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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | COVID-19 | Research

Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis

Author: Kuang-Ming Kuo

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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Abstract

An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I 2, and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.
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Literature
1.
Müller J, Kretzschmar M. Contact tracing – old models and new challenges. Infect Disease Modelling. 2021;6:222–31.
2.
3.
4.
Yang Y, Peng F, Wang R, Guan K, Jiang T, Xu G, Sun J, Chang C. The deadly coronaviruses: the 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J Autoimmun. 2020;109:102434.PubMedPubMedCentral
5.
6.
Urbaczewski A, Lee YJ. Information Technology and the pandemic: a preliminary multinational analysis of the impact of mobile tracking technology on the COVID-19 contagion control. Eur J Inform Syst. 2020;29(4):405–14.
7.
8.
Oyibo K, Morita PP. COVID Alert: factors influencing the adoption of exposure notification apps among canadian residents. Front Digit Health. 2022a;4:842661.PubMedPubMedCentral
9.
Trang S, Trenz M, Weiger WH, Tarafdar M, Cheung CMK. One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps. Eur J Inform Syst. 2020;29(4):415–28.
10.
Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, Parker M, Bonsall D, Fraser C. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491):eabb6936.PubMedPubMedCentral
11.
12.
Fernández-Niño JA, Peña-Maldonado C, Rojas-Botero M, Rodriguez-Villamizar LA. Effectiveness of contact tracing to reduce fatality from COVID-19: preliminary evidence from Colombia. Public Health. 2021;198:123–8.PubMed
13.
Fox G, Clohessy T, van der Werff L, Rosati P, Lynn T. Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications. Comput Hum Behav. 2021a;121:106806.
14.
Geber S, Friemel TN. Tracing-technology adoption during the COVID-19 pandemic: the multifaceted role of social norms. Int J Communication. 2022;16:247–66.
15.
16.
Guo H, Huang Z, Yeo JYP, Wang Y, Chow A. Psychosocial determinants of healthcare personnel’s willingness to carry real-Time locating system tags during daily inpatient care in hospital managing COVID-19 patients: Insights from a mixed-methods analysis. JAMIA Open. 2021;4(3):ooaa072. https://​doi.​org/​10.​1093/​jamiaopen/​ooaa072.
17.
Meier Y, Meinert J, Krämer NC. Investigating factors that affect the adoption of COVID-19 contact-tracing apps: a privacy calculus perspective. Technol Mind Behav. 2021;2(3):1–10.
18.
Oyibo K, Sahu KS, Oetomo A, Morita PP. Factors influencing the adoption of contact tracing applications: systematic review and recommendations. Front Digit Health. 2022b;4:862466.PubMedPubMedCentral
19.
20.
21.
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. Chichester: Wiley; 2009.
22.
McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM. Preferred reporting items for a systematic review and Meta-analysis of Diagnostic Test Accuracy Studies: the PRISMA-DTA Statement. JAMA. 2018;319(4):388–96.PubMed
23.
Kirca AH, Jayachandran S, Bearden WO. Market orientation: a Meta-Analytic Review and Assessment of its antecedents and impact on performance. J Mark. 2005;69(2):24–41.
24.
Rana NP, Dwivedi YK, Williams MD. A meta-analysis of existing research on citizen adoption of e-government. Inform Syst Front. 2015;17(3):547–63.
25.
Alsaad A, Al-Okaily M. Acceptance of protection technology in a time of fear: the case of Covid-19 exposure detection apps. Inform Technol People. 2022;35(3):1116–35.
26.
27.
Prakash AV, Das S. Explaining citizens’ resistance to use digital contact tracing apps: a mixed-methods study. Int J Inf Manag. 2022;63:102468.
28.
Protogerou C, Hagger MS. A checklist to assess the quality of survey studies in psychology. Methods in Psychology. 2020;3:100031.
29.
Alkhalifah A, Bukar UA. Examining the prediction of COVID-19 contact-tracing app adoption using an Integrated Model and Hybrid Approach Analysis. Front Public Health. 2022;10:847184.PubMedPubMedCentral
30.
