Editor’s note: This is the twelfth article since May 20, 2026 in an ongoing series by Dr. Andrew Maxwell, the Bergeron Chair in Technology Entrepreneurship in the Lassonde School of Engineering at York University. Every week – and occasionally every other week – we’ll present a new article by Maxwell, in a series whose wide-ranging and incisive themes encompass: Canada and innovation policy; productivity and industry; innovation frameworks; AI and higher education; research and intellectual property; technology adoption; entrepreneurship and commercialization; universities and higher education; entrepreneurship education; and AI and the future of work. 

 This is Part 2 of a two-part series on rebuilding competitiveness through culture, capability and system alignment. Part 1 was published on August 12, 2026.

1. The missing link between research and productivity

Canada’s research enterprise rests on a persistent assumption: that more investment in science and engineering automatically leads to greater prosperity. Yet while our research output continues to grow, productivity lags.
Analysis of Natural Sciences and Engineering Research Council of Canada (NSERC) project data shows that only about one-quarter of funded fields – advanced manufacturing, materials performance, process efficiency, clean energy – have a near-term connection to productivity. The remainder, mostly curiosity-driven or disciplinary, play a vital role in discovery but not in adoption.
This is not an argument against discovery. Curiosity-driven research fuels long-term breakthroughs. But we must recognize that only a portion of public research investment translates directly into productivity gains, and design our systems accordingly.

The goal is to increase that share – not to replace basic research but to make impact-oriented research visible, measurable, and intentionally supported.

2. Peer review: Reliable for rigour, not for impact

Peer review remains the cornerstone of academic quality assurance, but as designed it reinforces tradition rather than innovation. Studies of Social Sciences and Humanities Research Council of Canada (SSHRC), U.S. National Science Foundation, and U.K. Economic and Social Research Council competitions show that funding outcomes are often statistically random, highly correlated with institutional prestige, and prone to bias.
Beyond fairness, the system’s structure slows response to opportunity. Reviews can take months or years – time enough for an emerging technology or market window to close. Panels value safety and consensus; novelty and relevance can appear risky.
This is not an argument to abandon peer review, but to modernize it for impact. Research on research funding now shows alternative pathways:

• New Zealand allocates a share of its early-career grants by lottery among qualified proposals.
• The U.K. Research-on-Research pilots test behavioural nudges and alternate scoring systems to reward translational collaboration.
• Canada’s NSERC Alliance competition has already experimented with a partial lottery model.

Artificial intelligence could soon augment these efforts. Reinforcement-learning models can identify patterns among proposals that lead to measurable outcomes, helping agencies link review criteria to impact metrics.
Rigour is non-negotiable, but relevance must become visible.

3. The social science of innovation: The primordial driver

Innovation is not purely technical – it is social, institutional and behavioural. In Part 1 of this series, I argued  that productivity is rooted in culture; here we extend that logic.

Economics, psychology, management, sociology and public policy form the primordial science of innovation. They explain how organizations learn, how people perceive risk and how incentives shape adoption.
Countries that embrace this integration outperform others.

• Sweden’s Vinnova funds both technological and social innovation.
• The U.K.’s Catapult Centres pair market and user research with engineering development.
• The Netherlands’ TNO and Singapore’s A*STAR build cross-disciplinary teams that blend behavioural insight with technical design.

Canada’s challenge is not a lack of discovery, but the absence of applied social-science infrastructure – institutions capable of studying adoption, diffusion and organizational learning at scale. Understanding how innovation spreads is as important as discovering what to innovate.

4. Applying research in Canadian industry:

For Canada’s industries, productivity-oriented research could close the gap between discovery and use.

• In advanced manufacturing, collaborations like the Mosaic Array 3D printing initiative demonstrate how academic R&D can enhance process efficiency and supply-chain resilience.
• In cleantech and energy, programs such as Avatar Innovations in Calgary show that integrating behavioural economics, systems design and leadership development accelerates technology adoption.
• In urban mobility, projects like SARIT’s three-wheel electric vehicles illustrate how research partnerships can align engineering, policy, and user behaviour to create new markets.

At York University, the TechConnect program exemplifies how capability and adoption can evolve together. The program follows a structured learning journey that runs in parallel with research activities – helping graduate and undergraduate researchers identify users, validate needs and translate findings into real-world applications as their research unfolds.
These examples show that impact-oriented research doesn’t replace curiosity – it translates insight into advantage. When research institutions co-design with users, adoption accelerates and productivity gains compound.

5. Funding models that reward use:

Institutions such as Germany’s Fraunhofer Society and Finland’s VTT demonstrate how research, application and diffusion can coexist within one architecture. A core reason for their success lies in funding design.
One-third of Fraunhofer funding comes from federal base support, one-third from state governments, and one-third from user contracts.

