Analysis of factors influencing the adoption of MyTens application with the unified theory of acceptance and use of technology 2 (UTAUT 2) model on account managers at Telkom Indonesia

https://doi.org/10.35912/ijamis.v2i2.3229
Published: Jul 25, 2025

Abstract:

Purpose: To support the Five Bold Moves strategy, Telkom Indonesia launched the MyTEnS application to streamline B2B processes for Account Managers (AM). This study investigates the factors influencing MyTEnS adoption and the moderating roles of Age and Job Tenure.

Research methodology: A quantitative method was employed using the extended UTAUT-2 model, incorporating Personal Innovativeness and moderation by Age and Job Tenure. A total of 129 respondents participated, and data were analyzed using SmartPLS 3.0 with path analysis.

Results: Effort Expectancy, Hedonic Motivation, and Personal Innovativeness significantly influence Behavioral Intention. Facilitating Conditions and Behavioral Intention significantly affect Use Behavior. Meanwhile, Performance Expectancy, Social Influence, and Habit show no significant effect on Behavioral Intention. Moderating effects of Age and Job Tenure were significant only in the relationship between Facilitating Conditions and Behavioral Intention.

Conclusions: Personal Innovativeness emerged as the strongest predictor of Behavioral Intention, followed by Hedonic Motivation and Effort Expectancy. Behavioral Intention is the most significant factor influencing actual system usage.

Limitations: The study is limited by its cross-sectional design and focus on a single organization. Some hypotheses were unsupported, possibly due to limited construct measurement.

Contribution: This study extends the UTAUT-2 model with new variables and offers insights into B2B technology adoption within a telecommunications context.

Keywords:
1. Account Manager
2. MyTEnS
3. Personal Innovativeness
4. Technology Adoption
5. UTAUT-2
Authors:
1 . Yosef Febri Wiryawan
Telkom University, Bandung, Indonesia
https://orcid.org/0000-0001-5080-5032
2 . Dodie Tricahyono
Telkom University, Bandung, Indonesia
3 . Muhammad Awaluddin
Telkom University, Bandung, Indonesia
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How to Cite
Wiryawan, Y. F. ., Tricahyono, D., & Awaluddin, M. (2025). Analysis of factors influencing the adoption of MyTens application with the unified theory of acceptance and use of technology 2 (UTAUT 2) model on account managers at Telkom Indonesia. International Journal of Accounting and Management Information Systems, 2(2), 131–142. https://doi.org/10.35912/ijamis.v2i2.3229
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Issue & Section
Vol. 2 No. 2 (2024): August
Articles
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References

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    Wu, Q., Tian, J., & Liu, Z. (2025). Exploring the usage behavior of generative artificial intelligence: a case study of ChatGPT with insights into the moderating effects of habit and personal innovativeness. Current Psychology, 1-14. doi:https://doi.org/10.1007/s12144-024-07193-w

    Yuliani, P. N., Suprapti, N. W. S., & Piartrini, P. S. (2024). The Literature Review on UTAUT 2: Understanding Behavioral Intention and Use Behavior of Technology in the Digital Era. International Journal of Social Science and Business, 8(2), 208-222.

    Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 1-39. doi:https://doi.org/10.1007/s10639-024-13299-2

