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This study aimed to explore the spatial accessibility dynamics of urban parks and their driving forces from 1901 to 2010 in terms of the dynamic relationships between spatial morphology and road networks, taking Nanjing City as an example.

This study mapped and examined the spatiotemporal distribution of urban parks and road networks in four time points at Nanjing: the 1910s, 1930s, 1960s and 2010s, using the analysis methodology of spatial design network analysis, kernel density estimation and buffer analysis. Two approaches of spatial overlaying and data analysis were adopted to investigate the accessibility dynamics. The spatial overlaying compared the parks' layout and the road networks' core, subcore and noncore accessible areas; the data analysis clarified the average data on the city-wide and local scales of the road networks within the park buffer zone.

The analysis of the changing relationships between urban parks and the spatial morphology of road networks showed that the accessibility of urban parks has generally improved. This was influenced by six main factors: planning implementation, political policies, natural resources, historical heritage and cultural and economic levels.

The results provide a reference for achieving spatial equity, improving urban park accessibility and supporting sustainable urban park planning.

An increasing number of studies have explored the spatial accessibility of urban parks through the relationships between their spatial distribution and road networks. However, few studies have investigated the dynamic changes in accessibility over time. Discussing parks' accessibility over relatively long-time scales has practical, innovative and theoretical values; because it can reveal correlational laws and internal influences not apparent in short term and provide reference and implications for parks' spatial equity.

Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.

An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.

The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.

The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

The purpose of this paper is to discover similarities and differences in the construction industry in China and the United States by using data analytic tools on data crawled from social media platforms.

The method comprised comprehensive data analytics using network link analysis and natural language processing tools to discover similarities and differences of social networks, topics of interests and sentiments and emotions on different social media platforms.

From the research, it showed that all clusters (construction company, construction worker, construction media and construction union) shared similar trends on follower-following ratios and sentiment analysis in both social media platforms. The biggest difference between the two countries is that public accounts (e.g. company, media and union) on Twitter posted more on public interests, including safety and energy.

The research contributes to knowledge about an alternative method of data collection for both academia and industry practitioners. Statistical bias can be introduced by only using social media platform data. The analyzed four clusters can be further divided to reflect more fine-grained groups of construction industries. The results can be integrated into other analyses based on traditional methodologies of data collection such as questionnaire surveys or interviews.

The research provides a comparative study of the construction industries in China and the USA among four clusters using social media platform data.

As an unhealthy dependence on social media platforms, social media addiction (SMA) has become increasingly commonplace in the digital era. The purpose of this paper is to provide a general overview of SMA research and develop a theoretical model that explains how different types of factors contribute to SMA.

Considering the nascent nature of this research area, this study conducted a systematic review to synthesize the burgeoning literature examining influencing factors of SMA. Based on a comprehensive literature search and screening process, 84 articles were included in the final sample.

Analyses showed that antecedents of SMA can be classified into three conceptual levels: individual, environmental and platform. The authors further proposed a theoretical framework to explain the underlying mechanisms behind the relationships amongst different types of variables.

The contributions of this review are two-fold. First, it used a systematic and rigorous approach to summarize the empirical landscape of SMA research, providing theoretical insights and future research directions in this area. Second, the findings could help social media service providers and health professionals propose relevant intervention strategies to mitigate SMA.

As a new communication paradigm, social networking sites (SNS) have boosted information diffusion and viral marketing. Prior researchers have identified various factors affecting information dissemination on SNS. However, they often focus on limited factors and there is a lack of an integrated theoretical framework that explains aspects of relevant factors. Besides, the research on the impacts of relationships on individual retweeting behavior is still controversial. The purpose of this paper is to propose a theoretical framework to systematically investigate the determinants of individual dissemination behavior on SNS based on the elaboration likelihood model (ELM). Moreover, the authors also examine the relative importance of those relevant factors.

The authors randomly selected 1,250 members of Twitter and crawled posts published by each member since he/she created the Twitter account using Twitter API. The authors processed the data to create panel data and tested hypotheses with the panel logit model.

Factors both on the central route and on the peripheral route of ELM have positive impacts on individual dissemination behavior. Among them, information receiver-related factor and relationships-related factors are the most influential. Contrastingly, source-related factors are the least influential. Furthermore, the authors find that social tie strength mediates almost 50 percent of the effect of value homophily on individual dissemination behavior.

The authors are the first to directly apply ELM to examine individual dissemination behavior on SNS. By integrating factors into the two information processing routes, They incorporate relevant factors into the model and systematically analyze their impacts on individual retweeting behavior on SNS. The research offers at least one explanation for the contradictory findings about the effect of homophily on individual sharing behavior in previous research. The authors propose new variables that gauge topical relevance and interpersonal value homophily on SNS.

Sharing project information is critical for the success of medical crowdfunding campaigns. However, few users share medical crowdfunding projects on their social networks, and the sharing behavior of medical crowdfunding projects on social networking sites has not been well studied. Therefore, this study explored the factors and potential mechanisms influencing users’ sharing behaviors on networking sites.

A research model was developed based on the attribution-affect model of helping and social capital theory. Data were collected using a longitudinal survey. Partial least squares structural equation modeling was used to analyze the collected data. We conducted post hoc analyses to validate the results of the quantitative analysis.

The analysis results verified the effects of perceived external attribution, perceived uncontrollable attributions, and perceived unstable attributions on sympathy and identified the effect of sympathy and social characteristics of medical crowdfunding users on sharing behavior.

This research provides a comprehensive theoretical understanding of users’ sharing behavior characteristics and provides implications for enhancing the efficiency of medical crowdfunding activities.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

In recent years, the proliferation of social commerce (s-commerce) has attracted many researchers to investigate the drivers of individuals' intentions. However, the empirical results reported in these studies were fragmented and inconsistent. This has led various meta-analyses to synthesize these findings, but without including a large number of s-commerce studies. In addition, investigating meta-analytically the effects of moderators such as the six dimensions of Hofstede's national culture is still lacking.

Drawing on nine theories and models, this meta-analysis aims to summarize the findings reported in 109 s-commerce studies published between 2011 and 2021 and to examine the moderating role of national culture. The correlation coefficient (r) has been used as the main effect size for this study. Based on the random-effects method, the CMA V3 software has been employed to calculate the weighted mean effect sizes.

The meta-analysis results showed that all the 11 hypothesized direct relationships are positive and significant. The moderator results also revealed that five out of six cultural dimensions significantly moderate the examined associations.

This research serves to enrich the existing s-commerce literature by addressing contradictory and mixed results reported in the empirical studies. This study is one of the first of its kind to investigate the role of Hofstede's six cultural dimensions as moderators in the field of s-commerce using the meta-analytic techniques.

Despite the immense popularity of WhatsApp, there needs to be more reliable and valid scales for identifying and measuring users' motivation behind frequent WhatsApp status updates. This study explores the underlying motivations of users in updating their WhatsApp statuses and develops a scale to measure them.

Following a mixed-method research design, data were obtained from WhatsApp users in India. The current research furnishes results from five studies, involving both qualitative and quantitative approaches.

The results uncover seven unique motivations of users for keeping WhatsApp status, namely: social good, attention seeking, social relations, expressing views, life updates, inspiration and entertainment. Additionally, the empirical findings demonstrate the effects of the above dimensions on the positive and negative emotions of users.

This research is a pioneering effort in developing and validating a scale on WhatsApp status motivation construct. Key managerial and scholarly implications of the findings arising from this research are also discussed.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.