Linear Structural Equation Models Research Articles

Innovative factors affecting performance of local government organizations in Thailand

This dissertation aimed (1) to study factors that have causal influences that affect the performance of municipalities. (2) to study the causal relationship model of innovative organizations, innovative capabilities, service innovation and performance management that influences the performance of municipalities. (3) to develop a linear structural equation model of the influence of service innovation, innovation organization, innovative capabilities and performance management that affects the performance of local government organizations in the category of municipalities in Thailand. The sample group were 320 executives of local government organizations in the category of municipalities in Thailand. Sampling was done in 2 steps: stratified and systematic sampling. This study is a quantitative study, and the research tool was a structured questionnaire and data were analyzed by percentage, mean, standard deviation, skewness, kurtosis, and confirmatory component analysis and structural equation model analysis. The findings of this study demonstrate significant direct effects of innovative capabilities, service innovation and performance management on performance of local government organizations in Thailand. Moreover, it was observed that innovative organizations and innovative capabilities also exerted indirect effects on performance of local government organizations through their influence on performance management, serving as mediator variables. Through the development of a structural equation model, it was determined that innovative capabilities had the greatest overall influence on the performance of local government organizations in the category of municipalities in Thailand, followed by performance management, service innovation and innovation organization, respectively. This research study made executives of local government organizations in category of municipalities in Thailand can be used as a guideline in determining strategies for managing their organization to be able to keep up with changes in the digital world and to ensure that the performance of the local government organization achieves the specified goals in terms of both efficiency and effectiveness.

The role of self-compassion in the effects of living alone and quarantine on mental health and well-being during the COVID-19 pandemic: a longitudinal study

ABSTRACT Objectives: Previous research has shown that living alone and being quarantined during the COVID-19 pandemic were associated with poorer mental health and well-being. The aim of this study was to examine whether self-compassion buffered these adverse effects. Methods: 435 German adults were surveyed in three waves over six weeks. In all three waves, participants were asked whether they lived alone and whether they were quarantined. Self-compassion, mental health, and well-being were also assessed in all three waves. Results: In linear structural equation models adjusted for age, gender, and the respective outcome measure at T1, higher self-compassion at T2 was associated with better mental health and well-being at T3. However, there was no evidence that living alone and quarantine at T1 were associated with mental health and well-being at T3, or that self-compassion moderated these effects. Conclusions: Our study spanned six weeks, and one possible explanation for our nonsignificant findings is that the mental health effects of living alone, quarantine, and self-compassion are rather short-lived. In addition, individuals living alone and individuals in quarantine may not necessarily have been socially isolated or felt lonely, which may explain why no associations with mental health were found.

A study of the impact of ChatGPT self-efficacy on the information seeking behaviors in ChatGPT: the mediating roles of ChatGPT characteristics and utility

PurposeThe purpose of this study was to examine the factors that influence the information seeking behaviors of ChatGPT users. Specifically, we investigated how ChatGPT self-efficacy, ChatGPT characteristics and ChatGPT utility affect the frequency and duration of information seeking via ChatGPT. We also tested the mediating roles of ChatGPT characteristics and utility in the relationship between ChatGPT self-efficacy and information-seeking behaviors.Design/methodology/approachThis study adopts a quantitative approach and collects data from 403 ChatGPT users using an online questionnaire. The data are analyzed using linear regression and structural equation modeling (SEM).FindingsThe linear regression analyses revealed that ChatGPT self-efficacy is positively and significantly related to the information seeking behaviors in ChatGPT. Second, mediation analyses also showed that ChatGPT characteristics and utility significantly mediate the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT independently and sequentially.Originality/valueThis study is the first to investigate the factors and mechanisms that influence information-seeking behaviors in ChatGPT, a new phenomenon in the media landscape. The findings in this study suggest that ChatGPT self-efficacy acts as an important motivator for information-seeking behaviors in ChatGPT and that ChatGPT characteristics and utility provide information regarding potential mechanisms in the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT. The study contributes to the literature on information seeking, self-efficacy and generative AI.

