Factors Influencing the Allocation of Regional Sci-Tech Financial Resources Based on the Multiple Regression Model

Chengcheng Zhang,Sang-Bing Tsai

doi:10.1155/2021/6688549

Abstract

This article uses a multiple regression model to evaluate the extent to which financial resources in various regions can be put into the most appropriate direction through the financial system or financial market. This paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies. Based on the DEA model and Malmquist index model in the data envelopment analysis method, this paper uses deap software to calculate the efficiency of agricultural science and technology resource allocation in Gansu Province and the efficiency of agricultural science and technology operation in many provinces in the western region. This paper divides the allocation of scientific and technological innovation resources into the ability to allocate scientific and technological innovation resources and the allocation efficiency of scientific and technological innovation resources. By constructing a performance evaluation system for the allocation of scientific and technological innovation resources, it is found that there are obvious differences in the ability and efficiency of scientific and technological innovation resource allocation in various regions. This paper proposes the selected efficiency evaluation method, the multiple regression model, which uses the data from 2012 to 2018 to evaluate the efficiency of the allocation of scientific and technological financial resources in my country, calculates the efficiency value of each region, and conducts a comparative analysis of provinces and regions. Research shows that, through multiple regression model analysis, the total technical efficiency of technology finance in Northeast China rose from 0.759 fluctuations in 2012 to 0.922 in 2018, gradually reducing the difference with the eastern and western regions. The overall promotion plays a significant role. The innovation of this article lies in the use of multiple regression models to analyze the factors affecting the allocation of regional scientific and technological financial resources, aiming to improve the efficiency of financial resource allocation.

Highlights

Academic Editor: Sang-Bing Tsai is article uses a multiple regression model to evaluate the extent to which financial resources in various regions can be put into the most appropriate direction through the financial system or financial market. is paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies
Through multiple regression model analysis, the total technical efficiency of technology finance in Northeast China rose from 0.759 fluctuations in 2012 to 0.922 in 2018, gradually reducing the difference with the eastern and western regions. e overall promotion plays a significant role. e innovation of this article lies in the use of multiple regression models to analyze the factors affecting the allocation of regional scientific and technological financial resources, aiming to improve the efficiency of financial resource allocation
As a developing country, how to improve the efficiency of scientific and technological financial output is the primary issue considered by governments at all levels. erefore, this article explores the impact of various factors on the efficiency of science and technology finance from the perspectives of three technological finance entities, high-tech enterprises, governments, and venture capital companies, and studies how to use the least investment in science and technology to obtain the largest transformation of scientific and technological achievements, which is important for further improving China’s science and technology. e allocation of financial resources has great practical significance

Summary

Introduction

Academic Editor: Sang-Bing Tsai is article uses a multiple regression model to evaluate the extent to which financial resources in various regions can be put into the most appropriate direction through the financial system or financial market. is paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies. Is paper uses the Tobit model to conduct empirical analysis on the input and output data of technology finance in multiple provinces and cities and explores the impact of various factors on technology finance efficiency from the perspective of three technology finance entities: high-tech enterprises, governments, and venture capital companies. Is paper proposes the selected efficiency evaluation method, the multiple regression model, which uses the data from 2012 to 2018 to evaluate the efficiency of the allocation of scientific and technological financial resources in my country, calculates the efficiency value of each region, and conducts a comparative analysis of provinces and regions. E innovation of this article lies in the use of multiple regression models to analyze the factors affecting the allocation of regional scientific and technological financial resources, aiming to improve the efficiency of financial resource allocation. Cyril built a multilevel decision-making model on the basis of the problem of R&D resource allocation for hierarchical organization in 1981. e essence is to provide a theoretical basis, so that enterprises can better analyze the strategy of scientific and technological resource allocation [3]

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