UM Data Science Research Seminar
The UM Data Science Research Seminar Series are monthly sessions organised by the Institute of Data Science, on behalf of the UM Data Science Community, in collaboration with different departments across UM with the aim to bring together data scientists from Maastricht University to discuss breakthroughs and research topics related to Data Science.
This session is organised in collaboration with UNU-MERIT.
Time: 12:00 - 12:30
Title: How fast is this novel technology going to be a hit? Antecedents predicting follow-on inventions.
Speaker: Fabiana Visentin
Abstract: Despite the high interest of scholars in identifying inventions that have a big technological impact, little attention has been devoted to investigating how (fast) novel technologies embodied in these inventions are re-used in follow-on inventions. We overcome this limitation by empirically identifying novel technologies and mapping their re-use trajectories. We identify novel technologies as those that originate from new combinations of existing technological functionalities and trace these new combinations in follow-on inventions. We map each trajectory as S-shaped, defining its take-off time and full technological impact. Using patent data, we identify the trajectories of 10,782 novel technologies.
In searching for the antecedent characteristics of the novel technologies shaping their trajectories, we find that more complex novel technologies combining for the first time technological functionalities with a stronger science-based nature generate trajectories with a higher technological impact, but a somewhat longer take-off time. In contrast, combining for the first time technological functionalities that are similar to each other and familiar to the inventors’ community have a shorter take-off time but a lower technological impact.
Time: 12:30 - 13:00
Title: The role of early-career university prestige stratification on the future academic performance of scholars.
Speaker: Mario Gonzalez Sauri
Abstract: Prestige and mobility are important aspects of academic life that play a critical role during early-career. After PhD graduation, scholars have to compete for positions in the labour market. Unfortunately, many of them have few research products such that their inherent ability and skills remain mostly unobserved for hiring committees. Institutional prestige in this context is a key mechanism that signals the quality of candidates, and many studies have shown that a "good" affiliation can confer many opportunities for future career development. We know little, however, about how changes of scholar's institutional prestige during early-career relate to future academic performance.
In this paper, we use an algorithm to rank universities based on hiring networks in Mexico. We distinguish three groups of scholars that move Up, Down or Stay in the prestige hierarchy between PhD graduation and first job. After controlling for individual characteristics by matching scholars with equal training or the same first job institution, we find that scholars hired by their existing faculty sustain higher performance over their career in comparison to other groups. Interestingly, we find that scholars that move up the hierarchy exhibit, on average, lower academic performance than the other groups. We argue that the negative relation between upward ranking mobility and performance is related to the difficulties in changing research teams at an early-career stage and to the so-called "big-fish-small-pond" effect. We observe a high stratification of universities by prestige and a negative association between mobility and performance that can hinder the flows of knowledge throughout the science system.