Search results

Purpose: This chapter aims to study the modelling of conflict in the labour market in the conditions of automatization based on robots, Big Data and artificial intelligence (AI) from the position of countries’ inequality and conflict management.

Design/Methodology/Approach: It is determined that scientific literature has not formed the sufficient scientific and practical basis for determining the level of technological inequality of countries in the labour market in the conditions of automatization based on robots, Big Data and AI. The research objects are countries with the highest level of technological inequality from the position of automatization based on robots, Big Data and AI.

Findings: This chapter performs an overview of the factors of technological inequality of countries, which leads to the global conflict on the labour market in the conditions of automatization based on robots, Big Data and AI. It is supposed that using the technology and methods of the system of engineering knowledge within conflict management it is possible to find a non-standard solution, which ensures better optimization. A complex technical method proves its rationality and opens the perspectives for further development of the methodology and integration of the systems of knowledge on conflict management; still, from the position of conflict in the labour market in the conditions of automatization, there are not enough means of conflict management that could neutralize or partially solve such global conflict.

Originality/Value: It is proved that full automatization is a price paid by humans for prospering, while it is expected that new technologies will increase productivity and income. This will lead to the dismissal of certain employees and bankruptcy of the existing companies and productions, which is not that important for many large employers. For most employees, this is a conflict against the background of automatization, which leads to worse consequences for them.

The purpose of the research is to study the consequences of total (comprehensive) automatization of entrepreneurship for interested parties through the prism of competition human and artificial intellectual capital in production and distribution in Industry 4.0.

The research is conducted with application of scenario analysis, regression analysis, imitation modeling, forecasting and non-linear multi-parametric optimization with the simplex method.

The authors perform scenario modeling of competition between human and artificial intellectual capital in production and distribution in Industry 4.0 and offer recommendations for pro-active management of competition between human and artificial intellectual capital in production and distribution in Industry 4.0.

Contrary to the existing approach to studying competition between human and artificial intellectual capital in Industry 4.0, automatization of distribution, not production, is most preferable. This shows increase of the value of human intellectual capital in distribution during its automatization based on AI. This is an unprecedented and breakthrough conclusion for the modern economic science. It allows creating a completely new direction of research of competition between human and artificial intellectual capital in production and distribution in Industry 4.0, in which optimization of social consequences is achieved not by means of restraint of automatization but by means of its stimulation. The key condition is stimulation of automatization of distribution with limited automatization of production. Based on this conclusion, it is recommended to continue research in continuation of the presented work.

The purpose of this article is to determine the future proportion and variants of usage of human intellect and artificial intelligence (AI) in entrepreneurship of industry 4.0 that fits social entrepreneurship the most. It could be convergence (simultaneous utilization during the same entrepreneurial processes with the emphasis on unique features by the terms of the competition) or divergence (usage during different business processes by the terms of labor division).

The authors determine the influence of usage of human capital and AI on the efficiency of social entrepreneurship. The authors identify the perspective directions of usage of AI in social entrepreneurship and evaluate the readiness and interest in the implementation of these directions of concerned parties. The authors also model the optimal proportions and the variant of usage of human intellect and AI in social entrepreneurship in the conditions of Industry 4.0 in the future (until 2030).

It is found that social entrepreneurship will use the opportunities of Industry 4.0 for optimization of its activities until 2030, but will refuse from full automatization, using human intellect and AI at the same time.

The most perspective directions of application of AI at social companies are a collection of social goods and services, marketing studies and promotion of social goods and services. Neither convergence nor divergence of human and artificial intellectual capital does not fully conform to the interests of concerned parties. The most preferable (optimal) variant of usage of human intellect and AI in social entrepreneurship in the Industry 4.0 is human intelligent decision support.

Purpose: This chapter aims to test the existing argument in favour of technological discrimination of employees amid the COVID-19 pandemic and the post-pandemic period from the position of labour conflicts between express digitalization and their solutions.

Design/methodology/Approach: The review of the existing sources of research literature shows that their scientific basis for building a clear idea of labour conflicts between express digitalization and their solutions is not sufficient. To fill this gap in the system of scientific knowledge, we use the method of comparative analysis of statistical data. The research objects are the level of technological revolution, level of digitalization, and level of unemployment in the United States and several other countries.

Findings: The results of the research show that digitalization has a large impact on employment and the labour market; in particular, it is a precondition of not only new opportunities for creating new jobs but also increasing the current level of unemployment. Still, it should be noted that the data on the impact of digitalization on the creation of new jobs are rather contradictory.

Originality/Value: It is proved that express digitalization leads not only to the emergence of labour conflicts at the level of companies but also to a large level of unemployment around the world.

The purpose of the paper is to develop a model of distribution of human and machine labor at intellectual production in Industry 4.0.

