| Summary: | The objective of this study is to examine the concept of crowdsourcing and the corresponding method for employee selection. In recent years, there has been a growing trend towards the utilization of crowdsourcing, wherein both businesses and individuals harness the power of collective cooperation to provide solutions, ideas, or contributions across several domains, including but not limited to product development and scientific research. This study examines the concept of crowdsourcing as a means of gathering occupations or tasks completed by individuals with diverse qualifications. In this study, we examine the worker selection mechanism as explored in prior research and put forth a novel worker selection model incorporating a profile matching algorithm. The subsequent phase involves enhancing the profile matching algorithm to accommodate nested criteria for matching worker requirements. Upon concluding the investigation, a comparative analysis was conducted to assess the outcomes of matching workers' criteria using both single criterion and nested criteria. Additionally, the proposed formula was implemented to evaluate the case involving nested criteria. The findings reveal substantial disparities between the suggested workforces, particularly in terms of their composition. The proposed workforce with nested criteria exhibits a reduced and more precise numerical representation compared to the proposed workforce with a single criterion. This demonstrates that the utilization of the proposed selection model offers a viable solution to the challenge of identifying workers with layered criteria.
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