Research On Factors Affecting Behavior Of Participation In Crowdsourcing

Crowdsourcing, which rises with the development of internet, promotes companies toobtain a variety of low-cost external human resources to optimize the allocation ofhuman resources on a global scale. It’s of great importance for the development ofcompanies in the era of knowledge economy.Currently the studying on behavior of participation in crowdsourcing is little andmotivation research dominates in this field. This paper took the initiative to usequantitative methods and empirical research, basing on human resource perspectiveand person-environment fit (P-E fit) theory, with a combination of structural equationmodeling (SEM) and traditional regression analysis, to examine behavior ofparticipation in crowdsourcing. It not only expanded the research perspectives oncrowdsourcing and the range of application of P-E fit theory, but also made up for thelake of research on participation behavior, quantitative and empirical methods in thefield.In the field of traditional human resource management, employees’ behavior isfrequently regarded as the outcome of P-E fit. This paper treated the behavior ofparticipation in crowdsourcing as the outcome of person-crowdsourcing environmentfit for crowdsourcing participants are taking the role of external human resources.This research on factors affecting behavior of participation in crowdsourcing basedon study on the relationship between person-crowdsourcing environment fit andbehavior of participation was trying to help companies to guide and inspire people toparticipate in crowdsourcing.According to the motivation research, this paper established an initial conceptualmodel, assuming that complementary fit including needs-supplies fit anddemands-abilities fit had a significant influence on behavior of participation incrowdsourcing.This paper developed needs-supplies fit scale and demands-abilities fit scale forcrowdsourcing participants on the basis of work values scale and effort expectancy,facilitating condition scale derived from Unified Theory of Acceptance and Use of Technology (UTAUT). The two subscales constituted complementary fit scale. Datemeasured by complementary fit scale was used to establish a measured model inSEM software. Through confirmatory factor analysis (CFA) we got a five-factormodel indicating that crowdsourcing participants perceive five different dimensionsof complementary fit: openness to change, conservation, self-enhancementself-transcendence and demands-abilities fit. Conceptual model and hypotheses wereadjusted accordingly. Five dimensions of complementary fit were assumed to have asignificant influence on behavior of participation in crowdsourcing.The behavior of participation was defined as both task browsing and submission.Hypothesis testing by multinomial logistic regression showed that both openness tochange fit and demands-abilities fit have a significant influence on behavior ofparticipation in crowdsourcing. The affecting of fits differ in direction. The lower theopenness to change fit score, namely the smaller the perceived difference between thereal change and expectation, the greater chances that participants will browse tasksfor more change or reduce submission for unfulfilled needs. The higher thedemands-abilities fit score, namely the bigger perceived difference between theability and the demand of tasks, the greater chances that participants will browse tasksto seek more chances of submission. But in terms of submission, participants maymaintain or decrease submission for less challenge or felling of wasting of talents, orthey may increase submission for enough ability.Taking both reality and the data analysis into consideration, seekers can promotesubmission through increasing openness to change fit score, namely increasing supplyin change, and promote browsing through increasing demands-abilities fit score,namely helping participants to improve their ability.