Design and Study of Dynamic Mechanisms for User Generated Contents in Crowdfunding Systems
- Yulin FANG (Principal Investigator / Project Coordinator)Department of Information Systems
- Lele KANG (Co-Investigator)
- Keong Tae KIM (Co-Investigator)Department of Information Systems
- Chang Heon LEE (Co-Investigator)
DescriptionCrowdfunding gained popularity as an alternative means of seed financing for new ventures.In 2015, the crowdfunding industry was set to double again, approaching $34.4 billion in totalloan amount yearly worldwide. The fundamental concept of crowdfunding is to assemblefinancial capital from a large group of individuals connected to the Internet. Despite thepotential importance of User-Generated Content (UGC) in the forms of social media posts andproduct reviews about crowdfunded projects, few systematic studies have examined the effectof UGC on funding decision behavior. Furthermore, little is known about diverse contextualfactors moderating the relationship between the positive (or negative) UGC and backers’financing decision behavior. In particular, although we have witnessed proliferations ofcrowdfunding platforms with different designs, there is a scarcity of empirical examinationsabout how design features are optimized to improve project backers' decision behavior andimprove their UGC consumption patterns. To fill those research gaps, this project will,therefore, address the following research questions:(1) How does the positive or negative valence of UGCs influence backers’ financingdecision on crowdfunded projects?(2) How do contextual factors (consumer and product characteristics) moderate therelationship between the valence of UGCs and backers’ financing decision behavior?(3) How to design crowdfunding platforms that can facilitate funding decisions as well asstimulate platform users to consume relevant contents?Theoretical Foundation: To investigate the impact of positive or negative UGC on funders’decision behavior, we use two popular theories for information processing and attitudeformation, respectively, i.e., Elaboration Likelihood Model (ELM) and Signalling Theory.Panel Data & Econometric Analysis: We plan to gather data from one of the largestcrowdfunding platforms, including all variables about project initiators, description, backers,updates, comments, and related UGCs. We will use different econometric analysis, networkanalysis and text mining techniques to understand the effects of UGC valence and socialnetwork factors on the decision-making process.Validation of Results using Lab Experiment: After conducting research via econometric,social network, and text analytics based on the panel data, we will validate our findings byconducting lab experiments.Design of Crowdfunding Platforms to Stimulate Consumer Engagements: After analyzingempirical findings from both panel data analysis and lab experiments, we will develop aframework for designing better crowdfunding processes and interfaces that enhance thefunding process.This research provides academic and practical contributions through the combination ofinformation and economic theories and data analytics into the crowdfunding context. Theproject will provide useful insights and design principles that can be utilized to provide bettercrowdfunding services.
|Effective start/end date||1/01/18 → 31/08/21|