The study substantiates the need for a differentiated approach to evaluating social media marketing effectiveness for IT companies. The research is driven by the lack of methodologies that account for the specific characteristics of this sector, a gap that leads to the use of inappropriate metrics and ineffective management decisions. The article identifies and systematizes seven key industry factors that shape the specifics of evaluating social media marketing performance. A table outlining the application of unit metrics for various types of IT companies has been developed. Requirements for evaluating social media marketing effectiveness in the IT sector are formulated, taking into account factors such as the business model, the sales funnel stage, platform-specific nuances, and the necessity of integrating unit economics.

Using digital multinational companies as an example, this article analyzes the shortcomings and limitations of traditional transnationalization assessment tools – the transnationalization index, the internet intensity matrix, and the ease of foreign direct investment index. It demonstrates that all three approaches rely on physical assets, personnel, and geographically localized revenue, while digital multinational companies primarily expand internationally through intangible channels. A digital transnationalization index is proposed, based on three components: geographic reach, audience distribution, and audience engagement. This index is calculated using ByteDance and Meta Platforms as examples, demonstrating that the method can capture qualitatively different digital internationalization strategies that are not accessible using traditional metrics.

The intellectual component of the innovative functioning of an economy characterized by sustainable socio-economic development is examined. The study highlights the growing role of the intellectual component in the activities of economic entities aimed at enhancing the consumer attributes of products – regardless of their intended function – and boosting their competitiveness in both domestic and international markets. A decline is noted in the share of products whose novelty and originality are protected by patents for inventions, utility models, and industrial designs. Finally, the paper outlines ways to improve the intellectual component of innovation activities through the creation of cluster structures comprising scientific, educational, and industrial organizations and institutions.

The article presents the results of a comprehensive assessment of the banking system of the Republic of Belarus in the context of digital transformation. It examines three key priorities playing a pivotal role in the digital transformation of the country's banking system: crypto-banking, artificial intelligence, and the digital Belarusian ruble. The study makes it possible to evaluate the readiness of Belarus's regulatory framework and the preparedness of its banking system for the technological innovations being implemented.

In higher education, student academic performance is considered a key educational outcome. Consequently, improving academic performance is a pressing objective for many higher education institutions. Predicting and classifying student performance can provide valuable insights and facilitate the development of appropriate strategies to enhance academic results. In recent years, data mining methods have been widely applied and have demonstrated significant success in classification and prediction tasks. This article presents and analyzes the EASP student performance dataset. Seven machine learning methods were applied to classify and predict student academic performance: Multinomial Naïve Bayes, Support Vector Machines, Decision Trees, Logistic Regression, Random Forest, Gradient Boosting, and k-Nearest Neighbors. The performance of these methods was evaluated using precision, recall, the area under the ROC curve (AUC), and the F1-score. Experimental results demonstrated that Logistic Regression achieved the best predictive performance on the EASP test set, with a precision of 0.75.

The process-based approach is examined within the context of organizational digital transformation. Key aspects of digital transformation, artificial intelligence, and machine and deep learning are analyzed as tools for optimizing and improving process models. A process model is presented in the form of a business process map, serving as a foundation for digital transformation while ensuring management transparency, the elimination of function duplication, and the standardization of operations. It is demonstrated that the process-based approach can serve as a basis for the effective implementation of a digital transformation strategy, thereby enhancing the company's agility, resilience, and competitiveness.

In the context of the digital transformation of industry, quality management is faced with the need to revise traditional approaches based on sampling and statistical methods in favor of proactive and predictive models integrated into the enterprise's digital environment. However, the literature lacks a systematic analysis of quality management processes in terms of their suitability for digital transformation, which complicates the selection of effective strategies for implementing intelligent tools. The scientific novelty of this study lies in the systematization of key quality management processes, the identification of key barriers hindering digitalization, and the determination of factors influencing the suitability of processes for automation. Of particular interest is the analysis of the influence of the human factor, problems of integrating heterogeneous information systems, and limitations associated with the formalization of analytical processes on the effectiveness of digital transformation. The author's arguments favor a systems approach to digitalization, which involves preliminary analysis and adjustment of existing processes, phased implementation with an emphasis on management processes, and the development of personnel competencies as a prerequisite for success. The obtained results can be used by managers and quality specialists when planning digital transformation projects at industrial enterprises.