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Disruptive technologies often disrupt the careers of middle-skilled workers. The purpose of this paper is to investigate career transition strategies of middle-skilled workers that partially continue or expand their careers under the condition of disruptive technologies.

This paper established a conceptual framework of career transition strategies for middle-skilled workers by integrating the existing studies of disruptive technologies, technological trajectory transition, boundaryless and protean careers, and careers as repositories of knowledge.

The authors proposed three types of career transition strategies to partially prolong middle-skilled workers’ careers, namely, industry-oriented transition strategy which refers to a transfer to other occupations in the original industry, technology-oriented transition strategy which refers to a transfer to occupations with original technical skills in other industries, and comprehensive transition strategy which refers to a transfer to other occupations in the related industries. Further, this paper discusses the external conditions and individual competencies for each career transition strategy, and timing for implementing a career transition strategy from the perspective of the technology life cycle.

This paper focused on sustainable careers of middle-skilled workers under the condition of disruptive technologies, which received very little attention from the current literature. The findings also suggested for middle-skilled workers to develop a sustainable or long-term career in the current era of many disruptive technologies. The findings may also imply on how firms and government should contribute to help workers on handling scenarios of technological disruption.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Middle-skilled workers are the ones most likely to suffer due to disruptive technologies. This briefing considers the impact of disruptive technology, and looks at possible strategies for helping those workers find new employment.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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.

This study aims to empirically analyze the impact of technological innovation on the quantity and quality of employment in the hospitality industry.

Using the data of 30 provinces in China from 2010 to 2020, this paper makes an empirical analysis through the fixed effect model.

The results show that process innovation has a significant positive impact on employment quantity, while product innovation has a significant negative impact on employment quantity. The creative effect of process innovation and the substitution effect of product innovation offset each other, so in the long run, the impact of technological innovation on employment quantity is not significant. However, technological innovation has significantly improved the employment quality of the hospitality industry.

Because technological innovation has replaced part of the labor force, hospitality could guide the labor force in a positive direction. To promote innovation and retain talents, hotels should train employees’ digital thinking and attract high-skilled talents.

This research is unique in using process innovation and product innovation as the main measurement indicators of technological innovation, unlike previous studies that often relied on technological progress to conclude.

The purpose of this paper is to investigate which sectors are more vulnerable to human capital depreciation, with an emphasis on potential differences in skills and in ICT intensities.

The authors estimate an extended Mincerian earnings equation based on Neuman and Weiss’s (1995) model using the EU-KLEMS international database for 15 sectors for the period from 1980 to 2005. The authors also test structural ruptures in earnings and human capital depreciation in the labor market per decade controlling by technological intensity.

Human capital depreciation ranges from 1 to 6 percent. It is mainly significant in skill-intensive sectors regardless of the sector’s technological intensity. The analysis of structural breaks shows that human capital value indeed changed from decade to decade. It even appreciated in low skill-intensive sectors in the 1980s and in the high skill-intensive during the 1990s. Appreciation though, was mainly skill-biased.

Information about on-the-job-training and non-cognitive skills that can also affect human capital depreciation are not included due to lack of data.

To prevent human capital from depreciating in particular sectors and periods educational systems should provide the tools for ongoing lifelong learning at all skills levels. Education is subject to dynamic effects that should be addressed to increase the potential benefits of technological change.

First, instead of using cross-section analysis which is considered to be a pitfall in studying the depreciation of knowledge, the authors observe its dynamic on a longitudinal basis. Second, the international macro-sectoral approach goes beyond limited micro-sectoral analysis in certain countries.

The purpose of this paper is to analyze the impact of interstate migration of individuals with different qualification levels on human capital formation in the migrant’s place of origin.

A dynamic panel model with data from the National Household Sample Survey (Pesquisa Nacional por Amostra de Domicílios (PNAD)), between 2001 and 2013, is used.

