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Covid-19 Salgınında Hayat Eve Sığar (HES) Uygulamasının Kullanıcılar Tarafından Benimsenmesi: Ampirik Bir Çalışma

Year 2021, Volume: 14 Issue: 4, 367 - 376, 31.10.2021

Abstract

Milyonlarca insanın hastalanmasına ve ölümüne neden olan Covid-19 salgınını yönetmek ve yayılımını azaltmak önemli bir problem haline gelmiştir. Hükümetler ortaya çıkan bu salgını kontrol altında tutabilmek ve bulaş riskini önlemek amacıyla farklı politikalar üretmişlerdir. Bu kapsamda, T.C. Sağlık Bakanlığı tarafından bulaş riskini en aza indirmek ve risk durumunu kontrol altında tutabilmek amacıyla Hayat Eve Sığar (HES) uygulaması geliştirilmiştir. HES kodu üretme, yaşanılan bölgedeki hastalık yoğunluğunun izlenebilmesi ve karantina süresinin takip edilebilmesi gibi servisler kişileri HES uygulamasını kullanmaya yöneltmektedir. Sistemlerin etkin kullanımının sağlanması, son kullanıcıların bu sistemleri benimsemeleriyle doğrudan ilişkilidir. Bu bağlamda, yürütülen ampirik çalışmada HES uygulamasının etkin kullanımının sağlanması amacıyla kullanıcı niyetlerinin Kullanım Kolaylığı, Algılanan Fayda, Sosyal Çevre, Mobil Cihaz Kullanımı Öz-yeterliliği ve Uygulama Arayüzü faktörleri kapsamında incelenmesi amaçlanmıştır. Hazırlanan anket ile katılımcıların demografik bilgileri ve HES kullanımlarıyla ilgili bilgi toplanmıştır. Ayrıca belirlenen faktörleri ölçmek amacıyla bir ölçek geliştirilmiştir. Ölçek kartopu örnekleme yöntemiyle Türkiye genelinde toplamda 306 kişiye uygulanmıştır. Elde edilen verilerin ön analizleri yapıldıktan sonra faktörlerin birbirleri ve kullanıcı niyetleri üzerindeki etkisi Yapısal Eşitlik Modellemesi kullanılarak incelenmiştir. Bu çalışma ile HES uygulamasının kullanımına yönelik kişilerin algısını olumlu ve olumsuz etkileyen unsurlar öne çıkarılarak, sistemin etkin kullanımına fayda sağlanacağı ön görülmektedir.

