Volume 22 (2026) - In progress Download Cover Page

Determinants of Payroll Software Adoption in Accounting Education: A TAM-Based Study among University Students in an Emerging Economy

Article Number: e2026035  |  Available Online: April 2026  |  DOI: 10.22521/edupij.2026.22.35

Alejandro Valencia-Arias , Diana Yanet Gaviria Rodríguez , Juan Guillermo Arango Arango , Jackeline Valencia , Ever Cervera Cervera , Sebastián Cardona-Acevedo

Abstract

Background/Purpose. The integration of information systems in higher education has gained momentum, with accounting software becoming increasingly prominent in accounting education. Despite this trend, limited research has focused on how students perceive these tools. This study addresses this gap by analyzing the factors that influence the intention to use Siigo accounting software among accounting science students in Medellín, Colombia. The primary purpose is to understand students’ behavioral intentions regarding the adoption of this software in their academic training.

Materials/methods. The study applied the Technology Acceptance Model (TAM) as its theoretical foundation and employed a quantitative research approach. Data were collected using a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

Results. The analysis revealed that the variables "vocational training" and "perceived usefulness" significantly influenced students’ intention to use the Siigo software. In contrast, "perceived ease of use" did not exhibit a statistically significant effect on their behavioral intention.

Conclusion. The study provides original evidence on accounting students’ perceptions in an emerging-economy context. The findings suggest that enhancing students' understanding of the usefulness of accounting software and aligning it with vocational training objectives can foster greater adoption. Future research should explore strategies to integrate such tools more effectively into the academic curriculum to maximize their educational impact.

Keywords: Accounting software, university students, accounting education, technological acceptance, electronic payroll, higher education

References

Acosta-Prado, J. C., Montoya, O. H. L., & Villegas, J. H. (2020). Organizational culture and business success: SIIGO case. Dimensión Empresarial, 18(3). https://doi.org/10.15665/dem.v18i3.2422.

Ajzen, I. (1980). Understanding attitudes and predicting social behavior.

Al-Ghatrifi M, O., Al-Sahryani H, S., & Thottoli M, M. (2023). Insights and Driving Forces: Accounting Students’ Perspectives on Computerized Accounting. Revue Africaine de Management -African Management Review, 10(1), 1–12. https://doi.org/10.48424/IMIST.PRSM/ram-v1i10.44723

Alshurafat, H., Shbail, M. O. A., Masadeh, W. M., Dahmash, F., & Al-Msiedeen, J. M. (2021). Factors affecting online accounting education during the COVID-19 pandemic: an integrated perspective of social capital theory, the theory of reasoned action, and the technology acceptance model. Educ Inf Technol (Dordr, 26) (6), 6995–7013. https://doi.org/10.1007/s10639-021-10550-y.

Amin, M. K., Munira, S., Azhar, A., Amin, A., & Karim, M. T. (2016). Factors affecting employees’ behavioral intention to adopt an accounting information system (AIS. Bangladesh. 2016 19th International Conference on Computer and Information Technology (ICCIT. https://doi.org/10.1109/ICCITECHN.2016.7860249

Amora, J. T. (2021). Convergent validity assessment in PLS-SEM: A loadings driven approach. Data Analysis Perspectives Journal, 2(3), 1–6.

Assaker, G. (2020). Age and gender differences in online travel reviews and user-generated-content (UGC) adoption: extending the TAM with credibility theory. Journal of Hospitality Marketing & Management, 29(4), 428–449. https://doi.org/10.1080/19368623.2019.1653807.

Bashir, A., Bashir, S., Rana, K., Lambert, P., & Vernallis, A. (2021). Post COVID-19 adaptations; the shifts towards online learning, hybrid course delivery and the implications for biosciences courses in the higher education setting. In Frontiers in education (Vol. 6, p. 711619). Frontiers Media SA. https://doi.org/10.3389/feduc.2021.711619

Bayon, C., Abejo III, M., Guinocor, M., Garciano, M. J., Literatus, J., Reveche, S. J., & Cortes, S. (2024). Do social cognitive factors influence final year undergraduate students’ intentions to pursue advanced degrees? An examination of the moderating effect of sex. In Frontiers in Education (Vol. 9, p. 1329911). Frontiers Media SA. https://doi.org/10.3389/feduc.2024.1329911

Biduri, S., Hermawan, S., Maryanti, E., Rahayu, R. A., & Utami, N. (2021). The Effect of Computer Anxiety, Computer Attitude, Computer Self Efficacy and Accounting Knowledge on Accounting Students’ Understanding Using Accurate based Accounting Software. 2nd Annual Management, Business and Economic Conference (AMBEC, 50–54.

