Relações entre contabilidade e inteligência: caminhos de pesquisa
DOI:
https://doi.org/10.51341/1984-3925_2021v24n3a2Palabras clave:
Contabilidade, Inteligência, Revisão Sistemática de LiteraturaResumen
Objetivo: identificar de que forma se apresenta o desenvolvimento do conhecimento e quais as possíveis lacunas deste em pesquisas que envolvem a Contabilidade e os processos de Inteligência.
Método: estudo de natureza qualitativa e quantitativa. Realizou-se uma Revisão sistemática de literatura em que foram analisados 89 artigos publicados em periódicos científicos, coletados em julho de 2020.
Originalidade/Relevância: A contabilidade está cada vez mais inserida no processo de tomada de decisão e na gestão dos negócios de forma a ser relevante a apropriação de conceitos de Inteligência Estratégica junto à ciência da contabilidade, uma vez que a utilização das informações advindas do ambiente externo pode auxiliar na mensuração de forma mais assertiva dos fatos contábeis e na obtenção de melhores informações para a tomada de decisão.
Resultados: observa-se quatro principais enfoques de pesquisa nesse contexto: a utilização de sistema de Business Intelligence; o melhoramento de provisões contábeis; o monitoramento do ambiente para identificação de aspectos relevantes à vida financeira das organizações; e a automatização dos processos contábeis por meio de Artificial Intelligence e demais tecnologias.
Contribuições teóricas/metodológicas: O estudo traz como contribuição uma visualização sobre como os temas de contabilidade e inteligência se ramificam em enfoques diferentes de pesquisas. Portanto, foi possível identificar quatro enfoques no desenvolvimento do conhecimento e lacunas de pesquisas que envolvem a Contabilidade e os processos de Inteligência. Dois enfoques de pesquisa estão mais voltados ao uso de tecnologia e automatização de processos, um enfoque voltado ao aspecto mais gerencial de monitoramento de ambiente e um aspecto mais técnico voltado a apuração contábil. Como contribuição metodológica tem-se a forma de sistematização e ilustração dos processos de coleta.
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Acker, D., Horton, J., & Tonks, I. (2002). Accounting standards and analysts’ forecasts: the impact of FRS3 on analysts’ ability to forecast EPS. Journal of Accounting and Public Policy, 21(3), 193-217. https://doi.org/10.1016/S0278-4254(02)00049-2 DOI: https://doi.org/10.1016/S0278-4254(02)00049-2
Adegbile, A., Sarpong, D., & Meissner, D. (2017). Strategic Foresight for Innovation Management: A Review and Research Agenda. International Journal of Innovation and Technology Management, 14(4), 1-34. DOI: https://doi.org/10.1142/S0219877017500195
Aleksic, M., Vujnovic-Gligoric, B., & Uremovic, N. (2015). The Role And The Significance Of Forecast Accounting In Financial Preview. Casopis Za Ekonomiju I Trzisne Komunikacije, 5(2), 229-236.
Andrade, J. P., & Lopes Lucena, W. G. (2018). Analysis of performance of forecast models insolvency and the implementation of International Accounting Standards. Revista Ciencias Administrativas, 24(2). http://dx.doi.org/10.5020/2318-0722.2018.6563 DOI: https://doi.org/10.5020/2318-0722.2018.6563
Andriotti, F., & Freitas, H. (2008). A informação informal e a monitoração do ambiente: fontes e exploração/disseminação. Revista de Administração da Universidade Federal de Santa Maria, 1(3). https://doi.org/10.5902/19834659607 DOI: https://doi.org/10.5902/19834659607
Atkinson, A. A., Banker, R. D., Kaplan, R. S., & Young, M. (2000) Contabilidade gerencial. São Paulo: Atlas.
