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The purpose of this paper is to propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job-seeker qualifications and skills, against the vacancy provided by employers or job-agents.

The paper presents a framework of bidirectional jobseeker-to-vacancy matching system. Using occupational data from various sources such as the WageIndicator web survey, International Standard Classification of Occupations, European Skills, Competences, Qualifications, and Occupations as well as vacancy data from various open access internet sources and job seekers information from social networking sites, the authors apply machine learning techniques for bidirectional matching of job vacancies and occupational standards to enhance the contents of job vacancies and job seekers profiles. The authors also apply bidirectional matching of job seeker profiles and vacancies, i.e., semantic matching vacancies to job seekers and vice versa in the individual level. Moreover, data from occupational standards and social networks were utilized to enhance the relevance (i.e. degree of similarity) of job vacancies and job seekers, respectively.

The paper provides empirical insights of increase in job vacancy advertisements on the selected jobs – Internet of Things – with respect to other job vacancies, and identifies the evolution of job profiles and its effect on job vacancies announcements in the era of Industry 4.0. In addition, the paper shows the gap between job seeker interests and available jobs in the selected job area.

Due to limited data about jobseekers, the research results may not guarantee high quality of recommendation and maturity of matching results. Therefore, further research is required to test if the proposed system works for other domains as well as more diverse data sets.

The paper demonstrates how online jobseeker-to-vacancy matching can be improved by use of semantic technology and the integration of occupational standards, web survey data, and social networking data into user profile collection and matching.

The authors provide a novel interpretation of the relationship between skill demand and labour market concentration based on the training rationale.

The authors use a novel data set on Italian online job vacancies during 2013–2018 to analyse the relationship between labour market concentration and employers' skill demand. The authors construct measures of market concentration and skill intensity in the local labour market. The authors regress the measures of skill demand on market concentration, controlling for sector, occupations and other features of the labour market. The authors also use the Hausman–Nevo instrument for market concentration.

The authors show that employers in a highly concentrated labour market demand competencies associated with the ability of workers to learn faster (e.g. social skills) rather than actual knowledge. They also require less experience but higher education. These results are consistent with the hypothesis that employers in more concentrated labour markets are more prone to train their employees. Instead of looking for workers who already have job-specific skills, they look for workers who can acquire them faster and efficiently. The authors provide a theoretical framework within which to analyse these aspects as well as providing a test for the relevant hypotheses.

In addition to cross-countries differences in labour market regulations, the authors' findings suggest that policy authorities should consider the local labour market structure when studying workforce development programmes aimed at bridging the skill gap of displaced workers. Moreover, the authors show that market concentration can have relevant implications for human resource (HR) managers by affecting their recruitment behaviour through the demand for skills. In fact, concentrated markets tend to favour firms' collusion and anti-competitive behaviour that could strongly affect HR management practices.

The authors' paper innovates on the literature in a number of ways. First, the authors provide evidence of local labour market concentration in Italy. Second, the authors provide evidence of skill demand at the local level using a detailed skill taxonomy that goes beyond the classical distinction between high and low skills. Third, and most importantly, the authors provide evidence of the relationship between skill demand and labour market concentration. By analysing detailed skills and competencies, the authors take one step beyond understanding the features of labour demand in monopsonistic markets.

The purpose of this paper is to learn more about demand for competences is crucial for revealing the complex relationship between employee selection, different strands of education and training and labor market regulation.

Content analysis and statistics of job advertisements.

Employer skills requirements even for low- and medium-skilled jobs are highly specific. Formal education requirements are higher than they “should” be. No detectable “basic package” of general cognitive skills for low- and medium-skilled jobs was found. Employer demand focusses on non-cognitive skills and specific cognitive skills. Specificity of skill requirement across different sectors or occupation groups differs vastly between different types of low- and medium-skilled jobs and is linked to the interactive nature of the job, not to the qualifications or the experience required.

The analysis can be considered an initial feasibility test for a larger comparative cross-country project that would aim to understand labor demand in different EU countries.

The analysis could be used as input in designing labor market policy and life-long learning programs to integrate low-skilled and unemployed.

The research provides a tool to match disadvantaged workers to jobs for which they possess greater capabilities or to help them develop crucial skills for a given occupation.

This paper contributes to the HRM literature with a more demand-led approach to labor market policy. The authors reveal what role skills and upskilling can play in alleviating the problem of unemployment. The results can be useful for HR specialists and policy makers.

The purpose of this paper is to assess field experiments of labour and product markets that have attached photos to identify applicants (in the case of labour markets) or sellers/crowd funders (in the case of product markets).

The experiments seek to identify the contribution of attractiveness, race/ethnicity, skin colour, sexual orientation or religion to the behaviour of agents in markets. These experiments attach photos to CV to signal attractiveness, or the basis of being tested such as race/ethnicity, skin colour or religion.

Many experiments report significant findings for the impact of attractiveness or the identity revealed on positive callbacks to applicants.

The issue considered here, however, is to what extent it is attractiveness or other perceived characteristics that may have had an impact on the behaviour recorded in the experiments. The results of the studies covered in this paper, to a lesser extent those of Weichselbaumer (2004) and Baert (2017), are compromised by including photos, with the possibility the responses received were influenced not only by the basis being tested such as attractiveness, race/ethnicity or religion but by some other characteristic unintended by the researcher but conveyed by the photo.

