Improving Competitiveness: A Freelancer-Centric Approach for Recommending Reskilling Strategies

Online labor markets play a vital role in connecting freelancers and employers worldwide. Although most existing methods focus on helping employers find the best candidates, relatively little research addresses freelancer-centric approaches. In this study, we propose a freelancer-centric skill advisor (FESA) that combines a competitiveness evaluator and an optimization model. The competitiveness evaluator integrates a state-of-the-art deep learning recommender system with contrastive learning to calculate matching scores between freelancers and their desired tasks, whereas the optimization model prescribes skills a freelancer should acquire to improve their competitiveness for those tasks while making the learning experience manageable. FESA also supports two reskilling strategies: exploration and exploitation. We evaluate FESA against alternative recommendation models using a large-scale, real-world data set collected from a leading online labor market. We also conduct a controlled experiment with human participants to examine how real-world users respond to FESA recommendations and whether disclosing a freelancer’s ranking, a key output of FESA, affects reskilling motivation. In contrast to prior findings from traditional organizational settings, where ranking information generally encourages greater effort, we find that disclosing a freelancer’s low ranking significantly reduces motivation to reskill. This demotivating effect of ranking disclosure is more pronounced under the exploration reskilling strategy, which requires freelancers to acquire more challenging and unfamiliar skills, than under the exploitation reskilling strategy where freelancers are advised to acquire skills that are more closely aligned with their existing expertise. Our research highlights that designing a freelancer-centric recommendation system requires not only algorithmic innovation but also careful attention of how system design influences user motivation and engagement.

History: Pei-Yu Chen, Senior Editor; Zhengrui Jiang, Associate Editor.

Funding: Y. Chai is supported by the National Natural Science Foundation of China [Grant Nos. 72342011, 72322019, 72188101, and 72402001].

Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.0790.