Abstract
This study examines how technology diversification is related to patent quality by using text-based analysis. Technology diversification is a type of a firm’s R&D strategy aimed at extending the scope of intellectual property rights and thereby expanding its influence on competitors in the technology-based market. Diversifying technology stock has the benefits of becoming nimble in the fast-changing technological environment and enabling firms to affect the entrance of new players in the market. However, technological diversification has the risk of failing to concentrate R&D resources, which likely results in producing only fragmented patents that are less impactful on subsequent technology. This study uses text-mining analysis to develop a content-based indicator that can effectively measure the level of technology diversification. This work also introduces lexical diversity and patent-stock heterogeneity based on term-matrix similarity and performs an analysis of their potential as a consistent measure for diversification level. Regression analyses using lexical diversity produces meaningful implications. The results show that unlike the classification-based approach, lexical diversity has a consistent and positive correlation with forward citations, which suggests that all other factors being equal, diversified patent stocks are likely to have more impact on future technology.
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