Araştırma Makalesi
BibTex RIS Kaynak Göster

Implications for Human Resources Management from Labour Force Participation Rates of Countries by Education Level

Yıl 2025, Cilt: 21 Sayı: 2, 437 - 458, 29.12.2025

Öz

Labour force participation rates are a key indicator of economic structure, human capital development, and employment dynamics, with education level playing a crucial role in shaping workforce accessibility and productivity. This study examines the labour force participation rates across different education levels (basic, intermediate, high) in 34 European and surrounding countries between 2015 and 2023, aiming to provide strategic insights for human resources management (HRM). Using a quantitative approach, the study employs descriptive statistics and K-means clustering to analyse workforce segmentation, while Silhouette Scores assess the degree of convergence among countries over time. The findings indicate a significant decline in participation among individuals with lower education levels, particularly in countries such as Croatia and Latvia, whereas participation rates for highly educated individuals remain relatively stable. Additionally, clustering results suggest a gradual convergence in labour force dynamics, with certain countries shifting between clusters, reflecting structural shifts in workforce composition. These results highlight the need for targeted reskilling programs, flexible employment models, and education-driven labour policies to mitigate participation disparities. The study underscores the importance of international talent mobility, workforce digitalisation, and skill development initiatives in optimising HRM strategies. By providing an education-based labour market analysis, this research contributes to data-driven workforce planning and global HRM decision-making.

Etik Beyan

In the study, publicly available data were used. There is no need for ethics committee approval.

Destekleyen Kurum

The study was not supported by any institution.

