The relationship between a charity crowdfunding project's contents and donors' participation: An empirical study with deep learning methodologies (original) (raw)
Does Heart or Head Rule Donor Behaviors in Charitable Crowdfunding Markets?
International Journal of Electronic Commerce, 2016
Crowdfunding has matured into a meaningful online marketplace, both for traditional e-commerce activities and for charitable fundraising. For charities, crowdfunding presents novel donation behaviors, including those where donors may proactively seek out causes and give (often anonymously) to help others with whom they share little social connectivity. Understanding these behaviors is challenging compared to traditional fundraising behaviors, where charitable giving is partly explained by factors such as guilt avoidance, reciprocity, image, vicarious enjoyment, and group-level benefits. This suggests that some subset of charitable motivations is brought uniquely into focus in crowdfunding marketplaces. These marketplaces are often inhabited by fundseeking individuals and larger formal organizations. This adds further complexity, given that donors traditionally perceive and interact differently with charitable organizations and less formal fundraising entities. This study explores donation behavior in charitable crowdfunding based on the distinction between "pure altruism" and "warm glow" motivations. We offer a discriminatory model of donation behaviors toward individuals and organizations, which is then tested in a large-scale field study of Razoo.com. Findings suggest that donations to organizations are more influenced by outcome-related factors, such as fundraising targets and the likelihood of meeting that target, while donations to individuals are more influenced by interaction-related factors, such as the level of dialogue around a campaign.
What Contributes to a Crowdfunding Campaign's Success? Evidence and Analyses from GoFundMe Data
Journal of Social Computing, 2021
Researchers have attempted to measure the success of crowdfunding campaigns using a variety of determinants, such as the descriptions of the crowdfunding campaigns, the amount of funding goals, and crowdfunding project characteristics. Although many successful determinants have been reported in the literature, it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign's success. In this work, we focus on the performance of the crowdfunding campaigns on GoFundMe across a wide variety of funding categories. We analyze the attributes available at the launch of the campaign and identify attributes that are important for each category of the campaigns. Furthermore, we develop a fusion classifier based on the random forest that significantly improves the prediction result, thus suggesting effective ways to make a campaign successful.
Predictive models for charitable giving using machine learning techniques
PLOS ONE
Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more than 10% of total GDP. Despite the abundance of data available through tax forms and other sources, it is unclear which factors influence private donation, and a reliable predictive mechanism remains elusive. This study aims to develop predictive models to accurately estimate future charitable giving based on a set of potentially influential factors. We have selected several factors, including unemployment rate, household income, poverty level, population, sex, age, ethnicity, education level, and number of vehicles per household. This study sheds light on the relationship between donation and these variables. We use Stepwise Regression to identify the most influential variables among the available variables, based on which predictive models are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of charitable giving in the previous year are the most significant, independent variables. We propose three predictive models (MLR, ANN, and SVR) and validate them using 10-fold cross-validation method, then evaluate the performance using 9 different measuring criteria. All three models are capable of predicting the amount of future donations in a given region with good accuracy. Based on the evaluation criteria, using a test data set, ANN outperforms SVR and MLR in predicting the amount of charitable giving in the following year.
HCI 2018, 2018
Creating a good crowdfunding campaign is difficult. By understanding why people contribute to crowdfunding campaigns we can make campaigns better and raise more money. Crowdfunding websites allow entrepreneurs to make a pitch, which is watched by potential funders. This article describes a pilot of an experiment that measures how people react to both successful and unsuccessful pitches. In particular we are interested in emotional reactions and trust reactions. Unexpectedly, failed campaigns were watched more and were judged to have higher integrity. Perceived ability seemed to be the best predictor of a campaign's success. We hope to explore this subject in more depth with further experiments.
