Freelancers in online labor markets often display many skills in their profiles to increase their chances of being hired by different jobs. However, such behavior may lead their displaying skills to straddle multiple categories, that is, skill spanning. In this paper, we extend the concept of category spanning into online labor markets and empirically examine (a) how freelancers’ skill spanning affects employers’ hiring decisions for two different types of jobs (low-skilled and high-skilled jobs, respectively); and (b) how freelancers’ matching skills moderate the effects of skill spanning on employers’ hiring decisions. Based on a unique dataset of 12,428 high-skilled jobs and 19,875 low-skilled jobs in a leading online labor platform, we find that freelancers’ skill spanning has different impacts on employers’ hiring decisions for these two types of jobs. Specifically, for high-skilled jobs, freelancers’ skill spanning reduces their likelihoods of winning a contract; however, for low-skilled jobs, freelancers’ skill spanning and their winning probabilities demonstrate an inverse U-shape relationship. Furthermore, freelancers’ matching skills can moderate the negative effects of skill spanning for high-skilled jobs, but not for low-skilled jobs. Our findings provide guidelines for different stakeholders in online labor markets including freelancers and platform owners.
Fu, Yan; Feng, Juan; and Ye, Qiang, "Skill Spanning in Online Labor Market --- A Double-edged Sword?" (2022). JAIS Preprints (Forthcoming). 26.
Available at: https://aisel.aisnet.org/jais_preprints/26