Determinants of artificial intelligence use by accounting practitioners from the perspective of the technology readiness and acceptance model (TRAM)

• Authors: Adamek Jacek , Solarz Małgorzata

Title variants

PL

Uwarunkowania wykorzystania sztucznej inteligencji przez praktyków rachunkowości – perspektywa modelu gotowości technologicznej i akceptacji technologii (TRAM)

• Languages of publication: EN

Abstracts

EN

Purpose: The purpose of this paper is to assess the impact of dimensions of technology readiness (TR) on accounting office staffʼs intention to use artificial intelligence (AI), using the mediating factors of the technologyʼs perceived usefulness (PU) and perceived ease of use (PEOU). Methodology/research approach: The study was based on a purposive sample of 200 staff members of accounting offices in Poland. Structural equation modelling (SEM) was used to test the relationships between the TR personality dimensions and the cognitive dimensions (PU and PEOU) of the technology acceptance model (TAM). Findings: The findings indicate that respondentsʼ optimism affected the perceived usefulness of technology, while innovativeness affected the perceived ease of use of AI technologies. In addition, the cognitive dimensions of the TAM were confirmed to influence respondents' intention to use AI tools; thus, PU and PEOU mediate the relationship between TR dimensions and the intention to use technology. Research limitations/implications: The study focuses on a limited number of accounting practitioners. Moreover, the basis for the analysis was only two TR moderators: PU and PEOU. Originality/value: The articleʼs integration of knowledge from psychology, management and technology fosters a holistic approach to the issue of adopting technological innovations such as AI. The adopted research perspective enriches the theoretical contribution to the literature by identifying factors in AI technology adoption among Polish accountants. It also facilitates the application of the research results in practice as it takes into account the organisational and individual context of this process. Furthermore, the content of the article and the analysis contained therein can provide valuable support to organisations planning to use AI technologies, allowing them to improve their implementation strategies by considering both technical and human aspects.

PL

Cel: Celem artykułu jest ocena wpływu wymiarów gotowości technologicznej (TR) na zamiar użycia sztucznej inteligencji (AI) przez pracowników biur rachunkowych, przy wykorzystaniu czynników pośredniczących w postaci postrzeganej użyteczności (PU) oraz łatwości użycia (PEOU) tej technologii. Metodyka/podejście badawcze: Badanie oparto na celowej próbie, obejmującej 200 przedstawicieli biur rachunkowych w Polsce. Wykorzystując modelowanie równań strukturalnych (SEM) przetestowano relacje właściwe dla modelu TRAM, zachodzące między wymiarami gotowości technologicznej TR (tj. optymizmem, innowacyjnością, dyskomfortem i niepewnością) a wymiarami poznawczymi modelu akceptacji technologii TAM (PU i PEOU). Wyniki: Wyniki badań wskazują, że optymizm respondentów wpływa na postrzeganą użyteczność, a innowacyjność na postrzeganą przez nich łatwość użycia technologii AI. Dodatkowo potwierdzono, że wymiary poznawcze TAM wpływają na intencje użycia narzędzi AI, a więc PU i PEOU pośredniczą w związku między wymiarami gotowości technologicznej a zamiarem wykorzystania tej technologii. Ograniczenia/implikacje badawcze: Badanie skupia się na ograniczonej liczbie praktyków rachunkowości, ponadto podstawą prowadzonych analiz były tylko dwa moderatory TR tj. PU i PEOU. Oryginalność/wartość: Zawarta w artykule integracja wiedzy z zakresu psychologii, zarządzania i technologii, sprzyja holistycznemu ujęciu zagadnienia adopcji innowacji technologicznej, jaką jest AI. Przyjęta perspektywa badawcza wzbogaca teoretyczny wkład w literaturę, dotyczącą identyfikacji czynników adopcji technologii AI, przez polskich księgowych oraz ułatwia zastosowanie wyników badań w praktyce, ponieważ uwzględnia organizacyjny i indywidualny kontekst tego procesu. Ponadto treść artykułu i zawarte w nim analizy mogą stanowić cenne wsparcie dla organizacji planujących wykorzystanie technologii AI, pozwalając na doskonalenie strategii wdrożeniowych poprzez uwzględnienie zarówno ich aspektów technicznych, jak i ludzkich.

