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

Darke, P., Odou, P. and Voisin, D. (2019). "Promouvoir Les Comportements Pro-Environnementaux Grâce à L’hypocrisie Induite", Recherche et Applications en Marketing, 34(1), 78-94.

Open Access Download

Abstract Dans le domaine de la consommation pro-environnementale, les recherches se sont évertuées ces dernières années à expliquer l’écart existant entre les attitudes et les comportements effectifs. Trois études expérimentales montrent que lorsque la contradiction entre ce que les individus disent et ce qu’ils font est rendue saillante, c’est-à-dire dans une situation d’hypocrisie induite, ils réduisent de manière indirecte la dissonance cognitive qui en résulte en étant plus altruistes à l’égard d’associations qui agissent pour l’environnement mais pas pour des associations humanitaires. Cet effet de l’hypocrisie induite n’est plus significatif lorsque les individus ont pu, au préalable, affirmer leur Soi.

Deutsch, Y. and Weitzner, D. (2015). "Understanding Motivation and Social Influence in Stakeholder Prioritization", Organization Studies, 36(10), 1337-1360.

Open Access Download

Abstract Insight into organizational responses to stakeholder claims and influence attempts is critical to understand the challenges currently facing managers and organizations. Drawing on Kelman’s (1958) model of social influence, we advance the field’s understanding of the factors driving firm-level prioritization of competing stakeholder claims by developing a theoretical framework that accounts for both the stakeholder attributes that are important to relevant decision makers, and the decision makers’ motivations for accepting or rejecting the influence attempts of varying stakeholders. Our framework distinguishes itself from existing research by focusing on stakeholder prioritization, not salience, recognizing that stakeholder-related decisions result from group interaction and that important decision makers are not limited to those found within the classic boundaries of the firm. Consequently, we argue that decision makers are simultaneously stakeholders with attributes that might be relevant to other decision makers involved in prioritization. In addition, we identify a more extensive set of stakeholder attributes that includes powerlessness and illegitimacy.

Fang, X., Hu, P., Li, Z. and Tsai, W. (2013). "Predicting Adoption Probabilities in Social Networks", Information Systems Research, 24(1).

Open Access Download

Abstract In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.