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dc.contributor.author | Fajardo Pereira, Johana | spa |
dc.contributor.author | Toscano Hernández, Aníbal | spa |
dc.contributor.author | García Alarcón, Héctor | spa |
dc.contributor.author | Llanos Ayola, Jones | spa |
dc.date.accessioned | 2023-04-14T00:00:00Z | |
dc.date.accessioned | 2024-09-05T20:24:32Z | |
dc.date.available | 2023-04-14T00:00:00Z | |
dc.date.available | 2024-09-05T20:24:32Z | |
dc.date.issued | 2023-04-14 | |
dc.description.abstract | Objetivos: La inteligencia artificial se ha establecido como una fuerza disruptiva en una amplia gama de industrias, incluida la auditoría. En la última década, la Inteligencia artificial ha demostrado su capacidad para automatizar tareas, identificar patrones complejos y mejorar la precisión de los procesos de auditoría. El propósito fundamental de este estudio resumir y exponer los estudios científicos de la investigación relacionada con la inteligencia artificial y la auditoría a nivel mundial. Métodos: Se realizo un análisis bibliométrico que abarca un período de 37 años, desde 1984 hasta 2022. Para analizar y presentar los resultados se utilizó el paquete de análisis bibliométrico Biblioshiny, soportado en el programa R Studio, así como en el software VOSviewer, teniendo en cuenta 306 artículos y revisiones de literatura. Este enfoque cuantitativo nos permitió identificar patrones y tendencias en la investigación. Resultados: Los resultados reflejan cambios importantes en el número de publicaciones anuales al registrar que el 70,91% de los documentos se publicaron en los últimos 7 años (2016 a 2022) y solo el 29,08% fue publicado en los 30 años comprendidos entre 1984 y 2015. Además, entre las 234 revistas científicas con publicaciones relacionadas, se identifican las ocho principales que concentran un 12.8% de las publicaciones y acumulan 12.5% de las citaciones. El clúster más numeroso, representado en color rojo, resaltando los 10 principales “audit”, “Audit Quality”, “Auditing”, “Big Data”, “Big Data Analytics”, “Blockchain”, “Computers”, “Data Mining”, “Decision Making”. Conclusión: Esta investigación permite caracterizar la producción científica relacionada con la inteligencia artificial y la auditoria considerando la evolución temporal, características generales, redes de investigación con autores e instituciones, así como los clústeres temáticos de mayor relevancia en este campo de estudio. | spa |
dc.description.abstract | Background and objectives: Artificial intelligence has established itself as a disruptive force in a wide range of industries, including auditing. Over the last decade, Artificial Intelligence has demonstrated its ability to automate tasks, identify complex patterns, and improve the accuracy of audit processes. The fundamental purpose of this study is to summarize and present the scientific studies of research related to artificial intelligence and auditing worldwide. Methods: A bibliometric analysis was carried out covering a period of 37 years, from 1984 to 2022. To analyze and present the results, the Biblioshiny bibliometric analysis package was used, supported by the R Studio program, as well as the VOSviewer software, taking into account 306 articles and literature reviews. This quantitative approach allowed us to identify patterns and trends in the research. Findings: The results reflect important changes in the number of annual publications by recording that 70.91% of the documents were published in the last 7 years (2016 to 2022) and only 29.08% were published in the 30 years between 1984 and 2015. Furthermore, among the 234 scientific journals with related publications, the eight main ones are identified, which concentrate 12.8% of the publications and accumulate 12.5% of the citations. The most numerous cluster, represented in red, highlighting the top 10 “audit”, “Audit Quality”, “Auditing”, “Big Data”, “Big Data Analytics”, “Blockchain”, “Computers”, “Data Mining”, “Decision Making. Conclusion: This research allows to characterize the scientific production related to artificial intelligence and auditing, considering the temporal evolution, general characteristics, research networks with authors and institutions, as well as the most relevant clusters in this field. | eng |
