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Modeling the process of developing employee competencies and assessing their impact on an organization's performance is an urgent task. The study aims to develop a unified concept for modeling the process of employee competency development by implementing a corporate wellbeing program to achieve the workers’ target KPIs. The study consisted of two stages – modeling and a survey. A database on the components of the model is formed based on a survey of 727 individuals from different companies and economic sectors. The model is tested by means of preliminary analysis of the collected data, their clustering, assessment of interrelations of components, and systematization of the existing regularities. Fuzzy clustering of values, wellbeing elements, and individuals is constructed on multidimensional samples. Estimates of the probabilities of elements' transitions from clusters by values to clusters by activities and vice versa are obtained. The fuzzy clustering algorithm is developed in Python. The results show that for employees with a less pronounced value model, the wellbeing program in the company is of medium importance. Conversely, the wellbeing program in the company is of high importance for employees with prevailing social values. Employee clustering can suggest several propositions for the most efficient activities of the corporate wellbeing program according to the envisioned generalized employee value model. Conversely, it can help determine a candidate's optimal value profile for them to work effectively in the organization proceeding from the current corporate wellbeing program.
cluster analysis, competencies, economic and mathematical model, employee value model, key performance indicators, questionnaire survey, wellbeing program
1.1 Research background
There is no denying that the world community has been going through a continuous transformation of socioeconomic processes in recent years. The changes affect the operation of all institutions, from social to economic. Many organizations have already reformatted their strategic vector of development to focus on the real needs of end consumers (i.e., the product approach has been integrated into their business processes), which is necessary to create products that are in real demand in the market. People are the primary resource for creating such products in the first place.
Organizations increasingly often choose the system of shortterm planning where indicators and their target values are defined for one calendar year and the roadmap of measures to achieve them is revised quarterly. In this case, the primary issues identified by company management as part of planning are, on the one hand, the difficulty of assessing the input of each employee into the result and, on the other hand, establishing the components of the corporate environment that would be able to ensure maximum employee productivity. The challenge of answering these questions also lies in the fact that in addition to physical factors, the work of each individual is affected by psychological factors (meaning that an employee who is fully fit for their position and has been delivering the desired results in the past may now be entirely ineffective, for example, because of their emotional instability due to burnout).
Considering the above, we analyzed recent studies devoted to the processes of human capital development (including by means of wellbeing programs) and the assessment of their impact on an organization's performance.
1.2 Employees’ competencies and their human capital
A considerable number of studies examine the issues of developing both employees’ competencies and their human capital in the context of the transition to the fourth industrial revolution. Salman et al. [1] evaluate the influence of investment in various human resource management methods on the development of employee competencies. A study by Karpov et al. [2] proposes approaches to creating an educational environment in an organization for the development of its human capital. Rudyk et al. [3] investigate the primary issues in the advanced training and improvement of professional competencies among service industry enterprises. Loyalka et al. [4] analyze the effect of professional education and retraining on the development of human capital in China. A paper by Biao [5] proposes the concept of lifelong learning and analyzes its influence on human capital development. Several works examine the impact of the specifics of the corporate culture at an enterprise on various aspects of employees’ work, for instance, innovative activity [6, 7] or commitment to an organization [8]. Of note is research dedicated to analyzing the competencybased approach within Industry 4.0 [9]. The impact of an organization's corporate wellbeing system on its employees is now understood relatively poorly. Specifically, van Hierden et al. [10] propose an electronic healthcare model as an element of a wellbeing system. A study by Click and Dobbins [11] presents an analysis of the relationship between the elements of the wellbeing system and the level of financial literacy in staff.
