Investigating the Factors Affecting Accountants' Behavioral Intentions in Accounting Information System Adoption: Empirical Evidence of Unified Theory of Acceptance and Use of technology, and Task-Fit Model

Iranian Journal of Accounting, Auditing and Finance

مقاله 7، دوره 6، شماره 3 - شماره پیاپی 20، آبان 2022، صفحه 111-126   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/ijaaf.2022.42040

نویسندگان

Fereshteh Pourghanbari^{* 1}؛ Hassan Yazdifar²؛ Mahdi Faghani³

¹Department of Accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran

²Head of Accounting, Finance and Economics Department, Bournemouth University, England

³Assistant Professor, Department of Accounting, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran

چکیده

Accounting information systems have recently received many investments in the implementation, resulting in introducing of its technology and gaining importance. However, factors affecting the accounting information system's success are the adoption and use by accountants in organizations. The present study used the unified model of acceptance and the use of technology and the model of task-technology fit to investigate the factors affecting the accountants' behavioral intentions regarding an accounting information system adoption. The present study was a descriptive survey regarding the applied purpose and collecting data tools. The data were collected using a questionnaire distributed among accountants of companies listed on the 2020 Tehran Stock Exchange, and 200 questionnaires approved by structural equation modeling were analyzed by Smart PLS 3 software. The results showed a direct and positive association between all model constructs (i.e., self-efficacy, effort expectancy, performance expectancy, and perceived technology fit) in accounting information system adoption, except the facilitating conditions in the research.

کلیدواژه‌ها

Accounting Information Systems؛ Accountants' Behavioral Intention؛ Unified Theory of Acceptance and Use of Technology؛ Task- Technology Fit Model

