The "Make or Buy" decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

Identifying the performance factors of organizations is of utmost importance for labor studies for both empirical and theoretical research. The present study investigates the essential intra- and extra-organizational factors in determining the performance of firms using the European Company Survey (ECS) 2019 framework. The evolutionary computation method of genetic algorithm and the machine learning method of Bayesian additive regression trees (BART), are used to model the importance of each of the intra- and extra-organizational factors in identifying the firms’ performance as well as employee well-being. The standard metrics are further used to evaluate the accuracy of the proposed method. The mean value of the evaluation metrics for the accuracy of the impact of intra- and extra-organizational factors on firm performance are MAE = 0.225, MSE = 0.065, RMSE = 0.2525, and R2 = 0.9125, and the value of these metrics for the accuracy of the impact of intra- and extra-organizational factors on employee well-being are MAE = 0.18, MSE = 0.0525, RMSE = 0.2275, and R2 = 0.88. The low values of MAE, MSE and RMSE, and the high value of R2, indicate the high level of accuracy of the proposed method. The results revealed that the two variables of work organization and innovation are essential in improving firm performance well-being, and that the variables of collaboration and outsourcing, as well as job complexity and autonomy, have the greatest role in improving firm performance.

Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that “skills requirements and skill matching” and “employee voice” are the two factors that matter most in enhancing firm performance and wellbeing.

Platform works are swiftly turning into a big, perhaps game-changing force in the labor market. From low-skilled, low-paid services (like passenger transport) to high-skilled, high-paying project-based labor (like developing artificial intelligence algorithms), digital platforms can handle a wide range of tasks. Our paper discusses the platform-based content, working conditions, employment status, and advocacy problems. Terminological and methodological problems are dealt with in-depth in the course of the literature review, together with the ‘gray areas’ of work and employment regulation. To examine some of the complex dynamics of this fast-evolving arena, we focus on the unsuccessful market entry of the digital platform company Uber in Hungary 2016 and the relationship to institutional-regulatory platform-based work standards. Dilemmas about the enforcement of labor law regarding platform-based work are also paid special attention to the study. Employing a digital workforce is a challenge not only for labor law regulation but also for stakeholder advocacy.

Algorithmic management (am) has become a key research focus in the sociology of work, especially concerning platform work. However, am tools are also impacting traditional workplaces. This study investigates three main questions: the impact of ai on high vs. low-skilled jobs, its effect on employee's role, and the formation of collective voices around am, including non-traditional labour relations actors. The context is the Hungarian industrial relations system, known for low union membership and company-level bargaining. The study compares two cases: a medium-sized company in high-value-added business services and a Hungarian subsidiary of a multinational employing warehouse workers. Contrary to literature suggesting am reduces employee autonomy, the study finds its impact complex, decreasing employee's roles some areas while increasing it in others. Notably, transparency and wage predictability improved. The study also highlights the importance of considering new actors, such as clients and external consultants, in am analysis. Keywords