Covid-19 Contact Tracing Application Adoption: A Technology Readiness Model Perspective

  • Conference paper
  • First Online:
Innovations and Interdisciplinary Solutions for Underserved Areas (InterSol 2023)

Abstract

In late December 2019, there was an unforeseen outbreak of Coronavirus (COVID-19), which resulted in a global pandemic that claimed millions of lives. To allow individuals to travel freely and the economy to recover, government officials needed to be able to swiftly detect potential COVID-19 situations and track prospective encounters. There are numerous methods for doing contact tracing, one of them is to use contact tracking application. The COVID-19 contact tracing application allows for the tracking of people who encounter individuals who have COVID-19, regardless of where they are. The purpose of the research was to investigate the adoption of contact tracking applications through the theory of technology readiness in Nigeria. A cross-sectional survey was carried out using a non-probability sampling technique. Online questionnaires were sent via social media and email, with a total of 145 individuals taking part in the study. The data collected were analyzed using partial least squares (PLS) utilizing the SmartPLS-3 software to test the hypothesis generated by the research model presented in the study. The results obtained from the data collection and analysis revealed that six of the presented hypotheses were supported. Innovativeness was found to be strongly related to perceived usefulness while discomfort has a negative effect on perceived ease of use and usefulness. Implications of those findings are further discussed.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 64.19
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 79.11
Price includes VAT (France)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Hopkins, J.: Coronavirus Resource Center (2021). https://coronavirus.jhu.edu/map.html. Accessed 07 Aug 2021

  2. Ferguson, N., Laydon, D., Nedjati Gilani, G., et al.: Report 9: Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID19 Mortality and Healthcare Demand, Imperial College London (2020)

    Google Scholar 

  3. Sun, K., Viboud, C.: Impact of Contact Tracing on SARS-CoV-2 Transmission. Lancet Infect Dis (2020)

    Google Scholar 

  4. WHO. Contact tracing in the context of COVID-19. https://www.who.int/publications/i/item/contact-tracing-in-the-context-of-covid-19. Accessed 9 July 2021

  5. Alsdurf, H., et al.: COVID white paper. ar**v preprint ar**v:2005.08502 (2020)

  6. Ahmed, N., et al.: A survey of COVID-19 contact tracing apps. IEEE Access (2020). https://doi.org/10.1109/ACCESS

  7. Kleinman, R.A., Merkel, C.: Digital contact tracing for COVID-19. CMAJ: Can. Med. Assoc. Journal1⁄4Journal de L’Association Medicale Canadienne 192(24), E653–E656 (2020)

    Google Scholar 

  8. Vaughan, A.: The problems with contact-tracing apps. New Sci. (2020)

    Google Scholar 

  9. Eames, K.T.D., Keeling, M.J.: Contact tracing and disease control. Proceed. Roy. Soc. Lond. Ser. B 270(1533), 2565–2571 (2003)

    Article  Google Scholar 

  10. Shahroz, M., et al.: COVID-19 digital contact tracing applications and techniques: a review post initial deployments. Transp. Eng. 5, 100072 (2021)

    Article  Google Scholar 

  11. Lallmahomed, M.Z.I., Ab. Rahim, N.Z., Ibrahim, R., Rahman, A.A.: A preliminary classification of usage measures in information system acceptance: a Q-sort approach. Int. J. Technol. Diffus. 2(4), 25–47 (2011)

    Google Scholar 

  12. Straub, E.T.: Understanding technology adoption: theory and future directions for informal learning’. Rev. Educ. Res. 79(2), 625–649 (2009)

    Article  Google Scholar 

  13. Rogers, E.: Diffusion of Innovations. Free Press, New York (1983)

    Google Scholar 

  14. Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)

    Google Scholar 

  15. Davis, F.D.: Technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation. Sloan School of Management, Institute of Technology: Massachusetts (1986)

    Google Scholar 

  16. Venkatesh, V., David, F.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000)

    Article  Google Scholar 

  17. Parasuraman, A.: Technology readiness index (TRI): a multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res. 2(4), 307–320 (2000)

