Self-employment, work and health: A critical narrative review

BACKGROUND: Self-employment (SE) is a growing precarious and non-standard work arrangement internationally. Economically advanced countries that favor digital labor markets may be promoting the growth of a demographic of self-employed (SE’d) workers who are exposed to particular occupational diseases, sickness, and injury. However, little is known about how SE’d workers are supported when they are unable to work due to illness, injury, and disability. OBJECTIVE: Our objective was to critically review peer-reviewed literature focusing on advanced economies to understand how SE’d workers navigate, experience, or manage their injuries and illness when unable to work. METHODS: Using a critical interpretive lens, a systematic search was conducted of five databases. The search yielded 18 relevant articles, which were critically examined and synthesized. RESULTS: Five major themes emerged from the review: (i) conceptualizing SE; (ii) double-edged sword; (iii) dynamics of illness, injury, and disability; (iv) formal and informal health management support systems; and (v) occupational health services and rehabilitation. CONCLUSION: We find a lack of research distinguishing the work and health needs of different kinds of SE’d workers, taking into consideration class, gender, sector, and gig workers. Many articles noted poor social security system supports. Drawing on a social justice lens, we argue that SE’d workers make significant contributions to economies and are deserving of support from social security systems when ill or injured.

Understanding IT Value at the Managerial Level

Managerial ambidexterity is an important precursor to managerial seizing ability. However, ambidexterity can impose substantial costs. Yet information systems may help reduce these costs. We develop a model that includes an inverted U-shaped relationship between managerial ambidexterity and seizing ability. We propose that a manager's effective use of management support systems will mitigate the decline in seizing ability at higher levels of ambidexterity. We test our model with data collected over two time periods from 172 managers. Our results support our model, thereby generating implications for research and practice in IT value, managerial ambidexterity, and dynamic managerial capabilities.

Management Support Systems Type Business Intelligence (BI) and Factors Determining Their Implementation

The article contains the concept and analysis of basic types of Business Intelligence (BI) systems and analysis of critical success factors of BI implementation. The complexity and diversity of BI systems means that the success of their implementation requires a methodical foundation and proven scientific theories. Article focuses on analysis of factors determining the effectiveness of implementation and use of Business Intelligence (BI) tools in organizations, depending on the implementation method applied. Currently, organizations that decide to implement BI focus their attention on the method of system implementation. BI systems have become an indispensable element of the Industry 4.0 breakthrough. Industry 4.0 is related to the integration of the existing independent digital management systems and the creation of intelligent management support systems.

Food Industry 4.0 readiness in Hungary

In terms of production value, the food industry is the third-largest in Hungary, the first in Hungary in terms of the number of employees, and the first in Europe in the processing industry, as well as a significant user of resources. The research examined the state of art of digitalization readiness, focusing on I4.0 technologies, which supports the management to operate more efficiently the enterprise and to make better decisions. So the focus was on integrated enterprise information systems, management support systems, business intelligence systems, industry 4.0 technologies, and issues related to their application. The analysis based on an online questionnaire survey the request sent to 4.600 enterprises, the response rate was 5% which was representative of the branches of production, covered the Hungarian food and beverage manufacturing sectors in 2019. The companies were asked the most critical technologies in development, going towards Industry 4.0. The research tools were LimeSurvey, Mailing List Server, Excel, Power BI (Desktop, Publishing Server to distribute the results). The used analysing methods were making calculations, pivot tables, models, dasboards. We found that a significant portion of businesses, 78 %, use mobile devices in the manufacturing process. The three most relevant digital technologies are geolocating (GPS, GNSS), cloud computing, and sensor technology. The current level of digitalization and integration cannot be said to be high, but respondents are very optimistic about expectations. Improvements are expected in all areas in the next 2-3 years in terms of digitalisation and integration. Vertical integration involves, first and foremost, cooperation with partners in the supply chain. Horizontal integration means close, real-time connectivity and collaboration within the company. Unfortunately, between 6% and 15% of SMEs (approximately 9% on average) and large enterprises, 36% have a digital strategy. According to the survey, the sector needs significant improvement and creating a digitalization strategy.

Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System

Low-cost, flexible, easily maintainable and secure traffic management support systems are in demand. Internet-based real time bi-directional communication provides significant benefits to monitor road traffic conditions. Dynamic route computation is a vital requirement to make the traffic management system more realistic and reliable. Therefore, an integrated approach with multiple data feeds and Backpropagation (BP) Neural Network (NN) with Levenberg-Marquardt (LM) optimization is applied to predict the road weights. The results indicate that the proposed traffic system/tool with NN based dynamic weights computation is much more effective to find the optimal routes. The BP NN with LM optimization achieves 96.67% accuracy.