Job Skills Research Articles

Work engagement and burnout among Polish labour migrants.

The aim of the study was to examine the relationship between work engagement, burnout and coworker support, social climate at work, work-language proficiency and post-migration job skill change, among international migrant workers. Polish versions of Utrecht Work Engagement Scale, Burnout Assessment Tool, and the second Copenhagen Psychosocial Questionnaire were used for the measurement of the studied variables. All participants were recruited using announcements at Polish diaspora online forums and snowball sampling. The questionnaire was submitted to them in a form of an anonymous online survey. From 420 questionnaires filled in by the respondents, 411 were qualified for analysis. The results of a multiple regression analysis indicate that a positive perception of the social climate at work increases the scores of general work engagement (β = -0.22, p = 0.001), as well as all its subscales: dedication (β = -0.14, p = 0.002), vigor (β = -0.20, p = 0.001) and absorption (β = -0.22, p = 0.001). Support received from coworkers has a positive influence on the vigor dimension (β = -0.14, p = 0.002). Furthermore, work-language proficiency can be associated with more engagement F(1, 409) = 37.66, p = 0.000 and less burnout F(1, 409) = 11.68, p = 0.000 than limited work-language knowledge. In a similar manner, increased or similar job skill level compared to one's last employment in the sending country may be associated with better engagement F(2, 408) = 33.88, p = 0.000 and fewer symptoms of burnout F(1, 409) = 7.96, p = 0.005 than post-migration decrease of job skill. The perception of social climate at work and coworker support have shown to be positive predictors for particular dimensions of work engagement. Moreover, work language proficiency and job skill have been shown to associate with work engagement and burnout. Int J Occup Med Environ Health. 2024;37(4).

“Covid has stopped us all”: the experiences of migrant care workers in formal care settings in Germany

ABSTRACT This research examined how migrant care workers in eldercare institutions in Germany were affected by the COVID-19 pandemic, focusing on their personal experiences. The research is theoretically grounded in intersectionality and the prisoner of love framework. Drawing on 15 qualitative interviews with migrant care workers (MCWs) from Poland and Bosnia–Herzegovina, the main findings highlight that intersecting social positions play an important role in stratifying experiences for MCWs. Migrant workers with high skill levels and job security stressed similarities to their non-migrant colleagues in their day-to-day work experiences during the pandemic, foregrounding their shared professional self-image and creating solidarity between workers of all backgrounds. Strong workplace relationships, emphasising their professional identity, and adapting career plans were ways to mitigate the negative effects of the pandemic. The study’s results emphasise the need for a more balanced portrayal of the pandemic’s impact on migrant workers.

School Leadership Amidst the Push for CTE Curriculum

ABSTRACT Career and technical education (CTE) has long endured public scrutiny and bias, but unfortunately, the extent to which varying opinions have permeated secondary schools remains elusive. A quantitative methodology, was used to examine secondary administrator, counselor, and subject-specific teacher opinions in CTE options, role in conveying job skills, and school leader ability for implementation. The largest distinctions were between middle and high school teachers. Policy-makers can establish consistency in messaging to reduce the stigma toward CTE, help promote comprehensive professional learning across all educator types, and include social emotional learning to augment skills and diversify access to CTE programming.

The Effect of Job Skill Demands Under Artificial Intelligence Embeddedness on Employees' Job Performance: A Moderated Double-Edged Sword Model.

With the widespread application of AI technology, the skills and abilities required by employees in their work are undergoing fundamental changes, redefining the roles of employees. This research aims to explore the effect of job skill demands under AI embeddedness on well-being in organizations and job performance. Based on conservation of resources theory, this research randomly selected 479 employees from 8 companies in China using a time-lag method as samples, and conducted statistical analysis with ordinary least squares (OLS). This research found that, job skill demands under AI embeddedness will both increase employees' competency needs, promoting their well-being in organizations and job performance and decrease employees' job embeddedness, inhibiting their well-being in organizations and job performance. Meanwhile, technological anxiety moderated the impact of job skill demands under AI embeddedness on job embeddedness.