Kaynakça

  • Akdemir, B., & Duman, M. Ç. (2017). KADIN ÇALIŞANLARIN PERFORMANSINDA CAM TAVAN SENDROMU ENGELİ! International Journal of Academic Value Studies, 3(15), 517–526. www.javstudies.com
  • Angrist, N., Djankov, S., Goldberg, P. K., & Patrinos, H. A. (2021). Measuring human capital using global learning data. Nature, 592(7854), 403–408. https://doi.org/10.1038/s41586-021-03323-7
  • Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press.
  • Becker, R. (2019). Economic change and continuous vocational training in the work history: a longitudinal multilevel analysis of the employees’ participation in further training and the effects on their occupational careers in Germany, 1970–2008. Empirical Research in Vocational Education and Training, 11(1), 13–14. https://doi.org/10.1186/s40461-019-0079-x
  • Becker, R., & Blossfeld, H.-P. (2017). Entry of men into the labour market in West Germany and their career mobility (1945–2008). Journal for Labour Market Research, 50(1), 113–130. https://doi.org/10.1007/s12651-017-0224-6
  • Buchmann, M., & Malti, T. (2012). The future of young women’s economic role in a globalized economy: New opportunities, persisting constraints. New Directions for Youth Development, 2012(135), 77–86. https://doi.org/10.1002/yd.20030
  • Burdorf, A., Fernandes, R. C. P., & Robroek, S. J. W. (2023). Health and inclusive labour force participation. The Lancet, 402(10410), 1382–1392. https://doi.org/10.1016/S0140-6736(23)00868-1
  • Busse, R., Sun, L., & Zhu, V. (2015). Comparing value orientations of German and Chinese managers: impacts of demographic and business-related factors. Asia Pacific Business Review, 21(2), 170–187. https://doi.org/10.1080/13602381.2014.891400
  • Carraher, S. M., Gibson, J. W., & Buckley, M. R. (2006). Compensation satisfaction in the Baltics and the USA. Baltic Journal of Management, 1(1), 7–23. https://doi.org/10.1108/17465260610640840
  • Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200–210. https://doi.org/10.1016/j.eswa.2012.07.021
  • Chi, D. (2021). Research on the Application of K-Means Clustering Algorithm in Student Achievement. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 435–438. https://doi.org/10.1109/ICCECE51280.2021.9342164
  • Ehsan, R., & Sloam, J. (2020). Resources, Values, Identity: Young Cosmopolitans and the Referendum on British Membership of the European Union. Parliamentary Affairs, 73(1), 46–65. https://doi.org/10.1093/pa/gsy035
  • El Ouirdi, M., El Ouirdi, A., Segers, J., & Pais, I. (2016). Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe. Computers in Human Behavior, 57, 240–249. https://doi.org/10.1016/j.chb.2015.12.043
  • Englehardt, C. S., & Simmons, P. R. (2002). Organizational flexibility for a changing world. Leadership & Organization Development Journal, 23(3), 113–121. https://doi.org/10.1108/01437730210424057
  • Fa’rifah, R. Y., & Pramesti, D. (2022). Cluster Analysis of Inclusive Economic Development Using K-Means Algorithm. Jurnal Varian, 5(2), 171–178. https://doi.org/10.30812/varian.v5i2.1894
  • Fahim, A. M., Salem, A.-B. M., Torkey, F. A., & Ramadan, M. A. (2006). An efficient enhanced k-means clustering algorithm. Journal of Zhejiang University-SCIENCE A, 7(10), 1626–1633. https://doi.org/10.1631/jzus.2006.A1626
  • Gibson, J. L. (2015). The Effects of Workforce Trends and Changes on Organizational Recruiting: A Practical Perspective. Industrial and Organizational Psychology, 8(3), 383–387. https://doi.org/10.1017/iop.2015.54
  • Gurgu, E., & Savu, C. (2014). Human capital in the new economy. A post-revolutionary Romanian radiography. Contemporary Readings in Law and Social Justice, 6(1), 510.
  • Hanushek, E. A., & Kimko, D. D. (2000). Schooling, Labor-Force Quality, and the Growth of Nations. American Economic Review, 90(5), 1184–1208. https://doi.org/10.1257/aer.90.5.1184
  • Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
  • Jarinto, K., & Ridsomboon, L. (2024). “Clustering by Employee Personality”, Modern Working World Perspectives on Work Efficiency in the Organizations. WSEAS Transactions on Business and Economics, 21, 288–298. https://doi.org/10.37394/23207.2024.21.26
  • Jegede, O., & Muchie, M. (2024). Introduction to the Special Issue: Leveraging Global Value Chains for innovation and industrialization in Africa. African Journal of Science, Technology, Innovation and Development, 16(4), 451–458. https://doi.org/10.1080/20421338.2024.2361952
  • Kalleberg, A. L. (2001). Organizing Flexibility: The Flexible Firm in a New Century. British Journal of Industrial Relations, 39(4), 479–504. https://doi.org/10.1111/1467-8543.00211
  • Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892. https://doi.org/10.1109/TPAMI.2002.1017616
  • Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657. https://doi.org/10.1016/j.asoc.2009.12.025
  • Kumari, R. (2018). Economic growth, disparity, and determinants of female labor force participation. World Journal of Entrepreneurship, Management and Sustainable Development, 14(2), 138–152. https://doi.org/10.1108/WJEMSD-03-2017-0009
  • Lu, H., Hou, L., Zhou, W., Shen, L., Jin, S., Wang, M., Shang, S., Cong, X., Jin, X., & Dou, D. (2021). Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018. BMJ Open, 11(10), 1–10. https://doi.org/10.1136/bmjopen-2020-047348
  • Lv, W. (2011). Educational funds, educational level and human capital - Empirical analysis based on inter-province panel data of China. 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings, 1997, 2987–2991. https://doi.org/10.1109/AIMSEC.2011.6010691
