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Investigating Accessibility of Social Security System (SSS) Mobile Application: A Structural Equation Modeling Approach

Author

Listed:
  • Yung-Tsan Jou

    (Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan)

  • Klint Allen Mariñas

    (Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan
    School of Industrial Engineering and Engineering Management, Mapua University, Manila 1002, Philippines
    Department of Industrial Engineering, Occidental Mindoro State College, San Jose 5100, Philippines)

  • Charmine Sheena Saflor

    (Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan
    Department of Industrial Engineering, Occidental Mindoro State College, San Jose 5100, Philippines)

  • Michael Nayat Young

    (School of Industrial Engineering and Engineering Management, Mapua University, Manila 1002, Philippines)

Abstract

Due to the Philippines’ current condition in COVID-19, the Social Security System (SSS) has launched a mobile app as an intervention for walk-in appointments and another convenient way to exchange information. By integrating the extended Technology Acceptance Model (TAM) with the Theory of Planned Behavior (TPB), this study scrutinizes and investigates the various factors influencing the SSS mobile app’s accessibility. An online questionnaire composed of 60 items required at least 200 respondents. The researchers measured twelve latent variables, including social influence, awareness of service, computer self-efficacy, quality of internet connection, perceived ease of use, perceived usefulness, subjective norms, the impression of use, demographic, attitude towards using, behavioral intention to use, and actual use of SSS mobile app. The results of Structural Equation Modeling (SEM) indicated that awareness of service had a substantial direct influence on computer self-efficacy and perceived usefulness. In addition, computer self-efficacy had a substantial direct influence on the quality of internet connection and perceived ease of used, in which this perceived ease of use had a substantial direct influence on perceived usefulness and attitude toward using the mobile application. This study’s findings can be used to provide information on the key factors that have been identified as having a considerable effect on the app’s accessibility and further improving the overall service efficiency of the SSS mobile app. Finally, the study’s model construct will be valuable for researchers and other sectors investigating user–software interaction of applicable government or private service mobile applications.

Suggested Citation

  • Yung-Tsan Jou & Klint Allen Mariñas & Charmine Sheena Saflor & Michael Nayat Young, 2022. "Investigating Accessibility of Social Security System (SSS) Mobile Application: A Structural Equation Modeling Approach," Sustainability, MDPI, vol. 14(13), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7939-:d:851561
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    References listed on IDEAS

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    1. Choi, Kui-Son & Cho, Woo-Hyun & Lee, Sunhee & Lee, Hanjoon & Kim, Chankon, 2004. "The relationships among quality, value, satisfaction and behavioral intention in health care provider choice: A South Korean study," Journal of Business Research, Elsevier, vol. 57(8), pages 913-921, August.
    2. Kamal, Syeda Ayesha & Shafiq, Muhammad & Kakria, Priyanka, 2020. "Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)," Technology in Society, Elsevier, vol. 60(C).
    3. Grilli, Gianluca & Curtis, John & Hynes, Stephen & O’Reilly, Paul, 2018. "Sea Bass Angling in Ireland: A Structural Equation Model of Catch and Effort," Ecological Economics, Elsevier, vol. 149(C), pages 285-293.
    4. Ocampo, Lanndon & Yamagishi, Kafferine, 2020. "Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    5. Shahzad, Fakhar & Xiu, Guoyi & Shafique Khan, Muhammad Aamir & Shahbaz, Muhammad, 2020. "Predicting the adoption of a mobile government security response system from the user's perspective: An application of the artificial neural network approach," Technology in Society, Elsevier, vol. 62(C).
    6. Djimesah, Isaac Edem & Zhao, Hongjiang & Okine, Agnes Naa Dedei & Li, Yao & Duah, Elijah & Kissi Mireku, Kingsford, 2022. "Analyzing the technology of acceptance model of Ghanaian crowdfunding stakeholders," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    8. Cruz-Cárdenas, Jorge & Zabelina, Ekaterina & Deyneka, Olga & Guadalupe-Lanas, Jorge & Velín-Fárez, Margarita, 2019. "Role of demographic factors, attitudes toward technology, and cultural values in the prediction of technology-based consumer behaviors: A study in developing and emerging countries," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    9. Lam, Shun Yin & Chiang, Jeongwen & Parasuraman, A., 2008. "The effects of the dimensions of technology readiness on technology acceptance: An empirical analysis," Journal of Interactive Marketing, Elsevier, vol. 22(4), pages 19-39.
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    Cited by:

    1. Yung-Tsan Jou & Charmine Sheena Saflor & Klint Allen Mariñas & Michael Nayat Young, 2023. "Determining Factors Affecting Perceived Customer Satisfaction on Public Utility Bus System in Occidental Mindoro, Philippines: A Case Study on Service Quality Assessment during Major Disruptions," Sustainability, MDPI, vol. 15(4), pages 1-21, February.

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