The dovish turnaround: Germany's social benefit reform and job findings

AbstractOn the labor markets, recent decades were characterized by structural supply‐side reforms in many countries. Following its hawkish reforms from the 2000s, Germany has recently made a dovish turnaround. Conditions in basic income support for unemployed became more generous, combined with a focus on qualification and development. Before, a temporary moratorium on sanctions had been imposed, providing a unique policy shift. We analyze the consequences for job findings, building on large administrative data and a novel control group approach. The moratorium dampened job findings by 4% and the subsequent benefit reform by almost 6%—offsetting half of the positive effect of the 2000s reform. Considering reform objectives, so far, we find no improvement in skill level, job stability, or transitions to training.

The Critical Role of Soft Skills in Engineering: Enhancing Performance and Career Advancement

Today’s engineering graduates seeking jobs require a combination of technical abilities and the right soft skills, as organizations prefer a professional individual who has developed an overall personality that encompasses both professionalism and alignment with society, rather than relying solely on qualifications and academic scores. The 21st-century workforce has changed the way they define job skills, with managers prioritizing non-technical (soft) skills in addition to technical skills, as having these alone is no longer enough in today's competitive market place. This article aims to analyze the significance of soft skills in the engineering sector, as highlighted in the literature review. An understanding of these soft skills is crucial for organizations to optimize the levels of productivity amongst their employees and for individuals desiring to advance their futures within a competitive job landscape. This article attempts to shed light on these soft skills in the hopes of investing in training program designs that can maximize engineers professional efficacy and career successes within their industry, engineering.

Evaluating contexts for implementing a competency‐based curriculum in south and eastern Africa: A meta‐narrative review

AbstractMany countries have looked at schools as spaces that can impart requisite skills for future jobs through competency‐based education (CBE). Yet, there is limited comparative research evidence showing countries' implementation progress. The research design adopted for this study is a meta‐narrative. Stufflebeam's (2000) context, input, process and product evaluation model is used to reflect on the education systems of two countries, Rwanda and Zimbabwe, and the enactment of their competency‐based curricula. Data from the literature was analysed using a combination of thematic, content and narrative approaches. The findings of the study established four epistemic areas that influenced the growth of the CBE movement—competency‐based training, progressivism, competency assessment programmes and the 21st‐century CBE movement. Further results showed that the extent of support for education, literacy levels and proficiency in the language of instruction were some of the context‐determinant factors influencing CBE implementation. Despite some success stories, challenges in the areas of assessment, teaching and ICT integration were reported. The study made two recommendations. Firstly, countries can enhance effective implementation through the provision of guiding frameworks for continuous professional development to empower teachers on the knowledge and skills for a CBE. Secondly, there is a need to re‐examine the CBE approach to country‐specific contexts to assess relevance to prevailing national needs.

O PAPEL DAS COMPRAS PÚBLICAS EM DEFESA COMO O NOVO VETOR DE DESENVOLVIMENTO NACIONAL

This article discusses the importance of the defense industry as a driver of innovation and economic growth in the country. With the intensification of geopolitical challenges and the need to modernize the Armed Forces, Brazil has invested in acquisitions that encourage research and technological development. The proposed research analyzes how these strategic public purchases can not only strengthen military capacity, but also stimulate national industry, generating skilled jobs and promoting technology transfer. Collaboration between the government and the private sector is essential to maximize the benefits of these acquisitions, allowing technological advances to have a positive impact on various sectors such as health, energy and transport. In addition, these defense acquisitions can serve as an industrial policy tool, guiding investments in priority areas and contributing to the country's strategic autonomy. In this context, the research aims to demonstrate how defense acquisitions and orders are an essential vector for economic and technological development, with the potential to transform industrial capacity and national competitiveness in a global scenario.

