Skip to main content
Log in

Perceived information sensitivity and interdependent privacy protection: a quantitative study

  • Research Paper
  • Published:
Electronic Markets Aims and scope Submit manuscript

Abstract

From a theoretical point of view, previous research has considered information sensitivity in terms of potential negative consequences for someone who has disclosed information to others and that information becomes public. However, making information public could also have negative consequences for other individuals as well. In this study, we extend the concept of information sensitivity to include negative consequences for other individuals and apply it in a quantitative research study. The results prove that the extended concept of information sensitivity leads to a better understanding of privacy-related concepts especially in an interdependent privacy setting. We contribute to theory by defining the extended concept of information sensitivity and by drawing conclusions on how to use it in future privacy research studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. In particular, we researched on perceived information sensitivity as a 2nd order construct and also accounted for possible non-linear effects of perceived information sensitivity on the intention of the co-owner to protect the privacy of the original owner

References

  • Ajzen, I. (2006). Constructing a theory of planned behavior questionnaire. http://people.umass.edu/~aizen/pdf/tpb.measurement.pdf. Accessed 30 January 2017.

  • Alashoor, T., Keil, M., Liu, L., & Smith, J. (2015). How values shape concerns about privacy for self and others. In D. Leidner & J. Ross (Eds.), Proceedings of the Thirty Sixth International Conference on Information Systems, Dallas, USA.

  • Al-Natour, S., Benbasat, I., & Cenfetelli, R. (2009). The antecedents of customer self-disclosure to online virtual advisors. In H. Chen & S. Slaugther (Eds.), Proceedings of the 30th International Conference on Information Systems, Phoenix, USA.

  • Anderson, C. L., and Agarwal, R. (2009). “Genetic information altruists: how far and to whom does their generosity extend?” ICIS 2009 Proceedings.

  • Animesh, A., Pinsonneault, A., Yang, S.-B., & Oh, W. (2011). An Odyssey into virtual worlds: exploring the impacts of technological and spatial environments. MIS Quarterly, 35(3), 789–810.

    Google Scholar 

  • Bansal, G., Zahedi, F. M., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138–150.

    Google Scholar 

  • Batson, C. D., Duncan, D. B., Ackerman, P., Buckley, T., & Birch, K. (1981). Is empathic emotion a source of altruistic motivation? Journal of Personality and Social Psychology, 40(2), 290–302.

    Google Scholar 

  • Beaudry, A., & Pinsonneault, A. (2005). Understanding User Responses to Information Technology: A Coping Model of User Adaptation. MIS Quarterly, 29(3), 493–524.

    Google Scholar 

  • Bélanger, F., & Crossler, R. E. (2011). Privacy in the digital age: A review of information privacy research in information systems. MIS Quarterly, 35(4), 1017–1042.

    Google Scholar 

  • Bellekens, X., Hamilton, A., Seeam, P., Nieradzinska, K., Franssen, Q., and Seeam, A. (2016). “Pervasive eHealth services a security and privacy risk awareness survey,” in 2016 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA), London, United Kingdom, IEEE, pp. 1–4.

  • Berg, J., Dickhaut, J., & McCabe, K. (1995). Trust, reciprocity, and social history. Games and Economic Behavior, 10(1), 122–142.

    Google Scholar 

  • Biczók, G., & Chia, P. H. (2013). Interdependent privacy: Let me share your data. In A.-R. Sadeghi (Ed.), Financial cryptography and data security (pp. 338–353). Berlin: Springer.

    Google Scholar 

  • Bock, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87–111.

    Google Scholar 

  • Boyd, D. M., & Ellison, N. B. (2007). Social network sites: definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.

    Google Scholar 

  • Boyer O’Leary, M., Wilson, J. M., & Metiu, A. (2014). Beyond being there: the symbolic role of communication and identification in perceptions of proximity to geographically dispersed colleagues. MIS Quarterly, 38(4), 1219–1243.

