The purpose of this paper is to present a new concept – task affordance in crowdsourcing context, and build it as a theoretical lens to help the authors reconfigure the artifacts and process in task-oriented crowdsourcing projects. The paper differs from previous studies by focusing on the relationships between the task artifacts, systems and goal-directed actors in crowdsourcing process rather than on the pure examination of task properties.
An operational definition of task affordance was proposed and a pseudo-entity-relationship model based approach was employed to portrait the task affordance in online crowdsourcing context. Furthermore, the authors developed a typology of task affordance and decomposed the concept into five dimensions, namely, design affordance, presentation affordance, assignment affordance, task-platform fit affordance, and task-worker fit affordance. A preliminary analysis of task affordances across various crowdsourcing categories was also conducted to validate the proposed typological framework.
The findings show that the task affordances have varying degree and extend among the diverse crowdsourcing categories. For instance, task design affordances seem to be low in the crowd processing and crowd rating cases compared with that in the crowd solving and crowd creation cases. For another example, in terms of the task presentation affordance, crowd rating cases need the lowest affordance while the crowd creation cases need the highest affordance. Therefore, the authors would like to emphasize that the successful adoption, implementation, and design of the task-oriented crowdsourcing owes to the careful examination of the relationships among the actors, artifacts, and environment of the crowdsourcing projects.
To the authors’ best knowledge, this paper is the first study on conceptualizing the task affordance in online crowdsourcing context. The study contributes to the academic literature on a comprehensive overview of task-related studies in crowdsourcing, which are scattered in several information related fields. Furthermore, this research contributes directly to the area of information science and technology due to a common interest in studying the environments and contexts in which people, information and technology interact and interplay. Practically, this study may yield some implications for the requester and platform operator when designing the relevant tasks or developing the specific crowdsourcing platform.
The authors would like to thank Yingqi Chen and Fangqi Zheng for their intelligence and work on the coding of the empirical part in this study. This work is jointly supported by the National Science Foundation in China (No. 71403119, 71390521, 71473114), and the Ministry of Education, Humanities and Social Sciences Council in China (No. 13YJC870033).
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