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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.

This study aims to explore the impact of emotional cues in knowledge product descriptions on users’ willingness to gather information. It specifically focuses on how different types of textual emotional cues, including heuristic cues like “emotional titles” and systematic cues like “emotional synopses,” influence users’ information-gathering willingness and examines the mediating role of emotional arousal in this process.

A conceptual model was developed by integrating the heuristic-systematic model with cue utilization theory. The experimental design employed knowledge product descriptions from the “Knowledge Column” section of the Zhihu platform. A controlled experiment was conducted to investigate the effect of varying emotional cues in these descriptions on participants’ willingness to gather information.

The study identified two types of emotional cues – heuristic cues, such as “emotional titles,” and systematic cues, such as “emotional synopses” – that significantly and positively influence users' information-gathering willingness. Additionally, emotional arousal was found to mediate the relationship between emotional cues and users’ willingness to gather information in the context of knowledge payments.

This study confirms that emotional cues in knowledge product descriptions, mediated by emotional arousal, can enhance the information-gathering willingness of knowledge payment users. The research deepens the theoretical exploration of information behavior among online knowledge payment users, providing valuable insights for knowledge producers on effectively leveraging emotional cues to attract potential customers as well as offering guidance for knowledge payment users in their information-gathering practices.

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.

Building upon the resource-based view (RBV) and related research, this paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities. It provides micro-level evidence of AI’s influence on innovation behavior.

Drawing upon data from Chinese listed companies spanning the period from 2011 to 2022, this study employs a dual fixed-effects model and a mediation effects model to empirically analyze the influence of enterprise AI transformation on its innovation capability as well as the specific mechanisms involved.

The research reveals that AI transformation significantly enhances the innovation capability of enterprises. Heterogeneity analysis indicates that AI transformation exerts a stronger promoting effect on the innovation capability of non-technology firms, large enterprises and those within the manufacturing sector. Mechanism analysis further reveals that AI transformation enhances innovation capability by boosting enterprise profits, reducing costs and reinforcing internal control mechanisms. Further examination demonstrates that AI transformation elevates the quality, efficiency and eco-friendliness of enterprise innovation.

Firstly, this study employs text analysis methods from machine learning to construct artificial intelligence indicators at the firm level, providing stronger evidence of AI’s impact on corporate innovation capabilities. Secondly, it extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

This paper aims to integrate the latent semantic features of annual report text with accounting indicators to construct a financial fraud identification model, and quantitatively analyze the impact of different corporate risks on financial fraud behavior in different industries, providing a reference for identifying financial fraud.

This paper obtains 3,860 corporate annual report samples and accounting indicators from 2001 to 2020 through crawlers and the CSMAR database as our experimental subjects. By integrating latent semantic features with accounting indicators and textual language features, a new indicator system group is constructed. Based on this indicator system group, multiple model identification effects are compared and a stacking-based enterprise financial fraud identification model is constructed. In addition, an econometric model is established to verify the impact of latent semantic features related to enterprises on corporate financial fraud.

The experimental results show that the constructed stacking-based enterprise financial fraud identification model performs better than other machine learning models and can effectively identify financial fraud. The econometric model established for the latent semantic information of annual reports explains the impact of different corporate trends on fraud behavior in different industries.

This paper combines the textual latent semantic features of annual reports with accounting indicators, expands the scope of data analysis, introduces the idea of ensemble learning, updates the financial fraud identification algorithm and constructs an econometric model for further analysis, providing a reference for financial fraud identification.