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

Weber, S., & Weber, O. (2022). "How Fashionable Are We? Validating the Fashion Interest Scale through Multivariate Statistics", Sustainability, 14(4), 1946.

Open Access Download

Keywords
Abstract A person’s fashion interest describes how familiar a person is with fashion. There are major differences among consumers in terms of fashion interest that can be used as a segmentation criterion for markets. Understanding the drivers of clothing consumption can be used to develop strategies to address consumption habits, including overconsumption. Consequently, many studies have developed questionnaires and interview guidelines to define fashion interest or other fashion-related attitudes and behaviors. However, there is a gap in research about validating fashion scales. This study validates a fashion interest scale by comparing a random sample with a control group of fashion students, demonstrating differentiation between groups. We used principal component analysis (PCA) to explore the scale’s homogeneity and t-tests and analysis of variance (ANOVA) to validate the scale. The results suggest that the scale is homogeneous and has high validity. We conclude that the scale can be used as a tool to segment markets to gain faster and higher quality data and as a benchmark for other studies.