Open Access
Subscription Access
Open Access
Subscription Access
The Use of Data Mining Techniques in Environmental Accounting:A Comparison of Public and Private Sector Companies in India
Subscribe/Renew Journal
Environmental accounting as an essential tool for understanding the role played by the natural environment in the economy is useful for business decisions, especially for proactive environmental management activities. Since recent technological advancement paved the way for the use of big data to assist companies in the decision-making process and one of the best methods to the exploited large dataset is data mining, this study aims to examine the level of data mining techniques in environmental accounting within public and private sector companies in India. This study covering the States of Haryana for North, West Bengal for East, Maharashtra for West and Kerala as representative of South. With the use of structured questionnaires in the soft and hard copy at last 100 managers and 243 accountants randomly were selected. Six hypothesis has been considered that were evaluated in the statements with a five-point Likert scale. In this study, we used the classification and regression techniques as appropriate tools for data mining. The results of this study confirmed the null hypothesis ‘there is no significant difference existed in the level of data mining between the public and private sector industry in India’ rejects. Therefore, the levels of data mining within environmental accounting in terms of most aspects significantly is higher in the private sector than public sector companies in India.
Keywords
Environmental Accounting, Environmental Management, Data Mining, Industry in India.
Subscription
Login to verify subscription
User
Font Size
Information
- Yu, T., Lenzen, M., Gallego, B., & Debenham, J. (2006). A Data Mining System for Estimating a Large size Matrix in Environmental Accounting. International Conference on Artificial Intelligence Application and Innovation, 2007; Sydney: Australia Faculty of Information Technology, University of Technology, Sydney, NSW.
- Alshhadat, A. R., Ghaleb, E. R., & Amer, Q. (2018). The use of data mining techniques in accounting and finance as a corporate strategic tool: An empirical investigation on banks operating in emerging economies. International Journal of Economics and Business Research, 15(4), 442.
- Chinchuluun, A., Xanthopoulos, P., Tomaino, V., & Pardalos, P. (2010). Data mining techniques in agricultural and environmental sciences. International Journal of Agricultural and Environmental Information Systems, 1(5), 26–40.
- Common, M. (1996). Environmental and resource economics: An introduction (2nd ed.). Harlow, Essex: Addison Wesley Longman Ltd Publishers.
- Gilberto Carvalho, P., Ricardo, C., & Nelson Francisco Favila, E. (2008). Data mining for environmental analysis and diagnostic: A case study of upwelling ecosystem of Arraial do Cabo. Azilian Journal of Oceanography, 56(3), 1–12.
- Peter, R. (2013). Data mining solutions for the business environment. Database Systems Journal, 4(5), 21–29.
- Shah, K., Chauhan, P., & Potdar, M. (2014). Data mining techniques in parallel environment - a comprehensive survey. International Journal of Computer Applications, 108(1), 36–41.
- Rouse, M. (2017). Data Mining. TechTarget. Retrieved August 26, 2018 from https://searchsqlserver.techtarget.com/definition/data-mining
- Seifert, J. W. (2004). Data mining and the search for security: Challenges for connecting the dots and databases. Government Information Quarterly, 21(4), 461–480.
- Pechenizkiy, M., Puuronen, S., & Tsymbal, A. Why data mining research does not contribute to the business?. In Proc. of Data Mining for Business Workshop DMBiz (ECML/PKDD’05), Porto, Portugal.
- Kirkos, E., Spathis, C. H., & Manolopoulos, Y. (2007). Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995–1003.
- Sirikulvadhana, S. (2002). Data mining as a financial auditing tool. M.Sc. Thesis in Accounting, Swedish: School of Economics and Business Administration.
- Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications (2nd ed.). New York, NY: Springer-Verlag Publishers.
- Thuraisingham, B. (2000). A primer for understanding and applying data mining. IEEE Access, 2(1), 28–31.
- Mendes, R. P., & Vilela J. (2017). Privacy-preserving data mining: methods, metrics, and applications. IEEE Access, 5(9), 10562–10582.
- Crespo, F., & Weber, R. A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets and Systems, 150(2), 267–284.
- Amani, F. M., & Fadlallah, A. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 4(5), 32–58.
- Rahman, H. (2008). Data mining applications for empowering knowledge societies (2nd ed.). New York, NY: Information Science Reference (an imprint of IGI Global) Publishers.
- Data Mining Tutorial: Process, Techniques, Tools & Examples. 2018. Retrieved September 1, 2018 from https://www.guru99.com/data-mining-tutorial.html
- Scott, P. D., & Wilkins, E. (1999). Evaluating data mining procedures: Techniques for generating artificial data sets. Information and Software Technology, 41, 579–587.
- Ruxandra, P. (2013). Data mining solutions for the business environment. Database Systems Journal, 5(4), 21–29.
- Seifert, J. (2004). Data mining and the search for security: Challenges for connecting the dots and databases. Government Information Quarterly, 21, 461–480.
Abstract Views: 320
PDF Views: 0