Understanding Behavioural Intention to Adopt E-Invoicing in The Case of Malaysia Small and Medium Enterprises (SMEs): A UTAUT2

  • Authors

    https://doi.org/10.14419/a800db91

    Received date: August 29, 2025

    Accepted date: October 7, 2025

    Published date: January 8, 2026

  • E-Invoice; Unified Theory of Acceptance and Use of Technology 2; SMEs; Malaysia; technology adoption

  • Abstract

    This study examines the behavioral intention to adopt e-invoicing among small and medium-sized enterprises (SMEs) in Malaysia, utilizing the UTAUT2 framework. E-invoicing provides benefits that enable businesses to exchange invoices electronically, enhancing operational efficiency and compliance while reducing costs. This research aims to examine the factors influencing SMEs’ intention to adopt e-invoicing and identify the most significant factors. A quantitative survey was conducted among SME owners who were preparing for the early adoption of e-invoicing. The questionnaire measured seven factors, including performance expectancy, effort expectancy, social influence, facilitating conditions, trust, security, and perceived risk. The findings revealed that social influence is the strongest positive factor influencing adoption intention, which may involve the roles of peer, professional, and regulatory pressures. In contrast, security concerns and perceived risk negatively influence behavioural intention, highlighting the importance of addressing data protection and system reliability for e-invoices developed by the government. Performance expectancy, effort expectancy, facilitating conditions, and trust were not significant factors in the Malaysian context. The study contributes theoretically by extending UTAUT2 to incorporate security and risk factors in the e-invoicing context. It offers practical insights for policymakers and software developers to support SME digital adoption effectively.