1.3 Effect of employees’ competencies on the efficiency and productivity of individual business units and an organization
The next block of research concerns the assessment of the effect of employees’ competencies (or human capital) on the efficiency and productivity of both individual business units and an organization. In this line of research, Sivakalyan Kumar and Bhanu [12] analyze relationships between the competencies of a particular employee and the work performed by them. Studies by Ateke and Nwulu [13] and Otoo and Mishra [14] are aimed at determining the relationship between the development of employee competencies and a company’s organizational stability and efficiency. Nair et al. [15] explore the influence of leadership competencies (cognitive, emotional, and social intelligence) on employee performance. A study by Saleh [16] investigates the impact of motivational variables on employee performance in higher education. Rizka et al. [17] analyze employee efficiency by studying the effects of transformational leadership, employee engagement, and readiness for change. A study by Sakthi and Akila [18] assesses the effect of employees’ emotional competencies on their labor performance. An article by Mariyani et al. [19] addresses causal relationships between HRunit competencies, organizational culture, and employee performance.
1.4 Implication of employee burnout on their productivity and efficiency
It is worth noting separately several works concerning the implication of employee burnout on their productivity and efficiency. Iriani et al. [20] analyze the impact of leadership style, various rewards, and competencies on employee performance through the prism of employee job satisfaction. Kim and Jung [21] explore the influence of organizational culture and employee competencies on perceived stress, which has a negative physical and psychological effect on employees. A study by Daguplo et al. [22] evaluates the influence of the competencies of public institution employees on their satisfaction.
In previous studies, we considered the aspects of human capital development at various levels. Our 2020 paper [23] presents an economicmathematical optimization model for the formation of regional project portfolios to achieve the strategic goals of a region's development. Another study published in 2017 [24] proposes a model for forming a plan of corporate activities aimed at the development of human capital at a university.
It should be noted that the gap in the current research, which our research aims to address, is the following drawbacks of the currently available instruments, which diminish the efficiency of the corporate system of employee development and thereby narrow an organization's growth opportunities:
In view of the above, we can conclude that there is a deficit of muchneeded instruments. These tools are required, first, to describe the influence of various corporate policy components on the development of employees’ competencies and, as a result, on their performance and the efficiency of an organization overall. Second, these instruments must enable a company to determine the optimal set of measures to develop employees' competencies and reduce their level of burnout and, as a result, to make maximum progress towards achieving the target key performance indicator values of both individual employees and an organization at large.
1.5 Purpose and objectives of the study
The study aims to develop a unified concept for modeling the process of employee competency development by implementing a corporate wellbeing program to achieve the workers’ target KPIs.
For the purposes of achieving the goal of the study, the following research objectives were formulated:
– to develop a conceptual model for the development of employees’ competencies that enable them to obtain the target KPIs by means of implementing the optimal portfolio of activities within the corporate wellbeing program, considering a quantitative assessment of employee burnout;
– to analyze the compiled database in terms of fuzzy clustering of individuals' values, wellbeing elements, and individuals by multidimensional samples of values and activities, as well as estimating the probabilities of the transition of elements from clusters by values to clusters by activities.
The model actualizes the concepts of a corporate wellbeing program, values, competencies, burnout, and KPIs. The sets of activities in the wellbeing program, competencies, values, and KPIs of employees are clarified. A method is proposed for quantitative assessment of employee burnout. The study also describes the relationships between different components of the model: the cost of implementation of wellbeing program activities, employee competencies, values, engagement, loyalty, satisfaction, and workers' KPIs.
2.1 Study design
The study consisted of two stages – modeling and a survey. As part of the present research, a conceptual model for the development of employees' competencies was developed to achieve the target values of their KPIs through the formation and implementation of an optimal corporate wellbeing program considering employee burnout.
At the core of the conceptual model are:
2.2 Conceptual model for the development of employee competencies through the implementation of a corporate wellbeing program
The study examined a multiperiod dynamical problem in which time t = 0.1,…,T, where T is the number of quarters in the planning horizon. At each time point t the organization invests financial resources into various measures as part of the corporate wellbeing program that directly or indirectly influence the development of employees’ competencies or reduce their burnout at time point (t + 1). In this, employee competencies at time point t affect both the attainment of critical KPIs by employees and a company at time point t. An important aspect of the issue is that in commercial organizations achieving the target values of their KPIs at time t has a direct impact on the amount of financial resources invested in the program at time (t + 1).