References

  • M. N. Islam, I. Islam, K. M. Munim, and A. K. M. N. Islam, “A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis”, IEEE Access, 8, 145601–145610, 2020.
  • L. C. Ming et al., “Mobile health apps on COVID-19 launched in the early days of the pandemic: Content analysis and review”, JMIR mHealth uHealth, 8(9), 2020.
  • A. Nunes, T. Limpo, and S. L. Castro, “Acceptance of Mobile Health Applications: Examining Key Determinants and Moderators”, Frontiers in Psychology, 10, 2791, 2019.
  • F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, 13(3), 319-340, 1989.
  • A. Alsswey and H. Al-Samarraie, “Elderly users’ acceptance of mHealth user interface (UI) design-based culture: the moderator role of age”, Journal on Multimodal User Interfaces, 14(1), 49–59, 2020.
  • X. Zhang, X. Han, Y. Dang, F. Meng, X. Guo, and J. Lin, “User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance”, Informatics for Health and Social Care, 4282), 194–206, 2017.
  • A. Balapour, I. Reychav, R. Sabherwal, and J. Azuri, “Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps”, International Journal of Information Management, 49, 58–68, 2019.
  • R. R. Pai and S. Alathur, “Determinants of individuals’ intention to use mobile health: insights from India”, Transforming Government: People, Process and Policy, 13(3/4), 306–326, 2019.
  • X. T. Guo, J. Q. Yuan, X. F. Cao, and X. D. Chen, “Understanding the acceptance of mobile health services: A service participants analysis”, International Conference on Management Science and Engineering, Dallas, Texas, USA, 20-22 Eylül 2012.
  • M. Fishbein and I. Ajzen, Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, MA: Addison-Wesley, Massachusetts, ABD, 1975.
  • V. Thomas Sarver, “Ajzen and Fishbein’s ‘Theory of Reasoned Action’: A Critical Assessment”, Journal for the Theory of Social Behaviour, 13(2), 155–163, 1983.
  • F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models”, Management Science, 35(8), 982-1003, 1989.
  • D. Fındık-Coşkunçay, N. Alkiş, and S. Ozkan-Yildirim, “A structural model for students’ adoption of Learning Management Systems: An empirical investigation in the higher education context”, Educational Technology and. Soceity, 21(2), 13–27, 2018.
  • F. S. Esen, “Dijital Bankacılık Kullanımına Teknoloji Kabulü Temelli Bir Yaklaşım”, Bilişim Teknolojileri Dergisi, 13(4), 401–410, 2020.
  • H. Tatlı and F. Alaca, “Akademisyenlerin Mobil İnternet Tercihini Etkileyen Davranışsal ve Demografik Faktörler”, AJIT-e Online Academic Journal of Information Technology, 9(30), 121-136, 2018.
  • I. Arpaci, “Understanding and predicting students’ intention to use mobile cloud storage services”, Computers in Human Behavior, 58, 150–157, 2016.
  • B. Rahimi, H. Nadri, H. L. Afshar, and T. Timpka, “A systematic review of the technology acceptance model in health informatics”, Applied Clinical Informatics, 9, 604–634, 2018.
  • A. Garavand, M. Samadbeik, M. Kafashi, and S. H. Abhari, “Acceptance of health information technologies, acceptance of mobile health: A review article”, Journal of Biomedical Physics and Engineering, 7(4), 403–408, 2017.
  • C. Nadal, C. Sas, and G. Doherty, “Technology acceptance in mobile health: Scoping review of definitions, models, and measurement”, Journal of Medical Internet Research, 22(7), 2020.
  • R. J. Holden and B. T. Karsh, “The Technology Acceptance Model: Its past and its future in health care”, Journal of Biomedical Informatics, 43, 159–172, 2010.
  • Sarbadhikari S, Sarbadhikari SN. "The global experience of digital health interventions in COVID-19 management", Indian Journal of Public Health, 64, 117-124, 2020.
  • Internet: T.C. Sağlık Bakanlığı, “Hayat Eve Sığar.”, https://play.google.com/store/apps/details?id=tr.gov.saglik.hayatevesigar&hl=tr&gl=US, 16.01.2021.
  • A. Bandura, Social foundations of thought and action: Social cognitive theory, Englewood Cliffs, New Jersey: Prentice Hall, 1986.
  • A. Bandura, “Social cognitive theory of self-regulation”, Organizational Behavior and Human Decision Processes,50(2), 248–287, 1991.
  • S. A. Nikou and A. A. Economides, “Mobile-based assessment: Investigating the factors that influence behavioral intention to use”, Computers and. Education, 109, 56–73, 2017.
  • A. Field, Discovering Statistics Using SPSS, Sage publications, Londra, İngiltere, 2009.
  • J. F. Hair, W. C. Black, B. J. Babin, R. J. Anderson, and R. L. Tatham, Multivariate Data Analysis. Sixth Edition. Prentice Hall. New Jersey: Pearson Prentice Hall., 2006.
  • M. Q. Patton, “Qualitative Research”, Encyclopedia of Statistics in Behavioral Science, John Wiley & Sons, Ltd., New York, 2005.
  • J. P. Stevens, Applied multivariate statistics for the social sciences. Routledge, New York, 2012.
  • A. Field, Discovering statistics using SPSS, SAGE Publications, Londra, İngiltere, 2005.
  • W. W. Chin, “Issues and opinion on structural equation modeling”, MIS Quarterly, 22(1), 1, 1998.
  • D. X. Peng and F. Lai, “Using partial least squares in operations management research: A practical guideline and summary of past research”, Journal of Operations Management, 30(6), 6, 467–480, 2012.
  • C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, 18(1), 39–50, 1981.
  • M. El-Wajeeh, P. G. H. Galal-Edeen, and D. H. Mokhtar, “Technology Acceptance Model for Mobile Health Systems”, IOSR Journal of Mobile Computing & Application, 1(1), 21–33, 2014.
  • M. Yan, R. Filieri, and M. Gorton, “Continuance intention of online technologies: A systematic literature review”, International Journal of Information Management, 58, 102315, 2021.
  • C. Peng, Z. OuYang, and Y. Liu, “Understanding bike sharing use over time by employing extended technology continuance theory”, Transportation Research Part A: Policy and Practice, 124, 433–443, 2019.
  • L. C. Alain Yee-, “Understanding mobile commerce continuance intentions: An empirical analysis of chinese consumers”, Journal of Computer Information Systems, 53(4), 22–30, 2013.
  • N. Mohamed, R. Hussein, N. H. A. Zamzuri, and H. Haghshenas, “Insights into individual’s online shopping continuance intention”, Industrial Management and Data Systems, 114(9), 1453–1476, 2014.
  • H. Mohammadi, “Investigating users’ perspectives on e-learning: An integration of TAM and IS success model”, Computersin Human Behavior, 45, 359–374, 2015.
  • Z. Lin and R. Filieri, “Airline passengers’ continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge”, Transportation Research Part E: Logistics and Transportation Review, 81(C), 158–168, 2015.
  • J. Lu, “Are personal innovativeness and social influence critical to continue with mobile commerce?”, Internet Research, 24(2), 134–159, 2014.
  • A. Susanto, Y. Chang, and Y. Ha, “Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model”, Industrial Management and Data Systems, 116(3), 508–525, 2016.
  • S. Y. Park, M. W. Nam, and S. B. Cha, “University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model”, British Journal of Educational Technology, 43(4), 592–605, 2012.
  • R. Miao et al., “Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach”, International Journal of Production Research, 55(19), 5801–5815, 2017.
  • I. Ajzen, “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, 50(2), 179–211, 1991.
  • V. Venkatesh and F. D. Davis, “Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies”, Management Science, 46(2), 186–204, 2000.
  • C. C. Lee and M. C. Hsieh, “The influence of mobile self-efficacy on attitude towards mobile advertising”, 2009 International Conference on New Trends in Information and Service Science, Beijing, Çin, 30 Haziran- 2 Temmuz 2009.
  • I. F. Liu, M. C. Chen, Y. S. Sun, D. Wible, and C. H. Kuo, “Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community”, Computers and. Education., 54, 600–610, 2010.
  • Internet: I. Ajzen, “Constructing a theory of planned behavior questionnaire,” http://people.umass.edu/~aizen/pdf/tpb.measurement.pdf, 24.11. 2020.

