Contact us
![]() |
[email protected] |
![]() |
3275638434 |
![]() |
![]() |
Paper Publishing WeChat |
Useful Links
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Integrating Data Mining Into Managerial Accounting System: Challenges and Opportunities
Author(s)
Yihan Wang, Zhenkun Wang
Full-Text PDF
XML 2981 Views
DOI:10.17265/1537-1506/2016.01.004
Affiliation(s)
Yihan Wang, M.Sc., Nanjing University of Finance and Economics, Nanjing, China.
Zhenkun Wang, Ph.D., Nanjing University of Finance and Economics, Nanjing, China.
ABSTRACT
Data mining involves extracting information from large data sets, discovering the hidden relationships and unknown dependencies, and supporting strategic decision-making tasks. The alignment of data mining and business would bring benefits to the organization’s management. The study investigated the adoption of data mining technologies in managerial accounting system, concentrating on the challenges and opportunities. The research showed that with the technology adoption, managerial functions could be improved and current information system could be upgraded. Since the technical progresses are reshaping the world of business and accountancy, it is significant for accountants and finance professionals to exploit information technologies.
KEYWORDS
data mining, management accounting, accounting information system
Cite this paper
References
Ali, M., Khan, S. U., & Vasilakos, A. V. (2015). Security in cloud computing: Opportunities and challenges. Information Sciences, 305, 357-383.
Association of Chartered Certified Accountants [ACCA] & Institute of Management Accountants [IMA]. (2013). Digital Darwinism: Thriving in the face of technology change. Retrieved from http://www.accaglobal.com/content/dam/acca/global/PDF-technical/other-PDFs/Five-mins-on-Digital-Darwinism.pdf
Belfo, F., & Trigo, A. (2013). Accounting information systems: Tradition and future directions. Procedia Technology, 9, 536-546.
Bellomi, F., Cuel, R., & Biscaro, R. (2005). Ontology driven text mining for cost management processes. Proceedings from SWAP 2005, Semantic Web Applications and Perspectives.
Bode, J. (1998). Decision support with neural networks in the management of research and development: Concepts and application to cost estimation. Information & Management, 34(1), 33-40.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks & Applications, 19(2), 171-209.
Edwards, J. B. (2000). The new cost management culture: Where are we going? Journal of Corporate Accounting & Finance, 11(3), 3-8.
Eklin, M., Arzi, Y., & Shtub, A. (2009). Model for cost estimation in a finite-capacity stochastic environment based on shop floor optimization combined with simulation. European Journal of Operational Research, 194(1), 294-306.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, 3, 37-54.
Grabski, S. V., Leech, S. A., & Schmidt, P. J. (2011). A review of ERP research: A future agenda for accounting information systems. Journal of Information Systems, 25(1), 37-78.
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). New York, NY: Morgan Kaufmann.
Humphrey, A. (2005). SWOT analysis for management consulting. Retrieved from http://alumni.sri.com/newsletters/Dec-05.pdf
Institute of Management Accountants [IMA]. (2008). Definition of management accounting. Retrieved from http://www.imanet.org/PDFs/Public/Research/SMA/Definition%20of%20Mangement%20Accounting.pdf
Jans, M., Alles, M., & Vasarhelyi, M. (2013). The case for process mining in auditing: Sources of value added and areas of application. International Journal of Account Information System, 14(1), 1-20.
Koutanaei, F. N., Sajedi, H., & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing & Consumer Services, 27, 11-23.
Koyuncugil, A. S., & Ozgulbas, N. (2012). Financial early warning system model and data mining application for risk detection. Expert Systems With Applications, 39(6), 6238-6253.
Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032-2033.
Lanen, W., Anderson, S. W., & Maher, M. W. (2013). Fundamentals of cost accounting (4th ed.). New York, NY: McGraw-Hill.
Lankhorst, M. (2013). Enterprise architecture at work: Modelling, communication, and analysis. New York, NY: Springer.
Lucas, S., & Amiri, A. (1996). Statistical syntactic methods for high-performance OCR. IEE Proceedings—Vision, Image and Signal Processing, 143(1), 23-30.
Lum, K. T., Baker, D. R., & Hihn, J. M. (2008). The effects of data mining techniques on software cost estimation (IEMC Europe 2008, IEEE international, IEEE). Proceedings from Engineering Management Conference.
Medri, D. (2013). Big data & business: An on-going revolution. Statistics views. Retrieved from http://www.statisticsviews.com/details/feature/5393251/Big-Data--Business-An-on-going-revolution.html
O’Brien, J. A., & Marakas, G. M. (2011). Management information systems. New York, NY: McGraw-Hill.
Ortiz-Servin, J. J., Cadenas, J. M., Pelta, D. A., Castillo, A., & Montes-Tadeo, J. L. (2015). Nuclear fuel lattice performance analysis by data mining techniques. Annals of Nuclear Energy, 80, 236-247.
Piatetsky-Shapiro, G., & Frawley, W. J. (1991). Knowledge discovery in databases. Library Trends, 48, 1-13.
Professional Accountants in Business Committee. (2009). Evaluating and improving costing in organizations (international good practice guidance). Retrieved from http://www.fasab.gov/pdffiles/ifac_eval_and_improv_costing.pdf
Ren, X., Yan, D., & Hong, T. (2015). Data mining of space heating system performance in affordable housing. Building & Environment, 89, 1-13.
Sajadfar, N., & Ma, Y. (2015). A hybrid cost estimation framework based on feature-oriented data mining approach. Advanced Engineering Informatics, 29(3), 633-647.
Schmidt, D., Ostrouchov, G., Chen, W. C., & Patel, P. (2012). Tight coupling of R and distributed linear algebra for high-level programming with big data. High Performance Computing, Networking Storage and Analysis, SC Companion: IEEE, 113, 811-815.
Shi, S., Liu, C., Shen, Y., Yuan, C., & Huang, Y. (2015). AutoRM: An effective approach for automatic web data record mining. Knowledge-Based Systems, 89, 314-331.
Tan, P., Steinbach, M., & Kumar, V. (2011). Introduction to data mining (2nd ed.). New York, NY: Pearson.
Trigo, A., Varajão, J., Barroso, J., Soto-Acosta, P., Molina-Castillo, F. J., & Gonzalvez-Gallego, N. (2011). Enterprise information systems adoption in Iberian large companies: Motivations and trends. In M. Tavana (Ed.), Managing adaptability, intervention, and people in enterprise information systems. Hershey, PA: IGI Global.
Varajão, J., Trigo, A., & Barroso, J. (2009). Motivations and trends for IT/IS adoption: Insights from Portuguese companies. International Journal of Enterprise Information Systems (IJEIS), 5, 34-52.
Wijnmalen, D. J. D. (2007). Analysis of benefits, opportunities, costs, and risks (BOCR) with the AHP-ANP: A critical validation. Mathematical and Computer Modelling, 46(7), 892-905.
Williams, T. P., & Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43(43), 23-29.
Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction through interpretable genetic programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behaviour: Learning Analytics, Educational Data Mining and Data-Driven, Educational Decision Making, 47, 168-181.
Zhou, L., Lu, D., & Fujita, H. (2015). The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowledge-Based Systems, 85(C), 52-61.
Ziegler, C. N. (2012). Mining for strategic competitive intelligence: Foundations and applications. Berlin: Springer.