Alshami M, Abdulghafor R, Aborujilah A. Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 contact tracing application by malaysian users. Sustainability. 2022;14(11):6811.
31.
Bradshaw EL, Ryan RM, Noetel M, Saeri AK, Slattery P, Grundy E, Calvo R. Information Safety Assurances increase intentions to Use COVID-19 contact tracing applications, regardless of autonomy-supportive or Controlling Message Framing. Front Psychol. 2021;11:591638. https://​doi.​org/​10.​3389/​fpsyg.​2020.​591638.
32.
Chan EY, Saqib NU. Privacy concerns can explain unwillingness to download and use contact tracing apps when COVID-19 concerns are high. Comput Hum Behav. 2021;119:106718.
33.
34.
Duan SX, Deng H. Hybrid analysis for understanding contact tracing apps adoption. Industrial Manage Data Syst. 2021;121(7):1599–616.
35.
36.
37.
Jansen-Kosterink S, Hurmuz M, den Ouden M, van Velsen L. Predictors to Use Mobile apps for monitoring COVID-19 symptoms and contact tracing: Survey among Dutch Citizens. JMIR Formative Res. 2021;5(12):e28416. https://​doi.​org/​10.​2196/​28416.
38.
Jörling M, Eitze S, Schmid P, Betsch C, Allen J, Böhm R. To disclose or not to disclose? Factors related to the willingness to disclose information to a COVID-19 tracing app. Inform Communication Soc 2022.
39.
Nguyen VT, Nguyen CTH. Factors influencing intention to use the COVID-19 contact tracing application. J Comput Sci. 2022;18(6):453–62.
40.
41.
42.
Zhang Z, Vaghefi I. Continued use of contact-tracing apps in the United States and the United Kingdom: insights from a comparative study through the Lens of the Health Belief Model. JMIR Formative Research. 2022;6(12):e40302.PubMedPubMedCentral
43.
Ahmad M, Iram K, Jabeen G. Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China. Environ Res. 2020;190:109995.PubMedPubMedCentral
44.
Akinnuwesi BA, Uzoka F-ME, Fashoto SG, Mbunge E, Odumabo A, Amusa OO, Okpeku M, Owolabi O. A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19. Sustainable Oper Computers. 2022;3:118–35.
45.
Ali ZS, Dang H. Factors impacting the use of the NZ COVID Tracer application in New Zealand. Smart Health. 2022;24:100278.PubMed
46.
Chopdar PK. Adoption of Covid-19 contact tracing app by extending UTAUT theory: perceived disease threat as moderator. Health Policy and Technology. 2022;11(3):100651.PubMedPubMedCentral
47.
Chuenyindee T, Ong AKS, Prasetyo YT, Persada SF, Nadlifatin R, Sittiwatethanasiri T. Factors affecting the Perceived Usability of the COVID-19 contact-tracing application Thai Chana during the early COVID-19 Omicron Period. Int J Environ Res Public Health. 2022;19(7):4383.PubMedPubMedCentral
48.
49.
50.
Dzandu MD. Antecedent, behaviour, and consequence (a-b-c) of deploying the contact tracing app in response to COVID-19: evidence from Europe. Technol Forecast Soc Chang. 2023;187:122217.
51.
52.
Fernandes T, Costa M. Privacy concerns with COVID-19 tracking apps: a privacy calculus approach. J Consumer Mark. 2023;40(2):181–92.
53.
Fox G, van der Werff L, Rosati P, Endo PT, Lynn T. Examining the determinants of acceptance and use of mobile contact tracing applications in Brazil: an extended privacy calculus perspective. J Association Inform Sci Technol. 2021. https://​doi.​org/​10.​1002/​asi.​24602.
54.
55.
56.
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. Computers & Security. 2023;132:103338.
57.
58.
Hassandoust F, Akhlaghpour S, Johnston AC. Individuals’ privacy concerns and adoption of contact tracing mobile applications in a pandemic: a situational privacy calculus perspective. J Am Med Inform Assoc. 2021;28(3):463–71.PubMed
59.
60.
Horvath L, Banducci S, Blamire J, Degnen C, James O, Jones A, Stevens D, Tyler K. Adoption and continued use of mobile contact tracing technology: multilevel explanations from a three-wave panel survey and linked data. BMJ Open. 2022;12(1):e053327. https://​doi.​org/​10.​1136/​bmjopen-2021-053327.
