Predicting Communicative Behavior of Higher Institutions Employees during COVID-19 Pandemic: A Conceptual Assessment

Over the last three decades, a series of research has revealed how strategic communication may have a profound effect on how members of society behave. More precisely, communication theories have documented the media's ability to determine the agenda in society. The situational theory of problem-solving is particularly interesting because it explains why and how individual members of society communicate during a problematic situation. This hypothesis has been evaluated in a variety of contexts to determine its efficacy. However, few studies have been conducted to examine the influence of communication from corporations or governments to community members. The character of persons, particularly employees' information seeking and sharing behavior, has been significantly overlooked in the context of COVID-19. The purpose of this study is to investigate theoretically how employees to process, seek, forward, filter, choose, and exchange information regarding COVID 19.

Understanding the Health Behavior Decision-Making Process with Situational Theory of Problem Solving in Online Health Communities: The Effects of Health Beliefs, Message Credibility, and Communication Behaviors on Health Behavioral Intention

Online health communities (OHCs) offer users the opportunity to share and seek health information through these platforms, which in turn influence users’ health decisions. Understanding what factors influence people’s health decision-making process is essential for not only the design of the OHC, but also for commercial health business who are promoting their products to patients. Previous studies explored the health decision-making process from many factors, but lacked a comprehensive model with a theoretical model. The aim of this paper is to propose a research model from the situational theory of problem solving in relation to forecasting health behaviors in OHCs. An online questionnaire was developed to collect data from 321 members of online health communities (HPV Tieba and HPV vaccina Tieba) who have not received an HPV vaccination. The partial least squares structural equation modeling (PLS-SEM) method was employed for the data analysis. Findings showed that information selection and acquisition is able to forecast HPV vaccination intentions, perceived seriousness and perceived susceptibility can directly impact HPV vaccination intention and have an indirect impact by information selection and acquisition, and perceived message credibility indirectly affected HPV vaccination intention via information selection. The current paper supports health motivations analysis in OHCs, with potential to assist users’ health-related decision-making.