Applying Data Mining Techniques to Examine The Impact of The Employees' Needs on Job Satisfaction and Turnover Intention: A Case study of Chain Department Stores in Taiwan.

• Main Authors: HSU, CHIA-YU, 許家瑜
• Other Authors: CHEN, LI-FEI
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/k589t3

碩士 === 輔仁大學 === 企業管理學系管理學碩士班 === 105 === Increasing job satisfaction and reducing turnover intention are two of the most important issues in human resource management. The employees who get great performance appraisal and leaves the company makes negative influence to the operation of organization. Fulfilling employees’ needs can improve job satisfaction and turnover intention. However, different needs may play different weights. With the limited resource, it’s impossible for organizations to identify employees’ needs that may bring about the most impact on job satisfaction and turnover intention. The study applied artificial network, decision tree and association rule to examine the impact of employees’ needs on job satisfaction and turnover intention. A case of a chain department store in Taiwan was studied to demonstrate the effectiveness of the approaches. Base on three dimension from ERG theory, the study proposed twenty employees’ needs, then observing the degree of impact on job satisfaction and turnover intention in artificial neural network, using decision tree to explore the relation between employees’ attributes and employees’ needs and investigate the correlation of employees’ needs. The study summarized the above results and generated the effective strategies which can improve job satisfaction and reduce turnover intention.