Further education, its methods and selected characteristics of organisations: an empirical study of their association with organisations profitability
Abstract
Purpose – The paper presents the results of the study based on a sample of 358 organisations that focuses on further education and training (FET) of their employees. It specifically investigates which specific educational methods and various characteristics of organisations are associated with their financial performance.
Research methodology – The Dependency Aware Feature (DAF) selection method from statistical pattern recognition has been used to identify which of the 37 considered variables are most associated with the profitability indicators (ROA, ROCE, ROS).
Findings – The profitability indices are significantly associated with some of the specific methods of FET. Organisations should pay attention particularly to instructing, coaching and mentoring. The results also confirm the importance of talent management for organisations to be successful.
Research limitations – The examined sample consists solely of organisations operating in the Czech Republic. Shortly, we plan to extend the selection by including organisations from abroad. Practical implications – The study provides recommendations for HR managers for the goals they should focus on. Organisations should evaluate the impacts of FET; otherwise increasing investments in it may not have an effect.
Originality/Value – The originality of the current study lies in using a new methodology based on machine learning and respecting complex mutual relations among variables.
Keyword : further education and training, lifelong education, methods of education, organisations’ profitability, human resource management
How to Cite
Pudil, P., Somol, P., Mikova, I., Pribyl, V., & Komarkova, L. (2021). Further education, its methods and selected characteristics of organisations: an empirical study of their association with organisations profitability. Business, Management and Economics Engineering, 19(1), 111-130. https://doi.org/10.3846/bmee.2021.13952
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Bartonkova, H. (2010). Firemní vzdělávání [Corporate education] (1st ed.). Grada Publishing, a.s.
Beynon, M. J., Jones, P., Pickernell, D., & Packham, G. (2015). Investigating the impact of training influence on employee retention in small and medium enterprises: A regression-type classification and ranking believe simplex analysis on sparse data. Expert Systems, 32(1), 141–154. https://doi.org/10.1111/exsy.12067
Bhatti, U. A., Huang, M., Wu, D., Zhang, Y., Mehmood, A., & Han, H. (2019). Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Information Systems, 13(3), 329–351. https://doi.org/10.1080/17517575.2018.1557256
Calderon, A. C., Crick, T., & Tryfona, C. (2015). Developing computational thinking through pattern recognition in early years education. In Proceedings of the 2015 British HCI Conference (pp. 259–260). https://doi.org/10.1145/2783446.2783600
Cervelló-Royo, R., Guijarro, F., & Michniuk, K. (2015). Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data. Expert Systems with Applications, 42(14), 5963–5975. https://doi.org/10.1016/j.eswa.2015.03.017
Chen, Y.-S., Chang, B.-G., & Lee, C.-C. (2008). The association between continuing professional education and financial performance of public accounting firms. The International Journal of Human Resource Management, 19(9), 1720–1737. https://doi.org/10.1080/09585190802295363
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Czech Statistical Office. (2020). Organisational Statistics. https://www.czso.cz/csu/czso/organizationalstatistics
Davidović, G. R. (2020, September). Lifelong learning in pandemic situation–challenge and need. In 8th International Scientific Conference Technics and Informatics in Education (pp. 77–82). Faculty of Technical Sciences, Čačak, Serbia.
De Grip, A., & Sauermann, J. (2013). The effect of training on productivity: The transfer of on-the-job training from the perspective of economics. Educational Research Review, 8, 28–36. https://doi.org/10.1016/j.edurev.2012.05.005
Devijver, P. A., & Kittler, J. (1982). Pattern recognition: A statistical approach. Prentice/Hall International.
Dirani, K. M., & Nafukho, F. M. (2018). Talent management and development: Perspectives from emerging market economies. Advances in Developing Human Resources, 20(4), 383–388. https://doi.org/10.1177/1523422318803362
Egerová, D., Eger, L., Jirincova, H., & Ali Taha, V. (2013). Integrated talent management challenge and future for organisations in Visegrad Countries. NAVA.
Epstein, D. (2019). Range: How generalists triumph in a specialised world. Pan Macmillan.
Escobar, C. A., & Morales-Menendez, R. (2017). Machine learning and pattern recognition techniques for information extraction to improve production control and design decisions. In P. Perner (Ed.), Lecture notes in computer science: Vol. 10357. Advances in data mining. Applications and theoretical aspects (pp. 286–300). Springer, Cham. https://doi.org/10.1007/978-3-319-62701-4_23
Falola, H. O., Osibanjo, A. O., & Ojo, I. S. (2014). Effectiveness of training and development on employees’ performance and organisation competitiveness in the Nigerian banking industry. Bulletin of the Transilvania University of Braşov, 7(1), 161–170.
Folwarczna, I. (2010). Rozvoj a vzdělávání manažerů [Development and education of managers]. Grada Publishing a.s. (in Czech).
