Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge
Predicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This s...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/1/61 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588352189104128 |
|---|---|
| author | Francesc Font-Cot Pablo Lara-Navarra Claudia Sánchez-Arnau Enrique A. Sánchez-Pérez |
| author_facet | Francesc Font-Cot Pablo Lara-Navarra Claudia Sánchez-Arnau Enrique A. Sánchez-Pérez |
| author_sort | Francesc Font-Cot |
| collection | DOAJ |
| description | Predicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This study addresses these gaps by developing a multivariate AI-driven model for predicting startup survival, leveraging Lipschitz extensions, neural networks, and linear regression. Using a dataset of 20 startups, selected across diverse industries and evaluated on attributes such as team dynamics, market conditions, and financial metrics, the model demonstrated high accuracy and clustering capabilities. Key findings highlight the pivotal role of team dynamics and product differentiation in determining survival probabilities. By integrating conceptual insights with empirical data, the study bridges gaps in existing literature and offers a practical decision-making tool for entrepreneurs, investors, and policymakers. These findings underscore the importance of fostering collaborative, innovative ecosystems to enhance entrepreneurial success and societal well-being. |
| format | Article |
| id | doaj-art-7d32daa0d3914bafa5d3d198039044ad |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-7d32daa0d3914bafa5d3d198039044ad2025-01-24T13:35:19ZengMDPI AGInformation2078-24892025-01-011616110.3390/info16010061Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical KnowledgeFrancesc Font-Cot0Pablo Lara-Navarra1Claudia Sánchez-Arnau2Enrique A. Sánchez-Pérez3Information & Communication Faculty, Open University of Catalonia, Rambla Poblenou, 08018 Barcelona, SpainInformation & Communication Faculty, Open University of Catalonia, Rambla Poblenou, 08018 Barcelona, SpainSchool of Engineering, Universitat Valéncia, Avinguda de l’Universitat, 46100 Burjassot, SpainApplied Mathematics Department, Universitat Politècnica València, Camino de Vera, 46022 Valencia, SpainPredicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This study addresses these gaps by developing a multivariate AI-driven model for predicting startup survival, leveraging Lipschitz extensions, neural networks, and linear regression. Using a dataset of 20 startups, selected across diverse industries and evaluated on attributes such as team dynamics, market conditions, and financial metrics, the model demonstrated high accuracy and clustering capabilities. Key findings highlight the pivotal role of team dynamics and product differentiation in determining survival probabilities. By integrating conceptual insights with empirical data, the study bridges gaps in existing literature and offers a practical decision-making tool for entrepreneurs, investors, and policymakers. These findings underscore the importance of fostering collaborative, innovative ecosystems to enhance entrepreneurial success and societal well-being.https://www.mdpi.com/2078-2489/16/1/61startup survivalartificial intelligenceLipschitz extensionsneural networksentrepreneurial ecosystems |
| spellingShingle | Francesc Font-Cot Pablo Lara-Navarra Claudia Sánchez-Arnau Enrique A. Sánchez-Pérez Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge Information startup survival artificial intelligence Lipschitz extensions neural networks entrepreneurial ecosystems |
| title | Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge |
| title_full | Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge |
| title_fullStr | Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge |
| title_full_unstemmed | Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge |
| title_short | Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge |
| title_sort | startup survival forecasting a multivariate ai approach based on empirical knowledge |
| topic | startup survival artificial intelligence Lipschitz extensions neural networks entrepreneurial ecosystems |
| url | https://www.mdpi.com/2078-2489/16/1/61 |
| work_keys_str_mv | AT francescfontcot startupsurvivalforecastingamultivariateaiapproachbasedonempiricalknowledge AT pablolaranavarra startupsurvivalforecastingamultivariateaiapproachbasedonempiricalknowledge AT claudiasanchezarnau startupsurvivalforecastingamultivariateaiapproachbasedonempiricalknowledge AT enriqueasanchezperez startupsurvivalforecastingamultivariateaiapproachbasedonempiricalknowledge |