An analytical evaluation of Digital Technology Systems to Advance Green Startups in the Digital Economy

Integration of artificial intelligence and other smart technologies is increasingly important in startup ecosystems, and the analytical evaluation of these technologies is an essential need for guiding various decision-making processes in the sustainability transition. To help address this research gap, this study presents the results of an analytical evaluation that also effectively integrates multi-criteria decision analysis, is strong enough to quantify performance differences, and provides empirical validation when a predictive model is applied. Therefore, the successful advancement of green startups can be greatly enhanced by an approach that combines TOPSIS ranking and regression analysis about the contribution and interrelationship of digital technology systems. Meanwhile, the TOPSIS method is used to rank the digital technology alternatives so that the outcomes are closer to the ideal performance, identifying the efficiency of the systems and the trade-offs between cost and benefit. The results reveal that blockchain integration level, digital sustainability performance, and innovation capability significantly affect AI technology adoption, while the explanatory strength of the regression model decreases by insignificant cost-related and connectivity factors, respectively. From the multi-criteria evaluation on the three technology alternatives in green startup development, the IoT energy and resource monitoring system significantly affects scalability, energy efficiency, and emission reduction performance. The study discusses implications this analysis has for policymakers and startup developers to make strategic and evidence-based decisions about the diffusion and optimization of digital technology systems, had greater sustainability benefits while cost-intensive technologies lost significant ranking when scalability and deployment time were considered.