Grossman, R., & Salas, E. (2011). The transfer of training: What really matters: The transfer of training. International Journal of Training and Development, 15(2), 103–120. https://doi.org/10.1111/j.1468-2419.2011.00373.x
Haak-Saheem, W. (2020). Talent management in Covid-19 crisis: How Dubai manages and sustains its global talent pool. Asian Business & Management, 19, 298–301. https://doi.org/10.1057/s41291-020-00120-4
Hamblin, A. C. (1974). Evaluation and control of training. McGraw-Hill.
Henderson, A. J. (2003). The e-learning question and answer book: A survival guide for trainers and business managers. American Management Association.
Heslin, P. A., Keating, L. A., & Ashford, S. J. (2020). How being in learning mode may enable a sustainable career across the lifespan. Journal of Vocational Behavior, 117, 103324. https://doi.org/10.1016/j.jvb.2019.103324
Hite, L. M., & McDonald, K. S. (2020). Careers after COVID-19: Challenges and changes. Human Resource Development International, 23(4), 427–437. https://doi.org/10.1080/13678868.2020.1779576
Jain, A. K., Duin, R. P. W., & Jianchang Mao. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37. https://doi.org/10.1109/34.824819
Kaur, J. (2016). Impact of training and development programmes on the productivity of employees in the banks. Journal of Strategic Human Resource Management, 5(1). https://doi.org/10.21863/jshrm/2016.5.1.023
Khodaskar, A. A., & Ladhake, S. A. (2014). Pattern recognition: Advanced development, techniques and application for image retrieval. In 2014 International Conference on Communication and Network Technologies (pp. 74–78). Sivakasi, India. https://doi.org/10.1109/CNT.2014.7062728
Kirkpatrick, D. L. (1959). Techniques for evaluation training programs. Journal of the American Society of Training Directors, 13, 21–26.
Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs: The four levels (3rd ed). Berrett-Koehler.
Matusov, E. (2020). Pattern-recognition, intersubjectivity, and dialogic meaning-making in education. Dialogic Pedagogy: An International Online Journal, 8, 1–24. https://doi.org/10.5195/dpj.2020.314
Mayhew, K., & Anand, P. (2020). COVID-19 and the UK Labour Market. Oxford Review of Economic Policy, 36(Supplement 1), S215–S224. https://doi.org/10.1093/oxrep/graa017
Mehrdad, A., Salehi, M., & Ali, S. (2009). A study of on the job training effectiveness: Empirical evidence of Iran. International Journal of Business and Management, 4(11), 63–68. https://doi.org/10.5539/ijbm.v4n11p63
Mikova, I., Komarkova, L., & Pudil, P. (2019a). Support of development of non-profit organisations through special training programs for their managers. In CIBMEE 2019: Proceedings of the International Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering’2019” (pp. 468–479). Vilnius Gediminas Technical University, Vilnius, Lithuania. https://doi.org/10.3846/cibmee.2019.048
Mikova, I., Komarkova, L., Pudil, P., & Pribyl, V. (2019b). Comparison of usage and effectiveness of methods for further education. In L. Gomez Chova, A. Lopez Martinez, & I. Candel Torres (Eds.), 11th International Conference on Education and New Learning Technologies “Edulearn19 Proceedings” (pp. 4930–4937). Palma, Mallorca. IATED Academy. https://doi.org/10.21125/edulearn.2019.1230
Morley, M., Szlávicz, Á., Poór, J., & Berber, N. (2016). Training practices and organisational performance: A comparative analysis of domestic and international market oriented Organisations in Central & Eastern Europe. Journal for East European Management Studies, 21(4), 1–27. https://doi.org/10.5771/0949-6181-2016-4-406
Naranjo, R., & Santos, M. (2019). A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognition. Expert Systems with Applications, 133, 34–48. https://doi.org/10.1016/j.eswa.2019.05.012
Nikandrou, I., Apospori, E., Panayotopoulou, L., Stavrou, E., & Papalexandris, N. (2008). Training and firm performance in Europe: The impact of national and organisational characteristics. International Journal of Human Resource Management, 19(1), 2057–2078. https://doi.org/10.1080/09585190802404304
Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475–486. https://doi.org/10.1016/j.ssci.2019.06.001
Phillips, J. J. (1996). How much is the training worth? Training and Development, 50(4), 20–24.
Pudil, P., Blazek, L., Castek, O., Somol, P., Pokorna, J., & Kralova, M. (2014a). Identifying corporate performance factors based on feature selection in statistical pattern recognition: Methods, application, interpretation. Munipress. https://doi.org/10.5817/CZ.MUNI.M210-7557-2014
Pudil, P., Komarkova, L., & Mikova, I. (2017). Link between financial performance of organisations and selected aspects of further education. In European Conference on Management, Leadership & Governance (pp. 402–407). London, UK. Academic Conferences International Limited.
Pudil, P., Mikova, I., Komarkova, L., & Pribyl, V. (2019). Relation of selected factors of further education in organisations development and profitability of organisations. In CIBMEE 2019: Proceedings of the International Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering’2019” (pp. 247–254). Vilnius Gediminas Technical University, Vilnius, Lithuania. https://doi.org/10.3846/cibmee.2019.025
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