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Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method

Publié en ligne: 15 Jul 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 19 Jan 2022
Accepté: 29 Mar 2022
Détails du magazine
License
Format
Magazine
eISSN
2444-8656
Première parution
01 Jan 2016
Périodicité
2 fois par an
Langues
Anglais
Abstract

This article uses SPSS19.0 and AMOS21.0 statistical software to explore the relationship between corporate talent management and corporate performance. We use the structural equation model to quantitatively analyze the human resource management factors that affect logistics performance. Research shows that intellectual capital has a significant positive effect on enterprise innovation performance. The value proposition of employee training to customers and good customer service have an overall positive economic effect. Human capital plays a significant role in driving innovation performance only in the more mature stage of enterprise development.

Keywords

MSC 2010

Introduction

The talent gathering effect refers to the relative concentration of related talents in a certain time and space following a certain interconnection that exceeds their independent effects. The main characteristics of the talent gathering effect are as follows: (1) Information sharing effect. (2) Knowledge spillover effect. (3) Innovation effect. (4) Collective learning effect. (5) Incentive effect. (6) Time effect. (7) Regional effect. (8) Scale effect. The talent gathering effect is the process from quantitative change to qualitative change after gathering talents to a certain scale. In the process of this transformation, both individual talents and groups have undergone qualitative changes. Talent is not only knowledge growth and skill improvement, but more importantly, talent innovation ability and work efficiency are improved through talent accumulation. The talent gathering effect also enhances the cohesion between group members, and the overall functional advantages are brought into play [1]. The incentive is a process of motivating and cultivating talents and achieving better performance. It can promote talents to realize their own needs, satisfy group interests, and achieve organizational goals. Therefore, studying the incentive system under the accumulation of talents has theoretical significance and application value with the frequent flow of talents.

Construction of incentive system model based on talent gathering effect

The aggregate effect of talent accumulation is often closely related to a good and effective incentive system. Effective incentive channels and appropriate incentive measures will optimize the combination and allocation of gathered talents. It promotes the transformation of the talent gathering phenomenon to the talent gathering effect. The role of the incentive system on the talent accumulation effect mainly has the following three aspects:

One is to change work attitudes [2]. Through certain incentives (material incentives, spiritual incentives, value incentives), the desires of talents in the incentives are satisfied. They have a correct attitude and are full of passion at work.

The second is to improve workability. When talents get more incentives when they gather, they learn and work more actively and willingly accept more challenges [3]. This enables the overall improvement of workability and self-qualify, and the incentive effect, time effect, and collective learning effect of talent gathering are more prominent.

The third is to enhance the cohesion of talents. The incentive system will also supplement the improvement of talents' own ability and aggregation effect. Organizational benefits will be maximized when talent gathering takes effect. The organization will pay more attention to the motivation of talents [4]. A virtuous circle is formed between the talent gathering and the incentive system, contributing to the “secondary effect” of the talent gathering effect.

The basic model of the multi-level incentive system

An incentive system is an important subsystem of an organization's management information system. It is a multi-level incentive system composed of high-level (strategic level) incentives, middle-level (management level) incentives, and grass-roots (operational level) incentives. At the same level, it is composed of different types of technical talents and management talents. The basic idea of the incentive model proposed in this article is that the organization is composed of many different levels of functional departments. Each functional department performs its duties. There are different types of talents under the same functional department [5]. Only when we implement incentives for talents in different departments and implement hierarchical and sub-category incentives for different levels and categories of talents under the same department can we achieve a practical effect. In this paper, a linear programming model of multi-level excitation basic model is established by applying the mathematical modeling method to the above situation.

