# Influencing Factors of Employee Turnover Based on the Random Forest and DEMATEL Method: Evidence from the Qinghai Electric Power Company in China

Authors: Hao Xu; Jian Zheng; Renjing Liu; Xiangwen Zhang; Ru Zhang
Journal: Mathematical Problems in Engineering
 (2023)

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## Abstract

Under the background of the deepening reform of China’s electric power industry, how to make use of existing resources, keep existing employees, and identify and distinguish the influencing factors of turnover have become major problems faced by companies. At the same time, the identification of influencing factors also provides a reference for relevant enterprises to do a good job in talent reserve and recruitment. The influencing factors of employee turnover are numerous and interrelated. Identifying and distinguishing the influencing factors are the focus of academic research. Meanwhile, it provides a reference for relevant enterprises to do an excellent job in personnel reserve and recruitment. Aiming at the limitation of constructing a direct matrix based on experts’ scoring method in the traditional DEMATEL model, a model combining random forest and DEMATEL is proposed, applied to identify the influencing factors of employee turnover in electric power enterprises in Qinghai Province. Based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, the empirical analysis is conducted to analyze the influencing factors of employee turnover from two perspectives of the degree of centrality and the degree of causal. The research results show that marital status, household registration, political status, type of job, and the location of the work unit are the key influencing factors, providing a theoretical basis for the early warning of employee turnover and confirming the feasibility of the random forest-DEMATEL method proposed by us.


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## Body

## 1. Introduction

In the context of the rapidly updated digital economy and the “post-pandemic” era, the number of people leaving their jobs is on the rise overall, reaching record levels last year, and the trend is spreading across the globe. What is driving this large-scale trend? What will be the impact on economic and social development? According to the US Bureau of Labor Statistics on March 29, 2022, the number of Americans who quit their jobs rose to 4.4 million. The trend is also seen in France and Australia. In places such as Germany and Singapore, surveys suggest similar trends are on the rise, although they have yet to take shape.As a public social utility, the electric power industry is also the primary industry of national economic development. It plays a crucial role in promoting national economic construction and harmonious society construction [1]. As the world’s largest power company by revenue, the Qinghai Electric Power Company in China has an advantage in attracting talent and retaining employees. However, with the development of the social economy, electric power enterprises in western China suffer from severe brain drain and employee turnover. Many employees flock to developed areas in central and eastern China, among which Qinghai Electric Power Company is the most typical. Upon investigating the reason and cause of this phenomenon, the factor varied. Among many influencing factors, grasping the key factors and controlling them is the critical work that human resource management personnel of electric power enterprises urgently need to carry out.Employee turnover, especially the flow of outstanding and core talents, has greatly affected the performance of enterprises, and the massive cost of recruiting and training new employees has brought huge losses to enterprises. Therefore, modern enterprises are paying more and more attention to the problem of employee turnover [2]. The best way to predict employee turnover behavior is to monitor employee turnover tendencies. The critical issue is identifying the key factors affecting employee turnover.The existing research methods on influencing factors of employee turnover mainly focus on two aspects. On the one hand, the flow motivation model is constructed to analyze the influencing factors in employee turnover. Since March and Simon [3] established the first employee turnover model in 1958, many scholars have launched a series of models of employee turnover from various academic fields. These models can be divided into three categories. First, it mainly explains the rationality of employee turnover through quantitative analysis methods, which is habitually called the “mainstream model,” the most representative of which is the “Price-Muller” model. The second is the behavior mutation model, which examines the flow behavior of employees from a nonlinear perspective. The most prominent model is the “peak mutation” model. Third, there are some relatively complex decision-making theoretical models, among which the “expansion” model developed by Keser puts forward multiple path analysis methods [4]. However, these methods require strict assumptions, and their applicability is limited in today’s complex real conditions.On the other hand, through questionnaire surveys, correlation analysis, regression analysis, and other analysis methods, empirical research is carried out to analyze the factors affecting employee turnover behavior and determine the factors that play a crucial role in turnover behavior. For example, Ghosh et al. [5] aim to determine the main factors affecting employee turnover intention and empirically analyze the best predictors of employee turnover or retention intention by establishing a discriminant function through the factor analysis method. The research results show that affective commitment, normative commitment, and clear goals are the best predictors of employee turnover intention. Pang et al. [6] selected 200 Hong Kong travel agency employees as samples to empirically study the relationship between overall job satisfaction, job-hopping rate, job security, training opportunities, promotion opportunities, and employee turnover tendency. Rajapaksha [7] studied the influencing factors of employee turnover in the medium-scale garment industry in Sri Lanka, selected 280 operators as samples, collected data through interviews, semi-structured questionnaires, and other methods, and conducted an empirical study by using a multivariate Logit model. Skelton et al. [8] used Likert-type questionnaire measurements to analyze randomly selected individuals in the southeastern United States manufacturing facilities. They found that job satisfaction, job embeddedness, and mobility correlated significantly. Most of the influencing factors in the existing research results are directly selected and determined based on experience judgment, and then, logistic regression and multiple linear regression models are mainly used to analyze the correlation between influencing factors and employee turnover. However, there is a relative lack of research on systematically screening the influencing factors and identifying the role relationship and impact degree of factors on employee turnover-related aspects.Recently, Tsai et al. used the decision testing and evaluation laboratory (DEMATEL) method to identify the main influencing factors of employee turnover in convenience stores from the perspective of management and inferred the changes needed to reduce this high turnover rate [9]. The DEMATEL method is a method to identify and distinguish influential factors based on evaluating the relationship between influencing factors. Since the method was proposed in 1971, it has been widely used in the field of management. However, because there are many influencing factors in real management problems, and the relationship between influencing factors is usually complicated, it is difficult to accurately evaluate the relationship between influencing factors through expert scoring, which greatly limits the application of the DEMATEL method.In addition, some scholars use machine learning algorithms to predict human behavior and its influencing factors. For example, Gao et al. [10], Tang et al. [11], Wei et al. [12], and Wang et al. [13]. These methods provide many ideas for the application of machine learning algorithms in this research. In machine learning, a random forest is a classifier that contains multiple decision trees, and its output category is determined by the mode of the category output by the individual tree. Random forests can be used for both regression and classification tasks, and it is easy to see the relative importance of the input features of the model. The random forest algorithm is considered to be a very convenient and easy-to-use algorithm because it is the default assumption and usually produces a good prediction result. The number of assumptions is not that large, and what they represent is straightforward.In previous studies, the DEMATEL method is used to analyze the influencing factors of employee turnover, and the direct correlation matrix of influencing factors is mainly constructed in the way of expert scoring, so it is difficult to avoid subjective arbitrariness in the process of expert scoring. Meanwhile, the importance ranking of influence factors by the random forest method can be used as the correlation matrix of DEMATEL’s indicators, eliminating the resulting bias caused by subjectivity. In addition, the random forest method belongs to the machine learning method, which is suitable for large sample data sets and has a better effect on the accurate display of results.Based on the above practical and theoretical background, the scientific problem is using scientific and effective methods to identify the key factors from the numerous influencing factors of employee turnover of Qinghai Electric Power Company. This paper proposes to use the method of the random forest combined with DEMATEL to study the influencing factors of employee turnover in Qinghai Electric Power Company. Compared to previous studies, we use random forest weights to replace the traditional DEMATEL method of questionnaire or expert scoring. This method solves the correlation matrix between the influencing factors and indicators and avoids the influence of the subjectivity of the traditional DEMATEL method on the evaluation results. In addition, the model proposed by us has obvious advantages in processing large sample data sets. Therefore, using the random forest to solve the direct correlation matrix makes the analysis result more reliable.Based on this, the research ideas of this paper are as follows. First of all, we refer to a large number of relevant literature about employee turnover, and combined with the actual situation of Qinghai Electric Power Company, and construct the influencing factors index system of Qinghai Electric Power Company employee turnover. Second, aiming to limit the traditional DEMATEL model and combine it with the random forest model, we establish the identification model of factors of employee turnover in Qinghai Electric Power Company. Then, we calculate the main factors of employee turnover based on the employee turnover data of Qinghai Electric Power Company from 2012 to 2020. At last, the main factors of employee turnover in Qinghai Electric Power Company are analyzed from factors in the causal group and factors in the effect group. This study provides methodological guidance for Qinghai Electric Power Company to prevent employee turnover. At the same time, the study’s conclusion can also provide decision support for the employee demission warning of other similar provinces in western China.


