Convergence Technology Opportunity Discovery for Firms Based on Technology Portfolio Using the Stacked Denoising AutoEncoder (SDAE)

Technology convergence, as a key driving force of innovation, has brought a burgeoning of research attention. Although numerous studies on technology convergence have been carried out, there were limitations in consideration of a firm's capability in technology convergence. This article proposes a framework for "Convergence Technology Opportunity Discovery" (CTOD) based on firms' technical convergence competence manifested in their patent portfolios, market competition, and technological growth potential. The present research, by employing a stacked denoising autoencoder, a deep neural network-based collaborative filtering method, provides reliable latent preference toward convergence technology for individual firms. Our CTOD framework is applied to three information technology and biotechnology firms to elaborately demonstrate its validity. Ultimately, the proposed framework is expected to provide practical assistance to organizations seeking technology convergence opportunities in various fields.