There is evidence in the experimental work of a range of characteristics that photos convey of individuals and their impact on labour and product market outcomes such as success in obtaining a positive response to job applications and success in obtaining funding to finance projects in the product market. Suggestions are made for future experiments: evaluation of photos for a range of characteristics; use of a “no photo” application together with the photo applications; and evaluation of responses for any bias from unobservable characteristics using Neumark (2012).

This paper discusses for the first time three questions with some tentative answers. First, the researcher faces introducing further unobservable characteristics by using photos. Second, the researcher cannot fully control the experimental approach when using photos. Third, the researcher is able to accurately evaluate the impact of the photos used on the response/probability of call back. Field experiments using photos need to ensure they do this for the range of factors that have been shown to affect judgments and therefore potentially influence call back response. However, the issue remains whether the researcher has, in fact, identified all potential characteristics conveyed by the photos.

This study aims to investigate the relationship between the demand on “soft” skills and suggested salaries for IT specializations in Russia.

Based on the database of vacancies, econometric modeling and cluster analysis of job occupations are implemented.

The results show positive association between demand for “soft” skills and wage if the model is controlled for the working experience and narrow professional occupations. Findings provide evidence that there is significant wage premium for “soft” skills in cases when job positions either imply no experience or require specialists with at least three years of tenure.

This research provides new evidence on the relationship between “soft” skills and wage using job postings data from Russia. This paper identifies the presence of wage premium for “soft” skills among IT specialists if controlling for sub-specializations, year, region and working experience. The robustness checks indicate no significant changes in the obtained results.

Based on the legitimacy theory, this study sought to explore specific Green Human Resource Management Practices implemented by manufacturing firms in Ghana.

The study purposively sourced data from five manufacturing firms operating in Ghana, specifically, Cocoa Processing Company, Dannex Aryton Starwin PLC, Fan Milk Limited, Guinness Ghana Breweries Limited and Unilever Ghana PLC. The qualitative data from 10 interviewees were thematically analysed.

Pervasively, it was evident that Ghanaian manufacturing firms have integrated environmental concerns into their human resource management functions, hence they place more emphasis on online tools and platforms in attracting, selecting, involving, training, paying and managing the performance of employees. Lastly, manufacturing firms operating in Ghana give priority to online job applications ahead of manual applications.

In an era of sustainability, green human resource management practices provide a basis for the legitimation and sustenance of businesses through enhanced environmental performance and its resultant corporate image.

The study highlights the extent to which green human resource management is practised in a Sub-Saharan African country and the specific practices adopted in promoting the concept.

The implementation of BIM in the construction industry requires the coevolution of the various aspects of the BIM ecosystem. The human dimension is a very important dimension of the ecosystem necessary for BIM implementation. It is imperative to study this aspect of the BIM ecosystem both from the employer perspective and employee availability to provide insights for stakeholders (job seekers, employers, students, researchers, policymakers, higher education institutions, career advisors and curriculum developers) interested in the labour market dynamics.

To understand the BIM actor roles through the employer lens and the actual BIM actors in the construction industry, this study employed data mining of job adverts from LinkedIn and Mncjobs website. Content analysis was employed to gain insights into the data collected. Also, through a quantitative approach, the existing BIM actor roles were identified.

The study identified the employers' expectations of BIM actors; however, it is noted that the BIM actor recruitment space is still a loose one as recruiters put out open advertisements to get a large pool of applicants. From the data analysed, it is concluded that the BIM actor role is not an entirely new profession. However, it simply exists as construction industry professionals with BIM tool skills. Also, the professional development route is not well defined yet.

This study presents a realistic angle to BIM actor roles hence enhancing BIM implementation from the human perspective. The findings present an insight into the preferred against the actual.

This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.

Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.

This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.

This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

The purpose of this study is to discover the factors related to human resource (HR) digital transformation (DT) in Bahrain during coronavirus disease 2019 (COVID-19) and to analyze the impact of e-human resource management (e-HRM) on organizational performance. These factors are funding, information technology (IT) infrastructure, technical support, digital skills or talents, organizational culture, employee resistance and top management support. These factors were tested to determine whether they affected HR DT in Bahrain during COVID-19. There are no findings in the researched literature regarding the proposed factors that affect HR DT in Bahrain during COVID-19 in this study.

For data collection, a quantitative method was applied by conducting an online survey and distributing it to HR executives and employees from multiple organizations in Bahrain, both in the private and public sectors.

This study proposes a DT adoption model based on seven factors extracted from the literature review. A questionnaire was deployed, and accurate data were collected, processed and then analyzed. The logit model shows determinants factor to the DT adoption where all variables have a positive effect.

Using technology in an organization’s HR practices, known as e-HRM or HR DT, is becoming more crucial since the COVID-19 pandemic. Unlike European countries, the HR sector in Bahrain is not prepared to adopt the e-HRM process. This paper proposes a model that enables the HR sector to adopt digital technologies. This model is based on the key factors that enable an effective transition to the sector’s digitalization (e-HRM). Future research is sought to provide additional insights into the same factors and measure their effect on HR DT during COVID-19 in other countries.