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Article
Publication date: 28 April 2020

Agnessa O. Inshakova, Evgenia E. Frolova, Ekaterina P. Rusakova and Sergey I. Kovalev

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of Intellectual Capital, vol. 21 no. 4
Type: Research Article
ISSN: 1469-1930

Keywords

Article
Publication date: 20 February 2023

Cristina Fernandes, João Ferreira and Pedro Mota Veiga

The purpose of this study is use a bibliometric analysis to explore the relational nature of knowledge creation in WFM in operations. Companies live under constant pressure to…

Abstract

Purpose

The purpose of this study is use a bibliometric analysis to explore the relational nature of knowledge creation in WFM in operations. Companies live under constant pressure to find the best ways to plan their workforce, and the workforce emangement (WFM) is one of the biggest challenges faced by managers. Relevant research on WFM in operations has been published in a several range of journals that vary in their scope and readership, and thus the academic contribution to the topic remains largely fragmented.

Design/methodology/approach

To address this gap, this review aims to map research on WFM in operations to understand where it comes from and where it is going and, therefore, provides opportunities for future work. This study combined two bibliometric approaches with manual document coding to examine the literature corpus of WFM in operations to draw a holistic picture of its different aspects.

Findings

Content and thematic analysis of the seminal studies resulted in the extraction of three key research themes: workforce cross-training, planning workforce mixed methods and individual workforce characteristics. The findings of this study further highlight the gaps in the WFM in operations literature and raise some research questions that warrant further academic investigation in the future.

Originality/value

Likewise, this study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of WFM in operations.

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