Ayres, D., Huang, X. S., & Myring, M. (2017). Fair value accounting and analyst forecast accuracy. Advances in accounting, 37, 58-70. https://doi.org/10.1016/j.adiac.2016.12.004 DOI: https://doi.org/10.1016/j.adiac.2016.12.004
Baldwin-Morgan, A. A. (1995). Integrating artificial intelligence into the accounting curriculum. Accounting Education, 4(3), 217-229. https://doi.org/10.1080/09639289500000026 DOI: https://doi.org/10.1080/09639289500000026
Barnea, A., & Lakonishok, J. (1980). An analysis of the usefulness of disaggregated accounting data for forecasts of corporate performance. Decision Sciences, 11(1), 17-26. https://doi.org/10.1111/j.1540-5915.1980.tb01122.x DOI: https://doi.org/10.1111/j.1540-5915.1980.tb01122.x
Bardin L. Ánálise de conteúdo. SP: Edições 70, 2011.
Barth, M. E. (2014). Measurement in Financial Reporting: The Need for Concepts. Accounting Horizons, 28 (2). https://dx.doi.org/10.2139/ssrn.2235759 DOI: https://doi.org/10.2308/acch-50689
Barth, M. E.; & Shipper, K. (2008). Financial Reporting Transparency. Journal of Accounting, Auditing & Finance, 23 (2). https://doi.org/10.1177%2F0148558X0802300203 DOI: https://doi.org/10.1177/0148558X0802300203
Behrens, M. L., & Steinbart, P. J. (1992). Integrating expert systems and artificial intelligence in accounting: A description of the academic program at Memphis State University. Expert Systems with Applications, 4(2), 219-223. https://doi.org/10.1016/0957-4174(92)90113-7 DOI: https://doi.org/10.1016/0957-4174(92)90113-7
Berti, A. (2001). Contabilidade geral. São Paulo: Ícone.
Bradshaw, M. T., Christensen, T. E., Gee, K. H., & Whipple, B. C. (2018). Analysts’ GAAP earnings forecasts and their implications for accounting research. Journal of Accounting and Economics. https://doi.org/10.1016/j.jacceco.2018.01.003 DOI: https://doi.org/10.1016/j.jacceco.2018.01.003
Calof, J.L. and Wright, S. (2008), “Competitive intelligence: A practitioner, academic and inter‐disciplinary perspective”. European Journal of Marketing, 42(7/8), 717-730. https://doi.org/10.1108/03090560810877114 DOI: https://doi.org/10.1108/03090560810877114
Castanias, R. P., & Griffin, P. A. (1986). The effects of foreign‐currency translation accounting on security analysts’ forecasts. Managerial and Decision Economics, 7(1), 3-10. DOI: https://doi.org/10.1002/mde.4090070103
Chase, M. D., & Shim, J. K. (1991). Artificial intelligence and big six accounting: A survey of the current uses of expert systems in the modern accounting environment. Computers & industrial engineering, 21(1-4), 205-209. https://doi.org/10.1016/0360-8352(91)90089-O DOI: https://doi.org/10.1016/0360-8352(91)90089-O
Chen, A. Y., Comiskey, E. E., & Mulford, C. W. (1990). Foreign currency translation and analyst forecast dispersion: Examining the effects of statement of financial accounting standards no. 52. Journal of Accounting and Public Policy, 9(4), 239-256. https://doi.org/10.1016/0278-4254(90)90001-G DOI: https://doi.org/10.1016/0278-4254(90)90001-G
Cheng, Q. (2005). The role of analysts’ forecasts in accounting-based valuation: A critical evaluation. Review of Accounting Studies, 10(1), 5-31. https://doi.org/10.1007/s11142-004-6338-4 DOI: https://doi.org/10.1007/s11142-004-6338-4
Cho, J. S. (2013). The relation between accounting quality and security analysts’ target price forecast performance. Актуальні проблеми економіки, (3), 503-510.
