The Impact of Gender, Education, and Age on Installing a Proximity Tracing Application: Survey on a German Population

Marina Trkman – University of Ljubljana, Faculty of Public Administration; Gosarjeva ulica 5, 1000 Ljubljana, Slovenia

Abstract: During a crisis such as COVID-19 citizens of countries all over the world were asked to use a proximity tracing application voluntarily and in­stall it on their smartphones. Even though the use of the application in times of the pandemic crises was promoted as crucially important, many citizens re­fused to install it. In this paper, we raised the question of why. Previous litera­ture confirmed the impact of universal UTAUT predictors, namely, social in­fluence, performance expectancy and effort expectancy, on intention to use. However, the impact of the predictors has not yet been confirmed in actual use. We propose a research model to examine the direct influence of the pre­dictors on actual use. Furthermore, we assess if the impact of age, gender and education on PTA’s use behavior is significant. We present our preliminary re­sults on data collected in Germany.

7th International Scientific Conference – EMAN 2023 – Economics and Management: How to Cope With Disrupted Times, Ljubljana, Slovenia, March 23, 2023, SELECTED PAPERS, published by: Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-70-7, ISSN 2683-4510, DOI: https://doi.org/10.31410/EMAN.S.P.2023

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission. 

REFERENCES

Cobelli, N., Cassia, F., & Burro, R. (2021). Factors affecting the choices of adoption/non-adop­tion of future technologies during coronavirus pandemic. Technological Forecasting and Social Change, 169, 120814. https://doi.org/10.1016/j.techfore.2021.120814 

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technol­ogy: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982 

Farrelly, G., Trabelsi, H., & Cocosila, M. (2022). COVID-19 contact tracing applications: An analysis of individual motivations for adoption and use. First Monday. https://doi.org/10.5210/fm.v27i6.12324   

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial last squares structural equation modeling (PLS-SEM). 2nd edition. Sage publications.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Mar­keting Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/mtp1069-6679190202 

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/ebr-11-2018-0203  

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433. https://doi.org/10.1007/s11747-011-0261-6

Hassandoust, F., Akhlaghpour, S., & Johnston, A. C. (2021). Individuals’ privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy cal­culus perspective. Journal of the American Medical Informatics Association, 28(3), 463- 471. https://doi.org/10.1093/jamia/ocaa240 

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Mar­keting Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8 

Lin, J., Carter, L., & Liu, D. (2021). Privacy concerns and digital government: exploring citizen willingness to adopt the COVIDSafe app. European Journal of Information Systems, 1-14. https://doi.org/10.1080/0960085x.2021.1920857 

Mishra, A., Baker-Eveleth, L., Gala, P., & Stachofsky, J. (2023). Factors influencing actual us­age of fitness tracking devices: Empirical evidence from the UTAUT model. Health Mar­keting Quarterly, 40(1), 19-38. https://doi.org/10.1080/07359683.2021.1994170 

Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s Comments: A Critical Look at the Use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402  

Rowe, F. (2020). Contact tracing apps and values dilemmas: A privacy paradox in a neo-lib­eral world. International Journal of Information Management, 55, 102178. https://doi.org/10.1016/j.ijinfomgt.2020.102178 

Sharma, S., Singh, G., Sharma, R., Jones, P., Kraus, S., & Dwivedi, Y. K. (2022). Digital Health Innovation: Exploring Adoption of COVID-19 Digital Contact Tracing Apps. IEEE Trans­actions on Engineering Management, 1-17. https://doi.org/10.1109/tem.2020.3019033 

Trkman, M., Popovič, A., & Trkman, P. (2021). The impact of perceived crisis severity on inten­tion to use voluntary proximity tracing applications. International Journal of Information Management, 61, 102395. https://doi.org/10.1016/j.ijinfomgt.2021.102395 

Trkman, M., Popovič, A., & Trkman, P. (2023). The roles of privacy concerns and trust in volun­tary use of governmental proximity tracing applications. Government Information Quar­terly, 40(1), 101787. https://doi.org/10.1016/j.giq.2022.101787 

Velicia-Martin, F., Cabrera-Sanchez, J.-P., Gil-Cordero, E., & Palos-Sanchez, P. R. (2021). Re­searching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model. PeerJ Computer Science, 7, e316. https://doi.org/10.7717/peerj-cs.316 

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technolo­gy: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540