“…An overview of the relevant literature section and is commonly employed in empirical research (e.g. Esser and Olsen, 2012; Fana and Villani, 2022; Gallie and Zhou, 2013; Riso, 2020; Forth et al, 2017) 5 Figure 1 shows the already mentioned fact that digital innovation is more common than process innovation across all the regions. As to regional specificities, the most salient difference is the highest presence of innovative firms among Southern European countries and the lower propensity of Eastern European countries to engage in digital innovation compared to the other group of countries.Figure 1.Process innovation and digital innovation.…”
Using firm-level data from 28 European countries, this paper explores the relationship between two types of innovation (process and digital) and different forms of control (direct and indirect) at the workplace. We find that (1) digital innovation is more common than process innovation; (2) more innovative firms record higher levels of indirect control (especially related to algorithmic management) and lower levels of direct control; (3) the relationship between innovation and control is not uniform across European regions. These findings nurture the debate on the future of work as the process of digitalisation may promote a shift towards indirect forms of control and contribute to reducing the degree of direct control. Moreover, these changes may also affect the bargaining process and lead to a redefinition of managerial roles, though it should be acknowledged that social and institutional factors play an important role in shaping this process.
“…An overview of the relevant literature section and is commonly employed in empirical research (e.g. Esser and Olsen, 2012; Fana and Villani, 2022; Gallie and Zhou, 2013; Riso, 2020; Forth et al, 2017) 5 Figure 1 shows the already mentioned fact that digital innovation is more common than process innovation across all the regions. As to regional specificities, the most salient difference is the highest presence of innovative firms among Southern European countries and the lower propensity of Eastern European countries to engage in digital innovation compared to the other group of countries.Figure 1.Process innovation and digital innovation.…”
Using firm-level data from 28 European countries, this paper explores the relationship between two types of innovation (process and digital) and different forms of control (direct and indirect) at the workplace. We find that (1) digital innovation is more common than process innovation; (2) more innovative firms record higher levels of indirect control (especially related to algorithmic management) and lower levels of direct control; (3) the relationship between innovation and control is not uniform across European regions. These findings nurture the debate on the future of work as the process of digitalisation may promote a shift towards indirect forms of control and contribute to reducing the degree of direct control. Moreover, these changes may also affect the bargaining process and lead to a redefinition of managerial roles, though it should be acknowledged that social and institutional factors play an important role in shaping this process.
“…Además, estos autores proponen un marco teórico con un numero de tareas mucho más rico con respeto a la literatura de RBTC, que ha menudo se centra únicamente en el concepto de rutina. En línea con esta interpretación, Fana et al (2020) construyen una base de datos para cinco países europeos mostrando como, incluso entre países con un nivel de desarrollo parecido, puede haber una distribución de tareas no uniforme en los mismos sectores y ocupaciones. Según estos autores, las tareas de los trabajadores no son determinadas exclusivamente por la tecnología en uso, sino también por factores institucionales, a su vez resultado por aspectos culturales, distintas formas de organización del trabajo y relaciones sindicales etc.…”
Section: Empleo Y Ocupación En Las Cgvunclassified
“…11 En la misma línea, algunos estudios muestran como la relación entre tecnología y tareas puede variar entre países (e.g. Fana et al, 2020;Fana and Villani, 2022b).…”
Section: Empleo Y Ocupación En Las Cgvunclassified
Este articulo discute algunas cuestiones recientes acerca de la evolución de las cadenas globales de valor (CGV). En particular, se analizan el rol que la evolución reciente de los costos laborales, factores institucionales y tecnológicos pueden jugar en la modificación del mapa de comercio y producción global. Además, el artículo se centra en la relación entre CGV y mercado laboral, sosteniendo que esta dimensión de análisis es clave para entender a fondo las consecuencias de la reorientación productiva y que, sin embargo, queda en gran parte inexplorada en la literatura.
“…On a more disaggregated level, Fana and Villani ( 2022 ) decompose the automotive supply chain by analyzing the employment, the value-added, and the occupation structure for countries such as Germany, France, the United Kingdom, and Italy. They found that after the global financial crisis, the supply chains reorganized, being the Eastern Europe countries the ones who benefitted the more due to the strong position of Germany in the industry.…”
Emerging in the twenty-first century, Network Science provides practical measures to interpret a system’s interactions between the components and their links. Literature has focused on countries’ interconnections on the final goods, but its application on the value-added from a network perspective in trade is still imitated. This paper applies network science properties and a multi-regional input–output analysis by using the UNCTAD-Eora Global Value Chain Database on the Transport Equipment value added on 2017 to unwrap the specific structural characteristics of the industry. Results show that the industry is highly centralized. The center of the network is dominated by developed countries, mainly from Europe, the United States, and Japan. Emerging countries such as China, Mexico, Thailand, and Poland also have an important position. In addition, the structure reveals two sub-hubs located in East Europe and North America. By extending to community detection, the network consists of three different communities led by Germany, the United States, and the United Kingdom, associated with more significant value-added flows. The study concludes that flows are not always consistent with the economy’s geographical location as usually final goods analysis suggests, and highlight the need to continue using the complex network to reveal the world trade structure.
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