## 2. Index System of Influencing Factors of Employee Turnover

Scholars have conducted a lot of studies on the influencing factors of employee turnover. At present, the influencing factors of employee turnover mainly include four categories. Among them, the first category is individual factors. For example, Jolly et al. [14] proved that gender is an important factor affecting employee turnover in the catering industry. Sarwar et al. [15] found that marital status has a significant impact on employee turnover in private organizations. Hussain and Xian [16] obtained through empirical analysis that political status has an important impact on employee turnover in construction enterprises.The second category is education. For example, Mutenheri [17] found in his research that education level is an important factor affecting employee turnover in tourism enterprises. Kim [18] found through the investigation of 304 samples in the tourism and hotel industry that the school of graduation of employees was an important external factor affecting their turnover. Yang et al. [19] investigated 354 employees from four hotel groups in three provinces of China and found that the professional skills of employees had a positive effect on employee turnover.The third category is the work factor. Oruh et al. [20] confirmed that the type of work unit has a certain impact on employee turnover. Ju and Li [21] surveyed 1,531 samples of Korean labor and income groups and found that the type of jobs and the level of job titles of employees were highly correlated with employee turnover.The fourth category is environmental factors. Dellie et al. [22] showed that the workplace environment was a significant factor affecting employee turnover. Charlesworth and Isherwood [23] examined the employment status of Australian-born and overseas-born frontline workers using data from the 2016 National Aged Care Workforce Census and Survey. Their results show that the birthplace of an employee is an important factor affecting employee turnover.Employee turnover is the result of many complicated factors. The index system of influencing factors of employee turnover in Qinghai Electric Power Company has a series of intrinsically related indicators, reflecting the actual employee turnover situation from multiple perspectives. Therefore, establishing an employee turnover index system is helpful for quantitative, systematic, and operable research. This study conducted an in-depth excavation of existing literature through literature search and meta-analysis. At the same time, combined with the actual situation of human resources in Qinghai Electric Power Company, the influencing factors index system of employee turnover that conforms to the company’s characteristics is determined (as shown in Table1).Table 1
Influencing factors of staff turnover in Qinghai Electric Power Company.
Target layerFirst-level indicatorsSecond-level indicatorsInfluencing factors of employee turnoverIndividual factorsSexMarital statusPolitical statusEducation factorsType of graduation schoolLevel of educationProfessional skillsWork factorsType of work unitJob titleType of jobEnvironmental factorsBirthplaceHousehold registrationLocation of the work unit


## 3. Traditional DEMATEL Method

In influence factor identification, DEMATEL is a commonly used algorithm. The DEMATEL model is a method proposed by the Science and Human Affairs Project Group of Battelle Memorial Institute of the United States from 1972 to 1976 to study and solve complex and challenging problems [24]. It can improve the understanding of specific interrelated problem groups and complex cluster problems and determine feasible solutions through classification [25]. Different from other methods such as the analytic hierarchy process, the DEMATEL method does not require elements to be independent and can determine the correlation of various aspects of the system through the causal diagram to identify the fundamental influencing factors from numerous factors and provide a decision-making basis for solving engineering problems [26].In the following part, we introduce a few key definitions of the traditional DEMATEL approach.(1)
The direct correlation matrix is defined as follows:(1)B=bijn×n,wherebij is the influence degree of the ith indicator on the jth hand.(2)
The normalized direct correlation matrixX is defined, and X=s·B is specified, then(2)s=1max1≤i≤n∑j=1nbij,wherei,j=1,2,…,n.(3)
If the full correlation matrixT is defined, then(3)T=XI−X−1,whereI−X−1 is the inverse of I−X, and I is the identity matrix.(4)
define the centrality and causality of each indicator and defineR as the sum of all T lines to represent the impact degree of a factor. C is the sum of all columns of T, indicating the impact level. Then, we have as follows:(4)T=tijn×n,R=tin×1=∑j=1ntijn×1,C=tjn×1=∑i=1ntij1×n.Scholars defineRi+Ci as the centrality of index i, and the greater it is, the greater its importance is proved. At the same time, Ri−Ci is defined as the cause degree of indicator i, distinguishing the cause group from the resulting group. If the value of Ri−Ci of the indicator i is greater than 0, and this indicator belongs to the cause group. If the value of Ri−Ci of index i is less than 0, this index belongs to the resulting group. Among the many influencing factors, the influencing factors in the resulting group are the influencing results in the cause group.The key to the DEMATEL model is to establish the direct correlation matrix. In the studies of existing literature, most of the methods to show natural correlation matrices are questionnaires and expert scoring. Still, establishing a direct correlation matrix by questionnaire or expert scoring has the following two limitations: (1) For many complex problems, there are many influencing factors, and it is not practical to organize expert groups or questionnaires to score the importance of each influencing factor. (2) Although the algorithm does not directly adapt the assessment results of the expert group and carries out corresponding analysis, it still cannot avoid the influence of subjectivity, thus affecting the fairness of the results. These two shortcomings significantly affect the application range of the DEMATEL model [27–29].


## 4. Improved DEMATEL Model Based on Random Forest

Based on the limitation of the traditional DEMATEL method, this paper tries to use the random forest to solve the direct correlation matrix of each index. Random forest is an integrated learning algorithm first proposed by Leo Breiman and Adele Cutler. It is a classifier composed of multiple decision trees [30, 31]. Essentially, it is a combinatorial classifier, which can be expressed as follows:(5)hX,βk,k=1,…,n,where X is each characteristic variable (i.e., input variable). βk is an independent and identically distributed random vector and n represents the number of decision trees contained in the random forest. The random forest determines the final classification result according to each decision tree classifier’s simple majority voting method [32]. The schematic diagram of constructing the random forest is shown in Figure 1.Figure 1
Random forest structure map.The random forest is a commonly used machine learning algorithm, which has a significant feature that can evaluate the importance of a single component and sort these features according to their preference [33]. The specific steps to calculate the importance degree of a single element in the random forest are as follows [34–36].Step 1: Test the model performance of each decision tree that has been generated in the random forest with the corresponding out-of-bag (OOB) data, and calculate the out-of-bag data error rate, denoted as OOB Error 1. Out-of-bag data refers to that when a decision tree is established each time, one piece of data is obtained by repeated sampling and used for training the decision tree. At this time, about 1/3 of the data is not utilized and does not participate in the establishment of the decision tree. This part of the data can be used to evaluate the performance of the decision tree and calculate the prediction error rate of the model, which is called out-of-pocket data error.Step 2: Add noise interference that can randomly change the value of the sample at characteristicX to a feature X in all samples of OOB data generate a new data set and recalculate the out-of-bag data error rate, which is denoted as OOB Error 2.Step 3: The importance of featureX in the decision tree is (OOB Error 2-OOB Error 1). If the random forest contains N decision trees, the importance of this feature X in the random forest model is as follows:(6)∑OOBerror2−OOBerror1N.Based on feature importance, the steps of feature selection are as follows:Step 1: calculate the importance of each feature and sort it in descending order.Step 2: determine the proportion to be eliminated and eliminate the corresponding proportion of features according to the importance of features to obtain a new feature set.Step 3: repeat the process with the new feature set until there arem features left (m is the value set in advance).Step 4: select the feature set with the lowest out-of-pocket error rate according to each feature set obtained in the above process and the corresponding out-of-pocket error rate of the feature set.In the process of using the random forest method to analyze the importance of each index, the key point is to set the parameters of the random forest as shown in Table2.Table 2
The parameters of the random forest method.
Parameter nameParameter meaningParameter valuen_estimatorsThe more subtrees there are, the better the model performs. If the processor can bear it, the researchers can choose the highest value possible to achieve a better prediction effect and a more stable purpose10CriterionThe function that measures the quality of the splitGINImax_featuresThe maximum number of features considered during partitioningNonemax_depthMaximum depth of decision treeNonemin_samples_splitThe minimum number of samples required for internal node repartitioning limits the conditions for further subtree partitioning2min_samples_leafThe minimum number of samples of leaf nodes. This value limits the minimum number of samples with leaf nodes. If the number of leaf nodes is less than the number of samples, it will be pruned along with its sibling nodes1Through the above analysis, we propose the random forest-DEMATEL model. The specific implementation steps are as follows:Step 1: use the random forest to calculate the importance of each influencing factor.Step 2: calculate each influencing factor index’s direct correlation matrixB. Then, B can be expressed as follows:(7)B=bijn×nb11⋯b1n⋮⋱⋮bn1⋯bnn,where bii=0 and bij=wi∗/wj∗ denote the importance of the ith influencing factor index relative to the jth.Step 3: normalize the direct correlation matrixX. Then, X can be expressed as follows:(8)X=xijn×n=1max1≤i≤n∑j=1nbij•B,Step 4: calculate the full correlation matrixT. Then, T can be expressed as follows:(9)T=XI−X−1,Step 5: create a causal relationship. Let the centrality of each influencing factor be abscissa and cause degree to be ordinated.We use random forest weights to replace the traditional DEMATEL method of questionnaire or expert scoring. This method solves the correlation matrix between the influencing factors and indicators and avoids the influence of the subjectivity of the traditional DEMATEL method on the evaluation results. Therefore, using the random forest to solve the direct correlation matrix makes the analysis result more reliable.


