Systematic literature review: an analysis of skill mismatch measurement

• Main Authors: Saad, Zuria Akmal, Abdul Rahman, Hamirahanim, Hussain, Suhaidah, Hassan, Hazlin
• Format: Conference or Workshop Item
• Language: English; English
• Published: European Proceedings 2023
• Subjects: H Social Sciences (General); HD28 Management. Industrial Management
• Online Access: http://umpir.ump.edu.my/id/eprint/26539/1/ISEBA2022F034%2026122022.docx; http://umpir.ump.edu.my/id/eprint/26539/3/Systematic%20literature%20review%20an%20analysis%20of%20skill%20mismatch%20measurement.pdf; http://umpir.ump.edu.my/id/eprint/26539/; https://doi.org/10.15405/epfe.23081.34

The rapid growth of technology in the era of Industry 4.0 has caused the dynamic labor market to grow faster than ever before. This resulted in a mismatch between the jobs offered and the skills required. Thus, it raised the number of unemployability. The objective of this paper is to analyze the measurement of skill mismatch. Shortcomings and flaws in previous measurement methods and a broad definition of skill mismatch hindered the issues to be solved. The introduction of online job analysis has been seen as increasingly more valuable in measuring labor market conditions. Overcoming the issues such as cost, time lag, and biases, this measurement has been seen to be the new trend among scholars to shed the light on skill mismatch measurement. This paper analyzed 402 papers on online job data (vacancy, advertisement, portal) published from 2017 to 2022 from Scopus and Web of Science databases. Preferred Reporting Items for Systematic Review & Meta-Analyses (PRISMA) were used for this study. After the inclusion and exclusion criteria, ten papers from Scopus and five papers from the Web of Science database that matched with the criteria objective have been selected. Therefore, the study found that analyzing online job data is the new trend to be used in improving the labor market with more of the data could be used for the improvement to the previous method of measuring the skill mismatch problem