1. Meta-analyses are a reliable method for a quantitative research synthesis. They are, however, prone to specific biases that can be introduced in the process. Such a bias could exist if primary literature produces similar results if coming from the same authors. Authorship network bias is the non-independence of effect sizes introduced by the overlap of authors of primary studies. If not accounted for, it can severely impact the quality of meta-analysis and the conclusions drawn from it.

2. To account for such non-independence, multilevel models with author clusters as an additional hierarchy level were recently suggested. We propose a new method for the detection of non-independent effect sizes based on authorship networks and for their correction.

3. An analysis of simulated data demonstrates the effectiveness of the here-suggested new method. We further applied our new method to nine exemplary meta-analyses.

4. Our new method for detection and effective correction can be easily integrated in existing meta-analysis workflows, using the functionality already offered by R’s metafor package.

5. Our goal is to enhance the reliability of meta-analyses by highlighting potential authorship network bias and offering a method to address this often-overlooked bias.

Although extant research has highlighted the tangible benefits of bridging ties that interlink network communities, our understanding of the determinants of a firm's propensity to form new bridging ties is scarce. Drawing on the behavioral theory of the firm, we conceptualize the formation of new bridging ties as a consequence of decision-makers' bounded rationality and verify the effect of performance feedback on the share of new bridging ties. Additionally, we contend that decisions regarding forming new bridging ties in response to performance feedback are bounded by CEOs' experience. We use a longitudinal dataset of Chinese publicly listed firms in the pharmaceutical industry from 2010 to 2020. The results indicate that the magnitude of a firm's outperformance relative to its aspirations positively affects the share of new bridging ties, while the magnitude of a firm's underperformance relative to its aspirations has an inverted U-shaped relationship with the share of new bridging ties. CEOs' academic and political experience strengthens the positive relationship between the magnitude of a firm's outperformance relative to its aspirations and the share of new bridging ties. CEOs' political experience flattens the inverted U-shaped effect of the magnitude of a firm's underperformance relative to its aspirations on the share of new bridging ties.

Increasing transboundary crises necessitate the development of crisis management capabilities that transcend boundaries. In such situations, inter-governmental and cross-functional collaboration has become a common practice to address the complexities of governance challenges. This study employs Social Network Analysis to examine the structure, function, and evolution of policy collaboration networks in China in response to COVID-19 and SARS. Since the SARS outbreak, China has embraced a collaborative governance approach, considering the transboundary nature of COVID-19. This approach has led to the involvement of numerous specialized organizations engaged in economic and social development, contributing to the establishment of a larger and more loosely connected collaboration network. While the health department bears the primary responsibility for coordinating public health emergency management, diverse organizations with social governance and economic management functions have also emerged as key actors, providing crucial anti-epidemic information, knowledge, and resources to address this significant cross-border crisis.