Étude quantitative des déterminants technologiques d'acceptabilité de l'IA générative chez les experts-comptables au Maroc.
DOI:
https://doi.org/10.23882/ijdam.24173Palavras-chave:
IA générative, acceptabilité technologique, expertise comptable, MarocResumo
Este trabalho tem como objetivo identificar e analisar os determinantes tecnológicos da aceitabilidade da inteligência artificial generativa pelos contabilistas marroquinos. Diante da emergência dessas tecnologias que transformam as práticas profissionais, nossa problemática questiona especificamente a influência dos fatores tecnológicos na sua aceitação. Nossa metodologia baseia-se em uma abordagem quantitativa junto a 262 contabilistas inscritos na Ordem dos Contabilistas Certificados de Marrocos. Três determinantes tecnológicos foram examinados: a facilidade de uso percebida, a utilidade percebida e o antropomorfismo. Os resultados revelam que a facilidade de uso é o fator predominante (β=0,390, p<0,001), seguida pela utilidade percebida, que exerce uma influência moderada, mas significativa (β=0,170, p<0,01). Contrariamente às expectativas teóricas, o antropomorfismo não demonstra um efeito significativo (β=0,087, p>0,05). Este estudo demonstra que a aceitabilidade da IA generativa baseia-se principalmente em considerações ergonômicas, em vez de aspectos relacionais ou antropomórficos. Esses resultados sugerem a importância de priorizar a simplicidade de uso e a acessibilidade no desenvolvimento de soluções de IA destinadas aos profissionais da contabilidade.
Referências
Askell, A. et al. (2021). A General Language Assistant as a Laboratory for Alignment. https://doi.org/10.48550/arXiv.2112.00861
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics, 1(1), 71-81. https://doi.org/10.1007/s12369-008-0001-3
Bartlett, M. S. (1954). A note on the multiplying factors for various chi square approximations. Journal of the Royal Statistical Society, 16(Series B), 296-298.
Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 211-218. https://doi.org/10.17705/1jais.00126
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632-658. https://doi.org/10.1007/s11747-020-00762-y
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73. https://doi.org/10.2307/3150876
Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319. https://doi.org/10.1037/1040-3590.7.3.309
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
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. https://doi.org/10.1080/09639284.2021.1872036
Davis, F. D. (1989). 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., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Feuerriegel, S., Hörchler, L., Kühl, N., & Seyfried, F. (2024). Changing the economics of content production: A research agenda on text-generating AI. Information Systems Research. https://doi.org/10.1287/isre.2023.0214
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
Gotthardt, M., Koivulaakso, D., Paksoy, O., Saramo, C., Martikainen, M., & Lehner, O. (2020). Current state and challenges in the implementation of smart robotic process automation in accounting and auditing. ACRN Journal of Finance and Risk Perspectives, 9(1), 90-102. https://doi.org/10.35944/jofrp.2020.9.1.007
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, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
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.). Sage.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
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. (2022). The meta-analysis of partial least squares structural equation modeling (PLS-SEM) results in accounting research. Journal of International Financial Management & Accounting, 33(2), 182-211. https://doi.org/10.1111/jifm.12156
Hanetseder, C., Schulte-Nölke, H., & Amarouch, F. (2021). Digitalisierung in der Finanzbuchhaltung und die Zukunft des Berufsstandes. Der Betrieb, 74(19), 1017-1022.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
KEBE, P. I., El Bettioui, R., & COMBAUDON , C. (2024). L’évaluation de la performance des projets de R&D de la logique d’efficacite economique a la logique institutionnelle: Cas d’une entreprise énergétique. IJDAM • International Journal of Digitalization and Applied Management, 1(2), 92–120. https://doi.org/10.23882/ijdam.24134
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_30392
Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020). The digital transformation of external audit and its impact on corporate governance. Technological Forecasting and Social Change, 150, 119751. https://doi.org/10.1016/j.techfore.2019.119751
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Ordre des Experts-Comptables du Maroc [OEC]. (2024). Annuaire des experts-comptables inscrits au tableau de l'Ordre. Récupéré le 12/08/2024 de https://jecherchemonexpertcomptable.oecmaroc.com/
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2008). Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making, 2(2), 140-160. https://doi.org/10.1518/155534308X284417
Rocchetta, S. (2024). Resilience and Innovation: A Conceptual Approach. In Innovation-Research and Development for Human, Economic and Institutional Growth. IntechOpen. https://doi.org/10.5772/intechopen.113842
Shanahan, M. (2024). Talking About Large Language Models. arXiv preprint arXiv:2212.03551.
Sharma, K., Sharma, R., & Sharma, S. (2021). Technology acceptance model for the use of artificial intelligence in accounting and auditing: A conceptual framework. Journal of Management Information and Decision Sciences, 24(8), 1-13.
Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53.
Skidmore, S. M., & Smith, S. D. (2024). Impact of Generative Artificial Intelligence on Accounting and Financial Reporting. The CPA Journal, 94(1), 75-85.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., ... & Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
Vărzaru, A. A. (2022). Perceptions of accountants regarding artificial intelligence use in management accounting. Journal of Accounting and Management Information Systems, 20(4), 709-736. https://doi.org/10.24818/jamis.2021.04005
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 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), 425-478. https://doi.org/10.2307/30036540
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), 157-178. https://doi.org/10.2307/41410412
Waytz, A., Cacioppo, J., & Epley, N. (2010). Who sees human? The stability and importance of individual differences in anthropomorphism. Perspectives on Psychological Science, 5(3), 219-232. https://doi.org/10.1177/1745691610369336
Waytz, A., Heafner, J., & Epley, N. (2017). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113-117. https://doi.org/10.1016/j.jesp.2014.01.005
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Direitos de Autor (c) 2025 Abdellatif AZIKI, Moulay Hachem FADILI, Rachid EL BETTIOUI

Este trabalho encontra-se publicado com a Creative Commons Atribuição-NãoComercial 4.0.