Discovering Critical Factors in the Content of Crowdfunding Projects
Algorithms
Crowdfunding can simplify the financing process to raise large amounts of money to complete projects for startups. However, improving the success rate has become one of critical issues. To achieve this goal, fundraisers need to create a short video, attractive promotional content, and present themselves on social media to attract investors. Previous studies merely discussed project factors that affect crowdfunding success rates. However, from the available literature, relatively few studies have studied what elements should be involved in the project content for the success of crowdfunding projects. Consequently, this study aims to extract the crucial factors that can enhance the crowdfunding project success rate based on the project content description. To identify the crucial project content factors of movie projects, this study employed two real cases from famous platforms by using natural language processing (NLP) and feature selection algorithms including rough set theory (RST)...
The Common Donor's Motive: An Online Field Experiment
International Journal of Indian Psychology, 2021
During the COVID-19 pandemic, many organizations came forward to alleviate the sudden financial and primary needs of the dependent and disadvantaged population via online fundraisers; where the appeal is crucial in encouraging people to engage in prosocial behavior. Studies about social responsibility norm and the Empathy Joy hypothesis provisionally prove their role in enhancing prosocial behavior. We studied the difference in online donating behavior as a function of the motive through a field experiment. We circulated three kinds of messages on social media: 1-appealing to egoistic motive 2appealing to social responsibility 3-information only appeal (control) to evaluate their comparative effect on donating. The data suggests that underlying motivation had no influence on the choice of donation or the amount donated. The paper provides suggestions for organizers of fundraising campaigns based on past research findings and results of the current study.
Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning
Electronics, 2021
The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people that addresses health-related needs. However, the fact remains that medical fundraising from crowdfunding sources is relatively low and only a few studies have been conducted regarding this issue. Therefore, the performance predictions and multi-model comparisons of medical crowdfunding have important guiding significance to improve the fundraising rate and promote the sustainable development of medical crowdfunding. Based on the data of 11,771 medical crowdfunding campaigns from a leading donation-based platform called Weibo Philanthropy, machine-learning algorithms were applied. The results demonstrate the potential of ensemble-based machine-learning algorithms in the predictio...
Artificial Intelligence and the Future for Charities
International Journal of Non-Profit Sector Empowerment, 2023
Introduction Artificial intelligence (AI) is transforming our daily lives and shaping the future of work, communication, and the economy. The charity sector is not an exception. AI is a wide set of technologies that mimic human intelligence and can be used in various ways, such as speech recognition, decision-making, and language translation. It is changing the charity sector, where it is being used to offer online advice. Chatbots, which use "natural language processing (NLP)" and "machine learning (ML)," are being used to talk to people about climate change, homelessness, and other social issues (Green, 2021). This article explores the utilisation of AI in the charity sector, examining its benefits, potential risks, and challenges, as well as predicting its future prospects.
Measuring the Efficiency of Charitable Giving with Content Analysis and Crowdsourcing
2016
In the U.S., individuals give more than 200 billion dollars to over 50 thousand charities each year, yet how people make these choices is not well understood. In this study, we use data from CharityNavigator.org and web browsing data from Bing toolbar to understand charitable giving choices. Our main goal is to use data on charities' overhead expenses to better understand efficiency in the charity marketplace. A preliminary analysis indicates that the average donor is "wasting" more than 15% of their contribution by opting for poorly run organizations as opposed to higher rated charities in the same Charity Navigator categorical group. However, charities within these groups may not represent good substitutes for each other. We use text analysis to identify substitutes for charities based on their stated missions and validate these substitutes with crowd-sourced labels. Using these similarity scores, we simulate market outcomes using web browsing and revenue data. With ...
The Rich Get Richer? Limited Learning in Charitable Giving on donorschoose.org
2017
Crowdfunding websites allow anyone to raise money through many financial contributions from the 'crowd.' One commonly cited social benefit of crowdfunding is the democratization of access to capital. The degree of the democratization depends on how much ordinary people, who may not have financial access before crowdfunding, receive funding. Since many crowdfunders start out as a non-professional, learning is a critical factor for them to be successful. However, it is not clear that initially unsuccessful crowdfunders learn enough to prevent the rich get richer phenomenon. By analyzing a large dataset from donorschoose.org, we found that successful crowdfunders appear to learn more while unsuccessful crowdfunders frequently give up on crowdfunding. This result calls for design solutions that help crowdfunders learn from failure better.