Estimation of Uncertainty for Technology Evaluation Factors via Bayesian Neural Networks

Abstract

In contemporary times, science-based technologies are needed for launching innovative products and services in the market. As technology-based management strategies are gaining importance, associated patents need to be comprehensively studied. Previous studies have proposed predictive models based on patent factors. However, technology-based management strategies can influence the growth and decline of firms. Thus, this study aims to estimate uncertainties of the factors that are frequently used in technology-based studies. Furthermore, the importance of the factors may fluctuate over time. Therefore, we propose a Bayesian neural network model based on Flipout and four research hypotheses to evaluate the validity of our method. The proposed method not only estimates the uncertainties of the factors, but also predicts the future value of technologies. Our contribution is to (i) provide a tractable Bayesian neural network applicable to big data, (ii) discover factors that affect the value of technology, and (iii) present empirical evidence for the timeliness and objectivity of technology evaluation. In our experiments, 3781 healthcare-related cases of patents were used, and we found that the proposed hypotheses were all statistically significant. Therefore, we believe that reliable and stable technology-based management strategies can be established through our method.