Model HOT-FIT dalam Menilai Keberhasilan Implementasi Aplikasi Gemini serta Dampaknya pada Kepuasan Pengguna
Abstract
The rapid development of Artificial Intelligence (AI) has increased the use of the Gemini application to support information retrieval, task completion, and work productivity. This study aims to analyze the factors influencing the successful use of the Gemini application using the Human-Organization-Technology Fit (HOT-FIT) model. The variables examined include Service Quality, User Satisfaction, Organizational Structure, and Net Benefit. A quantitative approach with purposive sampling was employed. Data were collected from 100 respondents, consisting of students, lecturers, and employees, and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results indicate that all constructs meet the validity and reliability requirements. The R-Square values of 0,588 for User Satisfaction and 0,676 for Net Benefit demonstrate satisfactory explanatory power. Hypothesis testing shows that Service Quality has a positive and significant effect on User Satisfaction (β=0.767; p<0.001), User Satisfaction has a positive and significant effect on Net Benefit (β=0.274; p=0.023), and Organizational Structure has a positive and significant effect on Net Benefit (β=0.599; p<0.001). These findings indicate that the HOT-FIT model effectively explains the success of the Gemini application, highlighting the importance of service quality and organizational support in maximizing its benefits.
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References
Davenport & Ronanki, “Artificial intelligence for the real world. Harvard Business Review,” 96(1). [Online]. Available: https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
G. Deepmind, “Advances in Generative AI and Multimodal Reasoning,” Deep. Publ., 2024.
“The DeLone and McLean Model of Information Systems Success: A Ten-Year Update,” J. Manag. Inf. Syst., vol. 19, no. 4, pp. 9–30, Apr. 2003, doi: 10.1080/07421222.2003.11045748.
M. M. Yusof, J. Kuljis, A. Papazafeiropoulou, and L. K. Stergioulas, “An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit),” Int. J. Med. Inform., vol. 77, no. 6, pp. 386–398, Jun. 2008, doi: 10.1016/j.ijmedinf.2007.08.011.
S. Petter, W. DeLone, and E. McLean, “Measuring information systems success: models, dimensions, measures, and interrelationships,” Eur. J. Inf. Syst., vol. 17, no. 3, pp. 236–263, Jun. 2008, doi: 10.1057/ejis.2008.15.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.
R. N. Sinaga, M. Mitra, J. Yunita, R. Renaldi, and A. S. Efendi, “MODEL STRUKTURAL SISTEM INFORMASI MANAJEMEN RUMAH SAKIT BERDASARKAN FRAMEWORK HOT-FIT MODEL,” Media Penelit. dan Pengemb. Kesehat., vol. 35, no. 2, pp. 528–542, Jun. 2025, doi: 10.34011/jmp2k.v35i2.2537.
Z. Saputra, R. Siwi Pradini, and A. Naseh Khudori, “Analisis Model HOT-Fit terhadap Evaluasi Sistem Informasi Manajemen Puskesmas Kabupaten Lumajang dengan PLS-SEM,” J. Pendidik. dan Teknol. Indones., vol. 5, no. 1, pp. 227–236, Feb. 2025, doi: 10.52436/1.jpti.632.
M. Widyaningrum, M. R., Wiliyanarti, P. F., & Samiyah, “Validitas dan Reliabilitas Kuesioner Model HOT-FIT pada Penggunaan SIMRS,” J. Telenursing (JOTING, vol. 7, no. 2, 2025, doi: 10.31539/joting.v7i2.14529.
J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 8th ed. Boston, MA, USA: Cengage Learning, 2019.
J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, Jan. 2015, doi: 10.1007/s11747-014-0403-8.
Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295–336). Lawrence Erlbaum Associates.
C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, Feb. 1981, doi: 10.1177/002224378101800104.
M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” in Handbook of Market Research, Cham: Springer International Publishing, 2021, pp. 1–47. doi: 10.1007/978-3-319-05542-8_15-2.
F. (2022). Maghfiroh, H., Ahmad, M., Ramelan, A., & Adriyanto, “Fuzzy-PID in BLDC Motor Speed Control Using MATLAB/Simulink,” J. Robot. Control, vol. 3, no. 1, pp. 8–13, 2022, doi: 10.18196/jrc.v3i1.1096.
M. A. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using SmartPLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis. 2018.
M. Tavakol and R. Dennick, “Making sense of Cronbach’s alpha,” Int. J. Med. Educ., vol. 2, pp. 53–55, Jun. 2011, doi: 10.5116/ijme.4dfb.8dfd.
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