Developing general purpose technologies: An investigation in the green energy field

Purpose - This research analyzes the emergence of general purpose technologies. Specifically, we examine the relationship between how broadly organizations search across diverse knowledge domains in the invention process (i.e., their search breadth) and the technological generality of resulting inventive outcomes.Design/methodology/approach - We use patent data in order to identify inventions developed in the green energy sector. In particular, we refer to the IPC Green Inventory for patent collection. For the purpose of this study, we pose our attention to the "Alternative Energy Production" and "Energy Conservation" classes. Hence, we collected all the patents successfully filed at the U.S. PTO. from 1971 to 2009 that refer to the two green technological classes above mentioned. Since our aim is to assess the influence of an organization's breadth of search on the level of an invention's technological generality, the single patent is used as the unit of analysis. Our dependent variable assumes values that range from zero to one. In this case, a Tobit regression model is more appropriate for hypothesis testing.Originality/value - We reveal that search breadth is curvilinearly related to an invention's technological generality. Furthermore, we assess if a geographically dispersed inventive team moderates the costs and benefits of searching broadly, showing that it makes organizations more able to benefit from a wider search breadthPractical implications - Managers are advised of the double-edge word of a wide search breadth for the development of GPTs. Thereby, we suggest balancing the search efforts toward a wide range of knowledge domains, in order to avoid the risks to incur in the inability to gain returns from those efforts. Second, establishing dispersed teams may reduce the problems organizations face when they search broadly, being these useful to support the acquisition and integration of a diversified body of knowledge. Third, given the ever increasing need to develop more green general solutions, our findings guide organizations to focus on the conditions that are most critical for creating green GPTs in the energy field.