摘要
针对创新从投入到产出的多阶段性及各阶段价值转化时滞效应的差异性,本文提出了利用时间序列网络相似度与价值转化系统时滞效应关系来计算滞后期的方法。将创新价值链转化分为知识开发、成果转化和产业化三个阶段,在证明了价值转化系统的滞后效应与时间序列向量间的关系的基础上,构建各子系统的时间序列网络,进而采用余弦相似定理计算网络相似度,并求解最优映射关系以确定滞后效应及滞后期,利用中国1998—2016年省域创新价值转化数据,对创新价值链时滞效应进行验证。研究表明:价值转化系统的滞后效应与投入产出向量的时间序列网络结构有关,时间序列网络相似度能够反映价值转化参数向量的映射关系,其最大相似度反映了滞后效应,且对应的时间反映了滞后期;知识开发、成果转化和产业化阶段的平均滞后期分别为3~4年、2~3年和3~4年,专利驱动、论文驱动的价值转化期分别约为8.66年、9.66年;其中11个省份为双路径驱动、9个省份论文驱动创新速度较快、2个省份专利驱动速度较快;同时知识开发阶段与产业化阶段的滞后期存在互补,知识开发较长的地区产业化速度较快。
Technological innovation is an important way to improve economic development and enhance the countries′innovation competitiveness.The transformation efficiency of resource inputs to innovation outputs is a vital factor of national innovation development.It is worth noting that innovation is a value chain transfer process with multi-stages,from resource inputs to innovation outputs.But innovation value does not happen immediately when the organizations invest innovation resources,such as R&D funding,human resources,there is a lag period.This lag period leads to the time-lag effect at multi-stage innovation value chain.Therefore,it is meaningful to accurately evaluate the time-lag effect of value conversion in each stage.Calculating lag period is not only good at understanding the present situation of China′s innovation value transformation,but also providing parameters for the innovation efficiency measurement model.Additionally,the most important is that it can provide valuable decision-making basis for resource allocation of governments and innovation organizations.The time-lag effects express discriminating meanings during the different stages of innovation value transformation.This paper proposes a method to calculate the lag period based on the relationship between the similarity of time-series network and the time-lag effect of value conversion system.In this paper,we divide the transformation in innovation value chain into three stages:knowledge development,technical transformation and industrialization.Firstly,we prove the relationship between the time-lag effect of the innovation value conversion system and the time-series vector.Secondly,we build the inputs and outputs networks separately using the time series data and following the time series visualization approach.Thirdly,the network similarity between inputs and outputs is calculated by using the cosine similarity theorem.Then the optimal mapping solution indicate the lag period.Finally,an empirical study on time-lag effect in China′s provinces innovation value transformation is established to show the feasibility of our approach.We select data from 30 provinces(autonomous regions and municipalities)in China(excluding Hong Kong,Macao,Taiwan,and Tibet because of a lack of data).The time coverage of all research data is 1998 to 2016.In the start of the innovation stage,knowledge development stage,R&D investment and scientific research personnel full-time equivalent are considered the initial inputs.Considering the innovative outputs in the knowledge development stage include patents and research papers,this paper has produced two innovative value transformation chains.One is from inputs to the number of patent grants(K1),then vary to high-tech industry new product sales(T1),finally output the high-tech output value(I)(namely‘patent-driven’).The other one from the same inputs,capitals and human resources,but transform into to research papers in the knowledge generation phase,then produce high-tech industry new product sales(T2)and the proportion of high-tech output(I)gradually(namely‘paper-driven’).This research draws the following conclusions.(1)The time-lag effect of the innovation value conversion system is related to the time-series network structure of the input-output vector.The similarity between inputs and outputs time-series network can reflect the mapping relationship of the value conversion parameter.In addition,the maximum similarity reflects the time-lag effect,and years who have the maximum similarity reveal the lag period.(2)The average time-lag period of knowledge development stage,technical transformation stage and industrialization stage are 3-4 years,2-3 years and 3-4 years respectively.The patent-driven and paper-driven value conversion period is about 8.66 years and 9.66 years separately.The lag period of value transformation with paper-driven chain is more stable,but the collaboration effect between scientific papers and new products sales is fluctuating.Because scientific papers are results which explore different principles of technological innovation,while the new product is the technical program finally realized.They are not always synchronized at the same stage.(3)Comparing the lag period of each province with the national average,we can divide the innovation path into four categories.Dual-path driven means the province′s conversion time in both innovative value chains is faster than the national average.If the province′s patent-driven innovation value conversion has a short time lag,it names a patent-driven innovation.Instead,it is called paper-driven innovation.The remaining provinces have longer conversion time.In 30 provinces,11 provinces′innovation are dual-path driven,9 provinces are paper-driven innovation,and 2 provinces are patent-driven.(4)Further analyzing the stage differences of lag period,this paper finds the lag period in industrialization stage is longest at most provinces.This indicates that it takes a long time to materialize an industrial production project.Meanwhile,the lag period in knowledge development stage and industrialization stage are complementary.The provinces with longer lag period in knowledge development stage have shorter lag period in industrialization stage.Based on designing an approach to measure the lag period and time-lag effects on innovation value transformation,this paper also extends the suggestions on government policies in the following ways.Firstly,the governments can distribute their input resources to the distinct innovation stages according to the lag period of value transformation.Secondly,pay more attention to the regional innovation collaboration.Finally,the complementary phenomenon of the lag period between the knowledge development stage and the industrialization stage provides some recommendations for innovation management.Establishing a collaboration innovation network between universities,research institutions and enterprises can improve value conversion efficiency.This paper contributes to the existing literature in several ways.Theoretically,this paper establishes an analysis framework for time-lag effects of innovation value transformation systems.Then this paper demonstrates the mapping relationship between the parameter vector and the time-series network structure.Meanwhile,a method for calculation the time-lag effect and the lag period is also designed.It provides a direction and feasible way to study the multi-stage time-lag effect from the perspective of time-series network,and enriches the complex network application research system.Practically,we measure the lag period of each stage of the transformation of innovation value in China′s provinces.In addition,the results not only propose the path and direction for improving the innovation efficiency of value conversion,but also can be used to configure the lag period for the input-output analysis model.
作者
宋砚秋
胡军
齐永欣
Song Yanqiu;Hu Jun;Qi Yongxin(School of Management Science and Engineering,Central University of Finance and Economics,Beijing 100081,China)
出处
《科研管理》
CSSCI
CSCD
北大核心
2022年第3期192-200,共9页
Science Research Management
基金
国家自然科学基金:“创新生态系统协同演化机制设计及实证研究”(71872197,2019.01—2022.12)。
关键词
创新价值链
多阶段时滞效应
滞后期
时间序列网络
余弦相似度
innovative value chain
multi-stage time-lag effect
lag period
time series network
cosine similarity
