Machine Learning and Organizational Learning: Approaches, Relationships, and Ways of Mutual Support
- Art der Arbeit:
- Masterarbeit Wirtschaftsinformatik
It is commonly accepted today that organizations need to learn in order to survive in rapidly changing markets. The notion of "learning" is relevant to several areas in the organizational sphere. On one side, learning is considered important on an individual, collective, and even an overall organizational level. The research field of Organizational Learning investigates how knowledge, and often tacit knowledge, is produced, shared, and cultivated in the social system of an organization. Related, the research field of (Organizational) Knowledge Management studies how knowledge can be systematically documented, managed, and improved in an enterprise. Both research fields are predominantly concerned with forms of human knowledge. On the other side, recent decades have also brought forward advances in the area of Machine Learning. This research field is related to Artificial Intelligence (AI) and seeks to develop ways in which software systems can be said to "learn" on the basis of provided input data. Machine Learning research is often aimed at building algorithms that gradually improve formal description or prediction models, utilizing concepts from fields such as statistics and (theoretical) computer science.
Although the fields of Organizational Learning and Machine Learning evidently approach the notion of "learning" from quite distinct vantage points, both fields can nonetheless be said to study the generation and improvement of knowledge. Thus, the question arises how Machine Learning and Organizational Learning are related, how both theoretical outlooks may benefit each other, and how Organizational Learning and Machine Learning can be combined and supported in practice so as to improve overall organizational knowledge development.
This thesis has three goals. First, the student is asked to provide a thorough and critical review of central concepts and approaches from the research fields Organizational Learning and Machine Learning. This should also involve an examination of limitations and common deficiencies in relation to both areas. Second, the student should analyze and structure conceptual relationships between Organizational Learning and Machine Learning, and he or she should outline where practices relevant to both fields intermingle in organizational practice. Lastly, the student should develop actionable ideas about how Organizational Learning and Machine Learning can be combined and supported in practice. Depending on the interests of the student, it is also possible to develop a new tool to support the combination of Organizational Learning and Machine Learning. This tool might take the form of a supporting framework or a (modeling) method.
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