    Article  Google Scholar 

  18. Lin, C., Sher, H., Wang, Y.: Consumer adoption of e-service: integrating technology readiness with the technology acceptance model. In: Technology Management: A Unifying Discipline for Melting the Boundaries, pp. 483–488 (2005)

    Google Scholar 

  19. Roy, S.K., Balaji, M.S., Quazi, A., Quaddus, M.: Predictors of customer acceptance of and resistance to smart technologies in the retail sector. J. Retail. Consum. Serv. 42, 147–160 (2018)

    Article  Google Scholar 

  20. Parasuraman, A., Colby, C.L.: Techno-Ready Marketing: How and Why Your Customers Adopt Technology, vol. 224. Free Press, New York (2001)

    Google Scholar 

  21. Lin, C., Shih, H., Sher, P.: Integrating technology readiness into technology acceptance: the TRAM model. Psychol. Mark. 24(7), 641–657 (2007)

    Article  Google Scholar 

  22. Lin, J., Chang, H.: The role of technology readiness in self-service technology acceptance. Manag. Serv. Qual. Int. J. 21(4), 424–444 (2011)

    Article  Google Scholar 

  23. Godoe, P., Johansen, T.S.: Understanding adoption of new technologies: technology readiness and technology acceptance as an integrated concept. J. Eur. Psychol. Stud. 3(1), 38–52 (2012)

    Article  Google Scholar 

  24. Walczuch, R., Lemmink, J., Streukens, S.: The effect of service employees’ technology readiness on technology acceptance. Inf. Manag. 44, 206–215 (2007)

    Article  Google Scholar 

  25. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  26. Parasuraman, A., Colby, C.L.: An updated and streamlined technology readiness index: TRI 2.0. J. Serv. Res. 18(1), 59–74 (2015)

    Article  Google Scholar 

  27. Garcia, R., Calantone, R.: A critical look at technological innovation typology and innovativeness terminology: a literature review. J. Prod. Innov. Manag. Int. Publ. Prod. Dev. Manag. Assoc. 19(2), 110–132 (2002)

    Article  Google Scholar 

  28. Saunders, M., Lewis, P., Thornhill, A.: Research Methods for Business Students, 7th edn. Pearson Education, Nueva York (2016)

    Google Scholar 

  29. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate Data Analysis. Prentice Hall, Upper Saddle River (1998)

    Google Scholar 

  30. Mukherjee, A., Hoyer, W.D.: The effect of novel attributes on product evaluation. J. Consum. Res. 28(3), 462–472 (2001)

    Article  Google Scholar 

  31. Straub, D., Boudreau, M.C., Gefen, D.: Validation guidelines for IS positivist research. Commun. Assoc. Inf. Syst. 13(1), 380–427 (2004)

    Google Scholar 

  32. Ringle, C.M., Wende, S., Becker, J.M.: SmartPLS 3. Oststeinbek: SmartPLS (2020)

    Google Scholar 

  33. Hair, J.F., Bush, R.P., Ortinau, D.J.: Marketing Research: Within a Changing Information Environment. McGraw-Hill/Irwin, New York (2003)

    Google Scholar 

  34. Fornell, C., Larcker, D.: Structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)

    Article  Google Scholar 

  35. Kwang, N.A., Rodrigues, D.: A Big-Five Personality profile of the adaptor and innovator. J. Creative Behav. 36(4), 254–268 (2002)

    Article  Google Scholar 

  36. Brancheau, J.C., Wetherbe, J.C.: The adoption of spreadsheet software: testing innovation diffusion theory in the context of end-user computing. Inf. Syst. Res. 1(2), 115–143 (1990)

    Article  Google Scholar 

  37. Schepers, J., Wetzels, M.: A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Inf. Manag. 44(1), 90–103 (2007)

    Article  Google Scholar 

  38. Venkatesh, V.: Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11(4), 342–365 (2000)

    Article  Google Scholar 

  39. Vogelsang, K., Steinhüser, M., Hoppe, U.: A qualitative approach to examine technology acceptance (2013)

    Google Scholar 

  40. Rahmat, T.E., Raza, S., Zahid, H., Abbas, J., Sobri, F.A.M., Sidiki, S.N.: Nexus between integrating technology readiness 2.0 index and students’ e-library services adoption amid the COVID-19 challenges: implications based on the theory of planned behavior. J. Educ. Health Promot. 11 (2022)