Adoption of the Life Fits into Home (HES) Application by Users in the Covid-19 Outbreak: An Empirical Study

Year 2021, Volume: 14 Issue: 4, 367 - 376, 31.10.2021

Abstract

Managing the Covid-19 epidemic, which made millions of people sick and caused deaths, and reducing its spread have become an important problem. Governments have produced different policies in order to keep the emerging Covid-19 outbreak under control and to prevent the risk of contamination. In this context, The Life fits into home (HES) application was developed by the T.C. Ministry of Health in order to minimize the risk of contamination in people's daily lives and to keep the risk situation under control. Services such as generating HES code, monitoring the disease intensity in the living area and tracking the quarantine period direct people to use the HES application. Ensuring efficient use of systems is directly related to end users' adoption of these systems. In this context, this empirical study aims to examine user intentions within the scope of Ease of Use, Perceived Usefulness, Social Environment, Mobile Device Self-Efficacy and Application Interface in order to ensure the effective use of HES application. In the prepared questionnaire, demographic information of the participants and information about HES usage were collected. In addition, a scale was developed to measure the determined factors. The developed scale was applied to a total 306 people across Turkey by employing snowball sampling. After the preliminary analysis of the collected data, the effects of factors on each other and user intentions were examined using Structural Equation Modeling. It is predicted that to highlight the factors that affect the perception of the people regarding the use of the HES application positively and negatively will be beneficial to effective use of the application.

