research paper on aarogya setu app

Demarcating the Privacy Issues of Aarogya Setu App in Covid-19 Pandemic in India: An Exploration into Contact Tracing Mobile Applications from Elaboration Likelihood Model

• Conference paper
• First Online: 16 June 2022
• Cite this conference paper

• Nirmal Acharya   ORCID: orcid.org/0000-0002-2132-5184 8 &
• Abhishek Sharma   ORCID: orcid.org/0000-0001-8203-7568 9  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13333))

Included in the following conference series:

• International Conference on Human-Computer Interaction

1221 Accesses

1 Citations

Demand for contract tracing applications is significantly increasing as governments across the globe are relying on these mobile apps to help combat the spread of the COVID-19 virus. However, while this technology has a potential benefit, there is widespread concern that consumers’ fears around privacy and data protection prevent them from downloading such apps. By focusing on this emerging crisis, in this study, we investigate the potential obstacles imposed by privacy concerns (i.e., the perceived risk of accepting the app permission, the perceived risk of providing the information). This study also investigates the popularity of Aarogya Setu, the Indian government’s COVID-19 app. In doing so, we examine privacy concerns through the theoretical lens of the Elaboration Likelihood Model and explore the download intentions of new users. Using the above dimensions of privacy, we then propose a conceptual framework that depicts the influence of privacy concerns over the download intention of new users. Lastly, this paper provides suggestions to allow the Aarogya Setu to improve its perceived reliability among its users and increase downloads.

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

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Alanzi, T.: A review of mobile applications available in the app and google play stores used during the COVID-19 outbreak. J. Multidiscip. Healthc. 14 , 45 (2021)

Article   Google Scholar  

Angst, C.M., Agarwal, R.: Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS Q. 33 , 339–370 (2009)

Arevalo, F.N.: Decoding the public interest of Aarogya Setu, contact tracing app for managing the COVID19 pandemic in India. In: 2020 IEEE International Symposium on Technology and Society (ISTAS), pp. 508–512 (2020). https://doi.org/10.1109/ISTAS50296.2020.9462225

Bansal, G., Gefen, D.: The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decis. Support Syst. 49 , 138–150 (2010)

Basu, S.: Effective contact tracing for COVID-19 using mobile phones: an ethical analysis of the mandatory use of the aarogya setu application in India. Camb. Q. Healthc. Ethics 30 , 262–271 (2021)

Bhattacherjee, A., Sanford, C.: Influence processes for information technology acceptance: an elaboration likelihood model. MIS Q. 30 (4), 805–825 (2006)

Google Scholar  

Cho, H., Ippolito, D., Yu, Y.W.: Contact tracing mobile apps for COVID-19: privacy considerations and related trade-offs. aXiv preprint arXiv:2003.11511 (2020)

Cucinotta, D., Vanelli, M.: WHO declares COVID-19 a pandemic. Acta Bio-Med.: Atenei Parmensis 91 , 157–160 (2020)

Degirmenci, K.: Mobile users’ information privacy concerns and the role of app permission requests. Int. J. Inf. Manage. 50 , 261–272 (2020)

Dhar, T.: Aarogya Setu-carrying your privacy in your hands? Available at SSRN 3614506 (2020)

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 , 295–316 (2013)

Duan, W., Gu, B., Whinston, A.B.: Informational cascades and software adoption on the internet: an empirical investigation. MIS Q. 33 (1), 23–48 (2009)

Garg, S., Bhatnagar, N., Gangadharan, N.: A case for participatory disease surveillance of the COVID-19 pandemic in India. JMIR Public Health Surveill. 6 , e18795 (2020)

Gu, J., Xu, Y.C., Xu, H., Zhang, C., Ling, H.: Privacy concerns for mobile app download: an elaboration likelihood model perspective. Decis. Support Syst. 94 , 19–28 (2017)

Gupta, M., Abdelsalam, M., Mittal, S.: Enabling and enforcing social distancing measures using smart city and its infrastructures: a COVID-19 use case. arXiv preprint arXiv:2004.09246 (2020)

Gupta, R., Jithendranathan, T.: Fund flows and past performance in Australian managed funds. Account. Res. J. 25 , 131–157 (2012)

Haugtvedt, C.P., Petty, R.E.: Need for cognition and attitude persistence. ACR North American Advances (1989)

