An Application of Statistical Methods in Data Mining Techniques to Predict ICT Implementation of Enterprises  
[PERSON]  
1 Technical Faculty \"[PERSON]\" in Zrenjanin, University of Novi Sad, Djure Djakovica bb, 23000 Zrenjanin, Serbia 1  
[PERSON]  
1 Technical Faculty \"[PERSON]\" in Zrenjanin, University of Novi Sad, Djure Djakovica bb, 23000 Zrenjanin, Serbia 1  
[PERSON]  
1 Technical Faculty \"[PERSON]\" in Zrenjanin, University of Novi Sad, Djure Djakovica bb, 23000 Zrenjanin, Serbia 1  
[PERSON]  
1 Technical Faculty \"[PERSON]\" in Zrenjanin, University of Novi Sad, Djure Djakovica bb, 23000 Zrenjanin, Serbia 1  
[PERSON]  
1 Technical Faculty \"[PERSON]\" in Zrenjanin, University of Novi Sad, Djure Djakovica bb, 23000 Zrenjanin, Serbia 1  
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refers to the \"know-how\" regarding the application of ICT but can also be conceptualized as the \"engine\" of the development of technology and innovation [25]. In addition to the challenges of industrial development, the pandemic has also had a large impact on enterprises in various countries [26]. It has brought about stoppages in manufacturing and bottlenecks in global supply chains and has significantly affected the global economy.  
Overall, in the current body of literature, researchers have analyzed in detail the challenges and hallmarks of Industry 4.0 [17; 27; 28], the importance of the application of ICT technology in enterprises [29; 30], business performance and competitiveness [31; 32; 33; 34], and the globalization of markets [35; 36]. However, no research has been conducted in the transitional setting, in which factors that affect the intentions regarding the implementation of ICT in enterprises are analyzed. In this current study, a dataset (\(n=214\)) was analyzed to address this issue of ICT application. The data were obtained from micro-, small-, medium-, and large-sized enterprises that conducted business in the Republic of Serbia.  
The main goal of this study was to present the application of multiple statistical methods. First, simple/basic approaches are presented, including descriptive statistics, linear regression, logistic regression, a multicollinearity test, and a heteroscedasticity test. Next, data-mining algorithms are presented, including a Quick, Unbiased, Efficient, Statistical, Tree (QUEST) decision tree and a support vector machine (SVM) [37; 38; 39]. In addition, a feed-forward neural network was trained in order to provide a contrast with these approaches. However, neural networks work best if the dataset is large, whereas SVMs can be appropriate for smaller datasets.  
Through the use of the abovementioned statistical methods, we aimed to perform the extraction of data/data mining regarding potential predictors of ICT application and implementation intentions. This information could be of interest to entrepreneurs, managers, researchers, and governments.  
This paper includes four main sections (not including Sections 1 and 6). First, a concise overview of the research background on the modern business environment is provided. Here, the knowledge gap and the hypotheses are proposed. Next, the methodology is presented in more detail. Afterward, the research results are presented. Then, further results and the advantages and limitations of the study are discussed.