Economic and mathematical modeling of energy-saving and resource-saving innovations in the project approach to crisis and strategic management of hospitality industry enterprises based on corporate law and public administration: application of numerical methods - Hutsaliuk, O.; Panchyshyn, A.; Abliazov, D.; Buhaieva, M.; Pantielieieva, I.; Chen, M.; Vlasova, Ya.; Бугаєва, М. В. (My University, 2025)
This study investigates how energy and resource-saving innovations can enhance crisis and strategic management within the hospitality industry through mathematical modeling and analysis. We specifically explore the integration of these innovations with principles of corporate law and public administration, employing quantitative methods to assess their impact and effectiveness. Utilizing regression analysis and factor analysis’ numerical techniques, we identify key trends and relationships among variables such as the adoption of energy-efficient technologies ((X_1)), implementation of resource-saving practices ((X_2)), and employee training programs on sustainability ((X_3)). The regression model is expressed as (Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3 + \epsilon), where (Y) represents operational cost reduction. Qualitative data were analyzed using NVivo to systematically code and identify themes from interviews. Findings indicate that adopting energy-efficient technologies can lead to a reduction in operational costs by as much as 30%. Furthermore, organizations that implement resource-saving practices tend to experience increased customer satisfaction and loyalty, measured by a satisfaction index (S = \frac{\sum_{i=1}^{n} C_i}{n}). This study provides insights into the mathematical approaches that elucidate the relationship between energy efficiency, corporate law, and public administration. The use of quantitative methods enhancine the strategic decision-making processes within hospitality enterprises.
Keywords: numerical methods; energy efficiency; decision-making models; crisis management; hospitality industry; matematical analysis