INFORMATION SYSTEM FOR ASSESSING THE LEVEL OF HUMAN CAPITAL MANAGEMENT
Anzhelika Azarova
azarova.angelika@gmail.comVinnytsia National Technical University (Ukraine)
Larysa Azarova
Vinnytsia National Technical University (Ukraine)
Iurii Krak
Taras Shevchenko National University of Kyiv (Ukraine)
https://orcid.org/0000-0002-8043-0785
Olga Ruzakova
Vinnytsia Cooperative Institute (Ukraine)
https://orcid.org/0000-0002-4796-9703
Veronika Azarova
Borys Grinchenko Kyiv University (Ukraine)
Abstract
The article offers conceptual foundations for formalizing the process of assessing a level of human capital (HC) management at the enterprise using mathematical and computer modeling based on neural network technologies. The methodological approach for assessing the level of human capital management has been improved. This allows the use of neural network tools to identify accurately and reasonably the level of HC management with the help of self-learning multilayer perceptron. The weight coefficients of such a network were calculated. An appropriate artificial neural network – a multilayer perceptron – was built using the mathematical software MatLab and it was successfully diagnosed. The improved mathematical model for assessing the level of HC management at the enterprise makes it possible to display transparently a set of input parameters on a set of output solutions, to decompose such a process, and to simplify the procedure of its formalization. The designed neural network allows us to determine quickly and accurately the level of HC management at the enterprise. The conceptual approach proposed by the authors has several significant advantages over existing alternative methods: accuracy of assessment; taking into account a wide range of various evaluation parameters of impact; high speed of making decisions and self-learning ability. The proposed approach was successfully implemented to assess the level of HC management at 24 domestic enterprises. The information system "HC" developed by the authors allows to calculate the estimated parameters of the evaluation process; to determine the level of HC management based on the mathematical apparatus of the multilayer perceptron. Such estimates correlate with the estimates obtained by the experts of these enterprises which indicates the adequacy of the approach proposed by the authors. Therefore, the proposed information system for assessing the level of management of the HC allows accurate implementation of such a process with minimal time and money costs.
Keywords:
information system, multi-layer perceptron, human capital management, enterprise personnelReferences
[1] Abbas J.: Impact of total quality management on corporate sustainability through the mediating effect of knowledge management. Journal of Cleaner Production 244, 2020, 118806 [https://doi.org/10.1016/j.jclepro.2019.118806].
DOI: https://doi.org/10.1016/j.jclepro.2019.118806
Google Scholar
[2] Beijer S. et al.: The turn to employees in the measurement of human resource practices: a critical review and proposed way forward. Human Resources Management Journal 31(1), 2019, 1–17 [https://doi.org/10.1111/1748-8583.12229].
DOI: https://doi.org/10.1111/1748-8583.12229
Google Scholar
[3] Garg S., Jiang K., Lepak D. P.: HR practice salience: explaining variance in employee reactions to HR practices. The International Journal of Human Resource Management 32(2), 2020, 512–542 [https://doi.org/10.1080/09585192.2020.1792533].
DOI: https://doi.org/10.1080/09585192.2020.1792533
Google Scholar
[4] Greasley K., Thomas P.: HR analytics: The onto‐epistemology and politics of metricized HRM. Human Resources Management Journal 30(4), 2020, 494–507 [https://doi.org/10.1111/1748-8583.12283].
DOI: https://doi.org/10.1111/1748-8583.12283
Google Scholar
[5] Hauff S.: Analytical strategies in HRM systems research: a comparative analysis and some recommendations. The International Journal of Human Resource Management 32(9), 2019, 1923–1952 [https://doi.org/10.1080/09585192.2018.1547779].
DOI: https://doi.org/10.1080/09585192.2018.1547779
Google Scholar
[6] Heffernan M. et al.: HRM system strength and employee well-being: the role of internal process and open systems. Asia Pacific Journal of Human Resources 60, 2021, 171–193 [https://doi.org/10.1111/1744-7941.12302].
