The combination of artificial intelligence (AI) technology with the real economy has dramatically improved the efficiency of enterprises. However, the replacement of AI for employment also significantly impacts employees’ cognition and psychological state. Based on the Conservation of Resources Theory, the relationship between AI awareness and employee depression is explored in this article while examining the mediating role of emotional exhaustion, as well as the moderating role of perceived organizational support. Based on a sample of 321 respondents, the empirical results show that (1) AI awareness is significantly positively correlated with depression; (2) emotional exhaustion plays a mediating role between AI awareness and depression; (3) perceived organizational support negatively moderates the relationship between emotional exhaustion and depression; (4) perceived organizational support negatively moderates the mediating role of emotional exhaustion between AI awareness and depression. The research conclusions provide a theoretical basis for organizations to take measures to intervene in the negative impact of changes in AI technology on employees’ mental health.

Several previous studies have revealed a positive relationship between artificial intelligence (AI) technology development and employees’ employment, income, and job performance. If individuals can seize the opportunity to master the knowledge and skills relevant to the implementation of AI, they could make career progress and improve their workplace well-being (WWB). Based on the transactional theory of stress and resource conservation theory, we constructed a moderated mediation model to explore the relationship between AI opportunity perception and employees’ WWB and examine the mediating factor of informal learning in the workplace (ILW), as well as the moderating factor of unemployment risk perception (URP). Through a survey of 268 employees, our results showed the following: (1) AI opportunity perception was significantly positively correlated with employees’ WWB; (2) ILW played a mediating role in the positive relationship between AI opportunity perception and employees’ WWB; and (3) URP negatively moderated the mediating relationship of ILW between AI opportunity perception and employees’ WWB. Our research results have a guiding significance for enterprises seeking to promote WWB during AI application.

Background: Lockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available. These policies had significant economic impacts and caused social disruptions. Early re-opening is preferable, but it introduces the risk of a resurgence of the epidemic. Although the World Health Organization has outlined criteria for re-opening, decisions on re-opening are mainly based on epidemiologic criteria. To date, the effectiveness of re-opening policies remains unclear. Methods: A system dynamics COVID-19 model, SEIHR(Q), was constructed by integrating infection prevention and control measures implemented in Wuhan into the classic SEIR epidemiological model and was validated with real-world data. The input data were obtained from official websites and the published literature. Results: The simulation results showed that track-and-trace measures had significant effects on the level of risk associated with re-opening. In the case of Wuhan, where comprehensive contact tracing was implemented, there would have been almost no risk associated with re-opening. With partial contact tracing, re-opening would have led to a minor second wave of the epidemic. However, if only limited contact tracing had been implemented, a more severe second outbreak of the epidemic would have occurred, overwhelming the available medical resources. If the ability to implement a track-trace-quarantine policy is fixed, the epidemiological criteria need to be further taken into account. The model simulation revealed different levels of risk associated with re-opening under different levels of track-and-trace ability and various epidemiological criteria. A matrix was developed to evaluate the effectiveness of the re-opening policies. Conclusions: The SEIHR(Q) model designed in this study can quantify the impact of various re-opening policies on the spread of COVID-19. Integrating epidemiologic criteria, the contact tracing policy, and medical resources, the model simulation predicts whether the re-opening policy is likely to lead to a further outbreak of the epidemic and provides evidence-based support for decisions regarding safe re-opening during an ongoing epidemic. Keyords: COVID-19; Risk of re-opening; Effectiveness of re-opening policies; IPC measures; SD modelling.

Purpose: Using protest event analysis, this study aims to investigate which risk factors influence the severity of environmental protests in China, thus filling a gap in China-related environmental protest literature. Design/methodology/approach: Using a database derived from media coverage of 129 environmental protests in China from 2009 to 2015, this empirical study identifies underlying risk factors influencing environmental protest severity, quantifies these protest cases and verifies the associations between risk factors and severity using ordered logistic regression. Findings: The results show that higher environmental health threat, economic loss, distrust of local government, lack of local governmental response, improper local government action and higher population density are likely to increase environmental protest severity; however, contrary to expectations, environmental information disclosure has no significant effect. These findings illuminate the vital and variational role of local government throughout all stages of the evolutionary process in environmental protests. Moreover, public distrust of local government is the principal cause of these protests. Originality/value: This study enhances the understanding of how Chinese environmental protests arise from the identified risk factors and contributes to quantitative multi-case research in this area. Furthermore, the findings may help local governments in China, as well as in other countries, to enact positive measures to prevent serious environmental protests and improve their ability to address the environmental problems that cause protests. More effective governance can decrease the number and severity of environmental protests and thus promote social stability.

Using social exchange theory, we proposed a moderated mediation model to explore the relationship between unemployment risk perception and employee knowledge hiding under the disruption of artificial intelligence transformation. We conducted an empirical analysis of data from 274 respondents who completed a two-stage survey with an interval of about 2 weeks. The results were as follows: First, unemployment risk perception was positively related to employee knowledge hiding. Second, psychological contract breach mediated the relationship between unemployment risk perception and employee knowledge hiding. Third, mindfulness negatively moderated the relationship between psychological contract breach and employee knowledge hiding. Fourth, mindfulness negatively moderated the strength of the mediated relationship between unemployment risk perception and employee knowledge hiding via psychological contract breach. The research conclusions can help companies formulate policies to guide employees to actively share knowledge in artificial intelligence transformation contexts.

Introduction: Under the background of rapid progress of artificial intelligence (AI) technology, migrant workers are at a high risk of becoming unemployed due to their low levels of human capital. As for the influencing factors of unemployment risk perception, individual characteristics are an important one. However, there remain few studies exploring the impact of personality on the perception of technical unemployment risk among migrant workers under the impact of AI. Therefore, this study examines the relationship between proactive personality and migrant workers’ perception of technical unemployment risk, focusing on the mediating roles of AI learning and AI self-efficacy.Methods: This study collected data through the research platform “Credamo”. A total of 551 questionnaires were recovered in this survey, of which 446 valid questionnaires were reserved for research.Results: The research results show that: proactive personality was negatively correlated with migrant workers’ perception of technical unemployment risk; AI learning and AI self-efficacy mediated this relationship independently, and played a chain-mediating role in this relationship.Discussion: The present study contributes the perception of technical unemployment risk literature by highlighting the impact of proactive personality and the chain mediating role of AI learning and AI self-efficacy in a sample of Chinese migrant workers. The empirical results of this paper are considered as helpful for organizations to formulate the policies aimed at alleviating migrant workers’ perception of technical unemployment risk under the impact of AI technology.