## 5. Data Sources and Results Analysis

### 5.1. Data Collection and Preprocessing

As a typical electric power enterprise in the west of China, Qinghai Electric Power Company has experienced grave employee turnover in recent years. In this paper, the employee turnover data set of China Qinghai Electric Power Company from 2012 to 2020 was used for empirical analysis [37]. There were 2,666 records in total, and each record had 20 influencing factors. After investigation, we found that some fields were irrelevant to classification prediction, such as name, employee number, and id number, which were removed. Second, remove all fields with too many missing values (more than 30% of missing fields in this experiment). After such processing, we retained a total of 2,494 records. As shown in Table 3, each record was composed of 13 fields, among which the first 12 fields were the influencing factors of employee turnover in Qinghai Electric Power Company.Table 3
Basic settings for variables used in random forest.
Variable nameVariable descriptionVariable dimensionMeasurement levelEmployee data (input variables)Type of work unitThe type of unit the employee works inPower supply1Scientific research2Business3Construction4SexSex of the employeeMan1Woman2Marital statusThe employee’s marital statusMarried1Unmarried2BirthplaceThe employee’s birthplaceEastern1Western2Central3Household registrationThe household registration type of the employeeCity1Rural2Political statusThe employee’s political statusLeague member1Communist2Prospective communist3Mass4Type of graduation schoolThe type of school the employee graduated from“211 projects” colleges1“985 projects” colleges2Ordinary undergraduate colleges3Vocational colleges4Level of educationThe employee’s education levelPostgraduate1Undergraduate2Junior college student3Job titleThe employee’s job titleJunior title1Senior title2Intermediate title3Professional skillsEmployee’s professional skillsElectrician1Management2Computer3Finance4Engineering5Others6Communication7Type of jobThe employee’s type of jobService1Management2Skill3Technology3Others5Location of the work unitThe employee’s work areaSecond area1Third area2Fourth area3Fifth area4Sixth area5Desired target (output variables)TurnoverSucceed to turnoverNo0Yes1


### 5.2. Calculation Results

In this section, we give the calculation process and results. First, we used the random forest-DEMATEL model constructed in this paper and calculated the importance of 12 influencing factors of employee turnover based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020 after processing. Then, we calculate the direct influence matrixA according to the importance degree and then calculate the normalized direct influence matrix D, the comprehensive influence matrix T, and the centrality and cause degree. Finally, we draw the causality diagram of influencing factors of employee turnover with the centrality of 12 influencing factors as abscissa and causality as ordinate.


#### 5.2.1. Calculation of the Importance of Influencing Factors

According to the application of theR language analysis tool, the random forest algorithm was used to analyze the importance of the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, and the importance of 12 influencing factors of employee turnover was obtained. To facilitate calculation and result in analysis, we labeled the 12 factors as A1∼A12, as shown in Table 4.Table 4
Influencing factors of employee turnover.
Code nameFactorsImportant degreeA1Type of work unit12.045A2Sex5.877A3Marital status4.109A4Birthplace11.691A5Household registration4.415A6Political status12.811A7Type of graduation school12.679A8Level of education6.952A9Job title4.490A10Professional skills8.256A11Type of job7.998A12Location of the work unit12.648


#### 5.2.2. Calculation of the Direct Influence Matrix

According to the importance of the above 12 influencing factors, the direct influence matrixA of employee turnover is obtained using EXCEL software (omitted due to length reasons). The direct influence matrix is normalized to obtain the standardized direct influence matrix D, as shown in Table 5.Table 5
Influence matrixD after standardized treatment.
A1A2A3A4A5A6A7A8A9A10A11A12A100.0240.0170.0480.0180.0530.0520.0290.0180.0340.0330.052A20.10100.0350.0980.0370.1080.1070.0580.0380.0690.0670.106A30.1450.07100.1410.0530.1540.1530.0840.0540.0990.0960.152A40.0510.0250.01700.0190.0540.0540.0290.0190.0350.0340.053A50.1350.0660.0460.13100.1430.1420.0790.0500.0920.0900.142A60.0460.0230.0160.0450.01700.0490.0270.0170.0320.6180.049A70.0470.0230.0160.0460.0170.05000.0270.0180.0320.0310.090A80.0860.0420.0290.0830.0310.0910.09000.0320.0590.0570.139A90.1330.0650.0450.1290.0490.1410.1400.07700.0910.0880.139A100.1330.0350.0250.0700.0270.0770.0760.0420.02700.0040.076A110.0720.0360.0250.0720.0270.0790.0780.0430.0280.05100.078A120.0470.0230.01600.0460.0170.0500.0500.0270.0180.0320.0310


#### 5.2.3. Comprehensive Influence Matrix

According to the standardized direct influence matrixD and formula T=Xi−x−1, the comprehensive influence matrix T was obtained using MATLAB software, as shown in Table 6.Table 6
Comprehensive influence matrixT.
A1A2A3A4A5A6A7A8A9A10A11A12A10.0720.0560.0390.1120.0420.1230.1210.0670.0430.0790.1420.129A20.2450.0680.0810.2290.0870.2510.2480.1360.0880.1620.2900.264A30.3500.1650.0680.3280.1240.3590.3550.1950.1260.2310.4150.377A40.1230.0580.0410.0680.0440.1260.1250.0690.0440.0810.1460.133A50.3260.1530.1070.3050.0680.3340.3310.1810.1170.2150.3860.351A60.2120.1000.0700.1990.0750.1710.2160.1180.0770.1410.7850.229A70.1180.0550.0390.1110.0420.1210.0730.0660.0420.0780.1400.164A80.2120.0100.0700.1990.0750.2180.2150.0710.0760.1400.2520.274A90.3200.1510.1050.3000.1130.3290.3250.1780.0680.2120.3800.345A100.2290.0820.0570.1630.0620.1780.1770.0970.0630.0690.1660.187A110.1770.0850.0590.1680.0640.1840.1820.1000.0650.1190.1660.194A120.1140.0540.0370.1060.0400.1170.1150.0630.0410.0750.1350.076


#### 5.2.4. Comprehensive Impact Matrix Analysis

According to the three formulas corresponding toT, R, and C in random forest-DEMATEL, each factor’s influence degree, influence degree, cause degree, and centrality are calculated. The results are shown in Table 7.Table 7
Comprehensive impact matrix analysis table.
RRankCRank(R + C)Rank(R − C)RankA11.024112.49743.5228−1.47310A22.14951.12693.275101.0234A33.09310.773123.86632.3201A41.05792.28663.3439−1.2308A52.87520.836113.71242.0392A62.39342.51034.9032−0.1177A71.047102.48453.5317−1.4379A81.90261.34183.242110.5615A92.82630.849103.67661.9773A101.52981.60173.12912−0.0726A111.56273.40414.9661−1.84212A120.973122.72223.6965−1.74911


#### 5.2.5. Drawing the Causal Diagram

According to the comprehensive impact matrix analysis results, EXCEL software was used to draw the causality diagram of influencing factors of employee turnover with centrality as abscissa and causality as ordinate, as shown in Figure2, where the ordinate represents the degree of causal and the abscissa represents the degree of centrality. The influencing factors whose ordinate value is greater than 0 are the influencing factors in the cause group, including A3, A5, A9, A2, and A8. The influencing factors whose ordinate value is less than 0 are influencing factors in the effect group, including A10, A6, A4, A7, A1, A12, and A11. In addition, the value of abscissa represents the degree of centrality distribution of each influencing factor.Figure 2
Causal diagram of influencing factors of employee turnover in Qinghai Electric Power Company.


