Journal of Applied Economic Research
ISSN 2712-7435
Model of the Influence of Employee Competencies on Performance Considering Burnout
Lev S. Mazelis 1,
Gleb V. Grenkin 1,
Kirill I. Lavrenyuk 2
1 Vladivostok State University, Vladivostok, Russia
2 Smart Space (Samokat Tech), Saint-Petersburg, Russia
Abstract
The study was conducted as part of a current task of studying factors in the development of an organization's human capital that have a direct or indirect impact on employee productivity. The set of such factors includes indicators of the level of development of competencies and the degree of burnout of the individual. The work aims to develop economic and mathematical tools that would quantitatively describe the impact of employee competency values on their key performance indicators (KPIs), taking into account the level of burnout determined by the values of loyalty, involvement, and satisfaction. To achieve this goal, a hypothesis is put forward about the possibility of constructing a tool that makes it possible, based on a fuzzy classification of employees by level of competency development, to construct functional dependencies of KPIs on burnout indicators for each category. The initial data used were the results of a self-assessment of competencies and burnout of employees, mainly working in the areas of IT and HR in seven large Russian companies, calibrated by the immediate supervisors of the respondents, and the actual values of their KPIs. The work proposes an approach that includes two stages. As the first step, a fuzzy model is built that makes it possible, based on a weighted integral indicator of competency development with optimal weighting coefficients, to divide the range of values of the integral indicator into categories of uneven size, with the help of which it is possible to predict the achievement of KPIs. As the second step, an econometric model of the dependence of KPI on employee burnout indicators was built using the weighted least squares method to explain the dispersion of KPI values of employees belonging to individual competency categories in a fuzzy formulation, around the expected KPI value. The proposed tool will make it possible to predict the achievement of KPIs by employees depending on the input values of competencies and the level of burnout. In the future, this will make it possible to form an optimal portfolio of well-being program activities that will influence the development of employee competencies and reduce their level of burnout, and, therefore, contribute to the maximum progress in achieving target KPI values.
Keywords
competency; key performance indicator; burnout process; well-being program; multiple piecewise constant regression; fuzzy set approach.
JEL classification
J24References
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Acknowledgements
The study was supported by the Russian Science Foundation Grant No. 23-28-01333, https://rscf.ru/project/23-28-01333/, at Vladivostok State University.
About Authors
Lev Solomonovich Mazelis
Doctor of Economis, Professor, The Mathematics and Modeling Department, Vladivostok State University, Vladivostok, Russia (690014 Primorsky Krai, Vladivostok, Gogolya street, 41); ORCID https://orcid.org/0000-0001-7346-3960 e-mail: lev.mazelis@vvsu.ru
Gleb Vladimirovich Grenkin
Candidate of Physical and Mathematical Sciences, Assistant Professor, The Mathematics and Modeling Department, Vladivostok State University, Vladivostok, Russia (690014 Primorsky Krai, Vladivostok, Gogolya street, 41); ORCID https://orcid.org/0000-0002-1307-3757 e-mail: glebgrenkin@gmail.com
Kirill Igorevich Lavrenyuk
Group Leader, HR Management Tools Development Group, Smart Space (Samokat Tech), Saint-Petersburg, Russia (192019, Saint-Petersburg, Sedova street, 11А); ORCID https://orcid.org/0000-0002-9092-3196 e-mail: klavrenyuk@samokat.ru
For citation
Mazelis, L.V., Grenkin, G.V., Lavrenyuk, K.I. (2024). Model of the Influence of Employee Competencies on Performance Considering Burnout. Journal of Applied Economic Research, Vol. 23, No. 1, 227-250. https://doi.org/10.15826/vestnik.2024.23.1.010
Article info
Received November 12, 2023; Revised December 4, 2023; Accepted January 9, 2024.
DOI: https://doi.org/10.15826/vestnik.2024.23.1.010
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