| dc.description.abstract | Career development of employees is a continuous process in humankind’s lifelong development and growth. During the career journey of the employees, they will experience various occupations or jobs, in which they play a certain role, take their responsibilities or fulfill their obligations. When employees enter into the job market, they will devote to being promoted. However, under the transformation of the economic environment, enterprises have experienced the impacts of organizational intergration and reorganization, the decline of employee loyalty, as well as global talent competition, etc.. All of these reasons has result in driving the employees to consider their career developments. On the other hand, enterprises will expect the personal career development of their employees to fit with the organizational goals, which will lead to a better organizational performance. Based on the aforementioned perspective, previous studies regarding employee promotion adopted the employee performance as a tool to predict their possibilities of future promotion. When it comes to the factors of employees’ promotion, work performance is regarded as one of the main determinants. In the practice, however, firms are no longer evaluating work performance for promotion; instead, firms consider the employees’ longterm career planning currently when conducting promotion decisions, and further, develop a complete talent development program. Therefore, the main purpose of this study is to explore the key factors that influence career development and promotion via the decision tree algorithm in data mining, and further to establish a prediction model of employee promotion.
This study adopted the personnel data of the case enterprise as the research sample and conducted a C4.5 decision tree algorithm to classify and analyze the data. There were 1,344 valid samples with 49 input variables. The target variables were promotion, promotion of rank and promotion of sub-rank. The final output is the decision tree model and decision rule-sets. Of the valid samples, 80% were training data (1,076), which was used for building and training prediction models, and 20% were test data (268), which was used for testing the accuracy of the model. There were two phrases of the decision tree model building. The first phrase was to input all 49 variables into the prediction model, and conclude the key factors, including the overall performance, key talents, total annual leave hours and participation in mentoring program, which representing that the three key factors of the first phrase derived from the decision tree model was consistent with the promotion evaluation factors of the case company. As for the second phrase, the input variables directly related to promotion (such as performance evaluation, potential development and key talent etc.) are removed. The result of the extraction showed that the key factors, including tenure, reserved supervisor training hours, professional training hours, participation in mentoring program, etc. were related to talent development. It is possible to imply that the case company pays a significant attention on the preparation of the knowledge, skills and maturity before the promotion of employees. The accuracy of the six prediction models ranged from 74% to 89%, and the AUC value under the area of ROC curve ranged from 0.720 to 0.886, indicating that the predictability and discrimination of the prediction models were at a certain level.
According to the findings of this study, it is suggested that the enterprises should establish a systematic career development path. Firms should consider not only the performance and development potential of the employees, but also evaluate whether their professional capabilities, leadership, personality traits and experience as well as other conditions meet their criteria or even surpass the next position. By doing this, it is possible to put the right employee in the right position. Moreover, it is suggested that the future research could collect more data to continuously input into the model to verify and modify, in order to improve the discrimination of the prediction model. Furthermore, future studies could also explore different job systems, job levels, frequency and speed of promotion, etc. to understand the factors that affect the career development and promotion of employees. This could lead the firms to create strategies and programs to measure employees’ career development from a more pragmatic perspective. | en_US |