dc.format.mimetype | application/pdf | spa |
dc.identifier.doi | 10.32997/pe-2023-4575 | |
dc.identifier.eissn | 2463-0470 | |
dc.identifier.issn | 0122-8900 | |
dc.identifier.uri | https://hdl.handle.net/11227/17931 | |
dc.identifier.url | https://doi.org/10.32997/pe-2023-4575 | |
dc.language.iso | spa | spa |
dc.publisher | Universidad de Cartagena | spa |
dc.relation.bitstream | https://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/download/4575/3571 | |
dc.relation.citationendpage | 187 | |
dc.relation.citationissue | 2 | spa |
dc.relation.citationstartpage | 160 | |
dc.relation.citationvolume | 31 | spa |
dc.relation.ispartofjournal | Panorama Económico | spa |
dc.relation.references | Al-Sayyed, S.M.; Al-Aroud, S. F.; Zayed, L. M., (2021). The effect of artificial intelligence technologies on audit evidence. Accounting, 7(2), 281–288. https://www.growingscience.com/ac/Vol7/ac_2020_188.pdf | spa |
dc.relation.references | Appelbaum, D., (2016). Securing big data provenance for auditors: The big data provenance black box as reliable evidence. Journal of Emerging Technologies in Accounting, 13(1), 13–17. https://publications.aaahq.org/jeta/article-abstract/13/1/17/9219/Securing-Big-Data-Provenance-for-Auditors-The-Big?redirectedFrom=fulltext | spa |
dc.relation.references | Aria, M.; Cuccurullo, C., (2017). Bibliometrix: An r-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://www.sciencedirect.com/science/article/abs/pii/S1751157717300500?via%3Dihub | spa |
dc.relation.references | Atayah, O.F.; Alshater, M.M., (2021). Audit and tax in the context of emerging technologies: a retrospective analysis, current trends, and future opportunities. International Journal of Digital Accounting Research, 21, 95–128. https://www.uhu.es/ijdar/10.4192/1577-8517-v21_4.pdf | spa |
dc.relation.references | Bastani, H.; Bastani, O.; Sinchaisri, P., (2022). Improving human decision-making with machine learning. Academy of Management Proceedings, 2022(1). https://hamsabastani.github.io/tips.pdf | spa |
dc.relation.references | Boxwala, A.A.; Kim, J.; Grillo, J.M.; Ohno-Machado, L., (2011). Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal of the American Medical Informatics Association, 18(4), 498–505. https://academic.oup.com/jamia/article/18/4/498/2909142?login=false | spa |
dc.relation.references | Brown, B.; Balatsoukas, P.; Williams, R.; Sperrin, M.; Buchan, I., (2016). Interface design recommendations for computerised clinical audit and feedback: Hybrid usability evidence from a research-led system. International Journal of Medical Informatics, 94, 191–206. https://www.sciencedirect.com/science/article/pii/S138650561630171X | spa |
dc.relation.references | Brzezicki, M.A.; Bridger, N.E.; Kobetić, M.D.; Ostrowski, M.; Grabowski, W.; Gill, S.S.; Neumann, S., (2020). Artificial intelligence outperforms human students in conducting neurosurgical audits. Clinical Neurology and Neurosurgery, 192. https://www.sciencedirect.com/science/article/abs/pii/S0303846720300755?via%3Dihub | spa |
dc.relation.references | Cazazian, R., (2022). Blockchain technology adoption in artificial intelligence- based digital financial services, accounting information systems and audit quality control. August, 55–71. https://publications.aaahq.org/jeta/article-abstract/17/1/107/9324/Blockchain-Technology-Business-Data-Analytics-and?redirectedFrom=fulltext | spa |
dc.relation.references | Char, D.S.; Shah, N.H.; Magnus, D., (2019). Implementing machine learning in health care — addressing. The New England Journal of Medicine, 981–983, 2018–2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962261 | spa |
dc.relation.references | 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), 171–201. https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-679X.12407 | spa |
