Version 1
: Received: 9 October 2024 / Approved: 9 October 2024 / Online: 9 October 2024 (13:14:57 CEST)
How to cite:
Zhang, H.; He, Z. Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy. Preprints2024, 2024100687. https://doi.org/10.20944/preprints202410.0687.v1
Zhang, H.; He, Z. Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy. Preprints 2024, 2024100687. https://doi.org/10.20944/preprints202410.0687.v1
Zhang, H.; He, Z. Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy. Preprints2024, 2024100687. https://doi.org/10.20944/preprints202410.0687.v1
APA Style
Zhang, H., & He, Z. (2024). Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy. Preprints. https://doi.org/10.20944/preprints202410.0687.v1
Chicago/Turabian Style
Zhang, H. and Zhongbing He. 2024 "Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy" Preprints. https://doi.org/10.20944/preprints202410.0687.v1
Abstract
This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. By using information entropy, the study creates a model to gauge how research abilities are forming in enterprise clusters. To dive deeper, the China Pingmei Shenma Group was used as a case study. Data came from interviews, surveys, and text analysis. This case—focused on a state-owned enterprise cluster in China’s coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, changes in entropy were tracked, revealing the process of research development driven by both internal and external forces. The study shows that when a system can bring in external resources while reducing internal disorder, its research capability is greatly improved. In real-world terms, the research proves that measuring entropy is a valuable way to evaluate and enhance research abilities within enterprise clusters, offering a clear method to manage innovation systems in tech-driven industries.
Keywords
systems theory; information entropy; corporate basic research; cluster enterprises; related diversification
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
