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Innovation and Commercialization in the Canadian Bioproduct Industry

This study explores the complexities of product development, commercialization, and innovation in the Canadian bioproduct industry. It identifies gaps in understanding drivers for innovation and offers insights to policymakers. Data from Statistics Canada's Bioproducts Development Surveys in 2003 and 2006 inform the analysis of firm activities, industry characteristics, and regional trends. The study employs a conceptual framework and methods like negative binomial count data modeling to evaluate factors influencing product development and market dynamics. Factor analysis highlights the changing importance of benefits, barriers, and strategies in the industry. Future research directions include database linking, performance measurement, and industry dynamics understanding.

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Innovation and Commercialization in the Canadian Bioproduct Industry

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  1. Innovation and Commercialization in the Canadian Bioproduct Industry Pamela Laughland John Cranfield David Sparling University of Guelph

  2. Motivation • Industrial biotech an growing area of interest • Shift waste into something of value • CND’s resource based gives it a competitive advantage vis-à-vis biomass • Product development process is complex • Gap regarding commercialization and innovation activities and related drivers • Information gap to help policy makers understand better firm’s activities, industry structure and characteristics

  3. A changing industry? Source: Statistics Canada, Bioproducts Development Survey, 2003 & 2006

  4. Primary focus of enterprises Source: Statistics Canada, Bioproducts Development Survey, 2003 & 2006

  5. Regional location of enterprises Source: Statistics Canada, Bioproducts Development Survey, 2003 & 2006

  6. Raising capital Source: Statistics Canada, Bioproducts Development Survey, 2003 & 2006

  7. Raising capital, cont Source: Statistics Canada, Bioproducts Development Survey, 2003 & 2006

  8. Conceptual framework • Portfolio of products/projects – add or trim? • Uncertain of benefits • Maximize CE of portfolio of products under development or on the market • Choose level of hard and soft capital allocated to the respective product/project • Subject to a resource constraint on (hard and soft) capital • Non-negativity constraint on hard & soft capital

  9. Conceptual framework, cont • FOC equates net marginal benefits • Corner versus interior solution • Optimal level of hard & soft capital expressed as a share of overall capital

  10. Conceptual framework, cont • Resource based theory of the firm: sustained competitive advantage arises from heterogeneous resources and inimitatability

  11. Methods & Data • Share of (hard and soft) capital allocated to each product is a latent variable • Map to a count of the number of products • Negative bionomial count data model of number of products under development or on the market • Variables capturing internal and external resources to the firm (and how these might be deployed strategically), market environment

  12. Methods & Data, cont • 2003 & 2006 Bioproducts Development Survey • Internal: IP; firm size; age; BP R&D spending per employee; early/late focus; BP share of revenue; benefits, barriers and strategies for development; private firm • External: access capital; SR&ED; collaborations • Market: sub-sector of predominant focus; region

  13. Benefits, barriers & strategies • Likert scale response items • 1=low importance, 5=high importance • Analyzed using PCA (with varimax rotation)

  14. Benefits, barriers & strategies, cont 1=low importance, 5=high importance

  15. Marginal effects – internal factors

  16. Marginal effects – external factors

  17. Marginal effects – market factors

  18. Take home points • From 2003 to 2006, more smaller firms with slightly higher count of products • Impact of importance of benefits changed: product/sales versus cost/environmental • IP, collaborations & BP R&D expenditure positively associated with count of products • Large effects associated with sub-sector & some regional variables

  19. Future work • Need to be able to link databases to create panels • Link firms to measure performance in more desirable way • Understand industry dynamics better • Distribution of BP importance • Network effects • Non/semi-parametric analysis

  20. Any questions? THANK YOU

  21. Factor analysis: benefits, barriers & strategies

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