dc.relation.references | Cossío, A., (2018). Bots, machine learning, servicios cognitivos realidad y perspectivas de la inteligencia artificial en España, 2018. PWC, 1–34. https://www.pwc.es/es/publicaciones/tecnologia/assets/pwc-ia-en-espana-2018.pdf | spa |
dc.relation.references | Dai, J.; Vasarhelyi, M.A., (2017). Toward blockchain-based accounting and assurance. Journal of Information Systems, 31(3), 5–21. https://publications.aaahq.org/jis/article-abstract/31/3/5/1105/Toward-Blockchain-Based-Accounting-and-Assurance?redirectedFrom=fulltext | spa |
dc.relation.references | Denning, D.E., (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, 13(2), 222–232. https://ieeexplore.ieee.org/document/1702202 | spa |
dc.relation.references | Dickey, G.; Blanke, S.; Seaton, L., (2019). Machine learning in auditing. The CPA Journal, 89(6), 16–21. https://www.cpajournal.com/2019/06/19/machine-learning-in-auditing | spa |
dc.relation.references | Dungan, C.w; Chandlers, J. s., (1985). Auditor: A microcomputer-based expert system to support auditors in the field. University of South Florida at Sarasota, 2(4), 210–221. https://onlinelibrary.wiley.com/doi/10.1111/j.1468-0394.1985.tb00474.x | spa |
dc.relation.references | Earley, C.E., (2015). Data analytics in auditing: Opportunities and challenges. Business Horizons, 58(5), 493–500. https://www.sciencedirect.com/science/article/abs/pii/S0007681315000592 | spa |
dc.relation.references | Fan, L.; Yang, K.; Liu, L., (2020). New media environment, environmental information disclosure and firm valuation: Evidence from high-polluting enterprises in China. Journal of Cleaner Production, 277, 123253. https://www.sciencedirect.com/science/article/abs/pii/S0959652620332984 | spa |
dc.relation.references | Fedyk, A.; Khimich, N.; Fedyk, T., (2022). Is artificial intelligence improving the audit process ? Review of Accounting Studies, june, 938–985. https://link.springer.com/article/10.1007/s11142-022-09697-x | spa |
dc.relation.references | Fuentes-Doria, D.D.; Toscano-hernández, A. E.; Malvaceda-espinoza, E., (2020). Metodología de la investigacion (Juan Carlos Rodas Montoya (ed.). Editorial Universidad Pontificia Bolivariana. https://repository.upb.edu.co/handle/20.500.11912/6201 | spa |
dc.relation.references | Gangsar, P.; Bajpei, A.R.; Porwal, R., (2022). A review on deep learning based condition monitoring and fault diagnosis of rotating machinery. Noise & vibration worldwide, 095745652211396. https://journals.sagepub.com/doi/10.1177/09574565221139638 | spa |
dc.relation.references | Gentner, D.; Stelzer, B.; Ramosaj, B.; Brecht, L., (2018). Strategic foresight of future b2b customer opportunities through machine learning. Technology Innovation Management Review, 8(10), 5–17. https://timreview.ca/article/1189 | spa |
dc.relation.references | González, G.C.; Sharma, P.N.; Galletta, D.F., (2012). The antecedents of the use of continuous auditing in the internal auditing context. International Journal of Accounting Information Systems, 13(3), 248–262. https://www.sciencedirect.com/science/article/abs/pii/S1467089512000401 | spa |
dc.relation.references | 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. http://www.acrn-journals.eu/resources/jofrp09g.pdf | spa |
dc.relation.references | Groza, A.; Toderean, L.; Muntean, G.A.; Nicoara, S.D., (2021). Agents that argue and explain classifications of retinal conditions. Journal of Medical and Biological Engineering, 41(5), 730–741. https://www.researchsquare.com/article/rs-201690/v1 | spa |
dc.relation.references | Haenlein, M.; Kaplan, A., (2019). A brief history of artificial intelligence: California Management Review, 1–10. https://journals.sagepub.com/doi/abs/10.1177/0008125619864925 | spa |
dc.relation.references | Hu, K.H.; Chen, F.H.; Hsu, M.F.; Tzeng, G.H., (2021). Identifying key factors for adopting artificial intelligence-enabled auditing techniques by joint utilization of fuzzy-rough set theory and MRDM technique. Technological and Economic Development of Economy, 27(2), 459–492. https://journals.vilniustech.lt/index.php/TEDE/article/view/13181 | spa |
