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Digital transformation and innovation-driven development have become an international consensus. The purpose of this paper is to examine the effects of relationships, mechanisms and economic consequences between digital transformation and enterprise innovation quality in order to provide a benchmark for developing countries to implement digital transformation strategies and innovation-driven strategies and provide a major support for economic recovery in the post-coronavirus disease 2019 (COVID-19) era.

Using microdata from A-share listed enterprises in Shanghai and Shenzhen from 2010 to 2021, this study examines the relationship between digital transformation and enterprise innovation quality and further reveals the internal logic and economic consequences of digital transformation to improve enterprise innovation quality through the mediating effect and moderating effect models.

The results demonstrate that digital transformation is beneficial for improving enterprise innovation quality. The heterogeneity test demonstrates that digital transformation has a larger effect on improving enterprise innovation quality in non-state-owned enterprises and eastern enterprises in China. The mechanism test demonstrates that digital transformation can improve enterprise innovation quality by improving internal control quality and analyst attention. Furthermore, with the increase in enterprise innovation inputs, digital transformation plays a significantly stronger role in improving enterprise innovation quality. The extended analysis demonstrates that digital transformation can significantly improve enterprise financial performance by improving innovation quality.

First, the construction of the core explanatory variable digital transformation index in this study is based on the Python data analysis software, which calculates the frequency of digital transformation in the text of the business situation analysis portion of the annual report of the listed companies and then obtains the degree of digital transformation of the company in this year. There may be some deviation from the degree of digital transformation in the actual production and operation of enterprises. Second, in addition to internal control quality and analyst attention, are there other mediating mechanisms for the impact of digital transformation on the quality of enterprise innovation? Third, whether the moderating effect of innovation input on digital transformation and innovation quality is related to human capital factors of the research and development (R&D) team, such as the technical background of R&D personnel, etc.

This study enriches the relevant theories of digital transformation and broadens the research boundaries of digital transformation and enterprise innovation. This study's result provides an empirical basis for enterprises to improve enterprise innovation quality and financial performance from the perspective of digital transformation at the micro level and points out specific practical directions, combining theory with practice.

This paper aims to present a new trajectory scheduling method to generate a smooth and continuous trajectory for a hybrid machining robot.

The trajectory scheduling method includes two steps. First, a G3 continuity local smoothing approach is proposed to smooth the toolpath. Then, considering the tool/joint motion and geometric error constraints, a jerk-continuous feedrate scheduling method is proposed to generate the trajectory.

The simulations and experiments are conducted on the hybrid robot TriMule-800. The simulation results demonstrate that this method is effectively applicable to machining trajectory scheduling for various parts and is computationally friendly. Moreover, it improves the robot machining speed and ensures smooth operation under constraints. The results of the S-shaped part machining experiment show that the resulting surface profile error is below 0.12 mm specified in the ISO standard, confirming that the proposed method can ensure the machining accuracy of the hybrid robot.

This paper implements an analytical local toolpath smoothing approach to address the non-high-order continuity problem of the toolpath expressed in G code. Meanwhile, the feedrate scheduling method addresses the segmented paths after local smoothing, achieving smooth and continuous trajectory generation to balance machining accuracy and machining efficiency.

The purpose of this study is to (1) improve the spectral features of the second-order uniformly non-oscillatory (UNO) slope limiters, and (2) numerical simulation of the unified-form of generalized fully-coupled saturated thermo-poro-elastic systems in the axisymmetric cylindrical coordinate via cell-adaptive Kurganov-Tadmor (KT) central high-resolution scheme using the UNO limiters.

(1) The spectral features of the UNO limiter are improved by compression-adaptive MINMOD (MM) limiters, achieved by blending different types of MM limiters to achieve less numerical dissipation and dispersion. These blended MM limiters preserve the total variation diminishing (TVD) feature over non-uniform non-centered cells. Also, the spectral features of the central schemes using the UNO limiters are investigated. (2) For the thermo-poro-elastic problem, corresponding first-order hyperbolic system is provided, including flux, source, diffusion and nonlinear terms. Where, there are different interacting components in the source and flux terms. The nonlinear terms are also considered by the Picard-like linearization concept.

Compression-adaptive UNO limiters would be stable over adapted cells with centered and non-centered cells. The benchmarks confirm that both spectral features and numerical accuracy are improved. For the generalized thermo-poro-elastic problem, corresponding responses including the shock waves can properly be captured.

Studying heat effects (e.g. hot fluid or freezing) and explosions on tunnels. Also, the UNO limiters could be used for simulations of various systems of conservation laws.

The purpose of this study is to explore the relationships between shared leadership, psychological contract, felt obligation and employees’ creative deviance as well as to investigate the mediating role of psychological contract and the moderating role of felt obligation in these relationships.

This study used convenience sampling to obtain longitudinal data collected (interval of one week) from 348 frontline hotel employees. The SPSS Process Model 5 was used to test the moderated mediation model.

The results indicated that shared leadership has a significant positive impact on both employee creative deviance and psychological contract and psychological contract positively influences creative deviance. The results confirmed that the psychological contract mediated the relationship between shared leadership and employee creative deviance. The study also highlighted the moderating role of felt obligation on the relationship between shared leadership and employee creative deviance.

