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Is Innovation Good for European Workers? Beyond the Employment Destruction/Creation Effects, Technology Adoption Affects the Working Conditions of European Workers

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Abstract

This article contributes to better understanding the relations between innovation and the evolution of working conditions and employment quality. Most studies on employment and innovation focus on the impacts of innovation on employment variation and turnover. However, few empirical works explicitly study the transformative role of new technology adoption in the qualitative dimensions of jobs. This article investigates the effect of new technology adoption on job quality and working conditions. Based on the European Working Conditions Survey (EWCS) (2010), econometrics models identify at employee-level the combined influence of innovation with work organization practises on several job quality dimensions. We observe that new technology adoption is generally associated with better employment quality for workers in some ways, but, simultaneously, it leads to higher physical constraints and work-time intensity. Furthermore, our study highlights the heterogeneity of innovation diffusion effects according to work organization’s practices. Our results suggest that more consideration should be given to the impact of technology diffusion on job quality. The increasing constraints on working conditions from innovation and information and communication technology use call for regulation setting. This article is an original contribution in answering the claims for more in-depth research on the links between employment variation and work transformations due to technological change.

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Notes

  1. https://ec.europa.eu/info/strategy/european-semester/framework/europe-2020-strategy_en.

  2. For instance, the Oxford Living Dictionaries define innovation as a phenomenon that “make changes in something, especially in introducing new methods, ideas, or products.” The two manuals of reference in economics of innovation, the Oxford Handbook of Innovation (2004) and the Handbook of the economics of innovation (2010), point out the holistic and comprehensive aspects of innovation phenomenon leading to a strongly scattered field of research.

  3. The empirical literature on innovation put emphasis on several levels of distinctions between innovation production and innovation adoption, between incremental innovation and radical innovation, and regarding the level of novelty and the type of innovation (technological – process or product – organizational and even marketing), among others.

  4. From economic perspectives, innovation leads to several market failures that are difficult to deal with (great uncertainty, non-rival and, to some extent, non-excluable goods, and externalities).

  5. Besides it is not a proxy as are job quality framework or wage measures, since it is the direct measure of well-being.

  6. For more detail, see the “Introduction” section of this article, especially “The European Working Conditions Survey” that presents these mechanisms in greater details.

  7. Depending on the level of analysis (firm, industry or country-level), on the types of innovation used, and on the data collected, results could be substantially different: for further details, see the critical review by Calvino and Virgillito (2017).

  8. As we pointed out at the beginning of this article, the limited number of previous studies linking qualitative aspects of work and innovation dynamics could explain the weakness of surveys mixing the two.

  9. We have to note that all samples are as representative as possible in each country, with at least 1000 individuals; thus, the misinterpretation is not too great. Furthermore, to minimize this issue, our regressions, as well as all our descriptive statistics, are weighted by the sample weight variable provided.

  10. For each index, we also conducted a multiple correspondence analysis (MCA) on the question used to check the empirical proximity of the variable and confirm the conceptual links from the questions. In all MCAs, the first dimension represents at least 80% of the inertia and the second always less than 5%. This first test confirms the relevance of our synthetic variables.

  11. For the EWCS the use of index from 0 (minimum) to 1 (maximum) – or 0 to 100 – is the widespread in the literature.

  12. Physical constraints and work pressure are two indexes that are built negatively in terms of job quality view. When these indices are high, this means that level of physical constraints and work pressure are high; then, the job quality is low on these dimensions.

  13. Coefficients are 0.45 for organizational change and 0.19 for ICT use; both coefficients are significant at the 1% level.

  14. Table 14 shows the average score for each dimension by industry, results are in line with what we could expect.

  15. We also performed a correlation analysis and the industry level between CIS variables and the innovation measure from the EWCS: results were in line with the national level correlation (results are available on request).

  16. Based on the following reference manual: McCutcheon (1987) and Collins and Lanza (2013). The LCA has the advantage, compared to hierarchical clustering, to be less constraining in terms of computational power required, especially when the database is large, like in our case.

  17. We check the stability of the four classes’ choice (motivated by the Holm et al. (2010) analysis) by the two inertia criteria AIC and BIC. Both support the four classes’ choice.

  18. Moreover, work organization experienced is the result of a combination of several work organization practices.

  19. The regressions are weighted by the survey weight provided to take into account the selections bias of the dataset. It is also a way to reduce the heteroscedasticity, even if in our case the use of normalized indexes and dummy variable already partly manage it.

  20. Except for the age, we assume a quadratic relation, especially because age as a proxy of career advancement is known to have nonlinear effects on employment characteristics.

  21. Tables 5.1, 5.2, and 5.3, show the regression without the controls for the structural employee’s characteristics. All econometric results, complete tables and codes are available on request.

  22. A rich literature on workplace innovation stresses the link between an innovation environment and employees’ motivation and well-being (Aalbers et al., 2013; Eurofound, 2013 and Fu et al., 2015).

  23. Note that this variable of organizational change is strongly correlated with our main variable of innovation (technology adoption).

  24. This is in line with several studies confirming that new forms of work organization (HPWS) offer better contractual conditions, and better work environment, but at the same time increase demand and pressure through the higher involvement and level of responsibility offered (Rubery & Grimshaw, 2001; OECD, 2010; Greenan et al., 2012; Eurofound, 2015; Gallie, 2018).

  25. It is the less restrictive form of organization for the employee.

  26. Because control variables are numerous, they are not reported here but available upon request.

  27. As presented in Table 2, the work organization practices are strongly influenced by the occupation.

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Acknowledgements

This article was made in the context of the European project “Quality of jobs and Innovation generated Employment outcomes—QuInnE”(http://www.quinne.eu/) funded by the Horizon 2020 Framework Program of the European Commission. It is an updated version of the QuInnE Working Paper No. 9 (Mofakhami, 2018).

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  1. Centre d’Etude de l’Emploi et du Travail and LIRSA, CNAM, Paris, France

    Malo Mofakhami

  2. CEPN, Université Sorbonne Paris Nord - Paris XIII, Paris, France

    Malo Mofakhami

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Correspondence to Malo Mofakhami.

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Mofakhami, M. Is Innovation Good for European Workers? Beyond the Employment Destruction/Creation Effects, Technology Adoption Affects the Working Conditions of European Workers. J Knowl Econ 13, 2386–2430 (2022). https://doi.org/10.1007/s13132-021-00819-5

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