Machine learning-based empirical investigation of user’s perception of digitalisation in pandemic immunisation programs

References

• Abramova, O., Wagner, A., Olt, C.M., & Buxmann, P. (2022). One for all, all for one: Social considerations in user acceptance of contact tracing apps using longitudinal evidence from Germany and Switzerland. International Journal of Information Management, 64, 102473. https://doi.org/10.1016/j.ijinfomgt.2022.102473
   Web of Science ®Google Scholar
• Adams, J.S. (1963). Towards an understanding of inequity. The Journal of Abnormal and Social Psychology, 67(5), 422–436. https://doi.org/10.1037/h0040968
   PubMed Web of Science ®Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Alaparthi, S., & Mishra, M. (2021). BERT: a sentiment analysis odyssey. Journal of Marketing Analytics, 9(2), 118–126. https://doi.org/10.1057/s41270-021-00109-8
   Google Scholar
• Albastaki, Y. (2022). Assessing the perceived usability of an intelligent contact tracing app to prevent the spread of COVID-19 using SUS and TAM: Be aware Bahrain. Journal of Decision Systems, 33(2), 293–310. https://doi.org/10.1080/12460125.2022.2076299
   Web of Science ®Google Scholar
• Alsaad, A., & Al-Okaily, M. (2022). Acceptance of protection technology in a time of fear: The case of COVID-19 exposure detection apps. Information Technology & People, 35(3), 1116–1135. https://doi.org/10.1108/ITP-10-2020-0719
   Web of Science ®Google Scholar
• Amul, G.G., Ang, M., Kraybill, D., Ong, S.E., & Yoong, J. (2022). Responses to COVID‐19 in Southeast Asia: Diverse paths and ongoing challenges. Asian Economic Policy Review, 17(1), 90–110. https://doi.org/10.1111/aepr.12362
   Web of Science ®Google Scholar
• Azen, R., & Budescu, D.V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129–148. https://doi.org/10.1037/1082-989X.8.2.129
   PubMed Web of Science ®Google Scholar
• Banerjee, S.C., Banerjee, S., & Rai, P. (2022, October). Vaccine supply optimization and forecasting using random forest and ARIMA models. 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Banglore, India (pp. 1–5). IEEE.
   Google Scholar
• Belarmino, A.M., & Koh, Y. (2018). How E-WOM motivations vary by a hotel review website. International Journal of Contemporary Hospitality Management, 30(8), 2730–2751. https://doi.org/10.1108/IJCHM-02-2017-0055
   Web of Science ®Google Scholar
• Budescu, D.V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542–551. https://doi.org/10.1037/0033-2909.114.3.542
   Web of Science ®Google Scholar
• Carlsson Hauff, J., & Nilsson, J. (2023). Individual costs and societal benefits: The privacy calculus of contact-tracing apps. Journal of Consumer Marketing, 40(2), 171–180. https://doi.org/10.1108/JCM-03-2021-4559
   Web of Science ®Google Scholar
• Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J., & Blei, D. (2009). Reading tea leaves: How humans interpret topic models. Advances in neural information processing systems, 22. https://proceedings.neurips.cc/paper/2009/hash/f92586a25bb3145facd64ab20fd554ff-Abstract.html
   Google Scholar
• Chopdar, P.K. (2022). Adoption of COVID-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator. Health Policy and Technology, 11(3), 100651. https://doi.org/10.1016/j.hlpt.2022.100651
   PubMed Web of Science ®Google Scholar
• Cocosila, M., Farrelly, G., & Trabelsi, H. (2023). Perceptions of users and non-users of an early contact tracing mobile application to fight COVID-19 spread: A value-based empirical investigation. Information Technology & People, 36(5), 2088–2111. https://doi.org/10.1108/ITP-01-2021-0026
   Web of Science ®Google Scholar
• Dash, S.B., Jain, A., Dey, L., Dasgupta, T., & Naskar, A. (2023). Factors affecting contact tracing app user experience during COVID-19: An aspect-based sentiment analysis of user-generated review. Behaviour & Information Technology, 42(2), 249–264. https://doi.org/10.1080/0144929X.2022.2139758
   Web of Science ®Google Scholar
• Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Dzandu, M.D. (2023). Antecedent, behaviour, and consequence (a-b-c) of deploying the contact tracing app in response to COVID-19: Evidence from Europe. Technological Forecasting and Social Change, 187, 122217. https://doi.org/10.1016/j.techfore.2022.122217
   PubMed Web of Science ®Google Scholar
• Fernandes, T., & Costa, M. (2023). Privacy concerns with COVID-19 tracking apps: A privacy calculus approach. Journal of Consumer Marketing, 40(2), 181–192. https://doi.org/10.1108/JCM-03-2021-4510
   Web of Science ®Google Scholar
• Garousi, V., Cutting, D., & Felderer, M. (2022). Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps. Journal of Systems and Software, 184, 111136. https://doi.org/10.1016/j.jss.2021.111136
