Main research projects

More details on my Google Scholar page

Working Papers

Office at Offsite: How Temporary Colocation Shapes Communication in Fully-remote Organizations (with Prithwiraj Choudhury , Sujin Jang  and Victoria Sevcenko)
(Awarded the EGOS Best Paper Award 2023) 

Abstract:
This paper investigates the role of temporary colocation events and demographic diversity in shaping online communication in a fully remote organization. Motivated by the rise in remote work and the resulting increase in diversity within modern organizations, we examine the effectiveness of temporary colocation events, such as company retreats, as a tool to stimulate online interaction across demographic divides. Leveraging proprietary Slack communications data as well as data from an all-company retreat (including who shared taxi rides with whom) from a fully remote organization, we hypothesize and find that while these retreats enhance subsequent online communication, the benefits are less pronounced for pairs of demographically dissimilar employees. However, even brief periods of constrained temporary colocation with a limited number of interaction partners, such as sharing a taxi ride, can help overcome these barriers, increasing online interactions even for demographically dissimilar pairs of employees. This research contributes to the literature on remote work by highlighting the nuanced role of temporary colocation events in fostering communication. It also contributes to the literature on microgeography by highlighting important design features for the microgeography of temporary colocation events that help to maximize their benefits and foster a more inclusive communication culture in remote work environments.

Machine Learning in Healthcare: Mirage or Miracle for Breaking the Costs Dead-lock?(with Dominique Foray)

Abstract:
The ageing population in all developed economies and the limited productivity characterizing the healthcare sector are leading to alarmingly increasing costs. The current rapid advances in machine learn-ing (ML), a subfield of artificial intelligence (AI), offer new automation and prediction capabilities that could, if properly integrated, help address the healthcare costs deadlock. Are ML-driven solutions the ap-propriate ingredient to produce this necessary transformation, or are they condemned to face the same destiny as previous attempts to remodel healthcare delivery? This paper aims at bringing first elements to answer this question by providing both qualitative and quantitative evidence on the development of ML in healthcare and discussing the organizational and institutional conditions for the ML potential to be real-ized. Building on a novel search methodology for publications and patents in ML and on hospital surveys, our results reveal two major observations. On the one hand, while the publication rate in the field has tripled in the last decade, the level of patenting in ML applied to healthcare has so far been relatively low. This result has several potential explanations, such as the early stage of the technology, its rapid growth, and the emergence of new business models based on data accumulation and appropriation rather than patenting. On the other hand, the bulk of firms’ publications are produced by IT firms rather than by com-panies in healthcare. This last observation seems to be driven by the disruptiveness of the new ML tech-nology allowing the entry of new actors in healthcare. The technology producers benefit from their mas-tery of ML and the lack of investment and capabilities among health experts.

Pro-environmental behavior and morality: An economic model with heterogeneous preferences (with Boris Thurm)

Abstract:
Some individuals voluntarily engage in costly pro-environmental actions although their efforts have limited direct benefits. This paper proposes a novel economic model with heterogeneous agents explaining why. Each agent has a homo moralis type of preference, which combines selfishness and morality. Morality is modeled here as the payoff an agent receives if all other agents act like her. Our model builds on extant literature showing that homo moralis preferences have an evolutionary advantage to better evaluate the behavioral motives of agents. Shedding light on how people respond not only to economic but also moral incentives, we contribute to the ongoing policy debate on the design of efficient environmental policies.

Does It Pay to Do Novel Science? The Selectivity Patterns in Science Funding (SPP, 2021) (with Michele Pezzoni and Fabiana Visentin)

Abstract:
Public funding agencies aim to fund novel breakthrough research to promote the radical scientific discoveries of tomorrow. Identifying the profiles of scientists being financed to pursue their research is therefore crucial. This paper shows that the funding process is not always awarding the most novel scientists. Exploiting rich data on all applications to a leading Swiss research funding program, we find that novel scientists have a higher probability of applying for funds than non-novel scientists, but they get on average lower ratings by grant evaluators and have fewer chances of being funded. We discuss the implications for the allocation of scientific research spending.

COVID-19: Insights from Innovation Economists (SPP, 2020) (with George Abi Younes, Omar Ballester, Gabriele Cristelli, Gaétan de Rassenfosse, Dominique Foray, Patrick Gaulé, Gabriele Pellegrino, Matthias van den Heuvel, Elizabeth Webster and Ling Zhou)

Abstract:
This article provides the take of innovation economists on the COVID-19 pandemic. It is addressed to the general public and focuses on questions related to the Science, Technology, and Innovation (STI) ecosystem. It does not present new research findings. Instead, it provides a reading of current real-world developments using economic reasoning and relying on existing economic research. The first part of the report explains the root causes for a general underinvestment in Research and Development (R&D), with a particular focus on vaccines. The second part discusses several aspects related to current STI policy reactions. The third part of the report assesses some potential long-term impacts of the COVID-19 pandemic. The last part of the report offers some concluding thoughts. The STI policy response cannot be limited to the urgent need for ‘technological fixes.’ A second line of response involves the production of new knowledge to prevent outbreaks (ex-ante) or mitigate their effects (ex-post).

The Important Thing is not to Win, it is to Take Part: What if Scientists Benefit from Participating in Research Grant Competitions? (Research Policy, 2019) (with Michele Pezzoni and Fabiana Visentin)

Abstract:
“The important thing is not to win, it is to take part,” this famous saying by Pierre de Coubertin asserts that the value athletes draw from Olympic games lies in their participation in the event and not in the gold they collect during it. We find similar evidence for scientists involved in grant competitions. Relying on unique data from a Swiss funding program, we find that scientists taking part in a research grant competition boost their number of publications and average impact factor while extending their knowledge base and their collaboration network regardless of the result of the competition. Receiving the funds increases the probability of co-authoring with co-applicants but has no additional impact on the individual productivity.

At the Origins of Learning: Absorbing Knowledge Flows from Within the Team (JEBO, 2017) (with Michele Pezzoni and Fabiana Visentin)

Abstract:
Empirical studies document a positive effect of collaboration on team productivity. However, little has been done to assess how knowledge flows among team members. Our study addresses this issue by exploring unique rich data on a Swiss funding program promoting research team collaboration. We find that being involved in an established collaboration and team size foster the probability of an individual learning from the other team members. We also find that team members with limited experience are more likely to learn from experienced peers. Moreover, there is an inverted U-shaped effect of cognitive distance on the probability of learning from other team members.