Understanding the Effect of Task Descriptions on User Participation in Crowdsourcing Contests: A Linguistic Style Perspective

Abstract

Many employers are struggling with how to deliver attractive tasks on crowdsourcing platforms, where users can be effectively integrated into a company’s tasks. In this study, the linguistic style of crowdsourcing task descriptions is investigated, and an analysis is conducted on how such linguistic styles are related to a task description’s success in attracting participants. Based on uncertainty reduction theory as well as source credibility theory, an empirical analysis of 2,014 designing contests demonstrates that certain linguistic styles will reduce the uncertainty perceived by crowdsourcing solvers and increase employers’ credibility, generating positive effects on participation. It is also found that these observed effects are moderated by the magnitude of the rewards offered for completing crowdsourcing tasks. The results of this study inform the theories concerned on crowdsourcing participation, linguistics, as well as psychological processes, while offering the industry insight on how to describe their own crowdsourcing tasks better.