A Conceptual Framework of COVID-19 Location-Based Mobile Applications.
Main Article Content
Abstract
A leading problem that threatens the global economy and security of countries is the lack of immediate action and readiness to face a common threat. In addition to the mass vaccination and quarantine measures implemented as an emergency response to the rise of COVID-19, information systems and telecommunications were also utilized in the form of location-based mobile applications to control the mobility of global population, for contact tracing, and for compliance with quarantine measures. Early implementations of code and software in the form of location-based mobile apps as an emergency response, failed to face the waves of coronavirus due to the low adoption rates of smartphone users that led to minimization of citizens’ freedom. The scope of this paper is to examine the phenomenon of adoption of COVID-19 location-based mobile applications by the users of smartphones. This paper builds a novel conceptual framework for the factors that affect the adoption of COVID-19 location-based mobile applications.
Keywords:
Location-based mobile applications, Contact-tracing apps, COVID-19, Coronavirus
Article Details
How to Cite
OIKONOMIDIS, Theodoros; FOUSKAS, Konstantinos; VLACHOPOULOU, Maro.
A Conceptual Framework of COVID-19 Location-Based Mobile Applications..
Medical Research Archives, [S.l.], v. 12, n. 6, june 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5557>. Date accessed: 02 july 2024.
doi: https://doi.org/10.18103/mra.v12i6.5557.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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31. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989;13 (3):319-340. doi:10.2307/249008
32. Leong CY, Perumal T, Peng KW, Yaakob R. Enabling Indoor Localization With Internet of Things (IoT). In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE). IEEE; 2018:571-573. doi:10.1109/GCCE.2018 .8574489
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35. Lewis J. GPRS roaming the GRX way. Telecommun Int. 2003;37(2):32-33+38.
36. Venkatesh V, Thong JY, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. Published online 2012:157-178.
37. Crabbe M, Standing C, Standing S, Karjaluoto H. An adoption model for mobile banking in Ghana. Int J Mob Commun. 2009;7(5):515-543.
38. Fagan MH, Neill S, Wooldridge BR. Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. J Comput Inf Syst. 2008;48(3):31-37.
39. Junglas IA, Johnson NA, Spitzmüller C. Personality traits and concern for privacy: An empirical study in the context of location-based services. Eur J Inf Syst. 2008;17(4):387-402. doi:10.1057/ejis.2008.29
40. Allan A, Warden P. Got an iPhone or 3G iPad? Apple is recording your moves. O’Reilley Radar. Published online 2011.
41. Keith MJ, Thompson SC, Hale J, Lowry PB, Greer C. Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior. Int J Hum Comput Stud. 2013;71(12):1163-1173. doi:10.1016/j.ijhcs.2013.08.016
42. Chao CM. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol. 2019;10:1652. doi:10. 3389/fpsyg.2019.01652
43. Chen CC, Tsai JL. Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Gener Comput Syst. 2019;96: 628-638. doi:10.1016/j.future.2017.02.028
44. Behne A, Krüger N, Beinke JH, Teuteberg F. Learnings from the design and acceptance of the German COVID-19 tracing app for IS-driven crisis management: a design science research. BMC Med Inform Decis Mak. 2021;21(1):1-22.
45. Jaradat MIRM, Imlawi J, Al-Mashaqba AM. Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nursing Mobile Decision Support Systems Adoption: A Developing Country Perspective. Int J Interact Mob Technol. Published online 2018. doi:10.3991/IJIM.V12I2.8081
46. Venkatesh V, Morris MG. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q. Published online 2000:115-139.
47. Yuan Y, Raubal M. Extracting dynamic urban mobility patterns from mobile phone data Motivation • Various mobility patterns in urban area. Published online 2013:1-23.
48. Park Y, Chen JV. Acceptance and adoption of the innovative use of smartphone. Ind Manag Data Syst. 2007;107(9):1349-1365. doi:10.1108/02635570710834009
2. \AAman P, Liikkanen LA, Hinkka A. Enriching user experiences with location-sensitive music services. J Locat Based Serv. 2015;9(3):167-186. doi:10.1080/17489725.2015.1098737
3. Protecting consumer privacy in an era of rapid change. In: Protecting Consumer Privacy in an Era of Rapid Change. Nova Science Publishers, Inc.; 2011:5-86. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935082833&partnerID=40&md5=3018753145e7c1e571a6ea89b9f5705f
4. Turoff M, Chumer M, de Walle BV, Yao X. The design of a dynamic emergency response management information system (DERMIS). J Inf Technol Theory Appl JITTA. 2004;5(4):3.