Digital Competence of Young Adults in Poland

Digital transformation is a challenge for many areas of modern society functioning. The effects of digitalisation are particularly noticeable in the labour market and are expressed, among others, in new requirements of employers both for professionally active people and those entering the labour market. Technological advances and automation are reducing the number of low skilled jobs. Moreover, employees are required to be more flexible. As a consequence, there is a need to adapt to changing trends in the labour market, and to constantly learn and acquire new competences, including digital competences. This paper aims to identify vulnerabilities regarding digital competences and their determinants amongst young adults in Poland. Furthermore, detailed objectives were defined: (1) assessment of the level of digital competences of young adults in Poland; (2) identification of the gaps in this area; (3) identification of factors differentiating the level of digital competences. The catalogue of competences under study was taken from the European Digital Competence Framework, which includes 21 digital competences grouped into five areas: (information and data literacy; communication and collaboration; digital content creation; safety; problem solving). To achieve the objective, a nationwide survey was conducted. The sample consisted of 1,000 young adult respondents (aged 18–30), with at least secondary education, selected by quotas from panel participants. The research methods used statistical analysis, including descriptive statistics, regression modelling (stepwise regression), as well as t-tests for independent samples, and One-Way ANOVA. Research results indicate that digital competences of young adults vary depending on the area. The respondents are aware of their insufficient skills, especially in the area of digital content creation (skills connected with programming). A relatively considerable share of individuals have no intention of acquiring these competences as part of lifelong learning. Self assessment of competences also indicates that their level differs due to individuals’ gender, age, level of education, and profile of education.

Economic development through sustainable energy transition: the impact of machine learning-driven energy efficiency in industrial sectors

The transition to sustainable energy systems is a critical component in addressing global environmental challenges and fostering economic development. Industrial sectors, which are major consumers of energy and contributors to greenhouse gas emissions, play a pivotal role in this transition. Machine learning (ML) technologies have emerged as transformative tools for enhancing energy efficiency in these sectors, offering significant potential for economic and environmental benefits. This review explores the impact of ML-driven energy efficiency on economic development, focusing on its application within various industrial contexts. The integration of machine learning into energy management systems allows for advanced predictive analytics, real-time monitoring, and process optimization. ML algorithms can analyze large datasets to forecast energy demand, detect inefficiencies, and optimize operational processes, leading to substantial cost savings and energy reductions. For example, in manufacturing industries, ML-driven solutions have been employed to enhance predictive maintenance, optimize production processes, and reduce energy consumption, resulting in lower operational costs and increased productivity. Similarly, in industrial facilities such as chemical plants and data centers, ML has been used to streamline energy management and minimize waste, demonstrating its versatility and effectiveness across diverse industrial applications. The economic impacts of ML-driven energy efficiency are profound. By reducing energy costs and improving operational performance, industries can achieve significant financial savings, which in turn can be reinvested into further innovations and growth. Additionally, the adoption of ML technologies fosters job creation and skill development in the tech and energy sectors, contributing to broader economic growth. Industries that leverage ML for energy efficiency gain a competitive advantage, positioning themselves favorably in the market and tapping into new opportunities for innovation and expansion. However, the transition to ML-driven energy efficiency is not without challenges. Issues related to data availability and quality, high implementation costs, and industry-specific constraints must be addressed to fully realize the benefits. Policymakers play a crucial role in facilitating this transition by developing supportive policies and regulations that encourage the adoption of ML technologies in energy management. Machine learning offers a transformative approach to achieving energy efficiency in industrial sectors, with significant implications for economic development. By addressing the challenges and leveraging emerging opportunities, industries and policymakers can drive sustainable energy transitions that benefit both the environment and the economy. This review provides a comprehensive analysis of these impacts and offers strategic recommendations for advancing ML-driven energy efficiency in the industrial sector. Keywords: Economic Development, Sustainable Energy, Machine Learning, Industrial Sectors.

Digital competence is a must: Manager and safety representative perspectives on evolving job skills and balancing digital work environments in transportation, logistics, and home care

Digital competence is a must: Manager and safety representative perspectives on evolving job skills and balancing digital work environments in transportation, logistics, and home care

Impact assessment of COVID-19 and PMO15 on labour in the plantation-wood processing sector in Lao PDR with policy recommendations