    Google Scholar 

  • Buckel, T., & Thiesse, F. (2013). Predicting the disclosure of personal information on social networks: an empirical investigation. In R. Alt & B. Franczyk (Eds.), 11th International Conference on Wirtschaftsinformatik, Leipzig, Germany.

  • Cao, Z., Hui, K.-L., & Xu, H. (2018). An economic analysis of peer disclosure in online social communities. Information Systems Research, 29(3), 546–566.

    Google Scholar 

  • Carmines, E. G., and Zeller, R. A. 2008. Reliability and validity assessment, Newbury Park: Sage Publications.

    Google Scholar 

  • Chen, J., Ping, W., Xu, Y., & Tan, B. C.Y. (2009). Am i afraid of my peers? Understanding the antecedents of information privacy concerns in the online social context. In H. Chen & S. Slaugther (Eds.), Proceedings of the 30th International Conference on Information Systems, Phoenix, USA, pp. 1–18.

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for Business Research (pp. 295–336). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cialdini, R. B., Darby, B. L., & Vincent, J. E. (1973). Transgression and altruism: a case for hedonism. Journal of Experimental Social Psychology, 9(6), 502–516.

    Google Scholar 

  • Clark, R. D., & Word, L. E. (1974). Where is the apathetic bystander? Situational characteristics of the emergency. Journal of Personality and Social Psychology, 29(3), 279–287.

    Google Scholar 

  • Culnan, M. J. (1993). "How did they get my name?": An exploratory investigation of consumer attitudes toward secondary information use. MIS Quarterly, 17(3), 341–363.

    Google Scholar 

  • Davis, J. A. (1985). The logic of causal order. Iowa City: Sage.

    Google Scholar 

  • Davis, M. H., Soderlund, T., Cole, J., Gadol, E., Kute, M., Myers, M., & Weihing, J. (2004). Cognitions associated with attempts to empathize: how do we imagine the perspective of another? Personality & Social Psychology Bulletin, 30(12), 1625–1635.

    Google Scholar 

  • Deutsch, R., & Strack, F. (2006). TARGET ARTICLE: Duality Models in Social Psychology: From Dual Processes to Interacting Systems. Psychological Inquiry, 17(3), 166–172.

    Google Scholar 

  • Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80.

    Google Scholar 

  • Dinev, T., Xu, H., Smith, J. H., & Hart, P. (2013). Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22(3), 295–316.

    Google Scholar 

  • Eling, N., Krasnova, H., Widjaja, T., & Buxmann, P. (2015). Will you accept an app? Empirical investigation of the decisional calculus behind the adoption of applications on facebook. In D. Leidner & J. Ross (Eds.), Proceedings of the Thirty Sixth International Conference on Information Systems, Dallas, USA.

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading: Addison-Wesley Pub. Co..

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Gandy, O. H. (1993). The panoptic sort: A political economy of personal information. Boulder: Westview Press.

    Google Scholar 

  • Gerlach, J., Widjaja, T., & Buxmann, P. (2015). Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior. The Journal of Strategic Information Systems, 24(1), 33–43.

    Google Scholar 

  • Goel, V., and Perlroth, N. (2016). Yahoo says 1 billion user accounts were hacked. http://www.nytimes.com/2016/12/14/technology/yahoo-hack.html. Accessed 15 December 2016.

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 1–21.

    Google Scholar 

  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.

    Google Scholar 

  • Hess, T., Legner, C., Esswein, W., Maaß, W., Matt, C., Österle, H., Schlieter, H., Richter, P., & Zarnekow, R. (2014). Digital life as a topic of business and information systems engineering? Business & Information Systems Engineering, 6(4), 247–253.

    Google Scholar 

  • Hu, L.‐. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Google Scholar 

  • Hulland, J. (1999). Use of Partial Least Squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.

    Google Scholar 

  • James, T. L., Wallace, L., Warkentin, M., Kim, B. C., & Collignon, S. E. (2017). Exposing others’ information on online social networks (OSNs): Perceived shared risk, its determinants, and its influence on OSN privacy control use. Information Management, 54(7), 851–865.

