An uncertain search model for recruitment problem with enterprise performance
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This paper studies a dynamic recruitment problem with enterprise performance in the uncertain environment, in which a firm first interviews finite job applicants sequentially and then makes an employment decision according to results of the interview. Since the assessment of the firm about each interviewee’s capability is subjective and the interviewees are heterogeneous, it is reasonable to characterize these assessments as independent but not identically distributed uncertain variables. What’s more, an uncertain sequential search model is established to maximize the benefit of the recruitment firm. Moreover, an optimal search strategy is presented by adopting the principle of optimality and the reservation value rule. The results demonstrate that the threshold of recruitment decreases with the search cost, and increases with the enterprise performance level. In addition, we find that the low employment risk applicant will be preferred. Finally, some numerical examples are given to illustrate the effectiveness of the proposed model.
KeywordsUncertainty theory Sequential search Dynamic programming Labor market Enterprise performance
This work is supported by the National Natural Science Foundation of China (No. 71071106), the National Natural Science Foundation of China (No. 71371133), supported partially by Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20120032110071), and supported partially by Program for New Century Excellent Talents in Universities of China.
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