Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
Incentive misalignment in rewards-based crowd-funding occurs because creators may benefit disproportionately from fundraising, while backers may benefit disproportionately from the quality of project deliverables. The resulting principal-agent relationship means backers rely on campaign information to identify signs of moral hazard, adverse selection, and risk attitude asymmetry. We analyze campaign information related to fundraising, and compare how different information affects eventual backer satisfaction, based on an extensive dataset from Kickstarter. The data analysis uses a multi-model comparison to reveal similarities and contrasts in the estimated drivers of dependent variables that capture different outcomes in Kickstarter’s funding campaigns, using a linear probability model (LPM), which is a special case of the binary probability model. Our results reveal inconsistencies in funding information compared to backers’ satisfaction, and a plat-form-wide trend of decreasing satisfaction. The findings broadly suggest fundraising is influenced by information disclosure and backer feedback, while eventual backer satisfaction is closely potentially caused by in-formation about deferred compensation and long-term relationship-building.
Creators and Backers in Rewards-Based Crowdfunding: Will Incentive Misalignment Affect Kickstarter’s Sustainability?
Online
Incentive misalignment in rewards-based crowd-funding occurs because creators may benefit disproportionately from fundraising, while backers may benefit disproportionately from the quality of project deliverables. The resulting principal-agent relationship means backers rely on campaign information to identify signs of moral hazard, adverse selection, and risk attitude asymmetry. We analyze campaign information related to fundraising, and compare how different information affects eventual backer satisfaction, based on an extensive dataset from Kickstarter. The data analysis uses a multi-model comparison to reveal similarities and contrasts in the estimated drivers of dependent variables that capture different outcomes in Kickstarter’s funding campaigns, using a linear probability model (LPM), which is a special case of the binary probability model. Our results reveal inconsistencies in funding information compared to backers’ satisfaction, and a plat-form-wide trend of decreasing satisfaction. The findings broadly suggest fundraising is influenced by information disclosure and backer feedback, while eventual backer satisfaction is closely potentially caused by in-formation about deferred compensation and long-term relationship-building.
https://aisel.aisnet.org/hicss-54/os/sites/6