The basis of the methodology of the research is regression analysis. The analyzed variables are independent variables that characterize the level of development of human and machine labor in the economy of a country; dependent variables that reflect the effectiveness of the production, marketing and innovative business processes in the economy of country according to “The Global Competitiveness Report” (World Economic Forum); and dependent variables, which show the share of the sphere (agriculture, mining industry, processing industry and service sphere) in the structure of GDP of a country according to the statistics of the World Bank. For determining the change of regression dependencies in dynamics in the interests of reduction of the probability of statistical error, the research is conducted for 2010 and 2018 with application of trend analysis.

Based on the full selection of modern countries that conduct digital modernization, the authors determine statistical dependencies of effectiveness of business processes and development of the spheres of economy on the intensity of application of machine and human labor. This allowed determining significant differences in automatization of business processes: perspectives of application of machine labor are the widest in production and the narrowest in marketing, differentiated logic of organization of intellectual production in different spheres of economy and the specifics of automatization of business processes and spheres of economy in countries of different categories, one of which has to be taken into account during organization of intellectual production in Industry 4.0.

The developed model of optimal distribution of human and machine labor at intellectual production in Industry 4.0 will allow reducing disproportions in effectiveness of different business processes, development of different spheres of economy and growth rate of developed and developing countries. This explains its contribution into provision of well-balanced development of the modern global economic system.

Explores critically the economic thought of Norbert Wiener with special reference to automatization, of which he was the father and philosopher. Considers the concept and theory in economic science and Wiener’s economics as an axiological science. Examines long‐time and short‐time (contest‐free) economic analysis as discussed by Wiener. Further considerations include the analysis of contest and Wiener’s militarology. Automatization is given special reference and Wiener’s analysis is presented and the humane resolution of the problem discussed. Wienierian ideas are further examined and related to the human condition in a final section: The mandate of heaven.

The purpose of the paper is to develop a concept of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital.

The methods of regression and correlation analysis are used. The role of human and artificial intellectual capital for provision of effectiveness and competitiveness of the system of science and education is determined, as well as its correspondence to the modern challenges. Analysis of perspectives and limitations of automatization of the system of science and education based on AI is conducted; a model of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital is compiled.

It is established that in the system of science and education, the decisive production factor is intellectual capital, but human intellect does not necessarily have to dominate in its structure. AI is one of the most popular technologies of Industry 4.0 in the system of science and education, which has wide perspectives of practical implementation. As experience of the leading world universities, which had the highest level of digitization in 2018, showed, foundation on non-breakthrough digital technologies (computer equipment and Internet) does not allow opening the potential of increase of indicators of effectiveness and competitiveness of the system of science and education and bringing it in correspondence to the modern challenges based on digitization. However, correlation of activity of application of artificial intellectual capital with these indicators is four times higher (0.2), as compared to correlation of these indicators with activity of application of human intellectual capital (0.05). This shows demand for digitization of the system of science and education, but based on breakthrough digital technologies, of which AI should become the key one.

It is substantiated that higher education could be automatized based on AI by 85% and science by 63%. Therefore, on the whole in the system of science and education, the share of AI in the structure of intellectual capital could reach 74% ((85 + 63)/2). It is recommended to use the developed model of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital.

The purpose of the chapter is to study the possibilities of systemic intellectual support for managerial decisions in modern business systems and perspectives of authomatization of this process on the basis of intellectual technologies.

The methodology of the chapter includes the methods of systemic and problem analysis, analysis of causal connections, modeling, and formalization.

Advantages of usage of technologies of intellectual support for decisions in modern business systems are substantiated; they are connected to multitask character, full determination of possibilities and problems of the business system regardless of employees’ involvement in this process, and “scale effect” during making of managerial decisions. Also, drawbacks of intellectual support for decision-making in modern business systems are determined: incompleteness of authomatization of the process of making of managerial decision, foundation primarily on digital data, necessity for complex digitization of the business system, and the problem of security of digital data and intellectual technologies.

Large opportunities of systemic intellectual support for managerial decisions in modern business systems and wide perspectives of almost full authomatization of this process on the basis of intellectual technologies, accessible at all stages of the process of decision-making, are determined. For this, an algorithm of complex intellectual support for decisions in a modern business system is offered. The obtained results allow determining intellectual technologies of support for managerial decisions in modern business systems as a perspective direction of improving this process.

The purpose of the work is to solve the set problem and to study the competition and perspectives of division of labor of humans and machines during creation of intangible assets in Industry 4.0.

The research is performed with the help of regression and comparative analysis by building regression curves and with the help of the qualitative structural and logical analysis.

The authors perform an overview of the factors that determine the advantages and limits of participation in creation of intangible assets in Industry 4.0, determine the perspectives and compile recommendations for division of human and machine labor during creation of intangible assets in Industry 4.0.