مراجع

1. Alamin, A. A., Wilkin, C. L., Yeoh, W. and Warren, M. (2020). The Impact of Self-Efficacy on Accountants’ Behavioral Intention to Adopt and Use Accounting Information Systems. Journal of Information Systems, 34(3), pp. 31–46. https://doi.org/10.2308/isys-52617
2. Alamin, A. Yeoh, W. Warren, M. and Salzman, S. (2015). An empirical study of factors influencing accounting information systems adoption. In Proceedings of the Twenty-Third European Conference on Information Systems, pp. 1-11. ECIS. https://doi.org/10.18151/7217259
3. Alsyouf, A. (2021). Self-efficacy and personal innovativeness influence on nurses' beliefs about EHRS usage in Saudi Arabia: Conceptual model. International Journal of Management (IJM), 12(3). pp. 1049-1058. https://doi.org/10.34218/IJM.12.3.2021.096
4. Andwika V. R. and Witjaksono. R. W. (2020).Analysis of User Acceptance of ERP System on After Sales Function Using Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Int. J. Adv. Data Inf. Syst. 1(1), pp. 26-33. https://doi.org/10.25008/ijadis.v1i1.178
5. Aoun, C. Vatanasakdakul, S. and Li, Y. (2010). AIS in Australia: UTAUT application & cultural implication. ACIS 2010 Proceedings - 21st Australasian Conference on Information Systems.
6. Askarany, D. Smith, M. and Yazdifar, H. (2007). Attributes of innovation and the implementation of managerial tools: An activity-based management technique. International Journal of Business and Systems Research, 1(1), pp. 98–114.
https://doi.org/10.1504/IJBSR.2007.014776
7. Bandura, A. (2006). Toward a Psychology of Human Agency. Perspectives on Psychological Science, 1(2), pp. 164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x
8. Barclay, D. Higgins, C. and Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology studies, 2(2): pp. 285-309.
9. Boontarig, W. Chutimaskul, W. Chongsuphajaisiddhi, V. and Papasratorn, B. (2012). Factors influencing the Thai elderly intention to use the smartphone for e-Health services. SHUSTER 2012 - 2012 IEEE Symposium on Humanities, Science and Engineering Research, 479–483. https://doi.org/10.1109/SHUSER.2012.6268881
10. Chiu, C. M. and Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information and Management, 45(3), pp. 194–201. https://doi.org/10.1016/j.im.2008.02.003
11. Compeau, D. R. and Higgins, C. A. (2017). Computer Self-Efficacy: Measure and Initial Development of a Test. MIS Quarterly, 19(2), 189–211. https://www.astm.org/Standards/E2368.htm
12. Curtis, M. B. and Payne, E. A. (2008). An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing. International Journal of Accounting Information Systems, 9(2), pp. 104–121. https://doi.org/10.1016/j.accinf.2007.10.002
13. Damanpour, F. and Schneider, M. (2006). Phases of the adoption of innovation in organizations: Effects of environment, organization, and top managers. British Journal of Management, 17(3), pp. 215–236. https://doi.org/10.1111/j.1467-8551.2006.00498.x
14. Dowling, C. (2009). Appropriate audit support system use: The influence of auditor, audit team, and firm factors. Accounting Review, 84(3), pp. 771–810. https://doi.org/10.2308/accr.2009.84.3.771
15. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2).
16. Forward, S. E. (2009). The theory of planned behavior: The role of descriptive norms and past behavior in the prediction of drivers’ intentions to violate. Transportation Research Part F: Traffic Psychology and Behavior, 12(3), pp. 198–207. https://doi.org/10.1016/j.trf.2008.12.002
17. Gonzalez, G. C., Sharma, P. N. and Galletta, D. (2012). Factors influencing the planned adoption of continuous monitoring technology. Journal of Information Systems, 26(2), pp. 53–69. https://doi.org/10.2308/isys-50259
18. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 213-236.
19. Goodhue, D. L. (1998). Development and measurement validity of a task‐technology fit instrument for user evaluations of information system. Decision Sciences, 29(1), 105-138.
20. Hayashi, A. Chen, C. Ryan, T. and Wu, J. (2004). The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), pp. 139-154.
21. Katurura, M. C. and Cilliers, L. (2018). Electronic health record system in the public health care sector of South Africa: A systematic literature review. African journal of primary health care & family medicine, 10(1), pp. 1-8.https://doi.org/10.4102/phcfm.v10i1.1746
22. Mahzan, N. and Lymer, A. (2014). Examining the adoption of computer-assisted audit tools and techniques: Cases of generalized audit software use by internal auditors. Managerial Auditing Journal, 29(4), pp. 327–349. https://doi.org/10.1108/MAJ-05-2013-0877
23. Naheb, O. A. Sukoharsono, E. G. and Baridwan, Z. (2017). The influence of critical factors on the behavior intention to computerized accounting systems (CAS) in cement manufactures in Libya. The International Journal of Accounting and Business Society, 25(1), pp. 38-60. https://doi.org/10.21776/ub.ijabs.2017.25.1.7
24. Odeh, M. H. (2019). Factors Affecting the Adoption of Financial Information Systems Based on the UTAUT Model. International Journal of Academic Research in Accounting, Finance and Management Sciences, 9(2), pp. 108–116. https://doi.org/10.6007/IJARAFMS/v9-i2/6064
25. Özer, G. and Yilmaz, E. (2011). Comparison of the theory of reasoned action and the theory of planned behavior: An application on accountants’ information technology usage. African Journal of Business Management, 5(1), pp. 50-58. https://doi.org/10.5897/AJBM10.389
26. Salehi, M. Rostami, V. and Mogadam, A. (2010). The usefulness of Accounting Information System in Emerging Economy: Empirical Evidence of Iran. International Journal of Economics and Finance, 2(2), pp. 186–195. https://doi.org/10.5539/ijef.v2n2p186
27. Shahreki, J. and Nakanishi, H. (2016). The relationship between task technology fit and individual performance: case study in hotel industry in Malaysia. Journal of Soft Computing and Decision Support Systems, 3(6), pp. 1-15.
28. Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22(April), pp. 960–967. https://doi.org/10.1016/j.promfg.2018.03.137
29. Tilahun, M. (2019). A Review on 1Determinants of Accounting Information System Adoption. Science Journal of Business and Management, 7(1), pp. 17-22. https://doi.org/10.11648/j.sjbm.20190701.13
30. Venkatesh, V. Morris, M. G. Davis, G. B. and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, MIS Quarterly, 27(3), pp. 425-478. https://doi.org/10.2307/30036540
31. Venkatesh, V. Thong, J. and Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), pp. 328–376. https://doi.org/10.17705/1jais.00428
32. 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(1), pp. 157–178. https://doi.org/10.2307/41410412.
33. Wetzels, M. Odekerken-Schröder, G. and Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), pp. 177-195. https://doi.org/10.2307/20650284
34. Yazdifar, H. and Askarany, D. (2012). A comparative study of the adoption and implementation of target costing in the UK, Australia, and New Zealand. International Journal of Production Economics, 135(1), pp. 382–392. https://doi.org/10.1016/j.ijpe.2011.08.012
35. Zhou, T. Lu, Y. and Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), pp. 760-767. https://doi.org/10.1016/j.chb.2010.01.013