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With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Effective inter-agency information sharing can facilitate the internal administration and external service delivery of government agencies, as well as help them address some complex social issues and then promote social development. Thus, how to promote the success of inter-agency information sharing has attracted the attention of researchers and public administrators. A lot of research has investigated the influential factors of inter-agency information sharing; however, few studies have taken account of the governmental administrative systems, which may influence the collaborations among agencies. Given the composition of the Chinese governmental administrative systems, this study investigated and compared the factors that influence the inter-agency information sharing in vertical and horizontal dimensions. An extended technology–organization–environment (TOE) framework was used to organize the influential factors. The results show that marked differences in influential factors between vertical and horizontal inter-agency information sharing indeed exist. [ABSTRACT FROM AUTHOR]

Abundant health knowledge resources are available on social media to facilitate technology-enhanced knowledge learning among older adults. The objective of this study is to investigate the predictors and the underlying formation mechanism of older adults' intention to learn health knowledge on social media. We propose a novel model to examine how older adults' emotional state (i.e., health anxiety) and cognitive state (i.e., e-health literacy) during knowledge acquisition influence threat appraisal (i.e. perceived severity and perceived susceptibility) and coping appraisal (i.e. self-efficacy and perceived benefits), thereby shaping older adults' intention to learn health knowledge. Survey data from 337 Chinese older adult users of social media was collected to test the research model. Results reveal that perceived susceptibility, self-efficacy and perceived benefits exert positive effects on older adults' health knowledge learning intention, while the impact of perceived severity on health knowledge learning intention is not statistically significant; health anxiety is positively correlated with perceived severity and perceived susceptibility, and e-health literacy is a powerful predictor of self-efficacy and perceived benefits. This paper enriches the literature related to technology-enhanced knowledge learning and online health behavior among older adults. Effective strategies are proposed based on the findings for practitioners dedicated to promoting health knowledge via social media and older adults who apply health knowledge to address health-related needs. [ABSTRACT FROM AUTHOR]

Online health communities (OHCs) are important online health communication channels for older adults with diabetes to access health information. When seeking health information, they often disclose a variety of contextual information (e.g., socio-economic situations) in their questions. Selective contextual information disclosure is a type of communication strategy for users in OHCs to elicit replies from others. In this study, we adopted text analysis to investigate what contextual information older adults with diabetes disclose to articulate their information needs and used the fixed-effect Poisson model to examine the relationships between different types of contextual information disclosure and informational support receipt. Our analyses were based on a dataset of 4,505 questions and corresponding replies from an online diabetes community. The results showed that cognitive information is the most frequently disclosed contextual information, while older adults tend to disclose demographic information in their questions less. Providing demographic and situational details in questions can enhance informational support receiving, resulting in an increased number of informational supports. However, disclosing cognitive, affective, informational channels, or support information does not significantly affect the informational support receiving. These findings can contribute to extending our existing understanding of information seekers’ communication strategies in OHCs. [ABSTRACT FROM AUTHOR]

With the popularization of massive open online courses (MOOCs) in the e-learning landscape, more and more older adults are participating in MOOC learning activities. Understanding older adults' learning motivations in MOOCs will help MOOC developers design suitable learning systems and appropriate course content for older learners. Using a content analysis method, this study identifies six types of learning motivations of older adults in MOOCs: solving problems, acquiring knowledge, improving cognition, seeking fun, benefiting others, and social contact, among which improving cognition is unique to older adults. Based on the above classification, we found that the learning motivations among older adults vary with age and gender. The findings enrich the theories of education for older adults and of learning motivation, and can be used to improve the design of MOOCs for older adults. [ABSTRACT FROM AUTHOR]