   PubMed Web of Science ®Google Scholar
• Garrett, P.M., Wang, Y.-W., White, J.P., Kashima, Y., Dennis, S., & Yang, C.-T. (2022). High acceptance of COVID-19 tracing technologies in Taiwan: A nationally Representative survey analysis. International Journal of Environmental Research and Public Health, 19(6), 3323. https://doi.org/10.3390/ijerph19063323
   PubMed Web of Science ®Google Scholar
• Geber, S., & Ho, S.S. (2022). Examining the cultural dimension of contact-tracing app adoption during the COVID-19 pandemic: A cross-country study in Singapore and Switzerland. Information, Communication & Society, 26(11), 2229–2249. https://doi.org/10.1080/1369118X.2022.2082880
   Web of Science ®Google Scholar
• Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience: An overview of experience components that Co-create value with the customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005
   Google Scholar
• Gleeson, M., Neville, K., & Pope, A. (2020). The design and development of a digital contact tracing application to better facilitate the tracing of passengers in the event of a biological threat/pandemic. Journal of Decision Systems, 29(sup1), 482–488. https://doi.org/10.1080/12460125.2020.1848386
   Google Scholar
• González-Pizarro, F., Figueroa, A., López, C., & Aragon, C. (2022). Regional differences in information privacy concerns after the facebook-cambridge analytica data scandal. Computer Supported Cooperative Work (CSCW), 31(1), 33–77.
   Web of Science ®Google Scholar
• Grimmer, J., & Stewart, B.M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028
   Web of Science ®Google Scholar
• Griva, A., Zampou, E., Stavrou, V., Papakiriakopoulos, D., & Doukidis, G. (2022). A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data. Journal of Decision Systems, 33(1), 1–29. https://doi.org/10.1080/12460125.2022.2151071
   Web of Science ®Google Scholar
• Guner, H., & Acarturk, C. (2020). The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults. Universal Access in the Information Society, 19(2), 311–330. https://doi.org/10.1007/s10209-018-0642-4
   Web of Science ®Google Scholar
• Guo, Y., Barnes, S.J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using lateDirichletlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009
   Web of Science ®Google Scholar
• Gupta, R., Rathore, B., Srivastava, A., & Biswas, B. (2022). Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic. Computers & Industrial Engineering, 169, 108207. https://doi.org/10.1016/j.cie.2022.108207
   PubMed Web of Science ®Google Scholar
• Hoffman, D.L., & Franke, G.R. (1986). Correspondence analysis: Graphical representation of categorical data in marketing research. Journal of Marketing Research, 23(3), 213–227. https://doi.org/10.1177/002224378602300302
   Web of Science ®Google Scholar
• Hong, S.J., & Cho, H. (2023). Privacy management and health information sharing via contact tracing during the COVID-19 pandemic: A hypothetical study on AI-Based technologies. Health Communication, 38(5), 913–924. https://doi.org/10.1080/10410236.2021.1981565
   PubMed Web of Science ®Google Scholar
• Hong, S.J., & Tam, K.Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179. https://doi.org/10.1287/isre.1060.0088
   Web of Science ®Google Scholar
• Huang, M.H. (2005). Web performance scale. Information and Management, 42(6), 841–852. https://doi.org/10.1016/j.im.2004.06.003
   Web of Science ®Google Scholar
• Ibrahim, N.F., Wang, X., & Bourne, H. (2017). Computers in human behavior exploring the effect of user engagement in online brand communities: Evidence from Twitter. Computers in Human Behavior, 72, 321–338. https://doi.org/10.1016/j.chb.2017.03.005
   Web of Science ®Google Scholar
• Jacobi, C., van Atteveldt, W., & Welbers, K. (2016). Quantitative analysis of large amounts of journalistic texts using topic modeling. Digital Journalism, 4(1), 89–106. https://doi.org/10.1080/21670811.2015.1093271
   Web of Science ®Google Scholar
• Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074. https://doi.org/10.1016/j.dss.2019.113074
   Web of Science ®Google Scholar
• Kar, A.K. (2021). What affects usage satisfaction in mobile payments? modellinuser-generateded content to develop the “digital service usage satisfaction model. Information Systems Frontiers, 23(5), 1341–1361.
   PubMedGoogle Scholar
• Kim, H., Cho, I., & Park, M. (2022). Analyzing genderless fashion trends of consumers’ perceptions on social media: Using unstructured big data analysis through Latent Dirichlet Allocation-based topic modeling. Fashion and Textiles, 9(1), 1–21.
