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

Yiduo Shao , Chengquan Huang, Yifan Song, Mo Wang, Young Ho Song, and Ruodan Shao (Forthcoming). "Using Augmentation-based AI Tool at Work: A Daily Investigation of Learning-based Benefit and Challenge", Journal of Management, 0(0).

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Keywords
Abstract Augmentation-based artificial intelligence (AI) artifacts are increasingly being incorporated into the workplace. The coupling of employees and AI tools, given their complementary strengths, expands and expedites employees’ access to information and affords important learning opportunities. However, existing research has yet to fully understand the learning-based benefits and challenges for employees in augmentation. Integrating insights from AI augmentation literature and cognitive load theory, we conducted a daily diary study to understand employees’ experience using augmentation-based AI at work on a daily basis. We theorized and found that, on the one hand, frequent usage of augmentation-based AI during a workday was associated with greater knowledge gain and subsequently better task performance at the end of the workday. On the other hand, using augmentation-based AI frequently also led employees to experience information overload, which in turn impaired their performance and recovery at the end of the workday. In addition to elucidating the countervailing mechanisms, we identified employee openness to experience as a dispositional factor, and positive affect as a momentary state that shaped the effects of using augmentation-based AI over the workday. Our research has implications for understanding AI augmentation dynamics from a learning-based perspective, as well as AI’s impact on employees at large.

Dhanani, L.Y., Joseph, D.L., McCord, M.A., McHugh, B.C. and Shen, W. (2015). "Is a Happy Leader a Good Leader? A Meta-analytic Investigation of Leader Trait Affect and Leadership", Leadership Quarterly, 26, 557-576.

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Keywords
Abstract Organizational scholars have long been concerned with identifying traits that differentiate effective leaders from ineffective leaders. Although there has been renewed interest in the role of emotions in leadership, there is currently no quantitative summary of leader trait affectivity and leadership. Thus, the current paper meta-analyzed the relationship between leader trait affectivity and several leadership criteria, including transformational leadership, transactional leadership, leadership emergence, and leadership effectiveness. Results show that leader positive affect is positively related to leadership criteria, whereas leader negative affect is negatively related to leadership criteria, and regression analyses indicate that leader trait affect predicts leadership criteria above and beyond leader extraversion and neuroticism. Additionally, mediational analyses reveal that the relationship between leader trait affect and leadership effectiveness operates through transformational leadership. Taken together, these results contribute to the literature on emotions and leadership by highlighting the role of leader affect as a meaningful predictor of leadership.