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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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, research on the role of legal sanctions, research on different modes of governance and its intended and unintended consequences, and digitalization and the use of big data. Research in the department is