Keywords

EN

artificial intelligence; accounting; technology readiness; technology acceptance; TRAM

PL

sztuczna inteligencja; rachunkowość; gotowość technologiczna; akceptacja technologii; TRAM

• Publisher: Stowarzyszenie Księgowych w Polsce
• Journal: Zeszyty Teoretyczne Rachunkowości
• Year: 2025
• Issue: 49(2)
• Pages: 11-36

Contributors

author

Adamek Jacek

• Wroclaw University of Economics and Business, Faculty of Economics and Finance Department of Finance and Accounting

author

Solarz Małgorzata

• Wroclaw University of Economics and Business, Faculty of Economics and Finance, Department of Finance and Accounting

References

• Adamek J., Solarz M. (2023), Adoption factors in digital lending services offered by FinTech lenders, “Oeconomia Copernicana”, 14 (1), pp. 169–212; https://doi.org/10.24136/oc.2023.005.
• Afsay A., Tahriri A., Rezaee Z. (2023), A meta-analysis of factors affecting acceptance of information technology in auditing, “International Journal of Accounting Information Systems”, 49, 100608; https://doi.org/10.1016/j.accinf.2022.100608.
• Ajzen I. (1991), The Theory of Planned Behavior, “Organization Behavior and Human Decision Processes”, 50 (2), pp. 179–211; https://doi.org/10.1016/0749-5978(91)90020-T.
• Al Wael H., Abdallah W., Ghura H., Buallay A. (2024), Factors influencing artificial intelligence adoption in the accounting profession: the case of public sector in Kuwait, “Competitiveness Review: An International Business Journal”, 34 (1), pp. 3–27; https://doi.org/10.1108/CR-09-2022-0137.
• Anh N.T.M., Hoa L.T.K., Thao L.P., Nhi D.A., Long N.T., Truc N.T., Ngoc Xuan V. (2024), The effect of technology readiness on adopting artificial intelligence in accounting and auditing in Vietnam, “Journal of Risk and Financial Management”, 17 (1), 27; https://doi.org/10.3390/jrfm17010027.
• Bentler P.M., Bonett D.G. (1980), Significance tests and goodness of fit in the analysis of covariance structures, “Psychological Bulletin”, 88 (3), pp. 588–606; https://doi.org/10.1037/0033-2909.88.3.588.
• Blut M., Wang C. (2020), Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage, “Journal of the Academy of Marketing Science”, 48, pp. 649–669; https://doi.org/10.1007/s11747-019-00680-8.
• Carlson K.D., Herdman A.O. (2012), Understanding the impact of convergent validity on research results. “Organizational Research Methods”, 15 (1), pp. 17–32; https://doi.org/10.1177/1094428110392383.
• Chan R., Troshani I., Hill S.R., Hoffmann A. (2022), Towards an understanding of consumers’ FinTech adoption: the case of Open Banking, “International Journal of Bank Marketing”, 40 (4), pp. 886–917; https://doi.org/10.1108/IJBM-08-2021-0397.
• Chatterjee S., Rana N.P., Khorana S., Mikalef P., Sharma A. (2023), Assessing organisational users’ intentions and behaviour to AI integrated CRM systems: A meta-UTAUT approach, “Information Systems Frontiers”, 25 (4), pp. 1299–1313; https://doi.org/10.1007/s10796-021-10181-1.
• Commerford B.P., Dennis S.A., Joe J.R., Ulla J.W. (2022). Man versus machine: Complex estimates and auditor reliance on artificial intelligence, “Journal of Accounting Research”, 60 (1), pp. 171–201; https://doi.org/10.1111/1475-679X.12407.
• Damerji H., Salimi A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting, “Accounting Education”, 30 (2), pp. 107–130; https://doi.org/10.1080/09639284.2021.1872035.
• Davenport T.H., Ronanki R. (2018), Artificial intelligence for the real world, “Harvard Business Review”, 96 (1), pp. 108–116; https://blockqai.com/wp-content/uploads/2021/ 01/analytics-hbr-ai-for-the-real-world.pdf.
• Davis F.D., Bagozzi R.P., Warshaw P.R. (1989), User acceptance of computer technology: A comparison of two theoretical models, “Management Science”, 35 (8), pp. 982–1003; https://www.jstor.org/stable/2632151 (access 8.09.2023).
• Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, “MIS Quarterly” 13 (3), pp. 319–340; https://doi.org/10.2307/249008.