Problematic social Internet use and associations with ADHD symptoms in girls: a longitudinal observational study

BackgroundProblematic Internet use (i.e., Internet use that disrupts functioning in other important domains; PIU) is increasingly prevalent worldwide, particularly among youth. One form of PIU relates specifically to interpersonal interaction and communication, deemed social PIU. Social PIU has been linked to various forms of psychopathology, including attention-deficit/hyperactivity disorder (ADHD). Yet with limited longitudinal research, the direction of this association remains unclear. Moreover, little research investigates whether social PIU is linked to one or both symptom dimensions of ADHD (inattention vs. hyperactivity/impulsivity).MethodsThe present study utilized data from the largest extant longitudinal study of girls with childhood-diagnosed ADHD (N = 228). Linear regression and structural equation models were used to analyze social PIU as both a prospective predictor and outcome of ADHD symptoms.ResultsInattentive ADHD symptoms were positively associated with concurrent social PIU in initial regression models but were non-significant in path analyses. Social PIU was only marginally significant in predicting subsequent inattention six years later. Symptoms of hyperactivity/impulsivity were unrelated to social PIU among girls at either time point.ConclusionsInattentive ADHD symptoms were initially positively linked to concurrent problems with social Internet use, but bidirectional associations were non-significant in path analyses. Relations between PIU and ADHD in girls may be less robust than previously thought, although further longitudinal research with clinical samples is needed to clarify which groups of adolescents are particularly vulnerable to social PIU and its long-term effects.

The link between fear of disease progression and health promotion behaviors in patients with obstructive sleep apnea hypoventilation syndrome (OSAHS): the mediating role of social support

ObjectiveThis study aims to assess the levels of social support, fear of disease progression (FOP) and health promotion behaviors in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), and to examine the associations between FOP, social support, and health promotion behaviors in OSAHS patients, with a focus on exploring the mediating role of social support. The findings aim to provide insights for enhancing health promotion behaviors among OSAHS patients in China and to offer a theoretical foundation for healthcare professionals in devising intervention strategies to promote health behaviors in individuals with OSAHS.MethodsThis cross-sectional study included 307 patients diagnosed with OSAHS in Jinzhou City, Liaoning province. The survey instruments utilized included the Demographic Characteristics Questionnaire, Social Support Rating Scale (SSRS), Fear of Disease Progression Scale (Fop-Q-SF), and Health-Promoting Lifestyle Scale (HPLP II). Pearson correlation analysis was employed to assess correlations, while multiple linear regression and structural equation modeling were utilized to explore potential mediation effects.ResultsIn the OSAHS patient population, FOP (r=-0.55, p<0.001) and social support (r = 0.60, p<0.001) were found to be significantly correlated with health promotion behaviors. In the mediated effects model, social support was identified as a partial mediator in the association between FOP and health promotion behaviors among OSAHS patients, accounting for 59.00% of the total effect.ConclusionFOP demonstrates a direct relationship with health promotion behaviors in patients with OSAHS, with social support playing a mediating role in this connection. Healthcare professionals are advised to underscore the significance of social support in promoting the health of OSAHS patients to mitigate FOP and consequently improve health promotion behaviors.

Development of A Structural Equation Model of The Variables Affecting the Purchase Intention Innovation Solar Power Air-conditioning Systems of Small and Medium-Sized Enterprise

Purpose: This quantitative dissertation aimed 1) to investigate the impact of government policies, product innovation attributes, consumer innovativeness, and perceived value towards purchase intention,2) to construct a linear structural equation model that illustrates the influence of those factors on the purchase intention of innovative solar power airconditioning system of SMEs in the service sector. Method: The sample group was 320 executives of SMEs in the service sector. Sampling was by specific and quota random sampling. Data were collected by a structured questionnaire and analyzed by descriptive statistics, confirmatory component analysis, and structural equation model analysis. Results: The findings of this study demonstrate significant direct effects of government policy, consumer innovativeness, and consumer’s perceived value on purchase intention of innovative solar power air conditioning systems. Moreover, government policy and consumer innovativeness also exerted indirect effects on purchase intention through their influence on consumers’ perceived value, serving as mediator variables. Research Implication: Through the development of a structural equation model, consumer innovativeness had the greatest overall influence on the purchase intention of SMEs in the service sector, followed by consumer’s perceived value, government policy, and product innovation attributes, respectively. Originailty/Value: Airconditioning SMEs can used government policy, consumer innovativeness, and consumer’s perceived value to motivate consumers on purchase intention of innovative solar power air conditioning systems.