  1. Abdunool, K., Ali, M., Muhammad, S., & Warda, S. (2024). The Mediating Effect of Behavioral Intention In The Relationship Between Social Influence And Wireless Technology Usage. International Journal of Wireless & Mobile Networks (IJWMN), 16(6). doi:https://doi.org/10.5121/ijwmn.2024.16603
  2. Ariyantho, M. R., & Sutjipto, M. R. (2024). Transforming Financial Management: An RPA Implementation Case Study at PT Telkom Indonesia.
  3. Calista, A. F. I. (2024). PT Telkom Indonesia’s Digital Transformation Strategy Facing The Digital Era by Utilizing Corporate Communications. Universitas Islam Indonesia.
  4. Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247. doi:https://doi.org/10.1016/j.techfore.2024.123247
  5. Feng, K., & Haridas, D. (2025). A unified model integrating UTAUT-Behavioural intension and Object-Oriented approaches for sustainable adoption of Cloud-Based collaborative platforms in higher education. Scientific Reports, 15(1), 24767. doi:https://doi.org/10.1038/s41598-025-08446-9
  6. Foroughi, B., Iranmanesh, M., Asadi, S., Al-Emran, M., Ghobakhloo, M., & Batouei, A. (2025). Extending UTAUT2 to explore intention to use ChatGPT for travel planning: a hybrid PLS-ANN approach. Journal of Tourism Futures.
  7. García de Blanes Sebastián, M., Sarmiento Guede, J. R., & Antonovica, A. (2022). Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants. Frontiers in Psychology, 13, 993935.
  8. GC, S. B., Bhandari, P., Gurung, S. K., Srivastava, E., Ojha, D., & Dhungana, B. R. (2024). Examining the role of social influence, learning value and habit on students’ intention to use ChatGPT: the moderating effect of information accuracy in the UTAUT2 model. Cogent Education, 11(1), 2403287. doi:https://doi.org/10.1080/2331186x.2024.2403287
  9. Gharaibeh, M. K., & Arshad, M. R. M. (2018). Determinants of intention to use mobile banking in the North of Jordan: extending UTAUT2 with mass media and trust. Journal of Engineering and Applied Sciences, 13(8), 2023-2033.
  10. Hameed, M. A., & Arachchilage, N. A. G. (2020). A conceptual model for the organizational adoption of information system security innovations Security, Privacy, and Forensics Issues in Big Data (pp. 317-339): IGI Global.
  11. Indrawati, P. D. (2015). Metode penelitian manajemen dan bisnis konvergensi teknologi komunikasi dan informasi. Bandung: PT Refika Aditama.
  12. Kuntadi, A., Sumarwan, U., Najib, M., & Jahroh, S. (2020). The Effects of Gender and Tenure on the Relationship between Decision-Makers’ Behavioral Preferences and University’s Inno-Vations Adoption. Manag. Sci. Lett, 10, 3445-3452.
  13. Lin, W., & Jiang, P. (2025). Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China. Behavioral Sciences, 15(3), 295. doi:https://doi.org/10.3390/bs15030295
  14. Mir, F. A. (2025). An integrated autonomous vehicles acceptance model: Theoretical development and results based on the UTAUT2 model. Transportation Research Part F: Traffic Psychology and Behaviour, 112, 290-304.
  15. Nassar, A. A., Othman, K., & Nizah, M. (2019). The impact of the social influence on ICT adoption: Behavioral intention as mediator and age as moderator. International Journal of Academic Research in Business and Social Sciences, 9(11), 963-978. doi:https://doi.org/10.6007/ijarbss/v9-i11/6620
  16. Ngusie, H. S., Kassie, S. Y., Zemariam, A. B., Walle, A. D., Enyew, E. B., Kasaye, M. D., . . . Mengiste, S. A. (2024). Understanding the predictors of health professionals' intention to use electronic health record system: extend and apply UTAUT3 model. BMC Health Services Research, 24(1), 889.
  17. Nguyen, M.-H., Sari, N. P. W. P., Li, D., & Vuong, Q.-H. (2025). Impacts of social influence, social media usage, and classmate connections on Moroccan nursing students’ ICT using intention. Teaching and Learning in Nursing, 20(1), e106-e117. doi:https://doi.org/10.1016/j.teln.2024.08.014
  18. Pan, Y.-C., Jacobs, A., Tan, C., & Tehraini, J. (2022). Exploring Consumer Mobile Payment Adoption: A Multi-Country Study. Communications of the IIMA, 20(1). doi:https://doi.org/10.58729/1941-6687.1432
  19. Rita, R., Setiawan, M., & Yuniarinto, A. (2022). Continuance intention of mobile payment user in Indonesia: Gender and age as moderating variables. Neuroquantology.
  20. Rumangkit, S., Surjandy, S., & Billman, A. (2023). The effect of performance expectancy, facilitating condition, effort expectancy, and perceived easy to use on intention to using media support learning based on unified theory of acceptance and use of technology (UTAUT). Paper presented at the E3S Web of Conferences.
  21. Senshaw, D., & Twinomurinzi, H. (2021). The moderating effect of gender on adopting digital government innovations in Ethiopia. arXiv preprint arXiv:2108.09960.
  22. Shirali, G., Shekari, M., & Angali, K. A. (2018). Assessing reliability and validity of an instrument for measuring resilience safety culture in sociotechnical systems. Safety and health at work, 9(3), 296-307. doi:https://doi.org/10.1016/j.shaw.2017.07.010
  23. Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative higher education, 49(2), 223-245. doi:https://doi.org/10.1007/s10755-023-09686-1
  24. Susetyo, A. S. A., Abrar, A. N., Darwin, M. M., & Djunaedi, A. (2024). Strategic renewal through digital transformation: Insights from Indonesia’s telecom industry. Journal of Infrastructure, Policy and Development, 8(10). doi:http://dx.doi.org/10.24294/jipd.v8i10.7253
  25. Sutanto, S., Ghozali, I., & Handayani, R. S. (2018). Faktor-faktor yang memengaruhi penerimaan dan penggunaan sistem informasi pengelolaan keuangan daerah (sipkd) dalam perspektif the unified theory of acceptance and use of technology 2 (utaut 2) di kabupaten semarang. Jurnal Akuntansi Dan Auditing, 15(1), 37-68. doi:https://doi.org/10.14710/jaa.15.1.37-68
  26. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178. doi:https://doi.org/10.2307/41410412
  27. Wu, Q., Tian, J., & Liu, Z. (2025). Exploring the usage behavior of generative artificial intelligence: a case study of ChatGPT with insights into the moderating effects of habit and personal innovativeness. Current Psychology, 1-14. doi:https://doi.org/10.1007/s12144-024-07193-w
  28. Yuliani, P. N., Suprapti, N. W. S., & Piartrini, P. S. (2024). The Literature Review on UTAUT 2: Understanding Behavioral Intention and Use Behavior of Technology in the Digital Era. International Journal of Social Science and Business, 8(2), 208-222.
  29. Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 1-39. doi:https://doi.org/10.1007/s10639-024-13299-2