    Google Scholar 

  41. Alharbi, A., Osama, S.: Technology readiness and cryptocurrency adoption: PLS-SEM and deep learning neural network analysis. IEEE Access 9 (2021)

    Google Scholar 

  42. Chiu, W., Cho, H.: The role of technology readiness in individuals’ intention to use health and fitness applications: a comparison between users and non-users. Asia Pac. J. Mark. Logist. 33(3), 807–825 (2021)

    Article  Google Scholar 

  43. Nugroho, M.A., Fajar, A.: Effects of technology readiness towards acceptance of mandatory web-based attendance system. Procedia Comput. Sci. 124, 319–328 (2017)

    Article  Google Scholar 

  44. Long, M.A.: Understanding non-adopters’ intention to use internet pharmacy: revisiting the roles of trustworthiness, perceived risk and consumer traits. J. Eng. Technol. Manag. 59 (2021)

    Google Scholar 

  45. Wang, X., Wong, Y.D., Chen, T., Yuen, K.F.: Adoption of shopper-facing technologies under social distancing: a conceptualisation and an interplay between task-technology fit and technology trust. Comput. Hum. Behav. 124 (2021)

    Google Scholar 

  46. Flavián, C., Pérez-Rueda, A., Belanche, D., Casaló, L.V.: Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. J. Serv. Manag. 33(2), 293–320 (2022)

    Article  Google Scholar 

  47. Abu-Shanab, E.: E-government familiarity influence on Jordanians’ perceptions. Telematics Inform. 34(1), 103–113 (2017)

    Article  Google Scholar 

  48. Abu-Shanab, E.: Antecedents of trust in e-government services: an empirical test in Jordan. Transforming Gov. People Process Policy 8(4), 480–499 (2014)

    Google Scholar 

  49. Lallmahomed, M.Z., Lallmahomed, N., Lallmahomed, G.M.: Factors influencing the adoption of e-Government services in Mauritius. Telematics Inform. 34(4), 57–72 (2017)

    Article  Google Scholar 

  50. Krasnova, H., Spiekermann, S., Koroleva, K., Hildebrand, T.: Online social networks: why we disclose. J. Inf. Technol. 25(2), 109–125 (2010)

    Article  Google Scholar 

  51. Harborth, D., Pape, S.: A privacy calculus model for contact tracing apps: analyzing the use behavior of the German corona-warn-app with a longitudinal user study. Comput. Secur. (2023)

    Google Scholar 

  52. O’Callaghan, M.E., et al.: A national survey of attitudes to COVID-19 digital contact tracing in the republic of Ireland. Ir. J. Med. Sci. (2020)

    Google Scholar 

  53. Altmann, S., Milsom, L., Zillessen, H., Blasone, R., at al.: Acceptability of app-based contact tracing for COVID-19: cross-country survey study. JMIR mHealth uHealth 8(8) (2020)

    Google Scholar 

  54. Bonner, M., Naous, D., Legner, C., Wagner, J.: The (lacking) user adoption of COVID-19 contact tracing apps–insights from Switzerland and Germany. In: Proceedings of the 15th Pre-ICIS Workshop on Information Security and Privacy, vol. 1 (2020)

    Google Scholar 

  55. Van Slyke, C., Ilie, V., Lou, H., Stafford, T.: Perceived critical mass and the adoption of a communication technology. Eur. J. Inf. Syst. 16(3), 270–283 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Z. I. Lallmahomed .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Olaegbe, A.T., Lallmahomed, M.Z.I. (2024). Covid-19 Contact Tracing Application Adoption: A Technology Readiness Model Perspective. In: Seeam, A., Ramsurrun, V., Juddoo, S., Phokeer, A. (eds) Innovations and Interdisciplinary Solutions for Underserved Areas. InterSol 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-031-51849-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-51849-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51848-5

  • Online ISBN: 978-3-031-51849-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Navigation