Borhani, S. A., Babajani, J., Vanani, I. R., Anaqiz, S. S., & Jamaliyanpour, M. (2021). Adopting Blockchain Technology to Improve Financial Reporting by Using the Technology Acceptance Model (TAM. International Journal of Finance & Managerial Accounting, 6(22), 155–171.

Boulianne, E. (2014a). Impact of accounting software utilization on students’ knowledge acquisition: An important change in accounting education. Journal of Accounting & Organizational Change, 10(1), 22–48. https://doi.org/10.1108/JAOC-12-2011-0064.

Boulianne, E. (2014b). Impact of accounting software utilization on students’ knowledge acquisition: An important change in accounting education. Journal of Accounting & Organizational Change, 10(1), 22–48. https://doi.org/10.1108/JAOC-12-2011-0064

Cardona-Acevedo, S. (2025). E-learning technologies in secondary education: a literature review. Front Educ, Lausanne), 10. https://doi.org/10.3389/feduc.2025.1539763.

Carlson, K. D., & Herdman, A. O. (2012). Understanding the Impact of Convergent Validity on Research Results. Organizational Research Methods, 15(1), 17–32. https://doi.org/10.1177/1094428110392383

Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technol Forecast Soc Change, 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880.

Chrismastuti, A. A., Nugroho, R. S. A., Adriani, A., Purnamasari, V., & Ratnaningsih, S. D. A. (2019). Accounting software for MSMEs: Organizational and personal factors based on TAM theory. South East Asia Journal of Contemporary Business, Economics and Law, 19(1), 1–7.

Damerji, H., & Salimi, A. (2021a). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035

Damerji, H., & Salimi, A. (2021b). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035.

Davis, F. D. (1985). A technology acceptance model for empirically testing new enduser information systems: Theory and results.

Davis, F. D. (1989a). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.

Davis, F. D. (1989b). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Duman, H., Apak, İ., Yücenurşen, M., & Peker, A. A. (2015). Determining the anxieties of accounting education students: A sample of Aksaray University. Procedia Social and Behavioral Sciences, 174, 1834–1840. https://doi.org/10.1016/j.sbspro.2015.01.845

Fakhri, M. M., Fadhilatunisa, D., B, Y., Sari, N. R., & Rosidah. (2022). The use of the extended echnology acceptance model (tam) to measure behavioral intention users of zahir accounting software. Assets: Jurnal Ekonomi, Manajemen Dan Akuntansi, 12(1), 107–123. https://doi.org/10.24252/assets.v1i1.29048.

Ferri, L., Spanò, R., Ginesti, G., & Theodosopoulos, G. (2021). Ascertaining auditors’ intentions to use blockchain technology: Evidence from the Big 4 accountancy firms in Italy. Meditari Accountancy Research, 29(5), 1063–1087. https://doi.org/10.1108/MEDAR-03-2020-0829

García, M. B. (2023). Factors Affecting Adoption Intention of Productivity Software Applications Among Teachers: A Structural Equation Modeling Investigation. Int J Hum Comput Interact. https://doi.org/10.1080/10447318.2022.2163565.

Gaviria Rodriguez, D., Arango Arango, J., Valencia Arias, A., & Bran Piedrahita, L. (2019). Percepción de la estrategia aula invertida en escenarios Universitario. Revista mexicana de investigación educativa, 24(81), 593–614.

