Analysing Labour-Based Estimates of Automation and Their Implications. A Comparative Approach from an Economic Competitiveness Perspective

IF 4.4 1区 管理学 Q2 BUSINESS
Adrian Oţoiu, E. Țițan, D. Paraschiv, Vasile Dinu, D. Manea
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Abstract

Given the advent of Industry 4.0 and the importance of labour-based automation in ensuring competitiveness at the firm, regional cluster, or country level, the paper aims to explore, for the first time, the features of several estimates of occupational/labour automation and to assess the potential risks associated with it. A comparative analysis of the most well-established estimates of labour automation, the Occupational Information Network (O*NET) degree of automation estimates and Frey and Osborne’s future probabilities of automation was carried out to see whether, and to what extent, these estimates are compatible. Results show significant distributional differences between them, which are quantified into automation-triggered disruption risks at the occupational level, as current levels of labour automation are, in some cases, well below their future estimates. Work context features were used to derive a typology of occupations, which can explain up to one-third of the current, and up to half of the future levels of labour automation. Finally, we identified which occupations and occupational groups are likely to be affected by the highest risk of automation-induced displacement and estimated the magnitude of different disruption classes. Conclusions are compatible with other economywide assessments of the impact of labour automation on the workforce, thus being valuable inputs for corporate strategy, decision-makers and human resource planners as they address a growing need for quantitative insights useful for adapting the labour force structure, workers’ skills, and the task content of occupations to the competitiveness requirements related to the process of digitization in the Industry 4.0 context.
分析基于劳动力的自动化估计及其影响。经济竞争力视角下的比较研究
鉴于工业4.0的出现以及以劳动力为基础的自动化在确保公司、区域集群或国家层面的竞争力方面的重要性,本文旨在首次探讨职业/劳动力自动化的几种估计的特征,并评估与之相关的潜在风险。对最完善的劳动自动化估计,职业信息网络(O*NET)自动化程度估计和Frey和Osborne的未来自动化概率进行了比较分析,以查看这些估计是否兼容,以及在多大程度上兼容。结果显示它们之间存在显著的分布差异,这些差异被量化为自动化引发的职业层面的破坏风险,因为在某些情况下,当前的劳动自动化水平远低于他们未来的估计。工作环境特征被用来推导职业类型,这可以解释多达三分之一的当前和多达一半的未来劳动自动化水平。最后,我们确定了哪些职业和职业群体可能受到自动化导致的位移的最高风险的影响,并估计了不同破坏类别的程度。结论与劳动力自动化对劳动力影响的其他经济范围的评估相一致,因此对于企业战略、决策者和人力资源规划者来说是有价值的投入,因为他们解决了对定量见解的日益增长的需求,这些见解有助于调整劳动力结构、工人技能和职业任务内容,以适应与工业4.0背景下数字化进程相关的竞争力要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.30
自引率
2.70%
发文量
33
审稿时长
12 weeks
期刊介绍: The Journal of Competitiveness, a scientific periodical published by the Faculty of Management and Economics of Tomas Bata University in Zlín in collaboration with publishing partners, presents the findings of basic and applied economic research conducted by both domestic and international scholars in the English language. Focusing on economics, finance, and management, the Journal of Competitiveness is dedicated to publishing original scientific articles. Published four times a year in both print and electronic formats, the journal follows a rigorous peer-review process with each contribution reviewed by two independent reviewers. Only scientific articles are considered for publication, while other types of papers such as informative articles, editorial materials, corrections, abstracts, or résumés are not included.
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