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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2508.05491 |
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| _version_ | 1866911095990517760 |
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| author | Jafari, Seyed Mohammad Ali Dehkordi, Ali Mobini Chitsaz, Ehsan Yaghoobzadeh, Yadollah |
| author_facet | Jafari, Seyed Mohammad Ali Dehkordi, Ali Mobini Chitsaz, Ehsan Yaghoobzadeh, Yadollah |
| contents | The integration of Artificial Intelligence (AI) into startup evaluation represents a significant technological shift, yet the academic research underpinning this transition remains methodologically fragmented. Existing studies often employ ad-hoc approaches, leading to a body of work with inconsistent definitions of success, atheoretical features, and a lack of rigorous validation. This fragmentation severely limits the comparability, reliability, and practical utility of current predictive models.
To address this critical gap, this paper presents a comprehensive systematic literature review of 57 empirical studies. We deconstruct the current state-of-the-art by systematically mapping the features, algorithms, data sources, and evaluation practices that define the AI-driven startup prediction landscape. Our synthesis reveals a field defined by a central paradox: a strong convergence on a common toolkit -- venture databases and tree-based ensembles -- but a stark divergence in methodological rigor. We identify four foundational weaknesses: a fragmented definition of "success," a divide between theory-informed and data-driven feature engineering, a chasm between common and best-practice model validation, and a nascent approach to data ethics and explainability.
In response to these findings, our primary contribution is the proposal of the Systematic AI-driven Startup Evaluation (SAISE) Framework. This novel, five-stage prescriptive roadmap is designed to guide researchers from ad-hoc prediction toward principled evaluation. By mandating a coherent, end-to-end methodology that emphasizes stage-aware problem definition, theory-informed data synthesis, principled feature engineering, rigorous validation, and risk-aware interpretation, the SAISE framework provides a new standard for conducting more comparable, robust, and practically relevant research in this rapidly maturing domain |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework Jafari, Seyed Mohammad Ali Dehkordi, Ali Mobini Chitsaz, Ehsan Yaghoobzadeh, Yadollah Computational Engineering, Finance, and Science General Economics Economics The integration of Artificial Intelligence (AI) into startup evaluation represents a significant technological shift, yet the academic research underpinning this transition remains methodologically fragmented. Existing studies often employ ad-hoc approaches, leading to a body of work with inconsistent definitions of success, atheoretical features, and a lack of rigorous validation. This fragmentation severely limits the comparability, reliability, and practical utility of current predictive models. To address this critical gap, this paper presents a comprehensive systematic literature review of 57 empirical studies. We deconstruct the current state-of-the-art by systematically mapping the features, algorithms, data sources, and evaluation practices that define the AI-driven startup prediction landscape. Our synthesis reveals a field defined by a central paradox: a strong convergence on a common toolkit -- venture databases and tree-based ensembles -- but a stark divergence in methodological rigor. We identify four foundational weaknesses: a fragmented definition of "success," a divide between theory-informed and data-driven feature engineering, a chasm between common and best-practice model validation, and a nascent approach to data ethics and explainability. In response to these findings, our primary contribution is the proposal of the Systematic AI-driven Startup Evaluation (SAISE) Framework. This novel, five-stage prescriptive roadmap is designed to guide researchers from ad-hoc prediction toward principled evaluation. By mandating a coherent, end-to-end methodology that emphasizes stage-aware problem definition, theory-informed data synthesis, principled feature engineering, rigorous validation, and risk-aware interpretation, the SAISE framework provides a new standard for conducting more comparable, robust, and practically relevant research in this rapidly maturing domain |
| title | Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework |
| topic | Computational Engineering, Finance, and Science General Economics Economics |
| url | https://arxiv.org/abs/2508.05491 |