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This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Despite the growing attention being paid to the role of uncertainty in the competitive business environment, few studies have considered uncertainty as an antecedent factor and explored its direct impact on accelerating a firm’s innovation speed. This study develops a conceptual framework that examines the impacts of technological uncertainty and market uncertainty on innovation speed, building on complex adaptive theory. Furthermore, it is important to note that the internal resources of a firm and its external environment are not separate entities. In this study, we investigate the moderating role of a firm's internal and external resource ability (financial constraints level and organizational slack level) in the relationship between environmental uncertainty and innovation speed.

Our data sample is the panel data of China's A-share listed companies. The data year span is from 2000 to 2018. We use a hierarchical regression analysis model.

Our results reveal that both technology uncertainty and market uncertainty can promote innovation speed. Still, a firm’s organizational slack positively moderates the relationship between technology uncertainty and innovation speed, and financial constraints negatively moderate the relationship between demand uncertainty and innovation speed.

Our research contributes to the existing literature on uncertainty and extends its research perspective by no longer taking uncertainty as an environmental factor but exploring its direct impact. Still, our research focuses on innovation speed and discusses the impact of environmental uncertainty (including technology uncertainty and demand uncertainty) on firms’ innovation speed, expanding the limitations of previous research, which usually holds a relatively general perspective on innovation problems.

Women migrant street vendors are considered a vulnerable group in societies, thereby being hard hit during a crisis. This research aims to examine effects of COVID-19 social distancing on their businesses, consumption, health and general lives; solutions and mitigation strategies that they adopted in response to these adverse effects; and their recovery of socioeconomic lives after social distancing.

The data were collected from the women migrant street vendors in urban Vietnam. Descriptive statistics, probit model and ordered probit model were used for empirical analysis.

Women migrant street vendors faced immense challenges during social distancing. They also lacked solutions to sustain their businesses and were forced to resort to various mitigation strategies. Their socioeconomic lives were also slow to recover after social distancing. In addition, those experiencing greater business loss faced more adverse effects during social distancing and have more difficulties in the recovery of socioeconomic lives after social distancing.

This research highlights the importance of redesigning social policies to support women migrant street vendors during crises. It also emphasizes the need to formalize and legalize their activities to foster sustainable and inclusive development in the long term.

To the best of the authors’ knowledge, this research is among the first attempts to explore the effects of COVID-19 social distancing on the women migrant street vendors and to examine how they respond to these adverse effects.