Building Economic Resilience: Strategies for Sustainable Growth and Competitiveness

The lack of systematic organizational development (OD) methodologies and their limited application led to low productivity. Combining “systemic devel-opment” methods could help managers improve their management and leadership skills. Unfortunately, two created systematic development practices have spread in North America (C. Argyris and D. Schön’s Harvard Business School practice “Action Science”) and developed quite different operational performance instruments in Scandinavia. Therefore, there is a need for research on effective OD methods that can unify the measures, considering the developed management context. This research aims to develop a set of digital indicators for the evaluation of OD at enterprises using artificial neural networks. Quantitative estimation of indicators is planned to be carried out using Artificial Neural Network (ANN) system tools, which will more effectively replace previously widely used decision support systems based on comparative analysis. The competence of leadership and management of human resources in organizations contributes to the resilience capability that helps rapidly adapt to continuously changing business environments and maintain the competition in markets. The research will identify clusters of indicators and the most significant groups of indicators, based on which OD evaluation model will be created. The set of indicators will be digitalized for usage in artificial neural networks. This research presents the determination of clusters of digital quantitative parameters in known OD methodologies to evaluate OD and increase the productivity of organizations. Digital evaluation requires modern AI methods. An innovative intersection for classifying sets of indicators of OD methodologies is presented. Research results will provide leaders with digital indicators to evaluate OD and contribute to building leadership capacity in any country.

In the context of a rapidly evolving business landscape characterized by technological advancements and shifting market dynamics, organizations must prioritize the establishment of comprehensive quality management frameworks and the enhancement of adaptive operational strategies to ensure sustained competitiveness and effective responses to environmental changes. While companies usually assess quality by tracking overall defects, a more thorough examination of nonconformities can help identify specific areas that require management attention. This study reviews and tests potential categories of nonconformities within a logistics service-providing company. This will help organizations proactively identify, rectify, and prevent issues, thereby fortifying their resilience. Research on a logistics company’s non-conformance system revealed that the company’s resilience is closely linked to its ability to identify patterns of nonconformities, understand their causes, and address them promptly. However, the system’s impact on resilience is not immediate and can take up to 2 years as the quality system is analyzed and improved before a reduction in nonconformities becomes noticeable.

Previous studies have revealed that organizational resilience can be characterized by their intrinsic ability to maintain and regain a dynamically stable state, enabling them to successfully continue their activities after various shocks or function effectively in an environment of continuing threats. Scholars also highlight the prominence of employee engagement, trust, and commitment to strengthening organizational capacity. However, empirical evidence demonstrates that organizational resilience development patterns are commonly explored from the managerial level rather than the employee viewpoint. Consequently, this research aims to analyze the extent to which perceptions regarding the levels of organizational resilience are perceived equivalently from the standpoint of both management and employees. By gathering the data from 591 respondents and applying the structural equation modelling technique, this study elucidated that different dimensions of organizational resilience are positively interconnected in both groups and that perceptions regarding organizational resilience are similar (CMIN = 3.010; CFI = 0.924; TLI = 0.904 and RMSEA = 0.0041). Meanwhile, exogenous effects confirmed no direct effect of network dimension on change-ready & learning in the group of managers. In this group, we only confirmed the direct effects of the network on leadership & culture as well as leadership & culture to change-ready & learning.