Integrate UiPath into Robotic Process Automation in Accounting Course Teaching

Liang Xinna
Thailand
https://orcid.org/0009-0007-1594-0045
Changhan Li
Thailand
https://orcid.org/0009-0004-5768-6733
Keywords: UiPath, RPA, Behavioral intention, UTAUT, Structural Equation Modeling (SEM)
Published: Jan 17, 2025

Abstract

Background and Aim: The study aims to investigate impacting factors of behavioral intention of accounting students in using RPA as a burgeoning technology and selecting UiPath as the preferred tool in accounting course. This study also combines the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Decomposed Theory of Planned Behavior (DTPB) in an attempt to assess students' perceptions of learning with UiPath in the context of using these three models. Testify that perceived behavioral control and attitude play a significant role in behavioral intention to use.


Materials and Method: In this study, a total of 470 junior students in three accounting majors at a private university in Zhanjiang (Guangdong province, China) participated in the study. The research utilized structural equation modeling (SEM) for hypothesis testing.


Results: The study results show that perceived behavioral control and social influence had a more significant impact on behavioral intention to use, and provide relevant information for students majoring in accounting to implement RPA by using UiPath. This research progress is discussed effort expectancy, performance expectancy, and attitude, self-efficacy had a stronger significant impact on perceived behavioral control.


Conclusion: The influence of perceived behavioral control on behavioral intention to use highlights the significance of self-efficacy and facilitating conditions in enabling students to utilize UiPath. Another element impacting students' behavioral intentions toward UiPath is social influence, with a p-value below 0.05. The platform's adoption and acceptance have been greatly aided by the backing and impact of colleagues, advisors, and the broader scholarly circle. Collectively, these findings enhance our comprehension of the intricate factors affecting students' views and actions regarding financial numeration.

Article Details

How to Cite

Xinna , L., & Li, C. . (2025). Integrate UiPath into Robotic Process Automation in Accounting Course Teaching . International Journal of Sociologies and Anthropologies Science Reviews, 5(1), 177–192. https://doi.org/10.60027/ijsasr.2025.5264

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References

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Al-Debei, M. M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behavior perspective. Decision support systems, 55(1), 43-54.

Ambarwati, R.,& Mandasari, B. (2020).THE INFLUENCE OF ONLINE CAMBRIDGE DICTIONARY TOWARD STUDENTS’ PRONUNCIATION AND VOCABULARY MASTERY. Journal of English Language Teaching and Learning. 1(2), 50-55. DOI:10.33365/jeltl.v1i2.605

Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and cybernetics-Part A: Systems and humans, 30(4), 411-420.

Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher Education, 25, 1-11.

Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10-114.

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652-1654.

Deloitte. 2017. The 3rd annual global robotics survey. Retrieved from: https://www2. deloitte.com/lu/en/pages/technology/articles/robotsare-ready.html

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719-734.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.

Fussell, S. G., & Truong, D. (2022). Using virtual reality for dynamic learning: an extended technology acceptance model. Virtual Reality, 26(1), 249-267.

Gao, L., Vongurai, R., Phothikitti, K., & Kitcharoen, S. (2022). Factors influencing university students’ attitude and behavioral intention towards online learning platform in Chengdu, China. ABAC ODI Journal Vision. Action. Outcome, 9(2), 21-37.

Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1-8.

Kadir, A., AlHosani, A.H., Ismail, F., & Sehan, N. (2019). The effect of compensation and benefits on employee performance. In Proceedings of the 1st Asian Conference on Humanities, Industry, and Technology for Society, ACHITS 2019, 30-31 July 2019, Surabaya, Indonesia

Keys, B., & Zhang, Y. J. (2020). Introducing RPA in an undergraduate AIS course: Three RPA exercises on process automation in accounting. Journal of Emerging Technologies in Accounting Teaching Notes, 17(2), 8-45.

Khechine, H., Lakhal, S., Pascot, D., & Bytha, A. (2014). UTAUT model for blended learning: The role of gender and age in the intention to use webinars. Interdisciplinary Journal of E-Learning and Learning objects, 10(1), 33-52.

Kheng, L. L., Mahamad, O., & Ramayah, T. (2010). The impact of service quality on customer loyalty: A study of banks in Penang, Malaysia. International journal of marketing studies, 2(2), 57-60.

Kokina, J., & Blanchette, S. (2019). Early evidence of digital labor in accounting: Innovation with Robotic Process Automation. International Journal of Accounting Information Systems, 35, 100431.

Mahande, R. D., & Malago, J. D. (2019). An E-Learning Acceptance Evaluation through the UTAUT Model in a Postgraduate Program. Journal of Educators Online, 16(2), 1-10.

Mahmodi, M. (2017). The analysis of the factors affecting the acceptance of E-learning in higher education. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 8(1), e11158. Doi: 10.5812/ijvlms.11158

Manis, K. T., & Choi, D. (2019). The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100, 503-513.

McCoy, S., Galletta, D. F., & King, W. R. (2007). Applying TAM across cultures: the need for caution. European Journal of Information Systems, 16(1), 81-90.

Nair, K. S. (2018). Impact of robots in the financial sector. IOSR Journal of Business and Management (IOSR-JBM, 72-76. https://www.iosrjournals.org/iosr-jbm/papers/Conf.ADMIFMS1808-2018/Volume-1/11.%2072-76.pdf

Ngampornchai, A., & Adams, J. (2016). Students’ acceptance and readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, 13, 1-13.

Nor, K. M., & Pearson, J. M. (2008). An exploratory study into the adoption of Internet banking in a developing country: Malaysia. Journal of Internet Commerce, 7(1), 29-73.

Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation, and UTAUT 2 to understand e-learning adoption in the era of the COVID-19 pandemic. Education and Information Technologies, 27(8), 10705-10730.

Patri, P. (2021). Robotic process automation: challenges and solutions for the banking sector. Prateek Patri, Robotic Process Automation: Challenges and Solutions for the Banking Sector, International Journal of Management, 11(12), 2020.

Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2021). Robotic process automation and artificial intelligence in industry 4.0–a literature review. Procedia Computer Science, 181, 51-58.

Rizvi, A., & Srivastava, N. (2023). Exploring the Potentials of Robotic Process Automation: A Review. Journal of Informatics Electrical and Electronics Engineering (JIEEE), 4(2), 1-12.

Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet research, 14(3), 213-223.

Shore, L., Power, V., De Eyto, A., & O’Sullivan, L. W. (2018). Technology acceptance and user-centered design of assistive exoskeletons for older adults: A commentary. Robotics, 7(4), 78. DOI:10.3390/robotics7040078

Sivo, S. A., Ku, C. H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4), 72–91. https://doi.org/10.14742/ajet.2806

Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2018). Personality predictions based on user behavior on the Facebook social media platform. IEEE Access, 6, 61959-61969.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.

Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852.

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

Venkatesh, V., Thong, J.Y.L. and Xu, X. (2012) Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36, 157-178.

Wang, T. (2021). The impact of emerging technologies on the accounting curriculum and the accounting profession. Pacific Accounting Review, 34(4), 526-535.

Zahid, H., & Haji Din, B. (2019). Determinants of intention to adopt e-government services in Pakistan: An imperative for sustainable development. Resources, 8 (3), 128. https://doi.org/10.3390/resources8030128

Zhu, Y. Q., & Kanjanamekanant, K. (2023). Human–bot co-working: job outcomes and employee responses. Industrial Management & Data Systems, 123(2), 515-533.