Latin America has spent two decades expanding its scientific output and its innovation policy, yet it still lags far behind in innovation intensity. R&D spending averages about 0.5 percent of GDP, against roughly 2.3 percent in the OECD, and the productivity gap with advanced economies has not narrowed. This thesis argues that the problem is not only a lack of resources. The region has internationally recognized scientific institutions, firms willing to invest in innovation and growing demand. What falls short is the conversion of knowledge and technology into productivity-enhancing innovation. The thesis asks which conditions govern that conversion at four levels: knowledge systems, firm innovation strategies, macroeconomic environments and organizational capabilities.
Six empirical studies address the question. At the level of the scientific system, bibliometric and network analysis of five natural-resource-related fields (2004–2013) shows that science–industry co-publication is below one percent of output in most countries. It also shows that research departments in brokerage positions, which are linked to more diverse knowledge sources, collaborate more intensively with industry.
The thesis then introduces LAIS, the first harmonized multi-country firm-level innovation database for the region. LAIS combines thirty national surveys from ten countries (2007–2017) into about 119,900 observations and is publicly available through the Inter-American Development Bank. Using LAIS, the thesis identifies four firm innovation strategies. None of them resembles the science-intensive strategies documented in Europe. One of them, an "open management" strategy specific to the region, is associated with higher labor productivity. LAIS is also used to show that firm-level economic uncertainty lowers innovation investment, mainly tangible investment, and reinforces persistence in past innovation choices. The size of this effect is real but limited.
Finally, the thesis turns to artificial intelligence. In Argentine manufacturing, early machine-learning adoption is rare (2.1 percent) and depends mainly on accumulated digital complements rather than on broad human capital. A randomized controlled trial in Chile provides causal evidence that implementation-oriented AI training for executives increases firms' AI experimentation, production use and in-house AI capacity.
Taken together, the findings show that the main bottleneck lies in fragile knowledge connections and limited organizational and managerial capabilities, not in a lack of scientific activity or willingness to invest. The thesis draws out the implications for science, technology and innovation policy and for research on innovation and AI in Latin America.