Model 1-The basic model of multi-level incentives composed of different functional departments at different levels

(1) Conditional assumptions. Suppose an organization is divided into m tier structures to organize activities. Each level is composed of n departments, and the total cost of the various types of incentives for these departments at different levels is ca. How do we maximize the net value IC of the organizational incentive effect when the total cost of incentives for different departments under the i level is ci ? At this time, the mathematical meaning of establishing the incentive model can be explained as follows: Under certain linear constraints ca and ci, the amount of incentive distribution to different levels and different departments is calculated to maximize the net value of organizational incentive effect IC.

(2) Symbol description. We mark A as the utility coefficient matrix of organizational incentives. A=[a11a12a13a14a1na21a22a23a24a2na31a32a33a34a3nam1am2am3am4amm]=[A1A2A3Am] A = \left[ {\matrix{ {{a_{11}}} & {{a_{12}}} & {{a_{13}}} & {{a_{14}}} & - & - & {{a_{1n}}} \cr {{a_{21}}} & {{a_{22}}} & {{a_{23}}} & {{a_{24}}} & - & - & {{a_{2n}}} \cr {{a_{31}}} & {{a_{32}}} & {{a_{33}}} & {{a_{34}}} & - & - & {{a_{3n}}} \cr \cdots & \cdots & \cdots & \cdots & \cdots & \cdots & \cdots \cr {{a_{m1}}} & {{a_{m2}}} & {{a_{m3}}} & {{a_{m4}}} & - & - & {{a_{mm}}} \cr } } \right] = \left[ {\matrix{ {{A_1}} \cr {{A_2}} \cr {{A_3}} \cr \cdots \cr {{A_m}} \cr } } \right] Where Ai = (ai1 ai2 ai3ai(n−1) ain). Ai is expressed as a row vector of the organization's incentive utility coefficients for different organizational departments at level i. aij ≥ 0(1 ≤ im;1 ≤ jn), aij is expressed as the incentive coefficient for the j department of the i layer. The determination of this coefficient should be determined by the evaluation and scoring of various experts within the organization. X is the distribution matrix of organizational incentives X=[x11x12x13x14x1nx21x22x23x24x2nx31x32x33x34x3nxm1xm2xm3xm4xmm]=[X1X2X3Xm] X = \left[ {\matrix{ {{x_{11}}} & {{x_{12}}} & {{x_{13}}} & {{x_{14}}} & - & - & {{x_{1n}}} \cr {{x_{21}}} & {{x_{22}}} & {{x_{23}}} & {{x_{24}}} & - & - & {{x_{2n}}} \cr {{x_{31}}} & {{x_{32}}} & {{x_{33}}} & {{x_{34}}} & - & - & {{x_{3n}}} \cr \cdots & \cdots & \cdots & \cdots & \cdots & \cdots & \cdots \cr {{x_{m1}}} & {{x_{m2}}} & {{x_{m3}}} & {{x_{m4}}} & - & - & {{x_{mm}}} \cr } } \right] = \left[ {\matrix{ {{X_1}} \cr {{X_2}} \cr {{X_3}} \cr \cdots \cr {{X_m}} \cr } } \right] Where Xi = (xi1 xi2 xi3xi(n−1) xin), Xi is a row vector of the different allocations of incentives for different organizational departments at level i. xij ≥ 0(1 ≤ im;1 ≤ jn), xij is the amount of incentives allocated to the j department of the i level. aij xij is the amount of incentives allocated to the j department of the i level xij The incentive effect produced by the incentive coefficient aij. ai1xi1+ai2xi2+ainxin=AiXiT(1im) {a_{i1}}{x_{i1}} + {a_{i2}}{x_{i2}} + \cdots\,{a_{in}}{x_{in}} = {A_i}X_i^T\left( {1 \le i \le m} \right) . AiXiT=Yi {A_i}X_i^T = {Y_i} is expressed as the total incentive effect on the n departments of the i level. j=1nxij \sum\limits_{j = 1}^n {{x_{ij}}} represents the total incentive distribution of n departments in the i layer. Iic=Yij=1nxij I_i^c = {Y_i} - \sum\limits_{j = 1}^n {{x_{ij}}} is expressed as the net value of the incentive effect of the organization on the incentives of different departments at the i level. I=i=1mYi I = \sum\limits_{i = 1}^m {{Y_i}} is expressed as the total incentive effect of all departments after the organization is encouraged.

