As the coronavirus disease 2019 pandemic causes a general concern regarding the overall mental health of employees worldwide, policymakers across nations are taking precautions for curtailing and scaling down dispersion of the coronavirus. In this study, we conceptualized a framework capturing recurring troublesome elements of mental states such as depression and general anxiety, assessing them by applying standard clinical inventory. The study explores the extent to which danger control and fear control under the Extended Parallel Processing Model (EPPM) threat impact job insecurity, with uncertainty phenomenon causing afflicting effect on the experiential nature of depression heightened by anxiety. With the aim to explore the job insecurity relationship with anxiety and depression, and measure the impact of EPPM threat, an empirical study was conducted in the United States on a sample of 347 white collar employees. Demographic data, EPPM threat, job insecurity, anxiety, and depression data were collected via a standardized questionnaire during the coronavirus disease 2019 (COVID-19) pandemic. The questionnaire consisting of multi-item scales was distributed online. All the scale items were evaluated on a 5-point Likert scale. SEM software AMOS version 23 was used to perform confirmatory factor analysis with maximum likelihood estimation. In the structural model, relationships between the threat of COVID-19, job insecurity, anxiety, and depression were assessed. The findings of the study suggest that job insecurity has a significant impact on depression and anxiety, whereas the threat of COVID-19 has a significant impact on depression. Mediating effects of job insecurity and EPPM threat impact on anxiety were not established in the study. The study contributes to the apprehension of the repercussions of major environmental disruptions on normal human functioning, and it investigates the effects of self-reported protective behaviors on risk perception. The study also explains the underlying mechanisms of coping behavior as possible antecedents to mental disorders. When subjected to stressful events, heightened psychological arousal causes physical and psychological challenges of affected employees to manifest as behavioral issues.

This study has aimed to examine the empirically relationship between strategic management of organizational knowledge and awareness about artificial intelligence (AI) through mediating effect of learning climate in service sector of Saudi Arabia. For better understanding, a structural questionnaire was developed, and both questionnaire and interview techniques were applied to collect the data from targeted respondents. Statistical methods like confirmatory factor analysis, structural equation modeling and mediation technique were applied to check the direct and indirect effects with overall valid responses of 390. Findings under factor analysis specify that all the indicators of strategic management of knowledge, learning climate and awareness about AI have demonstrated good factor loadings. Additionally, results under structural equation model for strategic management of knowledge and awareness about AI indicates that knowledge acquisition, knowledge dissemination and knowledge responsiveness have their significant and positive relationship with AI awareness. While mediating effect of learning climate (LC), shows that relationship between knowledge acquisition and AI, between knowledge dissemination and AI is significantly mediated by LC under full sample. While mediating effect of LC between knowledge responsiveness and AI is not significant. In addition, practical implications of this study reveals the fact that management in all three layers of the business firms should consider these findings and positive role by learning climate to promote awareness about AI. However, this study has observed several limitations. At first overall impact of strategic management of knowledge is yet to be explored as this study has examined its impact through knowledge acquisition, dissemination, and responsiveness factors under sperate models. At second, study has a regional limitations and specific organizational implications only for the service sector is observed. At third, earlier studies have raised serious concern over data collection through questionnaire as it covers only one dimensions of employee's attitude. Other instruments like open ended non-structural interview should be reconsidered in more significant manner. Future studies could be reimplemented on similar topic while addressing these limitations.

Knowledge-management (KM) has received much attention because of the rapid growth of computers and information technology in shaping products and services. While new learning and innovations added value to the existing products and services, they also added a new dimension to economy, popularly coined as knowledge-based economy. The 21st century is often described as an era of "knowledge capitalism." This study has attempted to find links between KM practices and modern education and learning. This study has shown how KM practices can be used for education, training and learning purposes which in turn can help organizations leverage the skills and expertise of their workers and transform them into knowledge capital. This study also aims to identify such KM practices that create a knowledge economy. Data has been collected from six universities in public and private sectors in Saudi Arabia where KM practices can be seen and which have contributed to the national economy. Contribution/Originality: This study is going to be a useful contribution to the domain of knowledge economy in the Saudi Arabian context, particularly when the country is looking for a non-oil based economy, pursuing the 2030 vision. This study shows how learning and KM practices build up a knowledge economy. 2015) to help organizations become more competitive (Grant, 1996) to replicate and validate and update the existing knowledge (Shattock, 2009) through investment in R&D and training and development (Bratianu et al., 2011).