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.

In our study, we analyse data from the Hungarian Microcensus (2016) in order to map the proportion of Hungarian jobs threatened by the spread of automation. In doing so, we use the internationally well-known methodology of Carl Benedict Frey and Michael A. Osborne who estimated the probability of computerization for 702 occupations. The analysis was then repeated by Panarinen and Rouvinen for the Finnish labour market by converting the probabilities defined for the US occupational statistics to the European International Standard Classification of Occupations. Similar calculations were conducted for the Swedish and Norwegian labour markets. According to our results, almost every second Hungarian employee (44%) works in a job that is threatened by the development of digital technologies. The same ratio is 47% in the US and 53% in Sweden, while it is much lower in Finland (35%) and Norway (33%). It is especially alarming that 13% of the Hungarian workforce (i.e., almost 600,000 employees) works in an occupation where the probability of computerization is above 95%, while the number of those working in occupations where the same ratio is above 90% exceeds one million (i.e., 25% of the total Hungarian labour force). Diving deeper into the analysis, we can state that those with higher educational qualifications are more likely to work in an occupation that is more protected against computerization. Overall, there are no significant differences in the probability of computerization by gender; however, women are over-represented in the most endangered occupations.

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.

The paper aims to summarise the role of social integration in the labour market processes of the 2010s in Hungary. We have left behind a key decade when employment growth reached unprecedented proportions. However, this unprecedented growth had serious social costs, as those excluded from the labour market during the break-up of the welfare state were left without social protection, and because the financial situation of those entering the labour market improved in absolute terms. Still, their relative deprivation did not change or worsened, and in the meanwhile their ability to enforce their interests has been significantly reduced. That is, today’s Hungarian government wants to integrate the members of society into the labour market. To this end, it provides increasingly weak protection for those who, due to old age, long-term illness or unemployment, are forced to stay out of the labour market. Disintegration affects those with the fewest resources, the least capable of conflict and those unable to articulate their problems in public most: these are women, young people and older workers who are ‘traditionally’ in a worse position in the labour market, in addition to those who are forced out of the labour market. The question is whether the current social model or the situation of those who disintegrated from the labour market will continue to deteriorate after the economic slowdown.

A társadalomtudományi szakirodalomban a 2010-es évek óta ismét felerősödött a technológiai változás munkahelyromboló hatásától való aggodalom. Az elemzések közül a kvantitatív megközelítésű becslések dominálnak, miközben viszonylag keveset tudunk a vállalatokon belül zajló tényleges folyamatokról. A tanulmány ezt a tudáshiányt igyekszik mérsékelni, az automatizáció és a digitalizáció hatását vizsgálva három autóipari vállalatnál készített esettanulmány másodelemzésén keresztül. Az eredmények legfontosabb tapasztalata, hogy a vizsgált innovációk egyetlen esetben sem eredményeztek csökkenést az alkalmazottak számában. Szintén nagyon fontos kiemelni ezen innovációk munkafeltételekre gyakorolt pozitív hatásait, elsősorban a munkavállalói autonómia és elkötelezettség növekedését, a munkavállalók továbbképzését, a kereseti lehetőségek javulását és a kedvezőbb munkahelyi légkört.