Under employee competencies, we understand their inherent abilities or acquired knowledge, abilities, and skills that in aggregate ensure the efficient and rational functioning of staff as a production factor of development. For the purposes of this study, the list of competencies was actualized and systematized as follows:
Each employee of the organization is assigned the following vector function:
$X_i(t)=\left(x_{i 1}(t), \ldots, x_{i 20}(t)\right), i=1, \ldots, N$
where, $x_{i j}(t)$ is the level of mastery of competency j by the ith employee at time t; N is the number of employees.
In our study, $x_{i j}(t)$ is taken as a fuzzy variable showing the level of an employee's mastery of a competency. The value of this variable is determined using verbal assessments. For the linguistic variable $Y_1$ = <level of employee’s mastery of the competency>, the termset can be written as $V\left(Y_1\right)$ = {absent; minimal; low; average; high; very high}. The functions of affiliation are given in the form of fuzzy numbers: W(absent) = {0; 0; 0; 1}; W(minimal) = {0; 0; 1; 2}; W(low) = {0; 1; 2; 3}; W(average) = {1; 2; 3; 4}; W(high) = {2; 3; 4; 5}; W(very high) = {3; 4; 5; 5}.
Within the framework of the conceptual model, the development of competencies was carried out through the implementation of measures under the wellbeing program. A wellbeing program is part of an organization's corporate culture that seeks to create and further develop favorable working conditions that allow an employee to feel comfortable and unlock their potential. The list of wellbeing program measures was updated and systematized within the framework of this study:
It was presumed that on a quarterly basis, an organization invests financial resources in the implementation of the program measures to develop employees' competencies. In this case, a decision on the total amount of investments to be allocated for the implementation of the program over the time interval [t; t + 4] is made once at time point t. For commercial organizations, it is defined as a share β of its annual profit, and for nonprofit organizations – as a specific budget for employee development.
In turn, the value of change in the employee’s competency at time point (t + 1) depends on the structure of investment directed toward the realization of measures as part of the wellbeing program at time point t and is established through the following equation:
$\begin{gathered}x_{i j}(t+1)=x_{i j}(t)+\Delta x_{i j}(t) \\ \Delta x_{i j}(t)=f_j\left(x_{i j}(t), z_1(t), \ldots, z_{29}(t)\right)\end{gathered}$
where, $\Delta x_{i j}(t)$ is change in the level of mastery of competency j by the ith employee at time point t as a result of measures implemented under the wellbeing program; $z_k(t)$ is the amount of financial resources allocated for the implementation of the kth measure of the corporate wellbeing program at time t (in ths. rubles); k is the number of measures implemented under the wellbeing program.
An employee advances in achieving their KPIs by virtue of possessing specific competencies. In this connection, the development of competencies becomes the main tool for increasing their productivity and labor efficiency and, therefore, for increasing their performance results expressed in KPIs. Within the framework of this study, we proposed a unified list of employee KPIs ($y_{i p}$):
When building functional dependencies of the influence of an employee's competencies on their KPI values, it was necessary to identify the reasons why the same employees with the same level of competencies and unchanged business processes at different time points bring different results to an organization as expressed in their KPI. In this study, we hypothesized that the efficiency and effectiveness of the employee’s labor is influenced by the degree of their burnout. The degree of employee burnout is understood here as the physical and emotional state of the worker that directly or indirectly affects their speed and number of mistakes in realizing the business processes they are responsible for. As an assessment of the degree of employee burnout we used three $a_{l i}$ indicators:
These indicators were determined by means of a questionnaire survey of an organization's employees. Their assessment was performed on a 100point scale. The extent of burnout of the ith employee at time t was calculated as follows:
$a_i(t)=\frac{1}{3} \cdot\left(a_{1 i}(t)+a_{2 i}(t)+a_{3 i}(t)\right)$
where, $a_i(t)=[0 ; 20)$ is severe burnout of the ith employee at time point t; $a_i(t)=[20 ; 40)$ is burnout of the ith employee at time point t; $a_i(t)=[40 ; 60)$ is the ith employee being close to burnout at time point t; $a_i(t)=[60 ; 80)$ is the ith employee being motivated at time point t; $a_i(t)=[80 ; 100]$ is the ith employee being maximally motivated at time point t.
