A predictive patent level framework to anticipate convergence

This thesis proposes a patent level framework to anticipate convergence. The development of multidisciplinary products and services, particularly in recent years, has maintained the convergence of distinct disciplines and the emergence of novel interdisciplinary areas. Early prediction of convergence provides firms with new business opportunities and capabilities to anticipate threats and mitigate their impact. Despite efforts to investigate convergence patterns, studies seeking to anticipate the convergence patterns developing rapidly over industries, have remained scarce. To identify the main drivers of convergence, this thesis targets the dynamic investigation of convergence patterns from the emerging point of convergence at the technology level. As persistent integration of distinct technological domains advances a convergence process, particularly during the early stages, this research has employed a technology-level angle of view to analyse the convergence process. Examination of patent data, as sources of technical knowledge, supports the identification of distinct technical domains integration as well as convergence progress during early stages. To begin the analysis of convergence development patterns from the technology-level outset, the first research question tackles the significance of classifying technical knowledge integration and understanding the possibility of convergence initiation after unprecedented technical knowledge combinations. Using International Patent Classification (IPC) subclass codes to classify the technical domains, the occurrence of the technical knowledge combinations was followed to measure the progress in convergence. Since this thesis covers the convergence from the starting point at the technology level, the second research question seeks to address the significance of the starting point of convergence. Consequently, the third research question concentrates on patent-level indicators triggering convergence. The descriptive statistics of re-occurrence of knowledge combinations in subsequent years revealed that only a relatively few new combinations are developed in future inventions. This finding shows that the emergence of new combinations between distinct technical domains does not guarantee the re-occurrence of the respective combinations in future years. Thus, new combinations developing rapidly in subsequent years are substantial to be identified immediately. Concerning the second research question, the results of a negative binomial regression model indicated that the patents introducing new combinations, which are referred to as converged patents, can significantly influence the convergence process. The results justified the exploration of converged patents for identifying the influential combinations driving industry convergence processes. In regard to the third research question, the findings of the negative binomial regression also revealed that the influential new combinations advancing convergence need to be practical and useful for future innovations, compatible with various technical domains, and recognisable within converged patents. The Examination of the converged patents showed that the scientific support, level of claims in applications, size of inventor teams, and inventors¿ experience in the invention of converged patents maintain the usefulness and practicality of new combinations. Furthermore, the broad-based technical scope of a converged patent reflects the compatibility of new combinations with various technical domains. However, grant duration and the number of new combinations in converged patents increase the complexity of the patent, and therefore, disrupt the recognising of convergence. This thesis contributes to the convergence, patent, and knowledge combination literature. Pertaining to convergence literature, this research framework covers the dynamics of convergence from the emerging point at the technology level, which assists in anticipation of convergence. In regard to knowledge combination and patent literature, the theoretical contributions are related to the knowledge-level analysis of knowledge combinations, while the literature has focused on technology-level or firm-level study of new knowledge combinations. This thesis also provides insights for R&D experts in the forecasting of new technologies, for managers in acquiring substantial competencies, and for regulators in raising awareness of the development of new standards and regulatory frameworks.