双螺旋模型:知识吸收能力和创新效率的动态演变
Liangyou Cheng1, Yong Qiu2, Luwei Wang1
1School of Digital Economy and Management, Sichuan Technology and Business University, Meishan, China.
PloS one
|November 20, 2025
概括
本研究介绍了吸收能力和创新效率的"双螺旋"模型,揭示了它们的协同增长. 调查结果显示,吸收能力对创新产生积极影响,受政策和知识溢出影响的影响.
科学领域:
- 经济学 经济学 经济学
- 创新研究 研究 创新研究
- 技术管理 技术管理
背景情况:
- 传统模型往往将吸收能力和创新效率视为线性.
- 了解它们的动态,协同关系对于经济增长和技术进步至关重要.
研究的目的:
- 为吸收能力和创新效率提出"双螺旋"动态进化模型.
- 分析政策环境和知识溢出对这种关系的影响.
- 研究吸收能力在不同经济环境和时间段的影响的异质性.
主要方法:
- 利用来自29个国家的 (1960-2023) 面板数据.
- 采用固定效应模型和工具变量方法.
- 构建组合指标并执行集成回归与稳定性检查.
主要成果:
- 发现吸收能力对创新效率的微弱增强凸起的积极效应.
- 政策环境和知识溢出影响缓和了这种关系,过度吸收能力可能会抑制低溢出环境中的效率.
- 在经济发展的早期阶段和2000年以前,吸收能力的作用更为显著.
结论:
- 该研究通过引入一个动态的,非线性模型来推进创新效率的理论框架.
- 调查结果为制定有针对性的创新政策和企业战略提供了实际见解.
- 这是一个很棒的节目,这是一个很棒的节目.
相关概念视频
Gene Duplication and Divergence
7.8K
The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
7.8K
Induced-fit Model
88.6K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
88.6K
Theories of Dissolution: Diffusion Layer Model
1.6K
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
1.6K
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model
745
Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
745
Gene Evolution - Fast or Slow?
7.9K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
In contrast, regions which code...
7.9K
Gene Evolution - Fast or Slow?
3.4K
3.4K