  • Maket, L. J., Lamaon, L. G., & Kwonyike, J. (2015). Managing Diversity through Workplace Flexibility for Organizational Performance. International Journal of Academic Research in Business and Social Sciences, 5(4). https://doi.org/10.6007/IJARBSS/v5-i4/1581
  • Marrocu, E., & Paci, R. (2012). Education or Creativity: What Matters Most for Economic Performance? Economic Geography, 88(4), 369–401. https://doi.org/10.1111/j.1944-8287.2012.01161.x
  • Mok, K. H., Yu, K. M., & Ku, Y. wen. (2013). After massification: The quest for entrepreneurial universities and technological advancement in Taiwan. Journal of Higher Education Policy and Management, 35(3), 264–279. https://doi.org/10.1080/1360080X.2013.786857
  • Muttaqin, M. F. J. (2022). Cluster Analysis Using K-Means Method to Classify Sumatera Regency and City Based on Human Development Index Indicator. Seminar Nasional Official Statistics, 2022(1), 967–976. https://doi.org/10.34123/semnasoffstat.v2022i1.1299
  • Muttaqin, M. F. J., & Zulkarnain. (2020). Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator. Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020, 81–85. https://doi.org/10.1145/3400934.3400951
  • Neethirajan, S. (2023). Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors, 23(16). https://doi.org/10.3390/s23167045
  • Osmancevic, S., Großschädl, F., & Lohrmann, C. (2023). Cultural competence among nursing students and nurses working in acute care settings: a cross-sectional study. BMC Health Services Research, 23(1), 1–7. https://doi.org/10.1186/s12913-023-09103-5
  • Palpacuer, F. (2000). Competence-Based Strategies and Global Production Networks a Discussion of Current Changes and Their Implications for Employment. Competition & Change, 4(4), 353–400. https://doi.org/10.1177/102452940000400401
  • Patzina, A., & Wydra-Somaggio, G. (2020). Early careers of dropouts from vocational training: Signals, human capital formation, and training firms. European Sociological Review, 36(5), 741–759. https://doi.org/10.1093/esr/jcaa011
  • Peneder, M. (2007). A sectoral taxonomy of educational intensity. Empirica, 34(3), 189–212. https://doi.org/10.1007/s10663-007-9035-2
  • Perez‐Arce, F., & Prados, M. J. (2021). THE DECLINE IN THE U.S. LABOR FORCE PARTICIPATION RATE: A LITERATURE REVIEW. Journal of Economic Surveys, 35(2), 615–652. https://doi.org/10.1111/joes.12402
  • Pokharel, M., Bhatta, J., & Paudel, N. (2021). Comparative Analysis of K-Means and Enhanced K-Means Algorithms for Clustering. NUTA Journal, 8(1–2), 79–87. https://doi.org/10.3126/nutaj.v8i1-2.44044
  • Schim, S. M., Doorenbos, A. Z., & Borse, N. N. (2005). Cultural competence among Ontario and Michigan healthcare providers. Journal of Nursing Scholarship, 37(4), 354–360. https://doi.org/10.1111/j.1547-5069.2005.00061.x
  • Schömann, K., & Becker, R. (1995). Participation in Further Education over the Life Course: A Longitudinal Study of Three Birth Cohorts in the Federal Republic of Germany. European Sociological Review, 11(2), 187–208. https://doi.org/10.1093/oxfordjournals.esr.a036356
  • Shamsuddinova, S. (2024). The European Youth Guarantee scheme: A viable solution to youth unemployment? International Review of Education, 70(5), 819–847. https://doi.org/10.1007/s11159-024-10075-9
  • Sozen, H. C., Varoglu, D., Yeloglu, H. O., & Basim, H. N. (2016). Human or Social Resources Management: Which Conditions Force HR Departments to Select the Right Employees for Organizational Social Capital. European Management Review, 13(1), 3–18. https://doi.org/10.1111/emre.12063
  • Stegmaier, J., Krekel, E. M., & Bellmann, L. (2010). Aus- und Weiterbildung - Komplemente oder Substitute? REPORT - Zeitschrift Für Weiterbildungsforschung, 93, 41–54. https://doi.org/10.3278/REP1001W041
  • Tahsin, R., Rantu, S. B. A., Rahman, M., Salman, S., & Karim, M. R. (2025). Towards the adoption of AI, IoT, and Blockchain technologies in Bangladesh’s maritime industry: Challenges and insights. Results in Engineering, 25(December 2024), 103825. https://doi.org/10.1016/j.rineng.2024.103825
  • Wahyuni, S. N., Khanom, N. N., & Astuti, Y. (2023). K-Means Algorithm Analysis for Election Cluster Prediction. JOIV : International Journal on Informatics Visualization, 7(1), 1. https://doi.org/10.30630/joiv.7.1.1107
  • Wang, C. (2023). Development of Student Biochemical Index Monitoring System Based on K-means Cluster Analysis. 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), 1–5. https://doi.org/10.1109/ICICACS57338.2023.10100254
  • Weir-Smith, G. (2018). Spatiotemporal Variation of South African Jobless Trends: Policy Directions. Professional Geographer, 70(1), 94–102. https://doi.org/10.1080/00330124.2017.1325748
  • Yang, L., Peng, H., Yang, Y., Ouyang, L., & Li, Y. (2020). Situation and Countermeasures of the Management Team of the Elderly Care Institutions from the Perspective of the Combination of Medical and Health Care: A Cross-Sectional Study. Journal of Healthcare Engineering, 2020. https://doi.org/10.1155/2020/8826007
  • Zamani, F. E., Kusnandar, T., Silmi, F. E., & Rachman, R. (2023). Analysis of Public Service Satisfaction using Artificial Intelligence K-Means Cluster. Majalah Bisnis & IPTEK, 16(1), 181–187. https://doi.org/10.55208/bistek.v16i1.428
  • Zuzana, W. (2017). Comparison of requirements for brand managers responsible for competitiveness of brands: A cross-national study in the US and the Czech Republic. Journal of Competitiveness, 9(4), 148–163. https://doi.org/10.7441/joc.2017.04.10

