初始条件与新创科技企业生存

Founding conditions of high-tech startups and their survival

新创科技企业的生存与成长是创业研究的重要内容。尽管学界对科技企业初始条件与其生存的关系有着重要认识,但对这一作用的过程却知之甚少。为此,本文从企业资源观和组织生态学的视角出发,以中关村2006年创办的科技企业为例,运用Complementary log-log模型,重点研究了企业创立时的资源与条件(研发投入、内部金融资本、企业规模和产业集中度)对其生存风险的影响及其时效性。研究结果表明,创立年较高的内部金融资本和产业集中度将增加企业的生存风险,而较高的研发投入和企业规模将有利于降低科技企业的生存风险;同时,初始资源与条件对科技企业生存的影响呈现出不同的时效性。这些经验证据将有助于揭示中国科技型新创企业的生存过程。

The survival and growth of newly established high-tech startups are crucial and hot topics in innovation and entrepreneurship research academia;it is also an unignorable practical issue of high-tech industrial development in current China. Recently, scholars have concentrated on how founding conditions shape high-tech startups′ survival, and an increasing number of literatures argues that founding conditions would exert significant influence on high-tech startups′ exit rates. However, limitations in given studies hinder our further understandings. First, relatively scare knowledge concerns about how the survival stimulating effects of founding conditions change over time. Second, Chinese empirical evidence is nearly absent in this research line. To fill these gaps, building on resource-based view of the firm(RBV) and organizational ecology perspectives, this paper particularly focuses on when and how high-tech startups′ founding resources and market conditions i.e. R&D, internal financial resource, firm size, and industrial concentration at the founding year, affect their survival risk as time passes. To this end, this paper first proposed that the current resources and market conditions would prolong high-tech startups′ survival duration. Then, it further proposed that these founding resources and market conditions have positive influence on startups′ survival, but such effects decline over time differently.This paper uses a dataset of high-tech startups from Z-Park in Beijing to test the proposed theoretical hypotheses. These startups are established in 2006 and their fundamental operation information have been loyally recorded till 2011. In this dataset, it can identify which year a startup exits but the exact time point that a startup exits is unavailable. That is, the records of firms′ exit event are typical "firm-year" observations. To estimate such data, complementary log-log model(cloglog) is more robust and efficient than that of Cox model which is often used in firm survival studies. After cloglog modeling, this paper conducts robustness checks by using alternative models i.e. logit model and complementary log-log model with random effects considerations. Empirical findings support most of theoretical predictions. Firstly, it shows significant and robust influence of current internal resources and market conditions on high-tech startups′ survival. To be specific, industrial concentration has positive effects on high-tech startups′ survival risk, whereas R&D and firm size are negatively related to high-tech startups′ survival risk. However, internal financial resource shows insignificant survival motivating effects. Moreover, founding resources and market conditions have varied survival motivating influence. Specifically, R&D and firm size at the founding year help to prolong startups′ survival duration, whereas higher internal financial resource and industrial concentration at the founding year are likely to exacerbate high-tech startups′ survival risk. Although these founding resources and conditions matter for high-tech startups′ survival, their affecting durations are varied. That is, the survival motivating effect of founding industrial concentration lasts for a shorter time duration than that of firm size. Particularly, the founding effects of internal financial resources and R&D last for a much longer time than that of industrial concentration and firm size. Empirical findings in this paper have practical and theoretical implications. In practice, it contributes to unpacking the black box of thesurvival process of China′s high-tech startups, which may help entrepreneurs to better develop strategic planning. This study also contributes to innovation and entrepreneurship literature in serval ways. First, it provides a possible analytic lens by combining RBV and organization ecology perspective to explore when and to what extent founding conditions determine high-tech startups′ survival. Second, this study examines how the founding effects of internal resources and market conditions change over time, which provides fresh evidence to deepen the understanding of the influence of organizational imprinting on high-tech startups′ survival chance.