Productized OS Framework: Predicting Immigrant Startup Success in Canada Visa Program

Abstract

This study investigates the critical factors distinguishing successful from unsuccessful immigrant-founded startups in Canada, analyzing 100 ventures through a multi-metric lens. Building on theories such as signaling, resource-based view, and ecosystem approaches, the research identifies key differentiators across financial performance, innovation, team dynamics, cultural adaptation, and execution. The analysis reveals that successful startups exhibit more integrated and reinforcing performance patterns, while unsuccessful ones show fragmented, misaligned strategies. A major contribution of this work is the development of the Productized OS Framework, a predictive model for assessing immigrant-founded ventures, validated for both theoretical rigor and practical applicability. The study provides actionable insights for incubators, investors, immigrant founders, and policymakers, especially regarding the refinement of Canada's Startup Visa program. It also challenges deficit-based views of immigrant entrepreneurship, highlighting resilience and adaptive strengths. While limited by its cross-sectional scope, this research lays a strong foundation for future longitudinal studies exploring immigrant entrepreneurial success.