  • References

    1. Abu Gharrah, A. and Aljaafreh, A. (2021), Why students use social networks for education: extension of UTAUT2, Journal of Technology and Science Education, Vol. 11 No. 1, pp. 53-66,
    2. Ala’a, A., & Ramayah, T. (2023). Predicting the behavioural intention of Jordanian healthcare professionals to use blockchain-based EHR systems: an empirical study. Journal of System and Management Sciences, 13(4), 107-139.
    3. Almajali, D. A., Masa’Deh, R. E., & Dahalin, Z. M. (2022). Factors influencing the adoption of Cryptocurrency in Jordan: An application of the extended TRA model. Cogent Social Sciences, 8(1), 2103901.
    4. Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in society, 62, 101293.
    5. Ali, M. K. M., & Singh, C. K. A. (2025). Review of the benefits and challenges of E-Invoicing in Malaysia. Jurnal Sains Sosial, 9(1), 24.
    6. Ali, N. (2016). Adapting e-invoicing: benefits, challenges and future strategies to overcome challenges." Vaasa, Finland.
    7. Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659-673.
    8. Aranyossy, M. (2022, September). Technology adoption in the digital entertainment industry during the COVID-19 pandemic: An extended UTAUT2 model for online theater streaming. In Informatics (Vol. 9, No. 3, p. 71). MDPI.
    9. Asadi, S., Nilashi, M., Husin, A. R. C., & Yadegaridehkordi, E. (2017). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management, 18(4), 305-330.
    10. Bélanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. The journal of strategic information systems, 17(2), 165-176.
    11. Biloš, A., & Budimir, B. (2024). Understanding the adoption dynamics of ChatGPT among generation Z: Insights from a modified UTAUT2 model. Journal of theoretical and applied electronic commerce research, 19(2), 863-879.
    12. Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 831-846.
    13. BDO Malaysia. (2025, August 21). Guide to e-Invoicing in Malaysia: Everything you need to know. BDO. Retrieved from https://www.bdo.my/en-gb/insights/featured-insights/guide-to-e-invoicing-in-malaysia
    14. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9780203805534
    15. Chaveesuk, S., Chaiyasoonthorn, W., Kamales, N., Dacko-Pikiewicz, Z., Liszewski, W., & Khalid, B. (2023). Evaluating the determinants of consumer adoption of autonomous vehicles in Thailand—An extended UTAUT model. Energies, 16(2), 855.
    16. Chiao, H. M., Chen, Y. L., & Huang, W. H. (2018). Examining the usability of an online virtual tour-guiding platform for cultural tourism education. Journal of Hospitality, Leisure, Sport & Tourism Education, 23, 29-38.
    17. Cimperman, M., Makovec Brenčič, M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22–31.
    18. Chen, S. C., Wu, C. C., & Miau, S. (2015). Constructing an integrated e-invoice system: the Taiwan experience. Transforming Government: People, Process and Policy, 9(3), 370-383.
    19. Cheung, S. F., Pesigan, I. J. A., & Vong, W. N. (2023). DIY bootstrapping: Getting the nonparametric bootstrap confidence interval in SPSS for any statistics or function of statistics (when this bootstrapping is appropriate). Behavior Research Methods, 55(2), 474-490.
    20. Du, W., & Liang, R. Y. (2024). Teachers’ continued VR technology usage intention: An application of the UTAUT2 model. Sage Open, 14(1), 21582440231220112.
    21. Damali, U., Kocakulah, M., & Ozkul, A. S. (2021). Investigation of cloud ERP adoption in the healthcare industry through technology-organization-environment (TOE) framework: Qualitative study. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-14.
    22. FocusMalaysia. (2024, December 4). KPMG’s e-invoicing survey found Malaysian taxpayers at various stages of readiness. FocusMalaysia. Retrieved from https://focusmalaysia.my/kpmgs-e-invoicing-survey-found-malaysian-taxpayers-at-various-stages-of-readiness/
    23. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
    24. Gao, S., & Li, Y. (2021). An empirical study on the adoption of blockchain-based games from users’ perspectives. The Electronic Library, 39(4), 596-614.
    25. Gilbert, D., Balestrini, P., & Littleboy, D. (2004). Barriers and benefits in the adoption of e‐government. International Journal of Public Sector Management, 17(4), 286-301.
    26. Grassini, S., Aasen, M. L., & Møgelvang, A. (2024). Understanding university students’ acceptance of ChatGPT: Insights from the UTAUT2 model. Applied Artificial Intelligence, 38(1), 2371168.
    27. Gupta, K., & Arora, N. (2020). Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model: An Indian perspective. South Asian Journal of Business Studies, 9(1), 88-114.
    28. Hair, J. F. Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate Data Analysis: A Global Perspective, 7th Ed., Saddle River, NJ. Prentice-Hall International.