61.
Jahari SA, Hass A, Hass D, Joseph M. Navigating privacy concerns through societal benefits: a case of digital contact tracing applications. J Consumer Behav. 2022;21(3):625–38.
62.
63.
64.
65.
Kulyk O, Britton-Steele L, Paja E, Duckert M, Barkhuus L. “You have been in Close Contact with a Person Infected with COVID-19 and you may have been Infected”: Understanding Privacy Concerns, Trust and Adoption in Mobile COVID-19 Tracing Across Four Countries. Proceedings of the ACM on Human-Computer Interaction. 2022;6(MHCI):204. https://​doi.​org/​10.​1145/​3546739.
66.
Kurtaliqi F, Zaman M, Sohier R. The psychological reassurance effect of mobile tracing apps in Covid-19 era. Comput Hum Behav. 2022;131:107210.
67.
Lee JK, Lin L, Kang H. The influence of normative perceptions on the uptake of the covid-19 tracetogether digital contact tracing system: cross-sectional study. JMIR Public Health and Surveillance. 2021;7(11):e30462. https://​doi.​org/​10.​2196/​30462.
68.
Lin J, Carter L, Liu D. Privacy concerns and digital government: exploring citizen willingness to adopt the COVIDSafe app. Eur J Inform Syst. 2021;30(4):389–402.
69.
Nguyen TT, Nguyen TCAH, Tran CD. Exploring individuals’ adoption of COVID-19 contact-tracing apps: a mixed-methods approach. Libr Hi Tech. 2022;40(2):376–93.
70.
Nguyen TT, Tran Hoang MT, Phung MT. To our health! Perceived benefits offset privacy concerns in using national contact-tracing apps. Libr Hi Tech. 2023;41(1):174–91.
71.
Ntsiful A, Kwarteng MA, Inegbedion HE. How health-related messaging increase intentions to download and use mobile contact (COVID-19) tracing apps: preliminary findings. Cogent Social Sciences. 2022;8(1):2035912.
72.
Nunes N, Adamo G, Ribeiro M, Gouveia BR, Gouveia ER, Teixeira P, Nisi V. Modeling adoption, security, and privacy of COVID-19 apps: findings and recommendations from an empirical study using the Unified Theory of Acceptance and Use of Technology. JMIR Hum Factors. 2022;9(3):e35434.PubMedPubMedCentral
73.
Oldeweme A, Märtins J, Westmattelmann D, Schewe G. The role of transparency, trust, and social influence on uncertainty reduction in times of pandemics: empirical study on the adoption of COVID-19 tracing apps. J Med Internet Res. 2021;23(2):e25893. https://​doi.​org/​10.​2196/​25893.
74.
Oyibo K, Morita PP. COVID Alert: factors influencing the adoption of exposure notification apps among canadian residents. Front Digit Health. 2022;4:842661.PubMedPubMedCentral
75.
Oyibo K, Morita PP. Factors Influencing the Willingness to Download Contact Tracing Apps among the American Population. Limassol, Cyprus.: In: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization; 2023. p. 147–156.
76.
Prakash AV, Das S, Pillai KR. Understanding digital contact tracing app continuance: insights from India. Health Policy and Technology. 2021;10(4):100573.
77.
78.
Ross GM. I use a COVID-19 contact-tracing app. Do you? Regulatory focus and the intention to engage with contact-tracing technology. Int J Inform Manage Data Insights. 2021;1(2):100045.
79.
Saladdin IR, Handayani PW. The Influence of Individual, Process, Technology, and Environment Dimensions on Intention and Actual Usage of PeduliLindungi Application. In: 2022 10th International Conference on Information and Communication Technology (ICoICT): 2022; 2022: 47–52.
80.
Scholl A, Sassenberg K. How identification with the social environment and with the government guide the use of the official COVID-19 contact tracing app: three quantitative survey studies. JMIR mHealth and uHealth. 2021;9(11):e28146. https://​doi.​org/​10.​2196/​28146.
81.
