Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data

Lv Bo, Zhi Yechao, Guo Qiaoling
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Abstract

In recent years, the technology incubation platform is facing a new ecological environment. The background of big data brought by cloud computing and big data has increased the random disturbance effect on technology incubation platform. The failure of some technology incubation platforms has caused academic controversies. This paper conducts theoretical research and empirical test for these academic controversies, and the empirical conclusions of this paper provide a more comprehensive and reasonable explanation for current academic controversies. In order to describe the failure phenomena of technology incubation platform, this paper innovatively proposes the concept of failure effects and failure coefficients, constructs failure effects model and deduces the failure mechanism formula by using the principle of Stochastic Frontier Analysis (SFA). On the basis of literature research, combined with the background characteristics of the big data, 4 dependent variables and 14 random influence variables were selected, and the Chinese technology incubator platform was taken as an example to empirically analyze failure effects model. The paper finds that the independent variables can be divided into three categories: positive, negative and partially irrelevant. When corresponding to negatively correlate variables or unrelated variables, dependent variables will show the failure phenomenon, that is, partial failure of technology incubation platform.
科技孵化器失效的原因——大数据背景下科技孵化平台失效机理与实证研究
近年来,科技孵化平台面临着新的生态环境。云计算、大数据带来的大数据背景,加大了技术孵化平台的随机扰动效应。一些科技孵化平台的失败引发了学术界的争议。本文对这些学术争议进行了理论研究和实证检验,本文的实证结论为当前的学术争议提供了更为全面合理的解释。为了描述科技孵化平台的失效现象,本文创新性地提出了失效效应和失效系数的概念,运用随机前沿分析(SFA)原理,构建了失效效应模型,推导了失效机理公式。在文献研究的基础上,结合大数据的背景特征,选取4个因变量和14个随机影响变量,以中国科技孵化器平台为例,对失效效应模型进行实证分析。研究发现,自变量可分为积极、消极和部分不相关三类。因变量对应负相关变量或不相关变量时,会出现失效现象,即科技孵化平台部分失效。
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