A design science approach to developing and evaluating items for the assessment of transversal professional competences

PurposeRelying on a design science paradigm, the purpose of this paper is to describe the development and evaluation of items for an ICT artefact that supports the assessment of transversal professional competences within the validation of prior learning (VPL). To do so, the authors build a conceptual bridge between the Occupational Information Network (O*NET) and the European Qualifications Framework (EQF).Design/methodology/approachDesign science research paradigm, in particular the participatory development of candidate items and their evaluation in a multi-stakeholder approach.FindingsThe authors find that a self-assessment of professional competences should be comprised of 160 items in order to cover the breadth and depth of the O*NET in the hierarchical taxonomy. Such quantity of items sufficiently builds a conceptual bridge between the O*NET and the; EQF.Practical implicationsWhen designing procedures for the VPL, it is imperative to bear in mind the purpose of the validation procedure, in order to determine relevant stakeholders and their needs in advance as well as the; required language proficiency of the assessment instrument.Social implicationsThe innovative value of this approach lies in the combination of an underlying hierarchical taxonomy with assessment items that are developed based on the qualification standards of different Austrian professions. Together with specific verbs that were adapted for each particular item, an innovative self-assessment is proposed. Thereby the authors aim to account for some of the mentioned shortcomings of the EQF.Originality/valueThis paper applies a design science paradigm to develop an ICT artefact that should support the VPL. By reflecting on the design process, the authors introduce a theoretical bridge between the O*NET and the EQF. Thereby the authors aim to account for some of the mentioned shortcomings of the EQF.

The Role of Occupational Switching on Retirement of American Midlife Workers

An increasing number of midlife Americans are financially unprepared for retirement. This is a problem because of the increasing life expectancy that prolongs the need for financial resources. One way to resolve this problem is to postpone full retirement by having bridge jobs that provide more time to work and accumulate retirement savings. While having a bridge job means numerous labor market behavior such as working for different employers and reducing work hours and intensity, there is a limited number of studies focused specifically on how switching occupations can contribute to retirement decisions with a longer time frame. This study investigated the association between occupational switching and retirement patterns of American midlife workers aged between 50 to 59 years using the Health and Retirement Study longitudinal data from 2004 to 2016, Occupational Information Network data, and American Community Survey from 2003 to 2016. The changes in occupational demandingness before and after switching occupations were reflected by using mover design event study regression with fixed effects. In general, occupational switching is associated with later retirement until two to three years after switching occupations for both genders, yet this association varies by the directions of the change in occupational demandingness.

Building and Using Data Resources for Research on Job Characteristics, Health, and Retirement

A growing literature seeks to understand the relationship between the experience of work and important later-life outcomes. Rich longitudinal measurement of both sides of this equation in datasets such as the Health and Retirement Study (HRS) have made this research possible. These data take the form of self-reported experiences of work (such as physical demands, job flexibility, job satisfaction etc.). Increasingly, researchers are looking to add potentially complementary information on the work environment available in the Occupational Information Network (O*NET) database through a linkage using occupation and industry codes in the survey data. The session talks will describe research conducted using O*NET linked with HRS data as well as ongoing work to create a new data resource that will allow other researchers to undertake research with O*NET-HRS linked data. Each presentation will include some discussion of both the value and limits of using the linkage to O*NET. Carpenter will provide a detailed description a new project linking the 2019 O*NET data to the HRS for public use.This presentation explains the types of variables that will be made available in the O*NET-HRS occupation project and will provide examples for how the measures can be used in longitudinal HRS studies. Using O*NET-HRS linked data, Carr will present on work examining the role of preretirement job complexity in alternative retirement paths and cognitive performance. Helppie-McFall will used the linked data to discuss the role of mismatch between demands of work and workers’ ability to meet those demands in retirement decisions.

Stereotypical Descriptions of Computer Science Careers Are Not Representative of Most Computer Scientists

Using data from a large self-initiated online survey, we find that the career interests of many current and aspiring computer scientists diverge from the official profile of computer scientists established and promoted by the U.S. government – specifically that from the Department of Labor’s Occupational Information Network (O*NET). Five distinct profiles of career interests emerged from the data. Latent profile analysis suggests that many women in the profession value social and artistic expression in a way not currently recognized by official representations of computer scientists’ interests. Better admitting to a more nuanced and comprehensive picture of those interests has important implications for career guidance and workforce development and might help to address women’s underrepresentation in this STEM discipline.

A network analysis of cross-occupational skill transferability for the hospitality industry

Purpose This paper aims to examine transferable skills and viable career transition pathways for hospitality and tourism workers. Future career prospects are discussed, along with the importance of reskilling for low-wage hospitality workers. Design/methodology/approach A network analysis is conducted to model skill relationships between the hospitality industry and other industries such as health-care and information technology. Multiple data are used in the analysis, including data from the US Department of Labor Occupational Information Network (O*NET), wage data from the Bureau of Labor Statistics and job computerization data (Frey and Osborne, 2017). Findings Although hospitality workers have lower than average skills scores when compared to workers from other career clusters included in the analysis, they possess essential soft skills that are valuable in other industries. Therefore, improving hospitality workers’ existing soft skills may help them enhance their cross-sector mobility, which may allow them to obtain jobs with a lower likelihood of computerization. Practical implications The findings shed light on workforce development theories and practice in the hospitality industry by quantitatively analyzing cross-sector skill correlations. Sharpening transferable soft skills will be essential to enhancing hospitality workers’ career development opportunities. Originality/value To the best of the authors’ knowledge, this is the first study that specifically examines the skill taxonomy for the hospitality industry and identifies its connection with other in-demand career clusters.

