Abstract
Compensation experts advocate that competency-based pay can represent employee's job capabilities better than job-based pay. However, competencies exist complex relationships among each other. This leads to that competency-based pay have no formal and systematic evaluation process, and there is no common standard among different corporate departments. The study aims to develop a big data analytics framework for competency-based pay. The framework integrates stepwise regression, Random Forests, and Bayesian Network method to construct a competency analytics model. The model can explore that how competencies influence probabilities of employee gain high pay level and further give job seekers specific advice for competencies enhancing. The study cooperates with a Taiwanese indicative Job Bank Web site for empirical research. To validate the result, this framework extracts latent knowledge and patterns from huge data about job seekers and sets the validation index. The results assist various types of job seekers to understand what competencies labor market need actually. In the meantime, the enterprise can recruit suitable candidates based on the results. On the other hand, this study also provides the decision-making reference for government agencies to organize education and training course and finally enhances the commonality of all parties.