The adoption of industry 4.0 and digitalisation of Hungarian SMEs

Hungarian small- and medium-sized enterprises are facing the challenges of digitalisation and innovation to survive fierce competition in the era of Industry 4.0, and particularly of COVID-19. Survival in the heavily hit sectors depends on the degree of digitalisation and involvement in e-commerce. This paper aims to examine Hungarian SMEs’ current scale of digitalisation and adoption of Industry 4.0 technologies. It also analyses the role of the Hungarian government’s support for SMEs’ digital transformation. To this end, secondary data were collected from Eurostat, the European Commission and the Hungarian Central Statistical Office, including the Digital Economy and Society Index (DESI), indices of skills and innovation from SME performance reviews and sectoral business statistics. In processing the data, the study strictly followed the European Commission’ classification protocol, complemented by a qualitative analysis of reports and programmes related to digitalisation and Industry 4.0 in Hungary. The findings reveal that there is a further need for strengthening the digitalisation and innovation capacities of Hungarian SMEs. The effects of introduced measures could not be seen yet. Hence, the Hungarian government should continue to support SMEs’ digital transformation in order to increase their role in high-tech manufacturing and knowledge-intensive services.

Robust official business statistics methodology during COVID-19-related and other economic downturns

Official statistics has not properly researched and understood how its methods and models behave at times of downturns (and potentially in the corresponding situation of similarly paced (unpredictable and fast) growths). There is generally a wish to make methods robust to unusual changes, but these are often tackled situation by situation. Production of official statistics during COVID-19 has necessitated some radical changes in both data collection and statistical methods; these have been introduced with admirable speed and dedication, but this process would have been made easier with a body of research already in place to draw from. We discuss the issues with the robustness of statistical methods during downturns, and highlight the opportunity to gather data which can be analysed to give evidence for the most robust methods to use as protection against poor measurement during future downturns.

Forecasting Students’ Final Exam: Results Using Multiple Regression Analysis in an Undergraduate Business Statistics Course

This paper discusses the development of a multiple regression model to predict the final examination marks of students in an undergraduate business statistics course. The marks of a sample of 366 students in the Winter 2017 semester were used to fit the regression model. The final model contained three predictor variables namely two test marks and the homework assignment mark. The marks of another 194 students from Winter 2018 were used to validate the model. The model validation showed that it can be used for future cohorts of students for prediction. The two main objectives of the study were to use the model as a teaching tool in class and to use the model to predict final examination marks of future students.

Innovation intensity and skills in firms across five European countries

This study attempts to establish the importance of specific formally achieved higher skills for the innovation intensity in firms across a group of European countries. Innovation expenditures are calculated as the ratio to turnover and the main explanatory variable is the proportion of highly skilled employees (tertiary education in ICT-oriented or other fields). The analysis employs official data on innovation activities (Community Innovation Survey) in firms for the period 2004–2010, linked to registers on education and businesses as well as to the Structural Business Statistics including 34,000 observations. Estimation results show a strong significantly positive relationship between the innovation intensity and the proportion of highly ICT skilled employees. Higher skills outside the field of ICT are also important for the innovation activities. Control variables reveal that the innovation intensity significantly increases with joint national and EU funding while the role of firm age varies. The significant and negative link to firm size reveals a lack of advantages of scale, a finding possibly related to the use of a comprehensive measure of innovation activities. There are also indications that industry affiliation is essential for the innovation intensity.