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Ongoing automation processes may render a fair share of the existing jobs redundant or change their nature. This begs the question to what extent employees affected invest in training in order to strengthen their labour market position in times of uncertainty. Given the different national labour market regimes and institutions, there may be an important geographical dimension to the opportunities to cope with the challenges set by automation. The purpose of this study is to address both issues.

Using data from the 2016 European labour Force Survey, the authors estimate with logit and multi-level regression analyses how the automation risk of a worker's job is associated with the propensity of following non-formal education/training. The authors allow this relationship to vary across European countries.

The results show that employees in jobs vulnerable to automation invest relatively little in training. Also, there are significant differences across Europe in both the provision of training in general and the effect of automation on training provision.

While there is quite a lot of research on the structural labour market effects of automation, relatively little is known about the actions that employees take to deal with the uncertainty they are faced with. This article aims to contribute to our understanding of such mechanisms underlying the structural macro-level labour-market dynamics.

This study sought to examine the attitudes of blue-collar workers toward job automation. The study examined the relations between job automation, fear of job loss and self-actualization.

Using mixed methods (qualitative and quantitative analysis) with 539 participants overall, we examined employees' attitudes toward job automation through two separate studies conducted in a large public organization that employs blue-, white- and pink-collar employees. The blue-collar workers who participated consisted of waste collectors, gardeners and parking supervisors whose work is at risk of job automation.

We found that 74% of the blue-collar employees described technology as a “replacer” that simplifies and reduces human work activities, while only 3% perceived it as an “enabler” that could enrich their jobs and expand human potential. Fifty-three percent of the employees in the white-collar professions described technology as a “replacer,” and 36% perceived it as an “enabler.” Among pink-collar workers, 51% perceived technology as an “enabler,” while only 14% perceived it as a “replacer.” A positive relationship between job automation and self-actualization was evident for pink- and white-collar workers, but not for blue-collar workers.

This study sheds light on how employees in different types of jobs perceive technological advancements at work. A classification of the perception of technology as an “enabler” vs a “replacer” is presented. The relationships between job automation and self-actualization in different job types are explored.

Office automation as it is being introduced today aims “to automate office procedures”, as I.B.M. points out, not “merely to mechanize tasks”. It refers to the use of computer technology to process and transmit information, combining word and data processing. Automation also involves linking together today's many modern devices into “integrated office systems”. Because new technology is being developed to computerize the very flow of work in the office, its potential impact is qualitatively different from previous office equipment which “mechanized” or “automated” routine tasks.

The introduction of an automated library system brings changes in the working environment. If job design within the altered environment is not considered, the result can be employee apathy/hostility and the use of the system in unplanned ways. The idea of ‘good’ job design takes into account the needs of the individual as well as the organisational objectives. Automation also affects staff structures. This paper examines, by means of a literature survey carried out for a master's dissertation, the need for, and principles of, job design and the effects automation can have, and discusses the possible influences on staff structures.

This 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.

The responses obtained from the structured interview of 265 firms are used to build up the empirical models (conditional mean regression and quantile regression).

The 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.

From 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.

Based 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.

The purpose of this paper is to analyze whether specific jobs characteristics, which experts have identified as being more automation proof, are associated with reduced job insecurity.

Data come from a recent survey providing information on sources of job insecurity as well as on detailed job characteristics. The analysis is based on various regression models.

People who have jobs that involve lots of personal interaction are less likely to be concerned about losing their job because of automation, or because of other reasons, and are more likely to think their job will exist 50 years from now. Having a creative job does not change these concerns. The share of respondents who fear losing their job to automation is fairly small, and those who do, typically fear other sources of job insecurity as much or even more.

Developing interpersonal skills is more likely to be an effective strategy for reducing job insecurity than developing creative skills. The findings further suggest that policies aimed at automation are unlikely to suffice for the elimination of worry over job loss, as many workers who fear automation at the same time feel there are other reasons that might lead to the loss of their job.

There are very few studies that link fear of losing one’s job to automation to a job’s characteristics. The survey used here is unique in the level of detail provided on job characteristics.

The advent of extreme automation from new technologies such as artificial intelligence portends a massive increase in unemployment. The psychological impact of this threat on the workforce is critically important. This paper aims to examine the functioning of individuals' anxiety buffers in response to this threat.

A two-stage mixed-methods design is used. In stage 1, qualitative data are gathered through semi-structured interviews. In stage 2, quantitative data are collected through two experiments to assess the psychological impact of exposure to the threat.