### 5.3. Result Analysis

Based on the above calculation results, the influencing factors are analyzed from two perspectives of centrality and causality, and the following results are obtained.


#### 5.3.1. Analysis of the Degree of Centrality

The greater the centrality, the stronger the influence of this factor on employee turnover, and the smaller the centrality, the weaker the influence of this factor on employee turnover. According to the degree of centrality (i.e.,R + C), the two most significant factors affecting employee turnover are A11 and A6, whose centrality is 4.966 and 4.903, respectively. Other indicators have relatively little influence, and their centrality is between 3 and 4. It indicates that for staff turnover of Qinghai Electric Power Company, the post category of A11 and A6 are the most critical factors. The job type of employees in power companies often determines the promotion, interest, and specialty. Therefore, the initial appointment is the most crucial factor affecting employee turnover. In China, political status is essential. Party members have excellent qualities and dare to work under challenging conditions. Therefore, political status is also an important factor affecting Qinghai Electric Power Company’s employee turnover. Comparatively speaking, other factors have little influence on employee turnover of electric power enterprises in Qinghai Province.


#### 5.3.2. Analysis of the Degree of Causal

The reason degree is divided into positive and negative results. The positive influence indicates that this factor is the influencing factor in the causal group of employee turnover in Qinghai Electric Power Company. In contrast, the negative influence indicates that this factor is the influencing factor in the effect group. According to the value of causative degree (i.e.,R − C), among the 12 factors influencing employee turnover in Qinghai Electric Power Company, the factors in the causal group are A2, A3, A5, A8, and A9. The factors in the effect group are A6, A10, A1, A11, A4, A7, and A12.


#### 5.3.3. Analysis of the Factors in the Causal Group

The factors in the causal group are not easy to change due to external influence. They have a significant influence on employee turnover and other influencing factors. At the same time, the factors in the causal relationship are also affected by different factors. These kinds of factors indirectly affect employee turnover through other factors, which is the key factor to be considered in the early warning of employee turnover of Qinghai Electric Power Company. Among the many factors in the causal group, A3 is the most significant, followed by A5. Their degree of causal are the first and the second, and their degree ofcentrality is the third and the fourth. It indicates that marital status and birthplace influence the factors in the effect group and play an essential role in improving employee turnover. Therefore, managers in Qinghai Electric Power Company should prioritize them as critical factors. In the long run, Qinghai Electric Power employees are generally reluctant to turnover if they marry locally or if their household registration is local. In addition, although A2 and A8 are also factors in the causal group, their degree of centrality are lower, ranking 10th and 11th. This indicates that gender and education level of employees are not the main factors affecting employee turnover. Therefore, the managers of Qinghai Electric Power Company should not give priority to these two factors when formulating relevant measures. In addition, the factors A2, A3, A5, A8, and A9 in the causal group are not easy to change, which verifies the truth that the factors in the causal group are not easy to change.


#### 5.3.4. Analysis of the Factors in the Effect Group

The factors in the effect group are indirect influencing factors of employee turnover. These factors have a significant impact on employee turnover. They also influence and are influenced by other factors. In addition, these factors indirectly affect employee turnover through other factors. These factors are the early warning Qinghai Electric Power Company employee turnover needs to be considered. Among the factors in the effect group, the largest degree of causal is A11, followed by A12. The degree of centralities of the two factors is the first and the fifth. It shows that they play a vital role in improving employee turnover and should be considered a priority. In the short term, when the two factors remain relatively stable, the early warning of employee turnover in Qinghai Electric Power should start from the type of job and location of the work unit before the turnover. However, the degree of centrality of A1, A4, A10, and A12 is not high, indicating that they have little influence on employee turnover. Therefore, managers of Qinghai Electric Power Company can give priority to excluding these factors when formulating relevant measures. In addition, the factors (A6, A10, A1, A11, A4, A7, and A12) in the effect group are susceptible to other factors and have direct or short-term effects on employee turnover.


### 5.4. Contrast Experiment

To verify the effectiveness of the random forest-DEMATEL method proposed in this paper, the research group invited 10 experts from Qinghai Electric Power Company of China to score the correlation between the influencing factors of employee turnover. The scoring rules are as follows: 0 means no impact, 1 means very low impact, 2 means low impact, 3 means moderate impact, 4 means high impact, and 5 means very high impact. The direct matrix was constructed according to the expert scoring scale, and the DEMATEL method was used to calculate the (R + C) and (R − C) of each influencing factor. The results are shown in Table 8.Table 8
Comprehensive impact matrix analysis table.
RRankCRank(R + C)Rank(R − C)RankA11.318112.36743.6858−1.04910A21.95651.21993.175100.7374A33.20810.694123.90232.5161A40.98291.98262.9649−18A52.93120.701113.63242.232A62.40742.43434.8412−0.0277A70.966102.11753.0837−1.1519A81.85461.56383.417110.2915A92.90630.795103.70162.1113A101.63781.72173.35812−0.0846A111.76273.17514.9371−1.41312A120.885122.65823.5435−1.77311By comparing Tables7 and 8, although the calculated values of centrality and reason degree of the influencing factors of employee turnover in Qinghai Electric Power Company are different, the ranking and classification results of the influencing factors of employee turnover through centrality and reason degree are consistent. This indicates that the random forest-DEMATEL method has high accuracy in identifying the influencing factors of employee turnover. Therefore, it is practical and scientific to use the random forest-DEMATEL method to identify the influencing factors of employee turnover.


## 5.1. Data Collection and Preprocessing

As a typical electric power enterprise in the west of China, Qinghai Electric Power Company has experienced grave employee turnover in recent years. In this paper, the employee turnover data set of China Qinghai Electric Power Company from 2012 to 2020 was used for empirical analysis [37]. There were 2,666 records in total, and each record had 20 influencing factors. After investigation, we found that some fields were irrelevant to classification prediction, such as name, employee number, and id number, which were removed. Second, remove all fields with too many missing values (more than 30% of missing fields in this experiment). After such processing, we retained a total of 2,494 records. As shown in Table 3, each record was composed of 13 fields, among which the first 12 fields were the influencing factors of employee turnover in Qinghai Electric Power Company.Table 3
Basic settings for variables used in random forest.
Variable nameVariable descriptionVariable dimensionMeasurement levelEmployee data (input variables)Type of work unitThe type of unit the employee works inPower supply1Scientific research2Business3Construction4SexSex of the employeeMan1Woman2Marital statusThe employee’s marital statusMarried1Unmarried2BirthplaceThe employee’s birthplaceEastern1Western2Central3Household registrationThe household registration type of the employeeCity1Rural2Political statusThe employee’s political statusLeague member1Communist2Prospective communist3Mass4Type of graduation schoolThe type of school the employee graduated from“211 projects” colleges1“985 projects” colleges2Ordinary undergraduate colleges3Vocational colleges4Level of educationThe employee’s education levelPostgraduate1Undergraduate2Junior college student3Job titleThe employee’s job titleJunior title1Senior title2Intermediate title3Professional skillsEmployee’s professional skillsElectrician1Management2Computer3Finance4Engineering5Others6Communication7Type of jobThe employee’s type of jobService1Management2Skill3Technology3Others5Location of the work unitThe employee’s work areaSecond area1Third area2Fourth area3Fifth area4Sixth area5Desired target (output variables)TurnoverSucceed to turnoverNo0Yes1


## 5.2. Calculation Results

In this section, we give the calculation process and results. First, we used the random forest-DEMATEL model constructed in this paper and calculated the importance of 12 influencing factors of employee turnover based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020 after processing. Then, we calculate the direct influence matrixA according to the importance degree and then calculate the normalized direct influence matrix D, the comprehensive influence matrix T, and the centrality and cause degree. Finally, we draw the causality diagram of influencing factors of employee turnover with the centrality of 12 influencing factors as abscissa and causality as ordinate.


