Enhancing employee job satisfaction through organizational climate and employee happiness at work: a mediated–moderated model - Yu Jianchun (BMC Psychology)
Abstract Background The Chinese educational sector is dynamic; hence, there is a need to anchor the factors that influence faculty job satisfaction and performance. These are channeled through organizational climate (OrgC) and employee happiness (EmH). The growing integration of artificial intelligence applications (AIAs)—like ChatGPT—into the learning environment raises questions about AIAs’ moderating role in the relationship between EmH at work and EJoS. Purpose This research empirically examines the influence of OrgC on EmH, the direct and mediated impacts of EmH on EJoS, and the moderating effect of AIAs on the influence of EmH on EJoS. Design/Methodology Data was collected from faculty members of various Chinese universities. Using SmartPLS version 4.1, I have analyzed six hypotheses and the corresponding research questions. Findings The outcomes include favorable effects of OrgC on EmH and EJoS. EmH significantly correlates with EJoS, partially mediating the relationship between OrgC and EJoS. Interestingly, the research did not find evidence that AIAs moderated the relationship (ChatGPT) between EmH and EJoS. The predictors (OrgC and EmH) and moderation of AIAs explained a 51.9% change in EJoS, and EJoS explained a 13.3% variance in employee job performance. Conclusion This study’s findings support a supportive OrgC as the key instrument for improving employees’ happiness and job satisfaction. AI assistants, such as ChatGPT, provide relative efficiency and support but do not significantly affect how EmH at work relates to job satisfaction.