Evaluation of Digital Transformation and Upgrading in Emerging Industry Innovation Ecosystems: A Hybrid Model Approach

• Published in: Sustainability Vol. 17; no. 17; p. 7969
• Main Authors: Tian, Li, Sun, Long, Wang, Xueyuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Accuracy; Digital economy; Digital technology; Digital transformation; Economic aspects; Entropy; Environmental economics; Grounded theory; Industry; Information technology; Innovations; Literature reviews; Methods; Objectivity; Sustainable development; Technological innovations; Technology application; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su17177969

In order to scientifically and reasonably evaluate the digital transformation and upgrading level of “emerging industry” innovation ecosystems, this paper firstly uses the grounded theory to extract the factors influencing the digital transformation and upgrading of the emerging industry innovation ecosystems. Secondly, a cloud model is introduced to evaluate the importance of the influencing factors, select the important factors, and construct an evaluation index system. Thirdly, the projection pursuit model based on the quantum genetic algorithm is used to search for the optimal projection direction and determine the weight and comprehensive evaluation value of each index. Finally, the digital transformation and upgrading levels of 506 innovation subjects are divided into a budding level (I), growth level (II), and mature level (III) based on K-means and the SVM—most of which are at a medium–low level. Therefore, countermeasures and suggestions for promoting the digital transformation and upgrading of the emerging industry innovation ecosystems are put forward. This paper provides a systematic and complete method for the evaluation of digital transformation and upgrading of the emerging industry innovation ecosystems. Further, this paper promotes the combination of qualitative and quantitative analysis and realizes the effective integration of the overall logic chain of theoretical demonstrations, method design, and data analysis.