5. Fuchs C. Bill Gates Conspiracy Theories as ideology in the context of the COVID-19 crisis. In: Communicating COVID-19: Everyday Life, Digital Capitalism, and Conspiracy Theories in Pandemic Times. Emerald Publishing Limited; 2021:91-144.
6. Webster JG. ANALYZING THE PAST TO PREPARE FOR THE FUTURE: WRITING A LITERATURE REVIEW. MIS Q. 2002;26(2).
7. Ishtiaq M. Book Review Creswell, JW (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches . Thousand Oaks, CA: Sage. Engl Lang Teach. 2019;12(5):40.
8. Sharma H. How short or long should be a questionnaire for any research? Researchers dilemma in deciding the appropriate questionnaire length. Saudi J Anaesth. 2022;16(1):65-68. doi:10.4103/sja.sja_163_21
9. Ukpabi D, Olaleye S, Karjaluoto H. Factors influencing tourists’ intention to use COVID-19 contact tracing app. In: Information and Communication Technologies in Tourism 2021: Proceedings of the ENTER 2021 ETourism Conference, January 19–22, 2021. Springer; 2021:504-516.
10. Walrave M, Waeterloos C, Ponnet K. Adoption of a Contact Tracing App for Containing COVID-19: A Health Belief Model Approach. JMIR Public Health Surveill. 2020;6 (3):e20572. doi:10.2196/20572
11. Walrave M, Waeterloos C, Ponnet K. Ready or not for contact tracing? Investigating the adoption intention of COVID-19 contact-tracing technology using an extended unified theory of acceptance and use of technology model. Cyberpsychology Behav Soc Netw. 2021;24(6):377-383.
12. Liu DY, Hsu KS. A Study on User Behavior Analysis of Integrate Beacon Technology into Library Information Services. Eurasia J Math Sci Technol Educ. 2018;14(5):1987-1997. doi:10.29333/ejmste/85865
13. Rejikumar G, Sreedharan V. R, Saha R. An integrated framework for service quality, choice overload, customer involvement and satisfaction: Evidence from India’s non-life insurance sector. Manag Decis. 2019;59(4): 801-828. doi:10.1108/MD-12-2018-1354
14. Lin S, Shams S, Choi H, Meng D, Azari H. Estimation of wave velocity for ultrasonic imaging of concrete structures based on dispersion analysis. J Test Eval. 2020;48(2): 1095-1107.
15. Chen S, Yang J, Yang W, Wang C, Bärnighausen T. COVID-19 control in China during mass population movements at New Year. The Lancet. 2020;395(10226):764-766. doi:10.1016/S0140-6736(20)30421-9
16. Ming LC, Untong N, Aliudin NA, et al. Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review. Vol 8. JMIR Publications; 2020. doi:10.2196/19796
17. Budi NFA, Adnan HR, Firmansyah F, Hidayanto AN, Kurnia S, Purwandari B. Why do people want to use location-based application for emergency situations? The extension of UTAUT perspectives. Technol Soc. 2021;65:101480.
18. Wu X, Fu L, Yao Y, Fu X, Wang X, Chen G. GLP: A novel framework for group-level location promotion in geo-social networks. IEEEACM Trans Netw. 2018;26(6):2870-2883. doi:10.1109/TNET.2018.2879437
19. Kang JW, Namkung Y. The role of personalization on continuance intention in food service mobile apps: A privacy calculus perspective. Int J Contemp Hosp Manag. 2019;31(2):734-752. doi:10.1108/IJCHM-12-2017-0783
