An integrated methodology for startup assessment in Quality 4.0 using Thurstone’s Law of Comparative Judgment

SEE OUR PUBLICATION: Carrera G, Maisano DA, Mastrogiacomo L, Franceschini F (2025;), "An integrated methodology for startup assessment in Quality 4.0 using Thurstone’s Law of Comparative Judgment (3.42 MB)". International Journal of Innovation Science

Purpose – This paper addresses the challenge of early-stage evaluation of startups within the Quality 4.0 framework, which is characterized by various principles (e.g. continuous improvement, integration of modern technology, data-driven decision-making, etc.). Startups often lack historical data and operate in uncertain markets, making traditional evaluation methods inadequate. To overcome these limitations, this study introduces an integrated assessment methodology based on Quality 4.0 principles and Thurstone’s Law of Comparative Judgment (LCJ). The purpose of this paper is to provide a structured, adaptable framework for early-stage startup evaluation, ensuring a more reliable and systematic assessment of critical success factors.

Design/methodology/approach – This study develops an integrated evaluation methodology for startups by combining expert judgment with data-driven Quality 4.0 techniques. It is inspired by Thurstone’s LCJ and focuses on key evaluation criteria, including team characteristics, patents and market potential, strategic alliances, financial health and funding, and innovation and technology. A real-world case study is conducted to validate the approach, demonstrating its effectiveness in providing structured insights for stakeholders involved in the innovation ecosystem. Only aggregate and anonymized data were analyzed, ensuring confidentiality for both experts and startups.

Findings – The proposed methodology enhances the accuracy and reliability of startup evaluations by combining expert judgment with systematic assessment processes, aligned with Quality 4.0 principles. The real-world case study highlights the method’s practical benefits, particularly in identifying high-potential startups despite limited historical data.

Research limitations/implications – This study primarily focuses on early-stage startups, which may limit its applicability to more mature companies with established track records. In addition, while the methodology integrates expert judgment with systematic assessment tools, the subjectivity of expert evaluations may introduce biases. Future research could refine the approach by incorporating advanced artificial intelligence-driven analytics to enhance objectivity. Furthermore, broader validation across different industries and international contexts would help assess the model’s generalizability and effectiveness in diverse innovation ecosystems.

Originality/value – This paper contributes to Quality 4.0 applications in startup evaluation, by integrating systematic methodologies with expert-driven comparative judgment. It introduces a novel framework inspired by Thurstone’s LCJ, offering a structured yet flexible approach to assessing early-stage startups. Unlike traditional models that struggle with high uncertainty and limited historical data, this methodology exploits a blend of human expertise and technology-driven insights.