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

Tahmina Ahmed and Gregory D. Saxton (2026). "The Effects of Bots on Market Reactions to Earnings News", Journal of Information Systems, 1–23.

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Keywords
Abstract Social media platforms such as Twitter influence capital markets by rapidly disseminating information; yet, this environment is increasingly shaped by nonhuman bots. Building on theories of investor attention and information salience, we examine whether bots amplify market reactions to earnings news by directing attention toward larger surprises. Using machine learning to classify 12.02 million tweets discussing S&P 1,500 firms in 2018, we measure firm-specific abnormal bot activity and analyze its association with market responses to earnings announcements. We find that bot activity amplifies the relationship between earnings surprises and abnormal returns. Additional analyses reveal that this effect is stronger when bot sentiment is positive but diminishes with excessive positivity, varies by bot type, and is most pronounced for firms with fewer analysts, further supporting our investor attention argument. Our findings highlight bots’ roles as “attention amplifiers” and underscore the need for greater scrutiny of algorithmic actors in financial markets.

Guo, C. and Saxton, G. (2020). "Social Media Capital: Conceptualizing the Nature, Acquisition, and Expenditure of Social Media-Based Organizational Resources", International Journal of Accounting Information Systems, 36, 100443.

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Keywords
Abstract The near-universal organizational participation in social media is predicated on the belief there are some tangible or intangible new resources to be had through tweeting, pinning, posting, friending, and sharing. We argue the linchpin of any payoff from engagement in social media is a special form of social capital we refer to as social media capital, and offer a conceptual framework for understanding its nature, acquisition, and expenditure. This paper contributes to existing literature by elaborating a new type of organizational resource and then synthesizing and extending research on the processes through which organizations can translate social media efforts into meaningful organizational outcomes. Understanding this causal chain is critical not only for measuring the return on investment from social media use but also for developing accounting information systems that are both adaptable to social resources and better able to exploit the data analytic and forecasting capabilities of real-time social media data.