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To recruit and retain high-potential talent is critical for semiconductor companies to maintain competitive advantages in a modern knowledge-based economy. Conventional personnel selection methodologies focusing on static work and job analysis will no longer be appropriate for knowledge workers in high-tech industries. This paper aims to develop an effective data mining approach based on Rough Set Theory to explore and analyze human resource data for personnel selection and human capital enhancement. An empirical study was conducted in a leading semiconductor company in Taiwan to estimate the validity of the proposed approach for predicting work behaviors including performance and resignation. The results showed that latent knowledge can be discovered as a basis to derive specific recruitment and human resource management strategies. In particular, 29 rules have been adopted as references for recruiting the right talent. This paper concludes with discussions of empirical findings and future research directions.
Date of Publication: Nov. 2007