(3) The establishment of the model. {a11x11+a12x12++a1nx1n=Y1a21x21+a22x22++a2nx2n=Y2ai1xi1+ai2xi2++ainxin=Yiam1xm1+am2xm2++amnxmn=Ym \left\{ {\matrix{ {{a_{11}}{x_{11}} + {a_{12}}{x_{12}} + \cdots + {a_{1n}}{x_{1n}} = {Y_1}} \hfill \cr {{a_{21}}{x_{21}} + {a_{22}}{x_{22}} + \cdots + {a_{2n}}{x_{2n}} = {Y_2}} \hfill \cr \cdots \hfill \cr {{a_{i1}}{x_{i1}} + {a_{i2}}{x_{i2}} + \cdots + {a_{in}}{x_{in}} = {Y_i}} \hfill \cr \cdots \hfill \cr {{a_{m1}}{x_{m1}} + {a_{m2}}{x_{m2}} + \cdots + {a_{mn}}{x_{mn}} = {Y_m}} \hfill \cr } } \right.

The objective function is IC=max(Y1+Y2++Yi++Ym)i=1mj=1nxij {I^C} = \max \left( {{Y_1} + {Y_2} + \cdots + {Y_i} + \cdots + {Y_m}} \right) - \sum\limits_{i = 1}^m {\sum\limits_{j = 1}^n {{x_{ij}}} }

Among them, 0j=1nxijci 0 \le \sum\limits_{j = 1}^n {{x_{ij}}} \le {c_i} ; 0i=1mcica,ci 0 \le \sum\limits_{i = 1}^m {{c_i}} \le {c_a},{c_i} represents the total incentive distribution of the i level. ca represents the total distribution of organizational incentives [6] when the total cost of organizational input and incentive is fixed, how to allocate incentive input among departments reasonably to obtain the maximum economic benefits of the organization.

Model II-the basic model of multi-level incentives for different types of talents at different levels in the same department

(1) Conditional assumptions. Suppose an organizational department is divided into g departments. Each department is also composed of category h talents. The total cost of the various types of incentives for the organization to the department is cb. When the total cost of incentives for different talents in the i department is ci, how to maximize the net effect IC of organizational department incentives [7], under certain linear constraint conditions cb and ci, find out the amount of incentive distribution to different departments and different types of talents. This in turn maximizes the net effect IC of organizational department incentives.

(2) Symbol description. Suppose B is the utility coefficient matrix of departmental incentives. B=[b11b12b13b14b1hb21b22b23b24b2hb31b32b33b34b3hbg1xg2xg3xg4bgh]=[B1B2B3Bg] B = \left[ {\matrix{ {{b_{11}}} & {{b_{12}}} & {{b_{13}}} & {{b_{14}}} & - & - & {{b_{1h}}} \cr {{b_{21}}} & {{b_{22}}} & {{b_{23}}} & {{b_{24}}} & - & - & {{b_{2h}}} \cr {{b_{31}}} & {{b_{32}}} & {{b_{33}}} & {{b_{34}}} & - & - & {{b_{3h}}} \cr \cdots & \cdots & \cdots & \cdots & \cdots & \cdots & \cdots \cr {{b_{g1}}} & {{x_{g2}}} & {{x_{g3}}} & {{x_{g4}}} & - & - & {{b_{gh}}} \cr } } \right] = \left[ {\matrix{ {{B_1}} \cr {{B_2}} \cr {{B_3}} \cr \cdots \cr {{B_g}} \cr } } \right]

