DATA-DRIVEN APPROACH TO EXPLORE EMPLOYEES’ JOB NEEDS: AN EMPIRICAL STUDY OF DEPARTMENT STORE CHAIN IN TAIWAN
DOI:
https://doi.org/10.23055/ijietap.2022.29.5.7929Keywords:
Data mining, Human resource management, Employee job needs, Job satisfaction, Turnover intentionAbstract
Fulfilling employee job needs is key for increasing job satisfaction and reducing turnover intention. However, this psychological and behavioral process is complex, and employees with heterogeneous demographics may prioritize different psychological needs. Therefore, planning human resource strategies that can effectively fulfill employee needs which are critical to job satisfaction and turnover intention, is challenging for organizations. Data mining techniques were employed to investigate the complex and interactive effects of employee job needs and demographics on employee outcomes. Data were collected from 1579 employees of a company in Taiwan. The results revealed that data mining techniques can not only effectively identify meaningful relationships without prior hypotheses but can also discover previously unknown, non-general, and case-specific knowledge patterns. The findings can serve as guidelines for service managers attempting to address employee job needs to increase employee satisfaction, reduce turnover intention, and increase organizational competitiveness.
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