    29. Hammouri, Q., Aloqool, A., Saleh, B. A., Aldossary, H., al Frejat, S. Y., Halim, M., ... & Darawsheh, S. R. (2023). An empirical investigation on acceptance of e-wallets in the fintech era in Jordan: Extending UTAUT2 model with perceived trust. International Journal of Data & Network Science, 7(3).
    30. Hanafiah, M. H., Asyraff, M. A., Ismail, M. N. I., & Sjukriana, J. (2024). Understanding the key drivers in using mobile payment (M-Payment) among Generation Z travellers. Young Consumers, 25(5), 645-664.
    31. Hoblos, N., Joukhadar, G., Rabhi, L., Rabhi, F., & Hussain, W. (2024). Addressing e-Invoicing Adoption Challenges using API Ecosystems.
    32. He, Z., Yang, Y., Mu, Y., & Qu, X. (2025). Public acceptance of driverless buses: An extended UTAUT2 model with anthropomorphic perception and empathy. Communications in Transportation Research, 5, 100167.
    33. Herzallah, F., Abosamaha, A. J., Abu-Siam, Y., Amer, M., Sajjad, U., & Hamid, K. (2025). Determinants of mobile wallet usage among Gen Z: Extending the UTAUT2 model with moderating effects of personal innovativeness and gender. International Journal of Information Management Data Insights, 5(1), 100336.
    34. Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Education and Information Technologies, 25(5), 4615-4635.
    35. Huang, C. Y., & Kao, Y. S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015(1), 603747.
    36. Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2019). M-payment adoption for bottom of pyramid segment: an empirical investigation. International Journal of Bank Marketing, 37(1), 362-381.
    37. Heinemann, M., & Stiller, W. (2025). Digitalization and cross-border tax fraud: evidence from e-invoicing in Italy. International Tax and Public Finance, 32(1), 195-237.
    38. Hayes, A. F. (2018). Partial, conditional, and moderated mediation: Quantification, inference, and interpretation. Communication monographs, 85(1), 4-40.
    39. Jom EInvoice Sdn Bhd. (2025, July 11). e-Invoicing Malaysia: Latest update in July 2025 – Business complete guide adapting to Malaysia’s new e-invoicing regulations. Retrieved from https://jomeinvoice.my/malaysia-e-invoicing-guide/
    40. Jun, G., & Jaafar, N. I. (2011). A study on consumers’ attitude towards online shopping in China. International Journal of Business and Social Science, 2(22), 122-132.
    41. Kalınkara, Y., & Özdemir, O. (2024). Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model. Anatomical Sciences Education, 17(2), 319-336.
    42. Khalil, A., Haqdad, A., & Sultana, N. (2023). Educational Metaverse For Teaching And Learning In Higher Education of Pakistan. Journal of Positive School Psychology, 7(2).
    43. Khazaei, H. (2020). Integrating cognitive antecedents to UTAUT model to explain adoption of blockchain technology among Malaysian SMEs. JOIV: International Journal on Informatics Visualization, 4(2), 85-90.
    44. Kumar, N., Singh, M., Upreti, K., & Mohan, D. (2021, June). Blockchain adoption intention in higher education: role of trust, perceived security and privacy in technology adoption model. In International Conference on Emerging Technologies and Intelligent Systems (pp. 303-313). Cham: Springer International Publishing.
    45. Kumar, A., & Krishnamoorthy, B. (2020). Business analytics adoption in firms: A qualitative study elaborating TOE framework in India. International Journal of Global Business and Competitiveness, 15(2), 80-93
    46. Lakshmanan, K., & Shanmugavel, N. (2025). Investigation on the factors influencing the continuation intention to use digital wallet in the rural parts of India using extended UTAUT2. Asian Journal of Economics and Banking, 9(1), 22-47.
    47. Latif, M. I., & Zakaria, Z. (2020). Factors determine the behavioural intention in adopting the blockchain technology by Malaysian Public Sector Officers. Journal of Advanced Research in Business and Management Studies, 20(1), 34-43.
    48. Lee, J. M., Lee, B., & Rha, J. Y. (2019). Determinants of mobile payment usage and the moderating effect of gender: Extending the UTAUT model with privacy risk. International Journal of Electronic Commerce Studies, 10(1), 43-64.
    49. Lian, J. W. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35(1), 98-109.
    50. Liébana-Cabanillas, F., Molinillo, S., & Japutra, A. (2021a). Exploring the determinants of intention to use P2P mobile payment in Spain. Information Systems Management, 38(2), 165-180.
    51. Liébana-Cabanillas, F., Singh, N., Kalinic, Z., & Carvajal-Trujillo, E. (2021b). Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Information Technology and Management, 22(2), 133-161.
    52. Lin, X., Suanpong, K., Ruangkanjanases, A., Lim, Y. T., & Chen, S. C. (2022). Improving the sustainable usage intention of mobile payments: extended unified theory of acceptance and use of technology model combined with the information system success model and initial trust model. Frontiers in Psychology, 12, 634911.