This study contributes to the literature on shared leadership, psychological contract, felt obligation and creative deviance by providing empirical evidence of the mediating and moderating effects of psychological contract and felt obligation on the relationship between shared leadership and employee creative deviance. The findings offer practical insights into how organizations can leverage shared leadership to enhance employee creative deviance through psychological contract and felt obligation.

The purpose of this study is to contribute to empirical research on individual ambidexterity drivers. This paper analyzes the relationships between inclusive leadership, team knowledge acquisition, team knowledge sharing, digital tools usage and individual ambidexterity.

This study conducted a questionnaire survey of high-tech and manufacturing enterprises in China and obtained 75 leader questionnaires and 365 employee questionnaires. The hypotheses were tested using hierarchical and cross-level regressions.

The research indicates that inclusive leadership improves team knowledge acquisition and sharing. However, only team knowledge sharing significantly boosts individual ambidexterity, and not team knowledge acquisition. Thus, inclusive leadership fosters individual ambidexterity primarily through team knowledge sharing. Digital tools usage strengthens the impact of inclusive leadership on team knowledge sharing, thereby intensifying its effect on individual ambidexterity. However, digital tools usage weakens the effect of inclusive leadership on team knowledge acquisition.

First, this study addresses the call for research on ambidexterity at different levels, revealing the heterogeneous impact of team knowledge acquisition and sharing on individual ambidexterity. Second, this study developed a theoretical model to explore how leadership affects individual ambidexterity. Third, this study responds to the question that digitalization has won, but has leadership lost by investigating the role of digital tools usage in the relationship between inclusive leadership and team knowledge integration.

Governments worldwide are placing a greater emphasis on enhancing ecology and the environment as a result of escalating ecological issues. One possible approach is sustainable governance. This chapter explores the interrelated roles of internal control, environmental accounting, and environmental auditing mechanisms in promoting sustainable governance and green transformation. By looking at these three aspects, the chapter illustrates how integrated approaches can promote sustainable practices and guarantee adherence to environmental standards. The objective of this chapter is to present a thorough knowledge of the ways in which these components work together to support sustainability as a whole.

Team-based work is increasing within organizations nowadays. Despite calls for differentiation between individual and team levels of analysis, research on leadership effects at multiple levels is still limited. By integrating the Conservation of Resources and Social Cognitive theories, this paper aims to analyze the relationships between coaching leadership, team collective efficacy and individual exhaustion via multilevel modeling.

This paper opted for an exploratory study testing a 2-2-1 multilevel mediational model, positing team collective efficacy as a key factor in mediating the relationship between coaching leadership at the team level and exhaustion at the individual level. The hypotheses were tested on a sample of 311 employees, nested in 72 teams (Msize = 5.70, SDsize = 2.82 team members) of a large Italian company that provides financial services.

Results supported the positive association between coaching leadership and team collective efficacy, which, in turn, was negatively associated with team members’ average score in exhaustion and fully mediated the effect of coaching leadership on exhaustion, even controlling for team design features (i.e. task interdependence and team virtuality). The association between coaching leadership and exhaustion was only indirect, explained by team collective efficacy.

Because of the self-report nature of the measures and the cross-sectional nature of the data, this research results might raise problems of variance in common methods and not allow causal conclusions to be drawn. Therefore, researchers are encouraged to test the proposed propositions further.

This paper includes several practical implications for enhancing group efficacy beliefs, which have an impact on individual well-being within the team.

This paper addresses the lack of empirical findings on the multilevel nature of the relationship between coaching-based leadership, collective team effectiveness and individual exhaustion.

To maximize health synergistic benefits and provide a scientific foundation for enhancing air pollution control policies, this research proposed a causal inference framework with a decomposition ensemble prediction model for comprehensive policy assessment.

We introduced the CD-OASVR-Ensemble-CC method, a policy assessment approach that combined machine learning-based decomposition ensemble prediction (CD-OASVR-Ensemble) with counterfactual contrast (CC) analysis. The CD-OASVR-Ensemble model incorporated support vector regression (SVR) optimized through swarm intelligent optimization algorithms (OA), complete ensemble empirical model decomposition of adaptive noise (CD) and a linear ensemble model (Ensemble). This approach was applied to analyze patterns in historical PM2.5 data and predict the future trend. The predicted PM2.5 served as a counterfactual trend, enabling an evaluation of the policy’s environmental and health effects.

Analysis of prediction errors confirmed the proposed CD-OASVR-Ensemble model’s accuracy and robustness in predicting PM2.5 levels. The findings indicated that the implementation of “Regulation 2019” led to a significant reduction in PM2.5 concentration, preventing an estimated 7,960 premature deaths and saving approximately 2,602.95 m yuan in health-related economic costs.

This study introduces an innovative framework for evaluating the environmental effect of policies, with a specific emphasis on health synergistic benefits.

