & Dogruel, L. (2019). Default effects in app selection: German adolescents' tendency to adhere to privacy or social relatedness features in smartphone apps. Mobile Media & Communication, 1--20. https://doi.org/10.1177/2050157918819616Google ScholarGoogle Scholar
  24. JonDos Gmbh. (2018). Official Homepage of JonDonym. Retrieved January 16, 2018, from https://www.anonym-surfen.deGoogle ScholarGoogle Scholar
  25. Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121--1134. https://doi.org/10.1037/0022--3514.77.6.1121Google ScholarGoogle ScholarCross RefCross Ref
  26. Lankton, N. K., Mcknight, D. H., & Tripp, J. (2015). Technology, humanness, and trust: Rethinking trust in technology. Journal of the Association for Information Systems, 16(10), 880--918.Google ScholarGoogle ScholarCross RefCross Ref
  27. Lee, L., Fifield, D., Malkin, N., Iyer, G., Egelman, S., & Wagner, D. (2017). A usability evaluation of Tor launcher. Proceedings on Privacy Enhancing Technologies, (3), 90--109. https://doi.org/10.1515/popets-2017-0030Google ScholarGoogle ScholarCross RefCross Ref
  28. Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336--355. https://doi.org/10.1287/isre.1040.0032Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in is research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865--1883. https://doi.org/10.1287/mnsc.1060.0597Google ScholarGoogle ScholarCross RefCross Ref
  30. Masur, P. K., Teutsch, D., & Trepte, S. (2017). Entwicklung und validierung der online-privatheitskompetenzskala (OPLIS) [Development and validation of the Online Privacy Literacy Scale (OPLIS)]. Diagnostica, 63(4), 256--268. https://doi.org/10.1026/0012--1924/a000179Google ScholarGoogle ScholarCross RefCross Ref
  31. McKnight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems (TMIS), 2(2), 1--25. https://doi.org/10.1145/1985347.1985353Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Mineo, L. (2017). On internet privacy, be very afraid (Interview with Bruce Schneier). Retrieved February 20, 2018, from https://news.harvard.edu/gazette/story/2017/08/when-it-comes-to-internet-privacy-be-very-afraid-analyst-suggests/Google ScholarGoogle Scholar
  33. Morrison, B. (2013). Do we know what we think we know? An exploration of online social network users' privacy literacy. Workplace Review, April 2013.Google ScholarGoogle Scholar
  34. Naeini, P. E., Bhagavatula, S., Habib, H., Degeling, M., Bauer, L., Cranor, L., & Sadeh, N. (2017). Privacy expectations and preferences in an IOT world. In Symposium on Usable Privacy and Security (SOUPS).Google ScholarGoogle Scholar
  35. Park, Y. J. (2013). Digital literacy and privacy behavior online. Communication Research, 40(2), 215--236. https://doi.org/10.1177/0093650211418338Google ScholarGoogle Scholar
  36. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101--134. https://doi.org/10.1080/10864415.2003.11044275Google ScholarGoogle ScholarCross RefCross Ref
  37. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879--903. https://doi.org/10.1037/0021--9010.88.5.879Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Raber, F., & Krueger, A. (2017). Towards understanding the influence of personality on mobile app permission settings. In IFIP Conference on Human-Computer Interaction (pp. 62--82).Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.smartpls.com. Retrieved from http://www.smartpls.comGoogle ScholarGoogle Scholar
  40. Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The Media and technology usage and attitudes scale: An empirical investigation. Computer Human Behavior, 29(6), 2501--2511. https://doi.org/10.1016/j.pestbp.2011.02.012.InvestigationsGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  41. Rossnagel, H. (2010). The market failure of anonymity services. Lecture Notes in Computer Science (Incl. Subseries Lecture Notes in AI and Lecture Notes in Bioinformatics), 6033 LNCS, 340--354. https://doi.org/10.1007/978--3--642--12368--9_28Google ScholarGoogle Scholar
  42. Schmitz, C. (2015). LimeSurvey Project Team. Retrieved from http://www.limesurvey.orgGoogle ScholarGoogle Scholar
  43. Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325--343.Google ScholarGoogle ScholarCross RefCross Ref
  44. Singh, T., & Hill, M. E. (2003). Consumer privacy and the Internet in Europe: A view from Germany. Journal of Consumer Marketing, 20(7), 634--651.Google ScholarGoogle ScholarCross RefCross Ref
  45. Slyke, C. V., Johnson, R., Jiang, J., & Shim, J. T. (2006). Concern for information privacy and online consumer purchasing. Journal of the Association for Information Systems, 7(6), 415--444.Google ScholarGoogle ScholarCross RefCross Ref
  46. Smith, H. J., Dinev, T., & Xu, H. (2011). Theory and review information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989--1015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for is positivist research. Communications of the Association for Information Systems, 13, 380--427.Google ScholarGoogle ScholarCross RefCross Ref
  48. The Tor Project. (2018). Tor. Retrieved February 20, 2018, from https://www.torproject.orgGoogle ScholarGoogle Scholar
  49. Trepte, S., & Masur, P. K. (2017). Privacy attitudes, perceptions, and behaviors of the German population. Forum Privatheit Und Selbstbestimmung in Der Digitalen Welt.Google ScholarGoogle Scholar
  50. Trepte, S., Teutsch, D., Masur, P. K., Eicher, C., Fischer, M., Hennhöfer, A., & Lind, F. (2015). Do people know about privacy and data protection strategies? Towards the "Online Privacy Literacy Scale" (OPLIS). In S. Gutwirth, R. Leenes, & P. de Hert (Eds.), Reforming European Data Protection Law 20, Springer Netherlands. https://doi.org/10.1007/978--94-017--9385--8Google ScholarGoogle Scholar
  51. Weinberger, M., Zhitomirsky-Geffet, M., & Bouhnik, D. (2017). Factors affecting users' online privacy literacy among students in Israel. Online Information Review, 41(5), 582--597. https://doi.org/10.1108/OIR-05--2016-0127Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM SIGMIS Database: the DATABASE for Advances in Information Systems
    ACM SIGMIS Database: the DATABASE for Advances in Information Systems  Volume 51, Issue 1
    February 2020
    120 pages
    ISSN:0095-0033
    EISSN:1532-0936
    DOI:10.1145/3380799
    Issue’s Table of Contents

    Copyright © 2020 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 21 January 2020

    Check for updates

    Qualifiers

    • research-article

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader