Chair: Aaron Turpin, University of Toronto

Social Enterprise Transcendent: How a Theory of Organizational Form Outcomes Shows Social Enterprise to be the Business of the Future
Bruce Martin, Thompson Rivers University; Sofy Carayannopoulos, Wilfrid Laurier University

We create the foundation for a theory of organizational form outcomes by developing a model of organizational form influence on individual organization outcomes, and propositions that explain the main relationships involved. Among the more surprising findings from this work is that the newest, and least well studied organizational form, Social Enterprise, performs well compared to traditional for-profit ventures, suggesting that an economy built of only social enterprises, and no traditional for-profit only firms might produce the goods and services that societies need efficiently in the future.

Partnership dynamics that support social innovation by nonprofits
Micheal L. Shier, University of Toronto; Aaron Turpin, University of Toronto; John R. Graham, University of British Columbia (Okanagan)

This paper provides results from a nationally focused cross-sectional study of nonprofits (n=838) in Canada that sought to investigate the effects of within-partnership dynamics on nonprofit efforts to implement social innovations. Results show satisfactory reliability and construct validity of the measured latent variables of the newly developed Partnership Dynamics for Social Innovation Scale (CFI = 0.984; TLI = 0.975; RMSEA: 0.048; α=0.801). Of the factors measured, results also highlight the significant effects of structure of engagement and clarity of outcomes as key partnership dynamics significantly predicting social innovation implementation by nonprofits. The findings have implications to support successful partnership engagement involving nonprofits.

Theory meets practice: An Artificial Intelligence literature review engine for Social Innovation projects
Rahil Adeli, Simon Fraser University, Meg Holden, Simon Fraser University

The presentation introduces a prototype Artificial Intelligence literature review engine for social innovation projects in the philanthropic sector. This engine uses a dataset of selected social innovation articles collected in this research, as the input, and offers a customized dataset of selected ranked papers useful for the subject of a social project. The engine facilitates the process of finding the most relevant publications that contribute to academic evidence for designing the pathways for changes the project is intended to create. Moreover, an encompassing picture of research themes and trends in the academic literature of social innovation is built and introduced with the help of bibliometric analysis and data analytics techniques.