In studying the nature of employee burnout, we developed a hypothesis that this phenomenon stems from the diversion of employees’ expectations about a company’s corporate and work policies from reality (in this study, this implies the gap between reality and employees’ expectations about the elements of the wellbeing program implemented at a company). Thus, the functional dependence of the indicators of satisfaction, engagement, and loyalty of the ith employee on the divergence of their expectations from reality at time point t can be expressed as follows:
$a_{l i}(t)=g_{l i}\left(\left(b_{i 1}(t)\hat{b}_1(t)\right) \cdot \frac{\alpha_{i 1}(t)}{5}, \ldots,\left(b_{i k}(t)\hat{b}_k(t)\right) \cdot \frac{\alpha_{i k}(t)}{5}\right)$
where, $b_{i k}(t)$ is assessment of the ith employee’s expectations about the presence of the kth measure of the wellbeing program at a company at time point t (discrete value: 0 – the presence of the activity is unimportant to the employee; 1 – the presence of the activity is important); $\widehat{b}_k(t)$ – assessment of the factual realization of the kth activity under the corporate wellbeing program at time point t (discrete value: 0 – the activity is implemented; 1 – the activity is not implemented); $\alpha_{i k}(t)$ – importance of the kth activity of the wellbeing program being organized at a company for the ith employee at time point t, $\alpha_{i k}(t)$ being a fuzzy variable.
Importantly, the presence of one or another activity within the corporate wellbeing program at a company is indicated here by the direction of investment towards its implementation.
In the framework of this study, it is assumed that employees’ expectations about the presence of activities in an organization do not depend on their value models. Employee values are understood as their highest goals in life. All values are inherently immaterial, and any material component is but a means to achieve them. In this work, we propose a unified list of employee values:
In this way, the functional dependence of the ith employee’s expectations about the presence of the kth measure of the corporate wellbeing program at a company at time point t on the values of the ith employee at time point t can be expressed as follows:
$\alpha_{i k}(t)=h_k\left(d_{i 1}(t), \ldots, d_{i 10}(t)\right)$
where, $d_{i r}(t)$ is assessment of the importance of value r for the ith employee at time point t, $d_{i r}(t)$ being a fuzzy variable; r is the number of employee values.
Considering the above, the functional dependence between the value of the pth KPI indicator of the ith employee at time point t and the values of their competencies with consideration of burnout level can be expressed as follows:
$y_{i p}(t)=q_p\left(x_{i 1}(t), \ldots, x_{i 20}(t), a_i(t)\right)$
Figure 1. Conceptual model to describe the process of employee competency development in an organization
To give a summary, Figure 1 provides a description of the process of employee competency development by means of a corporate wellbeing program to achieve the target employee KPIs.
Based on the conceptual model, we formulated an optimization problem, the solution of which will allow us to determine the optimal structure of investment distribution by areas of investment for all moments of time on the planning horizon.
The target function of the optimization model has the form:
$I(T)=\sum_{i=1}^N \sum_{p=1}^6 \frac{y_{i p}(T)}{\underline{y}_{i p}(T)} \rightarrow \max$
where, $\underline{y}_{i p}(T)$ is the target value of the pth KPI of the ith employee at time point T.
The optimization variable is $z_k(t)$.