• Donmez-Turan A., Oren B. (2021), Technology Readiness as an Antecedent of Technology Acceptance Model: A Meta-analytic Approach, [in:] Musleh Al-Sartawi A.M., Razzaque A., Kamal M.M. (eds.), Artificial Intelligence Systems and the Internet of Things in the Digital Era (EAMMIS 2021), Lecture Notes in Networks and Systems, vol. 239, Springer, Cham; https://doi.org/10.1007/978-3-030-77246-8_47.
• Fishbein M., Ajzen I. (1975), Belief, attitude, intention, and behavior: An introduction to theory and research, Addison-Wesley, Reading, Mass; https://people.umass.edu/aizen/f&a1975.html.
• Fornell C., Larcker D.F. (1981), Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, “Journal of Marketing Research”, 18 (1), pp. 39–50; https://doi.org/10.1177/002224378101800104.
• Godoe P., Johansen S.T. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept, “Journal of European Psychology Students”, 3 (1), pp. 38–53 https://doi.org/10.5334/jeps.aq.
• Goodwin T. (2018), Digital Darwinism: Survival of the fittest in the age of business disruption, Kogan Page Publishers, London.
• Gursoy D., Chi O.H., Lu L., Nunkoo R. (2019), Consumers acceptance of artificially intelligent (AI) device use in service delivery, “International Journal of Information Management”, 49, pp. 157–169; https://doi.org/10.1016/j.ijinfomgt.2019.03.008.
• Hair Jr. J.F., Hult G.T.M., Ringle C.M., Sarstedt M., Danks N.P., Ray S. (2021), Partial least squares structural equation modelling (PLS-SEM) using R: A workbook, Springer Nature; https://library.oapen.org/handle/20.500.12657/51463.
• Hu Z., Ding S., Li S., Chen L., Yang S. (2019), Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model, “Symmetry”, 11 (3), 30340; https://doi.org/10.3390/sym11030340.
• Jackson D., Allen C. (2023), Technology adoption in accounting: The role of staff perceptions and organisational context, “Journal of Accounting and Organisational Change”, 20 (2), pp. 205–227; https://doi.org/10.1108/JAOC-01-2023-0007.
• Jackson D., Michelson G., Munir R. (2022), New technology and desired skills of early career accountants, “Pacific Accounting Review”, 34 (4), pp. 548–568; https://doi.org/10.1108/PAR-04-2021-0045.
• Jin C.H. (2020), Predicting the Use of Brand Application Based on a TRAM, “International Journal of Human-Computer Interaction”, 36 (2), pp. 156–171; https://doi.org/10.1080/10447318.2019.1609227.
• Kelly S., Kaye S.A., Oviedo-Trespalacios O. (2023), What factors contribute to the acceptance of artificial intelligence? A systematic review, “Telematics and Informatics”, 77, 101925; https://doi.org/10.1016/j.tele.2022.101925.
• Kim T., Chiu W. (2019), Consumer acceptance of sports wearable technology: The role of technology readiness, “International Journal of Sports Marketing and Sponsorship”, 20 (1), pp. 109–126; https://doi.org/10.1108/IJSMS-06-2017-0050.
• Li X., Zhou, Y. Liu Y., Wang X., Yuen K.F. (2023), Psychological antecedents of telehealth acceptance: A technology readiness perspective, “International Journal of Disaster Risk Reduction”, 91, 103688; https://doi.org/10.1016/j.ijdrr.2023.103688.
• Liébana-Cabanillas F., Marinkovic V., de Luna I.R., Kalinic Z. (2018), Predicting the determinants of mobile payment acceptance: a hybrid SEM-neural network approach, “Technological Forecasting and Social Change”, 129 (C), pp. 117–130; https://doi.org/10.1016/j.techfore.2017.12.015.
• Lin J.S.C., Chang H.C. (2011). The role of technology readiness in self-service technology acceptance, “Managing Service Quality”, 21 (4), pp. 424–444; http://dx.doi.org/10.1108/09604521111146289.
• Lu J., Yao J.E., Yu C.S. (2005), Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology, “The Journal of Strategic Information Systems", 14 (3), pp. 245–268; https://doi.org/10.1016/j.jsis.2005.07.0 03.
• Łada M., Martinek-Jaguszewska K. (2023), Autonomizacja procesów rachunkowości, “Zeszyty Teoretyczne Rachunkowości”, 47 (3), pp. 95–111. http://dx.doi.org/10.5604/01.3001.0053.7697.
