Suchergebnisse

AbstractHave people in China moved from more polluted cities to less polluted ones? We merge city-level air pollution data from 2003 to 2016 with migration data from a nationally representative sample. We estimate a linear model and a conditional logit model, and employ air pollution from distant sources carried by the wind as an instrument for local air pollution to address the potential concern that air pollution is endogenous to local economic activities. We make a distinction between out-migration that left some family members behind and whole-household out-migration, and discover that the former was more responsive to air pollution than the latter. The decline in net in-migration in response to an increase in air pollution was driven by both a decrease in gross in-migration and an increase in gross out-migration. We find suggestive evidence that out-migrants brought their children with them, but some aged parents were left behind.

This study integrates two lines of research: technologies as tools and technologies as social beings, under the theoretical framework of dynamic systems, to investigate the reciprocal dynamics between functional use and relational use of artificial intelligence (AI) voice assistants, and the mediating roles of self-disclosure and privacy concerns. A two-wave longitudinal survey was conducted among 354 AI voice assistant users across 2 months. Factor analysis results supported the conceptualization and operationalization of functional use and relational use of voice assistants. Results from the cross-lagged panel model confirmed that functional use and relational use reinforced themselves over time, respectively. Relational use increased subsequent functional use, and relational use reinforced itself through self-disclosure. Surprisingly, functional use did not increase subsequent relational use; instead, longitudinal mediation analysis showed that functional use reduced subsequent relational use due to the lack of self-disclosure. Furthermore, while self-disclosure increased subsequent privacy concerns, privacy concerns did not reduce subsequent self-disclosure.

The knowledge gap hypothesis predicts that information inequity will be amplified rather than attenuated by the media. Previous research has focused on the role of mass media exposure and has not examined the roles of social media and social networks in mitigating the gap. This study investigated the potential moderating roles of social media engagement, social networks, and the interaction between engagement and online and offline networks. Hypotheses were tested with data from a national sample survey ( N = 991) concerning political and health knowledge. More social media engagement predicted a smaller knowledge gap in the political domain but not the health domain. More diverse and denser social media networks predicted a smaller political, but not health, knowledge gap. Social media engagement interacted with mixed-media relationships to predict the political knowledge gap. More engagement with mixed-media relationships was associated with a smaller political knowledge gap.

This study examined how social media influence the knowledge gap between low and high socioeconomic status (SES) groups in artificial intelligence (AI), a highly debated scientific subject warranting immediate scholarly attention. A national survey of U.S. adults ( N = 965) was conducted. The results showed that education and social media exposure to AI information (SME) predicted greater AI knowledge, and SME did not moderate the SES-based AI knowledge gap. Furthermore, information elaboration moderated the association between SME and the AI knowledge gap. SME was associated with a smaller AI knowledge gap when information elaboration was high rather than low.

This study examined the effects of interactive mediated contact, intergroup contact enabled by social media engagement, on attitudes toward Ukrainian refugees, as well as the psychological mechanisms underlying these effects, such as empathy and perceived threat. A three-wave longitudinal survey of 555 US adults was conducted in March and May 2022. The results of a cross-lagged panel model showed that interactive mediated contact predicted greater empathy and perceived threat. Subsequently, these two factors predicted positive and negative attitudes toward Ukrainian refugees, respectively. Empathy and perceived threat mediated the effect of interactive mediated contact on intergroup attitudes over time. Furthermore, intergroup attitudes predicted increased empathy and decreased perceived threat subsequently, but did not predict further interactive mediated contact on social media. This study contributes to our understanding of mediated contact in social media environment.

Concerns over the harmful effects of social media have directed public attention to media literacy as a potential remedy. Current conceptions of media literacy are frequently based on mass media, focusing on the analysis of common content and evaluation of the content using common values. This article initiates a new conceptual framework of social media literacy (SoMeLit). Moving away from the mass media-based assumptions of extant approaches, SoMeLit centers on the user's self in social media that is in dynamic causation with their choices of messages and networks. The foci of analysis in SoMeLit, therefore, are one's selections and values that influence and are influenced by the construction of one's reality on social media; and the evolving characteristics of social media platforms that set the boundaries of one's social media reality construction. Implications of the new components and dimensions of SoMeLit for future research, education, and action are discussed.

This study extends the diffusion of innovations theory by considering the threat of technology and examining the adoption of artificial intelligence (AI) in the workplace over time from a dynamic, differential-effects perspective. Findings from a three-wave survey study reveal an association between the threat of AI (i.e., job security concerns) and increasingly negative attitudes toward AI adoption among employees over time. Relative advantage, compatibility, and observability correlated with more positive attitudes, whereas ease of use and trialability showed no significant association. In testing the differential effects on attitudes toward AI adoption among different groups of potential adopters, we found that trialability positively influenced attitudes only among employees who held a positive attitude previously. Observability and the threat of AI, however, were more influential among employees who held a negative attitude previously. Theoretical and practical implications were discussed.