Within the optimization model, the total amount of investments allocated for the implementation of the corporate wellbeing program activities is limited to the budget allocated for the planning period $\sum_{t=1}^T Z(t)$:
$\sum_{t=1}^T \sum_{k=1}^{29} z_k(t) \leq \sum_{t=1}^T Z(t)$
In accordance with the above, the multiperiod optimization model is as follows:
$I(T)=\sum_{i=1}^N \sum_{p=1}^6 \frac{y_{i p}(T)}{\underline{y}_{i p}(T)} \rightarrow \max , t=1, \ldots, T$
$y_{i p}(t)=q_p\left(x_{i 1}(t), \ldots, x_{i 20}(t), a_i(t)\right)$
$a_i(t)=\left(a_{1 i}(t)+a_{2 i}(t)+a_{3 i}(t)\right) \cdot \frac{1}{3}$
$a_{l i}(t)=g_{l i}\left(\left(b_{i 1}(t)\hat{b}_1(t)\right) \cdot \frac{\alpha_{i 1}(t)}{5}, \ldots,\left(b_{i k}(t)\hat{b}_k(t)\right) \cdot \frac{\alpha_{i k}(t)}{5}\right)$
$\alpha_{i k}(t)=h_k\left(d_{i 1}(t), \ldots, d_{i 10}(t)\right)$
$x_{i j}(t)=x_{i j}(t1)+\Delta x_{i j}(t1)$
$\Delta x_{i j}(t)=f_j\left(x_{i j}(t1), z_1(t1), \ldots, z_{29}(t1)\right)$
$\sum_{t=1}^T \sum_{k=1}^{29} z_k(t) \leq \sum_{t=1}^T Z(t)$
The solution of the optimization problem will help to create an efficient and transparent corporate environment in an organization, which will contribute to the growth of employee productivity and unleash their potential.
2.3 Survey
As a part of this study, a questionnaire survey of various target groups was conducted. The survey was conducted using the online service anketolog.ru. It included 39 questions aimed at selfassessment of the importance of personal values, wellbeing program activities, levels of proficiency in existing competencies, etc. For example, the request to assess the importance of values was formulated as "Assess the importance for you of each proposed value". The assessment was carried out using linguistic variables: "Not important", "Minimum importance", "Below average importance", etc. Linguistic variables, which were subsequently translated into fuzzy numbers, were used to smooth out the subjectivity and vagueness of the respondents' answers.
To collect data as part of the survey, the questionnaires were sent to top managers of seven Russian companies (Samokat, Avito, GazpromMedia, etc.), who distributed them among their employees, mainly in IT and HR management. The total number of respondents amounted to 727 people.
The target portrait of respondents was as follows:
2.4 Data analysis
Using the responses of survey participants, a fuzzy clustering of employee values, activities under the corporate wellbeing program, and respondents was carried out according to their assessments of the importance of values and expectations for the presence of particular activities.
The survey results are presented as trapezoidal fuzzy numbers $\left(c_1, c_2, l, r\right)$, where $c_1, c_2$ are coordinates of the vertices of the upper base of the trapezoid and l, r are the lengths of side projections.
Clustering of fuzzy data was performed by a variant of the fuzzy Cmeans method as described by Coppi et al. [25]. The method consists in minimizing the target function under some constraints:
$J=\sum_{i=1}^I \sum_{k=1}^K u_{i k}^m d^2\left(x_i, h_k\right)$
where, $x_i$ is the fuzzy coordinate vector of the ith sample element; $h_k$ is the fuzzy vector of the kth cluster; $u_{i k}$ is the measure of the ith element's belonging to the kth cluster; m is the specified parameter of fuzzification determining the degree of cluster fuzziness; I is the number of elements in the sample; K is the number of clusters.
The Euclidean distance between a pair of fuzzy points is used as a metric in the feature space:
$d^2\left(x, x^{\prime}\right)=w_c^2\left(\left\c_1c^{\prime}{ }_1\right\^2+\left\c_2c^{\prime}{ }_2\right\^2\right)+w_S^2\left( ll^{\prime}\left\^2+\right\ rr^{\prime} \^2\right)$
The limitations of the optimization problem are:
$\begin{gathered}0 \leq w_s \leq w_c, w_s+w_c=1 \\ \sum_{k=1}^c u_{i k}=1, u_{i k} \geq 0\end{gathered}$
The variables on which optimization is performed are $u_{i k}$ and $h_k$ and the metric parameters are $w_s$ and $w_c$. During the clustering algorithm the optimum is determined alternately based on $u_{i k}$ with fixed $h_k$ and $w_s$, $w_c$, followed by identification of the optimum by $h_k$, $w_s$, $w_c$ with fixed $u_{i k}$. The iterative process is repeated until the required accuracy in finding the parameters is achieved. The parameter m = 1.3. The clustering algorithm was developed in the Python language.
