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What is Your Information Worth? A Systematic Analysis of the Endowment Effect of Different Data Types

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Secure IT Systems (NordSec 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14324))

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Abstract

Various smartphone and web applications use personal information to estimate the user’s behaviour among others for targeted advertising and improvement of personalized applications. Often applications and web services offer only two choices, either accept their privacy policies or not use the services. Hereby, the general scenario is to pay applications and web services with personal data. As privacy policies are lengthy to read and not comprehensible, most users accept the terms and conditions without the awareness of potential consequences. Thus, most users are unaware of continuously being tracked by many applications installed on their smart devices or accept sharing personal data in exchange for using applications and services online. Therefore, this study attempts to shed some light on the willingness to pay for data protection when offered this option in a continuous data-sharing scenario, and the willingness to accept when offered the option to sell personal data to two different data requestors. The study (N = 500) is conducted via crowdsourcing and examines the monetary valuation of users with respect to different data-sharing scenarios and different data types to allow for a more fine-grained analysis of user preferences. Moreover, different influencing factors such as privacy concerns, awareness and intended behaviour are examined in relation to the user’s monetary valuation. The results show significant differences between willingness to pay and accept for ten different data types and the two sharing scenarios contributing to further empirical evidence for the endowment effect. However, the sharing scenarios seem to have not a big influence on willingness to pay but showed significant differences in willingness to accept. Furthermore, the privacy influencing factors seem to negatively correlate with willingness to pay and positively correlate with willingness to accept.

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Notes

  1. 1.

    The HTML files of the experiments and privacy nudges are available open source in the following GitHub repository: https://github.com/veraschmitt/MonVal_Experiment.git.

  2. 2.

    An example of the nudges for Google Maps can be found in the Appendix A.

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Author information

Authors and Affiliations

  1. Quality and Usability Lab, Technische Universität Berlin, Berlin, Germany

    Vera Schmitt, Daniel Sivizaca Conde, Premtim Sahitaj & Sebastian Möller

  2. Speech and Language Technology Lab, German Research Center for Artificial Intelligence, Kaiserslautern, Germany

    Vera Schmitt, Premtim Sahitaj & Sebastian Möller

Authors
  1. Vera Schmitt
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  2. Daniel Sivizaca Conde
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  3. Premtim Sahitaj
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  4. Sebastian Möller
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Corresponding author

Correspondence to Vera Schmitt .

Editor information

Editors and Affiliations

  1. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Lothar Fritsch

  2. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Ismail Hassan

  3. OsloMet - Oslo Metropolitan University, Oslo, Norway

    Ebenezer Paintsil

Appendices

Appendix

A Privacy Nudges, Demographics and Relation to Influencing Factors

See Figs. 13, 14, 15, 16, 17, 18 and 19.

Fig. 13.
figure 13

Example of the privacy nudges used in the control group (nudge on the left side) and the experimental group (information nudge and visual nudge on the right side).

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Fig. 14.
figure 14

Correlation between WTA and Privacy Concern with Age Categories.

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Fig. 15.
figure 15

Correlation between WTP and Privacy Concern with Age Categories.

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Fig. 16.
figure 16

Correlation between WTA and Privacy Concern with Education Categories.

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Fig. 17.
figure 17

Correlation between WTP and Privacy Concern with Education Categories.

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Fig. 18.
figure 18

Correlation between WTA and Privacy Concern with IT Background.

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Fig. 19.
figure 19

Correlation between WTP and Privacy Concern with IT Background.

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Schmitt, V., Conde, D.S., Sahitaj, P., Möller, S. (2024). What is Your Information Worth? A Systematic Analysis of the Endowment Effect of Different Data Types. In: Fritsch, L., Hassan, I., Paintsil, E. (eds) Secure IT Systems. NordSec 2023. Lecture Notes in Computer Science, vol 14324. Springer, Cham. https://doi.org/10.1007/978-3-031-47748-5_13

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  • DOI: https://doi.org/10.1007/978-3-031-47748-5_13

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