    53. Liu, N., & Ye, Z. (2021). Empirical research on the blockchain adoption–based on TAM. Applied Economics, 53(37), 4263-4275.
    54. Lutfi, A., Al-Okaily, M., Alshirah, M. H., Alshira’h, A. F., Abutaber, T. A., & Almarashdah, M. A. (2021). Digital financial inclusion sustainability in Jordanian context. Sustainability, 13(11), 6312.
    55. Menon, D., & Shilpa, K. (2023). Chatting with ChatGPT: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon, 9(11).
    56. Mogollón García, F. S., Rodríguez Chamorro, S. L., Núñez Puse, S. M., Quiroga Ríos, B. C., Garcés Aguilera, L. A., Arévalo Rodríguez, M. E., & Garcia Arismendiz, A. (2025). Electronic invoicing as a tax collection strategy in the context of Peruvian economic informality. Journal of Educational and Social Research, 15(3), 397. https://doi.org/10.36941/jesr-2025-0106
    57. Muquis, L., Ortega, W., & Arias, C. (2024, July). Technological Traceability and Taxation. The Duality Paradigm to Avoid Evasion. In International Conference in Information Technology and Education (pp. 156-167). Cham: Springer Nature Switzerland.
    58. Namatovu, A., & Kyambade, M. (2025). Leveraging AI in academia: University students’ adoption of ChatGPT for writing coursework (take home) assignments through the lens of UTAUT2. Cogent Education, 12(1), 2485522.
    59. NGUYEN, A. H., NGUYEN, T. P., & DANG, G. T. T. (2020). Determinants of E-invoice adoption: Empirical evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 7(7), 311-321.
    60. Nicolaou, A. I., & McKnight, D. H. (2006). Perceived information quality in data exchanges: Effects on risk, trust, and intention to use. Information systems research, 17(4), 332-351.
    61. Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139-4155.
    62. NST. (2024, August 1st). E-invoicing to kick off — Industry experts say many still unprepared. New Straits Times. Retrieved from https://www.nst.com.my/business/corporate/2024/08/1084747/e-invoicing-kick-industry-experts-say-many-still-unprepared
    63. Ooi, S. K., Ooi, C. A., Yeap, J. A. L., & Goh, T. H. (2021). Embracing Bitcoin: users’ perceived security and trust. Quality and Quantity, 55(4), 1219–1237.
    64. Pal, A., Herath, T., De’, R., & Rao, H. R. (2021). Is the convenience worth the risk? An investigation of mobile payment usage. Information systems frontiers, 23(4), 941-961.
    65. Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144.
    66. Polyportis, A., & Pahos, N. (2025). Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy. Behaviour & Information Technology, 44(2), 315-336.
    67. PricewaterhouseCoopers. (2024, July 29). Ready or not, e-invoicing has arrived [PDF]. PwC. Retrieved from https://www.pwc.com/my/en/assets/media/pwc-in-the-news/2024/pwc-my-290724-e-invoicing-has-arrived.pdf
    68. Qi, Y., & Che Azmi, A. (2021). Factors affecting electronic invoice adoption and tax compliance process efficiency. Transforming Government: People, Process and Policy, 15(1), 150-168.
    69. Rahman, S. A., Didarul Alam, M. M., & Taghizadeh, S. K. (2020). Do mobile financial services ensure the subjective well-being of micro-entrepreneurs? An investigation applying UTAUT2 model. Information Technology for Development, 26(2), 421-444.
    70. Raza, S. A., Qazi, Z., Qazi, W., & Ahmed, M. (2022). E-learning in higher education during COVID-19: Evidence from blackboard learning system. Journal of Applied Research in Higher Education, 14(4), 1603-1622.
    71. Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15-36.
    72. Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219-238.
    73. San, A. N. C., Seng, W. T., Yee, C. J., Siong, O. H., Hussain, N. C., & Khen, T. M. (2023, December). Preparing SMEs for e-invoice adoption: Assessing technology readiness and its segmentation. In Proceedings of the 11th International Conference on Business, Accounting, Finance and Economics (BAFE 2023) (pp. 303–315). Atlantis Press. https://doi.org/10.2991/978-94-6463-342-9_22
    74. Sandberg, K. W., Wahlberg, O., & Pan, Y. (2009, July). Acceptance of e-invoicing in SMEs. In International Conference on Engineering Psychology and Cognitive Ergonomics (pp. 289-296). Berlin, Heidelberg: Springer Berlin Heidelberg.
    75. Shin, S., & Lee, W. J. (2021). Factors affecting user acceptance for NFC mobile wallets in the US and Korea. Innovation & Management Review, 18(4), 417-433.
    76. Sleiman, K. A. A., Jin, W., Juanli, L., Lei, H. Z., Cheng, J., Ouyang, Y., & Rong, W. (2022). The factors of continuance intention to use mobile payments in Sudan. Sage Open, 12(3), 21582440221114333.
    77. Sobaih, A. E. E., Elshaer, I. A., & Hasanein, A. M. (2024). Examining students’ acceptance and use of ChatGPT in Saudi Arabian higher education. European Journal of Investigation in Health, Psychology and Education, 14(3), 709-721.
