Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into useful knowledge. Data analysis adapted to these changes when data mining was developed as an approach to data analysis from different perspectives which reveals significant hidden regularities. This paper presents conceptual characteristics of decision tree, an important data mining method which is, due to its explorative nature, exceptionally suitable for detection of data structure when analysing various problem situations. The empirical section of the paper demonstrates applicative characteristics of this method using CHAID algorithm in leadership studies: an interdependence of selected personal characteristics and the manager’s leadership style has been investigated. The aim of the paper is to develop a classification model for identification of the dominant leadership style. The study was conducted on the sample of 417 managers of privately owned small-sized enterprises in Serbia, using a specially designed questionnaire. The classification model identified the set of six statistically significant personal characteristics as predictors of dominant leadership style.
If the inline PDF is not rendering correctly, you can download the PDF file here.
Baran B. & Kılıç E. (2015). Applying the CHAID algorithm to analyze how achievement is influenced by university students’ demographics study habits and technology familiarity. Educational Technology & Society 18 (2) 323-335.
de Ville B. (2006). Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner. Cary NC: SAS Institute Inc.
Díaz-Pérez M. F. & Bethencourt-Cejas M. (2016). CHAID algorithm as an appropriate analytical method for tourism market segmentation. Journal of Destination Marketing & Management 5 (3) 275-282 doi:
Díaz-Pérez, M. F. & Bethencourt-Cejas, M. (2016). CHAID algorithm as an appropriate analytical method for tourism market segmentation. Journal of Destination Marketing & Management, 5 (3), 275-282, doi: 10.1016/j.jdmm.2016.01.006.)| false
Kağnicioğlu, H. C. & Moğol, M. (2014). Implementation of CHAID algorithm: a hotel case. International Journal of Research in Business and Social Science, 3(4), 42-52, doi: 10.20525/ijrbs.v3i4.116.)| false
Kim, S.S., Timothy, J. D. & Hwang, J. (2011). Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method. Tourism Management, 32(3), 544-554, doi: 10.1016/j.tourman.2010.04.008.)| false
Maimon O. & Rokach L. (Eds.) (2010). Data mining and knowledge discovery handbook (2nd ed). New York: Springer.
Nisbet R. Elder J. & Miner G. (2009). Handbook of statistical analysis and data mining applications. Amsterdam (etc.): Elsevier Inc.
Northouse P.G. (2012). Introduction to leadership: Concepts and Practice (2nd ed). Los Angeles (etc.): SAGE Publ.Inc.
Novotná M. (2012). The use of different approaches for credit rating prediction and their comparison. In: Proceedings of the 6th International Conference on Managing and Modelling of Financial Risks (pp. 448-457). [Availabe at SSRN: https://ssrn.com/abstract=2867. Accessed August 232016.]
Öcal N. Ercan K. M. & Kadıoğlu E. (2015). Predicting financial failure using decision tree algorithms: an empirical test on the manufacturing industry at Borsa Istanbul. International Journal of Economics and Finance 7(7) 189-206 doi:
Öcal, N., Ercan, K. M. & Kadıoğlu, E. (2015). Predicting financial failure using decision tree algorithms: an empirical test on the manufacturing industry at Borsa Istanbul. International Journal of Economics and Finance, 7(7), 189-206, doi:10.5539/ijef.v7n7p189.)| false
Republički zavod za statistiku (RZS) (2015). Preduzeća u Republici Srbiji prema veličini u 2014. godini (Radni dokument 90). Beograd: Republički zavod za statistiku.
Petković M. Janićijević N. Bogićević Milikić B. (2010). Organizacija (8th ed). Beograd: Ekonomski fakultet Univerziteta u Beogradu.
Popescu M. E. Andreica M. & Micu D. (2014). A method to improve economic performance evaluation using classification tree models. European Journal of Business and Social Sciences 3 (4) 249-256.
Rokach L. & Maimon O. (2008). Data mining with decision trees:theory and applications. New Jersey (etc.): World Scientific.
Shmueli G. Patel N.R. & Bruce P.C. (2010). Data mining for business intelligence concepts techniques and applications in Microsoft Office Excel with Xlminer (2nd ed). Hoboken New Jersey: John Wiley& Sons.
Soldić-Aleksić J. (2009). Prediktivni model segmentacije tržišta: primena modela logističke regresije i CHAID procedure. Marketing 40 (3) 129-138.
Stefanović N. & Stefanović Ž. (2007). Liderstvo i kvalitet. Kragujevac: Univerzitet u Kragujevcu Mašinski fakultet.
Stojanović-Aleksić V. (2007). Liderstvo i organizacione promene. Kragujevac: Univerzitet u Kragujevcu Ekonomski fakultet.
Stojanović Aleksić V. Stamenković M. & Milanović M. (2016). Analiza liderskih stilova u organizacijama u Srbiji: uticaj pola. Teme XL(4) 1383-1397.
Tufféry S. (2011). Data mining and statistics for decision making. Chichester: John Wiley & Sons.
Vercellis C. (2009). Business intelligence: data mining and optimization for decision making. Chichester: John Wiley & Sons.
Witten H. I. & Frank E. (2005). Data mining: practical machine learning tools and techniques (2nd ed). Amsterdam (etc.): Elsevier Inc.