What Does Google Opinion Rewards Require and Get from Users?

Authors

DOI:

https://doi.org/10.51480/1899-5101.16.1(33).5

Keywords:

communication, digital surveillance, counter-surveillance, data sharing, mobile apps

Abstract

This article focuses on the mobile app called “Google Opinion Rewards” (GOR), which is used as a data collection tool in market research and academic research. Developed by Google Surveys, GOR deals with voluntary participation of app users in data sharing in return for rewards. In-depth interviews were conducted with 12 participants from the USA, the UK and Turkey to gain comprehensive knowledge about the app ecosystem. The aim of the interviews was to understand the motivations of GOR users for using the app, and explore the counter-surveillance strategies users have developed to avoid surveillance. The findings indicate that most GOR users share their information recklessly even if they have security concerns and that users who are actively involved in surveillance, knowingly or unknowingly, and who want to maximise their income develop masking strategies against surveillance.

Author Biographies

Hasan Cem Çelik, Akdeniz University

Hasan Cem Çelik is a PhD graduate from the Faculty of Communication at Akdeniz University, Turkey. His research focuses on communication sciences, new media, radio-television and cinema.

Ömür Talay, Akdeniz University

Ömür Talay, a PhD candidate at the Faculty of Communication, Akdeniz University, Turkey. His research focuses on communication sciences, digital media, critical data studies, and advertising.

References

Afolabi, O., Adeshola, I., Ozturen, A., and Ilkan, M. (2020). The influence of context on privacy concern in smart tourism destinations. PEOPLE: International Journal of Social Sciences, 6(1), 282–293.

Alfnes, F., & Wasenden, O. C. (2022). Your privacy for a discount? Exploring the willingness to share personal data for personalized offers. Telecommunications Policy, 46(7), 1–10.

Arp, D., Quiring, E., Wressneger, C., and Rieck, K. 2017. Privacy threats through ultrasonic side channels on mobile devices. IEEE European Symposium on Security and Privacy (pp. 35–47).

Ataman, B., & Çoban, B. (2018). Counter-surveillance and alternative new media in Turkey. Information, Communication & Society, 21(7), 1014–1029.

Barnes, S. (2006). A privacy paradox: social networking in the United States. doi:10.5210/fm.v11i9.1394

Bauer, C., & Strauss, C. (2016). Location-based advertising on mobile devices. Management Review Quarterly, 66(3), 159–194.

Bauman, Z., & Lyon, D. (2013). Liquid surveillance: A conversation. John Wiley & Sons.

Benndorf, V., & Normann, H. T. (2018). The willingness to sell personal data. The Scandinavian Journal of Economics, 120(4), 1260–1278.

Book, T., & Wallach, D. S. (2015). An empirical study of mobile ad targeting. Accessed 11 February 2021. https://arxiv.org/abs/1502.06577

Burton, F. (2007). The secerts of counter-survelliance. Strafor global intelligence. Accessed 20 February 2021. https://worldview.stratfor.com/article/secrets-countersurveillance

Carnegie Mellon University, (2020). Self-reported COVID-19 symptoms show promise for disease forecasts. Accessed 20 February 2021. https://www.cmu.edu/news/stories/archives/2020/april/self-reported-covid-19-symptoms-disease-forecasts.html

Castelluccia, C., Guerses, S., Hansen, M., Hoepman, Jaap-Henk., Hoboken, J., and Vieira, B. (2017). Privacy and data protection in mobile applications: A study on the app development ecosystem and the technical implementation of GDPR Accessed 15 April 2021. https://pure.uva.nl/ws/files/42887337/22302384.pdf

Cecere, G., Le Guel, F., & Lefrere, V. (2020). Economics of free mobile apps: Personal data and third parties. Accessed 10 January 2021. https://ssrn.com/abstract=3136661

Cornesse, C., & Bosnjak, M. (2018). Is there an association between survey characteristics and representativeness? A meta-analysis. Survey Research Methods, 12(1), 1–13.

Dave, P. (2020). Google asks users about symptoms for Carnegie Mellon coronavirus forecasting effort. Accessed 10 February 2021. https://www.reuters.com/article/us-health-coronavirus-google-idUSKBN21B09Q

Demotriou, S., Merrill, W., Yang, W., Zhang, A., & Gunter, C. A. (2016). Annual Network and Distributed System Security Symposium. 1–15. Accessed 12 February 2021. http://youngwei.com/publication/pluto/

Donalek, J. G. (2004). Phenomenology as a qualitative research method. Urologic Nursing, 24(6), 516–517.

Fernandes, E. R., & Oliveira, J. V. D. (2020). Quanto valem seus dados? O caso Google Opinion Rewards. Revista de Direito e as Novas Tecnologias, 7.

Future of Privacy Forum, 2016. FPF mobile aaps study. Accessed 19 March 2021. https://fpf.org/wp-content/uploads/2016/08/2016-FPF-Mobile-Apps-Study_final.pdf

Gerber, N., Gerber, P., and Volkamer, M. (2018). Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior. Computers & Security, 77, 226–261.