Choi, K. S., Lee, S. J., Park, S. Y., & Yoo, Y. K. (2015). Accounting Conservatism, Changes in Real Investment, And Analysts’ Earnings Forecasts. Journal of Applied Business Research, 31(2), 727. https://doi.org/10.19030/jabr.v31i2.9163 DOI: https://doi.org/10.19030/jabr.v31i2.9163
Choo, C. W. (2002). Information management for the intelligent organization: the art of scanning the environment. Information Today, Inc.
D’Augusta, C. (2018). Does Accounting Conservatism Make Good News Forecasts More Credible and Bad News Forecasts Less Alarming? Journal of Accounting, Auditing & Finance. https://doi.org/10.1177%2F0148558X18780550
Davenport, T. H. (2000). Ecologia da informação: por que só a tecnologia não basta para o sucesso na era da informação. Futura.
Dorsman, A. B., Langendijk, H. P., & Praag, B. V. (2003). The association between qualitative management earnings forecasts and discretionary accounting in the Netherlands. The European Journal of Finance, 9(1), 19-40. https://doi.org/10.1080/13518470110099696 DOI: https://doi.org/10.1080/13518470110099696
Dull, RB, & Earp, J.B. (2000). Intelligent visualizations: Using artificial intelligence to improve accounting decisions. New Review of Applied Expert Systems and Emerging Technologies, 6.
Garvin, S.K., & Garvin, T.P. (1996). Considerations in the development of an environmental scanning system for the accounting academic department in an institution of higher education. Annual Meeting of the Decision Sciences Institute.
Gilad, B. (1989). The role of organized competitive intelligence in corporate-Strategy. Columbia Journal of World Business, 24(4), 29-35.
Glaum, M., Baetge, J., Grothe, A., & Oberdörster, T. (2013). Introduction of International Accounting Standards, disclosure quality and accuracy of analysts’ earnings forecasts. European Accounting Review, 22(1), 79-116. https://doi.org/10.1080/09638180.2011.558301 DOI: https://doi.org/10.1080/09638180.2011.558301
Grytz, R., & Krohn-Grimberghe, A. (2018, January). Business Intelligence & Analytics Cost Accounting: A Survey on the Perceptions of Stakeholders. In Proceedings of the 51st Hawaii International Conference on System Sciences. DOI: https://doi.org/10.24251/HICSS.2018.095
Hope, O. K. (2003a). Accounting policy disclosures and analysts’ forecasts. Contemporary Accounting Research, 20(2), 295-321. https://doi.org/10.1506/LA87-D1NF-BF06-FW1B DOI: https://doi.org/10.1506/LA87-D1NF-BF06-FW1B
Hope, O. K. (2003b). Disclosure practices, enforcement of accounting standards, and analysts’ forecast accuracy: An international study. Journal of accounting research, 41(2), 235-272. https://doi.org/10.1111/1475-679X.00102 DOI: https://doi.org/10.1111/1475-679X.00102
Horngren, C. T., Sundem, G. L., & Stratton, W. O. (2006). Contabilidade gerencial. 12. ed. São Paulo, Prentice Hall
Ionescu, I. (2012). The Newest Key to Unlock the Erp Systems “Accounting Intelligence”. AMIS 2012, 1243.
