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Predicting College Students’ Squared Platform Utilization: A Multiple Regression Analysis of the Technology Acceptance Model

Author

Listed:
  • Justine G. Landicho

    (Davao Central College, Toril, Davao City, Philippines)

  • Roberto Jr. P. Tacbobo

    (Davao Central College, Toril, Davao City, Philippines)

  • Alkhaser V. Sappayani

    (Davao Central College, Toril, Davao City, Philippines)

Abstract

This study explored the factors that influence college students’ use of the Squared Platform, a QR code-based attendance system created by the College Supreme Student Government of Davao Central College in Davao City. The research was anchored on the Technology Acceptance Model (TAM), which focuses on how perceived ease of use, perceived usefulness, and behavioral intention affect actual system use. Using a predictive-correlational design, data were collected from a stratified sample of 368 enrolled students from five different departments through a validated and reliable questionnaires. The responses were analyzed using frequency, percentage, mean, ANOVA, Pearson correlation, and multiple regression analysis. Findings revealed that students had a very high level of acceptance toward the platform across all TAM constructs. No significant differences were found in terms of year level, but there were significant differences across departments, especially in perceived usefulness. Strong positive relationships were also found between each TAM variable and students’ actual usage behavior, with behavioral intention having the strongest influence. The combined predictors significantly explained more than half of the variance in actual usage. The results support the Technology Acceptance Model, proving that ease of use and usefulness influence students’ intention and actual behavior in using educational technologies like the Squared Platform. Based on the findings, it is recommended that the school promote training, raise awareness, and offer department-specific support to improve technology adoption and ensure effective implementation across all programs.

Suggested Citation

  • Justine G. Landicho & Roberto Jr. P. Tacbobo & Alkhaser V. Sappayani, 2025. "Predicting College Students’ Squared Platform Utilization: A Multiple Regression Analysis of the Technology Acceptance Model," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(8), pages 7432-7444, August.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-8:p:7432-7444
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    References listed on IDEAS

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    1. Xiaole Wu & Walton Wider & Ling Shing Wong & Choon Kit Chan & Siti Sarah Maidin, 2023. "Integrating the technology acceptance model on online learning effectiveness of emerging adult learners in Guangzhou, China," International Journal of Education and Practice, Conscientia Beam, vol. 11(2), pages 129-140.
    2. Liqiu Zhou & Sijia Xue & Ruiqian Li, 2022. "Extending the Technology Acceptance Model to Explore Students’ Intention to Use an Online Education Platform at a University in China," SAGE Open, , vol. 12(1), pages 21582440221, March.
    3. 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.
    4. Meennapa Rukhiran & Sethapong Wong-In & Paniti Netinant, 2023. "User Acceptance Factors Related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
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