The literature dealing with the intersection between venture capital and academic spin-off is characterized by a huge number of scientific studies. In our opinion, there is the need to provide an overview of the main themes investigated. This could serve to highlight both outdated and emerging themes. Through a systematic review of the literature on the topic, we aim at identifying reasonable and significant clusters of research papers and articles in objectively justifiable patterns and sets that make possible the understanding of thematic areas evolution, that can be useful for the definition of future research avenues. To reach our objective, we will apply a structured text mining process to extract relevant knowledge from their abstract. More in details, a method to apply text categorization to shape available research abstracts into logical categories will be implemented. Finally, we will characterize each cluster of papers, corresponding to a different thematic area, discussing the evolution of trends over time and possible future research direction.
VENTURE CAPITAL FOR THE ENTREPRENEURIAL UNIVERSITY: A TEXT MINING REVIEW OF THE LITERATURE / Galati, F; Petroni, A; Carmignani, D.; Filippelli, Serena; Garziera, R.. - (2018), pp. 3398-3402. [10.21125/iceri.2018]
VENTURE CAPITAL FOR THE ENTREPRENEURIAL UNIVERSITY: A TEXT MINING REVIEW OF THE LITERATURE
Galati F;Petroni A;FILIPPELLI, SERENA;Garziera R.
2018-01-01
Abstract
The literature dealing with the intersection between venture capital and academic spin-off is characterized by a huge number of scientific studies. In our opinion, there is the need to provide an overview of the main themes investigated. This could serve to highlight both outdated and emerging themes. Through a systematic review of the literature on the topic, we aim at identifying reasonable and significant clusters of research papers and articles in objectively justifiable patterns and sets that make possible the understanding of thematic areas evolution, that can be useful for the definition of future research avenues. To reach our objective, we will apply a structured text mining process to extract relevant knowledge from their abstract. More in details, a method to apply text categorization to shape available research abstracts into logical categories will be implemented. Finally, we will characterize each cluster of papers, corresponding to a different thematic area, discussing the evolution of trends over time and possible future research direction.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.