Model-implied simulation-based power estimation for correctly specified and distributionally misspecified models: Applications to nonlinear and linear structural equation models.

Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking. We propose a new model-implied simulation-based power estimation (MSPE) method for the z-test that makes use of the asymptotic normality property of estimates of a wide class of estimators, the M-estimators, and give theoretical justification for the approach. M-estimators include maximum-likelihood, least squares estimates and limited information estimators, but also estimators used for misspecified models, hence, the new simulation-based power modeling method is widely applicable. The MSPE employs a parametric model to describe the relationship between power and sample size, which can then be used to determine the required sample size for a specified power rate. We highlight its performance in linear and nonlinear structural equation models (SEM) for correctly specified models and models under distributional misspecification. Simulation results suggest that the new power modeling method is unbiased and shows good performance with regard to root mean squared error and type I error rates for the predicted required sample sizes and predicted power rates, outperforming alternative approaches, such as the naïve approach of selecting a discrete selection of sample sizes with linear interpolation of power or simple logistic regression approaches. The MSPE appears to be a valuable tool to estimate power for models without an (asymptotic) analytical power estimation.

Vine copula based structural equation models

Gaussian linear structural equation models (SEMs) are often used as a statistical model associated with a directed acyclic graph (DAG) also known as a Bayesian network. However, such a model might not be able to represent the non-Gaussian dependence present in some data sets resulting in nonlinear, non-additive and non Gaussian conditional distributions. Therefore the use of the class of D-vine copula based regression models for the specification of the conditional distribution of a node given its parents is proposed. This class extends the class of standard linear regression models considerably. The approach also allows to create an importance order of the parents of each node and gives the potential to remove edges from the starting DAG not supported by the data. Further uncertainty of conditional estimates can be assessed and fast generative simulation using the D-vine copula based SEM is available. The improvement over a Gaussian linear SEM is shown using random specifications of the D-vine based SEM as well as its ability to correctly remove edges not present in the data generation using simulation. An engineering application showcases the usefulness of the proposals.

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery.

This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene regulatory network information into hypothesis testing for the difference between mean vectors and linear discriminant analysis, we aim to effectively capture and utilize previously validated external gene interaction information. Our method leverages a block-coordinate descent approach which enables us to incorporate mixed graph information into linear structural equation modeling, accommodating directed/undirected edges and potential cycles in gene regulatory networks. Extensive simulations under various data scenarios have demonstrated the effectiveness of our approach with improved power for gene pathway tests and disease classification over existing methods. An application to a lung cancer dataset from the Cancer Genome Atlas Program (TCGA) further exemplifies the potential of our graph-informed approach in empowering the detection of differentially expressed gene pathways and gene expression-based classification of different lung cancer stages. Our findings underscore the potential utility of incorporating gene regulatory network information in gene pathway analysis, setting the stage for future advancements in gene pathway discovery, disease diagnosis, and treatment strategies.

Mediation Analysis with Multiple Exposures and Multiple Mediators.

A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's disease neuroimaging initiative (ADNI), the proposed framework identifies protein deposition-brain atrophy-memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities.