Among them, Bi = (bi1 bi2 bi3bi(h−1) bih), Bi is expressed as a row vector of the incentive utility coefficient of different types of talents in i departments by the organization department. bij ≥ 0(1 ≤ ig;1 ≤ jh), bij is expressed as the incentive coefficient for the incentives of category j talents in i departments [8]. The determination of this coefficient should be determined by the evaluation and scoring of various experts within the organization's department. Y is the allocation matrix of organizational incentives. Y=[y11y12y13y14y1hy21y22y23y24y2hy31y32y33y34y3hyg1yg2yg3yg4ygh]=[Y1Y2Y3Yg] Y = \left[ {\matrix{ {{y_{11}}} & {{y_{12}}} & {{y_{13}}} & {{y_{14}}} & - & - & {{y_{1h}}} \cr {{y_{21}}} & {{y_{22}}} & {{y_{23}}} & {{y_{24}}} & - & - & {{y_{2h}}} \cr {{y_{31}}} & {{y_{32}}} & {{y_{33}}} & {{y_{34}}} & - & - & {{y_{3h}}} \cr \cdots & \cdots & \cdots & \cdots & \cdots & \cdots & \cdots \cr {{y_{g1}}} & {{y_{g2}}} & {{y_{g3}}} & {{y_{g4}}} & - & - & {{y_{gh}}} \cr } } \right] = \left[ {\matrix{ {{Y_1}} \cr {{Y_2}} \cr {{Y_3}} \cr \cdots \cr {{Y_g}} \cr } } \right] Where Yi = (yi1 yi2 yi3yi(h−1) yih), Yi. Y is expressed as a row vector of the different allocations of incentives for different types of talents in i departments by the organization department. yij ≥ 0(1 ≤ ig;1 ≤ jh), yij is expressed as the amount of incentive allocation for the incentives of category j talents in i departments. bij yij is expressed as the incentive effect produced by the incentive coefficient bij for the incentive distribution yij of the j type talents in i departments. bi1yi1+bi2yi2+bihyih=BiYiT(1ig) {b_{i1}}{y_{i1}} + {b_{i2}}{y_{i2}} + \cdots\,{b_{ih}}{y_{ih}} = {B_i}Y_i^T\left( {1 \le i \le g} \right) . Suppose BiYiT=Zi {B_i}Y_i^T = {Z_i} is expressed as the total incentive effect for h different types of talents in the i department. j=1hyij \sum\limits_{j = 1}^h {{y_{ij}}} represents the total amount of incentive distribution of h different types of talents in the i department. Iic=Zij=1hyij I_i^c = {Z_i} - \sum\limits_{j = 1}^h {{y_{ij}}} is expressed as the net value of the incentive effect of the department to the i department's h type talents. I=i=1gZi I = \sum\limits_{i = 1}^g {{Z_i}} is expressed as the total effect of all incentives generated by all departments in the department.

(3) The establishment of the model.

{b11y11+b12y12++b1hy1h=Z1b21y21+b22y22++b2ny2n=Z2bi1yi1+bi2yi2++bihyih=Ziyg1yg1+yg2yg2++yghygh=Zg \left\{ {\matrix{ {{b_{11}}{y_{11}} + {b_{12}}{y_{12}} + \cdots + {b_{1h}}{y_{1h}} = {Z_1}} \hfill \cr {{b_{21}}{y_{21}} + {b_{22}}{y_{22}} + \cdots + {b_{2n}}{y_{2n}} = {Z_2}} \hfill \cr \cdots \hfill \cr {{b_{i1}}{y_{i1}} + {b_{i2}}{y_{i2}} + \cdots + {b_{ih}}{y_{ih}} = {Z_i}} \hfill \cr \cdots \hfill \cr {{y_{g1}}{y_{g1}} + {y_{g2}}{y_{g2}} + \cdots + {y_{gh}}{y_{gh}} = {Z_g}} \hfill \cr } } \right.