References

  • M. N. Islam, I. Islam, K. M. Munim, and A. K. M. N. Islam, “A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis”, IEEE Access, 8, 145601–145610, 2020.
  • L. C. Ming et al., “Mobile health apps on COVID-19 launched in the early days of the pandemic: Content analysis and review”, JMIR mHealth uHealth, 8(9), 2020.
  • A. Nunes, T. Limpo, and S. L. Castro, “Acceptance of Mobile Health Applications: Examining Key Determinants and Moderators”, Frontiers in Psychology, 10, 2791, 2019.
  • F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, 13(3), 319-340, 1989.
  • A. Alsswey and H. Al-Samarraie, “Elderly users’ acceptance of mHealth user interface (UI) design-based culture: the moderator role of age”, Journal on Multimodal User Interfaces, 14(1), 49–59, 2020.
  • X. Zhang, X. Han, Y. Dang, F. Meng, X. Guo, and J. Lin, “User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance”, Informatics for Health and Social Care, 4282), 194–206, 2017.
  • A. Balapour, I. Reychav, R. Sabherwal, and J. Azuri, “Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps”, International Journal of Information Management, 49, 58–68, 2019.
  • R. R. Pai and S. Alathur, “Determinants of individuals’ intention to use mobile health: insights from India”, Transforming Government: People, Process and Policy, 13(3/4), 306–326, 2019.
  • X. T. Guo, J. Q. Yuan, X. F. Cao, and X. D. Chen, “Understanding the acceptance of mobile health services: A service participants analysis”, International Conference on Management Science and Engineering, Dallas, Texas, USA, 20-22 Eylül 2012.
  • M. Fishbein and I. Ajzen, Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, MA: Addison-Wesley, Massachusetts, ABD, 1975.
  • V. Thomas Sarver, “Ajzen and Fishbein’s ‘Theory of Reasoned Action’: A Critical Assessment”, Journal for the Theory of Social Behaviour, 13(2), 155–163, 1983.
  • F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models”, Management Science, 35(8), 982-1003, 1989.
  • D. Fındık-Coşkunçay, N. Alkiş, and S. Ozkan-Yildirim, “A structural model for students’ adoption of Learning Management Systems: An empirical investigation in the higher education context”, Educational Technology and. Soceity, 21(2), 13–27, 2018.
  • F. S. Esen, “Dijital Bankacılık Kullanımına Teknoloji Kabulü Temelli Bir Yaklaşım”, Bilişim Teknolojileri Dergisi, 13(4), 401–410, 2020.
  • H. Tatlı and F. Alaca, “Akademisyenlerin Mobil İnternet Tercihini Etkileyen Davranışsal ve Demografik Faktörler”, AJIT-e Online Academic Journal of Information Technology, 9(30), 121-136, 2018.
  • I. Arpaci, “Understanding and predicting students’ intention to use mobile cloud storage services”, Computers in Human Behavior, 58, 150–157, 2016.
  • B. Rahimi, H. Nadri, H. L. Afshar, and T. Timpka, “A systematic review of the technology acceptance model in health informatics”, Applied Clinical Informatics, 9, 604–634, 2018.
  • A. Garavand, M. Samadbeik, M. Kafashi, and S. H. Abhari, “Acceptance of health information technologies, acceptance of mobile health: A review article”, Journal of Biomedical Physics and Engineering, 7(4), 403–408, 2017.
  • C. Nadal, C. Sas, and G. Doherty, “Technology acceptance in mobile health: Scoping review of definitions, models, and measurement”, Journal of Medical Internet Research, 22(7), 2020.
  • R. J. Holden and B. T. Karsh, “The Technology Acceptance Model: Its past and its future in health care”, Journal of Biomedical Informatics, 43, 159–172, 2010.
  • Sarbadhikari S, Sarbadhikari SN. "The global experience of digital health interventions in COVID-19 management", Indian Journal of Public Health, 64, 117-124, 2020.
  • Internet: T.C. Sağlık Bakanlığı, “Hayat Eve Sığar.”, https://play.google.com/store/apps/details?id=tr.gov.saglik.hayatevesigar&hl=tr&gl=US, 16.01.2021.
  • A. Bandura, Social foundations of thought and action: Social cognitive theory, Englewood Cliffs, New Jersey: Prentice Hall, 1986.
  • A. Bandura, “Social cognitive theory of self-regulation”, Organizational Behavior and Human Decision Processes,50(2), 248–287, 1991.
  • S. A. Nikou and A. A. Economides, “Mobile-based assessment: Investigating the factors that influence behavioral intention to use”, Computers and. Education, 109, 56–73, 2017.