    Google Scholar 

  • Johnson, M. E. (2008). Information risk of inadvertent disclosure: an analysis of file-sharing risk in the financial supply chain. Journal of Management Information Systems, 25(2), 97–123.

    Google Scholar 

  • Junglas, I., Goel, L., Abraham, C., & Ives, B. (2013). The Social component of information systems—How sociability contributes to technology acceptance. Journal of the Association for Information Systems, 14(10), 585–616.

    Google Scholar 

  • Karwatzki, S., Trenz, M., Tuunainen, V. K., & Veit, D. (2017). Adverse consequences of access to individuals’ information: An analysis of perceptions and the scope of organisational influence. European Journal of Information Systems, 26(6), 688–715.

    Google Scholar 

  • Kehr, F., Wentzel, D., & Mayer, P. (2013). Rethinking the privacy calculus: on the role of dispositional factors and affect. In R. Baskerville & M. Chau (Eds.), Proceedings of the 34th International Conference on Information Systems, Milan, Italy, pp. 1–10.

  • Koohikamali, M., Peak, D. A., & Prybutok, V. R. (2017). Beyond self-disclosure: disclosure of information about others in social network sites. Computers in Human Behavior, 69), 29–42.

    Google Scholar 

  • Krasnova, H., & Veltri, N. F. (2010). Privacy calculus on social networking sites: explorative evidence from Germany and USA. In R. Sprague & S. Laney (Eds.), 43rd Hawaii International Conference on System Sciences (2010), Koloa, Kauai, Hawaii, pp. 1–10.

  • Leider, S., Möbius, M. M., Rosenblat, T., & Do, Q.-A. (2009). Directed altruism and enforced reciprocity in social networks *. Quarterly Journal of Economics, 124(4), 1815–1851.

    Google Scholar 

  • Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors. Decision Support Systems, 51(3), 434–445.

    Google Scholar 

  • Li, T., Pavlou, P., & dos Santos, G. L. (2013). What drives users’ website registration? A randomized field experiment. In R. Baskerville & M. Chau (Eds.), Proceedings of the 34th International Conference on Information Systems, Milan, Italy.

  • Lowry, P. B., D’Arcy, J., Hammer, B., & Moody, G. D. (2016). “Cargo cult” science in traditional organization and information systems survey research: A case for using nontraditional methods of data collection, including Mechanical Turk and online panels. JSIS, 25(3), 232–240.

    Google Scholar 

  • Lwin, M., Wirtz, J., & Williams, J. D. (2007). Consumer online privacy concerns and responses: a power–responsibility equilibrium perspective. Journal of the Academy of Marketing Science, 35(4), 572–585.

    Google Scholar 

  • 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. Information Systems Research, 15(4), 336–355.

    Google Scholar 

  • Metzger, M. J. (2007). Communication privacy management in electronic commerce. Journal of Computer-Mediated Communication, 12(2), 335–361.

    Google Scholar 

  • Milne, G. R. (1997). Consumer participation in mailing lists: a field experiment. Journal of Public Policy & Marketing, 16(2), 298–309.

    Google Scholar 

  • Milne, G. R., & Gordon, M. E. (1993). Direct mail privacy-efficiency trade-offs within an implied social contract framework. Journal of Public Policy & Marketing, 12(2), 206–215.

    Google Scholar 

  • Moon, Y. (2000). Intimate Exchanges: Using Computers to Elicit Self‐Disclosure From Consumers. Journal of consumer research, 26(4), 323–339.

    Google Scholar 

  • Morlok, T. (2016). Sharing is (not) caring - the role of external privacy in users’ information disclosure behaviors on social network sites. In T.-P. Liang & S.-Y. Hung (eds.), Proceedings of the 20th Pacific Asia Conference on Information Systems, Chiayi, Taiwan.

  • Mothersbaugh, D. L., Foxx, W. K., Beatty, S. E., & Wang, S. (2011). Disclosure antecedents in an online service context: the role of sensitivity of information. Journal of Service Research, 15(1), 76–98.

    Google Scholar 

  • Myers, D. G. (2009). Social psychology. New York: McGraw-Hill.