    78. Strzelecki, A. (2024). ChatGPT in higher education: Investigating bachelor and master students’ expectations towards AI tool. Education and Information Technologies, 1-25.
    79. Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive learning environments, 32(9), 5142-5155.
    80. Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11
    81. Sunardi, S., Ramadhan, A., Abdurachman, E., Trisetyarso, A., & Zarlis, M. (2022). Acceptance of augmented reality in video conference based learning during COVID-19 pandemic in higher education. Bulletin of Electrical Engineering and Informatics, 11(6), 3598-3608.
    82. Suo, W. J., Goi, C. L., Goi, M. T., & Sim, A. K. (2022). Factors influencing behavioural intention to adopt the QR-code payment: extending UTAUT2 model. International Journal of Asian Business and Information Management (IJABIM), 13(2), 1-22.
    83. Surya Bahadur G. C., 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. https://doi.org/10.1080/ 2331186X.2024.2403287
    84. Soliman, R. M. (2024). Private Sector’s Perceptions and Adoption of the E-invoicing System in Egypt.[Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt.edu/etds/2298
    85. Sundström, J. (2006). Adoption of electronic invoicing in smEs. Master Thesis. Lulea University of Technology.
    86. Sweet, M. N., Scott, D. M., & Hamiditehrani, S. (2023). Who will adopt private automated vehicles and automated shuttle buses? Testing the roles of past experience and performance expectancy. Transportation Planning and Technology, 46(1), 45-70.
    87. Tashtoush, M. A., AlAli, R., Wardat, Y., Alshraifin, N., & Toubat, H. (2023). The impact of information and communication technologies (ICT)-based education on the mathematics academic enthusiasm. Journal of Educational and Social Research, 13(3), 284-293.
    88. Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369-392.
    89. Tiwari, A. K., Marak, Z. R., Paul, J., & Deshpande, A. P. (2023). Determinants of electronic invoicing technology adoption: Toward managing business information system transformation. Journal of Innovation & Knowledge, 8(3), 100366
    90. Uche, D. Ben, Osuagwu, O. B., Nwosu, S. N., & Otika, U. S. (2021). Integrating Trust into Technology Acceptance Model (TAM), the Conceptual Framework for E-Payment Platform Acceptance. British Journal of Management and Marketing Studies, 4(4), 34–56.
    91. Upadhyay, N., Upadhyay, S., Abed, S. S., & Dwivedi, Y. K. (2022). Consumer adoption of mobile payment services during COVID-19: Extending meta-UTAUT with perceived severity and self-efficacy. International Journal of Bank Marketing, 40(5), 960-991.
    92. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478.
    93. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), 157–178.
    94. Wang, L., & Yi, Y. (2012). The impact of use context on mobile payment acceptance: An empirical study in China. In Advances in computer science and education (pp. 293-299). Berlin, Heidelberg: Springer Berlin Heidelberg.
    95. Wu, Z., & Liu, Y. (2023). Exploring country differences in the adoption of mobile payment service: the surprising robustness of the UTAUT2 model. International Journal of Bank Marketing, 41(2), 237-268.
    96. Weiqun, X., & Xing, L. (2025). Can the Digitalization of Tax Administration Promote Corporate Digital Transformation?——Empirical Evidence from China. International Review of Economics & Finance, 104634.
    97. Xero & Malaysia SME. (2025, May). From awareness to implementation: Assessing e-invoicing readiness among SMEs in Malaysia [White paper]. Malaysia SME. Retrieved from https://www.malaysiasme.com.my/wp-content/uploads/2025/05/Whitepaper-From-awareness-to-implementation-Assessing-e-invoicing-readiness-among-SMEs-in-Malaysia.pdf
    98. Yang, F., Ren, L., & Gu, C. (2022). A study of college students' intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon, 8(9).
    99. Yaseen, S. G., El Qirem, I. A., & Dajani, D. (2022). Islamic mobile banking smart services adoption and use in Jordan. ISRA International Journal of Islamic Finance, 14(3), 349-362.
    100. Yuen, K. F., Choo, L. Q., Li, X., Wong, Y. D., Ma, F., & Wang, X. (2023). A theoretical investigation of user acceptance of autonomous public transport. Transportation, 50(2), 545-569.
    101. Zissis, D., & Lekkas, D. (2011). Securing e-Government and e-Voting with an open cloud computing architecture. Government Information Quar-terly, 28(2), 239-251

  • Downloads

  • How to Cite

    Ilias , A. ., Baidi , N., K. Ghani , E. ., & Muhammad , K. . (2026). Understanding Behavioural Intention to Adopt E-Invoicing in The Case of Malaysia Small and Medium Enterprises (SMEs): A UTAUT2. International Journal of Accounting and Economics Studies, 13(1), 56-67. https://doi.org/10.14419/a800db91