Ionescu, Luminita. (2019). Big data, blockchain, and artificial intelligence in cloud-based accounting information systems. Analysis and Metaphysics, 18, 44-49. https://doi.org/10.22381/AM1820196 DOI: https://doi.org/10.22381/AM1820196
Janissek-Muniz, R., & Blanck, M. R. de M. (2014). Weak signals management, entrepreneurship and uncertainty: a relational theoretical essay under the perspective of intelligence. 11º CONTECSI. DOI: https://doi.org/10.5748/9788599693100-11CONTECSI/COMM-625
Kalantari, B., Mehrmanesh, H., & Saeedi, N. (2012). Ranking the Driving Affecting Factors on Management Accounting: Business Intelligence Approach. World Appl. Sci. J, 20(8), 1147-1151. https://doi.org/10.5829/idosi.wasj.2012.20.08.2482
Kim, J. B. (2016). Accounting flexibility and managers’ forecast behavior prior to seasoned equity offerings. Review of Accounting Studies, 21(4), 1361-1400. https://doi.org/10.1007/s11142-016-9372-0 DOI: https://doi.org/10.1007/s11142-016-9372-0
Kocsis, D. (2019). A conceptual foundation of design and implementation research in accounting information systems. International Journal of Accounting Information Systems, 34. https://doi.org/10.1016/j.accinf.2019.06.003 DOI: https://doi.org/10.1016/j.accinf.2019.06.003
Kumar Doshi, Harisai Anil; Balasingam, Suresh; Arumugam, Dhamayanthi. Artificial Intelligence as a paradoxical Digital Disruptor in the Accounting Profession: An Empirical Study amongst Accountants. International Journal of Psychosocial Rehabilitation, 24(2), 2020. https://doi.org/10.37200/IJPR/V24I2/PR200396 DOI: https://doi.org/10.37200/IJPR/V24I2/PR200396
Lee, Cheah Saw; Tajudeen, Farzana Parveen. Impact of Artificial Intelligence on Accounting: Evidence from Malaysian Organizations. Asian Journal of Business and Accounting, 13(1). https://doi.org/10.22452/ajba.vol13no1.8 DOI: https://doi.org/10.22452/ajba.vol13no1.8
Lesca, H. (2003). Veille stratégique : La méthode L.E.SCAnning®, Editions EMS. 180 p.
Lesca H., & Janissek-Muniz, R. (2015). Inteligência Estratégica Antecipativa e Coletiva: o Método L.E.SCAnning. Porto Alegre: Palotti, p. 188.
Li, M., Ning, X., Li, M., & Xu, Y. (2017). An approach to the evaluation of the quality of accounting information based on relative entropy in fuzzy linguistic environments. Entropy, 19 (4). https://doi.org/10.3390/e19040152 DOI: https://doi.org/10.3390/e19040152
Liu, C., & O’Farrell, G. (2013). The role of accounting values in the relation between XBRL and forecast accuracy. International Journal of Accounting and Information Management, 21(4), 297-313. https://doi.org/10.1108/IJAIM-03-2013-0023 DOI: https://doi.org/10.1108/IJAIM-03-2013-0023
Lotka, Alfred J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317-323.
Madan, D. B. (1985). Project Evaluation and Accounting Income Forecasts. Abacus, 21(2), 197-202. DOI: https://doi.org/10.1111/j.1467-6281.1985.tb00119.x
Marconi, M.A., & Lakatos, E.M. (2003). Fundamentos de metodologia científica. 5. ed. São Paulo: Atlas.
Marek-Klimczak, K., & Szafranski, G. (2013). Coincident and forecast relevance of accounting numbers. Accounting Research Journal, 26(3), 239-255. https://doi.org/10.1108/ARJ-09-2012-0076 DOI: https://doi.org/10.1108/ARJ-09-2012-0076
Meservy, R. D., Denna, E. L., & Hansen, J. V. (1992). Application of artificial intelligence to accounting, tax, and audit services: Research at Brigham Young University. Expert Systems with Applications, 4(2), 213-218. https://doi.org/10.1016/0957-4174(92)90112-6 DOI: https://doi.org/10.1016/0957-4174(92)90112-6
Moreira, R. de L., Encarnação, L. V., Bispo, O. N. de A., Colauto, R. D., & Angotti, M. (2013). A importância da informação contábil no processo de tomada de decisão nas micro e pequenas empresas. Revista Contemporânea de Contabilidade, 10(19), 119-140. https://doi.org/10.5007/2175-8069.2013v10n19p119 DOI: https://doi.org/10.5007/2175-8069.2013v10n19p119
Muñoz, D. L. C. (2009) Estudos empíricos de gestão de conhecimento orientados para sustentabilidade: uma revisão sistemática de literatura de 1998 a 2009. 2009. 220 f. Dissertação (Programa de Pós-Graduação de Engenharia e Gestão de Conhecimento) – Universidade Federal de Santa Catarina (UFSC).