Eğitim Seviyesine Göre Ülkelerin İşgücüne Katılım Oranlarından İnsan Kaynakları Yönetimi İçin Çıkarımlar

Yıl 2025, Cilt: 21 Sayı: 2, 437 - 458, 29.12.2025

Öz

İşgücüne katılım oranları ekonomik yapı, beşeri sermaye gelişimi ve istihdam dinamiklerinin önemli bir göstergesidir ve eğitim seviyesi işgücüne erişilebilirlik ve verimliliği şekillendirmede önemli bir rol oynamaktadır. Bu çalışma, insan kaynakları yönetimi (İKY) için stratejik öngörüler sağlamayı amaçlayarak, 2015-2023 yılları arasında 34 Avrupa ve çevre ülkesinde farklı eğitim seviyelerinde (temel, orta, yüksek) işgücüne katılım oranlarını incelemektedir. Nicel bir yaklaşım kullanan çalışmada, işgücü segmentasyonunu analiz etmek için tanımlayıcı istatistikler ve K-ortalamalar kümelemesi kullanılırken, Siluet Skorları zaman içinde ülkeler arasındaki yakınsama derecesini değerlendirmektedir. Bulgular, özellikle Hırvatistan ve Letonya gibi ülkelerde daha düşük eğitim seviyesine sahip bireyler arasında katılımda önemli bir düşüş olduğunu gösterirken, yüksek eğitimli bireyler için katılım oranları nispeten sabit kalmaktadır. Ayrıca, kümeleme sonuçları işgücü dinamiklerinde kademeli bir yakınsama olduğunu ve bazı ülkelerin kümeler arasında geçiş yaparak işgücü kompozisyonundaki yapısal değişimleri yansıttığını göstermektedir. Bu sonuçlar, katılım eşitsizliklerini azaltmak için hedefe yönelik yeniden beceri kazandırma programlarına, esnek istihdam modellerine ve eğitim odaklı işgücü politikalarına duyulan ihtiyacı vurgulamaktadır. Çalışma, İKY stratejilerinin optimize edilmesinde uluslararası yetenek hareketliliğinin, işgücünün dijitalleşmesinin ve beceri geliştirme girişimlerinin öneminin altını çizmektedir. Bu araştırma, eğitim temelli bir işgücü piyasası analizi sunarak, veri odaklı işgücü planlamasına ve küresel İKY karar alma süreçlerine katkıda bulunmaktadır.

Etik Beyan

Çalışmada kamuya açık veriler kullanılmıştır. Etik kurul onayına ihtiyaç yoktur.

Destekleyen Kurum

Çalışma herhangi bir kurum tarafından desteklenmemiştir.

