Unsupervised learning analysis of European working condition

• Published in: Cogent business & management Vol. 11; no. 1
• Main Authors: Adesina, Olumide S., Adedotun, Adedayo F., Alayande, Semiu A., Efe-Imafidon, Emmanuel O., Adesina, Tolulope F., Okagbue, Hillary I., Onayemi, Oluwakemi O.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 31-12-2024
• Subjects: Europe; Huifen (Helen) Cai, Middlesex University Business School, United Kingdom; job satisfaction; machine learning; principal component analysis; working condition
• ISSN: 2331-1975; 2331-1975
• DOI: 10.1080/23311975.2024.2316644

AbstractWorkers require good working conditions to enhance their job performance, in this study, we conducted a survey of European working conditions in 2022 and compared the results with that of 2016 using an unsupervised learning approach for exploratory data analysis and determining the relationships. Hence, the Principal Component Analysis (PCA) was adopted. The analyses were in two parts for both the 2016 and 2022 surveys. Following the PCA, the first part shows that European workers are mostly characterized by cheerfulness and good spirits. The second part reveals that European workers are best characterized by enthusiasm in their work. Test statistics showed that the European working condition for the two periods does not differ significantly. The working conditions in Europe have not been altered in the space of six years. This study recommends that the working condition in Europe should be improved so that employers would continue to give their best.