• Ma X., Huo Y. (2023), Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework, “Technology in Society”, 75, 102362. https://doi.org/10.1016/j.techsoc.2023.102362.
• Moron C.E., Diokno C.O.B. (2023), Level of Readiness and Adoption on the Use of Artificial Intelligence Technologies in the Accounting Profession, “Open Journal of Accounting”, 12, pp. 37–54; https://doi.org/10.4236/ojacct.2023.123004
• Norzelan N.A., Mohamed I.S., Mohamad M. (2024), Technology acceptance of artificial intelligence (AI) among heads of finance and accounting units in the shared service industry, “Technological Forecasting and Social Change”, 198, 123022; https://doi.org/10.1016/j.techfore.2023.123022.
• Nugroho M.A., Fajar M.A. (2017). Effects of technology readiness towards acceptance of mandatory web-based attendance system, “Procedia Computer Science”, 124, pp. 319–328; https://doi.org/10.1016/j.procs.2017.12.161.
• Odonkor B., Kaggwa S., Uwaoma P.U., Hassan A.O., Farayola O.A. (2024), The impact of AI on accounting practices: A review: Exploring how artificial intelligence is transforming traditional accounting methods and financial reporting, “World Journal of Advanced Research and Reviews”, 21 (1), pp. 172–188; https://doi.org/10.30574/wjarr.2024.21.1.2721.
• Parasuraman A., Colby C.L., (2001), Techno-ready marketing: How and why customers adopt technology, Simon and Schuster, New York.
• Parasuraman A. (2000), Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies, “Journal of service research”, 2 (4), pp. 307–320; https://doi.org/10.1177/109467050024001.
• Petkov R. (2020), Artificial Intelligence (AI) and the Accounting Function-A Revisit and a New Perspective for Developing Framework, “Journal of Emerging Technologies in Accounting”, 17, pp. 99–105; https://doi.org/10.2308/jeta-52648.
• Porter C.E., Donthu N. (2006), Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics, “Journal of business research”, 59 (9), pp. 999–1007; https://doi.org/10.1016/j.jbusres.2006.06.003.
• Rafdinal W., Senalasari W. (2021), Predicting the adoption of mobile payment applications during the COVID-19 pandemic, “International Journal of Bank Marketing”, 39 (6), pp. 984–1002; http://dx.doi.org/10.1108/IJBM-10-2020-0532.
• Richards M.B. (2023). Artificial Intelligence in Marketing Communication: Adoption Challenges and Opportunities Through a Lens of Cognitive Dissonance. “Journal of Marketing Development and Competitiveness”, 17 (3), pp. 18–22; https://articlearchives.co/index.php/JMDC/article/view/5941.
• Rudnicka P. (2021), Gotowość wobec technologii. Konteksty, definicja i pomiar, Wydawnictwo Uniwersytetu Śląskiego, Katowice; https://doi.org/10.1080/0144929X.2022.2054729.
• Salisbury W.D., Chin W.W., Gopal A., Newsted P.R. (2002), Better theory through measurement-Developing a scale to capture consensus on appropriation, “Information Systems Research”, 13 (1), pp. 91–103; https://doi.org/10.1287/isre.13.1.91.93.
• Shin S., Lee W.J. (2014), The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea, “Journal of Applied Business Research”, 30 (6), 1615; https://doi.org/10.19030/jabr.v30i6.8873.
• Shuhidan S.M., Awang Y., Taib, A., Rashid, N., Hasan, M.S. (2023), Technology Readiness Among Future Accountants Towards Digitalization of Accounting Profession, “Indonesian Journal of Sustainability Accounting and Management”, 7 (S1), pp. 1–12; https://doi.org/10.28992/ijsam.v7s1.878.
• Sohn K., Kwon O. (2020), Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products, “Telematics and Informatics”, 47, 101324; https://doi.org/10.1016/j.tele.2019.101324.
• Sudaryanto M.R., Hendrawan M.A., Andrian T. (2023), The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students artificial intelligence technology adoption, The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022), E3S Web of Conferences, vol. 388, article number 04055; https://doi.org/10.1051/e3sconf/202338804055.
