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PurposeThis paper aims to explore coopetition within the entrepreneurial ecosystem and answer the following two fundamental questions: How does coopetition affect the entrepreneurial learning and performance of startups? and What learning strategies should startups adopt to promote their growth in the coopetition activities?Design/methodology/approachUsing the structural equation model and instrumental variable, this study used a sample of 371 startups to test the hypotheses. Data comes from startups in Jiangsu, Shanghai and Zhejiang, China.FindingsThis study finds that the coopetition-performance relationship of startups is marginally negative. This study also finds that exploitative learning and exploratory learning positively mediate this relationship. Ecosystem's social capital can enhance the coopetition-exploration relationship, but the coopetition-exploitation relationship is not affected.Originality/valueMany studies propose that the coopetition-performance relationship is ambiguous, which makes it meaningful to explore startups individually. Based on the resource-based view and the knowledge-based view, this study deepen the works of Bouncken and Fredrich (2016c), that is, how startups can learn and grow through coopetition activities. This study proposes that coopetition is one of the foundations of the ecosystem and explore the coopetition-performance relationship in this special context. Thus, the present paper adds to the budding literature on the effects of the entrepreneurial ecosystem and to the literature on coopetition.

AbstractEntrepreneurship scholars consider the benefits of entrepreneurial activities to include regional innovation, employment, and growth. In recent years, the entrepreneurship literature has begun to focus on entrepreneurial contexts, especially the entrepreneurial ecosystem, which may exist at different levels, such as cities and countries. This study analyzes whether the entrepreneurial ecosystem promotes regional development and if so, how. Using fuzzy‐set qualitative comparative analysis, we employ the most recent available full data, for 2018, to conduct research on 265 urban entrepreneurial ecosystems in China and reveal the conditions that can stimulate regional entrepreneurship and development. Overall, our results indicate that the entrepreneurial ecosystem can well explain employment and innovation in the region, while there is only one configuration to promote economic growth, which emphasizes the impacts of formal institutions and finance. Our research explains how the elements within the entrepreneurial ecosystem interact to promote regional short‐term and long‐term development. The results have rich implications for policies on entrepreneurship and urban planning as well as practice.

We describe a new approach to multiple class pattern
classification problems with noise and high dimensional feature
space. The approach uses a random matrix X which has a specified
distribution with mean M and covariance matrix rij(Σs+Σε) between any two columns of X.
When Σε is known, the maximum likelihood estimators
of the expectation M, correlation Γ, and covariance
Σs can be obtained. The patterns with high dimensional
features and noise are then classified by a modified discriminant
function according to the maximum likelihood estimation results.
This new method is compared with a multilayer feed forward neural
network approach on nine digit recognition tasks of increasing
difficulty. Both methods achieved good results for those
classification tasks, but the new approach was more effective and
more efficient than the neural network method for difficult
problems.

BACKGROUND: The grass-roots civil servants are the final implementers and executors of a series of government policies and the fundamental force for social stability and harmonious development. However, the mental health problems of grass-roots civil servants have not got full attention. This study aimed to assess the impact of resilience on anxiety and depression among grass-roots civil servants in China. METHOD: From Oct to Dec 2019, 302 civil servants completed a series of questionnaires. The Civil Servants Stress Scale (CSSS) was used to assess the stress of civil servants. The Self-rating Depression Scale (SDS) and the Self-rating Anxiety Scale (SAS) were used to evaluate the depression and anxiety of participants, respectively. The resilience of civil servants evaluates by the Connor-Davidson Resilience Scale (CD-RSCI). We conducted the moderating and mediating analysis on the impact of resilience on depression and anxiety in grass-roots civil servants. RESULTS: There were significant differences in gender, education, position, relationship with coworkers, physical exercise, and monthly income for stress in grass-roots civil servants (P < 0.05). Resilience can negatively regulate the stress of grass-roots civil servants, and an effective mediator and moderator in the relationship between stress and anxiety and depression and the mediating effect ratios of 7.77 and 22.79%. CONCLUSION: Resilience has moderating and mediating effects on the relationship between stress and depression, and anxiety. The negative effects of stress on depression and anxiety of grass-roots civil servants can be buffered by resilience as a dynamic moderator directly and indirectly. These findings contribute to society and government better understand the mental health status of grass-roots civil servants and provide references and guidance for the formulation of corresponding management and prevention measures.