### 5.2.1. Calculation of the Importance of Influencing Factors

According to the application of theR language analysis tool, the random forest algorithm was used to analyze the importance of the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, and the importance of 12 influencing factors of employee turnover was obtained. To facilitate calculation and result in analysis, we labeled the 12 factors as A1∼A12, as shown in Table 4.Table 4
Influencing factors of employee turnover.
Code nameFactorsImportant degreeA1Type of work unit12.045A2Sex5.877A3Marital status4.109A4Birthplace11.691A5Household registration4.415A6Political status12.811A7Type of graduation school12.679A8Level of education6.952A9Job title4.490A10Professional skills8.256A11Type of job7.998A12Location of the work unit12.648


### 5.2.2. Calculation of the Direct Influence Matrix

According to the importance of the above 12 influencing factors, the direct influence matrixA of employee turnover is obtained using EXCEL software (omitted due to length reasons). The direct influence matrix is normalized to obtain the standardized direct influence matrix D, as shown in Table 5.Table 5
Influence matrixD after standardized treatment.
A1A2A3A4A5A6A7A8A9A10A11A12A100.0240.0170.0480.0180.0530.0520.0290.0180.0340.0330.052A20.10100.0350.0980.0370.1080.1070.0580.0380.0690.0670.106A30.1450.07100.1410.0530.1540.1530.0840.0540.0990.0960.152A40.0510.0250.01700.0190.0540.0540.0290.0190.0350.0340.053A50.1350.0660.0460.13100.1430.1420.0790.0500.0920.0900.142A60.0460.0230.0160.0450.01700.0490.0270.0170.0320.6180.049A70.0470.0230.0160.0460.0170.05000.0270.0180.0320.0310.090A80.0860.0420.0290.0830.0310.0910.09000.0320.0590.0570.139A90.1330.0650.0450.1290.0490.1410.1400.07700.0910.0880.139A100.1330.0350.0250.0700.0270.0770.0760.0420.02700.0040.076A110.0720.0360.0250.0720.0270.0790.0780.0430.0280.05100.078A120.0470.0230.01600.0460.0170.0500.0500.0270.0180.0320.0310


### 5.2.3. Comprehensive Influence Matrix

According to the standardized direct influence matrixD and formula T=Xi−x−1, the comprehensive influence matrix T was obtained using MATLAB software, as shown in Table 6.Table 6
Comprehensive influence matrixT.
A1A2A3A4A5A6A7A8A9A10A11A12A10.0720.0560.0390.1120.0420.1230.1210.0670.0430.0790.1420.129A20.2450.0680.0810.2290.0870.2510.2480.1360.0880.1620.2900.264A30.3500.1650.0680.3280.1240.3590.3550.1950.1260.2310.4150.377A40.1230.0580.0410.0680.0440.1260.1250.0690.0440.0810.1460.133A50.3260.1530.1070.3050.0680.3340.3310.1810.1170.2150.3860.351A60.2120.1000.0700.1990.0750.1710.2160.1180.0770.1410.7850.229A70.1180.0550.0390.1110.0420.1210.0730.0660.0420.0780.1400.164A80.2120.0100.0700.1990.0750.2180.2150.0710.0760.1400.2520.274A90.3200.1510.1050.3000.1130.3290.3250.1780.0680.2120.3800.345A100.2290.0820.0570.1630.0620.1780.1770.0970.0630.0690.1660.187A110.1770.0850.0590.1680.0640.1840.1820.1000.0650.1190.1660.194A120.1140.0540.0370.1060.0400.1170.1150.0630.0410.0750.1350.076


### 5.2.4. Comprehensive Impact Matrix Analysis

According to the three formulas corresponding toT, R, and C in random forest-DEMATEL, each factor’s influence degree, influence degree, cause degree, and centrality are calculated. The results are shown in Table 7.Table 7
Comprehensive impact matrix analysis table.
RRankCRank(R + C)Rank(R − C)RankA11.024112.49743.5228−1.47310A22.14951.12693.275101.0234A33.09310.773123.86632.3201A41.05792.28663.3439−1.2308A52.87520.836113.71242.0392A62.39342.51034.9032−0.1177A71.047102.48453.5317−1.4379A81.90261.34183.242110.5615A92.82630.849103.67661.9773A101.52981.60173.12912−0.0726A111.56273.40414.9661−1.84212A120.973122.72223.6965−1.74911


### 5.2.5. Drawing the Causal Diagram

According to the comprehensive impact matrix analysis results, EXCEL software was used to draw the causality diagram of influencing factors of employee turnover with centrality as abscissa and causality as ordinate, as shown in Figure2, where the ordinate represents the degree of causal and the abscissa represents the degree of centrality. The influencing factors whose ordinate value is greater than 0 are the influencing factors in the cause group, including A3, A5, A9, A2, and A8. The influencing factors whose ordinate value is less than 0 are influencing factors in the effect group, including A10, A6, A4, A7, A1, A12, and A11. In addition, the value of abscissa represents the degree of centrality distribution of each influencing factor.Figure 2
Causal diagram of influencing factors of employee turnover in Qinghai Electric Power Company.


## 5.2.1. Calculation of the Importance of Influencing Factors

According to the application of theR language analysis tool, the random forest algorithm was used to analyze the importance of the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, and the importance of 12 influencing factors of employee turnover was obtained. To facilitate calculation and result in analysis, we labeled the 12 factors as A1∼A12, as shown in Table 4.Table 4
Influencing factors of employee turnover.
Code nameFactorsImportant degreeA1Type of work unit12.045A2Sex5.877A3Marital status4.109A4Birthplace11.691A5Household registration4.415A6Political status12.811A7Type of graduation school12.679A8Level of education6.952A9Job title4.490A10Professional skills8.256A11Type of job7.998A12Location of the work unit12.648


## 5.2.2. Calculation of the Direct Influence Matrix

According to the importance of the above 12 influencing factors, the direct influence matrixA of employee turnover is obtained using EXCEL software (omitted due to length reasons). The direct influence matrix is normalized to obtain the standardized direct influence matrix D, as shown in Table 5.Table 5
Influence matrixD after standardized treatment.
A1A2A3A4A5A6A7A8A9A10A11A12A100.0240.0170.0480.0180.0530.0520.0290.0180.0340.0330.052A20.10100.0350.0980.0370.1080.1070.0580.0380.0690.0670.106A30.1450.07100.1410.0530.1540.1530.0840.0540.0990.0960.152A40.0510.0250.01700.0190.0540.0540.0290.0190.0350.0340.053A50.1350.0660.0460.13100.1430.1420.0790.0500.0920.0900.142A60.0460.0230.0160.0450.01700.0490.0270.0170.0320.6180.049A70.0470.0230.0160.0460.0170.05000.0270.0180.0320.0310.090A80.0860.0420.0290.0830.0310.0910.09000.0320.0590.0570.139A90.1330.0650.0450.1290.0490.1410.1400.07700.0910.0880.139A100.1330.0350.0250.0700.0270.0770.0760.0420.02700.0040.076A110.0720.0360.0250.0720.0270.0790.0780.0430.0280.05100.078A120.0470.0230.01600.0460.0170.0500.0500.0270.0180.0320.0310


