Youth Aspirations Towards Industry 4.0 Job Requirements: The Example of the Serbian Labor Market

Youth Aspirations Towards Industry 4.0 Job Requirements: The Example of the Serbian Labor Market

DOI: 10.4018/978-1-6684-9089-1.ch003
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Abstract

A push towards digitalization and a solid inflow of foreign investments has provided a basis to create new jobs and facilitate a smoother transition of youth into the Serbian labor market. Although still inordinately high when compared to its EU counterparts, the unemployment rate youth has decreased over the last decade following improved macroeconomic trends. Further positive outcomes necessitate targeted, labor market policies focused on strengthening employability - of interest to research are changes in the external environment (particularly Industry 4.0 requirements) and deficiencies caused by rigidity in formal education. Taking into consideration youth career aspirations and capacities, a Principal Component Factor Analysis was applied to data collected from the survey of youth (18-29 years old, n=125). The results help provide conclusions on youth attitudes related to learning preferences, additional skills required and the self-estimation of their digital-skill capacities. The analysis extracts five factors relevant for developing effective active labor market measures.
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Literature Review

A plethora of studies worldwide have investigated employers’ expectations of young professionals entering the labor market (Pang et al., 2019), many concluding there to be a deficiency or outright lack of skills in youth. Based on a literature review, Tsitskari et al. (2017) argue that, in addition to offering an academic degree, undergraduate programs often fail to equip students with the skills needed in the current job climate. This is a common perception among both employers and students in Serbia, who believe that educational institutions fall short of providing undergraduates with key ICT and soft skills (Matijevic & Šolaja, 2020) that are vital for the Industry 4.0. (Picatoste et al., 2018). Skill shortages can result in difficulties in obtaining employment (Baird & Parayitam, 2019) as well as poor employee performance (Akman & Turhan, 2018). Based on research surveying 361 graduate and under-graduate students, Islam (2022) reports that students are aware of the changing job market scenario, and they are trying to have those skills which will make them competent compared to the early years, but they are not prepared enough to accept the challenges faced in industry 4.0.

Overall, there is a significant demand for developed digital skills within the labor market as Picatoste et al. (2018) underscored that almost half of all new positions require them. Through analysis of the job advertisements aiming to identify skills and knowledge relevant for specific areas, Pejic-Bach et al. (2020) indicate that Industry 4.0 organizations are looking not for the traditionally educated industrial, software or electrical engineers, but for the experts with the multidisciplinary skills, which can help factories to create and improve low-end and high-end machine and device capabilities, using knowledge and skills in embedded and distributed system development. Further, according to Digital Opportunity traineeship, an EU initiative for developing digital competences and increasing the employment chances of youth, skills related to machine learning, quantum technology, big data and cybersecurity are highly in demand within the EU labor market. In addition, the initiative points to the significance of digital marketing competences, web design and software development, which are vital to run a successful business. Accordingly, a report from the association IAB Europe on human capital in the digital environment also highlights the importance of digital business skills in the contemporary labor market (IAB, 2019). Moreover, information and analytical skills together with competencies in the field of cross-media and social media are among the main training areas pertinent to recruiters, as found by a study conducted among employers and training specialists in the EU-28.

In the context of acquiring digital skills, diverse labor market preferences and constraints, as well as dissimilitude in competencies gained, the principal component technique of factor analysis has been often employed to help researchers in detecting the most important factors of youth professional aspirations as well as associations between specific determinants. Most studies conducted are within employee job satisfaction, employment expectations, digital competences, unemployment determinants and informal education programs. This technique has been employed in multiple studies such as Ozdamar-Keskin et al. (2015) who analyzed data gathered from 20,172 open and distant learners from Turkey. Aiming to group and classify the attitudes and statements of learners in their personal learning preferences, problem solving skills, project-work skills and abilities to use digital tools for learning purposes, their results indicate learners possess problem-solving and project-working skills which might help them overcome deficiencies resulting from poor formal education. However, low digital literacy limits their potential to utilize communication technologies for more demanding jobs and further learning purposes, with the authors going as far as to suggest that students prefer visual learning environments while being reluctant to collaborative online group work as found in modern business environments. They conclude that investing more effort into developing a collaborative approach to education is necessary to strengthen employability among students and life-long learners.

Key Terms in this Chapter

Employment policy: – a set of policies aimed at supporting individuals to find the best possible job with respect to their education background, skills and job aspirations. It also includes measures supposed to help creating better working environment including equal opportunities, protection of the workers’ rights, improvement of the capacities through education and trainings, etc.

Principal Component Analysis: – one of the techniques used within factor analysis approach. It reduces the number of variables making them easier for interpretation and visualization. As not requiring strong assumptions with regards to data, it could be useful tool for data exploration and analysis.

ICT Skills: – refer to the skills and abilities to use information and technology tools for different purposes – communication, data analysis, learning, etc. They are very important for job career prospects and therefore considered vital for conducting most jobs nowadays.

NEET: – youth in NEET status are youth who are not employed and not receiving an education or vocational training support. They belong to vulnerable youth population often facing difficulties and barriers to participate in labour market.

Industry 4.0: – an industrial revolution which refers to use of automation and data exchange in manufacturing processes. It is often associated with the use of advanced technologies such as Internet of Things (IoT), Artificial intelligence (AI), Big data, Robotics and many others.

Youth policy: – a set of policy instruments such as strategies, programs, and other public policy measures aimed at providing better opportunities for youth population.

Factor Analysis: – refers to statistical method used to describe variability among correlated variables. Its logic relies on reducing a large set of variables to smaller number of factors that have some common characteristics and are easily understandable.

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