Gaviria, D., Arango, J., Valencia-Arias, A., Palacios-Moya, L., Velez Holguin, R., & Gallegos Ruiz, A. L. (2022). Factors That Affect the Usage Intention of Virtual Learning Objects by College Students. Informatics, 9(3), 65. https://doi.org/10.3390/informatics9030065

Ghasemy, M., Teeroovengadum, V., Becker, J. M., & Ringle, C. M. (2020). This fast car can move faster: a review of PLS-SEM application in higher education research. Higher Education, 80, 1121–1152. https://doi.org/10.1007/s10734-020-00534-1

Hair Jr, 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.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130

Hamid, M. R. A., Sami, W., & Sidek, M. H. M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 12163. https://doi.org/10.1088/1742-6596/890/1/012163

Hanh, T. T. H., & Boonstra, W. J. (2019). What prevents small-scale fishing and aquaculture households from engaging in alternative livelihoods? A case study in the Tam Giang lagoon. Viet Nam. Ocean & Coastal Management, 182, 104943. https://doi.org/10.1016/j.ocecoaman.2019.104943.

Hassan, H., Mohamad Hsbollah, H., & Raja Mohd Ali, R. H. (2021). Accounting Software Application: Understanding Behavioural Intention to Use and the Moderating Role of Gender. International Business Education Journal, 14(2), 48–60. https://doi.org/10.37134/ibej.vol14.2.5.2021

Huang, S.-M., Wei, C.-W., Yu, P.-T., & Kuo, T.-Y. (2005). An empirical investigation on learners’ acceptance of e-learning for public unemployment vocational training. International Journal of Innovation and Learning, 3(2), 174–185.

Hung, Y. H., Chang, R. I., & Lin, C. F. (2015). Survey of software literacy, behavior and personal traits of freshmen accounting majors. British Journal of Educational Technology, 46(5), 1064–1069. https://doi.org/10.1111/bjet.12333.

Kalayou, M. H., Endehabtu, B. F., & Tilahun, B. (2020). The Applicability of the Modified TAM on the Sustainable Adoption of eHealth Systems in Resource Limited Settings. J Multidiscip Healthc, 13, 1827–1837.

Khera, Y., Whig, P., & Velu, A. (2021). Efficient effective and secured electronic billing system using AI. Vivekananda Journal of Research, 10, 53–60.

Kock, N. (2016). Hypothesis testing with confidence intervals and P values in PLS-SEM. International Journal of E-Collaboration, 12(3), 1–6.

Kock, N. (2017). Common Method Bias: A Full Collinearity Assessment Method for PLS-SEM. In L. En, N. H., & R. (Eds.), Partial Least Squares Path Modeling. Springer. https://doi.org/10.1007/978-3-319-64069-3_11

Lai, M. (2008). Technology readiness, internet self‐efficacy and computing experience of professional accounting students. Campus Wide Information Systems, 25(1), 18–29. https://doi.org/10.1108/10650740810849061

Larios, C., & Benavides, L. (2021). Impacto de la tecnología del programa SIIGO como sistema contable IV semestre [Trabajo de grado. Fundación Universitaria San Mateo.

Lazim, C. S. L. M., Ismail, N. D. B., & Tazilah, M. D. A. K. (2021). Application of TAM towards online learning during the COVID-19 pandemic: Accounting students' perspective. International Journal of Business, Economics and Law, 24(1), 13–20. https://doi.org/10.5267/j.msl.2020.4.032

Le, O., & Cao, Q. (2020). Examining the technology acceptance model using cloud-based accounting software of Vietnamese enterprises. Management Science Letters, 10(12), 2781–2788.

Lois, P., Tabouratzi, E., & Makrygiannakis, G. (2017). Accounting Information Systems course: perceptions of accounting and non-accounting students. EuroMed Journal of Business, 12(3), 258–268. https://doi.org/10.1108/EMJB-11-2016-0032.

Lutfi, A. (2022). Factors influencing the continuance intention to use accounting information systems in Jordanian SMEs from the perspectives of UTAUT: Top management support and self-efficacy as predictor factors. Economies, 10(4), 75. https://doi.org/10.3390/economies10040075

Machera, R. P., & Machera, P. C. (2017). Computerised Accounting Software; A Curriculum That Enhances an Accounting Programme. Universal Journal of Educational Research, 5(3), 372–385. https://doi.org/10.13189/ujer.2017.050310

Niankara, I., & Islam, A. R. M. (2023). The impact of B2P electronic payroll and G2P digital welfare on formal financial inclusion in the global open economy. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100034. https://doi.org/10.1016/j.joitmc.2023.100034

Ong, A. K. S. (2022). Determining the Factors Affecting a Career Shifter’s Use of Software Testing Tools amidst the COVID-19 Crisis in the Philippines. TTF-TAM Approach. Sustainability, 14(17), 11084. https://doi.org/10.3390/su141711084.

