This paper analyses the level of knowledge management implementation in organizations and the impact of control variables on knowledge management dimensions. In addition, the importance of knowledge management influence on the competitive sustainability of an organization is addressed. The idea of the research is to highlight key factors that affect the effectiveness and efficiency of knowledge management applications in transition conditions. Data for the study were obtained by surveying 520 respondents—managers of all levels from manufacturing enterprises in Serbia. A t-test was conducted in several variations. More precisely, the t-test analysis was conducted on the average assessments of knowledge management dimensions and with specific control variables. The findings indicate that knowledge management dimensions differ in domestic enterprises and foreign enterprises as well as that their impact differs across organizational levels. Financial performance (profitability, sales growth, asset growth, market share, competitive position in a specific industry, productivity, and salaries) was found to be a reliable indicator of knowledge management success. It can be concluded that knowledge management dimensions have an important role in transition countries, and it is evident that effective knowledge management is imperative for the competitive sustainability of an organization.

In this paper the impact and predictive effects of knowledge management on financial performance are analysed. The research also includes an analysis of the moderating effects of two moderators (National Origin and Enterprise Size) and their relationship. The data was gathered by interviewing 520 managers from manufacturing companies operating in Serbia. The most important conclusions of the research are: 1. High levels of knowledge management positively influence business performance, quality and competitiveness. 2. Financial performance is mainly influenced by the dimensions connected to the effects of knowledge management regarding its implementation and protection and the acquisition of competitive advantages. 3. Knowledge management strongly influences the market aspects of business, but also the increase of fixed assets. 4. Knowledge management has a slightly greater impact on financial performance in foreign enterprises (operating in Serbia) compared to domestic (Serbian) enterprises. The situation is similar when comparing medium and large enterprises (a higher impact) with small enterprises (a smaller impact). 5. The greatest effects are achieved when an effective organizational structure is in place, when the organization protects its knowledge, when it manages to materialize the knowledge gathered in the form of the efficient application of knowledge. Keyword : knowledge management, intellectual capital, financial performance, national origin of the enterprise, enterprises size, Serbia

This paper analyzes the influence and predictive effects of knowledge management on job satisfaction. The aim of this paper is to develop a model based on the effects of knowledge management on job satisfaction. The data were obtained by interviewing 520 managers of all levels from manufacturing enterprises in Serbia. The results showed that knowledge management has a complex influence on job satisfaction. It negatively affects the dimensions of Salary, Promotion, and Supervision and positively influences the Additional Benefits dimension, while having combined effects on the other dimensions. In a working environment where knowledge management levels are high, employees may perceive their salaries, rewards, and promotion opportunities as inadequate. This is because they feel they deserve more due to their knowledge, ability, engagement, and the enterprise's success. Serbian enterprises are often unable to pay their employees higher salaries. The theoretical importance of the results is precisely in the fact that the individual relations of the observed dimensions are determined and that the desired model is developed. The practical significance of the work is that leaders and managers, based on these relations, can see the possibilities and ways to improve certain aspects of knowledge management and job satisfaction.

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.