Gibler, C., Crussell, J., Erickson, J., & Chen, H. (2012). Androidleaks: Automatically detecting potential privacy leaks in android applications on a large scale. In Trust and Trustworthy Computing: 5th International Conference, TRUST 2012, Vienna, Austria, June 13–15, 2012. Proceedings 5 (pp. 291–307). Springer Berlin Heidelberg.

Google Play Store. (2020). Google Opinion Rewards. Accessed 20 February 2021. https://play.google.com/store/apps/details?id=com.google.android.apps.paidtasks&hl=tr&gl=US

Google Surveys Help. (2020). Targeting to Google Opinion Rewards. Accessed 01 February 2021.https://support.google.com/consumersurveys/answer/6013193?hl=en&ref_topic=6194671#zippy=%2Cdetermining-google-opinion-rewards-users-languages%2Cexpected-sampling-biases%2Cdifferences-by-country

Google Surveys Help. (2021). Types of questions. Accessed 01 February 2021. https://support.google.com/consumersurveys/answer/2446120?hl=en&ref_topic=6194671

Groenewald, T. (2004). A phenomenological research design illustrated. International Journal of Qualitative Methods, 3(1), 42–55.

Gurria, A. (2008). Ministerial Meeting on the future of the internet economy. Accesed March 2021.https://www.oecd.org/fr/sti/closingremarksbyangelgurriaoecdministerialmeetingonthefutureoftheinterneteconomy.htm

Harbach, M., DeLuca, A., Malkin, N., & Egelman, S. (2016). Keep on lockin’ in the free world: A multi-national comparison of smartphone locking. CHI ‚16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 4823–4827.

Ham, Chang-Dae. (2016). Exploring how consumers cope with online behavioral advertising. Journal of Advertising, 36(4), 632–658.

Han, M., Shen, S., Zhou, Y., Xu, Z., Miao, T., & Qi, J. (2019). An analysis of the cause of privacy paradox among SNS users: Take Chinese college students as an example.

Hogan, C., Atta, M., Anderson, P., Stead, T., Solomon, M., Banerjee, P., Sleigh, B., Shivdat, J., McAdams, A. W., & Ganti, L. (2020). Knowledge and attitudes of us adults regarding COVID-19. International Journal of Emergency Medicine, 53, 1–6.

Humby, C. (2006). Data is the new oil. Accessed 24 April 2021. http://ana.blogs.com/maestros/2006/11/data_is_the_new.html.

Hyrynsalmi, S., Suominen, A., Mäkilä, T., Järvi, A., & Knuutila, T. (2012). Revenue models of application developers in android market ecosystem. In Software Business: Third International Conference, ICSOB 2012, Cambridge, MA, USA, June 18–20, 2012. Proceedings 3 (pp. 209–222). Springer Berlin Heidelberg.

Jorgensen, Z., Chen, J., Gates, C. S., Li, N., Proctor, R. W., & Yu, T. (2015). Dimensions of risk in mobile applications: A user study. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy (pp. 49–60).

Jung, J., Shim, S. W., Jin, H. S., and Khang, H. (2015). Factors affecting attitudes and behavioral intention towards social networking advertising: A case of Facebook users in South Korea. Journal of Advertising, 35(2), 248–265.

Kadivar, J. (2015). Government Surveillance and Counter-Surveillance on Social and Mobile Media: The Case of Iran (2009). M/C Journal, 18(2).

Kanyadan, V., & Ganti, L. (2019). E-cigarette awareness among young adults. Palo Alto, 11(7), 1–10.

Karafiloski, E., & Mishev, A. (2017). Blockchain solutions for big data challenges: A literature review. In IEEE EUROCON 2017 – 17th International Conference on Smart Technologies (pp. 763–768). IEEE.

Kokolakis, S. 2017. Privacy attitudes and privacy behavior: A review of current research on the privacy paradox phenomenon. Computers & Security, 64, 122–134.

Kornstein, H. (2019). Under her eye: Digital drag as obfuscation and countersurveillance. Surveillance & Society, 17(5), 681–698.

Lazzarato, M. (1996). Immaterial labor. Trans. P. Callili and E. Emory. In Radical thought in Italy: A potential politics, ed. M. Hardt and P. Virno. Minneapolis: University of Minnesota Press.

Leontiadis, I., Efstratiou, C., Picone, M., & Mascolo, C. (2012). Don’t kill my ads! balancing privacy in an ad-supported mobile application market. In Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications (pp. 1–6).

Lyon, D. (2001). Surveillance society: Monitoring everyday life (Issues in society). Open University Press.

Lyon, D. (2007). Surveillance studies: An overview. Cambridge: Polity Press.

Marx, G. T. (2003). A Tack in the shoe: Neutralizing and resisting. Journal of Social Issues, 59(2), 369–390.

Meng, W., Ding, R., Chung, S. P., Han, S., & Lee, W. (2016). The price of free: privacy leakage in personalized mobile in-app ads. Conference: Network and Distributed System Security Symposium, (pp. 1–15).