dc.relation.references | Huang, F.; No, W.G.; Vasarhelyi, M. A.; Yan, Z., (2022). Audit data analytics, machine learning, and full population testing. Journal of finance and data science, 8, 138–144. https://www.sciencedirect.com/science/article/pii/S240591882200006X | spa |
dc.relation.references | Huang, F.; Vasarhelyi, M.A., (2019). Applying robotic process automation (RPA ) in auditing : A framework. International Journal of Accounting Information Systems, 100433. https://www.sciencedirect.com/science/article/abs/pii/S1467089518301738 | spa |
dc.relation.references | Huang, H.; Yang, Y.; Xie, A., (2022). Do over-conservative going concern audit opinions exist ? evidence from the prediction model approach. Economics Letters, 212. https://www.sciencedirect.com/science/article/abs/pii/S016517652200012X | spa |
dc.relation.references | Huerta, E.; Jensen, S., (2017). An accounting information systems perspective on data analytics and big data. Journal of Information Systems, 31(3), 101–114. https://publications.aaahq.org/jis/article-abstract/31/3/101/1097/An-Accounting-Information-Systems-Perspective-on?redirectedFrom=fulltext | spa |
dc.relation.references | Huq, A. M.; Hartwig, F.; Rudholm, N., (2022). Do audited firms have a lower cost of debt? International Journal of Disclosure and Governance, 19(2), 153–175. https://link.springer.com/article/10.1057/s41310-021-00133-1 | spa |
dc.relation.references | Issa, H.; Sun, T.; Vasarhelyi, M.A., (2016). Research ideas for artificial intelligence in auditing: the formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://publications.aaahq.org/jeta/article-abstract/13/2/1/9209/Research-Ideas-for-Artificial-Intelligence-in?redirectedFrom=fulltext | spa |
dc.relation.references | Kachroo, P.; Member, S.; Saiewitz, A.; Raschke, R.; Agarwal, S., (2019). A new language and input-output hidden markov model for automated audit inquiry. IEEE Intelligent Systems, 00(0), 1–8. https://ieeexplore.ieee.org/document/8948253 | spa |
dc.relation.references | Kokina, J.; Davenport, T.H., (2017). The emergence of artificial intelligence how automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://publications.aaahq.org/jeta/article-abstract/14/1/115/9198/The-Emergence-of-Artificial-Intelligence-How?redirectedFrom=fulltext | spa |
dc.relation.references | Lee, B.; Gately, L.; Lok, S.W.; Tran, B.; Lee, M.; Wong, R.; Markman, B.; Dunn, K.; Wong, V.; Loft, M.; Jalili, A.; Anton, A.; To, R.; Andrews, M.; Gibbs, P., (2022). Leveraging comprehensive cancer registry data to enable a broad range of research, audit and patient support activities. Cancers, 14(17), 1–12. https://www.mdpi.com/2072-6694/14/17/4131 | spa |
dc.relation.references | Leo Kumar; S.P., (2019). Knowledge-based expert system in manufacturing planning: state-of-the-art review. International Journal of Production Research, 57(15–16), 4766–4790. https://www.tandfonline.com/doi/abs/10.1080/00207543.2018.1424372 | spa |
dc.relation.references | Li, S., (2022). Discussion on the construction of enterprise internal audit informatization. Journal of Advanced Transportation, 2022. https://www.hindawi.com/journals/jat/2023/9839620/ | spa |
dc.relation.references | Maditati, D.R.; Munim, Z. H.; Schramm, H.J.; & Kummer, S., (2018). A review of green supply chain management: from bibliometric analysis to a conceptual framework and future research directions. Resources, Conservation and Recycling, 139, 150–162. https://www.sciencedirect.com/science/article/abs/pii/S0921344918302969?via%3Dihub | spa |
dc.relation.references | Moffitt, R.; Vasarhelyi., (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://publications.aaahq.org/jeta/article-abstract/15/1/1/9252/Robotic-Process-Automation-for-Auditing?redirectedFrom=fulltext | spa |