## 5.2.3. Comprehensive Influence Matrix

According to the standardized direct influence matrixD and formula T=Xi−x−1, the comprehensive influence matrix T was obtained using MATLAB software, as shown in Table 6.Table 6
Comprehensive influence matrixT.
A1A2A3A4A5A6A7A8A9A10A11A12A10.0720.0560.0390.1120.0420.1230.1210.0670.0430.0790.1420.129A20.2450.0680.0810.2290.0870.2510.2480.1360.0880.1620.2900.264A30.3500.1650.0680.3280.1240.3590.3550.1950.1260.2310.4150.377A40.1230.0580.0410.0680.0440.1260.1250.0690.0440.0810.1460.133A50.3260.1530.1070.3050.0680.3340.3310.1810.1170.2150.3860.351A60.2120.1000.0700.1990.0750.1710.2160.1180.0770.1410.7850.229A70.1180.0550.0390.1110.0420.1210.0730.0660.0420.0780.1400.164A80.2120.0100.0700.1990.0750.2180.2150.0710.0760.1400.2520.274A90.3200.1510.1050.3000.1130.3290.3250.1780.0680.2120.3800.345A100.2290.0820.0570.1630.0620.1780.1770.0970.0630.0690.1660.187A110.1770.0850.0590.1680.0640.1840.1820.1000.0650.1190.1660.194A120.1140.0540.0370.1060.0400.1170.1150.0630.0410.0750.1350.076


## 5.2.4. Comprehensive Impact Matrix Analysis

According to the three formulas corresponding toT, R, and C in random forest-DEMATEL, each factor’s influence degree, influence degree, cause degree, and centrality are calculated. The results are shown in Table 7.Table 7
Comprehensive impact matrix analysis table.
RRankCRank(R + C)Rank(R − C)RankA11.024112.49743.5228−1.47310A22.14951.12693.275101.0234A33.09310.773123.86632.3201A41.05792.28663.3439−1.2308A52.87520.836113.71242.0392A62.39342.51034.9032−0.1177A71.047102.48453.5317−1.4379A81.90261.34183.242110.5615A92.82630.849103.67661.9773A101.52981.60173.12912−0.0726A111.56273.40414.9661−1.84212A120.973122.72223.6965−1.74911


## 5.2.5. Drawing the Causal Diagram

According to the comprehensive impact matrix analysis results, EXCEL software was used to draw the causality diagram of influencing factors of employee turnover with centrality as abscissa and causality as ordinate, as shown in Figure2, where the ordinate represents the degree of causal and the abscissa represents the degree of centrality. The influencing factors whose ordinate value is greater than 0 are the influencing factors in the cause group, including A3, A5, A9, A2, and A8. The influencing factors whose ordinate value is less than 0 are influencing factors in the effect group, including A10, A6, A4, A7, A1, A12, and A11. In addition, the value of abscissa represents the degree of centrality distribution of each influencing factor.Figure 2
Causal diagram of influencing factors of employee turnover in Qinghai Electric Power Company.


## 5.3. Result Analysis

Based on the above calculation results, the influencing factors are analyzed from two perspectives of centrality and causality, and the following results are obtained.


### 5.3.1. Analysis of the Degree of Centrality

The greater the centrality, the stronger the influence of this factor on employee turnover, and the smaller the centrality, the weaker the influence of this factor on employee turnover. According to the degree of centrality (i.e.,R + C), the two most significant factors affecting employee turnover are A11 and A6, whose centrality is 4.966 and 4.903, respectively. Other indicators have relatively little influence, and their centrality is between 3 and 4. It indicates that for staff turnover of Qinghai Electric Power Company, the post category of A11 and A6 are the most critical factors. The job type of employees in power companies often determines the promotion, interest, and specialty. Therefore, the initial appointment is the most crucial factor affecting employee turnover. In China, political status is essential. Party members have excellent qualities and dare to work under challenging conditions. Therefore, political status is also an important factor affecting Qinghai Electric Power Company’s employee turnover. Comparatively speaking, other factors have little influence on employee turnover of electric power enterprises in Qinghai Province.


### 5.3.2. Analysis of the Degree of Causal

The reason degree is divided into positive and negative results. The positive influence indicates that this factor is the influencing factor in the causal group of employee turnover in Qinghai Electric Power Company. In contrast, the negative influence indicates that this factor is the influencing factor in the effect group. According to the value of causative degree (i.e.,R − C), among the 12 factors influencing employee turnover in Qinghai Electric Power Company, the factors in the causal group are A2, A3, A5, A8, and A9. The factors in the effect group are A6, A10, A1, A11, A4, A7, and A12.


### 5.3.3. Analysis of the Factors in the Causal Group

The factors in the causal group are not easy to change due to external influence. They have a significant influence on employee turnover and other influencing factors. At the same time, the factors in the causal relationship are also affected by different factors. These kinds of factors indirectly affect employee turnover through other factors, which is the key factor to be considered in the early warning of employee turnover of Qinghai Electric Power Company. Among the many factors in the causal group, A3 is the most significant, followed by A5. Their degree of causal are the first and the second, and their degree ofcentrality is the third and the fourth. It indicates that marital status and birthplace influence the factors in the effect group and play an essential role in improving employee turnover. Therefore, managers in Qinghai Electric Power Company should prioritize them as critical factors. In the long run, Qinghai Electric Power employees are generally reluctant to turnover if they marry locally or if their household registration is local. In addition, although A2 and A8 are also factors in the causal group, their degree of centrality are lower, ranking 10th and 11th. This indicates that gender and education level of employees are not the main factors affecting employee turnover. Therefore, the managers of Qinghai Electric Power Company should not give priority to these two factors when formulating relevant measures. In addition, the factors A2, A3, A5, A8, and A9 in the causal group are not easy to change, which verifies the truth that the factors in the causal group are not easy to change.


### 5.3.4. Analysis of the Factors in the Effect Group

The factors in the effect group are indirect influencing factors of employee turnover. These factors have a significant impact on employee turnover. They also influence and are influenced by other factors. In addition, these factors indirectly affect employee turnover through other factors. These factors are the early warning Qinghai Electric Power Company employee turnover needs to be considered. Among the factors in the effect group, the largest degree of causal is A11, followed by A12. The degree of centralities of the two factors is the first and the fifth. It shows that they play a vital role in improving employee turnover and should be considered a priority. In the short term, when the two factors remain relatively stable, the early warning of employee turnover in Qinghai Electric Power should start from the type of job and location of the work unit before the turnover. However, the degree of centrality of A1, A4, A10, and A12 is not high, indicating that they have little influence on employee turnover. Therefore, managers of Qinghai Electric Power Company can give priority to excluding these factors when formulating relevant measures. In addition, the factors (A6, A10, A1, A11, A4, A7, and A12) in the effect group are susceptible to other factors and have direct or short-term effects on employee turnover.


## 5.3.1. Analysis of the Degree of Centrality

The greater the centrality, the stronger the influence of this factor on employee turnover, and the smaller the centrality, the weaker the influence of this factor on employee turnover. According to the degree of centrality (i.e.,R + C), the two most significant factors affecting employee turnover are A11 and A6, whose centrality is 4.966 and 4.903, respectively. Other indicators have relatively little influence, and their centrality is between 3 and 4. It indicates that for staff turnover of Qinghai Electric Power Company, the post category of A11 and A6 are the most critical factors. The job type of employees in power companies often determines the promotion, interest, and specialty. Therefore, the initial appointment is the most crucial factor affecting employee turnover. In China, political status is essential. Party members have excellent qualities and dare to work under challenging conditions. Therefore, political status is also an important factor affecting Qinghai Electric Power Company’s employee turnover. Comparatively speaking, other factors have little influence on employee turnover of electric power enterprises in Qinghai Province.


## 5.3.2. Analysis of the Degree of Causal

The reason degree is divided into positive and negative results. The positive influence indicates that this factor is the influencing factor in the causal group of employee turnover in Qinghai Electric Power Company. In contrast, the negative influence indicates that this factor is the influencing factor in the effect group. According to the value of causative degree (i.e.,R − C), among the 12 factors influencing employee turnover in Qinghai Electric Power Company, the factors in the causal group are A2, A3, A5, A8, and A9. The factors in the effect group are A6, A10, A1, A11, A4, A7, and A12.