Does the nexus of corporate social responsibility and green dynamic capabilities drive firms toward green technological innovation? The moderating role of green transformational leadership

Dynamic organizations actively capitalize on social responsibility (CSR) and environmental recovery strategies, considering social and ecological challenges. However, how CSR-committed firms respond to changing environmental technologies and sense critical social and environmental trends is unknown. This study examines the role of CSR in developing green dynamic capabilities (GDC) of organizations, including resource reconfiguration, integration, and environmental insight capabilities, and it explains whether they strengthen organizational green technological innovation (GTI) performance. Specifically, it takes green transformational leadership (GTL) as the moderating variable in the relationship between GDC and GTI. The data collected from 295 manufacturing managers analyzed through linear regression and structural equation modeling indicated that CSR-committed firms have significant capability to integrate and reconfigure green resources and environment insight capability, leading toward improved GTI. The study establishes that GTL bolsters the relationship between resource integration and environmental insights aspects of GDC and GTI; while insignificant relationship exists for resource reconfiguration. Considering their pivotal green transformative role, it suggests that organizational leaders should prioritize sustainability, inspire vision, empower employees, and foster a culture of innovation to leverage the organizational GDC to boost GTI and concludes with policy implications.

The influence of parents on children's consciousness of gender equality: a multi-group structural equation modeling approach.

This study aimed to understand the consciousness of gender equality among school-aged children in China and its influencing factors using structural equation modeling to explore the pathways, intensity and group differences among these factors. A cross-sectional survey was conducted using stratified random whole-group sampling of primary school students in grades 1-6 and their parents who met the inclusion and exclusion criteria. In this study, 1,312 valid questionnaires were collected from a total of 1,500 school-aged children in Hunan Province, China (effective response rate of 87.5%). Statistical analysis was conducted using SPSS 26.0 and AMOS 24.0 software. Statistical inference consisted of t-tests, analysis of variance, the LSD test, Pearson correlation analysis, multiple stepwise linear regression analysis and structural equation modeling. School-aged children had the lowest consciousness of gender equality in the area of occupation and relatively higher consciousness in the areas of family and school. Children's age, gender, gender role, parent-child relationship, teacher-student relationship and parents' gender equality consciousness had predictive effects on children's consciousness of gender equality. The structural equation model constructed in this study is applicable to school-aged children of different genders. There was a significant difference in the structural equation modeling for children in different study period groups. In the education process, parents and teachers should attempt to improve their own consciousness of gender equality, integrate the concept of androgynous education, enhance close relationships with children, and adopt appropriate education methods according to the characteristics of different groups of children.

Direct and mediated impacts of mixed forests on Norway spruce infestation by European bark beetle Ips typographus

Climate change-induced windfalls and droughts exacerbate bark beetle outbreaks, severely impacting forest ecosystems. Despite extensive research on bark beetle infestation patterns, the question regarding the optimal spatial grid (SG) size for analyzing this phenomenon remains unresolved. The protective potential of natural forest complexity against bark beetles has been underestimated. We used remotely sensed data to fit Generalized Linear Models and structural equation models to explore the direct and environment-mediated effects of mixed forest cover (MFC) on Norway spruce forest infestation in central Europe. We assessed the effectiveness of various spatial grids (from 100 m to 1 km) for studying infestation by Ips typographus. We found a strong non-linear decline in infestation with increasing MFC across different spatial scales. The relationship between infestation and temperature was positive, while elevation had a negative effect on infestation, with higher infestation rates observed below 900 m. Direct effects of environmental predictors on infestation were significant at SGs of 100 m, 300 m, 400 m, and 500 m, but insignificant at 200 m and 1000 m SGs. Slope positively influenced infestation at 300 m and 400 m SGs. MFC exhibited significant indirect effects on infestation mediated by elevation, temperature, potential evapotranspiration, slope, and heat load index. Landscape variables played a significant role in the models at high-resolution spatial grids, whereas climate variables were influential in models at lower spatial grids of infestation data. We argue that mixed forest facilitates cooling, preserves water, enriches symbiotic fungal community, alleviating tree drought stress and mitigating infestation risk. Broad-leave trees’ non-host volatiles presumably disrupt olfactory signals, impeding beetles’ ability to locate host trees effectively. Our results underscore the potential of increasing mixed forest cover as a self-sustainable silvicultural measure for forest protection against I. typographus and for mitigating the effects of climate change.

