Intellectual Property (IP) is considered a valuable asset for a company and the relevance of effective IP management has been intensely discussed in the literature. However, research gets scarce when the focus is on SMEs and startups. Scholars and experts appoint the lack of knowledge on IP as one of the main reasons for the underperformance of small firms in the matter. Several initiatives attempted to solve the lack of knoweldge on IP unsuccessfully. Meanwhile, Brazil is seeking improvement in competitiveness and increasing its efforts on innovation. The number of startups sontrongly increased in the last years. This work uses Design Science Research to develop an innovative artifact to improve IP literacy among Brazilian startups. Using the design process stated in the methodology, we analyzed existing IP literacy systems and concluded with design recommendation for future systems. Additionally, we surveyed Brazilian startups to understand how these companies manage their IP and how they access knowledge. Consequently, we developed the system based on the recommendation from initial studies and evaluated it with potential users and IP experts in Brazil. Finally, Brazilian startups used the learning system and improved their knowledge on IP by increasing their capabilities of designing strategies to improve the IP value.

This thesis attempts to analyze a part of the big and complex process of how intellectual property ownership and technological innovation influence the performance of firms and their revenues. Here I analyze a firm's stock market performance as a function of the quantity and quality of intellectual property (patents) owned by the firm in context of the three US high-technology sectors, Pharmaceuticals, Semiconductors and Wireless. In these sectors, value of a firm is predominantly driven by the technologies which a firm owns. I use citation based indicators and number of claims to measure the quality of patents. This research presents empirical evidence for the hypothesis that in high-tech sectors, companies which generate better quality intellectual property perform better than average in the stock market. I also posit that firms which are producing better quality technologies (good R&D) invest more in R&D regardless of their market performance. Furthermore, though smaller firms get relatively less returns on quality and quantity of innovation, they tend to invest a bigger fraction of their total assets in R&D when they are generating high quality patents. Larger firms enjoy the super-additivity effects in terms of market performance as the same intellectual property gives better returns to them. In addition, returns to R&D are relatively higher in the pharmaceutical industry than semiconductor or wireless industries.

Valuing Intellectual Property assets is increasingly critical in today’s economy, where intangible assets constitute a significant portion of business value. This thesis addresses the challenges inherent in the IP valuation process, particularly the subjectivity and variability associated with different IP types and valuation methodologies. It proposes a new way to value IP assets, by building upon existing disaggregation methods, and by introducing the IP-heaviness classification system. The study aims to develop an objective valuation model for IP assets by introducing the IP-heaviness classification system. The goal of the model is to estimate the range of IP Contribution (IPC) to company value across different industry groups. Our study employed Kernel Density Estimation and Monte Carlo Simulation to analyze the dataset and generate a larger data sample. We then developed the IPH classification system, which categorizes industries based on their reliance on IP as a value contributor, grouping them by similar levels of IP dependence. This structured approach allows for a preliminary estimation of the IP contribution for each group, providing a standardized framework for IP valuation. Each IPH group was assigned its own probability density curve to represent its potential IPC value. Ultimately, our model produced confidence intervals for each IPH group, offering a reliable measure of the IP contribution within each category. Our findings reveal significant variability in the impact of IP on company value across different industries. Higher IPH groups, representing industries with substantial IP reliance, show a greater proportion of their value attributed to IP assets. Conversely, lower IPH groups, with less reliance on IP, exhibit lower IP contributions. The IPH classification system addresses the challenges of traditional IP valuation methods by providing a more objective and transparent approach. It enhances the comparability of companies within and across IPH groups and reduces subjectivity in the valuation process.