Racero, F. J., Bueno, S., & Gallego, M. D. (2020). Predicting Students’ Behavioral Intention to Use Open Source Software: A Combined View of the Technology Acceptance Model and Self Determination Theory. Applied Sciences, 10(8), 2711. https://doi.org/10.3390/app10082711

Rahmi N. U., Sari W., & Wulandari B. (2018). The effect of information technology, quality of accounting information and understanding of students on accounting software users. IOP Conf. Series: Journal of Physics. https://doi.org/10.1088/1742-6596/1230/1/012080

Rijanto, A. (2021). Blockchain Technology Adoption in Supply Chain Finance. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3078–3098. https://doi.org/10.3390/jtaer16070168.

Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. SmartPLS GmbH. http://www.smartpls.com

Rönkkö, M., & Cho, E. (2022). An Updated Guideline for Assessing Discriminant Validity. Organizational Research Methods, 25(1), 6–14. https://doi.org/10.1177/1094428120968614

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, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003

Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921

Schepman, A., Rodway, P., Beattie, C., & Lambert, J. (2012). An observational study of undergraduate students’ adoption of (mobile) notetaking software. Computers in Human Behavior, 28(2), 308–317. https://doi.org/10.1016/j.chb.2011.09.014

Silva, R., Tommasetti, R., Zaidan Gomes, M., & Silva Macedo, M. Á. (2020). How green is accounting? Brazilian students’ perception. International Journal of Sustainability in Higher Education, 21(2), 228–243. https://doi.org/10.1108/IJSHE-07-2019-0232

Stainbank, L. J., Reddy Jankeeparsad, T., & Algu, A. (2023). Using Accounting Software for Teaching and Learning in a Second Year Accounting Course. The African Journal of Information Systems, 15(1), 2.

Syafrudin, V. (2012a). An Empirical Study of Accounting Software Acceptance among Bengkulu City Students. Asian Journal of Accounting & Governance, 3.

Syafrudin, V. (2012b). An Empirical Study of Accounting Software Acceptance among Bengkulu City Students. Asian Journal of Accounting & Governance, 3.

Tam, T. (2013). What IT knowledge and skills do accounting graduates need? New Zealand Journal of Applied Business Research, 11(2), 23–42. https://doi.org/10.3316/informit.173501411990966.

Thottoli, M. M. (2021). Knowledge and use of accounting software: evidence from Oman. Journal of Industry University Collaboration, 3(1), 2–14. https://doi.org/10.1108/JIUC-04-2020-0005

Utami, N., & Yulianto, H. D. (2019). Significant Influence of Information Technology on the Use of Modern Accounting Software. IOP Conference Series: Materials Science and Engineering, 662(2), 22003. https://doi.org/10.1088/1757-899X/662/2/022003

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions Decision Sciences.

Vysochan, O., Vysochan O., Yasinska A., & Hyk, V. (2021). Selección de software de contabilidad para pequeñas y medianas empresas mediante el método Fuzzy Topsis. TEM Journal, 10(3), 1348–1356. https://doi.org/10.18421/TEM103-43

Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A TAM perspective. Information & Management, 51(2), 249–259. https://doi.org/10.1016/j.im.2013.12.003.

Wang, J., Li, C., Wu, J., & Zhou, G. (2023). Research on the Adoption Behavior Mechanism of BIM from the Perspective of Owners: An Integrated Model of TPB and TAM. Buildings, 13(7), 1745. https://doi.org/10.3390/buildings13071745.

Wessels, P. L. (2007). An analysis of the current IT education offered to accounting students at South African universities. South African Journal of Accounting Research, 21(1), 103–126. https://doi.org/10.1080/10291954.2007.11435128.