## 5.3.3. Analysis of the Factors in the Causal Group

The factors in the causal group are not easy to change due to external influence. They have a significant influence on employee turnover and other influencing factors. At the same time, the factors in the causal relationship are also affected by different factors. These kinds of factors indirectly affect employee turnover through other factors, which is the key factor to be considered in the early warning of employee turnover of Qinghai Electric Power Company. Among the many factors in the causal group, A3 is the most significant, followed by A5. Their degree of causal are the first and the second, and their degree ofcentrality is the third and the fourth. It indicates that marital status and birthplace influence the factors in the effect group and play an essential role in improving employee turnover. Therefore, managers in Qinghai Electric Power Company should prioritize them as critical factors. In the long run, Qinghai Electric Power employees are generally reluctant to turnover if they marry locally or if their household registration is local. In addition, although A2 and A8 are also factors in the causal group, their degree of centrality are lower, ranking 10th and 11th. This indicates that gender and education level of employees are not the main factors affecting employee turnover. Therefore, the managers of Qinghai Electric Power Company should not give priority to these two factors when formulating relevant measures. In addition, the factors A2, A3, A5, A8, and A9 in the causal group are not easy to change, which verifies the truth that the factors in the causal group are not easy to change.


## 5.3.4. Analysis of the Factors in the Effect Group

The factors in the effect group are indirect influencing factors of employee turnover. These factors have a significant impact on employee turnover. They also influence and are influenced by other factors. In addition, these factors indirectly affect employee turnover through other factors. These factors are the early warning Qinghai Electric Power Company employee turnover needs to be considered. Among the factors in the effect group, the largest degree of causal is A11, followed by A12. The degree of centralities of the two factors is the first and the fifth. It shows that they play a vital role in improving employee turnover and should be considered a priority. In the short term, when the two factors remain relatively stable, the early warning of employee turnover in Qinghai Electric Power should start from the type of job and location of the work unit before the turnover. However, the degree of centrality of A1, A4, A10, and A12 is not high, indicating that they have little influence on employee turnover. Therefore, managers of Qinghai Electric Power Company can give priority to excluding these factors when formulating relevant measures. In addition, the factors (A6, A10, A1, A11, A4, A7, and A12) in the effect group are susceptible to other factors and have direct or short-term effects on employee turnover.


## 5.4. Contrast Experiment

To verify the effectiveness of the random forest-DEMATEL method proposed in this paper, the research group invited 10 experts from Qinghai Electric Power Company of China to score the correlation between the influencing factors of employee turnover. The scoring rules are as follows: 0 means no impact, 1 means very low impact, 2 means low impact, 3 means moderate impact, 4 means high impact, and 5 means very high impact. The direct matrix was constructed according to the expert scoring scale, and the DEMATEL method was used to calculate the (R + C) and (R − C) of each influencing factor. The results are shown in Table 8.Table 8
Comprehensive impact matrix analysis table.
RRankCRank(R + C)Rank(R − C)RankA11.318112.36743.6858−1.04910A21.95651.21993.175100.7374A33.20810.694123.90232.5161A40.98291.98262.9649−18A52.93120.701113.63242.232A62.40742.43434.8412−0.0277A70.966102.11753.0837−1.1519A81.85461.56383.417110.2915A92.90630.795103.70162.1113A101.63781.72173.35812−0.0846A111.76273.17514.9371−1.41312A120.885122.65823.5435−1.77311By comparing Tables7 and 8, although the calculated values of centrality and reason degree of the influencing factors of employee turnover in Qinghai Electric Power Company are different, the ranking and classification results of the influencing factors of employee turnover through centrality and reason degree are consistent. This indicates that the random forest-DEMATEL method has high accuracy in identifying the influencing factors of employee turnover. Therefore, it is practical and scientific to use the random forest-DEMATEL method to identify the influencing factors of employee turnover.


## 6. Discussion and Conclusion

The traditional DEMATEL method mainly obtains the weight of each index based on the expert scoring method or questionnaire survey method and then constructs the direct matrix. The downside of this method is that it is difficult to avoid the arbitrary nature of the expert scoring process. Based on this, we try to use the importance of the random forest algorithm as the weight to construct the direct correlation matrix of each index in the DEMATEL method and achieve good results. The random forest-DEMATEL method overcomes the limitation of the traditional DEMATEL method which requires a questionnaire survey or expert scoring and greatly expands the application scope of the DEMATEL method. This method can effectively analyze the relationship between influencing factors and indicators of employee turnover in Qinghai Electric Power Company. Furthermore, the factors in the causal group and factors in the effect group are extracted to provide theoretical support and starting point for enterprise managers to prevent employee turnover. It enriches the theories and methods of influencing factors research to a certain extent and provides the possibility to extract the fundamental influencing factors effectively.Meanwhile, according to the random forest-DEMATEL method, the key factors of employee turnover in Qinghai Electric Power Company are “marital status,” “household registration,” “political status,” “type of job,” and “location of the work unit.” The most important ones are “political status” and “type of job,” which belong to the factors in the causal group, and these two factors also influence other factors on employee turnover. Adjusting the factors in the effect group factors (i.e., “type of job” and “location of the work unit”) can directly reduce the employee turnover rate. Through the quantitative research results of the model and the reality of Qinghai Electric Power Company, we can enrich the daily life of the employee outside work by opening the way for employee promotion, determining the employee needs and psychological expectations, and communicating effectively, improving material treatment and other measures to reduce employee turnover.Qinghai Electric Power Company could make the employee see the unimpeded and long-term development path, taking into account the employees’ unique development demand. Generally, the route divides into two kinds; one is the technical post and the other is the management post. The different development routes have the employee’s ability and decision, but no matter which route, the company could provide the corresponding support for the employee; company cannot confine employee development to a small space or a certain level which difficult to promote. Meanwhile, the company could provide learning conditions for employees, especially those who want to engage in technical posts. In addition, the company can increase the enjoyment of life outside of work and help employees adjust their mindset, give employees certain subsidies in material life and increase their housing provident fund.


### 6.1. Main Conclusions

This study combines the DEMATEL model with the random forest model to establish the identification model of the influencing factors of employee turnover in Qinghai Electric Power Company. Based on the employee turnover data of Qinghai Electric Power Company from 2012 to 2020, we also conducted verification and analysis and analyzed the key factors of employee turnover of Qinghai Electric Power Company from the causal group and effect group. The results show the adaptability and effectiveness of the random forest-DEMATEL method. Meanwhile, the conclusions provide the method guidance and decision support for the prevention of employee turnover in Qinghai Electric Power Company. The specific conclusions are as follows.(1)
Taking the influencing factors of employee turnover of Qinghai Electric Power Company as the research issue, we conducted an empirical analysis on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020. We obtained 12 influencing factors of employee turnover from 4 dimensions. The random forest-DEMATEL combined model was used to analyze the 12 factors, calculated the degree of centrality and the degree of causal, and distinguished the more critical factors, factors in the causal group, and factors in the effect group.(2)
We tried to use the importance degree solved by the random forest algorithm as the weight to construct the direct correlation matrix of each index in the DEMATEL method and achieve good results. The random forest-DEMATEL method overcomes the limitations of traditional DEMATEL, which requires questionnaires or expert scoring, and dramatically expands the application scope of the DEMATEL method. This method can well analyze the relationship between influencing factors and indicators of employee turnover and extract the most important influencing factors, to provide theoretical support and starting point for managers to prevent employee turnover. It enriches the theory and method of influencing factors research to a certain extent and allows it to extract the fundamental influencing factors effectively.(3)
Five vital influencing factors were calculated according to the degree of centrality and causal of each influencing factor, namely A3, A5, A6, A11, and A12. We got the two factors (A11 and A6) with the highest centrality based on the degree of centrality. According to the degree of causal, we divided the influencing factors into factors in the causal group and factors in the effect group. Among them, A3 and A5 are the factors in the causal group, which will long-term influence other factors and further influence employee turnover. A11 and A12 are the factors in the effect group, and their adjustment and optimization can directly improve employee turnover.(4)
We compared the results calculated by the random forest-DEMATEL method with those calculated by the DEMATEL method based on the traditional expert scoring. The results show that the proposed method is feasible and accurate.


### 6.2. Management Implications

Based on the analysis of the influencing factors of employee turnover and the development status of Qinghai Electric Power Company, we got the following management enlightenment from the enterprise level and individual level to further improve the problem of employee turnover.


