Study analyzes how the adoption of an innovation spreads
Research reveals that academic knowledge takes the longest to disseminate, while public interest is the fastest dimension, influenced by social networks.
Innovation is the driving force behind economic growth and technological progress. Governments and companies compete to anticipate trends, seeking competitive advantage in an increasingly dynamic market. In this context, the diffusion of innovation theory provides a robust framework for understanding how new ideas and technologies spread. A recent FGV study investigated the diffusion of deep learning, a machine learning technology, in three dimensions: public interest, patents and academic knowledge.
The study, conducted by Júlio César de Figueiredo of FGV EAESP and fellow researchers Carlos Takahashi and Eusebio Scornavacca, was published in Technological Forecasting and Social Change. Using the Bass model and data from sources such as Google Trends, Google Patents and the Scopus database, the researchers analyzed the take-off time of each diffusion dimension in 21 countries. In this way, it was possible to analyze how the diffusion process began with the growth of public interest, followed by the appearance of the first patents and then academic publications.
The research reveals 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 starts to become interested and involved with an innovation before it is formally patented or academically documented.
Social networks have played a central role in accelerating public interest. Countries such as Japan, Switzerland and the United States showed shorter take-off times in all dimensions. On the other hand, Australia and Turkey faced longer delays due to structural and political challenges.
The results challenge previous studies by showing that academic publications are the last stage in the diffusion of innovation. This reinforces the need for strategies to speed up the dissemination of academic knowledge. Thus, companies can use this analysis to predict market behavior, while governments can align innovation policies to strengthen their economies.
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