Deriving and applying core competencies for intellectual property-based startup entrepreneurs: a data-driven approach to technology evaluation

Abstract

This study identifies the core competencies of managers of intellectual property (IP)-based startups and proposes an evaluation model to enhance managerial competency criteria during technology assessments. Despite IP-based startups' role in fostering innovation and job creation, current research on these startups primarily emphasizes business feasibility, marketability, and technical viability while overlooking managerial competencies. Technology evaluations often prioritize secondary factors and neglect core managerial skills that are crucial for success. To address this gap, this study employed traditional methods (i.e., the Delphi method and expert workshops), alongside machine learning algorithms to derive the key competencies. This approach enables agile adaptation to the rapidly evolving technological and managerial landscape and moves beyond traditional behavior-focused evaluations. These derived competencies and growth rules are integrated with assessment center techniques, resulting in a novel, systematic, and clear evaluation framework. In this way, this study provides a robust model for accurately assessing the managerial competencies essential for achieving high performance in IP-based startups. The findings offer valuable insights for improving technology evaluation systems that can benefit both current managers and aspiring entrepreneurs.