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.

中文翻译：

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基于量子力学的知识创新框架

目的

由于知识创新的不连续性和不确定性特征无法用常规管理方法合理解释，以不确定性为出发点、关注复杂系统动态过程的量子力学在管理领域得到了探索性的应用。尽管开创性研究提供了理论新见解，但定量研究供不应求。本文旨在提出一个基于量子力学的定量研究框架，从而从动态系统进化的角度扩展量子力学在知识管理领域的应用。

设计/方法/方法

基于知识系统演化与原子运动的相似性比较，作者构建了知识系统的类原子结构，阐述了知识系统的演化机制，从而建立了量化模型。选择苹果和中兴通讯设备进行实证研究，论证模型对知识创新研究的有用性，探索两家公司独特的知识创新特征。

发现

一是动态知识的过渡力呈倒U型；适度积累动态知识不仅有利于将动态知识转化为静态知识，而且可以平衡知识力量范围的影响与动态知识转化之间的关系。二是现有知识逐渐被新知识所取代，高知识能量水平的知识密度随着带宽变窄而明显增加。第三，投资损失与资源配置、资源利用和投资前动态知识积累量有关。知识损失与知识相容系数呈负相关。

研究限制/影响

作者利用量子力学中的先进方法，合法地揭示了知识创新的产生机制。同时，作者捕捉到异质知识的非线性变换关系，揭示了传统模型无法量化的投资损失和知识损失方式的变化。在此过程中，作者不仅揭示了质量知识变革的原理，而且为制定灵活和有针对性的创新战略提供了实际意义。

原创性/价值

首先，通过提出一个完整的基于量子力学的框架，作者不仅补充了定量研究内容到知识创新文献中，提出了以量子力学的方式进行研究的号召，而且克服了基于知识的系统概念化和困难的问题。测量。其次，作者揭示了知识转化的不确定性变化，衡量了投资和知识的损失，有助于识别企业在知识创新中的缺陷。第三，作者探索了导致知识创新呈现非线性特征的内在机制，并捕捉到了影响知识创新出现的不同变量之间独特的动态关系。