Text Mining Enabled CEET@I Methodology for Business Model Analysis with the Case of Shared bicycle
Authors: 
Wei Li, Yanpeng He, Shugang MaAuthors Info & Claims
Pages 290 - 295
Abstract
The problem that “the successful business models fail to be duplicated easily” confuses the academia and industry. The CET@I methodology is the significant theoretical result about the problem. However, the CET@I methodology needs to be improved for it is unable to analyze the case of shared bicycle perfectly. Therefore, we investigate the documents about the shared bicycle through the text mining experiments which are basing on the Nature Language Processing technology. The results show that, the entrepreneurship is an important dimension of the tacit knowledge within business model. We incorporate the dimension of entrepreneurship into CET@I model to develop the CEET@I methodology; and then we demonstrate the validity of the proposed methodology. The current work provides a new perspective for the business model analysis in the context of sharing economy.
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- Text Mining Enabled CEET@I Methodology for Business Model Analysis with the Case of Shared bicycle
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Published In
July 2023
507 pages
ISBN:9798400708022
DOI:10.1145/3616712
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 December 2023
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- Research-article
- Research
- Refereed limited
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- Social Science Foundation of Hebei Province
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ICEME 2023
ICEME 2023: 2023 the 14th International Conference on E-business, Management and Economics
July 21 - 23, 2023
Beijing, China
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