A recent study conducted by Xiaolun Wang et al. has been published in Information & Management. This research conceptualize vendor–task fit from two dimensions – experience similarity and skill matching, and examines their distinct impacts on vendors’ bidding behavior (bidding prices) and bidding outcomes (winning probabilities). The work was completed collaboratively by Wang Xiaolun’s research team, which includes one undergraduate students from the College of Economics and Management at Nanjing University of Aeronautics and Astronautics, as well as researchers from institutions such as Nanjing University and Shanghai Academy of Social Sciences.

On online crowdsourcing platforms, to help employers identify the right vendor for the appropriate task from a variety of choices, it is important to evaluate the vendor–task fit (VTF), the degree to which a vendor’s competence fits a particular task. To address gaps in fit theories, we conceptualize VTF as consisting of two core dimensions – experience similarity and skill matching – based on a vendor’s experience and skills. From the perspective of a vendor’s cost–benefit analysis in crowdsourcing contests, we propose an intricate relationship between VTF, vendors’ bidding behavior (bidding prices), and bidding outcomes (winning probabilities). Using a sample of 5,141 bidding tasks collected from epwk.com from 2010 to 2023, we trained the Doc2Vec deep-learning algorithm to compute VTF. Our findings indicate that experience similarity lowers vendors’ bidding prices and improves bidding outcomes, while skill matching increases vendors’ bidding prices without affecting bidding outcomes. In addition, task complexity and capability level positively moderate the effect of experience similarity on bidding prices, whereas competition intensity has no significant effect. This study enriches theories of fit between tasks and vendors’ competencies in crowdsourcing contests, and offers practical insights for vendors, employers, and crowdsourcing platforms.

Information & Management, an internationally authoritative journal in information systems, is one of the top journals in the field of information science and technology management. It is ranked as ABS 3, SSCI-Q1, SCI-Q1 and FMS B. The journal aims to publish high‑quality research on the interaction between information systems, organizational processes and managerial decision‑making, covering topics such as digital innovation, knowledge management, e‑commerce, business analytics and IT strategy.

This paper can be cited as:

Wang Xiaolun, Zhao Tianrun, Zhao (Chris) Yuxiang, Gu Jie. Unpacking the vendor–task fit in crowdsourcing contests: Antecedents of vendors’ bidding behavior and outcomes.Information & Management, 2025, 62(8), 104239.

A complete version of this paper can be accessed via:

https://www.sciencedirect.com/science/article/abs/pii/S0378720625001429