The influence of educational and emotional support on e-learning acceptance: An integration of social support theory and TAM.

Flexible education is considered the primary function of e-learning, however, empirical evidence during the COVID-19 pandemic has also demonstrated that students may seek emotional comforts in e-learning to alleviate their negative emotions. This study aims to provide a holistic view of the antecedents of college students' e-learning acceptance by integrating social support theory with the technology acceptance model. Specifically, drawing upon social support theory, this study adopted perceived educational support and perceived emotional support as two driving factors and examined their influences on students' continuous intention in e-learning. The model was empirically validated using survey data from 512 college respondents in China during the first wave of the pandemic. Our results suggested that while perceived educational support exerts a major influence on e-learning acceptance, perceived emotional support also has an important role to play. Besides, the analytics results suggested that the two facets of support had different influencing patterns: perceived educational support has a positive and significant relationship with both perceived ease of use and perceived usefulness, whereas perceived emotional support solely has a significant relationship with perceived ease of use. Additionally, compared with the prior studies, the effect size ( ß ) between perceived ease of use and perceived usefulness is larger in the present study (COVID-19 context). These findings stress the need to better understand the mechanism by which social support influences college students' e-learning acceptance and to make use of various kinds of social supports to enhance perceived ease of use (e.g. human-computer interface), promote perceived usefulness, and ultimately motivate more students' continuance intention in e-learning.

The public needs more: The informational and emotional support of public communication amidst the Covid-19 in China.

Public communication is critical for responding to disasters. However, most research on public communication is largely focused on its informational support function, overlooking the emotional support that could equally offer. This study takes the lead to investigate their separate impacts. In particular, the variable public engagement, which is a function of the number of Shares, Likes, and Comments in a particular post, is introduced to benchmark the effect of public communication. Besides, considering the evolving nature of the crisis, their dynamic impacts across different COVID-19 pandemic stages are examined. Data from Dec 2019 to Jul 2020 were collected from 17 provincial government-owned social media (Weibo) accounts across COVID-19 in China with a Natural Language Processing-based method to compute the strengths of informational support and emotional support strength. An econometric model is then proposed to explore the impacts of two supports. The findings are twofold: the impact of emotional support on public engagement is empirically confirmed in the study, which is not in lockstep with the informational support; and their impacts on public communication are dynamic rather than static across stages throughout the crisis. We highlighted the importance of emotional support in public engagement by deriving its impact separately from informational support. The findings suggest incorporating both social supports to create stronger public communication tactics during crises.

The public needs more: The informational and emotional support of public communication amidst the Covid-19 in China

Public communication is critical for responding to disasters. However, most research on public communication is largely focused on its informational support function, overlooking the emotional support that could equally offer. This study takes the lead to investigate their separate impacts. In particular, the variable public engagement, which is a function of the number of Shares, Likes, and Comments in a particular post, is introduced to benchmark the effect of public communication. Besides, considering the evolving nature of the crisis, their dynamic impacts across different COVID-19 pandemic stages are examined. Data from Dec 2019 to Jul 2020 were collected from 17 provincial government-owned social media (Weibo) accounts across COVID-19 in China with a Natural Language Processing-based method to compute the strengths of informational support and emotional support strength. An econometric model is then proposed to explore the impacts of two supports. The findings are twofold: the impact of emotional support on public engagement is empirically confirmed in the study, which is not in lockstep with the informational support;and their impacts on public communication are dynamic rather than static across stages throughout the crisis. We highlighted the importance of emotional support in public engagement by deriving its impact separately from informational support. The findings suggest incorporating both social supports to create stronger public communication tactics during crises.

Exploring the pathway from seeking to sharing social support in e-learning: an investigation based on the norm of reciprocity and expectation confirmation theory.

E-learning is increasingly evidenced as a conduit for social support exchange among students and protects them against mental health issues, however, the cognitive process of how social support is exchanged remains unclear. This study uses a cognitive theory approach to achieve a more nuanced explanation of students sharing or giving social support in e-learning. Specifically, we adopted the Norm of Reciprocity and Expectation Confirmation Theory to reveal the interconnection between students' perceived social support and giving social support in learning. The model is empirically validated based on survey data of 512 respondents from college students across China regarding their e-learning experience during the first wave of Covid-19. Our findings suggest that the relationship between perceived social support and giving social support is significant and positive, and this relationship is partially mediated by confirmation of social support. These findings imply that e-learning can foster a self-reliant environment for social support exchange among college students. Under such an environment, students' cognitive process in their seeking and sharing social support is majorly following the norm of reciprocity and secondary expectation confirmation theory.

E-learning intention of students with anxiety: Evidence from the first wave of COVID-19 pandemic in China.

BACKGROUND: The COVID-19 pandemic has exposed the need to address the mental health issues for the future adoption of e-learning among massive students in higher education. This study takes a lead to investigate whether and how general anxiety will influence college students' e-learning intention to provide knowledge to better improve the e-learning technology. METHODS: We adopted the Technology acceptance model (TAM) to examine the difference between students with and without general anxiety in the e-learning intention where the students are classified based on the General Anxiety Disorder-2 scale. The model is empirically analyzed based on a survey of 512 college students in China regarding their e-learning experience in the first wave of the COVID-19 pandemic. RESULTS: Results demonstrate that the TAM is powerful in explaining the e-learning intention among college students with general anxiety. Besides, all effects associated with perceived usefulness (PU) are reinforced while those associated with perceived ease of use (PEOU) are attenuated in the anxiety group. The results suggest that instructors and higher education institutions should take advantage of the significant PU-intention relationship by providing quality e-learning, which is paramount to coping with the general anxiety among students. LIMITATIONS: This study provides a prototype attempt to investigate the influence of anxiety on e-learning where the different types of anxiety sources are synthesized. However, anxiety can stem from internal sources (computer anxiety, academic stress) and external sources (fear of the virus, lack of social interaction), which requires further investigations.

Understanding the impact of emotional support on mental health resilience of the community in the social media in Covid-19 pandemic.

BACKGROUND: Emotional support in social media can act as a buffer against the negative impact of affective disorders. However, empirical evidence relating to emotional support in social media and how it influences the wider public remains scanty. The objective of this study is therefore to conduct a prototype investigation into the translation mechanism of emotional support in social media, providing empirical evidence for practitioners to use to tackle mental health issues for the wider public. METHODS: A regression model is proposed to examine the relationship between perceived and received emotional support. Received emotional support is set as the dependent variable and measured using public activity. Perceived emotional support is derived using Natural Language Processing (NLP)-based content analysis. The model is then analyzed using a panel date with a total number of 61,297 posts from 17 Weibo accounts in 17 provincial administrative units in China. RESULTS: The relationship between perceived and received emotional support is not linear but complex, suggesting that translation of emotional support is not automatic. Further, our empirical evidence suggests that the translation of emotional support in social media is affected by frequency and pandemic stage. LIMITATIONS: The study does not examine the direct relationship between perceived and received emotional support, instead adopting public activity as a proxy for the latter construct. In addition, the relationship between perceived and received emotional support is more complex than linear, requiring further model and theory development.