Study analyzes how the adoption of an innovation spreads
Innovation is the driving force of economic growth and technological advancement. Governments and businesses compete to anticipate trends, seeking competitive advantage in an increasingly dynamic marketplace. Within this context, the Diffusion of Innovation Theory (IDT) provides a robust framework for understanding how new ideas and technologies propagate. This particular study investigates the diffusion of Deep Learning, a machine learning technology, through three dimensions: public interest, patents, and academic knowledge.
The study, conducted by Júlio César de Figueiredo (FGV EAESP) and researchers Carlos Takahashi and Eusebio Scornavacca, was published in Technological Forecasting and Social Change. Using Bass's model and data from sources such as Google Trends, Google Patents, and the Scopus Base, the researchers analyzed the take-off time of each diffusion dimension in 21 countries. Thus, it was possible to analyze what the diffusion process began with the growth of public interest, followed by the emergence of the first patents followed by academic publications.
The study revealed that public interest is the first dimension of innovation diffusion, followed by patents and, finally, academic publications. On average, the diffusion of interest preceded patents by 7.7 months, while patents appeared 10 months before academic publications. Therefore, this sequence shows that the public begins to be interested in and involved with an innovation before it is formally patented or academically documented.
Social media has played a central role in accelerating public interest. Countries such as Japan, Switzerland and the United States had shorter take-off times in all dimensions. Australia and Turkey faced greater delays, due to structural and political challenges.
The results challenge previous studies by showing that academic publications are the last step in the diffusion of innovation. This reinforces the need for strategies to speed up the dissemination of academic knowledge. Thus, companies can use these analyses to predict market behavior, while governments can align innovation policies to strengthen their economies.
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