FACTORS INFLUENCING THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN ACCOUNTING AMONG MICRO, SMALL MEDIUM ENTERPRISES (MSMES)
DOI:
https://doi.org/10.55197/qjssh.v5i1.323Keywords:
artificial intelligence, accounting, micro small medium enterprises, MalaysiaAbstract
This study investigated the factors that influence the adoption of artificial intelligence (AI) in accounting among Malaysian Micro, Small Medium Enterprises (MSMEs). It focused on three factors based on the Technology, Organization, and Environment (TOE) framework, and the Diffusion of Innovation (DOI) theory. The factors examined include compatibility, complexity, security and privacy, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations. A quantitative approach was employed to collect data which were then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) software. The study’s findings indicate that compatibility, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations significantly impact AI adoption in MSMEs. However, the study did not find significant influences of complexity as well as security and privacy on AI adoption of Malaysian MSMEs. In conclusion, this study’s findings offer valuable insights on how organizations can effectively navigate these factors to achieve successful AI adoption in their accounting practices.
References
Al-Dmour, R., Amin, E.A., Saad, N., Zaidan, H. (2022): Interrelated Factors Influencing the Adoption of Big Data Applications: Empirical Study in Jordan. – Jordan Journal of Business Administration 18(2): 203-222.
Alshamaila, Y., Papagiannidis, S., Li, F. (2013): Cloud computing adoption by SMEs in the north east of England: A multi‐perspective framework. – Journal of Enterprise Information Management 26(3): 250-275.
Alsheibani, S.A., Cheung, Y., Messom, C., Alhosni, M. (2020): Winning AI Strategy: Six-Steps to Create Value from Artificial Intelligence. Lake City, United States, Americas Conference on Information Systems 11p.
Ashok, M.L., MS, D. (2019): Emerging trends in accounting: an analysis of impact of robotics in accounting, reporting and auditing of business and financial information. – International Journal of Business Analytics and Intelligence 7(2): 28-34.
Asiaei, A., Ab. Rahim, N.Z. (2019): A multifaceted framework for adoption of cloud computing in Malaysian SMEs. – Journal of Science and Technology Policy Management 10(3): 708-750.
Bauer, M., van Dinther, C., Kiefer, D. (2020): Machine learning in SME: an empirical study on enablers and success factors. – Americas Conference on Information Systems 11p.
Chen, D.Q., Preston, D.S., Swink, M. (2015): How the use of big data analytics affects value creation in supply chain management. – Journal of Management Information Systems 32(4): 4-39.
Chin, W.W., Marcolin, B.L., Newsted, P.R. (2003): A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. – Information Systems Research 14(2): 189-217.
Chong, A.Y.L., Lin, B., Ooi, K.B., Raman, M. (2009): Factors affecting the adoption level of c-commerce: An empirical study. – Journal of Computer Information Systems 50(2): 13-22.
Chukwudi, O., Echefu, S., Boniface, U., Victoria, C. (2018): Effect of artificial intelligence on the performance of accounting operations among accounting firms in South East Nigeria. – Asian Journal of Economics, Business and Accounting 7(2): 1-11.
Damerji, H., Salimi, A. (2021): Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. – Accounting Education 30(2): 107-130.
Das, P.K. (2021): Impact of Artificial Intelligence on Accounting. – Sumerianz Journal of Economics and Finance 4(1): 17-24.
Davenport, T.H., Ronanki, R. (2018): Artificial intelligence for the real world. – Harvard Business Review 96(1): 108-116.
Diamantopoulos, A., Siguaw, J.A. (2006): Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. – British Journal of Management 17(4): 263-282.
Dijkstra, T.K., Henseler, J. (2015): Consistent and asymptotically normal PLS estimators for linear structural equations. – Computational Statistics & Data Analysis 81: 10-23.
Elbanna, A. (2013): Top management support in multiple-project environments: an in-practice view. – European Journal of Information Systems 22: 278-294.
El-Haddadeh, R., Osmani, M., Hindi, N., Fadlalla, A. (2021): Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics. – Journal of Business Research 131: 402-410.
Emetaram, E., Uchime, H.N. (2021): Impact of Artificial Intelligence (AI) on Accountancy Profession. – Journal of Accounting and Financial Management 7(2): 15-25.
Faccia, A., Al Naqbi, M.Y.K., Lootah, S.A. (2019): Integrated cloud financial accounting cycle: how artificial intelligence, blockchain, and XBRL will change the accounting, fiscal and auditing practices. – In Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing 7p.
Gangwar, H. (2018): Understanding the determinants of big data adoption in India: An analysis of the manufacturing and services sectors. – Information Resources Management Journal (IRMJ) 31(4): 1-22.
Ghani, E.K., Ariffin, N., Sukmadilaga, C. (2022): Factors Influencing Artificial Intelligence Adoption in Publicly Listed Manufacturing Companies: A Technology, Organisation, and Environment Approach. – International Journal of Applied Economics, Finance and Accounting 14(2): 108-117.
Ghobakhloo, M., Arias‐Aranda, D., Benitez‐Amado, J. (2011): Adoption of e‐commerce applications in SMEs. – Industrial Management & Data Systems 111(8): 1238-1269.
Gupta, S., Drave, V.A., Dwivedi, Y.K., Baabdullah, A.M., Ismagilova, E. (2020): Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. – Industrial Marketing Management 90: 581-592.
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.
Hair, J.F., Hult, G.T.M., Ringle, C.M. Sarstedt, M. (2017): A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). – 2nd. ed. Thousand Oaks: Sage Publications 384p.
Hair, J.F., Ringle, C.M., Sarstedt, M. (2015): PLS-SEM: Indeed a Silver Bullet. – Journal of Marketing Theory and Practice 19(2): 139-151.
Hair, J.F., Gabriel, M., Patel, V. (2014): AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. – Brazilian Journal of Marketing, 13(2): 44-55.
Hair, J.F., Black, W., Babin, B.J. Anderson, R.E. (2010): Multivariate Data Analysis: A Global Perspective. – 7th ed. Upper Saddle River: Pearson Education 761p.
Hannan, T.H., McDowell, J.M. (1984): The determinants of technology adoption: The case of the banking firm. – The RAND Journal of Economics 15(3): 328-335.
Hao, H., Padman, R., Sun, B., Telang, R. (2018): Quantifying the impact of social influence on the information technology implementation process by physicians: A hierarchical Bayesian learning approach. – Information Systems Research 29(1): 25-41.
Hasan, A.R. (2021): Artificial Intelligence (AI) in accounting & auditing: A Literature review. – Open Journal of Business and Management 10(1): 440-465.
Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., Ketchen Jr, D.J., Hair, J.F., Hult, G.T.M., Calantone, R.J. (2014): Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). – Organizational Research Methods 17(2): 182-209.
Hu, L.T., Bentler, P.M. (1998): Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. – Psychological Methods 3(4): 424-453.
Ionescu, L. (2019): Big data, blockchain, and artificial intelligence in cloud-based accounting information systems. – Analysis and Metaphysics 18: 44-49.
Karlinsky, S.S., O'Leary, D., O'Leary, D.E., Watkins, P.R. (1992): Tax-based expert systems: A first principles approach. – Expert Systems in Finance 21p.