The primary objective of this research was to 1) explore the impacts of COVID-19 on availability of, and demand for, labor in the plantation-wood processing sector in Lao PDR; 2) the Government and industry response measures to COVID-19; and 3) the impacts of 2016 Prime Ministers’ Order (No. 15/PM, PMO15) aimed at addressing illegal logging and controlling log exports were also considered, as representative of a similar ‘shock’ to the sector. The research, undertaken in Vientiane Capital City, adopted a mainly qualitative methodology utilizing literature reviews and semi-structured interviews to gather data from the government, industry and workers. Micro, small and medium sized companies and their employees were surveyed. The research found that Lao wood manufacturers were affected by the shutdowns in response to COVID-19 and there were follow-on effects up and down value chains. The experiences of Laos were found to be similar to those experienced in wood producing countries globally. Supply issues and access to markets were key factors impacting the ability of factories to continue to operate and retain workers. Government protection measures were effective but challenging to implement, and access to support was difficult. Impacts on male and female differed. Awareness of, and access to, mandatory social security insurance by workers was very low. Other forms of social ‘safety-net’ were more apparent and important, including company-based programs, charity, and family/self-help. Improved dissemination of information about the need for, and benefits of, social security should occur to mitigate future shocks. The high dependence on foreign workers to fill skilled jobs in the sector remains an issue. Coupled with reforms in response to PMO15, to improve factory and product standards and ongoing export restrictions, there is a need further build Lao skills and capacity. Current efforts to up-skill Lao workers for employment in emerging wood product industries must be maintained, and improved recognition of Lao-acquired competencies is needed. Market needs analysis including the development of product dossiers is required together with the building of relevant technical expertise. Without this the Lao wood processing sector will be remain vulnerable to future shocks.

ИССЛЕДОВАНИЕ РЫНКА КАРЬЕРНОГО КОНСУЛЬТИРОВАНИЯ В РОССИИ

Modern socio-economic processes and changes in social structure are intensifying the need for career counseling. Increased competition in the labor market requires job seekers to have skills for effective job searching, resume writing, and successful performance in interviews, consequently increasing the demand for career counselors. The research aims to analyze current trends in the field of career counselor vacancies after the pandemic in Russia, including an analysis of changes in demand for career counseling and understanding of company preferences when posting such vacancies. The study offers a comprehensive analysis of the demand for career counseling after the pandemic, using statistical data and information on vacancies from various platforms. The practical significance of the research lies in the development of strategies for career program advancement, understanding the needs of employees and job seekers, as well as identifying specific features of posting vacancies for career counselors on different platforms. The theoretical and methodological aspects of researching the issues of career counseling in Russia assess the transformation of socio-economic processes and the modern social structure, analyze the global market for career counseling, and investigate government support programs. The research confirmed the relevance, significance, and potential for further development of career counseling in Russia in the context of contemporary challenges in the labor market.

Empowering ASEAN’s Education for RE Jobs: A Readiness Assessment

Abstract The global imperative to address climate change and the depletion of traditional fossil fuel sources has underscored the need for a significant transition toward sustainable energy. ASEAN Member States embark on a journey towards energy transition toward low carbon economy. This transition necessitates a substantial workforce, with projections from the 7th ASEAN Energy Outlook estimative around 3.9 - 5.5 million jobs in renewables alone. Thus, adequate preparation for these roles is essential. Educational institutions’ role in the ASEAN region is imperative as the spearhead in preparing the workforce with the needed knowledge and skills for green energy jobs. This paper analyses the region’s formal education system’s readiness to cultivate skilled workforce candidates to meet the growing demand for green energy jobs. This study examines the current state of RE education in the ASEAN region through several indicators and provides best practices highlighting that various challenges are causing limited and varying progress. Addressing the gaps, the study recommended several efforts by relevant stakeholders to enhance educational readiness to propel ASEAN renewables development.

Women empowerment: challenges and opportunities for sustainable development goals

PurposeThe paper aims to explore gaps in women's empowerment in rural areas, specifically focusing on the alignment of skill development with job creation. The overarching goal is to understand how firms can contribute to achieving the 5th Sustainable Development Goal (SDG) related to women's empowerment through skill development and job creation programs.Design/methodology/approachThe paper reviews information available in various formats to analyse the existing landscape of women's empowerment, skill development and job creation in rural areas. It likely involves a literature review synthesising insights from diverse sources, including academic studies, government initiatives and reports from researchers working in the field. The methodology may also encompass an examination of success cases and challenges faced by government programs.FindingsGender disparities: Over 80% of the global impoverished population is in rural areas, with women constituting 70% of this demographic. Challenges in policy implementation: Despite women-centred policies, challenges persist in enabling rural women to recognise the potential of remunerative skills for improving their lives. Linking skills and decision-making: Scholarly work highlights the connection between remunerative skills and decision-making, emphasising the importance of financial independence for women's empowerment. Gap in job opportunities: Lack of industry and commercial setups in rural areas poses a significant challenge, hindering the recognition of the value that skill development can offer.Originality/valueThe paper reviews information available in various formats to analyse the existing landscape of women's empowerment, skill development and job creation in rural areas. It likely involves a literature review synthesising insights from diverse sources, including academic studies, government initiatives and reports from researchers working in the field. The methodology may also encompass an examination of success cases and challenges faced by government programs.