Kaynakça

  • Akdemir, B., & Duman, M. Ç. (2017). KADIN ÇALIŞANLARIN PERFORMANSINDA CAM TAVAN SENDROMU ENGELİ! International Journal of Academic Value Studies, 3(15), 517–526. www.javstudies.com
  • Angrist, N., Djankov, S., Goldberg, P. K., & Patrinos, H. A. (2021). Measuring human capital using global learning data. Nature, 592(7854), 403–408. https://doi.org/10.1038/s41586-021-03323-7
  • Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press.
  • Becker, R. (2019). Economic change and continuous vocational training in the work history: a longitudinal multilevel analysis of the employees’ participation in further training and the effects on their occupational careers in Germany, 1970–2008. Empirical Research in Vocational Education and Training, 11(1), 13–14. https://doi.org/10.1186/s40461-019-0079-x
  • Becker, R., & Blossfeld, H.-P. (2017). Entry of men into the labour market in West Germany and their career mobility (1945–2008). Journal for Labour Market Research, 50(1), 113–130. https://doi.org/10.1007/s12651-017-0224-6
  • Buchmann, M., & Malti, T. (2012). The future of young women’s economic role in a globalized economy: New opportunities, persisting constraints. New Directions for Youth Development, 2012(135), 77–86. https://doi.org/10.1002/yd.20030
  • Burdorf, A., Fernandes, R. C. P., & Robroek, S. J. W. (2023). Health and inclusive labour force participation. The Lancet, 402(10410), 1382–1392. https://doi.org/10.1016/S0140-6736(23)00868-1
  • Busse, R., Sun, L., & Zhu, V. (2015). Comparing value orientations of German and Chinese managers: impacts of demographic and business-related factors. Asia Pacific Business Review, 21(2), 170–187. https://doi.org/10.1080/13602381.2014.891400
  • Carraher, S. M., Gibson, J. W., & Buckley, M. R. (2006). Compensation satisfaction in the Baltics and the USA. Baltic Journal of Management, 1(1), 7–23. https://doi.org/10.1108/17465260610640840
  • Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200–210. https://doi.org/10.1016/j.eswa.2012.07.021
  • Chi, D. (2021). Research on the Application of K-Means Clustering Algorithm in Student Achievement. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 435–438. https://doi.org/10.1109/ICCECE51280.2021.9342164
  • Ehsan, R., & Sloam, J. (2020). Resources, Values, Identity: Young Cosmopolitans and the Referendum on British Membership of the European Union. Parliamentary Affairs, 73(1), 46–65. https://doi.org/10.1093/pa/gsy035
  • El Ouirdi, M., El Ouirdi, A., Segers, J., & Pais, I. (2016). Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe. Computers in Human Behavior, 57, 240–249. https://doi.org/10.1016/j.chb.2015.12.043
  • Englehardt, C. S., & Simmons, P. R. (2002). Organizational flexibility for a changing world. Leadership & Organization Development Journal, 23(3), 113–121. https://doi.org/10.1108/01437730210424057
  • Fa’rifah, R. Y., & Pramesti, D. (2022). Cluster Analysis of Inclusive Economic Development Using K-Means Algorithm. Jurnal Varian, 5(2), 171–178. https://doi.org/10.30812/varian.v5i2.1894
  • Fahim, A. M., Salem, A.-B. M., Torkey, F. A., & Ramadan, M. A. (2006). An efficient enhanced k-means clustering algorithm. Journal of Zhejiang University-SCIENCE A, 7(10), 1626–1633. https://doi.org/10.1631/jzus.2006.A1626
  • Gibson, J. L. (2015). The Effects of Workforce Trends and Changes on Organizational Recruiting: A Practical Perspective. Industrial and Organizational Psychology, 8(3), 383–387. https://doi.org/10.1017/iop.2015.54
  • Gurgu, E., & Savu, C. (2014). Human capital in the new economy. A post-revolutionary Romanian radiography. Contemporary Readings in Law and Social Justice, 6(1), 510.
  • Hanushek, E. A., & Kimko, D. D. (2000). Schooling, Labor-Force Quality, and the Growth of Nations. American Economic Review, 90(5), 1184–1208. https://doi.org/10.1257/aer.90.5.1184
  • Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
