Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309267 
Year of Publication: 
2022
Citation: 
[Journal:] The Journal of Technology Transfer [ISSN:] 1573-7047 [Volume:] 48 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2022 [Pages:] 1327-1358
Publisher: 
Springer US, New York, NY
Abstract: 
It is of key importance to identify the degree of novelty and probability of incorporation of business ideas in an early stage, so that targeted support of these different types of entrepreneurship is possible. Selection of business ideas for investments and support programs rely on quantitative and qualitative metrics. The qualitative assessment, however, is biased by subjective impressions and experiences of the decision-maker. Therefore, this paper examines the narrative of business idea descriptions to improve the identification of the degree of novelty and to enhance the estimation of the incorporation probability by advancing the objectivity of qualitative metrics. The paper aims to answer two questions: (1) Are there differences in topic prevalences in novel and non-novel business ideas?, and (2) Does the composition of topics related to a business idea influence its incorporation probability? Structural topic modelling and classification tree analysis are applied on business idea descriptions from a competition in Bremen, Germany, from 2003 until 2019. The results show that business idea descriptions are a rich source of information to identify novel and non-novel business ideas with higher incorporation prospects.
Subjects: 
Business idea
Machine learning
Incorporation
JEL: 
O30
L26
O38
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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