#### 6.2.1. Enterprise Level

“Type of job” and “political status” are the two influencing factors of employee turnover with the highest degree of centrality. They are the most important factors affecting employee turnover in Qinghai Electric Power Company. Therefore, Qinghai Electric Power Company should consider appropriate post-deployment according to the characteristics of each employee for newly recruited personnel.“Marital status” and “household registration” are the two influencing factors of employee turnover with the highest cause degree. This indicates that during the recruitment period, the relevant human resources management personnel should pay attention to the marital status and the source of employees, which can effectively alleviate the problem of employee turnover.In addition, managers in Qinghai Electric Power Company must pay attention to the management of employee turnover. After the turnover of some employees occurs, the enterprise should review itself in time, find out the problems existing in the organization, and correct the problems in time. The human resources management department of the company shall timely grasp the current situation of employee job satisfaction and turnover intention, formulate countermeasures to reduce talent turnover, and improve the welfare and working environment. Enterprises should strengthen the importance of humanistic care and other aspects. In general, the company needs to start from multiple perspectives such as organizational culture, the establishment of a common organizational vision, and human resource management.


#### 6.2.2. Personal Level

Employees should actively enhance their values and attitude to achieve personal development and constantly improve their political outlook. In addition, employees cultivate their organization, coordination, leadership, management, innovation, strain, and other abilities, integrating ideological and moral cultivation with scientific and cultural knowledge, and combining professional and technical levels with efficient management and scientific practice. In this way, we can promote the construction of compound talents and adapt to the development of enterprises.


### 6.3. Research Limitations and Future Prospects

The random forest-DEMATEL method proposed by us is applicable when the target factors are known, which may influence the scope of application of the method. In addition, the influencing factors of employee turnover in power companies in other provinces of China are different from those in Qinghai Province, which may lead to different research results on influencing factors. In future research, employee turnover data sets of electric companies from several provinces in western China will be selected for analysis to further support the research method in this paper. In addition, this paper does not pay attention to the influence mechanism of the influencing indicators of employee turnover in Qinghai electric power enterprises. In future research, this topic needs to be further improved and further studied from the perspective of influence mechanism.


## 6.1. Main Conclusions

This study combines the DEMATEL model with the random forest model to establish the identification model of the influencing factors of employee turnover in Qinghai Electric Power Company. Based on the employee turnover data of Qinghai Electric Power Company from 2012 to 2020, we also conducted verification and analysis and analyzed the key factors of employee turnover of Qinghai Electric Power Company from the causal group and effect group. The results show the adaptability and effectiveness of the random forest-DEMATEL method. Meanwhile, the conclusions provide the method guidance and decision support for the prevention of employee turnover in Qinghai Electric Power Company. The specific conclusions are as follows.(1)
Taking the influencing factors of employee turnover of Qinghai Electric Power Company as the research issue, we conducted an empirical analysis on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020. We obtained 12 influencing factors of employee turnover from 4 dimensions. The random forest-DEMATEL combined model was used to analyze the 12 factors, calculated the degree of centrality and the degree of causal, and distinguished the more critical factors, factors in the causal group, and factors in the effect group.(2)
We tried to use the importance degree solved by the random forest algorithm as the weight to construct the direct correlation matrix of each index in the DEMATEL method and achieve good results. The random forest-DEMATEL method overcomes the limitations of traditional DEMATEL, which requires questionnaires or expert scoring, and dramatically expands the application scope of the DEMATEL method. This method can well analyze the relationship between influencing factors and indicators of employee turnover and extract the most important influencing factors, to provide theoretical support and starting point for managers to prevent employee turnover. It enriches the theory and method of influencing factors research to a certain extent and allows it to extract the fundamental influencing factors effectively.(3)
Five vital influencing factors were calculated according to the degree of centrality and causal of each influencing factor, namely A3, A5, A6, A11, and A12. We got the two factors (A11 and A6) with the highest centrality based on the degree of centrality. According to the degree of causal, we divided the influencing factors into factors in the causal group and factors in the effect group. Among them, A3 and A5 are the factors in the causal group, which will long-term influence other factors and further influence employee turnover. A11 and A12 are the factors in the effect group, and their adjustment and optimization can directly improve employee turnover.(4)
We compared the results calculated by the random forest-DEMATEL method with those calculated by the DEMATEL method based on the traditional expert scoring. The results show that the proposed method is feasible and accurate.


## 6.2. Management Implications

Based on the analysis of the influencing factors of employee turnover and the development status of Qinghai Electric Power Company, we got the following management enlightenment from the enterprise level and individual level to further improve the problem of employee turnover.


### 6.2.1. Enterprise Level

“Type of job” and “political status” are the two influencing factors of employee turnover with the highest degree of centrality. They are the most important factors affecting employee turnover in Qinghai Electric Power Company. Therefore, Qinghai Electric Power Company should consider appropriate post-deployment according to the characteristics of each employee for newly recruited personnel.“Marital status” and “household registration” are the two influencing factors of employee turnover with the highest cause degree. This indicates that during the recruitment period, the relevant human resources management personnel should pay attention to the marital status and the source of employees, which can effectively alleviate the problem of employee turnover.In addition, managers in Qinghai Electric Power Company must pay attention to the management of employee turnover. After the turnover of some employees occurs, the enterprise should review itself in time, find out the problems existing in the organization, and correct the problems in time. The human resources management department of the company shall timely grasp the current situation of employee job satisfaction and turnover intention, formulate countermeasures to reduce talent turnover, and improve the welfare and working environment. Enterprises should strengthen the importance of humanistic care and other aspects. In general, the company needs to start from multiple perspectives such as organizational culture, the establishment of a common organizational vision, and human resource management.


### 6.2.2. Personal Level

Employees should actively enhance their values and attitude to achieve personal development and constantly improve their political outlook. In addition, employees cultivate their organization, coordination, leadership, management, innovation, strain, and other abilities, integrating ideological and moral cultivation with scientific and cultural knowledge, and combining professional and technical levels with efficient management and scientific practice. In this way, we can promote the construction of compound talents and adapt to the development of enterprises.


## 6.2.1. Enterprise Level

“Type of job” and “political status” are the two influencing factors of employee turnover with the highest degree of centrality. They are the most important factors affecting employee turnover in Qinghai Electric Power Company. Therefore, Qinghai Electric Power Company should consider appropriate post-deployment according to the characteristics of each employee for newly recruited personnel.“Marital status” and “household registration” are the two influencing factors of employee turnover with the highest cause degree. This indicates that during the recruitment period, the relevant human resources management personnel should pay attention to the marital status and the source of employees, which can effectively alleviate the problem of employee turnover.In addition, managers in Qinghai Electric Power Company must pay attention to the management of employee turnover. After the turnover of some employees occurs, the enterprise should review itself in time, find out the problems existing in the organization, and correct the problems in time. The human resources management department of the company shall timely grasp the current situation of employee job satisfaction and turnover intention, formulate countermeasures to reduce talent turnover, and improve the welfare and working environment. Enterprises should strengthen the importance of humanistic care and other aspects. In general, the company needs to start from multiple perspectives such as organizational culture, the establishment of a common organizational vision, and human resource management.


## 6.2.2. Personal Level

Employees should actively enhance their values and attitude to achieve personal development and constantly improve their political outlook. In addition, employees cultivate their organization, coordination, leadership, management, innovation, strain, and other abilities, integrating ideological and moral cultivation with scientific and cultural knowledge, and combining professional and technical levels with efficient management and scientific practice. In this way, we can promote the construction of compound talents and adapt to the development of enterprises.


## 6.3. Research Limitations and Future Prospects

The random forest-DEMATEL method proposed by us is applicable when the target factors are known, which may influence the scope of application of the method. In addition, the influencing factors of employee turnover in power companies in other provinces of China are different from those in Qinghai Province, which may lead to different research results on influencing factors. In future research, employee turnover data sets of electric companies from several provinces in western China will be selected for analysis to further support the research method in this paper. In addition, this paper does not pay attention to the influence mechanism of the influencing indicators of employee turnover in Qinghai electric power enterprises. In future research, this topic needs to be further improved and further studied from the perspective of influence mechanism.



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*Source: 7798674-2023-01-11.xml*