The objective function is: IC=max(Z1+Z2++Zi++Zm)i=1gj=1hyij {I^C} = \max \left( {{Z_1} + {Z_2} + \cdots + {Z_i} + \cdots + {Z_m}} \right) - \sum\limits_{i = 1}^g {\sum\limits_{j = 1}^h {{y_{ij}}} }

Among them, 0j=1hyijci 0 \le \sum\limits_{j = 1}^h {{y_{ij}}} \le {c_i} ; 0i=1gcicb,ci 0 \le \sum\limits_{i = 1}^g {{c_i}} \le {c_b},{c_i} represents the total incentive distribution of the i department. cb represents the total amount of incentives allocated by the organization [9]. The significance of the research lies in how to make reasonable incentive distribution within the department to obtain the maximum economic benefits of the department when the cost of the organization's investment in incentives for a certain department is constant.

The basic model of talent accumulation effect based on the incentive system

This model solves IC in the above-mentioned multi-level excitation model to obtain I. The model constructed based on the analysis of different states before and after the talent accumulation effect reveals the changes in the talent accumulation effect. Then help us propose different incentives.

Conditional assumptions

Assume that the initial state before the talent accumulation effect is P0. The talent accumulation effect works under the I mechanism of the multi-level incentive system. This makes the state of the talent accumulation effect change, and the change state is P1.

Symbol description

α(> = 0) is expressed as the incentive multiplier. This refers to the chain effect generated by individuals or groups of talents in the multi-level incentive system I. It is a variable that magnifies the incentive effect. C is expressed as the total cost of the implementation of the incentive process. It mainly includes four parts: currency cost C1, time cost C2, opportunity cost C3, and other costs C4. I is expressed as the incentive effect. This refers to the organization's investment in material incentives, spiritual incentives, value incentives, and career incentives for individual talents or groups in an incentive process. These inputs are combined in different proportions to stimulate the talents. It can be described by a utility indifference curve, and it is here that I is a variable. Under the condition that the total amount of input factors is constant, the indifference curves described by different proportions of input factors are the same. P0 represents the initial state of talent gathering and is a constant [10]. For individual talents, this refers to the working attitude and efficiency of the talents without motivation. For the talent group, this refers to the output or efficiency of the organizational department. P1 is expressed as the state of talents gathering after the incentive system acts, which is a variable. It refers to the work efficiency of talents after implementing incentives, the effectiveness or output of organizational departments.

Establishment of the model

Therefore, the talent accumulation effect model can be simply expressed by the following formula: P1=P0+αIC {P_1} = {P_0} + \alpha I - C

The talent accumulation effect refers to the effect that the aggregate role of talents in the accumulation process exceeds the effects of their independent roles. The overall effect of the talent accumulation effect is greater than the sum of the partial effects, and the talent accumulation effect is effective. From the transformation of formula (9), the increase of talent gathering can be obtained, ΔP=P1P0=αIC \Delta P = {P_1} - {P_0} = \alpha I - C

When αIC = 0 is not excited, the constant α = 0, I = C = 0 is excited at this time. The incentive system does not work, and the state of talent gathering has not changed. At this time ΔP = 0. In the incentive state, the incentive effect I varies with the cost C of the input incentive. I > C or I < C, the talent gathering effect may not change at this time. From equation (10), it can be seen that the talent accumulation effect only takes effect when ΔP > 0 is.

The following is an analysis of the talent accumulation effect and the incentive effect. We obtain the formula (9) for the talent accumulation effect P and the incentive effect variable I to obtain dp/dI=a {d_p}/{d_I} = a

It can be seen that the effectiveness of the talent accumulation effect is closely related to the incentive multiplier. That is, the talent gathering effect can only take effect when a is large enough. We transform the formula (1) into: a=ΔP+CI a = {{\Delta P + C} \over I}

The following analysis and discussion of equation (12) are as follows:

(1) 0 < a ≤ 1 time indicates that the excitation system is invalid, and the excitation multiplier has a negative effect. Incentive cost C > and incentive utility I indicate that the structure of the incentive system needs to be changed at this time;

If 0 < a ≤ 1 AND ΔP ≤ 0 represents the incentive cost and CI incentive effect, the incentive system is invalid. The incentive multiplier has a negative effect. At the same time, the talent gathering effect is also ineffective, and the talents are still in a state of dissociation. The system has not formed a cohesive force to reach a state of gathering talents.