   Web of Science ®Google Scholar
• Kostka, G., & Habich-Sobiegalla, S. (2022). In times of crisis: Public perceptions toward COVID-19 contact tracing apps in China, Germany, and the United States. New Media & Society.
   Google Scholar
• Kumar, A., Bala, P.K., Chakraborty, S., & Behera, R.K. (2024). Exploring antecedents impacting user satisfaction with voice assistant app: A text mining-based analysis on Alexa services. Journal of Retailing and Consumer Services, 76, 103586. https://doi.org/10.1016/j.jretconser.2023.103586
   Web of Science ®Google Scholar
• Kumar, A., Chakraborty, S., & Bala, P.K. (2023). Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. Journal of Retailing and Consumer Services, 73, 103363. https://doi.org/10.1016/j.jretconser.2023.103363
   Web of Science ®Google Scholar
• Kurtaliqi, F., Zaman, M., & Sohier, R. (2022). The psychological reassurance effect of mobile tracing apps in the COVID-19 era. Computers in Human Behavior, 131, 107210. https://doi.org/10.1016/j.chb.2022.107210
   PubMed Web of Science ®Google Scholar
• Lassi, Z.S., Naseem, R., Salam, R.A., Siddiqui, F., & Das, J.K. (2021). The impact of the COVID-19 pandemic on immunization campaigns and programs: A systematic review. International Journal of Environmental Research and Public Health, 18(3), 988. https://doi.org/10.3390/ijerph18030988
   PubMed Web of Science ®Google Scholar
• Li, C., Li, H., & Fu, S. (2023). Coping with COVID-19 using contact tracing mobile apps. Industrial Management & DataIndustrial Management & Data Systems, 123(5), 1440–1464. https://doi.org/10.1108/IMDS-05-2022-0293
   Web of Science ®Google Scholar
• Lin-Hi, N., Haensse, L., Hollands, L., & Blumberg, I. (2023). The role of ethics in technology acceptance: Analysing resistance to new health technologies on the example of a COVID-19 contact-tracing app. Journal of Decision Systems, 33(1), 1–31. https://doi.org/10.1080/12460125.2023.2171390
   Web of Science ®Google Scholar
• Liu, X.-X., Yang, J., Fong, S., Dey, N., Millham, R.C., & Fiaidhi, J. (2022). All-people-test-based methods for COVID-19 infectious disease dynamics simulation model: Towards citywide COVID testing. International Journal of Environmental Research and Public Health, 19(17), 10959. https://doi.org/10.3390/ijerph191710959
   PubMed Web of Science ®Google Scholar
• Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures, 12(2–3), 93–118. https://doi.org/10.1080/19312458.2018.1430754
   Web of Science ®Google Scholar
• Matt, C. (2021). Campaigning for the greater good? – how persuasive messages affect the evaluation of contact tracing apps. Journal of Decision Systems, 31(1–2), 189–206. https://doi.org/10.1080/12460125.2021.1873493
   Web of Science ®Google Scholar
• Mehta, M., De, S.J., & Chattopadhyay, M. (2022). Elucidating the role of emotion in privacy concerns: A text-convolutional neural network (text-CNN)-based tweets analysis of contact tracing apps. Australasian Journal of Information Systems, 26. https://doi.org/10.3127/ajis.v26i0.3687
   Web of Science ®Google Scholar
• Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1985). A conceptual Model of service quality and its implications for future research. Journal of Marketing, 49(4), 41–50. https://doi.org/10.1177/002224298504900403
   Web of Science ®Google Scholar
• Park, K., Nie, S.N., Zhang, J.C., & Asthana, S. (2024). Public relations lessons from the pandemic: A systematic review of the COVID-19 research in public relations published from 2020 to early 2023. Public Relations Review, 50(2), 102452. https://doi.org/10.1016/j.pubrev.2024.102452
   Web of Science ®Google Scholar
• Pavlou, P.A. (2014). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
   Google Scholar
• Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Polzer, T., & Goncharenko, G. (2022). The UK COVID-19 app: The failed co-production of a digital public service. Financial Accountability & Management, 38(2), 281–298. https://doi.org/10.1111/faam.12307
   Web of Science ®Google Scholar
• Prakash, A.V., & Das, S. (2022). Explaining citizens’ resistance to use digital contact tracing apps: A mixed-methods study. International Journal of Information Management, 63, 102468. https://doi.org/10.1016/j.ijinfomgt.2021.102468
   PubMed Web of Science ®Google Scholar
• Praveen, S.V., & Ittamalla, R. (2021). Analyzing Indian citizen’s perspective towards government using wearable sensors to tackle COVID-19 crisis—A text analytics study. Health Policy and Technology, 10(2), 100521.