Shahidi N, Tossan V, Bourliataux-Lajoinie S, Cacho-Elizondo S. Behavioural intention to use a contact tracing application: the case of StopCovid in France. J Retailing Consumer Serv. 2022;68:102998.
82.
83.
Suh A, Li M. Digital tracing during the COVID-19 pandemic: user Appraisal, emotion, and Continuance Intention. Sustainability. 2021;13(2):608.
84.
Thenoz E, Chouk I, Ferrandi J-M, Gonzalez C, Rowe F, Siadou-Martin B. The adoption of contact-tracing applications and the integration of a Health Pass. J Global Inform Manage. 2023;31(5):1–23.
85.
Tomczyk S, Barth S, Schmidt S, Muehlan H. Utilizing health behavior change and technology acceptance models to predict the adoption of COVID-19 contact tracing apps: cross-sectional survey study. J Med Internet Res. 2021;23(5):e25447. https://​doi.​org/​10.​2196/​25447.
86.
Tran CD, Nguyen TT. Health vs. privacy? The risk-risk tradeoff in using COVID-19 contact-tracing apps. Technol Soc. 2021;67:101755.PubMedPubMedCentral
87.
Trkman M, Popovič A, Trkman P. The impact of perceived crisis severity on intention to use voluntary proximity tracing applications. Int J Inf Manag. 2021;61:102395.
88.
van der Waal NE, de Wit J, Bol N, Ebbers W, Hooft L, Metting E, van der Laan LN. Predictors of contact tracing app adoption: integrating the UTAUT, HBM and contextual factors. Technol Soc. 2022;71:102101.
89.
Velicia-Martin F, Cabrera-Sanchez JP, Gil-Cordero E, Palos-Sanchez PR. Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model. PeerJ Comput Sci. 2021;7:1–20.
90.
Verpaalen IAM, Holland RW, Ritter S, van Hooff M, Ebbers W, Hooft Lt, Metting E, van der Laan LN. Resistance to contact tracing applications: the implementation process in a social context. Comput Hum Behav. 2022;134:107299.
91.
92.
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 Social Netw. 2021;24(6):377–83.
93.
Yuduang N, Ong AKS, Prasetyo YT, Chuenyindee T, Kusonwattana P, Limpasart W, Sittiwatethanasiri T, Gumasing MJJ, German JD, Nadlifatin R. Factors influencing the Perceived effectiveness of COVID-19 Risk Assessment Mobile Application MorChana in Thailand: UTAUT2 Approach. Int J Environ Res Public Health. 2022;19(9):5643.PubMedPubMedCentral
94.
Rosenthal R. The file drawer problem and tolerance for null results. Psychol Bull. 1979;86(3):638–41.
95.
Davis FD. Perceived usefulness, perceived ease of Use, and user Acceptance of Information Technology. MIS Q. 1989;13(3):319–40.
96.
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27(3):425–78.
97.
Laufer RS, Wolfe M. Privacy as a Concept and a Social Issue: a Multidimensional Developmental Theory. J Soc Issues. 1977;33(3):22–42.
98.
Dinev T, Bellotto M, Hart P, Russo V, Serra I, Colautti C. Privacy calculus model in e-commerce - a study of Italy and the United States. Eur J Inform Syst. 2006;15(4):389–402.
99.
Rosenstock IM. The Health Belief Model and Preventive Health Behavior. Health Educ Behav. 1974;2(4):354–86.
100.
Rogers RW. A Protection Motivation Theory of Fear Appeals and attitude change. J Psychol. 1975;91(1):93–114.PubMed
101.
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211.
102.
Fishbein M, Ajzen I. Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley Reading, MA; 1975.
103.
Chen D-G, Peace KE. Applied Meta-analysis using R. Boca Raton, FL: Taylor and Francis Group; 2013.
104.
Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.PubMed
105.
Riley RD, Higgins JPT, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011;342:d549.PubMed
106.
IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open. 2016;6(7):e010247.PubMedPubMedCentral
107.
Hofstede G, Hofstede GJ, Minkov M. Cultures and organizations: Software of the mind. 3rd ed. New York: McGraw-Hill; 2010.
108.
