The alleviation effect of knowledge integration on anxiety in parental vaccine decision-making supported by online communities

Purpose To support vaccine decision-making, a growing number of parents use online communities to obtain informational and emotional support; however, relatively high information heterogeneity and polarization in the online environment make it challenging for parents to make informed vaccine decisions based on the systematic processing of conflicting information. In this context, this study aims to focus on the relationship between parents’ knowledge integration and rational and experiential decision-making and the mediating effect of anxiety on this relationship. Design/methodology/approach A theoretical model incorporating the direct and indirect effects of knowledge integration and anxiety on decision-making is proposed and tested through partial least squares structural equation modeling with survey data from 223 parents. Findings Knowledge integration negatively affects anxiety. Knowledge integration has a direct positive effect on rational decision-making and an indirect negative effect on experiential decision-making. Practical implications These insights into the key role of knowledge integration in parental vaccine decision-making under information heterogeneity and polarization provide support for practical strategies to encourage knowledge integration and alleviate anxiety in online communities. Originality/value This study underscores the importance of knowledge integration in vaccine decision-making under information heterogeneity and polarization and reveals distinct mechanisms underlying the effects of knowledge integration on decision-making dominated by rational and experiential modes. The findings also provide insights into the information processing mechanisms underlying the knowledge integration of subjects with insufficient prior knowledge in the non-organizational context.

A Rule-Based Reasoner for Underwater Robots Using OWL and SWRL

Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement “If … Then … Else …”. Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.