The world is on the verge of the fourth industrial revolution that will considerably influence society and human life. Today human being is surrounded by technological advancement and every day we face new sophisticated technological systems that affect our daily lives. The business environment is being influenced by Industry 4.0 significantly and a massive transformation in labour market can be observed. The digital economy has become a disruptive factor in several sectors and it has shown a major impact on the logistic industry in terms of workforce transformation. The question that arises is that to what extent the logistic sector is ready for the digital transformation in Industry 4.0 and what factors should be considered by industry players, governments and multi-stakeholders in order to simplify workforce transformation. This study followed a qualitative approach using Grounded Theory to explain the phenomenon of workforce transformation within the logistic sector in Industry 4.0. Furthermore, a literature review was used to explain the role of human resource management in simplification of this process .The findings show that there is a lack of adequate awareness about the impact of the digital transformation on labour. Furthermore, it discusses the role of human resource management as an agent of change in Industry 4.0. The current research presents recommendations for different stakeholders on how to prepare the current and future workforce for the upcoming changes.This study is significant in the sense that it will add to the existing literature and provide practitioners with vital information that can be used to simplify the digital transformation of logistic industry by preparing labor market.
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This research focuses on exit choices within SMEs. In this study, “exit choice” refers to the decision to opt for either liquidation or sale of the firm. The predictions focus on human-capital and firm-resource variables. The hypotheses are tested on a set of 158 owners of small firms, the majority of which are micro-firms with 0–9 employees. The results of a series of binominal logistic regression analyses show that firm-resource characteristics (previous sales turnover, the firm’s independence from its owner, and firm size), together with one aspect of the owner’s specific human capital (the owner’s acquisition experience), predict exit choice. The conclusions have been made with caution, as the dataset is relatively small and the number of predictors is limited.
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This two-wave complete panel study aims to examine human resource management (HRM) bundles of practices in relation to social support [i.e., leader–member exchange (LMX; Graen & Uhl-Bien, 1995), coworker exchange (CWX; Sherony & Green, 2002)] and employee outcomes (i.e., work engagement, employability, and health), within a context of workers aged 65+, the so-called bridge workers (Wang, Adams, Beehr, & Shultz, 2009). Based upon the social exchange theory (Blau, 1964; Gouldner, 1960), and the Job Demands-Resources (JD-R; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) framework, it was hypothesized that HRM bundles at Time 1 would increase bridge workers’ outcomes at Time 2, and that this relationship would be mediated by perceptions of LMX and CWX at Time 2. Hypotheses were tested among a unique sample of Dutch bridge employees (N = 228). Results of several structural equation modeling analyses revealed no significant associations between HRM bundles, and social support, moreover, no significant associations were found in relation to employee outcomes. However, the results of the best-fitting final model revealed the importance of the impact of social support on employee (65+) outcomes over time.
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Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
The Dutch main water systems face pressing environmental, economic and societal challenges due to climatic changes and increased human pressure. There is a growing awareness that nature-based solutions (NBS) provide cost-effective solutions that simultaneously provide environmental, social and economic benefits and help building resilience. In spite of being carefully designed and tested, many projects tend to fail along the way or never get implemented in the first place, wasting resources and undermining trust and confidence of practitioners in NBS. Why do so many projects lose momentum even after a proof of concept is delivered? Usually, failure can be attributed to a combination of eroding political will, societal opposition and economic uncertainties. While ecological and geological processes are often well understood, there is almost no understanding around societal and economic processes related to NBS. Therefore, there is an urgent need to carefully evaluate the societal, economic, and ecological impacts and to identify design principles fostering societal support and economic viability of NBS. We address these critical knowledge gaps in this research proposal, using the largest river restoration project of the Netherlands, the Border Meuse (Grensmaas), as a Living Lab. With a transdisciplinary consortium, stakeholders have a key role a recipient and provider of information, where the broader public is involved through citizen science. Our research is scientifically innovative by using mixed methods, combining novel qualitative methods (e.g. continuous participatory narrative inquiry) and quantitative methods (e.g. economic choice experiments to elicit tradeoffs and risk preferences, agent-based modeling). The ultimate aim is to create an integral learning environment (workbench) as a decision support tool for NBS. The workbench gathers data, prepares and verifies data sets, to help stakeholders (companies, government agencies, NGOs) to quantify impacts and visualize tradeoffs of decisions regarding NBS.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
