CONF Inada_ACADEMYOFMANAGEMENTPROCEEDINGS2025._2025/IDIAP The Greatest Challenge For Startups: Computational Text Analysis on Swiss Ventures Inada, Takahiro Villatoro-Tello, Esaú Park, Jung Pulcrano, Jim Leleux, Benoit F. BERTopic Data Mining Text Analysis topic detection Academy of Management Proceedings 2025. 2025 https://journals.aom.org/doi/abs/10.5465/AMPROC.2025.17544poster URL This study showcases the application of a computational text analytics approach to explore the distinct challenges faced by entrepreneurs across various industries. By analyzing data from 1,351 startup competition applications in Switzerland, the research provides a repeatable and scalable method for understanding sector-specific entrepreneurial obstacles. The analysis reveals sectoral differences, highlighting distinct challenges in areas such as platform, energy/cleantech, artificial intelligence, and medical industries. Sales and marketing challenges are particularly pronounced in the medical sector, which also underscores the critical role of pre-entry experience in understanding customer needs. These challenges differ notably from those in sectors with similar regulatory and product life cycle demands. The study offers actionable insights for business managers and policymakers, advocating for tailored strategies to address the unique needs of entrepreneurs in different industries.