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Social networking site use, personality, user habit, and subjective wellbeing: a Kazakhstani pilot study
Considering the prevalence and increased use of online social networking sites (SNSs), the present study investigated the association between visiting SNSs and users' subjective wellbeing. Data were collected from 251 participants and were analyzed using partial least square-based structural equation modeling. The findings showed that there was no significant direct influence of SNS use on users' subjective wellbeing. Additionally, the study found a significant mediating influence of passion on the association between SNS visits and subjective wellbeing. Moreover, the study did not find any significant negative mediating impact of obsession with SNS visits and association with subjective wellbeing. Among various personality traits, openness to experience had a positive moderating impact and neuroticism had a negative moderating impact on the association between SNS visits and subjective wellbeing among SNS users. The study provides implications for managers and parents regarding improved SNS use and increased subjective wellbeing
An Examination of Missing Person Social Media Engagement Through Data Mining and Experimentation: An Application of the Crisis and Emergency Risk Communication Model
According to the Federal Bureau of Investigation (FBI), approximately 600,000 individuals are reported missing each year in the United States (2022). When missing person cases do not meet alert (e.g., AMBER) criteria, law enforcement often utilize social media to crowdsource information to ultimately return the missing home. Therefore, guided by the crisis and emergency risk communication model (CERC; Reynolds & Seeger, 2005) and its recently clarified propositions (Miller et al., 2021), the purpose of this dissertation was to (a) identify strategies law enforcement use to crowdsource missing person information and (b) experimentally test message characteristics that facilitate prosocial sharing of missing person posts on social media. In study one, a quantitative content analysis of 600 extracted missing person X (Twitter) posts identified that all CERC model message characteristics (i.e., timeliness, accuracy, source credibility, empathy, action-orientation, respect) were present in current law enforcement crowdsourcing posts. Additionally, a linear regression analysis indicated that timeliness, empathy, and respect predict message engagement (i.e., retweets, likes, replies) and were used to inform experimental messages in study two. In study two, participants (N = 377) who were 18 years or older and use X (Twitter) were randomly assigned one pilot tested experimental missing person message (i.e., timeliness, empathy, respect, or control). Parallel multiple mediation analyses indicated that timeliness is positively related to self-efficacy and uncertainty; empathy is positively related to self-efficacy, knowledge of risks and resources, and emotional turmoil; and respect is positively related to self-efficacy and uncertainty as well as negatively related to emotional turmoil. Additionally, self-efficacy, uncertainty, and emotional turmoil are positively related to behavioral intention whereas only self-efficacy and emotional turmoil can predict actual behavior. Finally, indirect relationships exist between timeliness and behavioral intention through self-efficacy and uncertainty; empathy and behavioral intention through self-efficacy and emotional turmoil; as well as respect and behavioral intention through self-efficacy, uncertainty, and emotional turmoil. This inquiry offers theoretical implications by being one of the first to experimentally investigate the recently clarified propositions of the CERC model. Practically, this work provides law enforcement with clear recommendations on crafting missing person messages on social media