Digitalization is the core component of future development in the 4.0 industrial era. It represents a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide. Diverse triggering effects shape future digitalization trends. Thus, the main research goal in this study is to use sustainable competitiveness pillars (such as social, economic, environmental and energy) to evaluate international digitalization development. The proposed empirical model generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33 European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s binominal distribution to derive training and testing samples and the entire analysis has been adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear regression and ANN model summary report valuable results with a high degree of coefficient of determination (R2>0.9 for all models). Research findings state that the digitalization process is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating other relevant factors that emerge in the environment. Indicators report the consumption of electrical energy in industry and households and GDP per capita to achieve the strongest effect.

In this paper, SWOT analysis and fuzzy Analytic Hierarchy Process (FAHP) have been employed to structure and determine the importance of all identified alternatives for developing strategic management in a local district heating plant in Serbia. Weaknesses are identified as drivers of technological slowdown in the plant. The outcome of the prioritization highlights the importance of solving challenges regarding weaknesses by relying on a variety of opportunities that emerge in the environment. The potential of restoring the equipment of the district heating plant is found to be the most important alternative for strategic development.

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The process of globalization forces market changes in the form of intense competition. Economies can survive by getting competitive advantage in the global market through developing innovation. The main target of this empirical research is to discover the most important innovation components that constitute structure of the global innovation index (GII) and judge their influence in emerging BRICS economies. Innovation process is discussed on the grounds of GII ranking scores accumulated from 2011 to 2021. The research outcome of the Principal Component Analysis adopted nine components that represent seven dimensions. Extracted components are further used in the regression analysis to establish a multiple linear regression (MLR) equation for predicting the GII score used in the overall ranking. Derived regression solution introduced valuable MLR results with high coefficient of determination where 98.2% of the GII values are explained by the extracted components. The dominant effects on GII are attained in innovation components that include general infrastructure and knowledge workers. Moreover, comparison analysis of the actual and computed GII scores illustrated 99.1% overlap between the two values. Evaluated results of the PCA-MLR analysis serve to investigate the success in developing innovation performances in emerging economies by comparing innovation index accomplished by BRICS.