How Does Artificial Intelligence Shape Audit Firms?

Does artificial intelligence (AI) displace auditors? We exploit the staggered hiring of AI employees at audit office locations across the United States as a proxy for the use of AI at local audit offices. The main findings are as follows. First, relative to audit offices that do not yet hire AI employees, those that do hire AI employees have a 4.3% increase in the number of auditor jobs, particularly among junior and midlevel auditors. Second, using AI is associated with an increased demand for soft skills (e.g., cognitive skills) in auditor jobs. Third, audit offices that use AI have more accurate going concern and internal control opinions. Semistructured interviews of 11 seasoned audit partners confirm that investment in AI is centralized at the national level, but the decision to deploy it often resides at the local audit office level. Notably, none of the partners believe that AI has replaced or will replace human auditors. Overall, our study—comprising both empirical and qualitative data—suggests that using AI does not replace auditors, but rather changes the skills required for these jobs and improves audit quality. This paper was accepted by Suraj Srinivasan, accounting. Funding: K. K. F. Law’s research is supported by the Start-Up Grant from Nanyang Technological University and the Ministry of Education (MOE), Singapore, under its Academic Research Fund Tier 1 [Grant RG128/20]. M. Shen’s research is supported by the Start-Up Grant from the National University of Singapore [Grant A-0003914-00-00] and the MOE under its Academic Research Fund Tier 1 [Grant A-8000098-00-00]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.04040 .

Rationalizing the Mathematics Requirements for a Master of Biostatistics Program: A Case Study and Commentary

Within a 2-year Master of Biostatistics program, we reconsidered the mathematics requirements for admission, and also the premise that the most distinguishing feature of a top candidate is deep exposure to mathematics. Our assessment took place within a broad curriculum review intended to enhance alignment: aligning programmatic goals with job skills, aligning the use of mathematics within our curriculum with programmatic goals, aligning our admission criteria with our curriculum, and aligning our application materials with these admission criteria. We developed a specific list of mathematical skills required by the curriculum, and are revising the application materials to include self-report on applicant's exposure to and functional mastery of those skills. We illustrate how functional mastery is operationally defined. Our criteria for identifying top candidates was broadened to include those with especial skills in analytics, biology and/or communication (i.e., the three conceptual pillars of our program). Deep mathematical training is one of many ways to become a top candidate. Within STEM fields such as biostatistics, and despite the commonly held assumption that admission criteria should emphasize depth of mathematical training, a systematic analysis suggests that this assumption imposes a gratuitous requirement on applicants. Reconsidering this assumption can help remove unnecessary barriers, reduce challenges, and support success for aspiring or emerging biostatisticians from diverse or multifaceted backgrounds. It is one way to contribute to a more inclusive and equitable learning environment in our STEM discipline. We believe similar programs might benefit from performing this type of analysis and reflection.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.

Employers feedback on the job performances of the Bachelor of Arts in English Language graduates

The study examined the employers feedback on the job performances of the Bachelor of Arts in English Language (BAEL) graduates of Kalinga State University. Employer's feedback is vital in honing future nation-builders as it provides input in evaluating a curricular program to determine whether it is congruent with the demands of the industries. It used an explanatory sequential research design, explicitly employing a quantitative-quality approach. Significantly, it revealed that the employers of the graduates are very satisfied with their job performances in the areas of competence, commitment, collaboration, and credibility. The employers identified the graduates’ strengths in terms of job traits such as flexibility, initiative, creativity, dependability, leadership potential, and intellect. However, the employers also identified their weaknesses as a lack of self-confidence and a lack of time management. The study further revealed that most employers require new recruits to have job skills such as communications skills, Information and Communication Technology (ICT) skills, cooperative skills, leadership skills, and research skills. Therefore, the study concludes that the BAEL graduates perform in their jobs with very much satisfaction from their employers. It significantly implies an effective curricular program, producing BAEL graduates with the required competence, commitment, collaborative skills, and credibility to perform in their chosen jobs.

From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.