Automation, Skills and the Future of Work: What do Workers Think?

Mulas‐Granados, Carlos; Varghese, Richard; Wallenstein, Judith; Boranova, Vizhdan; deChalendar, Alice

doi:10.2139/ssrn.3524309

Cited by 3 publications

(7 citation statements)

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“…The inadequacy of existing literature suggests the need to examine automation of low-skilled, routine and manual jobs in developing countries. Second, whereas most literature on perceptions of automation does not attend to specific contexts, Mulas-Granados et al (2019) suggest developmental and information contexts influence how workers perceive automation. Accordingly, this article embeds the analysis of automation within the macro-contexts of developmentalism – i.e.…”

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 96%

“…A study that analyses the Eurobarometer survey finds that 74 percent of respondents express concern about robots and believe AI might threaten more jobs than it would create, although more educated Europeans are less concerned. According to the same study, respondents who had heard of AI were more likely to have a favourable view of it (Mulas-Granados et al, 2019). An analysis of low- and middle-skilled workers in 11 countries finds workers feel more positive than negative about automation.…”

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 99%

“…An analysis of low- and middle-skilled workers in 11 countries finds workers feel more positive than negative about automation. Still, negative perceptions are prevalent among older, poorer and less-educated workers (Mulas-Granados et al, 2019). Daniel and Hogarth’s (1990) study of workers in the United Kingdom complements the above studies by showing that when workers see technological change as progress and investment for longer-term job security, they support technical change.…”

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 99%

“…Daniel and Hogarth’s (1990) study of workers in the United Kingdom complements the above studies by showing that when workers see technological change as progress and investment for longer-term job security, they support technical change. Mulas-Granados et al (2019) further compare workers’ attitudes across 11 countries. They find workers in developing countries tend to be more positive about automation than those in developed countries; also, workers in China have the second-least negative view on automation.…”

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 99%

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Upgrading China through Automation: Manufacturers, Workers and the Techno-Developmental State

Lei

2021

Work, Employment and Society

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This article analyses how local states, electronics manufacturers and low-skilled workers perceive and make decisions about automation under China’s techno-developmentalism. Since the early 2010s, local states have made automation – specifically, the substitution of robots for human workers – the linchpin of their techno-developmentalist strategy and set statistical targets to facilitate policy implementation. Although manufacturers realised the limitations of such substitution, most continue to overstate the power of robots in order to receive material and symbolic benefits from local states, which rely on manufacturers to achieve their statistical targets. Meanwhile, most low-skilled workers embrace the state’s vision and see automation as beneficial for national progress, although these workers are the most excluded by state policy. Essentially, China’s techno-developmentalism has led to symbiotic state-capital relations that marginalise low-skilled workers, while reproducing a national sociotechnical imaginary that prioritises abstract notions of technological progress over the actual efficacy of automation, labour protection and social equality.

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Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 96%

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 99%

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 99%

Section: Analysing Automation Under Techno-developmentalismmentioning

confidence: 99%

See 2 more Smart Citations

Upgrading China through Automation: Manufacturers, Workers and the Techno-Developmental State

Lei

2021

Work, Employment and Society

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“…Using a survey-based approach, we also relate to a small but evolving set of papers that survey aspects of automation concerns (e.g., Dekker et al, 2017;McClure, 2018;Mulas-Granados et al, 2019;Rodriguez-Bustelo et al, 2020;or Gallego et al, 2022) and the implications of occupational risk exposure to automation for policy preferences (e.g., Mulas-Granados et al, 2019;Zhang, 2019;Thewissen and Rueda, 2019;Jeffrey, 2021;or Gallego et al, 2022). Compared to our paper, these surveys do not consider different types of automation angst (economy-wide, personal and distributional), their respective determinants, and associations with policy attitudes and individual labor-market strategies to cope with automation.…”

Section: Introductionmentioning

confidence: 99%

The End of Work is Near, Isn’t It? Survey Evidence on Automation Angst

2022

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Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work

Zhang

Niu

et al. 2023

Systems

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This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming.

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Automation, Skills and the Future of Work: What do Workers Think?

Cited by 3 publications

References 0 publications

Upgrading China through Automation: Manufacturers, Workers and the Techno-Developmental State

Upgrading China through Automation: Manufacturers, Workers and the Techno-Developmental State

The End of Work is Near, Isn’t It? Survey Evidence on Automation Angst

Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work

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