Neely, M. P., & Cook, J. S. (2011). Fifteen years of data and information quality literature: developing a research agenda for accounting. Journal of Information Systems, 25(1). https://doi.org/10.2308/jis.2011.25.1.79 DOI: https://doi.org/10.2308/jis.2011.25.1.79
Nespeca, A., & Chiucchi, M. S. (2018). The Impact of Business Intelligence Systems on Management Accounting Systems: The Consultant’s Perspective. In Network, Smart and Open (pp. 283-297). Springer, Cham. https://doi.org/10.1007/978-3-319-62636-9_19 DOI: https://doi.org/10.1007/978-3-319-62636-9_19
Newman, M. R., Gamble, G. O., Chin, W. W., & Murray, M. J. (2013). An Investigation of the Impact Publicly Available Accounting Data, Other Publicly Available Information and Management Guidance on Analysts’ Forecasts. In New Perspectives in Partial Least Squares and Related Methods (pp. 315-339). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8283-3_22 DOI: https://doi.org/10.1007/978-1-4614-8283-3_22
O’leary, D. E. (1991). Artificial intelligence and expert systems in accounting databases: Survey and extensions. Expert Systems with Applications, 3(1), 143-152. https://doi.org/10.1016/0957-4174(91)90095-V DOI: https://doi.org/10.1016/0957-4174(91)90095-V
Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465. https://doi.org/10.1016/S0169-2070(02)00058-4 DOI: https://doi.org/10.1016/S0169-2070(02)00058-4
Pae, J., & Thornton, D. B. (2010). Association between accounting conservatism and analysts’ forecast inefficiency. Asia‐Pacific Journal of Financial Studies, 39(2), 171-197. https://doi.org/10.1111/j.2041-6156.2010.00008.x DOI: https://doi.org/10.1111/j.2041-6156.2010.00008.x
Patrascu, L., Ratiu, I. G., Paraschivescu, A. O., & Radu, F. (2010, January). Accounting forecast models and calculation. In Proceedings of the 4th WSEAS international conference on Computer engineering and applications (pp. 76-79). World Scientific and Engineering Academy and Society (WSEAS).
Pirttimäki, V. (2007). Comparative study and analysis of the intelligence activities of large Finnish companies. Journal of Competitive Intelligence and Management. http://urn.fi/URN:NBN:fi:tty-200810021126
Pope, P. F. (2003). Discussion of disclosure practices, enforcement of accounting standards, and analysts’ forecast accuracy: An international study. Journal of Accounting Research, 41(2), 273-283. http://www.jstor.org/stable/3542403 DOI: https://doi.org/10.1111/1475-679X.00103
Rezende, D. A. (2012) Planejamento de estratégias e informações municipais para cidade digital: guia para projetos em prefeituras e organizações públicas. São Paulo: Atlas.
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58. https://doi.org/10.1016/j.accinf.2018.03.001 DOI: https://doi.org/10.1016/j.accinf.2018.03.001
Rios, F. L. C., & Janissek-Muniz, R. (2014). Uma proposta de relação de requisitos funcionais para um software de apoio ao processo de Inteligência. Revista Eletrônica de Administração, 20(2), 425-460. http://dx.doi.org/10.1590/1413-2311056201238165 DOI: https://doi.org/10.1590/1413-2311056201238165
Rogers, J. L., & Van Buskirk, A. (2013). Bundled forecasts in empirical accounting research. Journal of Accounting and Economics, 55(1), 43-65. https://doi.org/10.1016/j.jacceco.2012.06.001 DOI: https://doi.org/10.1016/j.jacceco.2012.06.001
Rousseau, B., & Rousseau, R. (2000). LOTKA: A program to fit a power law distribution to observed frequency data. Cybermetrics: International Journal of Scientometrics, Informetrics and Bibliometrics, (4), 4.