20. Tam KY, Ho SY. Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Inf Syst Res. 2005;16 (3):271-291.
21. Chong A, Ngai E. What Influences Travellers’ Adoption of a Location-based Social Media Service for Their Travel Planning? PACIS 2013 Proc. Published online June 18, 2013. https://aisel.aisnet.org/pacis2013/210
22. Choi S. What promotes smartphone-based mobile commerce? Mobile-specific and self-service characteristics. Internet Res. 2018;28(1):105-122. doi:10.1108/IntR-10-2016-0287
23. Hubert M, Blut M, Brock C, Backhaus C, Eberhardt T. Acceptance of Smartphone-Based Mobile Shopping: Mobile Benefits, Customer Characteristics, Perceived Risks, and the Impact of Application Context. Psychol Mark. 2017;34(2):175-194. doi:10.100 2/mar.20982
24. Zhao Y, Yin F, Gunnarsson F, Amirijoo M, Hendeby G. Gaussian Process for Propagation Modeling and Proximity Reports Based Indoor Positioning. In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). IEEE; 2016:1-5. doi:10.1109/VTCSpri ng.2016.7504255
25. Richard JE, Meuli PG. Exploring and modelling digital natives’ intention to use permission-based location-aware mobile advertising. J Mark Manag. 2013;29(5-6):698-719. doi:10.1080/0267257X.2013.770051
26. Behne A, Krüger N, Beinke JH, Teuteberg F. Learnings from the design and acceptance of the German COVID-19 tracing app for IS-driven crisis management: a design science research. BMC Med Inform Decis Mak. 2021;21(1):238. doi:10.1186/s12911-02 1-01579-7
27. Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model | SpringerLink. Accessed April 1, 2021. https://link.springer.com/article/10.1007/s10796-017-9774-y
28. Yun H, Han D, Lee CC. Understanding the use of location-based service applications: do privacy concerns matter? J Electron Commer Res. 2013;14(3):215.
29. Venkatesh V, Bala H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis Sci. 2008;39(2):273-315. doi:10.1111/j.1540-5915.2008.00192.x
30. Venkatesh V, Bala H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis Sci. 2008;39(2):273-315. doi:10.1111/j.1540-5915.2008.00192.x
31. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989;13 (3):319-340. doi:10.2307/249008
32. Leong CY, Perumal T, Peng KW, Yaakob R. Enabling Indoor Localization With Internet of Things (IoT). In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE). IEEE; 2018:571-573. doi:10.1109/GCCE.2018 .8574489
33. Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003;27(3):425-478. doi:10.2307/30036540
34. Wei LY, Yeh MY, Lin G, Chan YH, Lai WJ. Discovering point-of-interest signatures based on group features from geo-social networking data. In: Proceedings - 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013. ; 2013:182-187. doi:10.1109/TAAI.2013.45
35. Lewis J. GPRS roaming the GRX way. Telecommun Int. 2003;37(2):32-33+38.
36. Venkatesh V, Thong JY, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. Published online 2012:157-178.
37. Crabbe M, Standing C, Standing S, Karjaluoto H. An adoption model for mobile banking in Ghana. Int J Mob Commun. 2009;7(5):515-543.
38. Fagan MH, Neill S, Wooldridge BR. Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. J Comput Inf Syst. 2008;48(3):31-37.
39. Junglas IA, Johnson NA, Spitzmüller C. Personality traits and concern for privacy: An empirical study in the context of location-based services. Eur J Inf Syst. 2008;17(4):387-402. doi:10.1057/ejis.2008.29
40. Allan A, Warden P. Got an iPhone or 3G iPad? Apple is recording your moves. O’Reilley Radar. Published online 2011.
41. Keith MJ, Thompson SC, Hale J, Lowry PB, Greer C. Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior. Int J Hum Comput Stud. 2013;71(12):1163-1173. doi:10.1016/j.ijhcs.2013.08.016
42. Chao CM. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol. 2019;10:1652. doi:10. 3389/fpsyg.2019.01652
43. Chen CC, Tsai JL. Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Gener Comput Syst. 2019;96: 628-638. doi:10.1016/j.future.2017.02.028
44. Behne A, Krüger N, Beinke JH, Teuteberg F. Learnings from the design and acceptance of the German COVID-19 tracing app for IS-driven crisis management: a design science research. BMC Med Inform Decis Mak. 2021;21(1):1-22.
45. Jaradat MIRM, Imlawi J, Al-Mashaqba AM. Investigating the Moderating Effects of Self-Efficacy, Age and Gender in the Context of Nursing Mobile Decision Support Systems Adoption: A Developing Country Perspective. Int J Interact Mob Technol. Published online 2018. doi:10.3991/IJIM.V12I2.8081
46. Venkatesh V, Morris MG. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q. Published online 2000:115-139.
47. Yuan Y, Raubal M. Extracting dynamic urban mobility patterns from mobile phone data Motivation • Various mobility patterns in urban area. Published online 2013:1-23.
48. Park Y, Chen JV. Acceptance and adoption of the innovative use of smartphone. Ind Manag Data Syst. 2007;107(9):1349-1365. doi:10.1108/02635570710834009