Automation in Colombia: assessing skills needed for the future of work

PurposeThe purpose of this paper is to identify workforce skills that protect an occupation from elimination due to automation technology.Design/methodology/approachThe authors apply a Gaussian process (GP) classifier, based on the level of non-automatable work activities in an occupation, to USA and Colombian occupational datasets.FindingsThe authors find that communication, interpersonal relationship management and decision-making skills are most important in occupations that are resistant to automation.Research limitations/implicationsThe results are based on work activities data from the Occupational Information Network (O*NET) database developed for the USA labor market. This dataset does not capture significant differences in work activities, where they exist, for the same occupation between the two countries. The findings are also limited to Colombia. Readers should be careful to extrapolate the findings outside of this geography.Originality/valueThe authors discover that automation is likely to be a global phenomenon that can only be slightly mitigated by cultural and political factors.

Racial and ethnic differentials in COVID-19-related job exposures by occupational standing in the US

Researchers and journalists have argued that work-related factors may be partly responsible for disproportionate COVID-19 infection and death rates among vulnerable groups. We evaluate these issues by describing racial and ethnic differences in the likelihood of work-related exposure to COVID-19. We extend previous studies by considering 12 racial and ethnic groups and five types of potential occupational exposure to the virus: exposure to infection, physical proximity to others, face-to-face discussions, interactions with external customers and the public, and working indoors. Most importantly, we stratify our results by occupational standing, defined as the proportion of workers within each occupation with at least some college education. This measure serves as a proxy for whether workplaces and workers employ COVID-19-related risk reduction strategies. We use the 2018 American Community Survey to identify recent workers by occupation, and link 409 occupations to information on work context from the Occupational Information Network to identify potential COVID-related risk factors. We then examine the racial/ethnic distribution of all frontline workers and frontline workers at highest potential risk of COVID-19, by occupational standing and by sex. The results indicate that, contrary to expectation, White frontline workers are often overrepresented in high-risk jobs while Black and Latino frontline workers are generally underrepresented in these jobs. However, disaggregation of the results by occupational standing shows that, in contrast to Whites and several Asian groups, Latino and Black frontline workers are overrepresented in lower standing occupations overall and in lower standing occupations associated with high risk, and thus may be less likely to have adequate COVID-19 protections. Our findings suggest that greater work exposures likely contribute to a higher prevalence of COVID-19 among Latino and Black adults and underscore the need for measures to reduce potential exposure for workers in low standing occupations and for the development of programs outside the workplace.

Developing China’s workforce skill taxonomy reveals extent of labor market polarization

China, the world’s second largest economy, is transitioning into an advanced, knowledge-based economy after four decades of rapid economic development. However, China still lacks a detailed understanding of the skills that underly the Chinese labor force, and the development and spatial distribution of these skills. Similar data has proven essential in other contexts; for example, the US standardized skill taxonomy, Occupational Information Network (O*NET), played an important role in understanding the dynamics of manufacturing and knowledge-based work, and the potential risks from automation and outsourcing. Here, we use Machine Learning techniques to bridge this gap, creating China’s first workforce skill taxonomy, and map it to O*NET. This enables us to reveal workforce skill polarization into social-cognitive skills and sensory-physical skills, and to explore China’s regional inequality in light of workforce skills, and compare it to traditional metrics such as education. We build an online tool for the public and policy makers to explore the skill taxonomy: skills.sysu.edu.cn. We also make the taxonomy dataset publicly available for other researchers.

Connecting O*Net® Database to Cybersecurity Workforce Professional Certifications

The Occupational Information Network O*NET is considered the primary source of occupational information in the U.S. I explore here possible uses of O*NET data to inform cybersecurity workforce readiness certification programs. The O*NET database is used to map out education requirements and how they relate to professional certifications as required by employers and job designers in accordance with the National Initiative for Cybersecurity Careers and Studies (NICCS). The search focuses on the “Information Security Analysts” occupation as listed on O*NET, Careeronestop, U.S. Bureau of Labor Statistics (BLS), and finally tied back to NICCS source work role to identify certifications requirements. I found that no site has listed any certification as required, desirable or mandatory. NICCS offered general guidance to potential topics and areas of certification. Careeronestop site provided the ultimate guidance for this role certification. Professional certifications are still not integrated in the Cybersecurity Workforce Framework official guidance.

Dream Jobs and Employment Realities: How Adolescents’ Career Aspirations Compare to Labor Demands and Automation Risks

Despite a rapidly changing labor market, little is known about how youth’s career goals correspond to projections about the future of work. This research examined the career aspirations of 3,367 adolescents (age 13–18 years) from 42 U.S. states. We conducted a large-scale coding effort using the Occupational Information Network (O*NET) to compile the vocational interests, educational requirements, and automation risk levels of career aspirations. Results revealed that most adolescents aspired to careers with low potential for automation. However, there were large discrepancies between the sample’s aspirations and the types of jobs available when the sample entered the workforce. Almost 50% of adolescents aspired to either an investigative or artistic career, which together account for only 8% of the U.S. labor market. There were also notable trends across age and gender, such that aspirations were more gendered among younger adolescents, whereas older adolescents appeared less influenced by gender stereotypes. Overall, findings indicate important discrepancies between young people’s dream jobs and employment realities. We discuss how lofty career aspirations can have both positive and negative effects, and we present implications for career theories and workforce development initiatives aimed at promoting a more dynamic future workforce.