This study aims to explain the privacy paradox, wherein individuals, despite privacy concerns, are willing to share personal information while using AI chatbots. Departing from previous research that primarily viewed AI chatbots from a non-anthropomorphic approach, this paper contends that AI chatbots are taking on an emotional component for humans. This study thus explores this topic by considering both rational and non-rational perspectives, thereby providing a more comprehensive understanding of user behavior in digital environments.

Employing a questionnaire survey (N = 480), this research focuses on young users who regularly engage with AI chatbots. Drawing upon the parasocial interaction theory and privacy calculus theory, the study elucidates the mechanisms governing users’ willingness to disclose information.

Findings show that cognitive, emotional and behavioral dimensions all positively influence perceived benefits of using ChatGPT, which in turn enhances privacy disclosure. While cognitive, emotional and behavioral dimensions negatively impact perceived risks, only the emotional and behavioral dimensions significantly affect perceived risk, which in turn negatively influences privacy disclosure. Notably, the cognitive dimension’s lack of significant mediating effect suggests that users’ awareness of privacy risks does not deter disclosure. Instead, emotional factors drive privacy decisions, with users more likely to disclose personal information based on positive experiences and engagement with ChatGPT. This confirms the existence of the privacy paradox.

This study acknowledges several limitations. While the sample was adequately stratified, the focus was primarily on young users in China. Future research should explore broader demographic groups, including elderly users, to understand how different age groups engage with AI chatbots. Additionally, although the study was conducted within the Chinese context, the findings have broader applicability, highlighting the potential for cross-cultural comparisons. Differences in user attitudes toward AI chatbots may arise due to cultural variations, with East Asian cultures typically exhibiting a more positive attitude toward social AI systems compared to Western cultures. This cultural distinction—rooted in Eastern philosophies such as animism in Shintoism and Buddhism—suggests that East Asians are more likely to anthropomorphize technology, unlike their Western counterparts (Yam et al., 2023; Folk et al., 2023).

The findings of this study offer valuable insights for developers, policymakers and educators navigating the rapidly evolving landscape of intelligent technologies. First, regarding technology design, the study suggests that AI chatbot developers should not focus solely on functional aspects but also consider emotional and social dimensions in user interactions. By enhancing emotional connection and ensuring transparent privacy communication, developers can significantly improve user experiences (Meng and Dai, 2021). Second, there is a pressing need for comprehensive user education programs. As users tend to prioritize perceived benefits over risks, it is essential to raise awareness about privacy risks while also emphasizing the positive outcomes of responsible information sharing. This can help foster a more informed and balanced approach to user engagement (Vimalkumar et al., 2021). Third, cultural and ethical considerations must be incorporated into AI chatbot design. In collectivist societies like China, users may prioritize emotional satisfaction and societal harmony over privacy concerns (Trepte, 2017; Johnston, 2009). Developers and policymakers should account for these cultural factors when designing AI systems. Furthermore, AI systems should communicate privacy policies clearly to users, addressing potential vulnerabilities and ensuring that users are aware of the extent to which their data may be exposed (Wu et al., 2024). Lastly, as AI chatbots become deeply integrated into daily life, there is a growing need for societal discussions on privacy norms and trust in AI systems. This research prompts a reflection on the evolving relationship between technology and personal privacy, especially in societies where trust is shaped by cultural and emotional factors. Developing frameworks to ensure responsible AI practices while fostering user trust is crucial for the long-term societal integration of AI technologies (Nah et al., 2023).

The study’s findings not only draw deeper theoretical insights into the role of emotions in generative artificial intelligence (gAI) chatbot engagement, enriching the emotional research orientation and framework concerning chatbots, but they also contribute to the literature on human–computer interaction and technology acceptance within the framework of the privacy calculus theory, providing practical insights for developers, policymakers and educators navigating the evolving landscape of intelligent technologies.

This study aims to assess how envy, both directly and indirectly, through negative behaviors such as ostracism, negative word-of-mouth and alignment with the negative behaviors of superiors, influences innovative behavior based on the cultural dimension of individualism versus collectivism.

The data was collected using a survey applied to 305 individuals between October 2022 and June 2023. The model developed was analyzed and validated using partial least squares estimation with structural equation modeling (PLS-SEM) and PLS-SEM multigroup analysis techniques.

The results suggest that for individualistic individuals, negative word-of-mouth exerts a greater positive mediating influence on the relationship between envy and ostracism, and that envy exerts a greater positive influence on both alignment with the negative behaviors of superiors and on ostracism. In addition, the results indicate that negative word-of-mouth and ostracism together negatively influence the relationship between envy and innovative behavior.

This research provides empirical evidence that envy triggers negative behavior in both individualistic and collectivist individuals. Thus, in practical terms, envy can be considered as something more primitive that goes beyond the accepted values of sociability, especially in the organizational environment.

The main contribution of this research is to understand the effects of envy on innovative behavior, based on a conceptual model that considers the mental programs that differentiate one group of individuals from another. In addition, it presents theoretical and empirical implications that provide descriptive evidence of behaviors, making it possible to broaden the psychological understanding of them. In this specific sense, this research differs from other organizational studies, whose objectives are to standardize behavior.