How "Task-Individual Fit" Influences User Contribution Behaviors in Citizen Science

Abstract

This paper presents a study on how task-individual fit influences user attitudes, self-efficacy, and contribution behaviors in a citizen science project. The paper selected task complexity and individual training to create four task-individual fit scenarios to examine effects of task-individual fit. We developed a botanical classification citizen science task and experimented with collecting data. In the formal experiment, we collected 183 valid subject data. Data analysis was performed by MANOVA and multiple regression analysis methods to verify the research model. Results of analysis of variance show significant differences in self-efficacy, quantity, and accuracy among the four groups and in user attitudes among some groups. Regression analysis shows that self-efficacy and quantity positively affect accuracy; user attitude affects accuracy by affecting self-efficacy; user attitude positively affects self-efficacy. This paper's findings provide both theoretical and practical implications for citizen science project design.