Nath, S. (2015). Madscope: Characterizing mobile in-app targeted ads. MobiSys ‚15: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services May 2015, (pp. 59–73). doi:10.1145/2742647.2742653

Pew Research Center. (2021). Questionnaire design. Accessed 10 April 2021. https://www.pewresearch.org/methods/u-s-survey-research/questionnaire-design/

Polykalas, S. E., and Prezerakos, G. N. (2019). When the mobile app is freethe products is your personal data. Digital Policy, Regulation and Government, 21(2), 89–101.

Prince, J. T., & Wallsten, S. (2022). How much is privacy worth around the world and across platforms? Journal of Economics & Management Strategy, 31(4), 841–861.

Qui, J. L. (2014). Goodbye iSlave: Foxconn, digital capitalism, and networked labor resistance. Society: Chinese Journal of Sociology/Shehui, 34(4), 119–137.

Razaghpanah, A., Nithyanand, R., Vallina-Rodriguez, N., Sundaresan, S., Allman, M., Kreibich, C., & Gill, P. (2018). Apps, trackers, privacy, and regulators: A global study of the mobile tracking ecosystem. The 25th Annual Network and Distributed System Security Symposium 1–15. San Diego.

Ruktanonchai, N. W., Ruktanonchai, C. W., Floyd, J. R., & Tatem, A. J. (2018). Using Google location history data to quantify fine-scale human mobility. International. Journal of Health Geographics, 17(1), 1–13. doi:10.1186/s12942-018-0150-z

Sanchez, F. J. S., Aguado, J. M., and Martinez, I. (2019). Privacy paradox in the mobile environment: The influence of the emotions. Professional de la Informacion, 28(2), 1–11.

Sandelowski, M. (2000). Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed‐method studies. Research in nursing & health, 23(3), 246–255.

Segijn, C. E., Voorveld, H., and Vakeel, K. A. (2021). The role of ad sequence and privacy concerns in personalized advertising: An eye-tracking study into synced advertising effects. Journal of Advertising, 50(3), 320–329.

Sell, R., Goldberg, S., & Conron, K. (2015). The utility of an online convenience panel for reaching rare and dispersed populations. PLoS ONE 10(12), 1–10.

Shoaibi, D. A., & Rassan, I. A. (2012). Mobile advertising using location-based services. 2012 IEEE First International Conference on Internet Operating Systems. doi:10.1109/icios.2012.15

Shklovski, I., Mainwaring, S. D., Skúladóttir, H. H., and Borgthorsson, H. 2014. Leakiness and creepiness in app space. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems-CHI ’14. doi:10.1145/2556288.255742

Statista. (2021). Distribution of free and paid apps in the Apple App Store and Google Play as of Accessed 16 March 2021. https://www.statista.com/statistics/263797/number-of-applications-for-mobile-phones/

Taddicken, M. (2014). The ‘privacy paradox’ in the social web: the impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self-disclosure. Comput-Med Commun, 19(2), 248–273.

Tay, S. W., Teh, P. S., and Payne, S. J. (2021). Reasoning about privacy in mobile application install decisions: Risk perception and framing. International Journal of Human-Computer Studies, 145, 1–11.

Turow, J. (2011). The daily you: How the new advertising industry is defining your identity and your worth. Yale University Press.

Ullah, I., Boreli, R., Kaafar, M. A., & Kanhere, S. S. (2014). Characterising user targeting for in-app mobile ads. IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) 547–552.

Wenz, A., Jäckle, A., and Couper, M. P. 2019. Willingness to use mobile technologies for data collection in a probability household panel. Survery Research Methods, 13(1), 1–22.

Wilson, D. (2012). Counter-Surveillance: Protest and Policing. Plymouth Law and Criminal Justice Review, 4, 33–42.

Winegar, A. G., & Sunstein, C. R. (2019). How much is data privacy worth? A preliminary investigation. Journal of Consumer Policy, 42, 425–440.

Zhang, Y., Yang, M., Xu, B., Yang, Z., Gu, G., Ning, P., Wang, S. X., & Zang, B. (2013). Vetting undesirable behaviors in android apps with permission use analysis. CCS ‚13: Proceedings of the 2013 ACM SIGSAC Conference on Computer & communications security (pp. 611–622).

Zhou, Y., Zhang, X., Jiang, X., & Freeh, V. W. (2011). Taming information-stealing smartphone applications (on android). In Trust and Trustworthy Computing: 4th International Conference, TRUST 2011, Pittsburgh, PA, USA, June 22–24, 2011. Proceedings 4 (pp. 93–107). Springer Berlin Heidelberg.

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power: Barack Obama’s books of 2019. Profile books.

Downloads

Published

2023-10-17

How to Cite

Çelik, H. C., & Talay, Ömür. (2023). What Does Google Opinion Rewards Require and Get from Users?. Central European Journal of Communication, 16(1(33), 79-100. https://doi.org/10.51480/1899-5101.16.1(33).5

Issue

Section

Scientific Papers