During and after the Covid-19 outbreak, people’s precautionary measures of not visiting public venues like cinema halls or multiplexes were replaced by watching treasured videos or films in private settings. People are able to watch their favourite video contents on a variety of internet-connected gadgets thanks to advanced technologies. As a result, it appears that the Covid-19 outbreak has had a substantial impact on people’s inclination to continue using video streaming services. This study attempted to establish an integrated framework that describes how people change their health behaviours during pandemic conditions using the health belief model (HBM), as well as the mediating effect of HBM constructs over ECM constructs such as continuous intention to subscribe to OTT video streaming services among subscribers. The study looked at the impact of three perceived constructs, susceptibility, severity, and self-efficacy, on the confirmation/adoption of over-the-top (OTT) video streaming services during the lethal pandemic (Covid-19). The study focused on new OTT video streaming service subscribers, and 473 valid replies were collected. Path analysis and multivariate analytical methods, such as structural equation modelling (SEM), were used to estimate construct linkages in the integrated framework. Perceived severity has been identified as the most influential factor in confirmation/adoption, followed by perceived susceptibility. The results also showed that satisfied users/subscribers are more likely to use OTT video streaming services. The mediators, confirmation/adoption, perceived usefulness, and satisfaction were used to validate the influence of perceived susceptibility on continuance intention. Furthermore, contactless entertainment enhances security for users/subscribers by allowing them to be amused across several internet-based venues while adhering to social distance norms.

This study examines the impact of emotional intelligence (EI) and employee motivation on employee performance within the telecommunication industry in the Sultanate of Oman. The target population consisted of 4344 non-managerial employees across nine telecommunication companies, including Omantel, Ooredoo, Vodafone, Oman Broadband Company, Awasr Oman & Co, TEO, Oman Tower Company L.L.C, Helios Tower, and Connect Arabia International. Employing a deductive research approach, finally data were collected via an online survey from 354 respondents. The hypotheses were tested using multiple regression analysis. The results indicate that all dimensions of EI self-awareness, self-regulation, empathy, and social skills positively and significantly influence employee performance, with social skills having the strongest effect. Furthermore, both intrinsic motivation factors, such as work itself and career development, and extrinsic motivation factors, including wages, rewards, working environment, and co-worker relationships, significantly enhance employee performance. The interaction between EI and employee motivation was found to amplify these positive effects. Among control variables, age and education level showed significant impacts, while gender did not. These findings underscore the critical role of both emotional intelligence and motivation in driving employee performance. The study suggests that managers and policymakers should adopt integrated strategies that develop EI competencies and enhance motivational factors to optimize employee performance, thereby contributing to the success of organizations in the telecommunication sector.

This study scrutinizes the allocation of financial aid for climate change adaptation from OECD/DAC donors, focusing on its effectiveness in supporting developing countries. With growing concerns over climate risks, the emphasis on green development as a means of adaptation is increasing. The research explores whether climate adaptation finance is efficiently allocated and what factors influence OECD/DAC donor decisions. It examines bilateral official development assistance in the climate sector from 2010 to 2021, incorporating climate vulnerability and adaptation indices from the ND-GAIN Country Index and the IMF Climate Risk Index. A panel double hurdle model is used to analyze the factors influencing the financial allocations of 41,400 samples across 115 recipient countries from 30 donors, distinguishing between the decision to select a country and the determination of the aid amount. The study unveils four critical findings. Firstly, donors weigh a more comprehensive range of factors when deciding on aid amounts than when selecting recipient countries. Secondly, climate vulnerability is significantly relevant in the allocation stage, but climate aid distribution does not consistently match countries with high vulnerability. Thirdly, discerning the impact of socio-economic vulnerabilities on resource allocation, apart from climate vulnerability, is challenging. Lastly, donor countries’ economic and diplomatic interests play a significant role in climate development cooperation. As a policy implication, OECD/DAC donor countries should consider establishing differentiated allocation mechanisms in climate-oriented development cooperation to achieve the objectives of climate-resilient development.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

This paper provides a disaster resilience-based approach. For the definition of the approach, a three-step method (definition of components, analysis of the resilience pillars and definitions of resilience-based actions) has been followed. To validate the approach, an application scenario for mitigating the COVID-19 pandemic is provided in the paper. The proposed approach contributes to stimulating the co-responsibility quadruple helix of actors in the implementation of actions for disaster management. Moreover, the approach is adaptable and flexible, as it can be used to manage different kinds of disasters, adjusting or changing itself to meet specific needs.

Taking Guizhou University of Commerce as an example, the author pointed out the problems in current Japanese language teaching in universities and proposed improvement measures from three aspects, aiming to explore a suitable path for Japanese language teaching in commerce colleges.