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JonDonym Users’ Information Privacy Concerns

  • David Harborth
  • Sebastian PapeEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 529)

Abstract

Privacy concerns as well as trust and risk beliefs are important factors that can influence users’ decision to use a service. One popular model that integrates these factors is relating the Internet Users Information Privacy Concerns (IUIPC) construct to trust and risk beliefs. However, studies haven’t yet applied it to a privacy enhancing technology (PET) such as an anonymization service. Therefore, we conducted a survey among 416 users of the anonymization service JonDonym [1] and collected 141 complete questionnaires. We rely on the IUIPC construct and the related trust-risk model and show that it needs to be adapted for the case of PETs. In addition, we extend the original causal model by including trust beliefs in the anonymization service provider and show that they have a significant effect on the actual use behavior of the PET.

Keywords

Internet Users’ Information Privacy Concerns IUIPC Anonymity services Privacy concerns Trust beliefs Risk beliefs 

Notes

Acknowledgments

This research was partly funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371. In addition, we thank Rolf Wendolski (JonDos GmbH) for his help during the data collection process.

Supplementary material

References

  1. 1.
    JonDos Gmbh: Official Homepage of JonDonym (2018). https://www.anonym-surfen.de
  2. 2.
    David, E.E., Fano, R.M.: Some thoughts about the social implications of accessible computing. In: Proceedings 1965 Fall Joint Computer Conference (1965). http://www.multicians.org/fjcc6.html
  3. 3.
    Bédard, M.: The Underestimated Economic Benefits of the Internet. Economic Notes, Regulation series. The Montreal Economic Institute, Montreal (2016)Google Scholar
  4. 4.
    Mineo, L.: On internet privacy, be very afraid (Interview with Bruce Schneier), August 2017. https://news.harvard.edu/gazette/story/2017/08/when-it-comes-to-internet-privacybe-very-afraid-analyst-suggests/
  5. 5.
    Singh, T., Hill, M.E.: Consumer privacy and the internet in Europe: a view from Germany. J. Consum. Mark. 20(7), 634–651 (2003)CrossRefGoogle Scholar
  6. 6.
    Malhotra, N.K., Kim, S.S., Agarwal, J.: Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model. Inf. Syst. Res. 15(4), 336–355 (2004)CrossRefGoogle Scholar
  7. 7.
    Naeini, P.E., et al.: Privacy expectations and preferences in an IoT world. In: Symposium on Usable Privacy and Security (SOUPS) (2017)Google Scholar
  8. 8.
    Heales, J., Cockcroft, S., Trieu, V.-H.: The Influence of privacy, trust, and national culture on internet transactions. In: Meiselwitz, G. (ed.) SCSM 2017. LNCS, vol. 10282, pp. 159–176. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58559-8_14CrossRefGoogle Scholar
  9. 9.
    Raber, F., Krueger, A.: Towards understanding the influence of personality on mobile app permission settings. In: Bernhaupt, R., Dalvi, G., Joshi, A., K. Balkrishan, D., ONeill, J., Winckler, M. (eds.) INTERACT 2017. LNCS, vol. 10516, pp. 62–82. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68059-0_4CrossRefGoogle Scholar
  10. 10.
    Borking, J.J., Raab, C.: Laws, PETs and other technologies for privacy protection. J. Inf. Law Technol. 1, 1–14 (2001)Google Scholar
  11. 11.
    Spiekermann, S.: The desire for privacy: insights into the views and nature of the early adopters of privacy services. Int. J. Technol. Hum. Interact. 1(1), 74–83 (2005)CrossRefGoogle Scholar
  12. 12.
    Lee, L., Fifield, D., Malkin, N., Iyer, G., Egelman, S., Wagner, D.: A usability evaluation of tor launcher. In: Proceedings on Privacy Enhancing Technologies, no. 3, pp. 90–109 (2017)Google Scholar
  13. 13.
    Benenson, Z., Girard, A., Krontiris, I.: User acceptance factors for anonymous credentials: an empirical investigation. In: 14th Annual Workshop on the Economics of Information Security (WEIS), pp. 1–33 (2015)Google Scholar
  14. 14.
    Janic, M., Wijbenga, J.P., Veugen, T.: Transparency enhancing tools (tets): an overview. In: 2013 Third Workshop on Socio-Technical Aspects in Security and Trust (STAST), pp. 18–25. IEEE (2013)Google Scholar
  15. 15.
    Hair, J., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19(2), 139–152 (2011)CrossRefGoogle Scholar
  16. 16.
    Hair, J., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Thousand Oaks (2017)zbMATHGoogle Scholar
  17. 17.
    Stewart, K.A., Segars, A.H.: An empirical examination of the concern for information privacy instrument. Inf. Syst. Res. 13(1), 36–49 (2002)CrossRefGoogle Scholar
  18. 18.
    Pavlou, P.A.: Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 7(3), 101–134 (2003)CrossRefGoogle Scholar
  19. 19.
    Harborth, D., Pape, S.: Exploring the hype: investigating technology acceptance factors of Pokémon Go. In: 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 155–168 (2017)Google Scholar
  20. 20.
    Harborth, D., Pape, S.: Privacy concerns and behavior of Pokémon go players in Germany. In: Hansen, M., Kosta, E., Nai-Fovino, I., Fischer-Hübner, S. (eds.) Proceedings of IFIP Summer School on Privacy and Identity Management (IFIPSC2017), pp. 314–329. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-92925-5_21CrossRefGoogle Scholar
  21. 21.
    Schmitz, C.: LimeSurvey Project Team (2015). http://www.limesurvey.org
  22. 22.
    Ringle, C.M., Wende, S., Becker, J.M.: SmartPLS 3 (2015). http://www.smartpls.com
  23. 23.
    Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2015)CrossRefGoogle Scholar
  24. 24.
    Malhotra, N.K., Kim, S.S., Patil, A.: Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research. Manag. Sci. 52(12), 1865–1883 (2006)CrossRefGoogle Scholar
  25. 25.
    Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P.: Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88(5), 879–903 (2003)CrossRefGoogle Scholar
  26. 26.
    Blome, C., Paulraj, A.: Ethical climate and purchasing social responsibility: a benevolence focus. J. Bus. Eth. 116(3), 567–585 (2013)CrossRefGoogle Scholar
  27. 27.
    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Lawrence Earlbaum Associates, HillsDale (1988)zbMATHGoogle Scholar
  28. 28.
    Chin, W.W.: The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (ed.): Modern Methods for Business Research, pp. 295–336. Lawrence Erlbaum, Mahwah (1998)Google Scholar
  29. 29.
    Rosen, L., Whaling, K., Carrier, L., Cheever, N., Rokkum, J.: The media and technology usage and attitudes scale: an empirical investigation. Comput. Hum. Behav. 29(6), 2501–2511 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Chair of Mobile Business and Multilateral SecurityGoethe UniversityFrankfurtGermany

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