   Web of Science ®Google Scholar
• Rai, P., & Bera, S. (2022, December). A comparative evaluation model for assessing solar energy capacity development of multiple geographical alternatives. 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Kuala Lumpur, Malaysia (pp. 1187–1191). IEEE.
   Google Scholar
• Rai, P., Bera, S., & Ray, P. (2022). Assessing technological impact on vaccine supply chain performance. Industrial Management & Data Systems, 122(8), 1938–1955. https://doi.org/10.1108/IMDS-08-2021-0488
   Web of Science ®Google Scholar
• Rathore, A.K., & Ilavarasan, P.V. (2020). Pre-and post-launch emotions in new product development: Insights from twitter analytics of three products. International Journal of Information Management, 50, 111–127. https://doi.org/10.1016/j.ijinfomgt.2019.05.015
   Web of Science ®Google Scholar
• Schmitt, B. (1999). Experimental marketing. Journal of Marketing Management, 15(1–3), 53–67. https://doi.org/10.1362/026725799784870496
   Google Scholar
• Shahidi, N., Tossan, V., Bourliataux-Lajoinie, S., & Cacho-Elizondo, S. (2022). Behavioural intention to use a contact tracing application: The case of StopCovid in France. Journal of Retailing and Consumer Services, 68, 102998. https://doi.org/10.1016/j.jretconser.2022.102998
   Web of Science ®Google Scholar
• Sheth, J.N., Newman, B.I., & Gross, B.L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170. https://doi.org/10.1016/0148-2963(91)90050-8
   Web of Science ®Google Scholar
• Shin, D., Lee, S., & Hwang, Y. (2017). How do credibility and utility play in the user experience of health informatics services? Computers in Human Behavior, 67, 292–302. https://doi.org/10.1016/j.chb.2016.11.007
   Web of Science ®Google Scholar
• Sv, P., Ittamalla, R., & Subramanian, D. (2022). How optimistic do citizens feel about digital contact tracing?–perspectives from developing countries. International Journal of Pervasive Computing and Communications, 18(5), 518–526.
   Web of Science ®Google Scholar
• Tonidandel, S., & LeBreton, J.M. (2011). Relative importance analysis: A useful supplement to regression analysis. Journal of Business and Psychology, 26(1), 1–9. https://doi.org/10.1007/s10869-010-9204-3
   Web of Science ®Google Scholar
• Tran, C.D., & Nguyen, T.T. (2021). Health vs. privacy? The risk-risk tradeoff in using COVID-19 contact-tracing apps. Technology in Society, 67, 101755. https://doi.org/10.1016/j.techsoc.2021.101755
   Web of Science ®Google Scholar
• Vaish, N., Goel, N., & Gupta, G. (2022, January). Machine learning techniques for sentiment analysis of hotel reviews. 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India (pp. 01–07). IEEE.
   Google Scholar
• Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Verma, S., & Garg, N. (2023). Exploring intergenerational differences in technology-oriented ethical behavior. Kybernetes, 52(6), 2164–2180. https://doi.org/10.1108/K-09-2021-0897
   Web of Science ®Google Scholar
• Verpaalen, I.A.M., Holland, R.W., Ritter, S., van Hooff, M., Ebbers, W., Hooft, L.’., Metting, E., & van der Laan, L.N. (2022). Resistance to contact tracing applications: The implementation process in a social context. Computers in Human Behavior, 134, 107299. https://doi.org/10.1016/j.chb.2022.107299
   Web of Science ®Google Scholar
• Wedel, M., & Kannan, P.K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413
   Web of Science ®Google Scholar
• WHO. (2022). World health statistics 2022: Monitoring health for the SDGs, sustainable development goals. Retrieved June 21, 2023, from https://iris.who.int/bitstream/handle/10665/356584/9789240051140-eng.pdf
   Google Scholar
• Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID-19 pandemic: Using latent dirichlet Allocation for topic modeling on Twitter. Public Library of Science ONE, 15(9), e0239441.
   Google Scholar
• Yuan, B., Ye, J., Wu, X., & Yang, C. (2023). Applying latent Dirichlet allocation and support vector regression to the aesthetic design of medical nursing beds. Journal of Computing and Information Science in Engineering, 23(5), 051014.
   Web of Science ®Google Scholar
• Zabel, S., Schlaile, M.P., & Otto, S. (2023). Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing. Computers in Human Behavior, 143, 107699. https://doi.org/10.1016/j.chb.2023.107699
   PubMed Web of Science ®Google Scholar
• Zhu, Z., Nakata, C., Sivakumar, K., & Grewal, D. (2003). Fix it of leave it? customer recovery from self-service technology failures. Journal of Retailing, 89(1), 15–29. https://doi.org/10.1016/j.jretai.2012.10.004
   Web of Science ®Google Scholar