Enhancing Biotechnological Innovation in SMEs: An Improved Decision Tree Method for Assessing Commercial Viability and Technological Advancement

Abstract

This study introduces an enhanced decision tree methodology tailored to evaluate the innovation performance of small and medium-sized enterprises (SMEs) in the biotechnology sector. Recognizing the critical role of innovative processes in driving commercial success and technological advancement in biotech, we propose a decision tree framework that not only assesses profitability and decision-making outcomes but also integrates key biotechnological metrics. By analyzing data from 200 management and staff members within several Chinese biotechnological SMEs, we utilize descriptive statistics, linear regression, and ANOVA tests to evaluate the correlation between service management, innovation performance, and commercial outcomes. Preliminary findings suggest that factors such as technology learning ability, market competitiveness, and customer satisfaction are significantly correlated with enhanced innovation performance. Conversely, traditional metrics like inventory turnover show negative correlations, indicating distinct dynamics in biotech enterprises. The results underscore the potential of specialized decision trees in aiding SMEs to refine strategic decisions and bolster competitiveness in the biotechnology industry. Future research should further validate the improved decision tree algorithm and explore its application across different biotechnological contexts to foster operational excellence and sustainable growth.