Abstract
Tools that detect and transform privacy sensitive information in user content have been proposed to enhance privacy in contexts such as social media. However, previous research has found that privacy-related concerns can be higher in these types of tools compared to similar non-privacy tools. In this paper, we focus on adoption of these tools and investigate how the knowledge that a data-processing tool has a privacy purpose affects privacy-related factors of intention to use such a tool, when compared with a similar tool with a non-privacy-related purpose. We conducted a user study where we described a privacy-enhancing and a non-privacy-enhancing photo manipulation app to two groups of participants. The results show that general and context-specific privacy-related perception has different effects for the two types of apps. In particular, although participants perceived the same level of privacy risk towards both types of apps, this risk only had a significant negative effect on intention to use in the case of the privacy-enhancing app. Furthermore, disposition to value privacy increased both perceived risk and intention to use the privacy-enhancing app. We discuss these findings in the context of the diffusion of privacy-enhancing tools for user content.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bracamonte, V., Pape, S., Loebner, S.: “All apps do this”: comparing privacy concerns towards privacy tools and non-privacy tools for social media content. Proc. Priv. Enhancing Technol. 2022(3) (2022)
Bracamonte, V., Tesfay, W.B., Kiyomoto, S.: Towards exploring user perception of a privacy sensitive information detection tool. In: 7th International Conference on Information Systems Security and Privacy (2021)
Corner, M., Dogan, H., Mylonas, A., Djabri, F.: A usability evaluation of privacy add-ons for web browsers. In: Marcus, A., Wang, W. (eds.) HCII 2019. LNCS, vol. 11586, pp. 442–458. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23535-2_33
Dijkstra, T.K., Henseler, J.: Consistent partial least squares path modeling. MIS Q. 39(2), 297–316 (2015)
Dinev, T., McConnell, A.R., Smith, H.J.: Research commentary—informing privacy research through information systems, psychology, and behavioral economics: thinking outside the “APCO’’ box. Inf. Syst. Res. 26(4), 639–655 (2015). https://doi.org/10.1287/isre.2015.0600
Dinev, T., Xu, H., Smith, J.H., Hart, P.: Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. Eur. J. Inf. Syst. 22(3), 295–316 (2013). https://doi.org/10.1057/ejis.2012.23
Hair, J., Hult, G.T.M., Ringle, C., Sarstedt, M., Danks, N., Ray, S.: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (2021)
Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M.: When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. (2019)
Harborth, D., Pape, S.: How privacy concerns and trust and risk beliefs influence users’ intentions to use privacy-enhancing technologies – the case of tor. In: 52nd Hawaii International Conference on System Sciences (HICSS) 2019, pp. 4851–4860 (2019). https://scholarspace.manoa.hawaii.edu/handle/10125/59923
Harborth, D., Pape, S.: How privacy concerns, trust and risk beliefs and privacy literacy influence users’ intentions to use privacy-enhancing technologies - the case of tor. ACM SIGMIS Database: DATABASE Adv. Inf. Syst. 51(1), 51–69 (2020). https://doi.org/10.1145/3380799.3380805. https://dl.acm.org/doi/abs/10.1145/3380799.3380805
Harborth, D., Pape, S., Rannenberg, K.: Explaining the technology use behavior of privacy-enhancing technologies: the case of tor and Jondonym. Proc. Priv. Enhancing Technol. (PoPETs) 2020(2), 111–128 (2020). https://doi.org/10.2478/popets-2020-0020. https://content.sciendo.com/view/journals/popets/2020/2/article-p111.xml
Hasan, R., Crandall, D., Fritz, M., Kapadia, A.: Automatically detecting bystanders in photos to reduce privacy risks. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 318–335 (2020). https://doi.org/10.1109/SP40000.2020.00097
Henseler, J.: PLS-MGA: a non-parametric approach to partial least squares-based multi-group analysis. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds.) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. STUDIES CLASS, pp. 495–501. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24466-7_50
