Detecting technological maturity from bibliometric patterns. (1st September 2022)

• Record Type: Journal Article
• Authors: Cauthen, Katherine; Rai, Prashant; Hale, Nicholas; Freeman, Laura; Ray, Jaideep
• Is Part Of: Expert systems with applications. Volume 201(2022)
• Publication Date: 2022-09-01
• Subjects: Technology life cycle -- Machine learning -- Artificial neural network -- Data augmentation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117177
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Identify emergent technologies from open-source indicators using binary classifier. Transition between emergence and growth is encoded in the shape of the curve. Artificial neural network learns technology maturity based on derivatives of curve. Data augmentation improves classifier performance by increasing training corpus. Abstract: The capability to identify emergent technologies based upon easily accessed open-source indicators, such as publications, is important for decision-makers in industry and government. The scientific contribution of this work is the proposition of a machine learning approach to the detection of the maturity of emerging technologies based on publication counts. Time-series of publication counts have universal features that distinguish emerging and growing technologies. We train an artificial neural network classifier, a supervised machine learning algorithm, upon these features to predict the maturity (emergent vs. growth) of an arbitrary technology. With a training set comprised of 22 technologies we obtain a classification accuracy ranging from 58.3% to 100% with an average accuracy of 84.6% for six test technologies. To enhance classifier performance, we augmented the training corpus with synthetic time-series technology life cycle curves, formed by calculating weighted averages of curves in the original training set. Training the classifier on the synthetic data set resulted in improved accuracy, ranging from 83.3% to 100% with an average accuracy of 90.4% for the test technologies. The performance of our classifier exceeds that of competing machine learning approaches in the literature, which report an average classification accuracy of only 85.7% at maximum. Moreover, in contrast to current methods our approach does not require subject matter expertise to generate training labels, and it can be automated and scaled.