The results of the performed research confirmed the general hypothesis that machine technologies allow improving the innovative, marketing and organizational and managerial activities and activities in the sphere of R&D through automatization of certain stages of the process of creation of intangible assets. The authors determine the factors that define the contribution of machine technologies in this process and their competitive advantages as compared to human intellectual capital during creation of intangible assets. These advantages prove the possibility and expedience of division of human and machine labor during creation of intangible assets.

The unmanned aerial vehicles (UAVs) entered into their development stage when different applications became real. One of those application areas is agriculture. Agriculture and transport currently follow infrastructure as the top industries in the world UAV market. The agricultural UAV can be acquired as a ready-made, built by its future user or UAV-as-a-service (UaaS) way. This paper aims to help the UAVs’ users to choose the right sensors for agricultural purposes. For that sake, the overview of the types and application areas of onboard sensors is presented and discussed. Some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring UAVs for their purposes related to the agricultural area.

The agricultural UAVs’ onboard specialised sensors have been analysed, described and evaluated from the farmer’s operational point of view. That analysis took into consideration the agricultural UAVs’ types of missions, sensor characteristics, basics of the data processing software and the whole set of UAV-sensor-software operational features. As the conclusions, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented.

Services performed by the UAVs for the agriculture businesses are the second in the UAV services world market, and their growth potential is around 17% compound annual growth rate in the next years. As one of the quickest developing businesses, it will attract substantial investments in all related areas. They will be done in the research, development and market deployment stages of that technology development. The authors can expect the new business models of the equipment manufacturers, service providers and sellers of the equipment, consumables and materials. The world agricultural UAVs’ services market will be divided between the following two main streams: the UAVs’ solutions dedicated to the individual farmers, systems devoted to the companies giving the specialised services to individual farmers, in the form of UaaS. It will be followed by the two directions of the agriculture UAV set optimisation, according to each of the above streams’ specific requirements and expectations. Solutions for the individual users will be more straightforward, universal and more comfortable to operate but less effective and less accurate than systems dedicated to the agricultural service provider. UAVs are becoming important universal machines in the agriculture business. They are the newcomers in that business but can change the processes performed traditionally. Such an example is spraying the crops. UAVs spray the rice fields in Japan on at least half of them every year. The other is defoliating the cotton leaves, which only in one China province takes place on a few million hectares every year (Kurkute et al., 2018). That trend will extend the range of applications of UAVs. The agricultural UAV will take over process after process from the traditional machines. The types and number of missions and activities performed by agricultural UAVs are growing. They are strictly connected with the development of hardware and software responsible for those missions’ performance. New onboard sensors are more reliable, have better parameters and their prices are reasonable. Onboard computers and data processing and transmitting methods allow for effective solutions of automatisation and autonomy of the agricultural UAVs’ operation. Automatisation and autonomous performance of the UAVs’ agricultural missions are the main directions of the future development of that technology. Changing the UAV payload allows for its application to a different mission. Changing the payload, like effectors, is quite simple and does not require any special training or tooling. It can be done in the field during the regular operation of the agricultural UAV. Changing the sensor set can be more complicated, because of the eventually required calibrating of those sensors. The same set of sensors gives a possibility to perform a relatively broad range of missions and tasks. The universal setup consists of the multispectral and RGB camera. The agricultural UAV equipped with such a set of sensors can effectively perform most of the crop monitoring missions. The agriculture business will accept the optimised sensor-computer-software UAV payload set, where its exploitation cost and operational simplicity are the critical optimisation factors. Simplicity, reliability and effectiveness of the everyday operation are the vital factors of accepting the agricultural UAV technology as a widespread working horse.

Performed research studies have been done taking into consideration the factors influencing the real operational decisions made by the farmers or companies offering UAV services to them. In that case, e.g. the economical factors have been considered, which could prevail the technical complexity or measuring accuracy of the sensors. Then, drawn conclusions can be not accurate from the scientific research studies point of view, where the financing limits are not so strict.

The main goal of the paper is to present the reasons and factors influencing the “optimised” solution of the configuration of agricultural UAV onboard sensors set. It was done at the level useful for the readers understanding the end-users expectations and having a basic understanding of the sensors-related technologies. The paper should help them to configure an acceptable agricultural UAV for the specific missions or their servicing business.

Understanding the technology implications related to the applying of agricultural UAVs into everyday service is one of the main limits of that technology market deployment. The conclusions should allow for avoiding the misunderstanding of the agricultural UAVs’ capabilities and then increasing their social acceptance. That acceptance by the farmers is the key factor for the effective introduction of that technology into the operation.

Presented conclusions have been drawn on the base of the extensive research of the existing literature and web pages, and also on the own experience in forestry and agriculture and other technical applications of the onboard sensors. The experience in practical aspects of the sensors choosing and application into several areas have been also used, e.g. manned and unmanned aeroplanes and helicopters applied in similar and other types of missions.