• Vărzaru A.A. (2022), Assessing Artificial Intelligence Technology Acceptance in Managerial Accounting, “Electronics”, 11, 2256; https://doi.org/10.3390/electronics11142256.
• Venkatesh V., Davis F.D. (1996), A model of the antecedents of perceived ease of use: Development and test, “Decision sciences”, 27 (3), pp. 451–481; https://doi.org/10.1111/j.1540-5915.1996.tb00860.x.
• Venkatesh V., Davis F.D. (2000), A theoretical extension of the technology acceptance model: Four longitudinal field studies, “Management Science”, 46 (2), pp. 186–204; https://doi.org/10.1287/mnsc.46.2.186.11926.
• 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), pp. 425–478; https://doi.org/10.2307/30036540.
• Venkatesh V., Bala H. (2008), Technology acceptance model 3 and a research agenda on interventions, “Decision sciences”, 39 (2), pp. 273–315; https://doi.org/10.1111/j.1540-5915.2008.00192.x.
• Venkatesh V., Thong J. Y., 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.
• Venkatesh V. (2022), Adoption and use of AI tools: a research agenda grounded in UTAUT, “Annals of Operations Research”, 308 (1), pp. 641–652; https://doi.org/10.1007/s10479-020-03918-9.
• Walczuch R., Lemmink J., Streukens S. (2007), The effect of service employees’ technology readiness on technology acceptance, “Information and Management”, 44 (2), pp. 206–215; https://doi.org/10.1016/j.im.2006.12.005.
• Wiese M., Humbani M. (2020), Exploring technology readiness for mobile payment app users, “The International Review of Retail, Distribution and Consumer Research”, 30 (2), pp. 123–142; https://doi.org/10.1080/09593969.2019.1626260.
• Wong K.K.K. (2013), Partial least squares structural equation modelling (PLS-SEM) techniques using SmartPLS, “Marketing Bulletin”, 24 (1), pp. 1–32; http://marketing-bulletin.massey.ac.nz/v24/mb_v24_t1_wong.pdf.
• Zemánkova A. (2019), Artificial intelligence and blockchain in audit and accounting: Literature review. “WSEAS Transactions on Business and Economics”, 16 (1), pp. 568–581; https://wseas.com/journals/bae/2019/b245107-089.pdf.
• Zhang T., Lu C., Kizildag M. (2018), Banking ‘on-the-go’: examining consumers’ adoption of mobile banking services, “International Journal of Quality and Service Sciences”, 10 (3), pp. 279–295; https://doi.org/10.1108/IJQSS-07-2017-0067.
• 2024 Report The State of Accounting Workflow Automation (2024), FinacialCents, https://financial-cents.com/resources/articles/2024-report-state-of-accounting-workflow-automation/.
• Artificial Intelligence Index Report 2023, Stanford University, https://hai.stanford.edu/ai-index/2023-ai-index-report.
• Doung Q. (2024), The impact of artificial intelligence on accounting and finance: A global perspective, IMA, https://www.imanet.org/research-publications/ima-reports/the-impact-of-artificial-intelligence-on-accounting-and-finance.
• Fishbein M., Ajzen I. (1975), Belief, attitude, intention and behavior: An introduction to theory and research, Addison-Wesley, Reading, Mass; https://people.umass.edu/aizen/f&a1975.html (access 10.10.2023).
• Nowakowska E., Wilczyńska-Baraniak J., Ciepiela P., Miernik A. (2024), Nowoczesny CFO w transformującej się firmie, KPMG; https://kpmg.com/pl/pl/home/insights/2024/10/nowoczesny-cfo-w-transformujacej-sie-firmie-2024.html.
• Nowoczesne narzędzia w księgowości – gdzie jesteśmy i co nas czeka w przyszłości (2022), Wolters Kluwer, Warszawa; https://www.wolterskluwer.com/pl-pl/solutions/informacje/raport-nowoczesne-narzedzia-w-ksiegowosci (access 10.10.2024).
• Rogers E.M. (1995), Diffusion of Innovations, The Free Press, New York (retrieved 13.10.2024); http://www.lamolina.edu.pe/postgrado/pmdas/cursos/innovacion/lecturas/Obligatoria/17%20-%20Rogers%201995%20cap%206.pdf.
• The State of AI in Accounting Report 2024, Karbon Report (2024); https://karbonhq.com/resources/state-of-ai-accounting-report-2024/.