Hindu. Data | How safe is Aarogya Setu compared to COVID-19 contact tracing apps of other countries? (2020)

Jennings, R.: TikTok, Explained. Vox (2019)

Jung, Y., Park, J.: An investigation of relationships among privacy concerns, affective responses, and coping behaviors in location-based services. Int. J. Inf. Manage. 43 , 15–24 (2018)

Kelley, P.G., Bresee, J., Cranor, L.F., Reeder, R.W.: A “nutrition label” for privacy. In: Proceedings of the 5th Symposium on Usable Privacy and Security, pp. 1–12 (2009)

Kelly, L., Kerr, G., Drennan, J., Fazal-E-Hasan, S.M.: Feel, think, avoid: testing a new model of advertising avoidance. J. Mark. Commun. 27 (4), 343–364 (2019). https://doi.org/10.1080/13527266.2019.1666902

Krehling, L., Essex, A.: A security and privacy scoring system for contact tracing apps. J. Cybersecur. Priv. 1 , 597–614 (2021)

Krosnick, J.: Attitude strength: an overview. In: Petty, R.E., Krosnick, J.A. (eds.) Attitude Strength: Antecedents and consequences. Erlbaum, Hillsdale (1995)

Liu, J.K., et al.: Privacy-preserving COVID-19 contact tracing app: a zero-knowledge proof approach. IACR Cryptol. ePrint Arch., 2020, 528 (2020)

Lowry, P.B., Moody, G., Vance, A., Jensen, M., Jenkins, J., Wells, T.: Using an elaboration likelihood approach to better understand the persuasiveness of website privacy assurance cues for online consumers. J. Am. Soc. Inform. Sci. Technol. 63 , 755–776 (2012)

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 , 336–355 (2004)

Nagori, V.: “Aarogya Setu”: the mobile application that monitors and mitigates the risks of COVID-19 pandemic spread in India. J. Inf. Technol. Teach. Cases 11 , 66–80 (2021)

Park, D.-H., Lee, J., Han, I.: The effect of on-line consumer reviews on consumer purchasing intention: the moderating role of involvement. Int. J. Electron. Commer. 11 , 125–148 (2007)

Petty, R.E., Cacioppo, J.T.: Attitudes and Persuasion: Classic and Contemporary Approaches. Westview Press, Boulder (1996)

Petty, R.E., Cacioppo, J.T.: Communication and Persuasion: Central and Peripheral Routes to Attitude Change. Springer, Heidelberg (2012)

Priester, J.R., Petty, R.E.: The influence of spokesperson trustworthiness on message elaboration, attitude strength, and advertising effectiveness. J. Consum. Psychol. 13 , 408–421 (2003)

Sengupta, J., Johar, G.V.: Effects of inconsistent attribute information on the predictive value of product attitudes: toward a resolution of opposing perspectives. J. Consum. Res. 29 , 39–56 (2002)

Sharma, A.: The role of IoT in the fight against Covid-19 to restructure the economy. In: Stephanidis, C., et al. (eds.) HCII 2021. LNCS, vol. 13097, pp. 140–156. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90966-6_11

Chapter   Google Scholar  

Singhal, T.: A review of coronavirus disease-2019 (COVID-19). Indian J. Pediatr. 87 , 1–6 (2020)

Spears, J.L.: The effects of notice versus awareness: an empirical examination of an online consumer’s privacy risk treatment. In: 46th Hawaii International Conference on System Sciences, pp. 3229–3238. IEEE, Hawaii (2013)

Susilo, D.: Unlocking the secret of E-loyalty: a study from Tiktok users in China. Int. J. Econ. Bus. Entrep. (IJEBE) 3 , 37–49 (2020)

Tam, K.Y., Ho, S.Y.: Web personalization as a persuasion strategy: an elaboration likelihood model perspective. Inf. Syst. Res. 16 , 271–291 (2005)

Tang, J., Akram, U., Shi, W.: Why people need privacy? The role of privacy fatigue in app users’ intention to disclose privacy: based on personality traits. J. Enterp. Inf. Manage. (2020)

Times, T.E.: Aarogya Setu’s not all that healthy for a person’s privacy (2020)

Wang, D., et al.: Epidemiological characteristics and transmission model of Corona Virus Disease 2019 in China. J. Infect. 80 , e25 (2020)

