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Paper Number
1627
Paper Type
Completed
Description
In recent years, higher education institutions (HEI) have expanded their involvement in diverse transfer activities (TA), extending beyond traditional teaching and research roles. These TA are often heterogeneous and informal, which makes measuring their full scope and effects challenging. In this article, we propose a new and straightforward to implement approach for mastering this task. In a first step, we theoretically derive three different dimensions of transfer, namely the transfer of knowledge, technology and personnel. For each of these categories, we develop an artificial intelligence (AI) optimized keyword list. Finally, we use these lists and apply web mining techniques and natural language processing (NLP) to measure TA from German HEI. To this end, we analyze a total of 299,229 texts from 376 German HEI websites. Our study shows that our proposed approach represents an effective and valuable tool for measuring TA from HEI and provides a foundation for further research.
Recommended Citation
Schmitt, Michelle; Schröder, Christian; Beck, Günter W.; and Werner, Arndt, "Investigating German Higher Education Institutions' Transfer Activities: New Measurements Based on Web Mining" (2023). ICIS 2023 Proceedings. 18.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/18
Investigating German Higher Education Institutions' Transfer Activities: New Measurements Based on Web Mining
In recent years, higher education institutions (HEI) have expanded their involvement in diverse transfer activities (TA), extending beyond traditional teaching and research roles. These TA are often heterogeneous and informal, which makes measuring their full scope and effects challenging. In this article, we propose a new and straightforward to implement approach for mastering this task. In a first step, we theoretically derive three different dimensions of transfer, namely the transfer of knowledge, technology and personnel. For each of these categories, we develop an artificial intelligence (AI) optimized keyword list. Finally, we use these lists and apply web mining techniques and natural language processing (NLP) to measure TA from German HEI. To this end, we analyze a total of 299,229 texts from 376 German HEI websites. Our study shows that our proposed approach represents an effective and valuable tool for measuring TA from HEI and provides a foundation for further research.
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