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Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period

Author

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  • Thanatorn Chuenyindee

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines
    Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand)

  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines)

  • Yogi Tri Prasetyo

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines)

  • Satria Fadil Persada

    (Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Malang 65154, Indonesia)

  • Reny Nadlifatin

    (Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia)

  • Thaninrat Sittiwatethanasiri

    (Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand)

Abstract

Thai Chana is one of the mobile applications for COVID-19 disease-control tracking, especially among the Thais. The purpose of this study was to determine factors affecting the perceived usability of Thai Chana by integrating protection motivation theory, the extended technology acceptance model, and the system usability scale. In all, 800 Thais participated and filled an online questionnaire with 56 questions during the early COVID-19 omicron period (15 December 2021 to 14 January 2022). Structural equation modeling (SEM) showed that the understanding of COVID-19 has significant effects on perceived severity and perceived vulnerability, which subsequently leads to perceived usefulness. In addition, perceived usefulness and perceived ease of use have significant direct effects on attitude, which subsequently leads to the intention to use, actual use, and perceived usability. This study is one of the first studies that have analyzed the mobile application for COVID-19 disease-control tracking. The significant and substantial findings can be used for a theoretical foundation, particularly in designing a new mobile application for disease-control tracking worldwide. Finally, protection motivation theory, the extended technology acceptance model, and the system usability scale can be used for evaluating other disease-control tracking mobile applications worldwide.

Suggested Citation

  • Thanatorn Chuenyindee & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Satria Fadil Persada & Reny Nadlifatin & Thaninrat Sittiwatethanasiri, 2022. "Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period," IJERPH, MDPI, vol. 19(7), pages 1-16, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:7:p:4383-:d:787697
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    References listed on IDEAS

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    Cited by:

    1. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kirstien Paola E. Robas & Satria Fadil Persada & Reny Nadlifatin & James Steven A. Matillano & Dennis Christian B. Macababbad & Jigger R. Pabustan & Kurt And, 2023. "Determination of Factors Influencing the Behavioral Intention to Play “Mobile Legends: Bang-Bang” during the COVID-19 Pandemic: Integrating UTAUT2 and System Usability Scale for a Sustainable E-Sport ," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    2. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Ralph Andre C. Roque & Jan Gabriel I. Garbo & Kirstien Paola E. Robas & Satria Fadil Persada & Reny Nadlifatin, 2022. "Determining the Factors Affecting a Career Shifter’s Use of Software Testing Tools amidst the COVID-19 Crisis in the Philippines: TTF-TAM Approach," Sustainability, MDPI, vol. 14(17), pages 1-24, September.
    3. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Nattakit Yuduang & Reny Nadlifatin & Satria Fadil Persada & Kirstien Paola E. Robas & Thanatorn Chuenyindee & Thapanat Buaphiban, 2022. "Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand," IJERPH, MDPI, vol. 19(13), pages 1-28, June.

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