Organizational resilience (OR) increases when the company has the ability to anticipate, plan, make decisions, and react quickly to changes and disruptions. Thus the company should focus on the creation and implementation of proactive and innovative solutions. Proactive processing of information requires modern technological solutions and new techniques used. The main focus of this study is to propose the best technique of Machine Learning (ML) in the context of accuracy for predicting the attributes of the organizational resilience potential. Based on the calculations, the research includes estimating them through the applications of regression and machine learning methods. The dataset is obtained from the results of the our survey based on the questionnaire consisting of 48 items mainly established on OR attributes formed on ISO 22316:2017 standard. Based on the outcomes of the study, it can be stated that the optimal technique in the context of accuracy for predicting the attributes of the organizational resilience potential is ensemble methods. The k-nearest neighbor (KNN) filtering-based data pre-processing technique for stacked ensemble classifier is used. The stacking is achieved with three base classifiers namely Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). The chosen ensemble method should be implemented in an organization systemically according to the circle of innovation, and should support the quality of managerial decision-making process by increasing the accuracy of organizational resilience potential prediction, and indication of the importance of attributes and factors affecting the potential for organizational resilience.

Objectives: This paper presents the results of research on the scale of psychosocial risks among miners. A comparative analysis was made, comparing the research results with the data obtained from workers in the following industries: metal, energy, chemical and construction – along with an...

This article shows the results of research on psychosocial risks for a group of machine and plant operators (n = 1014) from the construction, chemical, energy, mining, metal and food industries in Poland. The Psychosocial Risk Scale designed in Nofer Institute of Occupational Medicine (NIOM) by Moscicka-Teske and Potocka was used to indicate the occurrence of general and specific occupational stressors and the level of their stressfulness. The results revealed that the studied machine and plant operators experience job context stress – related to working environment features concerning work organization – more frequently than job content stressors – related to the type of tasks they perform. Moreover, a correlation analysis between work features and the health and occupational functioning of the respondents revealed significant but weak relationships between the variables (from −0.08 to −0.23). Comparative analysis revealed the differences between the studied sectors. Such a comparison makes it possible to set goals for each sector and to attempt to improve the distinctive areas.

The article presents the problem of evaluation of safety climate in a production facility. It shows selected, most popular and available tools for measuring its level. On the basis of that there has been presented author's questionnaire for evaluation of safety climate level which includes, among other, the following issues: I. Knowledge of work safety, II. Values and beliefs regarding work conditions, III. Clear communication regarding health and safety at work, IV. Attitudes towards health and safety services - personal influence on health and safety at work, V. Superiors’ approach to health and safety problems, VI. Evaluation of training in the area of health and safety, VII. Stress resilience, VIII. Motivation to safe behavior, IX. Making use of cultural offer. To each of the issues there have been assigned five-question modules, to achieve clarity and legibility of the questionnaire. There has also been used five-point Likert scale. To confirm the validity of the above assumptions there has been carried out a piloting research which allowed to simplify and modify the questionnaire. The obtained results can be referred to age, education and job experience of workers. The final result is a collective comparison in a form of a radar chart, which shows general level of safety climate in the analyzed production facility, and allows for an indicator approach to the problem.

This article presents a comprehensive analysis of the occurrence of innovative solutions in accordance with CSR in various companies. It turns out that 71% of medium-sized enterprises (50–249 people) and 64% of smaller companies (10–49 people and up to 9 people) declare the introduction of innovative solutions in CSR strategies, which proves that innovations are not limited to large companies. A responsible approach to business can affect competitiveness and positive public perception. The implementation of a CSR strategy increases profits, improves image, increases customer loyalty, attracts media attention, and opens up new markets. The hindering factor is the additional cost of implementing CSR activities, insufficient knowledge and awareness of CSR, and unclear regulations and market conditions, as well as the need to adapt the organizational structure and culture. CSR strategies are important for the success of enterprises and society, and solving potential problems allows for effective introduction of responsible practices. In order to obtain the presented data, two research methods were used: an analysis of the international literature sources and a survey by manufacturing firms in the Silesian Voivodeship.