DOI: https://doi.org/10.1111/1744-7941.12302
Google Scholar
[7] Hermans M., Ulrich M. D.: How symbolic human resource function actions affect the implementation of high‐performance work practices: The mediating effect of influence on strategic decision‐making, Human Resources Management Journal 31(4), 2021, 1063–1081 [https://doi.org/10.1111/1748-8583.12361].
DOI: https://doi.org/10.1111/1748-8583.12361
Google Scholar
[8] Ivanov S. et al.: Formation of Logit-Model for Predicting the Probability of Bankruptcy of Ukrainian Enterprises. Science and Innovation 19(1), 2023, 36–48 [https://doi.org/10.15407/scine19.01.036].
DOI: https://doi.org/10.15407/scine19.01.036
Google Scholar
[9] Jeronimo H., Correia de Lacerda T., Lopes Henriques P.: From Sustainable HRM to Employee Performance: A Complex and Intertwined Road. European Management Review 17(4), 2020, 871–884 [https://doi.org/10.1111/emre.12402].
DOI: https://doi.org/10.1111/emre.12402
Google Scholar
[10] Matviychuk A., Lukianenko O., Miroshnychenko I.: Neuro-fuzzy model of country's investment potential assessment. Fuzzy economic review 24(2), 2019, 65–88 [https://doi.org/10.25102/fer.2019.02.04].
DOI: https://doi.org/10.25102/fer.2019.02.04
Google Scholar
[11] Miao R. et al.: High-performance work systems and key employee attitudes: the roles of psychological capital and an interactional justice climate. The International Journal of Human Resource Management 32(2), 2020, 443–477 [https://doi.org/10.1080/09585192.2019.1710722].
DOI: https://doi.org/10.1080/09585192.2019.1710722
Google Scholar
[12] Melnyk L. et al.: Transformation of the human capital reproduction in line with Industries 4.0 and 5.0. Problems and Perspectives in Management 19(2), 2021, 480–494 [https://doi.org/10.21511/ppm.19(2).2021.38].
DOI: https://doi.org/10.21511/ppm.19(2).2021.38
Google Scholar
[13] Saks A. M.: Caring human resources management and employee engagement, Human Resource Management Review 32(3), 2022, 100835 [https://doi.org/10.1016/j.hrmr.2021.100835].
DOI: https://doi.org/10.1016/j.hrmr.2021.100835
Google Scholar
[14] Snell S., Morris S.: Time for realignment: the HR ecosystem. Academy of Management Perspectives 35(2), 2019, 219–236 [https://doi.org/10.5465/amp.2018.0069].
DOI: https://doi.org/10.5465/amp.2018.0069
Google Scholar
[15] Sorribes J., Celma D., Martinez-Garcia E.: Sustainable human resources management in crisis contexts: Interaction of socially responsible labor practices for the wellbeing of employees, Corporate Social Responsibility and Environmental Management 28(1), 2021, 720–741 [https://doi.org/10.1002/csr.2111].
DOI: https://doi.org/10.1002/csr.2111
Google Scholar
[16] Subramony M., Guthie J. P., Dooney, J.: Investing in HR? Human resource function investments and labor productivity in US organizations, The International Journal of Human Resource Management 32(2), 2021, 307–330 [https://doi.org/10.1080/09585192.2020.1783343].
DOI: https://doi.org/10.1080/09585192.2020.1783343
Google Scholar
Authors
Anzhelika Azarovaazarova.angelika@gmail.com
Vinnytsia National Technical University Ukraine
Authors
Larysa AzarovaVinnytsia National Technical University Ukraine
Authors
Iurii KrakTaras Shevchenko National University of Kyiv Ukraine
https://orcid.org/0000-0002-8043-0785
Authors
Veronika AzarovaBorys Grinchenko Kyiv University Ukraine
Statistics
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