Attracting solvers' participation in crowdsourcing contests: The role of linguistic signals in task descriptions

Abstract

Many companies gain external expertise, lower their costs and generate publicity by using crowdsourcing platforms to complete tasks by leveraging the power of the crowd. However, the number of solvers attracted by crowdsourcing tasks varies widely. Although some well-known crowdsourcing contests have attracted large numbers of participants, many tasks still suffer from low participation rates. Prior research aimed at solving this problem has focused on factors such as task rewards and durations while overlooking whether a well-written description might motivate solvers to choose a task. Based on signalling theory, this study investigates the effect of task descriptions on solvers' participation by focusing on informational and affective linguistic signals. Our model is validated by analysing 13 929 descriptions posted in single-winner tasks on epwk.com, a Chinese competitive crowdsourcing platform. For informational linguistic signals, the results reveal that there are inverted U-shaped relationships between both concreteness and specificity and solver participation, whereas linguistic accuracy has a positive effect on solver participation. For affective linguistic signals, positive emotional words have a positive relationship with solver participation, whereas negative emotional words have the opposite effect. Theoretical and practical implications are discussed.