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research-article

Modeling Participation Behaviors in Design Crowdsourcing Using A Bipartite Network-Based Approach

[+] Author and Article Information
Zhenghui Sha

Assistant Professor, Department of Mechanical Engineering, University of Arkansas, Fayetteville, Arkansas 72701
zsha@uark.edu

Ashish M. Chaudhari

Graduate Research Assistant, School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907
chaudha5@purdue.edu

Jitesh H. Panchal

Associate Professor, School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907
panchal@purdue.edu

1Corresponding author.

ASME doi:10.1115/1.4042639 History: Received September 14, 2018; Revised January 17, 2019

Abstract

This paper studies the participation behaviors in design crowdsourcing by modeling interactions between participants and design contests as a bipartite network. Such a network consists of two types of nodes, participant nodes and design contest nodes, and the links indicating participation decisions. The exponential random graph models (ERGMs) are adopted to test whether participants' decisions are interdependent, e.g., whether one participant's decision depends on participation decisions of others. ERGM enables the utilization of different network configurations (e.g., stars and triangles) to characterize forms of interdependencies and identify the factors that influence link formation. A case study on the field data from GrabCAD, an online design crowdsourcing platform, is performed. Our results indicate that designer-, contest-, incentive- and interdependence-related factors have significant effects on participation in GrabCAD contests. The results reveal some unique features about the incentives of GrabCAD. For example, the fraction of total prize allocated to the first prize negatively influences participation, whereas the absolute amount does not. The contest popularity modeled by alternating k-star network statistic has a significant influence, whereas associations between participants modeled by alternating 2-path network statistic does not have a significant influence. These insights are useful to system designers for initiating effective crowdsourcing in support of product design and development. The approach is validated by applying the estimated ERGMs to predict participants' decisions, and comparing with actual participation.

Copyright (c) 2019 by ASME
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