Development of a utaut model in the gayatri application in improving employee performance in health service facilities of the Mojokerto city health, population control and family planning department
DOI:
https://doi.org/10.56294/saludcyt20262568Keywords:
UTAUT, Knowledge, Performance, AgeAbstract
Introduction: implementation technology aims to optimize communication to increase benefits such as high productivity, employee welfare, and consumer satisfaction. Goal development theory reception technology is integrated with integrated construction-predictive key intent and uses subsequent behavior known as The Unified Theory of Acceptance and Use of Technology (UTAUT). The UTAUT model has been used to test reception technology in several sectors, such as maintenance health, e-government, mobile Internet, systems companies, mobile banking, and applications.
Methods: In this cross-sectional study, a quantitative study was conducted using survey methods and research data collection, whereas a design study used an analytic correlation. Ethical approval for this study was obtained from Strada University Indonesia.
Results: The analysis results show that Performance Expectancy (PE) has a significant influence on Behavioral Intention (BI) and BI has a significant effect to Use Behavior (UB). In addition , UB had a significant influence on Employee Performance. Effort Expectancy (EE) has a significant effect on BI and no direct influence on UB and performance through two-stage mediation.
Conclusion: The results show that performance expectancy influences employee performance through behavioral intention and use behavior; facilitating conditions have no influence on employee performance through behavioral intention and use behavior; social influence influences employee performance through behavioral intention and use behavior; facilitating conditions have no influence on employee performance through use behavior; use behavior has no influence on employee performance through use behavior; and use behavior has no influence on employee performance with age as a moderating variable.
References
1. Saxena S, RT, dkk. Comparison of conventional and real-time PCR for monitoring of respiratory syncytial virus among pediatric patients in Northern India 2011–2014. IJPSR. 2019;10(5):2294–300. doi:10.13040/IJPSR.0975-8232.10(5).2294-00. DOI: https://doi.org/10.13040/IJPSR.0975-8232.10(5).2294-00
2. Momani AM. The unified theory of acceptance and use of technology. Int J Sociotechnol Knowl Dev. 2020;12(3):79–98. doi:10.4018/IJSKD.2020070105. DOI: https://doi.org/10.4018/IJSKD.2020070105
3. Ayaz A, Yanartaş M. An analysis on the UTAUT: Acceptance of EDMS. J Elsevier. 2020. doi:10.1016/j.chbr.2020.100032. DOI: https://doi.org/10.1016/j.chbr.2020.100032
4. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of IT: Toward a unified view. MIS Q. 2003;27(3):425–78. doi:10.2307/30036540.
5. Venkatesh V, Thong JYL, Xu X. Unified theory of acceptance and use of technology. J Assoc Inf Syst. 2016;17:328–76. DOI: https://doi.org/10.17705/1jais.00428
6. Nurhayati S, Anandari D, Ekowati W. UTAUT model to predict health information system adoption. Kemas. 2019;15(1):89–97. doi:10.15294/kemas.v15i1.12376. DOI: https://doi.org/10.15294/kemas.v15i1.12376
7. Trivedi SK, Patra P, Srivastava PR, Kumar A, Ye F. Factors affecting intention to adopt digital tech. IEEE Trans Eng Manag. 2022;71:13814–26. DOI: https://doi.org/10.1109/TEM.2022.3182361
8. Blut M, Yee A, Chong L, Tsiga Z, Venkatesh V. Meta-analysis of UTAUT. SSRN. 2022.
9. Venkatesh V et al. User acceptance of IT: Toward a unified view. 2012;27(3):425–78. DOI: https://doi.org/10.2307/30036540
10. Neves C, Oliveira T, Cruz-Jesus F, Venkatesh V. Extending UTAUT for sustainable technologies. Int J Inf Manag. 2025;80:102838. DOI: https://doi.org/10.1016/j.ijinfomgt.2024.102838
11. Nurmala I et al. Promosi Kesehatan. Airlangga University Press; 2018.
12. Amnas MB et al. Determinants of FinTech adoption. J Risk Financ Manag. 2023;16(12). DOI: https://doi.org/10.3390/jrfm16120505
13. Wibowo AH et al. Pengaruh performance expectancy... JPTIIK. 2019;3(9):9047–53.
14. Afonso CM et al. Gender in UTAUT. Proc 7th Int Conf Partial Least Squares. 2012.
15. Putri VS, Mahadian AB. Pengaruh ekspektasi kinerja... eProceedings Manag. 2021;8(3).
16. Rofiah C, Suhermin S. Performance expectancy in e-health. IJRAR. 2022.
17. Mansour AT et al. Behavioral intention in e-government SMEs. Turk J Comput Math Educ. 2021;12(1):1520–8.
18. Li F et al. Aging, self-efficacy, time pressure. Psychol Res Behav Manag. 2022;15:1043–54. DOI: https://doi.org/10.2147/PRBM.S359624
19. Gates A. Knowledge management impact. Eur J Inf J Manag. 2024;3(3):1–13. DOI: https://doi.org/10.47941/ejikm.2065
20. Kusumawardani N. SIPKD usage factors. In: Digital Era 4.0. Routledge; 2020.
21. Khairunnisa P, Setyowati MS. SIPD–SIKD satisfaction factors. Asian J Soc Humanit. 2024;3(1):98–116. DOI: https://doi.org/10.59888/ajosh.v3i1.432
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Copyright (c) 2026 Arief Fardiansyah , Ahmad Yunus, Ratna Wardani, Nurwijayanti (Author)
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