Publications Database
Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Search Results
Simon J. Blanchard, Theodore J. Noseworthy, Ethan Pancer, and Maxwell Poole (2023). "Extraction of Visual Information to Predict Crowdfunding Success", Production and Operations Management, 32(12), 4172-4189.
Abstract
Researchers have increasingly turned to crowdfunding platforms to gain insights into entrepreneurial activity and dynamics. While previous studies have explored various factors influencing crowdfunding success, such as technology, communication, and marketing strategies, the role of visual elements that can be automatically extracted from images has received less attention. This is surprising, considering that crowdfunding platforms emphasize the importance of attention‐grabbing and high‐resolution images, and previous research has shown that image characteristics can significantly impact product evaluations. Indeed, a comprehensive review of empirical articles (n = 202) utilized Kickstarter data, focusing on the incorporation of visual information in their analyses. Our findings reveal that only 29.70% controlled for the number of images, and less than 12% considered any image details. In this manuscript, we contribute to the existing literature by emphasizing the significance of visual characteristics as essential variables in empirical investigations of crowdfunding success. We review the literature on image processing and its relevance to the business domain, highlighting two types of visual variables: visual counts (number of pictures and number of videos) and image details. Building upon previous work that discussed the role of color, composition, and figure–ground relationships, we introduce visual scene elements that have not yet been explored in crowdfunding, including the number of faces, the number of concepts depicted, and the ease of identifying those concepts. To demonstrate the predictive value of visual counts and image details, we analyze Kickstarter data using flexible machine learning models (Lasso, Ridge, Bayesian additive regression trees, and eXtreme Gradient Boosting). Our results highlight that visual count features are two of the top three predictors of success and highlight the ease at which researchers can incorporate some information about visual information. Our results also show that simple image detail features such as color matter a lot, and our proposed measures of visual scene elements can also be useful. By supplementing our article with R and Python codes that help authors extract image details (https://osf.io/ujnzp/), we hope to stimulate scholars in various disciplines to consider visual information data in their empirical research and enhance the impact of visual cues on crowdfunding success.Anderson, K. and Saxton, G. (2016). "Babies, Smiles, and Status Symbols: The PersuasiveBabies, Smiles, and Status Symbols: The Persuasive Effects of Images in Small-Entrepreneur Crowdfunding Requests Effects of Images in Small-Entrepreneur Crowdfunding Requests", International Journal of Communication, 10, 1764-1785.
Abstract
This article examines the persuasive effects of images in the context of online peer-topeer microfinance. The theoretical framework—based in self-presentation and impression management—relates micro-entrepreneurs’ loan-request image choices to lending decisions and lenders’ perceptions of the borrower’s trustworthiness and need. We explore effects of three specific visuals: (1) genuine enjoyment (Duchenne) smiles; (2) material status symbols; and (3) babies, children, and husbands. Using loan-request image data from 323 women micro-entrepreneurs on the Kiva.org website, results suggest smiling behavior is not associated with funding speed. However, loan-request images that include a baby are associated with significantly quicker funding, and those that include a man or an indication of relative material well-being are associated with delays in the average funding speed.Saxton, G. and Wang, L. (2014). "The Social Network Effect: The Determinants of Donations on Social Media Sites", Nonprofit & Voluntary Sector Quarterly, 43, 850-868.