Abstract
Freelancers in online labor markets often display many skills in their profiles to increase their chances of being hired. However, such behavior may lead to the skills they display straddling multiple categories, that is, “skill spanning.” In this paper, we extend the concept of category spanning into online labor markets in the form of skill spanning and empirically examine (1) how freelancers’ skill spanning affects employers’ hiring decisions for two different types of jobs (low- and high-skill jobs, respectively), and (2) how freelancers’ skill matching moderates the effects of skill spanning on employers’ hiring decisions. Based on a unique dataset of 12,428 high-skill jobs and 19,875 low-skill jobs on a leading online labor platform, we find that freelancers’ skill spanning has different impacts on employers’ hiring decisions for these two job types. Specifically, for high-skill jobs, freelancers’ skill spanning reduces their likelihood of winning contracts; however, for low-skill jobs, freelancers’ skill spanning and their probabilities of winning contracts demonstrate an inverse U-shape relationship. Furthermore, freelancers’ skill matching can moderate the negative effects of skill spanning for high-skill jobs but not for low-skill jobs. Our findings provide guidelines for different stakeholders in online labor markets, including freelancers and platform owners.
Recommended Citation
Fu, Yan; Feng, Juan; and Ye, Qiang
(2022)
"Skill Spanning in the Online Labor Market: A Double-Edged Sword?,"
Journal of the Association for Information Systems, 23(3), 750-778.
DOI: 10.17705/1jais.00739
Available at:
https://aisel.aisnet.org/jais/vol23/iss3/3
DOI
10.17705/1jais.00739
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