Exposure to the threat of extreme automation reduces self-esteem, faith in the worldview and attachment security. When self-esteem and attachment security are under attack, they are ineffective as anxiety buffers, and anxiety levels increase. Additionally, there is a distal effect such that during a period of distraction, the threatened anxiety buffers are reinforced and return to their normal levels.

This study is limited to a homogenous culture in which work is highly salient. Future research should include other cultures, other methods of exposure and further examine the distal effects.

The study examines the previously underexplored issue of individuals' psychological response to the impending changes in the workforce because of technological advancements.

This study aims to estimate the value of the impact from digital transformation (DX) focusing on its automation effect, looking at the time and cost savings coming from the substitution effect with an adoption of digital technologies. For example, cloud and artificial intelligence technologies such as ChatGPT have the potential to change ways of working, substituting and replacing several of the tasks that are currently carried out by public administration (PA) employees and labor processes underpinning PA services.

The paper outlines a new framework to estimate the potential impact of DX on the public sector. The authors apply this framework to estimate the value of the impact of DX on the Italian PA, defining the latter by the collection of the value of its labor (i.e. PA workforce salaries) and by the collection of the value of its outputs (i.e. public services’ costs).

This study ultimately maps out the magnitude and trends of how likely the PA occupations and services could be substituted in a wider process of DX. To do this, the authors apply their framework to the Italian PA, and they triangulate secondary data collection, from official accounts of the Italian Ministry of Economics and the National Statistical Institute, with methodological antecedents from the UK Office for National Statistics and experts’ insights. Results provide a snapshot on the type and magnitude of PA jobs and services projected to be affected by automation over the next 10 years.

To the best of the authors’ knowledge, this paper provides for the first time an approach to estimate the value of the impact of DX on the public sector in a data-constrained environment – or in the lack of the required primary data. Once applied to the Italian PA, this approach provides a granular map of the automatability of each of the PA occupations and of the PA services. Finally, this paper mentions preliminary insights on potential challenges related to equity in public sector jobs and implications on recruitment processes.

This paper aims to evaluate the impact of automation on job displacement and reshoring in the apparel industry. It also compares with predictions on the same subject matter by the existing literature and, thus, provides future research agenda for further studies.

Primary data were collected through 27 semi-structured in-depth interviews. The grounded theory was used for thematic and network analyzes, which traced the drivers and barriers, as well as the impact of automation and reshoring.

Initially, automation decreases human interactions in any specific production section. However, it increases productivity, quality and cost advantages, which invoke growth and further employment in clothing firms. The employment of unskilled workers decreases in the long run when automation is well adopted in the system. Automation does not stimulate reshoring but may support relocation initiatives of production sites around the centers of global value chains (GVCs). This GVC-based relocation may create job displacement in apparel manufacturing nations in Asia while bringing employment opportunities to Sub-Saharan African countries, Europe and North America.

Little empirical research has been conducted on the impact of automation on the apparel industry. This study predicts that human interventions will dominate the sewing of fashionable and sophisticated apparel products while automation may replace many human workers for basic garment items in the foreseeable future.

In the wake of the COVID-19 pandemic organisations are adapting to a new environment of global talent shortages, economic uncertainty and geo-political turmoil. As an outcome, the organisational strategies of digital transformation and remote working have been accelerated in the race to boost innovation, competitivity and attract staff. This has led to the rise of two new organisational dynamics: the increase of virtual teams (VTs) and focus on widespread work automation. However, despite the rise of these two related phenomena, literature does not connect them as one research area, and there is a gap in the understanding of the new employee wellbeing needs they form and how to respond to them. This paper aims to bridge this gap through a systematic literature across these areas.

This paper conducts a systematic literature review across the areas of leadership, VTs and automation over the past three years.

In this review, a number of newly arising employee wellbeing needs are identified such as fear of job displacement, a lack of self-efficacy and social cohesion, poor relationships with leaders and more. In addition, this paper recommends three fundamental research gaps to be addressed by future studies: 1. How to build and cultivate the new leadership skills needed to support VTs and workplace automation? 2. How to design work in a way that caters for employee wellbeing needs when operating in VTs or hybrid teams and working on or with workplace automation? 3. How to design work in a way that builds and emphasises the new employee skillsets to support augmentation and solves for the new employee wellbeing needs experienced by workplace automation?

This paper provides a novel contribution to literature by centralising current schools of thought across the cross-disciplinary themes and synthesising literature to recommend new wellbeing and leadership skills for organisations to focus on, alongside producing a new research agenda for scholars to focus.