Is automation stealing manufacturing jobs? Evidence from South Africa’s apparel industry

Parschau, Christian; Hauge, Jostein

doi:10.1016/j.geoforum.2020.07.002

Cited by 38 publications

(44 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Particularly, the potential of automation technologies to increase productivity and facilitate reshoring and nearshoring is gaining growing attention both from policy and industry actors (ILO, 2019;Altenburg et al, 2020). However, many barriers still exist, both financial and technical, to the adoption of automation technologies in the apparel industry (Parschau & Hauge, 2020).…”

Section: Long-term Implications: Consolidation Reconfiguration and Drivers Of Competitivenessmentioning

confidence: 99%

“…(3) Supporting technology adoption and skills development to upgrade in the value chain. While there is a strong trend towards automation, recent studies suggest that a gap persists between what is technologically feasible and what can economically be implemented at scale in developing countries (Altenburg et al, 2020;Parschau & Hauge, 2020). This means that low wages are likely to remain a key determinant of investment decisions and that, by some estimates, developing countries have 10-15 years, perhaps longer, before the advent of fully automated production (Altenburg et al, 2020).…”

Section: Inclusive and Sustainable Apparel Value Chainmentioning

confidence: 99%

See 1 more Smart Citation

COVID‐19’s impacts on global value chains, as seen in the apparel industry

Navarrete

Hauge

López‐Gómez

2021

Development Policy Review

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Long-term Implications: Consolidation Reconfiguration and Drivers Of Competitivenessmentioning

confidence: 99%

Section: Inclusive and Sustainable Apparel Value Chainmentioning

confidence: 99%

COVID‐19’s impacts on global value chains, as seen in the apparel industry

Navarrete

Hauge

López‐Gómez

2021

Development Policy Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the adoption of AI-driven technologies is still low, its effect is yet to be materialized. Although job displacement caused by that AI-driven automation is seen in some industries, the overall effect on employment, either in advanced or in developing countries, is still inconclusive (Parschau and Hauge, 2020).…”

Section: Automation and Employmentmentioning

confidence: 99%

Existence of asymmetry between wages and automatable jobs: a quantile regression approach

Baigh

Yong

Cheong

2021

IJSE

View full text Add to dashboard Cite

PurposeThis study aims to explore, in the context of Machinery and Equipment sector of Malaysia, the association between average wages and share of employment in automatable jobs, specifically whether the association between average wages and share of employment automatable jobs is asymmetric in nature.Design/methodology/approachThe responses obtained from the structured interview of 265 firms are used to build up the empirical models (conditional mean regression and quantile regression).FindingsThe conditional mean regression findings show that employment levels in some low-waged, middle-skilled jobs are negatively associated with average wages. Furthermore, the quantile regression results add that firms that possess higher levels of share of employment in automation jobs are found to have a stronger association to average wages than those possessing a lower share of employment in automation jobs.Practical implicationsFrom the theoretical perspective, the findings of this study add to the body of knowledge of the theory of minimum wages and the concept of job polarization. From a policy perspective, the findings of this study can serve as a critical input to standard setters and regulators in devising industrial and as education policies.Originality/valueBased on the assumption of a constant average policy effect on automatable jobs, conditional mean regression models have been commonly used in prior studies. This study makes the first attempt to employ the quantile regression method to provide a deeper understanding of the relationship between wages and employment in automatable jobs.

show abstract

“…However, employee adaptability with Industry 4.0, extent of digital transformation of the organization and employee reward and recognition the mean scores were higher for the service sector compared to manufacturing organizations. Services are labour intensive, simultaneous production consumption, service induced variability and hence employees are key in success of services(Allmendinger & manufacturing organizations are automated compared to services(Frohm et al, 2008;Parschau & Hauge, 2020), therefore, respondents felt that in service extent of digital transformation of organization is important compared to manufacturing.…”

mentioning

confidence: 99%

Conceptualizing Industry 4.0 readiness model dimensions: an exploratory sequential mixed-method study

Antony

Sony

McDermott

2021

TQM

View full text Add to dashboard Cite

PurposeOrganizations use Industry 4.0 readiness models to evaluate their preparedness prior to the implementation of Industry 4.0. Though there are many studies on Industry 4.0 readiness models, the dimensions of readiness differ. Besides, there is no study empirically validating the readiness model in different sectors or types of organization. The purpose of this study is to conceptualize the dimensions of the Industry 4.0 readiness model and subsequently evaluate the criticality of these dimensions in manufacturing, service, small and medium-sized enterprises (SMEs) and large enterprises (LEs).Design/methodology/approachThe study uses an exploratory sequential mixed method design. In phase one, 37 senior managers participated through a purposive sampling frame. In phase two, 70 senior managers participated in an online survey.FindingsThe results of the study indicated that the Industry 4.0 readiness model has 10 dimensions. Further, the criticality of the dimensions as applied to different sectors and type of organizations is put forward. This study will help manufacturing, services, SMEs and LEs to evaluate Industry 4.0 readiness before commencing the deployment of Industry 4.0.Practical implicationsThe findings can be very beneficial for Industry 4.0 practitioners and senior managers in different organisations to understand what readiness dimensions need to be considered prior to implementation of Industry 4.0 technology.Originality/valueThis paper makes an attempt to conceptualize the Industry 4.0 readiness model and utilizes an exploratory mixed method for critically evaluating the dimensions related to the model.

show abstract

Is automation stealing manufacturing jobs? Evidence from South Africa’s apparel industry

Cited by 38 publications

References 38 publications

COVID‐19’s impacts on global value chains, as seen in the apparel industry

COVID‐19’s impacts on global value chains, as seen in the apparel industry

Existence of asymmetry between wages and automatable jobs: a quantile regression approach

Conceptualizing Industry 4.0 readiness model dimensions: an exploratory sequential mixed-method study

Contact Info

Product

Resources

About