An Application of Statistical Methods in Data Mining Techniques to Predict ICT Implementation of Enterprises

Abstract

Globalization, Industry 4.0, and the dynamics of the modern business environment caused by the pandemic have created immense challenges for enterprises across industries. Achieving and maintaining competitiveness requires enterprises to adapt to the new business paradigm that characterizes the framework of the global economy. In this paper, the applications of various statistical methods in data mining are presented. The sample included data from 214 enterprises. The structured survey used for the collection of data included questions regarding ICT implementation intentions within enterprises. The main goal was to present the application of statistical methods that are used in data mining, ranging from simple/basic methods to algorithms that are more complex. First, linear regression, binary logistic regression, a multicollinearity test, and a heteroscedasticity test were conducted. Next, a classifier decision tree/QUEST (Quick, Unbiased, Efficient, Statistical Tree) algorithm and a support vector machine (SVM) were presented. Finally, to provide a contrast to these classification methods, a feed-forward neural network was trained on the same dataset. The obtained results are interesting, as they demonstrate how algorithms used for data mining can provide important insight into existing relationships that are present in large datasets. These findings are significant, and they expand the current body of literature.