Wang, L., Hu, H.-H., Yan, J., Mei, M.Q.: Privacy calculus or heuristic cues? The dual process of privacy decision making on Chinese social media. J. Enterp. Inf. Manag. 33 (2019)

WHO. World Health Organization - India Situation Report (2020)

Worldometers. Worldometer: Coronavirus Cases (2022). https://www.worldometers.info/coronavirus/ : Worldometers. Accessed 21 Oct 2021 2020

Wottrich, V.M., van Reijmersdal, E.A., Smit, E.G.: The privacy trade-off for mobile app downloads: the roles of app value, intrusiveness, and privacy concerns. Decis. Support Syst. 106 , 44–52 (2018)

Xu, H., Teo, H.-H., Tan, B.C.Y., Agarwal, R.: Research note—effects of individual self-protection, industry self-regulation, and government regulation on privacy concerns: a study of location-based services. Inf. Syst. Res. 23 , 1342–1363 (2012)

Zwitter, A., Gstrein, O.J.: Big data, privacy and COVID-19–learning from humanitarian expertise in data protection. 5 , 4 (2020)

Download references

Author information

Authors and affiliations.

University of Southern Queensland, Toowoomba, Australia

Nirmal Acharya

Swinburne University of Technology, Melbourne, Australia

Abhishek Sharma

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nirmal Acharya .

Editor information

Editors and affiliations.

San Jose State University, San Jose, CA, USA

Abbas Moallem

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

Acharya, N., Sharma, A. (2022). Demarcating the Privacy Issues of Aarogya Setu App in Covid-19 Pandemic in India: An Exploration into Contact Tracing Mobile Applications from Elaboration Likelihood Model. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2022. Lecture Notes in Computer Science, vol 13333. Springer, Cham. https://doi.org/10.1007/978-3-031-05563-8_28

Download citation

DOI : https://doi.org/10.1007/978-3-031-05563-8_28

Published : 16 June 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-05562-1

Online ISBN : 978-3-031-05563-8

eBook Packages : Computer Science Computer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• IVRS - 1921
• Skip to main content
• A + A A - A A
• Screen Reader

• How Setu Works
• COVID Updates

Media Gallery

How AarogyaSetu Knows about your COVID-19 positive status?

Your Sample is Collected at Lab

Collected Sample is analyzed

Lab uploads the test results to ICMR Portal. This is mandatory for AarogyaSetu to know, if you have tested +ve

if you test +ve, ICMR Shares your details with AarogyaSetu

AarogyaSetu Changes your status to COVID+ve (RED) & initiate contact tracing

Note: Inspite of testing positive for covid-19, if your Aarogyasetu App status is still showing safe, then you should contact the testing lab (from where your test samples were submitted) and check whether your correct details have been uploaded on the ICMR portal by the lab.

#CoWIN Registration with Aarogya Setu App

The complete process of registration of Co-WIN via Aarogya Setu App.

How does Aarogya Setu work?

Aarogya Setu uses contact tracing to record details of all the people you may have come in contact with, as you go about your normal activities. If any one of them, at a later point in time, tests positive for COVID-19, you are immediately informed and proactive medical intervention is arranged for you.

For registration of Aarogyasetu, the individual needs an Indian mobile number operated in India.

Android Version 5 & above and iOS version 10.3 & above

COVID-19 Across India

Download App

Why? Aarogya Setu

One stop solution.

Aarogyasetu has transformed into National Health App, brining you a whole plethora of digital health services powered by Ayushman Bharat Digital Mission (ABDM). Using Aarogyasetu, you can now register for your Ayushman Bharat Health Account (i.e., Digital Health ID) and leverage it for your interaction with participating healthcare providers, and allows you to receive your digital lab reports, prescriptions and diagnosis seamlessly from verified healthcare professionals and health service providers.

Consult your Doctor

Aarogyasetu helps you to schedule online doctor appointments powered by eSanjeevani OPD Application. You can schedule doctor appointments and consult with doctor from the comfort of your home. Your ePrescription will be readily available once synced.

COVID-19 Vaccination

Aarogyasetu helps you to schedule/reschedule/cancel your appointments for covid-19 vaccination. Using Aarogyasetu you can download the vaccination certificate or can request for changes in certificate as well. Your certificate will be readily available once synced.