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. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483–485. Barocas, S., Hardt, M., & Narayanan, A. (2021). Fairness in Machine Learning. Retrieved from https
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. This research is carried out using a variety of research methods primarily emphasizing large-scale archival data and quantitative analyses. Entrepreneurial Decision-Making and Ecosystems: This strand focuses
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, and innovation strategies. We also execute large research projects in collaboration with leading organizations on, for instance, horticulture innovation and in the creative industries. Our research is
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of the ERIM Business Processes, Logistics, and Information Systems (LIS), Supply Chain Management (SCM) section doctoral program is to be at the forefront of developments in its domain. We are committed
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in marketing as well as related fields. They deeply care about open science practices (e.g., data sharing and open-source software) and frequently host seminars to encourage knowledge exchange. In
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publish their research in top journals in marketing as well as related fields. They deeply care about open science practices (e.g., data sharing and open-source software) and frequently host seminars
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approaches that integrate both qualitative and quantitative research (e.g., combining big data or machine learning with in-depth fieldwork) can also be pursued. Method selection and mastery are viewed as part
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and implement the research independently and under guidance of the promotor and supervisor. This entails the set-up of the research, the selection of methods and theories, data collection and analysis
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-ups, NGOs and social enterprises to large multinational enterprises, can address these challenges. In other words, we interrogate how organisations impact society and the natural environment, and how
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0.8 fte - 0.8 fte Introduction The Russian invasion of Ukraine has demonstrated that large-scale war in Europe can no longer be considered unthinkable. This has increased the need for a thorough