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The social innovations have been together with the advanced technology in the 21 st century, taking an essential role in social structures and their informatization processes. Information technology has become an indispensable factor not only in industry and service businesses, but also in governing systems at the micro (cities and regions) and macro (state and nations) levels. The information processes, which includes automation, have in the last few years an important impact on the transformation from "classical" governance into the "smart governance". In the paper are presented the best practices which show how could social innovations, together with the advanced technology also lead to the several democratic changes in the urban environment. It can be concluded that it will in many ways reorganize public decisionmakings, create changes in democratic processes that are in accordance with socioeconomic and technological development, and will represent the basis for the emergence of the so-called the smartest social community and the ensuing novel processes of organization and operation.

Early warning systems are becoming increasingly important in high-risk industries, because of their potential to detect all kinds of subtle threats and opportunities, that is, weak signals, in order to avoid strategic surprises. However, it is an under-researched area within the context of smart factories. For the purpose of the study, semi-structured group interviews were used to investigate how managers at a smart factory, a highly innovative global supplier in the automotive industry sense weak signals, perceive the role of intuition, smart systems and business model adjustments. The results of the study show that managers perceive early warning systems as highly important for timely response to development changes and that both smart systems and intuition play an essential role in detecting and responding to weak signals. Based on this study, we propose a managerial early warning system model with four stages, namely, identifying, screening, appraising and responding to weak signs, within the context of a smart factory.

Background and purpose: Professional drivers as a group are exposed to high risk of developing low back pain due to ergonomic factors and work conditions. The purpose of the study was to examine to what extent the low back pain occurs among Slovene professional drivers as a result of the development of various psychosocial factors. Methodology: The study involved 275 respondents (professional bus drivers, car/van drivers, international truck/ lorry drivers, and ambulance car drivers). Hypotheses were tested using multivariate statistical method (regression analysis) and analysis of variance. Data were collected by structured questionnaire comprised of three parts: socio-demographic data, basic psychosocial factors causing low back pain, and incidence, duration and severity of low back pain as a result of psychosocial risk factors, was implemented. Results:The results of quantitative survey suggest that low back pain is mostly caused by lifting and carrying heavy loads, inadequate working conditions, poor physical fitness, regular nights out, shift work, and stress. Only the impact of gender on low back pain distress among professional drivers was confirmed, predominantly among bus drivers and lorry drivers on international routes. Low back pain occurrence was less common, albeit not statistically significant, among professional drivers of vans and passenger cars. Conclusion: Our study suggests that psychosocial factors are also important cause for the development of low back pain among professional drivers and can limit the quality of their social and professional lives.

The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage.

The purpose of the paper is to contribute to the development of best practices at emerging factories of the future, i.e. smart factories of Industry 4.0. Smart factories need to develop effective managerial early warning systems to identify and respond to subtle threats or opportunities, i.e. weak signals, in order to adapt to an ever-changing environment in a timely manner and thus gain or maintain a competitive advantage on the market. These factories need to develop and implement a several-stage early warning system that is specific to their industry. The aim of our study is, with the help of semi-structured group interviews, to examine which stages of a managerial early warning system are present in the case of a global innovative supplier in the automotive industry. As such, a four-stage managerial early warning system model for a knowledge-based automotive smart factory is proposed, in which aggregate activities and management decision-making strategies are defined for each stage, with the importance of intuition being taken into consideration. We found that managers rely on intuition and extensive analysis for satisficing strategies and teamwork for optimizing strategies, when using their managerial early warning system.

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