This ⅓-⅓-⅓ model anchors priorities in real demand and ensures that scientific excellence and market relevance reinforce one another.
Imagine adapting that model for Canada’s national centres of excellence, global innovation clusters, or university research institutes – tying a portion of budgets to user co-funding. Such an approach would naturally align incentives, stimulate collaboration and measure success through adoption.
When users invest, research becomes grounded in reality. User funding should shape the research landscape, influencing what questions are asked and increasing the likelihood of uptake.

6. Measuring productivity outcomes:

To build legitimacy and accountability, impact must be measurable. Productivity metrics vary by context but share a common principle: tracking what research enables, not just what it produces.
At the firm level:

• Output per employee.
• Time-to-adoption or scale-up.
• Efficiency gains (energy, materials, process).

At the sector level:

• Diffusion rates of key technologies.
• Improvements in management quality or digital maturity.
• Strength of industry–research collaboration networks

At the system level:

• Value-added per research dollar.
• Employment growth in innovation-intensive sectors.
• Contribution of research-enabled exports.

These indicators complement, rather than replace, traditional academic metrics. They reflect what truly matters: how research contributes to competitiveness and capability.

 Learning from the research on research movement:

The global Research on Research community, led by the UK Research on Research Institute and the Organisation for Economic Development and Co-operation Innovation Growth Lab, treats research funding as a living experiment. Their pilots test new evaluation tools – impact-weighted assessments, outcome lotteries, real-time learning loops.
Such experimentation offers lessons for Canada. The challenge is not to measure more, but to measure meaningfully. Each major program –from Discovery Grants to Alliance to Canada Foundation for Innovation – should articulate its theory of change: what kinds of productivity or societal outcomes it seeks, and how it will know progress is occurring.
Disappointingly, SSHRC appears to be stepping back from its earlier efforts to strengthen research-impact frameworks, precisely when the need for such evidence is greatest. Impact measurement is not bureaucratic – it is cultural, signalling what we collectively value.

8. Reforming institutions for responsiveness:

Even the most forward-looking research loses relevance if institutions cannot move at the pace of innovation. Current review and approval cycles – sometimes 18 months or more – are incompatible with markets that shift in weeks.
Canada’s research ecosystem needs decision processes as agile as innovation itself:

• Use AI-enabled triage to route proposals by complexity and urgency.
• Pilot quarterly micro-competitions to seize emerging opportunities.
• Fund “learning portfolios” that adapt criteria based on observed outcomes.

Reform is not only structural but cultural. Universities and agencies alike must evolve from hierarchies of compliance to systems of learning – rewarding adaptation, feedback, and collaboration.

9. Building capability through talent and incentives:

Every research project also trains people. Yet most graduate training still focuses narrowly on discovery rather than diffusion. To enhance national capability, innovation literacy – the ability to navigate markets, regulation and behavioural change – should be integral to every STEM and social-science program.
At York University, the TechConnect program exemplifies this shift. Its structured, parallel learning journey connects research with real users, enabling students and faculty to validate needs, co-design solutions and translate insights into adoption in real time.
Universities can lead this transformation by aligning promotion and tenure incentives with collaboration and adoption evidence. Recognizing partnership impact alongside publications embeds productivity thinking into institutional DNA.

10. Canada’s strategic opportunity:

The global environment is shifting rapidly. U.S. policy volatility and trade fragmentation are forcing industries to diversify supply chains and seek new markets. These disruptions create a unique window for Canada to lead in productive, values-based innovation.
Meeting this moment requires redirecting focus from consumption toward capacity-building—from short-term subsidies to long-term competitiveness. Investments in applied research, infrastructure and diffusion will pay off in resilience, not just growth.
By linking research more tightly to productivity, Canada can position itself as a trusted partner in emerging global value chains – offering reliability, sustainability and skill.

11. From research to learning systems:

Ultimately, impact arises from learning systems – where discovery, adoption and evaluation inform one another continuously.
Every research program should be a feedback loop:

• Design with users.
• Measure what changes.
• Adapt based on evidence.

Culture made productivity possible; capability will make it real. The next frontier for Canadian innovation lies not in doing more research, but in learning more effectively from the research we already fund.

References

Illustrative sources and institutional examples referenced in this article include:

OECD (2024). Science, Technology and Innovation Outlook.

OECD (2023). Innovation Strategy for a Digital World.

Rogers, E. (2003). Diffusion of Innovations (5th ed.).

Bloom, N. & Van Reenen, J. (2010). Why Do Management Practices Differ Across Firms and Countries? J. Econ. Persp. 24 (1).

Sharpe, A. (2022). The State of Productivity in Canada. CSLS.

Fraunhofer-Gesellschaft (Germany), VTT (Finland), Vinnova (Sweden), TNO (Netherlands), A*STAR (Singapore), Catapult Centres (U.K.).

UK Research on Research Institute (RoRI) and OECD Innovation Growth Lab (IGL).

NSERC (2024). Alliance ECR Lottery Pilot.

York University (2024). TechConnect Program Overview.

R$