Brenton Stewart

Interests

Teaching

515: Organization of Information

Research

Information Behavior;Information Use;New Technology Adoption; Academic Libraries;

Courses

Scholarly Contributions

Journals/Publications

• Ju, B., Park, S., Stewart, J. B., & Jin, T. (2025).

  Perceived Quality of Health Information on Wikipedia: A Sociodemographic Perspective

  . Libri International Journal of Libraries and Information Studies.
  More info

  While Wikipedia is a widely used source for medical and health information, few studies have examined disparities in perceived information quality based on gender, race, and age. This study investigates how Wikipedia users evaluate the quality of health-related content and whether perceptions differ across these sociodemographic groups. A total of 321 adult Wikipedia readers and contributors participated in a survey assessing five dimensions of information quality: presentation, trustworthiness, reliability, currency, and topic coverage. Descriptive statistics and multivariate analysis of variance (MANOVA) were used to evaluate perceived quality and identify group differences. Overall, both genders viewed the quality of Wikipedia’s health information positively. However, statistically significant gender differences were identified across the five quality factors, with males reporting higher ratings than females. Significant differences between White and non-White participants were also found in four of the five dimensions, excluding currency, with non-White participants reporting lower perceptions of quality. No significant age-based differences were identified. These findings contribute to a more nuanced understanding of how different demographic groups assess health content on Wikipedia. By highlighting disparities in perceptions, this study provides valuable insights for improving Wikipedia’s inclusivity and trustworthiness in health communication.
• Stewart, J. B., & Ju, B. (2025).

  Looking for information and avoiding it: Covid-19 and Black Americans

  . Journal of Librarianship and Information Science.
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  This study investigates information avoidance and information seeking behaviors among Black Americans during the COVID-19 pandemic. We proposed two distinct research models to examine how COVID-19 vaccine knowledge, eHealth literacy, and experiences with racial discrimination influenced individuals’ propensity to either avoid or seek information related to COVID-19. A national online survey of 222 Black Americans was conducted; data were analyzed using multiple regression and Spearman’s rho. Results from the first regression model indicated that higher COVID-19 vaccine knowledge and eHealth literacy levels were associated with increased information seeking. The second model demonstrated that as an individual’s COVID-19 vaccine knowledge and eHealth literacy increase, they are less likely to avoid information related to COVID-19. Unexpectedly, the models revealed complex relationships with discrimination: individuals experiencing higher levels of everyday discrimination were more likely to actively avoid certain types of information, yet they also tended to engage in increased information-seeking behaviors. This study contributes to the nascent understanding of information behaviors in minoritized populations by elucidating how societal experiences can impact information practices.

Proceedings Publications

• Ju, B., & Stewart, J. B. (2025).

  Are They Getting What They Expected? User Confirmation and Satisfaction with Generative AIs

  . In Proceedings of the Association for Information Science and Technology, 62, 946--950.
  More info

  The purpose of this study is to explore users' expectations of LLMs, examine their confirmation of perceived system performance, and examine how these factors influence their overall satisfaction with the system. We analyzed data collected from LLM users through an online survey using Welch's ANOVA and regression analysis. The findings demonstrate that users' expectations and confirmation of LLMs are fluid across different socio-cultural variables, spanning age, gender, and educational levels. Additionally, users' perceived system performance, of LLMS, significantly influences their confirmation of the system. Specifically, both perceived usefulness and perceived ease of use have a statistically significant effect on confirmation. Both of our sub-models demonstrate that perceived system performance influences users' confirmation of a given system, and users' confirmation is a strong determinant of their satisfaction. Furthermore, our results indicate an uneven distribution and penetration of AI technologies with respect to age, gender and educational level.

Presentations

• Ju, B., & Stewart, J. B. (2025).

  Are They Getting What They Expected? User Confirmation and Satisfaction with Generative AIs

  . Annual Meeting for the Association for Information Science and Technology. Washington, DC- ONLINE: Association of Information Science & Technology.