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Data Analysis for Prediction of Workforce Requirements of the Job Market in the Digital Age |
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| รหัสดีโอไอ | |
| Creator | Rachawit Tipsena |
| Title | Data Analysis for Prediction of Workforce Requirements of the Job Market in the Digital Age |
| Publisher | Department of Information Science Faculty of Humanities and Social Sciences, Khon Kaen University |
| Publication Year | 2566 |
| Journal Title | Journal of Information Science |
| Journal Vol. | 41 |
| Journal No. | 3 |
| Page no. | 93-109 |
| Keyword | Data Analytics, Prediction, Manpower, Workforce, Job Market |
| URL Website | https://www.tci-thaijo.org/index.php/jiskku/index |
| Website title | Journal of Information Science |
| ISSN | 2773-8841 |
| Abstract | This review article proposes the results of a literature synthesis on data analysis to predict the workforce requirements of the job market in the digital age and the trends in the future demand for manpower and the essential skills of the workforce in an age of digital disruption. The synthesized information was accessed from four Thai and global online databases, namely ThaiJo, Science Direct, Emerald Insight, and Springer Link, and used two search engines for online academic literature, namely Semantic Scholar and Google Scholar. To begin a database search, keywords related to the research were defined to provide academic literature and research papers containing relevant topics of academic review and published between 2011 and 2021. The received literature includes research articles and academic articles. There were 10 titles total, divided into 2 Thai articles and 8 English articles. Conducting content synthesis and summarizing the body of knowledge related to data analysis for predicting workforce requirements of the digital age job market. The results of the review revealed that there were 15 data analysis algorithms for predicting techniques, including EDFR, Documentary Analysis, Box-Jenkins, Winters Additive Exponential Smoothing, Combined Forecasting, Naïve Bayes, Decision Tree, Decision Rules, C4.5, Grey Model, Regression Model, Support Vector Machines, k-Nearest Neighbor, Regression Analysis, and the AODE Algorithm. These techniques are used to analyze data in the programs SPSS, Weka, Matlab, SAS, R, and Visual Basic. Data analysis will compare the effectiveness of each technique to determine the most precise outcome to generate predictive modelling, which is most likely used to estimate supply and demand, as well as calculate the workforce required for the organization or a project in progress. In addition, the future demand of the workforce indicates that automation tends to replace human employment in 2020–2025, such as the use of artificial intelligence in the manufacturing industry. However, there is still a need for workers with new skills in areas such as skills to operate, maintain, and manage technology. It’s therefore critical that the workforce is required to continuously activate new skills to meet the expectations of the workforce market that are subject to change in the form of employment in the digital age. |
