Many online labor markets face challenges in matching service providers with employers because of the high uncertainty in online environments compared to traditional ones. While prior studies have mostly focused on the uncertainty that employers face with service providers, our study explores the uncertainty service providers face when selecting projects on which to bid. Specifically, our study examines the role of project description in the Call for Bids (CFB) that describes the services requested by employers, in shaping service providers’ bidding behavior and the matching between service providers and employers. Drawing upon transaction cost economics, we examine three key dimensions of project descriptions: (a) codifiability (provisions of detailed procedures), (b) flexibility (provisions of renegotiation opportunities), and (c) provisions of outcome standards. We first use machine learning algorithms to quantify the three key dimensions based on unstructured textual information. Second, we use Bayesian mediation analysis and a randomized experiment to examine the role of these three key dimensions in matching efficiency and to explore the mediating role of three key bid attributes (i.e., number of bids, average price of bids, average quality of bids). Findings reveal: First, codifiability is positively associated with matching efficiency by reducing the number of bids received by employers. Second, flexibility is negatively associated with matching efficiency by raising the average price of bids. Third, outcome standards are positively linked to matching efficiency by reducing the number, albeit improving the average quality, of bids. By focusing on the project description and analyzing the textual content of CFB, our study contributes to the literature by enhancing the matching efficiency of online labor markets and facilitating the contracting of IT services in online labor markets.
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