Effects of artificial intelligence learning environments on engineers’ intercultural communication competence: mediating role of learning adaptability

ABSTRACT The purpose of this study is to investigate the impact of artificial intelligence learning environments on the intercultural communication competence of engineers. Engineering students at Chinese higher education institutions were invited to participate, and 990 students (510 males and 480 females) from different school types, majors, and grades completed an online survey. After analyses with multivariate linear regression and structural equation modelling, the results show that different dimensions of AI learning environments – including personalized learning, use of technology, and cooperation and interaction – have significant positive impacts on engineers’ intercultural communication competence and learning adaptability. At the same time, learning adaptability plays a mediating role between AI learning environments and intercultural communication competence. This study suggests that AI learning environments may have the potential to (i) enhance engineers’ intercultural communication competence and learning adaptability, an (ii) indirectly improve engineers’ intercultural communication competence through enhancements in their learning adaptability.

Different parenting styles and rural Children’s mental Health: The mediational role of mental resilience

The baseline data from the China Education Panel Survey (CEPS) 2014–2015 was employed to construct linear regression models and structural equation models, which were used to investigate the effects of different parenting styles on the mental health of rural left-behind children. The study found that parenting behaviors and parenting attitudes in two different modes, intergenerational parenting and parental parenting, both have an impact on the mental health of rural left-behind children, with no significant difference between the two. Nevertheless, the quality of the relationship between children and their mothers has a considerable impact on their mental health. Secondly, parenting behavior exerts a direct and indirect influence on the mental health mechanism of these children, the direct effect is considerably more pronounced than the indirect effect; the impact of parenting attitudes on the mental health of left-behind children is contingent upon the mediating variable of mental resilience. In order to mitigate the adverse effects of parental absence on the mental health of left-behind children, it is imperative that families enhance their parenting attitudes, reinforce parent–child communication, and cultivate a nurturing family environment. Schools must implement human-centered education, enhance the professionalism of teachers, and extend greater care and support to left-behind children. Furthermore, the state and the government must prioritize the development of the rural economy and strive to narrow the urban–rural disparity.

Tree size partially mediates the influence of climate on fruit and seed production in Ricinodendron heudelotii (Baill.) Heckel

AbstractUnderstanding environmental drivers of fruit and seed production in wild edible fruit species is essential for their valorisation. Ricinodendron heudelotii is an oilseed plant whose kernels are particularly treasured in cosmetic and medical industries. This study assessed fruits and seeds production patterns of R. heudelotii. Data were collected for three consecutive years on 30 trees in the phytodistricts of Plateau, Pobè and South Borgou where it naturally occurs. Tree dbh, total height, crown diameter, number of fruits and seed mass were measured. Climatic data were obtained from NASA Power Database. Data were analysed using linear (seed mass) and generalised linear (number of fruits) mixed models and structural equation modelling. Fruit and seed production were significantly higher in the South‐Borgou phytodistrict (1433 ± 1144 fruits, 4.26 ± 3.39 kg of Dry matter per tree) and Plateau phytodistrict (1.66 ± 0.35 g DM/seed). Tree dbh further significantly mediated the effect of minimum temperature, and relative humidity on seed mass. Tree dbh, irradiance and dry season rainfall were identified as the best‐predictors of seed mass production (kg DM). These results improved current knowledge of the fruit and seed production of R. heudelotii and are of significant importance for the exploitation of the species.

Microbial communities mediate the effect of cover cropping on soil ecosystem functions under precipitation reduction in an agroecosystem

Cover cropping is a sustainable agricultural practice that profoundly influences soil microbial communities and ecosystem functions. However, the responses of soil ecosystem functions and microbial communities to cover cropping under the projected changes in precipitation, remain largely unexplored. To address this gap, a field experiment with cover cropping (control, hairy vetch, ryegrass, and hairy vetch plus ryegrass) and precipitation reduction (ambient precipitation and 50 % reduction in ambient precipitation) treatments was conducted from 2018 to 2020 in an agroecosystem located in the Guanzhong Plain of China. Soil ecosystem functions related to nutrient storage, nutrient cycling, and organic matter decomposition were measured to assess the soil multifunctionality index and bacterial and fungal communities were determined by Illumina NovaSeq sequencing. The results indicated that cover cropping enhanced soil multifunctionality index, and reduced precipitation strengthened this effect. Microbial community composition, rather than microbial diversity, was significantly altered by cover cropping regardless of precipitation reduction. Cover cropping increased the microbial network complexity and stability, but this effect was dampened by reduced precipitation. The microbial community composition and network complexity significantly and positively correlated with soil multifunctionality index under ambient and reduced precipitation conditions. Linear regression analyses and structural equation models collectively demonstrated that the increase in soil multifunctionality index was attributed to cover cropping-induced changes in microbial community composition and network complexity, irrespective of precipitation reduction. This study highlights the crucial role of microbial communities in driving the response of soil multifunctionality to cover cropping in the context of reduced precipitation, which has important implications for agricultural management and sustainability under future climate change scenarios.

