Analisis Penerimaan Teknologi Layanan Distribusi Hyperlokal Menggunakan Model TAM pada PT Inspirasi Bisnis Nusantara

Tiara Ananda Faradia; Anita Anita; Arief Nurdini; Ardhy Lazuardy

doi:10.61132/globe.v3i2.1012

Authors

Tiara Ananda Faradia Universitas Gunadarma
Anita Anita Universitas Gunadarma
Arief Nurdini Universitas Gunadarma
Ardhy Lazuardy Universitas Gunadarma

DOI:

https://doi.org/10.61132/globe.v3i2.1012

Keywords:

Customer Acceptance, Huling service, Hyperlocal distribution, PLS-SEM, Technology Acceptance Model (TAM)

Abstract

The rapid pace of urbanization has intensified competition among businesses, prompting the adoption of innovative strategies to enhance sales performance and customer engagement. PT Inspirasi Bisnis Nusantara, under the HAUS! brand, introduced “Huling,” a hyperlocal distribution service utilizing electric bikes (e-bikes) equipped with integrated storage boxes to improve delivery efficiency and environmental sustainability. This study aims to evaluate customer acceptance of the Huling service by applying the Technology Acceptance Model (TAM). The model incorporates five core variables: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Towards Usage (ATU), Behavioral Intention (BI), and Actual Usage (AU). A quantitative research approach was employed, collecting data from 258 respondents through structured questionnaires. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to assess the relationships among the TAM variables. The findings reveal that PEOU has a significant positive effect on both PU and ATU, indicating that ease of use not only improves perceptions of usefulness but also fosters favorable attitudes toward adoption. PU significantly influences ATU and BI, suggesting that perceived benefits drive both positive attitudes and intentions to use the service. Furthermore, BI has a direct and significant effect on AU, confirming that stronger behavioral intentions translate into actual utilization. The analysis highlights the strongest impact along the pathway PEOU → ATU → BI → AU, underscoring the critical role of user-friendly design and positive attitudes in driving customer adoption. This research contributes to the literature by demonstrating the applicability of TAM in the hyperlocal distribution sector, offering practical implications for service providers seeking to enhance user acceptance through improved usability and perceived value. The findings suggest that optimizing ease of use and communicating tangible benefits can significantly increase customer engagement and sustained usage of innovative delivery services.

References

Chau, P. Y. K., & Hu, P. J.-H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699–719. https://doi.org/10.1111/j.1540-5915.2001.tb00978.x

CNBC Indonesia. (2024, January 31). Gerai e-bike keliling hingga menu hits, inovasi bisnis Haus! Indonesia. CNBC Indonesia. https://www.cnbcindonesia.com/market/20240131160753-19-510564/gerai-e-bike-keliling-hingga-menu-hits-inovasi-bisnis-haus-indonesia

Davis, F. (1987). User acceptance of information systems: The technology acceptance model (TAM) [Doctoral dissertation, Massachusetts Institute of Technology].

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624

Hamid, R. (2019). Structural equation modeling (SEM) berbasis varian: Konsep dasar dan aplikasi program SmartPLS 3.2.8 dalam riset bisnis.

Olayode, O. O., Jamei, E., & Alex, F. J. (2025). Integration of e-bikes in public transportation based on their impact, importance, and challenges: A systematic review. Multimodal Transportation, 4, 100182. https://doi.org/10.1016/j.multra.2024.100182

Permadi, A., Irawati, T., & Widada, B. (2023). Analisis perilaku pengguna website sistem informasi akademik menggunakan Technology Acceptance Model (TAM). Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), 11(1), 17–28. https://doi.org/10.30646/tikomsin.v11i1.728

Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003

Sarstedt, M., Ringle, C., & Hair, J. (2017). Partial least squares structural equation modeling. Springer.

Sri, R., & Permata, S. F. (2021). Peningkatan kemampuan analisa data tugas akhir mahasiswa melalui pelatihan program SmartPLS. Jurnal Masyarakat Mandiri, 5(6), 3576–3590. https://doi.org/10.31764/jmm.v5i6.5748

Sukardi, H. M. (2022). Metode penelitian pendidikan tindakan kelas: Implementasi dan pengembangannya. Bumi Aksara.

Sundararajan, A. (2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. MIT Press.

Telukdarie, A., Munsamy, M., & Mohlala, P. (2020). Analysis of the impact of COVID-19 on the food and beverages manufacturing sector. Sustainability, 12(22), 9331. https://doi.org/10.3390/su12229331

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540