Start Date
10-12-2017 12:00 AM
Description
In online labor markets, a Call for Bids (CFB) serves as a form of contracts with the description of services required from service providers. It helps service providers understand and perform the project by reducing the uncertainty about the required services. In this study, we (1) theorize the nature of description uncertainty in CFBs from three dimensions—codifiability, requirements, and flexibility, and (2) examine their respective role in matching efficiency between employers and service providers. We use content analysis and deep learning algorithms to analyze unstructured textual data and test our model using archival data from a major online labor platform. The preliminary results show that different dimensions of description uncertainty have different empirical effects on a project’s matching outcome. Our findings provide rich implications for employers, service provider, and platform owners. Also, our text mining approach can be applied in other fields that involve analyzing large-scale textual data.
Recommended Citation
Guo, Xue; Gong, Jing; and Pavlou, Paul, "Call for Bids to Improve Matching Efficiency: Evidence from Online Labor Markets" (2017). ICIS 2017 Proceedings. 20.
https://aisel.aisnet.org/icis2017/Peer-to-Peer/Presentations/20
Call for Bids to Improve Matching Efficiency: Evidence from Online Labor Markets
In online labor markets, a Call for Bids (CFB) serves as a form of contracts with the description of services required from service providers. It helps service providers understand and perform the project by reducing the uncertainty about the required services. In this study, we (1) theorize the nature of description uncertainty in CFBs from three dimensions—codifiability, requirements, and flexibility, and (2) examine their respective role in matching efficiency between employers and service providers. We use content analysis and deep learning algorithms to analyze unstructured textual data and test our model using archival data from a major online labor platform. The preliminary results show that different dimensions of description uncertainty have different empirical effects on a project’s matching outcome. Our findings provide rich implications for employers, service provider, and platform owners. Also, our text mining approach can be applied in other fields that involve analyzing large-scale textual data.