dc.relation.references | Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Altman, D.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.A.; Clark, J.; Clarke, M.; Cook, D.; D’Amico, R.; Deeks, J.J.; Devereaux, P.J.; Dickersin, K.; Egger, M.; Ernst, E.; Tugwell, P., (2009). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. PLOS Medicine, 6(7). https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000097 | spa |
dc.relation.references | Molina, A.; Rodellar, J.; Boldú, L.; Acevedo, A.; Alferez, S.; Merino, A., (2021). Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks. Computers in Biology and Medicine, 136(July). https://www.sciencedirect.com/science/article/abs/pii/S0010482521004741?via%3Dihub | spa |
dc.relation.references | Montoya Hernández, A.Y.; Valencia Duque, F.J., (2019). Inteligencia artificial al servicio de la auditoría: Una revisión sistemática de literatura. RISTI, 27, 213–226. https://www.risti.xyz/issues/ristie27.pdf | spa |
dc.relation.references | Mugwira, T., (2022). Internet related technologies in the auditing profession: A wos bibliometric review of the past three decades and conceptual structure mapping. Revista de Contabilidad-Spanish Accounting Review, 25(2), 201–216. https://revistas.um.es/rcsar/article/view/428041 | spa |
dc.relation.references | Noordin, N.A.; Hussainey, K.; Hayek, A.F., (2022). the use of artificial intelligence and audit quality: An analysis from the perspectives of external auditors in the UAE. Journal of Risk and Financial Management, 15(8). https://www.mdpi.com/1911-8074/15/8/339 | spa |
dc.relation.references | Oala, L.; Murchison, A.G.; Balachandran, P.; Choudhary, S.; Fehr, J.; Leite, A.W.; Goldschmidt, P.G.; Johner, C.; Schörverth, E.D.M.; Nakasi, R.; Meyer, M.; Cabitza, F.; Baird, P.; Prabhu, C.; Weicken, E.; Liu, X.; Wenzel, M.; Vogler, S.; Akogo, D.; Wiegand, T., (2021). Machine learning for health: Algorithm auditing & quality control. Journal of Medical Systems, 45(12). https://link.springer.com/article/10.1007/s10916-021-01783-y | spa |
dc.relation.references | Omoteso, K., (2012). The application of artificial intelligence in auditing : Looking back to the future. Expert Systems with Applications, 39(9), 8490–8495. https://www.sciencedirect.com/science/article/abs/pii/S095741741200111X?via%3Dihub | spa |
dc.relation.references | Pejic bach, M., (2010). Profiling intelligent systems applications in fraud detection and prevention : survey of research articles. University of Zagreb, 80–85. https://ieeexplore.ieee.org/document/5416118 | spa |
dc.relation.references | Pérez Dávila, F.L., (2017). Filosofía y ciencia, generadoras de conocimiento en investigación educativa. Revista Interamericana de Investigación, Educación y Pedagogía, 10(1), 255–276. https://revistas.usantotomas.edu.co/index.php/riiep/article/view/4762 | spa |
dc.relation.references | Perianes-Rodríguez, A.; Waltman, L.; Eck, N.J.Van., (2016). Constructing bibliometric networks : A comparison between full and fractional counting. Journal of Informetrics, 1–38. https://www.sciencedirect.com/science/article/abs/pii/S1751157716302036?via%3Dihub | spa |
dc.relation.references | Rijwani, P.; Jain, S., (2022). software effort estimation development from neural networks to deep learning approaches. Journal of Cases on Information Technology, 24(4), 1–16. https://www.igi-global.com/gateway/article/296715 | spa |
dc.relation.references | Rozinat, A.Ã.; Aalst, W.M.P.Van Der., (2008). Conformance checking of processes based on monitoring real behavior. Information Systems 33, 33, 64–95. https://www.sciencedirect.com/science/article/abs/pii/S030643790700049X?via%3Dihub | spa |
dc.relation.references | Saibene, A.; Assale, M.; & Giltri, M., (2021). Expert systems: Definitions, advantages and issues in medical field applications. Expert Systems with Applications, 177. https://www.sciencedirect.com/science/article/abs/pii/S0957417421003419?via%3Dihub | spa |
dc.relation.references | Salijeni, G.; Samsonova-Taddei, A.; Turley, S., (2019). Big data and changes in audit technology: contemplating a research agenda. Accounting and Business Research, 49(1), 95–119. https://www.tandfonline.com/doi/abs/10.1080/00014788.2018.1459458 | spa |