The results indicate that the migration of high-skilled people boosts school attendance in fifth grade elementary school and first year high school, but it does not affect the levels of those entering first year in higher education. However, the migration of low-skilled workers discourages people from entering higher education, as those living in less developed areas do not need higher education qualifications to get higher incomes. Thus, they migrate to developed areas with the education levels they already have. The brain gain hypothesis is not, therefore, confirmed in the context of higher education attendance.

This paper’s contribution is its investigation into the effect of interstate migration on human capital formation in Brazil, through testing the brain gain hypothesis in a national context. In addition, it also analyzes the impact of the migration of people of low and intermediate qualification levels on human capital, with a view to verifying if the mobility of people with other levels of qualification could discourage the formation of human capital.

The purpose of this paper is to investigate self-competence—the ability to act responsibly on one's own—and likely nonlinear wage returns across different levels of self-competence as part of training curricula.

The authors identify the teaching of self-competence at the occupational level by applying machine-learning methods to the texts of occupational training curricula. Defining three levels of self-competence (high, medium, and low) and using individual labor market data, the authors examine nonlinearities in wage returns to different levels of self-competence.

The authors find nonlinear returns to teaching self-competence: a medium level of self-competence taught in an occupation has the largest wage returns compared to low or high levels. However, in occupations with a high cognitive requirement profile, a high level of self-competence generates positive wage returns.

This paper first adds to research on the importance of teaching noncognitive skills for economic outcomes, which recently—in addition to personality traits research—has primarily focused on social skills by introducing self-competence as another largely unexplored but important noncognitive skill. Second, the paper studies not only average but also nonlinear wage returns, showing that the right level of self-competence is crucial, i.e. neither teaching too little nor too much self-competence provides favorable returns because of trade-offs with other skills (e.g. technical or professional skills). Third, the paper also examines complementarities between cognitive skills and noncognitive skills, again pointing toward nonlinear returns, i.e. only in occupations with a high cognitive requirement profile, high levels of self-competence generate positive wage returns.

This paper aims to provide a sobering and unique view of technological unemployment and job changes by identifying endangered jobs and skills, as well as the essential up-skills critical to employees’ performance, which cannot be replaced by technology.

A systematic literature review, used because it is replicable, transparent, and scientific, was implemented to examine the current and future technological influences on employment, job outlook, work structure, and human resource development (HRD).

The study concludes that HRD professionals should promptly reexamine their social responsibility relative to the technological influence on workers by focusing developmental efforts on employees’ human skills while assisting workers’ transition to a skill-polarized workplace. HRD professionals should play a major role in facilitating employees’ coexistence with robots in the workplace.

While recognizing the valuable contributions of previous researchers with similar concerns, this comprehensive review provides an amalgamated and updated view, which reveals the escalating and combined challenges of a skill-polarized workplace, a tendency of technological unemployment for those positioned in middle-skill jobs, and an increased demand for employees with a higher level of human skills.

The purpose of this paper is to examine the likely impact of AI robotics technology on the labor market through the lens of comparative advantage.

The first section reviews the recent success of AI in outperforming humans in cognitive intense activities such as Go, poker and other strategic games, which portends a shift in comparative advantage in human brain power work to machines. It notes the potential for a portfolio of specialized computer algorithms to compete with human general intelligence in work. The analysis contributes to the debate between economists dubious about claims that AI robotics will disrupt work and futurists who expect many jobs to be fully automated in coming years. It advances three “laws of robo-economics” to guide thinking about the new technologies and presents evidence that growing robot intensity has begun to impact the job market.

The paper finds that the case for AI robotics substantially changing the world of work and the distribution of income is more compelling than the case that it will have similar impacts on wages and employment as past technological changes. It advances an ownership solution to spread the benefits of AI robot-driven automation widely.

To the extent that who owns the robots rules the world, it argues for a concerted social effort to widen the “who” in ownership from the few to the many. It reviews policies to expand employee ownership of their own firm and of the stream of revenue via profit-sharing and gain-sharing bonuses. But the paper notes that ensuring that growth of AI robotics benefits all through ownership will require expansion of workers’ and citizens’ stake in business broadly, through collective investment via pension funds, individual investment in mutual funds and development of sovereign wealth funds.