Star and Prolific inventors: Empirical analysis of differences in education and career

The study of innovation has spread over the last fifty years in an ascending climax of importance. The process of technological innovation has been studied in depth by numerous academic fellows in order to understand how it is composed and on which factors it relies on. From Schumpeter, the innovation pioneer, to Rogers: academics question themselves about the phenomenon that, etymologically, introduces new systems, new orders, new methods. However the question of who conceived the invention of the century regarding his origins, formation and professional career is a much more recent issue. This thesis aims to investigate the correlation between Star Scientists and their professional careers and their education by means of statistical tools such as mainly multivariate regression analysis. The work is divided into two chronologically different phases: the first phase of information gathering and database creation and the second analysis of data collected through statistical tools such as multivariate regression. This thesis proposes to collect two samples of inventors: the first sample consisting of successful inventors who participated as finalists or won the EPO award (the Star Inventors) and the control sample consisting of the Prolific Inventors, i.e., those who have patented a lot during their career none of their inventions has achieved scientific or commercial success. The peculiarity of this thesis consists primarily in the construction of the database: data about the inventors candidates has been collected for the EPO award from 2010 to 2014 and information about the Prolific Inventors with a deep research through social networks, academic articles, search engines and online encyclopedias. This first phase of data collection turned out to be more smooth for the EPO sample and a little more arduous for the sample containing the prolific inventors: the information on the Star inventors is widely available because of their fame due to their inventions; on the other hand, prolific inventors are mainly traced through the information they share on social networks. Therefore, the timing dedicated to the data collection of the prolific inventors proved to be long and with a success rate of just over 40%: in order to obtain 114 complete subjects, more than 250 subjects were analyzed. Once the first phase of data collection and database creation has been completed, the statistical analysis and regression phase of the information retrieved begins. It is necessary to establish the variables and their role before carrying out a statistical analysis. Therefore the dependent variable is represented by the issue that the subject is a Star scientist or not, the variables of interest, or independent variables, are instead the factors through which the phenomenon is studied: we are interested in determining whether it is possible to distinguish a Star Scientific from a Prolific inventor on the basis of his education and his professional career. Finally there are the control factors that represent the general context in which the study is carried out, usually represented mainly by biographical information: nationality, age, gender, the sector of work, the field of studies. Multivariate regression analysis is used to confirm and deepen the information derived from the statistical analysis.