Sabau, E.M., Sgardea, F.M., Budacia, L.C.G., & Paunescu, M. (2009). The Accounting Information - Tool for Competitive Intelligence Systems. Proceedings of the 4th International Conference on Business Excellence.
Schoemaker, P. J., & Day, G. S. (2009). How to make sense of weak signals. Leading Organizations: Perspectives for a New Era, 2, 37-47.
Smieliauskas, W., Bewley, K., Gronewold, U., & Menzefricke, U. (2016). Misleading Forecasts in Accounting Estimates: A Form of Ethical Blindness in Accounting Standards? Journal of Business Ethics, 1-21. https://doi.org/10.1007/s10551-016-3289-1 DOI: https://doi.org/10.1007/s10551-016-3289-1
Sohn, B. C. (2012). Analyst forecast, accounting conservatism and the related valuation implications. Accounting & Finance, 52, 311-341. https://doi.org/10.1111/j.1467-629X.2011.00428.x DOI: https://doi.org/10.1111/j.1467-629X.2011.00428.x
Sun, Y., & Xu, W. (2012). The role of accounting conservatism in management forecast bias. Journal of Contemporary Accounting & Economics, 8(2), 64-77. https://doi.org/10.1016/j.jcae.2012.05.002 DOI: https://doi.org/10.1016/j.jcae.2012.05.002
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://doi.org/10.1016/j.accinf.2016.07.005 DOI: https://doi.org/10.1016/j.accinf.2016.07.005
Valente, N. T. Z., & Fujino, A. (2016). Atributos e dimensões de qualidade da informação nas Ciências Contábeis e na Ciência da Informação: um estudo comparativo. Perspectivas em Ciência da Informação, 21(2). https://doi.org/10.1590/1981-5344/2530 DOI: https://doi.org/10.1590/1981-5344/2530
Varum, C. A., & Melo, C. (2010). Directions in scenario planning literature–A review of the past decades. Futures, 42(4), 355-369. https://doi.org/10.1016/j.futures.2009.11.021 DOI: https://doi.org/10.1016/j.futures.2009.11.021
Vidigal, F. (2013). Competitive intelligence: functional practices, goals and infrastructure of companies in Brazil. Transinformação, 25(3), 237-243. DOI: https://doi.org/10.1590/S0103-37862013000300006
Xu, M., & Kaye, R. (2007). The nature of strategic intelligence, current practice and solutions. In Managing Strategic Intelligence: Techniques and Technologies (pp. 36-54). IGI Global. DOI: https://doi.org/10.4018/978-1-59904-243-5.ch003
Wahab, S., Teitel, K., & Morzuch, B. (2017). How Analysts and Whisperers Use Fundamental Accounting Signals to Make Quarterly EPS Forecasts. Journal of Accounting, Auditing & Finance, 32(3), 401-422. https://doi.org/10.1177%2F0148558X15613040 DOI: https://doi.org/10.1177/0148558X15613040
Ye, S. (2017). 29. Research on the Enterprise Accounting Statement Evaluation and Financial Management Optimization based on Computer Artificial Intelligence Method. Boletín Técnico, 55(20). DOI: https://doi.org/10.1108/JPBAFM-29-01-2017-B002
Zhai, J., & Wang, Y. (2016). Accounting information quality, governance efficiency and capital investment choice. China Journal of Accounting Research, 9(4). https://doi.org/10.1016/j.cjar.2016.08.001 DOI: https://doi.org/10.1016/j.cjar.2016.08.001
Zhang, Yingying et al. (2020). The Impact of Artificial Intelligence and Blockchain on the Accounting Profession. IEEE Access, 8,110461-110477. https://doi.org/10.1109/ACCESS.2020.3000505 DOI: https://doi.org/10.1109/ACCESS.2020.3000505
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