    Google Scholar 

  • Nissenbaum, H. F. (2010). Privacy in context: technology, policy, and the integrity of social life. Stanford: Stanford Law Books an imprit of Standford University Press.

    Google Scholar 

  • Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: a principal -- agent perspective. MIS Quarterly, 31(1), 105–136.

    Google Scholar 

  • Petronio, S. S., & Altman, I. (2002). Boundaries of privacy: Dialectics of disclosure. Albany: State University of New York Press.

    Google Scholar 

  • Phelps, J., Nowak, G., & Ferrell, E. (2000). Privacy concerns and consumer willingness to provide personal information. Journal of Public Policy & Marketing, 19(1), 27–41.

    Google Scholar 

  • Poremba, S. M. (2012). How friends spoil your social-media privacy. http://www.nbcnews.com/id/47711709/ns/technology_and_science-security/t/how-friends-spoil-your-socialmedia-privacy/#.WdTU28hJa70. Accessed 9 October 2017.

  • Posey, C., & Ellis, S. (2007). Understanding self-disclosure in electronic communities: an exploratory model of privacy risk beliefs, reciprocity, and trust. In J. Hoxmeier & S. Hayne (Eds.), Proceedings of the 13th American Conference on Information Systems, Keystone, Colorado, pp. 1–11.

  • Pu, Y., & Grossklags, J. (2014). An economic model and simulation results of app adoption decisions on networks with interdependent privacy consequences. International Conference on Decision and Game Theory for Security, 246–265.

  • Pu, Y., & Grossklags, J. (2015). Using conjoint analysis to investigate the value of interdependent privacy in social app adoption scenarios. In D. Leidner & J. Ross (Eds.), Proceedings of the thirty sixth international conference on information systems, Dallas.

  • Ringle, C. M., Wende, S., and Becker, J.-M. (2014). SmartPLS 3. http://www.smartpls.com. Accessed 21 February 2017.

  • Roberts, S. C. (2011). Applied evolutionary psychology. Oxford: Oxford University Press.

    Google Scholar 

  • Santrock, J. W. (2014). A topical approach to life-span development. New York: McGraw-Hill Education.

    Google Scholar 

  • Schreiner, M., & Hess, T. (2015). Why are consumers willing to pay for privacy? An application of the privacy-freemium model to media companies. In J. Becker, J. vom Brocke & M. de Marco (Eds.), Twenty-Third European Conference on Information Systems (ECIS), Münster, Germany.

  • Schwarz, A., Rizzuto, T., Carraher-Wolverton, C., Roldan, J. L., & Barrera-Barrera, R. (2017). Examining the Impact and Detection of the "Urban Legend" of Common Method Bias. ACM Sigmis Database, 48(1), 93–119.

    Google Scholar 

  • Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of Privacy Concern among Online Consumers. Journal of Public Policy & Marketing, 19(1), 62–73.

    Google Scholar 

  • Son, J.-Y., & Kim, S. S. (2008). Internet users' information privacy-protective responses: a taxonomy and a nomological model. MIS Quarterly, 32(3), 503–529.

    Google Scholar 

  • Spiekermann, S., & Korunovska, J. (2017). Towards a value theory for personal data. Journal of Information Technology, 32(1), 62–84.

    Google Scholar 

  • statista.com (2016). Share of internet users in the United States who have shared passwords to online accounts with friends or family as of May 2016, by age group. https://www.statista.com/statistics/676114/us-sharingof-online-passwords-with-family/. Accessed 8 September 2017.

  • statista.com (2018). Most famous social network sites worldwide as of January 2018, ranked by number of active users (in millions). https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-ofusers/. Accessed 20 March 2018.

  • Steelman, Z. R., Hammer, B. I., & Limayem, M. (2014). Data collection in the digital age: Innovative alternatives to student samples. MIS Quarterly, 38(2), 355–378.

    Google Scholar 

  • Stein, M.-K., Newell, S., Wagner, E. L., & Galliers, R. D. (2015). Coping with information technology: Mixed emotions, vacillation and non-conforming use patterns. MIS Quarterly, 39(2), 367–392.

