With the increasing pace of digital technology innovation and commercialization, monitoring commercial diffusion of technologies becomes more important for organizations. Technology monitoring is fundamental to R&D planning, technology management, and strategic decision-making. Despite its importance, monitoring the diffusion of technologies at the commercial lifecycle stage relies on crude methods, such as “snapshot-in-time” surveys and keyword counts. These approaches are in stark contrast to novel and rapidly advancing methods for monitoring technologies at the precommercial lifecycle stages, such as fundamental scientific research and applied R&D.
We address this imbalance by proposing a specialized method for monitoring the commercial diffusion of technology. The method recognizes phases in technology adoption by organizations and captures the temporal progression of the diffusion process. One of the central elements of the proposed method is the classification of text, which relies on qualitative content coding. Our approach to coding leverages the insights from innovation diffusion research and is sensitized specifically to detect phases in technology adoption by organizations. The approach is illustrated with the case of artificial intelligence (AI) diffusion among S&P 500 companies during the 2004–2019 period. Our first contribution is a new method for monitoring the commercial diffusion of technologies. It provides transparent, replicable, updatable, and granular results, which can complement survey-based technology monitoring. The second contribution is empirical evaluation of AI diffusion in the context of leading firms in North America.
SIGADIT Best Paper Award, Copenhagen Denmark, 11th December, 2022
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