Industry 4.0 (I4.0) approaches, frameworks, and technologies have gained an increasing relevance in order to gain sustainable and competitive advantages for industrial enterprises and for small and medium enterprises (SMEs), as well. Contrary to previous studies, which are mainly focused on companies, we conducted a questionnaire-based survey on inhabitants, in an attempt to examine general awareness about I4.0 concepts, in the region of South India. Our findings revealed a rather poor informational level of I4.0 concept and its components, which consequently leads to inadequate future actions and expectations. Moreover, respondents with prior information about I4.0 framework tend to have rather positive opinions and expectations of possible future trends. We emphasize that insufficient knowledge of the potential workforce regarding I4.0 concepts, especially in a region with ascending demographic development, can be considered as one of the main barriers for a successful and sustainable future development towards the 4th industrial revolution.

The impact of corporate intangibles on a company’s market value has been a widely debated topic. A large body of literature has separately examined the industry’s effect- or firm-specific attributes, such as industry type, company size, company age, or indebtedness and profitability, on the motivation to disclose information on intangible assets, but without considering a comprehensive view. This paper examines the role intangible assets play in a firm’s market valuation besides other firm-specific characteristics. The reducted dataset we use in this study comprises 250 publicly traded companies operating in four different business sectors in France, Germany, and Switzerland for the ten years from 2009 to 2018. Based on the panel data regression models, the study provides an extension of previous knowledge about the effect intangible assets may have on the investors’ view of a company’s value, where the value added of this paper is the empirical evidence of a possible link between the intangible assets’ disclosure and the market value of German, French, and Swiss enterprises. The importance of our contribution lies in a comparative analysis carried out to reveal substantial differences in the impact of intangible assets and innovation activity on the market value firms in three European countries and across four industry sectors. Although the results show the positive impact of intangible assets on the companies’ market value, we suggest that investors still assess companies based on their profitability rather than considering the information on intangible assets the enterprises disclose in their financial statements.

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This chapter provides a questionnaire-based survey on individuals, with an attempt to examine general awareness about I4.0, in the South India region. Results show inadequate future expectations regarding effects on several aspects of respondents’ lives, which mostly stem from a rather poor informational level of I4.0. Conversely, rather positive opinions and expectations of possible future trends come from respondents with prior information about I4.0. Thus, from a broader discussion we generalize, that for sustainable and successful development leading towards the Fourth Industrial Revolution, sufficient information and knowledge base within the potential labour force, especially in a particular region with promising demography, should be achieved. We find obtained information not negligible from SMEs management perspective and successful development of organization and network models.

In this study, we assessed the efficiency of compulsory lower secondary education. We selected three variables that may significantly affect students’ performance in a particular country. First, we assumed that student scores achieved in PISA testing determine the number of monetary funds spent on these three variables, specifically student–teacher ratio, class size, and the annual number of hours spent in school. Second, we evaluated the efficiency of education in a sample of 24 different OECD countries, comparing the students’ performance in PISA 2018. Third, we used the two-stage data envelopment analysis with a bootstrapping procedure for estimating technical efficiency scores. Finally, we applied OLS and quantile regression, where our regression estimates in both models showed a positive effect of GDP per capita on students’ achievement across countries. The positive impact of GDP per capita was significant only for the least efficient countries. Conversely, the level of impact of parental education was much stronger and more positive for the inefficient countries and proved to be negative for more efficient countries.