dc.relation.references | Sammour, T.; Cohen, L.; Karunatillake, A.I.; Lewis, M.; Lawrence, M.J.; Hunter, A.; Moore, J.W.; Thomas, M.L., (2017). Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics. Techniques in Coloproctology, 21(11), 869–877. https://link.springer.com/article/10.1007/s10151-017-1701-1 | spa |
dc.relation.references | Schetinin, V.; Jakaite, L.; & Krzanowski, W. (2018)., Artificial Intelligence in medicine bayesian averaging over decision tree models for trauma severity scoring. Artificial Intelligence in Medicine, 84, 139–145. https://www.sciencedirect.com/science/article/abs/pii/S0933365717301100?via%3Dihub | spa |
dc.relation.references | Sun, Z.; Wan, J.; Yin, L.; Cao, Z.; Luo, T.; Wang, B., (2022). A blockchain-based audit approach for encrypted data in federated learning. Digital Communications and Networks, 8(5), 614–624. https://www.sciencedirect.com/science/article/pii/S2352864822000979?via%3Dihub | spa |
dc.relation.references | Sutton, S.G.; Holt, M.; & Arnold, V., (2016). “The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60–73. https://www.sciencedirect.com/science/article/abs/pii/S1467089516300823?via%3Dihub | spa |
dc.relation.references | Talaviya, T.; Shah, D.; Patel, N.; Yagnik, H.; & Shah, M., (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73. https://www.sciencedirect.com/science/article/pii/S258972172030012X?via%3Dihub | spa |
dc.relation.references | Tiberius, V.; Hirth, S., (2019a). Impacts of digitization on auditing: A delphi study for Germany. Journal of International Accounting, Auditing and Taxation,” 37, 100288. https://www.sciencedirect.com/science/article/abs/pii/S1061951819300084?via%3Dihub | spa |
dc.relation.references | Tiberius, V.; Hirth, S., (2019b). Impacts of Digitization on Auditing: A delphi Study for germany. Journal of International Accounting, Auditing and Taxation, 100288. https://www.sciencedirect.com/science/article/abs/pii/S1061951819300084?via%3Dihub | spa |
dc.relation.references | Turing., (1950). Computing machinery and intelligence. Mind, 49, 433–460. https://phil415.pbworks.com/f/TuringComputing.pdf | spa |
dc.relation.references | Zandi, D.; Reis, A.; Goodman, K., (2019). New ethical challenges of digital technologies, machine learning and artificial intelligence in public health : a call for papers. February, 1–2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307511/pdf/BLT.18.227686.pdf | spa |
dc.relation.references | Zhou, G., (2021). Research on the development of cpa audit from the perspective of artificial intelligence. E3S Web of Conferences, 251, 1–4. https://www.e3s conferences.org/articles/e3sconf/abs/2021/27/e3sconf_ictees2021_01056/e3sconf_ictees2021_01056.html | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
dc.rights.creativecommons | Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. | spa |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0 | spa |
dc.source | https://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/view/4575 | spa |
dc.subject | Accounting | eng |
dc.subject | Artificial Intelligence | eng |
dc.subject | Automation | eng |
dc.subject | Digitalization | eng |
dc.subject | Financial Auditing | eng |
dc.subject | Auditoría financiera | spa |
dc.subject | Automatización | spa |
dc.subject | Contabilidad | spa |
dc.subject | Digitalización | spa |
dc.subject | Inteligencia Artificial | spa |
dc.title | Inteligencia Artificial y Auditoría: Tendencias de la literatura científica | spa |
dc.title.translated | Artificial Intelligence and Auditing: Trends in scientific literature | eng |
dc.type | Artículo de revista | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.coarversion | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.local | Journal article | eng |
dc.type.redcol | http://purl.org/redcol/resource_type/ARTREF | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
dspace.entity.type | Publication | spa |
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