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The purpose of this paper is to explore how and when employee smart technology, artificial intelligence, robotics and algorithms (STARA) awareness affects job crafting through challenge appraisal and threat appraisal and provides positive stress mindset as a moderator.

The survey data was collected from 319 employees in four Chinese companies. The hypotheses were tested using Mplus 7.0 and regression analysis.

The results indicate that STARA awareness positively prompts approach job crafting via challenge appraisal and also positively predicts avoidance job crafting via threat appraisal. Meanwhile, positive stress mindset enhanced the mediating effect of challenge appraisal and weakened the mediating effect of threat appraisal.

Leaders should prioritize hiring high-positive-stress mindset candidates for jobs, and organizations should also cultivate employees’ positive stress mindset.

Building on the cognitive appraisal theory of stress, this study reveals the underlying mechanism and boundary conditions behind the linkage of STARA awareness and job crafting.

This paper aims to examine the role of local/provincial government in influencing corporate social and environmental reporting (CSER) in China, and more specifically, how the underlying economic and political factors associated with local government have influenced the quality of CSER.

The authors used 234 environmentally sensitive companies listed on the Shanghai and Shenzhen Stock Exchanges during 2013 and 2015 as the research sample to test the relationship between CSER and local government’s political connection and economic prioritisation and the potential mediating effect of local economic prioritisation.

The analysis provides evidence that local/provincial government’s political geographical connectedness with the central government has directly and positively influenced the level of CSER, while local prioritisation of economic development has a direct but negative effect on CSER in China. In addition, local/provincial prioritisation of economic development has mediated the relationship between local–central political geographical connectedness and CSER.

While local/provincial governments are heavily influenced by the coercive pressure from the central government, they also act in their own political and economic interests in overseeing CSER at the local level. This study raises the question about the effectiveness of the top-down approach to improving CSER in China and suggests that the central government may need to focus more on coordinating and harmonising different local/provincial governments’ interests to enable achieving a common sustainability goal.

The authors provide evidence revealing how the economic and political contexts of local government have played a significant role in shaping CSER in China. More specifically, this paper addresses a gap in the literature by highlighting the importance of local government oversight power for CSER development and how such oversight is determined by local prioritisation of economic development and political geographical connectedness of local and central governments.

We examined who is more likely to use flexible work arrangements (FWAs) to alleviate work-family conflict (WFC) and under what conditions the use of FWAs actually reduces WFC.

We tested the model using survey data collected at two time points from 217 employees.

Proactive employees are more likely to use flextime to alleviate WFC (b = −0.03; 95% biased-corrected CI: [−0.12, −0.01]) and this mediation relationship is not moderated by their level of low work-to-nonwork boundary permeability. In addition, only when proactive employees have a low work-to-nonwork boundary permeability does their use of flexplace alleviate WFC (b = −0.07, 95% bias-corrected CI: [−0.1613, −0.0093]).

We expand our understanding of who is more likely to utilize FWAs by identifying that employees with proactive personality are more likely to use flextime and flexplace. We also advance our understanding regarding the conditions whereby FWA use helps employees reduce WFC by identifying the moderating role of work-to-nonwork boundary permeability on the relationships between both flextime and flexplace use on WFC.

This study evaluates the evolution from traditional to nontraditional financial intermediation (NTFI) in Chinese commercial banks from 2006 to 2021, analysing its impact on bank performance and risk.

Accounting measures are used to construct granular activity data, and factor analysis is employed to develop a financial intermediation evolution (FIE) index. The fully modified OLS (FMOLs) estimator is used for nonstationary data analysis, and difference-in-differences (DID) analysis is used for robustness check.

Chinese banks exhibit unique evolution in financial intermediation compared to developed economies, with greater inter-bank variations over time than intra-bank differences. From 2006 to 2017, three paths were identified: Investment, Fee and Repo models, with a fourth path (the Investment2 model) emerging post-2017. Only the Repo model enhances bank returns (ROA & ROE), while shifts towards NTFI increase liquidity and leverage risks across all models. The evolution of bank business models and their consequent implications on performance and risks are influenced by regulatory objectives and banks’ endeavours in regulatory arbitrage.

Major stakeholders in the banking sector can gain a better understanding of financial intermediation and associated market behaviour, performance and risks, with significant implications for banking regulations and crisis management.

This study provides the first comprehensive overview of the evolution of Chinese commercial banks’ financial intermediation activities over an extended period, uncovering the unique characteristics distinct from the developed economies.

Introduction: The current study provides a conceptual base for big data analytics in the insurance industry that might be useful for academics, researchers, and practitioners conducting future studies in the respective field.

Purpose: This study intends to investigate the transformative impact of big data analytics in the insurance industry. It elaborates on how advanced data analytics techniques are reshaping the insurance landscape, from customer acquisition and retention to risk modeling, pricing, and claims management.

