Home-bias in online fundraising: an analysis of international reward-based crowdfunding

FILATOV, George (2021). Home-bias in online fundraising: an analysis of international reward-based crowdfunding. Doctoral, Sheffield Hallam University.

Filatov_2021_DBA_AnalysisInterntionalReward-based.pdf - Accepted Version
Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview
Link to published version:: https://doi.org/10.7190/shu-thesis-00442


Home Bias is the recognized tendency of individuals to choose geographically proximate interaction partners. In business finance, Home Bias is to the detriment of both investors and entrepreneurs because it promotes an uneven distribution of capital and contributes to the Global Finance Gap. The aim of this thesis is to examine the existence of Home Bias in the emerging financing channel of reward-based crowdfunding. Crowdfunding, in general, is different from traditional financing because it shifts the entire fundraising process to a digital space on the internet. Moreover, it introduces new community-based trust mechanisms and eliminates some of the distance-related costs. The focus of this thesis lies on reward-based crowdfunding, which is currently the most popular, unrestricted and, therefore, most international form of crowdfunding. To assess whether international reward-based crowdfunding is prone to Home Bias, this thesis employs a Negative Binomial regression model that examines the relationship between the count of crowdfunding project backers and their respective distance to entrepreneurs. The model builds on an aggregate data sample of 1,118,654 project-specific country-to-country investment observations (from 211,695 projects) that occurred on Kickstarter platform between 2009 and 2020, making it the largest and most up to date crowdfunding study. Although large sample or “Big Data” models provide many advantages (e.g., higher representativeness), and have been commonly used in the crowdfunding literature, they however introduce some caveats that have been mostly ignored by previous research. One main issue that might distort results in Big Data models is that they are capable to identify marginally small patterns in the data that, although statistically significant in terms of p-values, might have little relevance in practice. Therefore, this thesis goes beyond the traditional analysis of statistical significance and devotes great attention to the assessment of different marginal effect sizes to identify the practical relevance of findings. The thesis also investigates the effect of additional variables that may have potential effect on the count of backers namely GDP per capita of backers and entrepreneurs, project category, third-party endorsements, herding behaviour and Covid-19 pandemic. The results suggest that although geographical distance appears to have a statistically significant negative influence on the count of backers, its practical effect is very small. This indicates that Home Bias has a comparably small relevance in international reward-based crowdfunding and that entrepreneurs should not overestimate its impact when planning their crowdfunding campaigns. Moreover, neither individual wealth of backers nor entrepreneurs, project category or global economic crises seem to affect the success of crowdfunding campaigns in a practically relevant manner. However, herding behaviour and third-party endorsements do seem to have a statistically and practically relevant influence on the count of backers and, therefore, should be considered in the planning of crowdfunding campaigns. The overall findings of this thesis suggest that some of the prior research in crowdfunding might have overestimated the practical relevance of certain influencing factors (e.g., geographical distance and individual wealth), perhaps by focusing too much on statistical significance while ignoring the capability of Big Data models to identify marginally small and practically irrelevant patterns in the data.

Item Type: Thesis (Doctoral)
Thesis advisor - Issa, Samah
Thesis advisor - Scheck, Barbara
Thesis advisor - Garrow, Nigel
Additional Information: Director of studies: Samah Issa / Supervisors: Barbara Scheck and Nigel Garrow
Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
Identification Number: https://doi.org/10.7190/shu-thesis-00442
Depositing User: Colin Knott
Date Deposited: 16 May 2022 15:49
Last Modified: 11 Oct 2023 12:32
URI: https://shura.shu.ac.uk/id/eprint/30229

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics