This study provides a nuanced understanding of AI’s impact on productivity and employment using machine learning models and Bayesian Network Analysis. Data from 233 employees across various industries were analyzed using logistic regression, Random Forest, and XGBoost, with 5-fold cross-validation. The findings reveal that high levels of AI tool usage and integration within organizational workflows significantly enhance productivity, particularly among younger employees. A significant interaction between AI tools usage and integration (β = 0.4319, p < 0.001) further emphasizes the importance of comprehensive AI adoption. Bayesian Network Analysis highlights complex interdependencies between AI usage, innovation, and employee characteristics. This study confirms that strategic AI integration, along with targeted training programs and ethical frameworks, is essential for maximizing AI’s economic potential.

This study explores a hybrid forecasting framework for air pollutant concentrations (PM_10, PM_2.5, and NO₂) that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) models with Bidirectional Long Short-Term Memory (BiLSTM) networks. By leveraging SARIMA’s strength in linear and seasonal trend modeling and addressing nonlinear dependencies using BiLSTM, the framework incorporates Box-Cox transformations and Fourier terms to enhance variance stabilization and seasonal representation. Additionally, attention mechanisms are employed to prioritize temporal features, refining forecast accuracy. Using five years of daily pollutant data from Romania’s National Air Quality Monitoring Network, the models were rigorously evaluated across short-term (1-day), medium-term (7-day), and long-term (30-day) horizons. Metrics such as RMSE, MAE, and MAPE revealed the hybrid models’ superior performance in capturing complex pollutant dynamics, particularly for PM_2.5 and PM₁₀. The SARIMA combined with BiLSTM, Fourier, and Attention configuration demonstrated consistent improvements in predictive accuracy and interpretability, with attention mechanisms proving effective for extreme values and long-term dependencies. This study highlights the benefits of combining statistical preprocessing with advanced neural architectures, offering a robust and scalable solution for air quality forecasting. The findings provide valuable insights for environmental policymakers and urban planners, emphasizing the potential of hybrid models for improving air quality management and decision-making in dynamic urban environments.

Companies use social business intelligence (SBI) to identify and collect strategically significant information from a wide range of publicly available data sources, such as social media (SM). This study is an SBI-driven analysis of a company operating in the insurance sector. It underlines the contribution of SBI technology to sustainable profitability of a company by using an optimized marketing campaign on Facebook, in symmetry with a traditional e-mail campaign. Starting from a campaign on SM, the study identified a client portfolio, processed data, and applied a set of statistical methods, such as the index and the statistical significance (T-test), which later enabled the authors to validate research hypotheses (RH), and led to relevant business decisions. The study outlines the preferences of the selected group of companies for the manner in which they run a marketing campaign on SM in symmetry with an e-mail-run campaign. Although the study focused on the practical field of insurance, the suggested model can be used by any company of any industry proving that BI technologies is the nexus of collecting and interpreting results that are essential, globally applicable, and lead to sustainable development of companies operating in the age of globalization. The results of the study prove that symmetrical unfolding (time and opportunity symmetry) of SM marketing campaigns, and using email, could lead to better results compared to two separate marketing campaigns. Moreover, the outcomes of both campaigns showed convergence on SBI platforms, which led to higher efficiency of management of preferences of campaign beneficiaries in the insurance sector.

Business intelligence and analytics (BI&A) have recently emerged as a strategic approach to managerial tasks, providing opportunities to improve work performance. Despite the growing interest in evaluating cases of BI&A adoption, to the best of our knowledge, few studies have addressed the influence of data-driven culture and the effects of BI&A adoption specifically on the work performance of managers. The aim of this study is to assess whether a data-driven culture predicts the adoption of BI&A in companies and its impact on decision-making effectiveness and managerial performance. This novel research model was tested with 180 managers from Romanian companies that work with BI&A tools. Based on PLS-SEM data analysis, our findings suggest that a data-oriented culture is a strong predictor of BI&A adoption and decision-making effectiveness. The results also confirm that BI&A utilization positively impacts decision-making effectiveness and individual work performance. The primary implication drawn from empirical evidence is that executives should prioritize the cultivation of a data-driven culture within their organizations, as this is essential for enhancing managerial performance through the adoption of business intelligence and analytics.

The aim of our article is to provide arguments for a poly-contextual and dynamic approach to information security risk culture. We consider the correlation of memes as DNA (DeoxyriboNucleic Acid) of the mind with knowledge and the organizational context. Our approach is interpretivist, reflective and dialectic (Cecez-Kecmanovic, 2011). It seeks to overcome the limits of knowledge induced by the highly mathematical models that are featured in specialized literature and often taken over in software applications.Yet we have to consider the subjectivism of the information that we process (Von Bayer, 2004). Depending on the country or the region, we can notice that there are discrepancies between our own perceptions and the perceptions of our fellows. Human behaviour adjusts depending on our own experiences that are also specific to the environment in which we live (Lorenz, 1969).Can actual information security risk assessment models provide objective, sci-entific information on a wide range of social and technological risks? Can indi-viduals develop unique and precise judgments that can be limited only to math-ematic forms and calculus? “Risk does not exist ‘out there’, independent of our minds and cultures, waiting to be measured” (Slovic,1992). As early as the 1950s opera-tional risk theoreticians stated that risk cannot be defined beyond human per-ceptions (Rappaport, 1953)