A quantum mechanics-based framework for knowledge-based innovation. Issue 3 (31st July 2021)

• Record Type: Journal Article
• Authors: Zhao, Jianyu; Li, Shengliang; Xi, Xi; Gong, Caiyan
• Is Part Of: Journal of knowledge management. Volume 26:Issue 3(2022)
• Page Start: 642
• Page End: 680
• Publication Date: 2021-07-31
• Subjects: Knowledge-based innovation -- Quantum mechanics -- Transition -- Knowledge-based system -- Evolution; Knowledge management -- Periodicals; 658.4
• DOI: 10.1108/JKM-12-2020-0920
• Languages: English
• ISSNs: 1367-3270

Abstract

Abstract : Purpose: Because the discontinuous and uncertain characteristics of knowledge-based innovation cannot be reasonably interpreted by conventional management approaches, quantum mechanics which begins with uncertainty and concerns with a dynamic process of the complex system, has been exploratorily used in the management field. Although the theoretical new insights are provided by pioneering studies, quantitative research is in short supply. This paper aims to propose a quantum mechanics-based framework for quantitative research, thus extending the application of quantum mechanics in the knowledge management area from a dynamic system evolutionary standpoint. Design/methodology/approach: Based on the similarity comparison between knowledge-based system evolution and atomic motion, the authors construct the atom-like structure of the knowledge-based system and elaborate the evolutionary mechanism of the knowledge-based system, thereby establishing the quantitative model. Apple and Zhongxing Telecom Equipment were selected for an empirical study to demonstrate the usefulness of the models for research on knowledge-based innovation and explore the unique knowledge-based innovation characteristics of the two firms. Findings: First, the transition force of dynamic knowledge shows an inverted U shape; accumulating dynamic knowledge to a moderate degree not only facilitates transforming dynamic knowledge into static knowledge but also balances the relationship between the influence of knowledge force range and dynamic knowledge transformation. Second, existing knowledge is gradually substituted by new knowledge and knowledge density at a high knowledge energy level distinctly increases with a narrower bandwidth. Third, the investment loss is associated with resource configuration, resource utilization and the amount of accumulative dynamic knowledge before investment. Knowledge loss is negatively correlated with the knowledge compatibility coefficient. Research limitations/implications: The authors use the advanced method in quantum mechanics to legitimately unveil the emergence mechanism of knowledge-based innovation. Meanwhile, the authors capture the non-linear transformation relationship of heterogeneous knowledge and expose the change in ways of both investment loss and knowledge loss that cannot be quantified by conventional models. In doing so, the authors not only reveal the principle of qualitative knowledge change but also offer practical implications for developing flexible and targeted innovation strategies. Originality/value: First, by proposing a complete quantum mechanics-based framework, the authors not only supplement the quantitative research contents to knowledge-based innovation literature which proposed calls to conduct research in way of quantum mechanics but also overcome the difficulties of knowledge-based system conceptualization and measurement. Second, the authors reveal the uncertain change of knowledge transformation and measure the loss of investment and knowledge, which contribute to identifying defects of firms in knowledge-based innovation. Third, the authors explore the internal mechanism that led to knowledge-based innovation exhibits non-linear characteristics and capture unique dynamic relationships between different variables which affect the emergence of knowledge-based innovation.