Demographic Factors as Determinants of Garment Workers’ Performance
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This study examined the influence of demographic characteristics and training exposure on the performance of garment workers. Using a descriptive-quantitative research design, 100 respondents from the local garment sector were surveyed with a structured questionnaire capturing sex, age, educational attainment, years of experience, eligibility certifications, and training attendance. Data were analyzed using descriptive statistics and independent t-tests. Results showed that eligibility certifications and training attendance significantly affect worker performance, while sex, age, educational attainment, and years of experience do not. These findings emphasize the critical role of structured training programs and formal certifications in enhancing productivity and effectiveness in the garment industry. Recommendations include implementing targeted training initiatives and supporting skill certification to optimize workforce performance and sustain industry growth.
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1. Ramel, R. M. (2020). Pedagogical practices and strategies of technology and livelihood education teachers in a public high school. International Journal of Scientific and Research Publications, 10(11), 1-9.
2. Almazan, J. P., & Pascual, N. A. (2020). Adjustment and challenges of technology and livelihood education teachers in K to 12 curriculum. International Journal of Advanced Research and Publications, 4(11), 1-61
3. Bautista, J. C., & Pascual, N. A. (2021). Effectiveness of technology and livelihood education (TLE) learning modules in the academic performance and attitude of grade 10 students. AsianAcademic Journal of Multidisciplinary Research, 3(1), 1-92
4. Pascual, N. A., & Almazan, J. P. (2021). Readiness of technology and livelihood education students in taking the licensure examination for teacher. International Journal of Advanced Research and Publications, 5(1), 1-73
5. Pascual, N. A., & Bautista, J. C. (2020). Access, attitude and interest: Their influence on engagement and performance of students in technology and livelihood education. Central Philippine University Journal of Science, 23(1), 1- 144
6. ADDIE in Action: A Transformational Course Redesign Process. (2019). Journal of Instructional Research, 8, 1-12. https://files.eric.ed.gov/fulltext/EJ1252113.pdf
7. Al-Azawei, A., Parslow, P., & Lundqvist, K. (2023). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 39(1), 1-19.
8. Al-Rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2019). Using TAM to measure the user acceptance of e-learning among university students in Malaysia. Journal of Computing in Higher Education, 31(3), 699-719.
9. Chen, Y., Chen, P., & Tsai, C. (2022). Understanding technology enhanced learning acceptance: An integrated model of TAM.
Copyright (c) 2025 Lileth O. Ulbeda, Roey C. Sumaoy, Stephany A. Hayahay, Anamie A. Ajon
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