Methodology: The current study incorporates a thorough review of existing literature, industry reports and academic articles related to big data analytics in the insurance sector. The aim is to develop an in-depth comprehension of the subject by synthesizing key insights and trends.

Findings: The findings underscore the evolutionary potential of the insurance sector through big data analytics, emphasizing its role as a catalyst for innovation, efficiency, and growth. Embracing data-driven strategies is essential for insurers to adapt to evolving market dynamics, meet customer expectations, and maintain a competitive edge in the digital age.

Green finance aims to promote sustainable financial activities, environmental conservation and ecological balance. This study examines how renewable energy consumption (REN), technological innovation (TEC) and green finance (GRF) influence CO2 emissions in Vietnam from 2000 to 2022.

We utilize a novel three-stage methodology including quantile-on-quantile regression, wavelet coherence and wavelet-quantile regression to explore the relationship in the structure of intercorrelation in terms of quantile, time and frequency.

The findings show that Vietnam will increase environmental quality for higher green development. Specifically, there is a negative influence of TEC, REN and GRF on CO2 emissions across different quantiles and timescales.

The study recommends policies that support green development and reduce carbon emissions, such as increasing the use of renewable energy and conducting well-planned research to achieve a carbon-free, sustainable environment.

This article looks into the effects of GRF, TEC and REN on CO2 emissions in Vietnam. Some studies argue that green development in underdeveloped nations is insufficient to reduce CO2 emissions, thereby limiting the sample to a few advanced economies. Adopting diverse methodologies demonstrates the varied and intricate nature of understanding CO2 drivers. Additionally, our work makes detailed policy implications for Vietnam to meet its net-zero emission target and achieve sustainable development by 2050.

Taking the support of social capital theory and conservation of resource theory, the present study explores the mediating role of rumination and moderating role of mindfulness in the relationship of workplace ostracism (WO) and workplace withdrawal (WW).

The data were collected in two waves from 467 employees working in hotels located in Delhi NCR region of India. The hypothesised relationships were investigated by macro-PROCESS (Hayes, 2013).

The results found a mediating impact of rumination on WO and WW relationship. It further supported the moderating effect of mindfulness in weakening the association between WO and WW via rumination.

This study identified mindfulness as an essential mechanism by which WO may be regulated to control employee's tendency to ruminate. Rumination may initially be prevented in organisations by regulating the primary effect of WO on employees' decisions for WW.

By linking the research model with the social capital theory, the study has contributed to the existing body of knowledge. The study is the first of its kind in India to examine the impact of hypothesised associations on the hotel industry. The findings of the study would help the industry in understanding the role of mindfulness in reducing aberrant behaviours at workplace.

The purpose of this study is to examine the impact of green bonds on common prosperity in China. Green bonds have gained significant attention as a means to address financial challenges and promote environmental protection. This research aims to investigate the influence of green bonds on common prosperity by utilizing the system-generalized method of moments (SYS-GMM) and analyzing panel data from prefecture-level cities. The study also explores the theoretical mechanisms and heterogeneous relationships between green bonds and common prosperity, providing valuable guidance for advancing economic and social well-being in China.

This study employs a system-generalized method of moments (SYS-GMM) as the methodology to investigate the influence of green bonds on common prosperity in China. Panel data from prefecture-level cities for the period 2014 to 2020 are utilized for analysis. The SYS-GMM approach allows for the examination of dynamic relationships and control of endogeneity issues. By utilizing this methodology, the study aims to provide robust and reliable findings on the impact of green bonds on common prosperity, considering the specific context of China's ecological civilization development and financial challenges faced by energy-saving and environmental protection enterprises.

The findings of this research indicate several important outcomes. Firstly, common prosperity in China experienced substantial growth between 2014 and 2020. Secondly, green bonds have demonstrated a clear and positive impact on common prosperity. They contribute to the enhancement of common prosperity by driving industrial structure upgrading and fostering green technology innovation. Lastly, the study reveals that the positive influence of green bonds on common prosperity is particularly pronounced in the western region of China. These findings highlight the significance of green bonds in promoting sustainable economic development and societal well-being.

This study contributes to the existing literature by examining the impact of green bonds on common prosperity in China, utilizing the system-generalized method of moments (SYS-GMM) and panel data analysis. The research not only adds to the understanding of the relationship between green bonds and economic well-being but also provides insights into the theoretical mechanisms and heterogeneous relationships involved. The findings showcase the positive influence of green bonds on common prosperity, emphasizing their role in addressing financial challenges, promoting environmental protection, and driving sustainable development. The study's conclusions offer valuable guidance for policymakers, financial institutions, and stakeholders in advancing common prosperity in China.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.