    Google Scholar 

  • Sun, Y., Wang, N., Shen, X.-L., & Zhang, J. X. (2015). Location information disclosure in location-based social network services: Privacy calculus, benefit structure, and gender differences. Computers in Human Behavior, 52), 278–292.

    Google Scholar 

  • Sutanto, J., Palme, E., Tan, C.-H., & Phang, C. W. (2013). Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users. MIS Quarterly, 37(4), 1141.

    Google Scholar 

  • van Eerde, W., & Thierry, H. (1996). Vroom's expectancy models and work-related criteria: A meta-analysis. Journal of Applied Psychology, 81(5), 575–586.

    Google Scholar 

  • Vroom, V. H. (1964). Work and motivation. New York: Wiley.

    Google Scholar 

  • Wacks, R. (1989). Personal Information: Privacy and the Law. Oxford: Clarendon Press.

    Google Scholar 

  • Weible, R. J. (1993). Privacy and data: An empirical study of the influence of types of data and situational context upon privacy perceptions. Doctoral Dissertation.

  • Wirth, J. (2018). Dependent variables in the privacy-related field: a descriptive literature review. In T. Bui (Ed.), Proceedings of the 51st Hawaii International Conference on System Sciences, Waikoloa Village, Hawaii, pp. 3658–3667.

  • Xie, E., Teo, H.-H., & Wan, W. (2006). Volunteering personal information on the internet: Effects of reputation, privacy notices, and rewards on online consumer behavior. Marketing Letters, 17(1), 61–74.

    Google Scholar 

  • Zhang, Y., He, D., & Sang, Y. (2013). Facebook as a platform for health information and communication: a case study of a diabetes group. Journal of Medical Systems, 37(3), 9942.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jakob Wirth.

Additional information

Responsible Editors: Christian Matt and Manuel Trenz

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Digitization of the Individual

Appendix

Appendix

Post-hoc analyses: Second-order construct and non-linear effects

Besides the given post-hoc analysis concerning the non-supported hypothesis H1, we also evaluated two other research opportunities:

  1. 1)

    One might consider perceived information sensitivity to be a reflective-formative second-order construct called perceived information sensitivity overall. It would be formative in a way that perceived information sensitivity is caused by perceived information sensitivity for the original owner and perceived information sensitivity for the co-owner. It would be reflective since for both first-order constructs the indicators would reflect the corresponding construct (Hair et al. 2017). Such a second-order construct would also be in line with the conceptualization of perceived information sensitivity, stating that perceived information sensitivity consists of perceived negative consequences for both co-owners and original owners. We checked our research model accordingly and can state that such a second-order construct is valid (weight of perceived information sensitivity for the original owner: 0.573; weight of perceived information sensitivity for the co-owner: 0.686; p value <0.001). Furthermore, the effect of this second-order construct on the dependent variable is positive and significant (beta-coefficient: 0.235; p value <0.01). Future research could therefore use this second-order construct as a starting point to conduct research in that area.

  2. 2)

    The results indicate that perceived information sensitivity for the co-owner becomes less important when perceived information sensitivity for the original owner is high. This calls for the examination of a non-linear quadratic effect: the effect of perceived information sensitivity for the original owner seems to become more important the higher the perception is. However, our results prove that such an effect is non-significant (p value >0.05). Furthermore, such a non-linear effect should be grounded on a strong theoretical basis which is not the case in such a post-hoc analysis (Hair et al. 2017). However, future research could build on this result and think deeper about possible non-linear effects concerning perceived information sensitivity.

Previous research about perceived information sensitivity

Table 4 Previous research about perceived information sensitivity

Previous research about perceived information sensitivity

Table 5 Items used

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wirth, J., Maier, C., Laumer, S. et al. Perceived information sensitivity and interdependent privacy protection: a quantitative study. Electron Markets 29, 359–378 (2019). https://doi.org/10.1007/s12525-019-00335-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12525-019-00335-0

Keywords

JEL classification

Navigation