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This study investigates the effects of convergence of science and technology on innovation impact, specifically how convergence helps R&D organizations to apply scientific knowledge to their R&D activities. In addition to direct effects of convergence, we address the moderating effects of scientific capacity, knowledge spillover, and knowledge maturity from the knowledge side. The empirical analysis, which employs a zero inflated negative binomial regression model uses data on 2074 patents granted to US organizations from the pharmaceutical industry. The results show that an increase in the proportion of scientific knowledge in convergence has a positive and curvilinear relationship with innovation impact. Also, we find that the organization's scientific capacity, regional scientific knowledge spillover, and knowledge maturity positively moderate the relationship between convergence and innovation impact. Our findings underline the importance of convergence between science and technology as well as provide implications on how to improve the outcome of an organization's research and development process.

Abstract:The phenomenon of innovation growing rapidly and having a shorter lifespan is a structural change due to the development of ICT (Information and Communications Technology), diverse investment methods, and reduced pattern of innovation adoption. For ICT companies to survive and maintain their success in this ever-changing environment, they need to succeed in fulfilling both productivity and accuracy of innovation. To sustain their innovative success, ICT companies should consistently maintain the direction of innovation towards consumers. The present study analyzes various cases of ICT companies which succeeded or failed to maintain their prior innovative success, and suggests consumer-centric innovation as a solution. To create consumer-centric innovations, companies have to (1) predict the dynamically evolving demand of consumers and continuously transform; (2) proactively employ observation method and big data analysis to discover hidden demands; and (3) identify hassles such as wastes, inconveniences, and anxieties, and put effort in solving these hassles.