  • A. Field, Discovering Statistics Using SPSS, Sage publications, Londra, İngiltere, 2009.
  • J. F. Hair, W. C. Black, B. J. Babin, R. J. Anderson, and R. L. Tatham, Multivariate Data Analysis. Sixth Edition. Prentice Hall. New Jersey: Pearson Prentice Hall., 2006.
  • M. Q. Patton, “Qualitative Research”, Encyclopedia of Statistics in Behavioral Science, John Wiley & Sons, Ltd., New York, 2005.
  • J. P. Stevens, Applied multivariate statistics for the social sciences. Routledge, New York, 2012.
  • A. Field, Discovering statistics using SPSS, SAGE Publications, Londra, İngiltere, 2005.
  • W. W. Chin, “Issues and opinion on structural equation modeling”, MIS Quarterly, 22(1), 1, 1998.
  • D. X. Peng and F. Lai, “Using partial least squares in operations management research: A practical guideline and summary of past research”, Journal of Operations Management, 30(6), 6, 467–480, 2012.
  • C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, 18(1), 39–50, 1981.
  • M. El-Wajeeh, P. G. H. Galal-Edeen, and D. H. Mokhtar, “Technology Acceptance Model for Mobile Health Systems”, IOSR Journal of Mobile Computing & Application, 1(1), 21–33, 2014.
  • M. Yan, R. Filieri, and M. Gorton, “Continuance intention of online technologies: A systematic literature review”, International Journal of Information Management, 58, 102315, 2021.
  • C. Peng, Z. OuYang, and Y. Liu, “Understanding bike sharing use over time by employing extended technology continuance theory”, Transportation Research Part A: Policy and Practice, 124, 433–443, 2019.
  • L. C. Alain Yee-, “Understanding mobile commerce continuance intentions: An empirical analysis of chinese consumers”, Journal of Computer Information Systems, 53(4), 22–30, 2013.
  • N. Mohamed, R. Hussein, N. H. A. Zamzuri, and H. Haghshenas, “Insights into individual’s online shopping continuance intention”, Industrial Management and Data Systems, 114(9), 1453–1476, 2014.
  • H. Mohammadi, “Investigating users’ perspectives on e-learning: An integration of TAM and IS success model”, Computersin Human Behavior, 45, 359–374, 2015.
  • Z. Lin and R. Filieri, “Airline passengers’ continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge”, Transportation Research Part E: Logistics and Transportation Review, 81(C), 158–168, 2015.
  • J. Lu, “Are personal innovativeness and social influence critical to continue with mobile commerce?”, Internet Research, 24(2), 134–159, 2014.
  • A. Susanto, Y. Chang, and Y. Ha, “Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model”, Industrial Management and Data Systems, 116(3), 508–525, 2016.
  • S. Y. Park, M. W. Nam, and S. B. Cha, “University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model”, British Journal of Educational Technology, 43(4), 592–605, 2012.
  • R. Miao et al., “Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach”, International Journal of Production Research, 55(19), 5801–5815, 2017.
  • I. Ajzen, “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, 50(2), 179–211, 1991.
  • V. Venkatesh and F. D. Davis, “Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies”, Management Science, 46(2), 186–204, 2000.
  • C. C. Lee and M. C. Hsieh, “The influence of mobile self-efficacy on attitude towards mobile advertising”, 2009 International Conference on New Trends in Information and Service Science, Beijing, Çin, 30 Haziran- 2 Temmuz 2009.
  • I. F. Liu, M. C. Chen, Y. S. Sun, D. Wible, and C. H. Kuo, “Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community”, Computers and. Education., 54, 600–610, 2010.
  • Internet: I. Ajzen, “Constructing a theory of planned behavior questionnaire,” http://people.umass.edu/~aizen/pdf/tpb.measurement.pdf, 24.11. 2020.
There are 49 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Nurcan Alkış 0000-0002-6393-8907

Duygu Fındık Coşkunçay 0000-0002-8932-5615

Publication Date October 31, 2021
Submission Date February 20, 2021
Published in Issue Year 2021 Volume: 14 Issue: 4

Cite

APA Alkış, N., & Fındık Coşkunçay, D. (2021). Covid-19 Salgınında Hayat Eve Sığar (HES) Uygulamasının Kullanıcılar Tarafından Benimsenmesi: Ampirik Bir Çalışma. Bilişim Teknolojileri Dergisi, 14(4), 367-376.