If 0 < a ≤ 1 AND ΔP > 0 the incentive system is invalid, the incentive multiplier has a positive effect, and the talent accumulation effect also has an effect. This situation is only a theoretical assumption. This is rare in practice. Because people in the organization are economic people. He is unwilling to pay more work when the normal incentive input can't make him fully satisfied.

(2) a > time indicates that the excitation system is effective, and the excitation multiplier has a positive effect. Incentive utility Incentive cost and incentive effect have an incentive effect both inside and outside the department. If a > 1 AND ΔP ≤ 0 indicates that the excitation system is effective, the excitation multiplier has a positive effect. However, the influence of the incentive multiplier is not large enough, the cohesion of talent gathering is not enough, and the talent gathering effect is ineffective. Therefore, the incentive system should be improved, and the incentive intensity of the organization or department should be increased. If a > 1 AND ΔP > 0 is time, it means that the incentive system and the talent accumulation effect are effective simultaneously. This is a result we want to pursue in the motivation process.

From the above analysis, we can see that the incentive system and the talent accumulation affect work simultaneously. There is no doubt that time a > 1 AND ΔP > 0 is our best choice in the process of incentive implementation. Therefore, in the incentive process, we must pay attention to the positive role of the incentive multiplier in the incentive system and the talent accumulation effect. We need to create a positive incentive atmosphere, solidarity, mutual assistance, and orderly competition. This has played a positive role in promoting the formation and promotion of the talent gathering effect.

Discussion

The talent gathering effect refers to the relative concentration of related talents in a certain time and space following a certain interrelationship that exceeds their independent effects. The incentive system is a multi-level incentive system composed of high-level (strategic level) incentives, middle-level (management level) incentives, and grass-roots (operational level) incentives [11]. This is an important means to stimulate the potential of talents and tap the potential of talents. Undoubtedly, an effective incentive system plays a vital role in forming and promoting the talent gathering effect.

Under certain constraints of the total cost of input incentives, we should implement incentives at different levels, categories, and differences. Adopting a single incentive method does not necessarily achieve the expected incentive effect of the organization. Therefore, a variety of coexisting incentive methods should be adopted in the process of implementing incentives. In the different stages of the organization's development, different proportions of incentive measures are adopted. The degree of talent accumulation effect is positively related to the role of the inventive system's incentive effect. To increase the talent gathering effect by a large margin, it is necessary to actively create an incentive atmosphere of competition, unity, friendship, and mutual assistance in implementing incentives. This will maximize the effect of the incentive multiplier. The incentive input behavior of the incentive multiplier in a certain department will cause a chain effect between various departments. This allows the output of all sectors to increase. This article believes that the organization's investment in incentives can rely on material incentives and must be combined with spiritual incentives, individual and organizational collective incentives, and value incentives. At the same time, we must create a positive organizational culture. Develop organized collective learning groups and amplify the collective learning effect of talent gathering. Create a good incentive environment for gathering scientific and technological talents and amplify the effectiveness of the incentive multiplier. In this way, the integration and expansion effect of incentives and the gathering of scientific and technological talents are realized.

Conclusion

Since the incentive system is a complex and dynamic system composed of many factors, we need to consider incentives from many aspects. This article only conducts a quantitative analysis and research on the incentive system from the perspective of mathematical methods from the organization's side. No more consideration is given to the subjective initiative of talents and the dynamic balance of the system. Therefore, establishing an effective dynamic multi-level incentive system between the talents and the organization still needs to be further studied.

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