Impacts of news media coverage on Canadian medical crowdfunding campaigns

While donation-based crowdfunding for health-related purposes raises hundreds of millions of dollars yearly, most campaigns fail to meet their fundraising goals. Crowdfunding campaigners are advised to seek traditional news media coverage of their campaigns to increase donor interest and fundraising success. In this study, we seek to better understand what happens to donor behavior via donation-based crowdfunding campaigns after they receive news media coverage. While research has focused on the impact of social media sharing on donation-based crowdfunding, academic analyses of the impact of news media coverage is largely speculative. We searched the Newsstream and Factiva databases for Canadian news coverage of domestic donation-based health-related crowdfunding campaigns. This news coverage was paired with the crowdfunding campaign reported on in the story. Campaign text and daily fundraising totals and donor amounts were recorded for the 7 days before and after publication of the news article. The authors identified emergent patterns in this data around the amplification of personal information from the crowdfunding campaign to a wider audience and inclusion of new personal details. This process identified 17 relevant pairs of news stories and crowdfunding campaigns over a review period of just under 5 months in 2021–22. These campaigns raised a total of CAD$443,134 (median CAD$20,030) out of a total goal of CAD$772,500 (median CAD$40,000) or 57.4% of the requested funds. Median campaign donations and donor numbers increased for the 3 days following publication of the news article. Our exploratory analysis shows a relationship between crowdfunding campaigns that receive news media coverage and the numbers of donations and total amount donated shortly after this coverage. Campaigners may feel pressure to participate in news media coverage in order to reach their fundraising goals. Media coverage has implications for campaign recipient privacy and the equitable distribution of health-related funding. This exploratory analysis establishes the need for additional research on this topic.     

The Relationship Between School Quality and U.S. Multi-family Housing Rents

The Journal of Real Estate Finance and Economics - Drawing on a sample, of U.S. multi-family apartment and school data from across 45 U.S. Core Based Statistical Areas, this paper examines the...     

Impact of corporate social responsibility on brand trust and brand loyalty: Case of Uber

The purpose of this research is to investigate the structural relationship between Uber corporate social responsibility (CSR), brand trust, and brand loyalty. Stakeholder theory is the theoretical foundation of the study. The CSR subdimensions of eco-friendliness, quarantine, user privacy, user safety, and driver well-being are used. Amazon Mechanical Turk was employed for data collection. The number of observations is 405. A structural equation model was implemented to test the research hypotheses. This study demonstrated the accountability of stakeholder theory for Uber brand management by showing a significant link between quarantine, user privacy, user safety, driver well-being, brand trust, and brand loyalty. This study contributes to the literature by validating the explanatory power of stakeholder theory in the case of Uber brand management.     

Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy by training simpler “off-the-shelf” artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross-sectional regressions, we link the forecasting performance of a long short-term memory network to stock characteristics at the market microstructure level, suggesting that “information-rich” stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes.     

Willingness to Pay for Attributes of Location Efficiency

The Journal of Real Estate Finance and Economics - This paper examines U.S. residential consumer willingness to pay for location efficiency, a normative advancement of new urbanism. Drawing on a...     

Exploring pre-service teachers’ technology-integration belief and scientific inquiry in a teacher-development course

International Journal of Technology and Design Education - We implemented design practices for a total of 85 pre-service teachers in a teacher-development course. This mixed-method study explored...