Behavioral Sciences

As artificial intelligence (AI) products become increasingly integrated into daily life, AI dependence has gained significant public and scholarly attention. While existing research has primarily examined its impact on students, few studies have investigated its association with employee career development, particularly work engagement. Our one-year longitudinal study involving 1108 employees aged 21–60 examined the long-term effect of AI dependence on work engagement, incorporating work self-efficacy as a mediator and gender as a moderator. Using three-wave survey data, we found that AI dependence at Time 1 (T1) directly predicted work engagement at Time 3 (T3), and also exerted a significant indirect effect through work self-efficacy at Time 2 (T2). While the mediating effect of T2 work self-efficacy showed no gender differences, the direct effect of T1 AI dependence on T3 work engagement was significantly stronger among male employees. These findings systematically address questions regarding the long-term, mediated, and gender-differentiated effects of AI dependence. They provide important warnings regarding AI dependence prevention and deliver practical implications for maintaining and enhancing employee self-efficacy and engagement in the AI era.

This study examined how perceived tax–benefit reciprocity and identification with the country explain tax morale of immigrants versus the native-born population, and how immigrants’ tax morale evolves over time. The study used data from an online survey of 536 people of working age, which were analyzed using Confirmatory Factor Analysis in AMOS, the PROCESS procedure, and ANOVA with Scheffe tests in SPSS ver. 28. The results showed that immigrants have lower tax morale than natives, which is partly explained by their weaker identification with the host country. Low dissatisfaction of immigrants with received benefits versus paid taxes partially buffers the negative relationship between identification and tax morale. After 25 years of residing in the host country, differences in tax morale between immigrants and natives largely disappear, suggesting a gradual process of adjustment and alignment with host society norms. Policy implications of the study suggest that tax morale among immigrants can be improved by strengthening their identification with the host country and enhancing perceptions of fairness in the tax–benefit exchange.

Although perceived posttraumatic growth (PPTG) has been examined in a wide variety of potentially traumatic and/or adverse events, very few studies have examined PPTG in response to experienced discrimination. Further, there is a strong need to better understand the factors that contribute to psychological outcomes following discrimination. The purpose of the current study was to examine correlates and predictors of unique variance in both positive (PPTG) and negative [perceived posttraumatic depreciation (PPTD)] trauma-related outcomes in response to discrimination experienced. A sample of 323 undergraduates from the United States (ages 18–58) who have experienced discrimination completed an online survey. The majority of participants indicated experiencing racial discrimination (51%), followed by gender discrimination (17%), and religious discrimination (10%). The results revealed that event centrality, perceived injustice, core beliefs, and resilience were all significantly associated with both PPTG and PPTD. In multiple regression models, core beliefs and resilience were unique predictors of both PPTG and PPTD. Specifically, core beliefs positively predicted PPTG and PPTD, and resilience positively predicted PPTG and negatively predicted PPTD. We conclude that experienced discrimination can powerfully alter one’s core beliefs, leaving the individual both more vulnerable to psychological depreciation, but also grants an opportunity for potential growth. We believe these findings can help clinicians better understand how to help individuals struggling with experienced discrimination.

COVID-19 lockdown announcements triggered global waves of panic buying, leading to widespread panic buying of essential goods and supply chain disruptions. Although the acute phase of the pandemic has passed, panic buying continues to emerge during natural disasters, extreme weather events, and other crisis-related disruptions, highlighting the ongoing need for evidence-based strategies to address its psychological drivers. Social cognition constructs, including willingness, intentions, attitudes, subjective norms, and risk perceptions, have been identified as modifiable psychological predictors of panic buying. However, few studies have experimentally tested theory-driven interventions aimed at modifying these mechanisms. This study evaluated the effectiveness of a brief, online intervention based on integrated social cognition models in reducing panic-buying-related cognitions during a hypothetical lockdown scenario. A pre-registered randomized controlled trial was conducted with Australian grocery shoppers (N = 140), who were randomly allocated to an intervention or control condition. Participants completed self-report measures assessing their willingness, intentions, attitudes, subjective norms, and risk perceptions at both pre- and post-intervention times. The hypotheses were partially supported. Compared with the control condition, the intervention group reported greater reductions across targeted psychological constructs. For hygiene products, significant decreases were observed across all five constructs, and for non-perishable foods, willingness, intention, and attitudes significantly decreased. For cleaning products, reductions were evident for attitudes, subjective norms, and intentions. These findings suggest that theory-informed, scalable interventions can effectively modify the social cognition processes underlying panic buying. This study extends existing research and demonstrates the potential for brief, theory-based communication strategies to reduce panic-buying-related cognitions. Future research should evaluate these interventions in real-world settings and explore mechanisms to target automatic cognitive processes.