Discovering technological opportunities of cutting-edge technologies: A methodology based on literature analysis and artificial neural network

Antonello Cammarano, Vincenzo Varriale, Francesca Michelino and Mauro Caputo

Technological Forecasting and Social Change, 2024, vol. 209, issue C

Abstract: This paper presents a methodology for discovering technological opportunities of cutting-edge technologies by capturing information from literature. Technological opportunities are expressed in the form of business applications of technologies in untested contexts, i.e. unexplored industries and business processes. The paper underscores the advantages of discovering opportunities starting from existing emerging practices presented in scientific papers, in contrast with the current state of the art that prefers other datasets. The business cases presented in journals are converted into the triad technology-industry-process and the impact on the business performance is reported. From the analysis of 33,285 papers, 14,739 existing triads have been captured. An artificial neural network has been trained using this dataset, enabling accurate forecasting of the potential impact of vacant combinations technology-industry-process. The methodology has been tested on 11 cutting-edge technologies: 3D printing, artificial intelligence, blockchain, computing, digital applications, geo-spatial technologies, immersive environments, internet of things, open & crowd-based platforms, proximity technologies, robotics. For each technology, a technological opportunity map is provided to show the best vacant areas for future implementation. The methodology distinguishes between combinations with uncertain and confident expected impact, so that companies can focus on the most promising areas. Implications for both practice and academia are discussed.

Keywords: Technological opportunity; Cutting-edge technologies; Vacant technology; Technological forecasting; Technological convergence; Emerging technologies (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:209:y:2024:i:c:s0040162524006097

DOI: 10.1016/j.techfore.2024.123811

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