3.1 Survey findings
The distribution of respondents’ answers on the importance of the assessed values is presented in Table 1.
Table 1. Respondents' answers on the importance of the values
Importance of the Value 


Absent 
Minimal 
Low 
Average 
High 
Very high 
Freedom 
2 
3 
13 
93 
226 
390 
Family 
3 
4 
15 
68 
131 
506 
Enjoyment 
1 
2 
19 
86 
223 
396 
Security 
8 
6 
27 
77 
200 
409 
Power 
39 
69 
122 
242 
138 
117 
Tradition 
81 
103 
142 
198 
106 
97 
Achievement 
4 
3 
23 
82 
226 
389 
Future 
13 
22 
58 
149 
223 
262 
Socialization 
21 
29 
87 
210 
189 
191 
Asocialization 
225 
130 
133 
131 
58 
50 
3.2 Cluster analysis
As a result of clustering of respondents' values across the sample $\left\{d_{i r}(t), i=1, \ldots, I, r=1, \ldots, 10\right\}$, we obtained the following clusters:
Clustering of the respondents' expectations about the availability of certain activities under the corporate wellbeing program in a company for the sample $\left\{\alpha_{i k}(t), i=1, \ldots, I, k=1, \ldots, 29\right\}$ resulted in three clusters:
Next, we conducted the clustering of respondents’ ranking of values on the sample $\left\{d_{i r}(t), i=1, \ldots, I, r=1, \ldots, 10\right\}$, which indicates three clusters: at the level of affiliation 0.9, cluster 1 includes 66 points, cluster 2 – 78 points, and cluster 3 – 127 points. Notably, with the affiliation level of 0.6, the number of points in cluster 1 amounts to 186, in cluster 2 – 219, and in cluster 3 – 183.
The results of a comparative analysis of clusters in terms of respondents' ranking of values are presented in Figure 2. For each value, the confidence interval of the median of the fuzzy mean rank at the confidence level of 0.9 is provided.
Figure 2. Clustering of respondents by the importance of values
The point of constructing confidence intervals for cluster rank median values is to identify significant differences between them. If the confidence intervals overlap for a particular parameter, it can be concluded that there are no statistically significant qualitative differences between the clusters by this parameter [26].
Analyzing Figure 2, we can see that clusters 2 and 3 are comprised by employees whose value models are based on strong (lower limit of the confidence interval greater than 4.0) values of "Family", "Enjoyment", "Security", "Freedom", and "Achievement" (i.e., the values in the "social cluster"). Cluster 3 has significant differences in the totalitarian values of "Power", "Tradition", and "Asocialization" (which are less pronounced in cluster 3, in contrast to cluster 2). In its turn, cluster 1 differs significantly from the others in that all values in it are less pronounced by importance than in the other two clusters. We can assume that this cluster includes employees who at the current moment do not have an established value model (proportions relative to clusters 2 and 3 remain the same).
Clustering of respondents’ ranking of activities within the wellbeing program on the sample $\left\{\alpha_{i k}(t), i=1, \ldots, I, k=1, \ldots, 29\right\}$ demonstrates the presence of three clusters: at the level of affiliation 0.9, cluster 1 includes 173 points, cluster 2 – 45, and cluster 3 – 45. With the level of affiliation equaling 0.6, the number of points in cluster 1 reaches 274, in cluster 2 – 243, and in cluster 3 – 96.
The results of the comparative analysis of clusters in terms of respondents' ranking of their expectations for the availability of certain activities within the corporate wellbeing program are presented in Figure 3. For each measure, the confidence interval of the median of the fuzzy mean rank at the confidence level of 0.9 is given.
Analyzing Figure 3, we shall describe the obtained clusters as follows: cluster 1 includes employees for whom the formation and development of the wellbeing program is of high importance for performing professional activities; cluster 2 comprises employees who attribute medium importance to this issue; cluster 3 is the workers who attribute low importance to the implementation of the program. Unlike in respondent clustering by the importance of values, here all the indicators demonstrate significant differences.
As part of the study, we constructed probability matrices of respondents' transitions from clusters by values to clusters by expectations of activities and vice versa. The obtained probabilities are presented in Table 2.
