A Graphic Model for Evaluating & Developing Employee Performance and its Empirical Study
2. Empirical study
This paper takes a limited company A in Dongguan, Guangdong province as a sample to perform empirical study on performance graph analysis. Company A was founded in 1996. It is in an industrial district in Dongguan, Guangdong province. And it is a Taiwan-funded company. This paper tends to analyze the performance features of 20 employees in the human resource department (HR, for short next) in company A by performance graph theory. 2.1 Index analysis
According to company A’ s strategy, this paper constitutes an index system as follow for the HR department by goal analysis (Du, 2007), job analysis(Yang, Bai & Zheng, 2002), competence analysis (Jon, 2005), and motivation analysis (Bai, 1996, p27-31). See to table 1.
2.2 Make evaluation by factor analysis
The constitution of employee performance is complicated. Its inner factors are mutually affected and interacted. There is greater relevance among these indexes. Therefore, we choose the factor analysis method in evaluation that can overcome the effects of relevance between indexes on the evaluation on one hand, and on the other hand, it can overcome the subjectivity of traditional methods, such as the weighted average method. The factor analysis can follow these procedures. (1) Index standardization. Standardize the initial scores of employees in the four aspects. Suppose the mathematical expectation is 0 and the error of estimate is 1. (2) Calculate the correlation coefficient matrix. Get the correlation coefficient matrix after the data standardization. (3) Get chief factors. Identify the factor whose feature value is larger than 1 and its accumulated contribution ratio exceeds 80% based on the correlation coefficient matrix. Take these factors as chief factors. (4) Calculate the factor load. Get the factor load matrix according to the formula: factor load = eigenvector * SQRT (eigenvalue). (5) Calculate the factor score. Calculate the factor score coefficient matrix by the Bartlette method and get the factor scores. (6) Calculate the integrated scores. Take the divergence contribution ratio of each factor as weight and get the integrated score of each sample in the four aspects. (7) Standardize the scores. Standardize the scores for the sake of further analysis. Transform the scores into 0-9 mark dimensions according to formula
s%
j= +µˆ
s
jδˆ
. According to calculations, we can get the scores of 20 employees in competence, motivation, job fitness, and job output as follow. See to table 2.2.3 Performance vector analysis 2.3.1 Vector expression
In order to help decision-maker to understand the distribution of employees in the performance vector space, we put the scores in a competence-motivation -job-fitness vector space as follow by origin 7.0. See to figure 1.
2.3.2 Employee system cluster
In order to exert the advantage that the evaluation system based on process view for performance can directly guide the improvement of performance, we should classify employees. Here we adopt the system clustering method to perform system clustering analysis on competence, motivation, and job fitness by SPSS13.0. Then we can get the following clustering tree. See to figure 2
Considering the performance distribution of 20 employees in the performance vector space, we classify the 20 employees into 7 clusters. See to table 3.
2.4 Performance facet analysis 2.4.1 The surface analysis on facets
Make surface analysis on the three main facets of employee performance (competence, motivation, and job fitness). In other words, it is to analyze the surface of certain employee cluster’s every facet in the performance vector space. Draw a surface of employee performance’s point collection from different angles by origin7.0. Get the macro performance features by analyzing the development of these surfaces. See to table 4 as follow.
2.4.2 Facet regression analysis
Perform regression analysis on the three facets (competence, motivation, and job fitness) and analyze the situation of each facet in these employees. The regression analysis figure for each facet is as follow. See to figure 3.
From the regression analysis figure and table (absence in this paper), we can draw the following conclusions. (1) “Competence-motivation” analysis conclusion. There is stronger positive correlation between motivation and competence and their relation is stable. It proves that competence is one of main factors that affect motivation for these employees. (2) “Competence-job fitness” analysis conclusion. There is stronger positive correlation between job fitness and competence, what indicates that competence is one of main factors that affect job fitness. (3) “Job fitness-motivation” analysis conclusion. The correlation between motivation and job fitness is complex. In general, it firstly increases then decreases and then increases. As the job fitness is in the section 4-6, the motivation begins to decrease. It indicates that motivation is significantly affected by job fitness. The degree of job fitness disappoints certain employees’ motivation. (4) “Motivation-competence” analysis conclusion. There is weak positive correlation between competence and motivation. It proves that motivation is not an important factor that affects competence. Competence has its own characteristics in distribution. (5) “Job fitness-competence” analysis conclusion. There is weak positive correlation between competence and job fitness. It indicates that job fitness is not a main factor that affects competence. Competence has its own characteristics in distribution. (6) “Motivation-job fitness” analysis conclusion. There is weak correlation between job fitness and motivation. It indicates that motivation is not a main factor that affects job fitness. Job fitness has its own characteristics in distribution.
According to the analysis above, the macro performance of employees in this HR department has the following characteristics. (1) Employee competence is stable and high, and seldom affected by other factors. The integrated competence of employees in this department is evenness and stable, and seldom affected by job nature, content, motivation, and emotion. (2) The motivation is greatly affected by competence, showing a positive correlation. The higher the competence is, the higher the motivation is. It indicates that the motivation in this department is competence-oriented. (3) Job fitness is greatly affected by competence, showing a stronger positive correlation. The higher the competence is, the higher the job fitness is. It indicates that the job arrangement in this department is competence-oriented. (4) Motivation is greatly affected by job fitness, showing a complex correlation. Some employees have higher motivation because of job arrangement. And some have lower motivation because of job arrangement. It indicates that there is certain problem in this department that has already affected the work passion of some employees.
Therefore, this department should change the competence-oriented job arrangement and motivation. Because the competence-oriented job arrangement will hurt some low-competence employees’ motivation, and the motivation of low-competence employees is already at a lower level, the constant positive feedback will lead to continuous decrease of motivation. Moreover, because competence has stronger positive effects on motivation and job fitness, we can improve the performance of employees by means of learning.