当前复杂严峻的内外部环境给企业发展带来前所未有的冲击，如何提升企业创新韧性成为亟待解决的现实问题。本文明确了企业创新韧性的内涵和特征及度量方法，讨论了影响企业创新韧性的内外部因素，从提供冗余资源、缓解融资约束、优化资源配置几个角度分析了影响企业创新韧性的理论机制。为进一步验证，选取风险投资这一影响企业创新韧性的重要变量进行实证检验。以中国创业板2009—2020年上市公司季度数据为样本，合并了企业的专利数据、风险投资、风险投资机构相关数据。研究发现风险投资支持显著提升了企业创新韧性，这种效应因不同企业特征存在异质性。此外风险投资的类别、性质、属地对这种效应发挥了显著的调节作用。基于研究结果提出相关政策建议，为企业在动荡环境中生存和发展提供了有益的参考。

The current complex and severe internal and external environment brings unprecedented impact to enterprise development, and how to improve enterprise innovation resilience has become a realistic problem to be solved. This paper clarifies the connotation and characteristics of enterprise innovation resilience and its measurement method, discusses the factors affecting enterprise innovation resilience, and analyzes the theoretical mechanisms affecting enterprise innovation resilience from several perspectives of providing redundant resources, alleviating financing constraints, and optimizing resource allocation. For further validation, venture capital, an important variable influencing firm innovation resilience, is selected for empirical analysis. Based on the quarterly data of listed companies in GEM from 2009 to 2020, this paper combines the patent data, venture capital data and venture capital organization data. It is found that venture capital support significantly enhances firms' innovation resilience, and this effect is heterogeneous by firm characteristics. In addition, the type, nature, and location of venture capital play a significant moderating role on this effect. Based on the research results, relevant policy recommendations are proposed, which provide useful references for firms to survive and grow in turbulent times.

This study theorizes about the behavioral and knowledge creation implications of betting on the losing technology in a competing technology situation and focuses on three main outcomes. First, in a situation with competing technological options, firms that invest initially in the losing technology will be less successful subsequently in building new knowledge in the winning technology because their experience with failure will lead them to update their expectations of the industry and choose to pursue less risky alternatives. Second, two classic risk-reducing strategies—investing in both technologies or entering after uncertainty is resolved—will not be completely effective. Firms investing in both technologies are likely to suffer the incentive and coordination-driven innovation penalties of generalists, while late entrants will suffer learning disadvantages. Third, the possession of key and relevant complementary assets—upstream and downstream—will positively moderate the observed inertial effect on firms that backed the failed technology and generalists that backed both technologies, as these complementary assets will increase incentives to adapt to the winning technology. I find empirical support for my hypotheses using a novel data set on the evolution of the global flat panel display industry from 1964 to 2003 to investigate the technological competition between plasma and liquid crystal displays. Results show that firms initially pursuing plasma generated less subsequent knowledge in liquid crystal displays, and that firms betting on both technologies were also slow to build knowledge in liquid crystal displays. Meanwhile, firms with upstream and downstream complementary assets were able to moderate, but not overcome, this barrier to knowledge creation. The findings have implications for our study of technological evolution and adaption, for learning from failure and reinforcement learning, and for the relationship between resource partitioning and adaptation.

基于2011—2019年中国内地30个省份面板数据，运用系统GMM和门槛效应模型，检验数字普惠金融对高技术制造业创新韧性的影响以及消费升级和研发投入强度的门槛效应。结果表明，数字普惠金融及其覆盖广度、使用深度和数字化程度均对高技术制造业创新韧性具有显著正向影响；消费升级、研发投入强度分别在数字普惠金融对高技术制造业创新韧性的影响中表现出单一门槛效应和双重门槛效应。进一步的地区异质性研究发现，数字普惠金融仅对中西部地区高技术制造业创新韧性起到提升作用，消费升级在东部和中西部地区分别表现出双重门槛效应与单一门槛效应，研发投入强度在东部和中西部地区均表现出单一门槛效应。研究结果有助于丰富数字普惠金融应用于高技术制造业相关研究，为高技术制造业利用数字普惠金融提升创新韧性提供实践启示。