  • Jarinto, K., & Ridsomboon, L. (2024). “Clustering by Employee Personality”, Modern Working World Perspectives on Work Efficiency in the Organizations. WSEAS Transactions on Business and Economics, 21, 288–298. https://doi.org/10.37394/23207.2024.21.26
  • Jegede, O., & Muchie, M. (2024). Introduction to the Special Issue: Leveraging Global Value Chains for innovation and industrialization in Africa. African Journal of Science, Technology, Innovation and Development, 16(4), 451–458. https://doi.org/10.1080/20421338.2024.2361952
  • Kalleberg, A. L. (2001). Organizing Flexibility: The Flexible Firm in a New Century. British Journal of Industrial Relations, 39(4), 479–504. https://doi.org/10.1111/1467-8543.00211
  • Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892. https://doi.org/10.1109/TPAMI.2002.1017616
  • Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657. https://doi.org/10.1016/j.asoc.2009.12.025
  • Kumari, R. (2018). Economic growth, disparity, and determinants of female labor force participation. World Journal of Entrepreneurship, Management and Sustainable Development, 14(2), 138–152. https://doi.org/10.1108/WJEMSD-03-2017-0009
  • Lu, H., Hou, L., Zhou, W., Shen, L., Jin, S., Wang, M., Shang, S., Cong, X., Jin, X., & Dou, D. (2021). Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018. BMJ Open, 11(10), 1–10. https://doi.org/10.1136/bmjopen-2020-047348
  • Lv, W. (2011). Educational funds, educational level and human capital - Empirical analysis based on inter-province panel data of China. 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings, 1997, 2987–2991. https://doi.org/10.1109/AIMSEC.2011.6010691
  • Maket, L. J., Lamaon, L. G., & Kwonyike, J. (2015). Managing Diversity through Workplace Flexibility for Organizational Performance. International Journal of Academic Research in Business and Social Sciences, 5(4). https://doi.org/10.6007/IJARBSS/v5-i4/1581
  • Marrocu, E., & Paci, R. (2012). Education or Creativity: What Matters Most for Economic Performance? Economic Geography, 88(4), 369–401. https://doi.org/10.1111/j.1944-8287.2012.01161.x
  • Mok, K. H., Yu, K. M., & Ku, Y. wen. (2013). After massification: The quest for entrepreneurial universities and technological advancement in Taiwan. Journal of Higher Education Policy and Management, 35(3), 264–279. https://doi.org/10.1080/1360080X.2013.786857
  • Muttaqin, M. F. J. (2022). Cluster Analysis Using K-Means Method to Classify Sumatera Regency and City Based on Human Development Index Indicator. Seminar Nasional Official Statistics, 2022(1), 967–976. https://doi.org/10.34123/semnasoffstat.v2022i1.1299
  • Muttaqin, M. F. J., & Zulkarnain. (2020). Cluster Analysis Using K-Means Method to Classify Indonesia Regency/City based on Human Development Index Indicator. Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020, 81–85. https://doi.org/10.1145/3400934.3400951
  • Neethirajan, S. (2023). Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors, 23(16). https://doi.org/10.3390/s23167045
  • Osmancevic, S., Großschädl, F., & Lohrmann, C. (2023). Cultural competence among nursing students and nurses working in acute care settings: a cross-sectional study. BMC Health Services Research, 23(1), 1–7. https://doi.org/10.1186/s12913-023-09103-5
  • Palpacuer, F. (2000). Competence-Based Strategies and Global Production Networks a Discussion of Current Changes and Their Implications for Employment. Competition & Change, 4(4), 353–400. https://doi.org/10.1177/102452940000400401
  • Patzina, A., & Wydra-Somaggio, G. (2020). Early careers of dropouts from vocational training: Signals, human capital formation, and training firms. European Sociological Review, 36(5), 741–759. https://doi.org/10.1093/esr/jcaa011
  • Peneder, M. (2007). A sectoral taxonomy of educational intensity. Empirica, 34(3), 189–212. https://doi.org/10.1007/s10663-007-9035-2
  • Perez‐Arce, F., & Prados, M. J. (2021). THE DECLINE IN THE U.S. LABOR FORCE PARTICIPATION RATE: A LITERATURE REVIEW. Journal of Economic Surveys, 35(2), 615–652. https://doi.org/10.1111/joes.12402