Henseler, J., Ringle, C.M., Sarstedt, M.: Testing measurement invariance of composites using partial least squares. Int. Mark. Rev. (2016)
Jarvenpaa, S.L., Tractinsky, N., Saarinen, L.: Consumer trust in an internet store: a cross-cultural validation. J. Comput.-Mediated Commun. 5(2), JCMC526 (1999). https://doi.org/10.1111/j.1083-6101.1999.tb00337.x
Kang, R., Brown, S., Dabbish, L., Kiesler, S.: Privacy attitudes of mechanical Turk workers and the U.S. public. In: 10th Symposium on Usable Privacy and Security (SOUPS 2014), pp. 37–49 (2014)
Klesel, M., Schuberth, F., Henseler, J., Niehaves, B.: A test for multigroup comparison using partial least squares path modeling. Internet Res. 29(3), 464–477 (2019). https://doi.org/10.1108/IntR-11-2017-0418
Kock, N., Hadaya, P.: Minimum sample size estimation in PLS-SEM: the inverse square root and gamma-exponential methods. Inf. Syst. J. 28(1), 227–261 (2018)
Kokolakis, S.: Privacy attitudes and privacy behaviour: a review of current research on the privacy paradox phenomenon. Comput. Secur. 64, 122–134 (2017)
Korayem, M., Templeman, R., Chen, D., Crandall, D., Kapadia, A.: Enhancing lifelogging privacy by detecting screens. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 4309–4314. Association for Computing Machinery (2016). https://doi.org/10.1145/2858036.2858417
Li, Y., Vishwamitra, N., Knijnenburg, B.P., Hu, H., Caine, K.: Effectiveness and users’ experience of obfuscation as a privacy-enhancing technology for sharing photos. Proc. ACM Hum.-Comput. Interact. 1(CSCW), 67:1–67:24 (2017). https://doi.org/10.1145/3134702
Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
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). https://doi.org/10.1287/isre.1040.0032
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)
Ringle, C.M., Wende, S., Becker, J.M.: SmartPLS 3 (2015)
Sarstedt, M., Hair, J.F., Ringle, C.M., Thiele, K.O., Gudergan, S.P.: Estimation issues with PLS and CBSEM: where the bias lies! J. Bus. Res. 69(10), 3998–4010 (2016)
Schaub, F., et al.: Watching them watching me: browser extensions impact on user privacy awareness and concern. In: Proceedings 2016 Workshop on Usable Security. Internet Society (2016). https://doi.org/10.14722/usec.2016.23017
Sleeper, M., et al.: “I read my Twitter the next morning and was astonished”: a conversational perspective on Twitter regrets. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 3277–3286. Association for Computing Machinery (2013). https://doi.org/10.1145/2470654.2466448
Smith, H.J., Milberg, S.J., Burke, S.J.: Information privacy: measuring individuals’ concerns about organizational practices. MIS Q. 20(2), 167–196 (1996). https://doi.org/10.2307/249477
Spiel, K., Haimson, O.L., Lottridge, D.: How to do better with gender on surveys: a guide for HCI researchers. Interactions 26(4), 62–65 (2019). https://doi.org/10.1145/3338283
Venkatesh, V., Thong, J.Y., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 157–178 (2012)
Wang, Y., Norcie, G., Komanduri, S., Acquisti, A., Leon, P.G., Cranor, L.F.: “I regretted the minute I pressed share”: a qualitative study of regrets on Facebook. In: Proceedings of the Seventh Symposium on Usable Privacy and Security, SOUPS 2011, pp. 1–16. Association for Computing Machinery (2011). https://doi.org/10.1145/2078827.2078841
Xu, H., Dinev, T., Smith, J., Hart, P.: Information privacy concerns: linking individual perceptions with institutional privacy assurances. J. Assoc. Inf. Syst. 12(12) (2011). https://doi.org/10.17705/1jais.00281
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Bracamonte, V., Pape, S., Löbner, S. (2024). Factors of Intention to Use a Photo Tool: Comparison Between Privacy-Enhancing and Non-privacy-enhancing Tools. In: Meyer, N., Grocholewska-Czuryło, A. (eds) ICT Systems Security and Privacy Protection. SEC 2023. IFIP Advances in Information and Communication Technology, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-031-56326-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-56326-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56325-6
Online ISBN: 978-3-031-56326-3
eBook Packages: Computer ScienceComputer Science (R0)