提出创新韧性的概念，认为创新韧性是创新面临外部冲击时保持系统稳定甚至进化为更高创新水平的能力，并以高技术产业为研究对象，探究创新韧性对高技术产业创新的影响机制，分析创新韧性对创新产出的影响大小、特征、规律。利用高技术产业数据，通过面板数据模型、面板门槛模型实证研究创新韧性与高技术产业创新产出的关系。结果表明，当前我国创新韧性对创新产出存在积极贡献；创新韧性与创新产出呈倒U型关系，创新韧性中等时对创新产出的影响最大；低创新产出下，创新韧性负向影响创新产出；创新韧性的弹性系数随研发经费、研发人员增加先减小后增大，中等研发投入时，创新韧性对创新产出的影响最小。

Even though organizational researchers have acknowledged the role of social and environmental business practices in contributing to organizational resilience, this work remains scarce, possibly because of the difficulties in measuring organizational resilience. In this paper, we aim to partly remedy this issue by measuring two ways in which organizational resilience manifests through organizational outcomes in a generalized environmental disturbance—namely, severity of loss, which captures the stability dimension of resilience, and time to recovery, which captures the flexibility dimension. By isolating these two variables, we can then theorize the types of social and environmental practices that contribute to resilience. Specifically, we argue that strategic social and environmental practices contribute more to organizational resilience than do tactical social and environmental practices. We test our theory by analyzing the responses of 963 U.S.-based firms to the global financial crisis and find evidence that support our hypotheses.

Performance feedback research addresses how firms respond to performance that diverges from their aspirations. Whereas the majority of research in this vein involves financial performance, we apply this framework to product quality performance, arguing that when performance diverges either below or above aspirations, firms will pursue a slower subsequent product introduction rate, either to identify the cause of the underperformance or to incorporate the successful product characteristics in the case of overperformance. We also investigate whether our predictions hold when two boundary conditions are applied. Since product quality aspirations are derived from the “reputations for quality” of the firm and its peers, we argue that the stability of these reputations will amplify the delaying effects of below- and above-aspiration performance. Consistent with research on firm responses to financial performance, we also predict that greater sales revenues relative to sales aspirations will attenuate the delaying effects of aspiration-relative performance divergence. Our analysis of 1,332 video games released by 48 publishers from 2006 to 2009 is largely consistent with these predictions.

在心理学和其他社科研究领域, 大量实证文章建立中介效应模型, 以分析自变量对因变量的影响过程和作用机制。检验中介效应最流行的方法是Baron和Kenny的逐步法, 但近年来不断受到批评和质疑, 有人甚至呼吁停止使用其中的依次检验, 改用目前普遍认为比较好的Bootstrap法直接检验系数乘积。本文对相关的议题做了辨析, 并讨论了中介分析中建立因果关系的方法。综合新近的研究成果, 总结出一个中介效应分析流程, 并分别给出显变量和潜变量Mplus程序。最后介绍了中介效应模型的发展。

<p>Mediation models are frequently used in the research of psychology and other social science disciplines. Mediation indicates that the effect of an independent variable on a dependent variable is transmitted through a third variable, which is called mediator. In most applied research, Baron and Kenny&rsquo;s (1986) causal steps approach has been used to test mediating effect. In recent years, however, many methodological researchers questioned the rationality of the causal steps approach, and some of them even attempted to stop its use. Firstly, we clarify the queries on the causal steps approach one by one. Secondly, we propose a new procedure to analyze mediating effects. The new procedure is better than any single method that constitutes the procedure in terms of Type I error rate and power. The proposed procedure can be conducted by using observed variables and/or latent variables. Mplus programs are supplied for the procedure with observed variables and/or latent variables. Finally, this article introduces the development of mediation models, such as mediation model of ordinal variables, multilevel mediation, multiple mediation, moderated mediation, and mediated moderation.</p>

Correlation and correlation‐based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness‐of‐fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation‐based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness‐of‐fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation‐based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation‐based measures should not be used to assess the goodness‐of‐fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.