The effects of intellectual property rights on knowledge spillovers and innovation: An empirical investigation

FDI is believed to be a conduit of new technologies between countries. The first chapter of this dissertation studies the advantages of outward FDI for the home country of multinationals conducting research and development abroad. We use patent citations as a proxy for technology spillovers and we bring empirical evidence that supports the hypothesis that a U.S. subsidiary conducting research and development overseas facilitates the flow of knowledge between its host and home countries.^ The second chapter examines the impact of intellectual property rights (IPR) reforms on the technology flows between the U.S. and host countries of U.S. affiliates. We again use patent citations to examine whether the diffusion of new technology between the host countries and the U.S. is accelerated by the reforms. Our results suggest that the reforms favor innovative efforts of domestic firms in the reforming countries rather than U.S. affiliates efforts. In other words, reforms mediate the technology flows from the U.S. to the reforming countries.^ The third chapter deals with another form of IPR, open source (OS) licenses. These differ in the conditions under which licensors and OS contributors are allowed to modify and redistribute the source code. We measure OS project quality by the speed with which programming bugs are fixed and test whether the license chosen by project leaders influences bug resolution rates. In initial regressions, we find a strong correlation between the hazard of bug resolution and the use of highly restrictive licenses. However, license choices are likely to be endogenous. We instrument license choice using (i) the human language in which contributors operate and (ii) the license choice of the project leaders for a previous project. We then find weak evidence that restrictive licenses adversely affect project success.^

A systems approach to describing the process of individual adoption of IT innovations

Research on the adoption of innovations by individuals has been criticized for focusing on various factors that lead to the adoption or rejection of an innovation while ignoring important aspects of the dynamic process that takes place. Theoretical process-based models hypothesize that individuals go through consecutive stages of information gathering and decision making but do not clearly explain the mechanisms that cause an individual to leave one stage and enter the next one. Research on the dynamics of the adoption process have lacked a structurally formal and quantitative description of the process. ^ This dissertation addresses the adoption process of technological innovations from a Systems Theory perspective and assumes that individuals roam through different, not necessarily consecutive, states, determined by the levels of quantifiable state variables. It is proposed that different levels of these state variables determine the state in which potential adopters are. Various events that alter the levels of these variables can cause individuals to migrate into different states. ^ It was believed that Systems Theory could provide the required infrastructure to model the innovation adoption process, particularly applied to information technologies, in a formal, structured fashion. This dissertation assumed that an individual progressing through an adoption process could be considered a system, where the occurrence of different events affect the system's overall behavior and ultimately the adoption outcome. The research effort aimed at identifying the various states of such system and the significant events that could lead the system from one state to another. By mapping these attributes onto an “innovation adoption state space” the adoption process could be fully modeled and used to assess the status, history, and possible outcomes of a specific adoption process. ^ A group of Executive MBA students were observed as they adopted Internet-based technological innovations. The data collected were used to identify clusters in the values of the state variables and consequently define significant system states. Additionally, events were identified across the student sample that systematically moved the system from one state to another. The compilation of identified states and change-related events enabled the definition of an innovation adoption state-space model. ^