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Erasmus Universiteit Rotterdam, Rotterdam School of Management

Erasmus Universiteit Rotterdam, Rotterdam School of Management

44 Projects, page 1 of 9
  • Funder: Netherlands Organisation for Scientific Research (NWO) Project Code: VI.Veni.241E.060

    The recent worsening of relationships between countries led to a drastic reduction in firms’ foreign investment, which has worsened the economic situation in regions that rely on foreign investment. This project examines when and why firms reduce investments and what policymakers can do to avert these reductions, by examining three dimensions of geopolitical tensions: (1) how tensions create spillovers for third, noninvolved countries, (2) how supranational lobbying (i.e., EU) may mitigate the effect of tensions within an economic union, and (3) whether nationalistic voting in municipalities worsens the effects of tensions. This project supports policymakers and firms in battling deglobalization.

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  • Funder: Netherlands Organisation for Scientific Research (NWO) Project Code: 439.19.001

    Global fresh produce supply chains are important for Dutch logistics and agro-food. Health certificates form a weak spot, however. The project aims to improve handling of health certificates for international phytosanitary and veterinary trade flows by digitalization. Scientific output is a valid instrument for measurement of benefits of electronic certificates.

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  • Funder: Netherlands Organisation for Scientific Research (NWO) Project Code: 407-12-002

    More than other entrepreneurs, creative entrepreneurs face the classical dilemma between the head and the heart. They are compelled to develop their entrepreneurship in order to survive, but at the same time they are afraid that this will be at the cost of their creativity and authenticity. This research project therefore investigates the trade-offs between cultural and business orientations of creative enterprises, and their effects on performance. Performance includes cultural performance (reputation), business performance (profits and growth) and international performance. The research includes an analysis of collaboration projects between creative enterprises and firms from other sectors, and will study the role of innovative capabilities, business models (?next revenue models?) and cultural and business networks of creative enterprises. By doing so, this project adds to our insight in three important performance dimensions of the creative industry: growth (?next value?, ?next growth models?), creative impact on other industries (?next ecosystems?) and international development (?next global markets?). The project consists of three subprojects. The first one focuses on business models of creative enterprises themselves, the second on networks of collaboration, and the third which on innovative capabilities. The project includes case studies and large scale empirical research in which we will compare our results with data collected from other sectors. We collaborate with firms and organizations from both the creative industry and other industrial sectors. The external partners contribute to the research itself and play a crucial role in the dissemination of our common insights to creative enterprises and firms in other sectors.

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  • Funder: Netherlands Organisation for Scientific Research (NWO) Project Code: VI.Vidi.221E.010

    Many people work in multiple teams at the same time. While this means that they get to meet many colleagues across their different teams, it also means that they have little time to build meaningful relationships in each of their teams. This may leave them to feel treated like an outsider rather than being fully included in the workplace. This project will examine how working on multiple teams at the same time affects workplace inclusion of women and men and develop practical solutions to offset potential negative effects that working in multiple teams can have on workplace inclusion of employees.

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  • Funder: Netherlands Organisation for Scientific Research (NWO) Project Code: VI.Veni.221E.073

    Behavioral interventions are a valuable tool for policymakers to tackle persistent societal problems such as climate change. However, researchers have increasingly challenged predominant one-size-fits-all approaches that use the same interventions for everyone. I propose a scalable deep machine learning approach that personalizes behavioral interventions. The approach first predicts the effectiveness of behavioral interventions and then identifies the optimal intervention mix and timing for each individual. Understanding differences in individuals’ susceptibilities to interventions will enable researchers to offer more nuanced guidance to policymakers. Policymakers can use the proposed approach to trigger behavioral change more effectively and increase welfare.

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