White K, Peloza J. Self-benefit versus Other-Benefit Marketing Appeals: their effectiveness in Generating charitable support. J Mark. 2009;73(4):109–24.
109.
Feinberg J. Psychological Egoism. In J. Feinberg & R. Shafer-Landau (Eds.), Reasons & Responsibility: Readings in Some Basic Problems of Philosophy (15th ed.,). Cengage Learning. 2013;501-513.
110.
White K, Habib R, Dahl DW. A review and Framework for thinking about the Drivers of Prosocial Consumer Behavior. J Association Consumer Res. 2019;5(1):2–18.
111.
World Health Organization: Digital tools for COVID-19 contact tracing. In., vol. 2020. Genevan, Switzerland: World Health Organization,; 2020.
112.
Smith HJ, Dinev T, Xu H. Information privacy research: an Interdisciplinary Review. MIS Q. 2011;35(4):980–1016.
113.
Schade M, Piehler R, Warwitz C, Burmann C. Increasing consumers’ intention to use location-based advertising. J Prod Brand Manage. 2018;27(6):661–9.
114.
Hsieh SH, Lee CT. Traces of mobility: examining location disclosure on social networks with mobile location tagging. Telematics Inform. 2020;49:101366.
115.
Ciucci M, Gouarderes F. National COVID-19 contact tracing apps. In. Edited by Service EEPR; 2020.
116.
Kummer T-F, Ryschka S, Bick M. Why do we share where we are? The influence of situational factors on the conditional value of check-in services. Decis Support Syst. 2018;115:1–12.
117.
Schepers J, Wetzels M. A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Inf Manag. 2007;44(1):90–103.
118.
Davis FD, Bagozzi RP, Warshaw PR. User Acceptance of Computer Technology: a comparison of two theoretical models. Manage Sci. 1989;35(8):982–1003.
119.
Mcknight DH, Carter M, Thatcher JB, Clay PF. Trust in a specific technology: an investigation of its components and measures. ACM Trans Manage Inform Syst. 2011;2(2):Article.
120.
Dinev T, Hart P. An extended privacy calculus model for E-commerce transactions. Inform Syst Res. 2006;17(1):61–80.
121.
Chang SE, Liu AY, Shen WC. User trust in social networking services: a comparison of Facebook and LinkedIn. Comput Hum Behav. 2017;69:207–17.
122.
123.
Rivis A, Sheeran P. Descriptive norms as an additional predictor in the theory of planned behaviour: a meta-analysis. Curr Psychol. 2003;22(3):218–33.
124.
Sheeran P, Maki A, Montanaro E, Avishai-Yitshak A, Bryan A, Klein WMP, Miles E, Rothman AJ. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: a meta-analysis. Health Psychol. 2016;35(11):1178–88.PubMed
125.
Jordan RE, Adab P, Cheng KK. Covid-19: risk factors for severe disease and death. BMJ. 2020;368:m1198.PubMed
126.
Tajfel H, Turner JC. The social identity theory of intergroup behavior. In: Political Psychology Edited by Jost JT, Sidanius J, 1st edn. New York: Psychology Press; 2004: 276–293.
127.
128.
Venkatesh V, Thong L, Xu JY. Consumer Acceptance and Use of Information Technology: extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012;36(1):157–78.
129.
Vishkin A, Kitayama S, Berg MK, Diener E, Gross-Manos D, Ben-Arieh A, Tamir M. Adherence to emotion norms is greater in individualist cultures than in collectivist cultures. J Personal Soc Psychol. 2023;124(6):1256–76.
130.
Milberg SJ, Smith HJ, Burke SJ. Information privacy: Corporate Management and National Regulation. Organ Sci. 2000;11(1):35–57.
131.
Norberg PA, Horne DR, Horne DA. The privacy paradox: personal information disclosure intentions versus behaviors. J Consum Aff. 2007;41(1):100–26.
132.
Taylor S, Todd PA. Understanding Information Technology usage - a test of competing models. Inform Syst Res. 1995;6(2):144–76.
Metadata
Title
Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis
Author
Kuang-Ming Kuo
Publication date
01-12-2023
Publisher
BioMed Central
Keyword
COVID-19
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02313-1

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