  • Pokharel, M., Bhatta, J., & Paudel, N. (2021). Comparative Analysis of K-Means and Enhanced K-Means Algorithms for Clustering. NUTA Journal, 8(1–2), 79–87. https://doi.org/10.3126/nutaj.v8i1-2.44044
  • Schim, S. M., Doorenbos, A. Z., & Borse, N. N. (2005). Cultural competence among Ontario and Michigan healthcare providers. Journal of Nursing Scholarship, 37(4), 354–360. https://doi.org/10.1111/j.1547-5069.2005.00061.x
  • Schömann, K., & Becker, R. (1995). Participation in Further Education over the Life Course: A Longitudinal Study of Three Birth Cohorts in the Federal Republic of Germany. European Sociological Review, 11(2), 187–208. https://doi.org/10.1093/oxfordjournals.esr.a036356
  • Shamsuddinova, S. (2024). The European Youth Guarantee scheme: A viable solution to youth unemployment? International Review of Education, 70(5), 819–847. https://doi.org/10.1007/s11159-024-10075-9
  • Sozen, H. C., Varoglu, D., Yeloglu, H. O., & Basim, H. N. (2016). Human or Social Resources Management: Which Conditions Force HR Departments to Select the Right Employees for Organizational Social Capital. European Management Review, 13(1), 3–18. https://doi.org/10.1111/emre.12063
  • Stegmaier, J., Krekel, E. M., & Bellmann, L. (2010). Aus- und Weiterbildung - Komplemente oder Substitute? REPORT - Zeitschrift Für Weiterbildungsforschung, 93, 41–54. https://doi.org/10.3278/REP1001W041
  • Tahsin, R., Rantu, S. B. A., Rahman, M., Salman, S., & Karim, M. R. (2025). Towards the adoption of AI, IoT, and Blockchain technologies in Bangladesh’s maritime industry: Challenges and insights. Results in Engineering, 25(December 2024), 103825. https://doi.org/10.1016/j.rineng.2024.103825
  • Wahyuni, S. N., Khanom, N. N., & Astuti, Y. (2023). K-Means Algorithm Analysis for Election Cluster Prediction. JOIV : International Journal on Informatics Visualization, 7(1), 1. https://doi.org/10.30630/joiv.7.1.1107
  • Wang, C. (2023). Development of Student Biochemical Index Monitoring System Based on K-means Cluster Analysis. 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), 1–5. https://doi.org/10.1109/ICICACS57338.2023.10100254
  • Weir-Smith, G. (2018). Spatiotemporal Variation of South African Jobless Trends: Policy Directions. Professional Geographer, 70(1), 94–102. https://doi.org/10.1080/00330124.2017.1325748
  • Yang, L., Peng, H., Yang, Y., Ouyang, L., & Li, Y. (2020). Situation and Countermeasures of the Management Team of the Elderly Care Institutions from the Perspective of the Combination of Medical and Health Care: A Cross-Sectional Study. Journal of Healthcare Engineering, 2020. https://doi.org/10.1155/2020/8826007
  • Zamani, F. E., Kusnandar, T., Silmi, F. E., & Rachman, R. (2023). Analysis of Public Service Satisfaction using Artificial Intelligence K-Means Cluster. Majalah Bisnis & IPTEK, 16(1), 181–187. https://doi.org/10.55208/bistek.v16i1.428
  • Zuzana, W. (2017). Comparison of requirements for brand managers responsible for competitiveness of brands: A cross-national study in the US and the Czech Republic. Journal of Competitiveness, 9(4), 148–163. https://doi.org/10.7441/joc.2017.04.10
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Tuğçe Şimşek 0000-0003-3256-4348

Gönderilme Tarihi 2 Şubat 2025
Kabul Tarihi 23 Haziran 2025
Yayımlanma Tarihi 29 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 21 Sayı: 2

Kaynak Göster

APA Şimşek, T. (2025). Implications for Human Resources Management from Labour Force Participation Rates of Countries by Education Level. Ekonomik ve Sosyal Araştırmalar Dergisi, 21(2), 437-458.

İletişim Adresi: Bolu Abant İzzet Baysal Üniversitesi İktisadi ve İdari Bilimler Fakültesi Ekonomik ve Sosyal Araştırmalar Dergisi 14030 Gölköy-BOLU

Tel: 0 374 254 10 00 / 14 86 Faks: 0 374 253 45 21 E-posta: iibfdergi@ibu.edu.tr

ISSN (Basılı) : 1306-2174 ISSN (Elektronik) : 1306-3553