Texture and clay mineralogy as main drivers of the priming effect in temperate forest soils

Abstract Background and aims This work aimed to determine how the soil parameters affect the magnitude and direction of priming effect (accelerated or decreased decomposition of native SOM under addition of new organic substrates, PE) in temperate acidic forest soils. Methods Thirteen topsoil samples were incubated for 163 days with the addition of maize residues. Soil respiration was measured and natural isotope labelling was used in order to separate the respiration sources (SOM, maize and PE). The effect of soil parameters on PE was studied using linear regression and structural equation modelling (SEM). Results Soils with high C/N ratio showed the lowest magnitude of cumulative PE (R2 = 0.321, p &lt; 0.05) and the longest negative PE period. A positive relationship was found between PE and the pH (R2 = 0.511, p &lt; 0.05). SEM analysis showed that pH and C/N ratio has direct (β = 0.50) and indirect (β = 0.20, via modifying soil texture and mineralogy) effect on PE. Soil texture and mineralogy had a significant effect on PE: texture affects the proportions of soil respiration sources and PE was reduced by the dithionite–citrate–bicarbonate–extractable Al (AlDCB, R2 = 0.454, p &lt; 0.05), silt + clay (R2 = 0.421, p &lt; 0.05), non-swelling clay mineral (R2 = 0.575, p &lt; 0.05) and illite (R2 = 0.522, p &lt; 0.05) contents. SEM analysis also highlighted that the AlDCB, illite and silt + clay contents has a great effect (β=−0.59) on the PE. Conclusion The silt + clay content and mineral composition of the soil, including the Al oxide and illite contents may thus significantly inhibit the magnitude of PE, and consequently the decomposition of SOM under acidic conditions.

PathGPS: discover shared genetic architecture using GWAS summary data.

The increasing availability and scale of biobanks and "omic" datasets bring new horizons for understanding biological mechanisms. PathGPS is an exploratory data analysis tool to discover genetic architectures using Genome Wide Association Studies (GWAS) summary data. PathGPS is based on a linear structural equation model where traits are regulated by both genetic and environmental pathways. PathGPS decouples the genetic and environmental components by contrasting the GWAS associations of "signal" genes with those of "noise" genes. From the estimated genetic component, PathGPS then extracts genetic pathways via principal component and factor analysis, leveraging the low-rank and sparse properties. In addition, we provide a bootstrap aggregating ("bagging") algorithm to improve stability under data perturbation and hyperparameter tuning. When applied to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggests multiple new hypotheses for future investigations.

How Government Actions Influence People’s Intentions to Share on Social Media During Shanghai’s Lockdown for COVID-19: A Third-Person Effect Perspective

During the COVID-19 pandemic, China’s “Zero-COVID” policy and strong government actions led to significant disruptions in daily life. Using Shanghai’s 2022 lockdown as an example, this study investigated how the government’s actions influenced people’s intentions to share information about risks and content with positive emotions. By employing the Third-Person Effect theory, this study further explores the underlying socio-psychological mechanism. A survey was conducted with 7,962 participants, and multiple linear regressions and structural equation models were applied to analyze the data. We found that (1) the policies that led to inconvenience whetted the dissemination of risk information and restricted the sharing of positive-emotion content; (2) “status-led” motivation was the critical reason why people shared both types of messages; and (3) Third-Person Perception mediated both of these influences. These findings expand our understanding of social communication during public health crises, particularly how the Third-Person Effect works.