Figure 3. Clustering of respondents by assessment of the importance of implementation of certain activities under the wellbeing program in the company
Table 2. Probability matrices of transitions from clusters in one category to clusters in the other category
Clusters by Values 
Probability 
Clusters by Measures 
Clusters by Measures 
Probability 
Clusters by Values 
Cluster 1 
0.21 
Cluster 1 
Cluster 1 
0.16 
Cluster 1 
0.50 
Cluster 2 
0.40 
Cluster 2 

0.29 
Cluster 3 
0.44 
Cluster 3 

Cluster 2 
0.45 
Cluster 1 
Cluster 2 
0.38 
Cluster 1 
0.41 
Cluster 2 
0.36 
Cluster 2 

0.14 
Cluster 3 
0.26 
Cluster 3 

Cluster 3 
0.58 
Cluster 1 
Cluster 3 
0.56 
Cluster 1 
0.35 
Cluster 2 
0.30 
Cluster 2 

0.08 
Cluster 3 
0.14 
Cluster 3 
The results given in Table 2 show that cluster 1 by the importance of employee values is made up of 21% of employees in cluster 1 by the importance of wellbeing program activities, 50% of employees in cluster 2, and 29% in cluster 3. In other words, for employees with a less pronounced value model, the wellbeing program in the company is of medium importance.
Conversely, cluster 1 formed by the importance of the wellbeing program activities consists of 16% of employees in cluster 1 by values, 40% in cluster 2, and 44% in cluster 3. This means that the wellbeing program in the company is of high importance for employees with prevailing social values.
The study highlights the shortcomings of existing tools for modeling human capital development through the implementation of a wellbeing program. The proposed conceptual model allows us to overcome several of them. For example, the presence of a dynamic relationship between achieving target KPI values and the amount of investment allocated to the implementation of the wellbeing program allows to implement a multiperiod development of employee competencies and create a tool for an optimal portfolio of wellbeing activities and short or mediumterm planning for their implementation. The use of a fuzzy set approach within the framework of a conceptual model allows us to consider existing uncertainties and risks when making decisions on the development of employee competencies. Within the framework of the model, the relationships of its individual components are logically described: wellbeing program activities, competencies, burnout, values, etc. In subsequent studies, economic and mathematical models will be built that functionally describe these relationships, which will allow the development of an optimization model for determining a portfolio of activities of wellbeing programs to maximize progress towards achieving organizational goals.
The conceptual model has theoretical significance because it is the basis for further research. A description of the system for developing employee competencies through the implementation of the wellbeing program in the future will allow us to develop several tools that have high practical significance for company and HR management. This is due to the fact that recently, a trend has emerged aimed, on the one hand, at increasing the efficiency of management of an organization’s financial resources and, on the other hand, at its continuous development and the formation of competitive advantages that will allow it to occupy the necessary market share for survival in the global economy in the context of increasing competition.
As a result of the research, a conceptual model for the development of employees' competencies was developed to achieve the target values of their KPIs through the formation and implementation of an optimal corporate wellbeing program considering employee burnout.
We conducted a questionnaire survey of different target groups, in which 727 respondents from different organizations were interviewed. Based on the responses obtained, a fuzzy clustering by values and assessments of expectations for the availability of certain activities within the corporate wellbeing program in an organization was constructed.
The obtained findings indicate that employee clustering can suggest several propositions for the most efficient activities of the corporate wellbeing program according to the envisioned generalized employee value model. Conversely, it can help to determine the optimal value profile of a candidate for them to work effectively in the organization proceeding from the current corporate wellbeing program.
In the continuation of the study, we will first formalize the functional dependencies between the components of the conceptual model in the form of economic and mathematical models. Secondly, a dynamic optimization model for forming an optimal set of measures of the corporate wellbeing program for the maximum possible progress in achieving KPIs will be constructed and a method for its solution will be proposed.
The study was conducted under a grant of the Russian Science Foundation No. 232801333, https://rscf.ru/project/232801333/ at the Vladivostok State University.
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