In the current competitive global marketplace, innovation is key for high-tech firms to thrive. Open innovation offers a promising approach, but its effectiveness remains unclear. Therefore, this research explored the connection between open innovation, knowledge management capability, and innovation performance within high-tech firms. We used a mediation approach to highlight the central role of knowledge management capability in the relationship between open innovation and innovation performance. We used a survey questionnaire approach to collect data from the 462 employees of high-tech firms on open innovation, knowledge management capability, and innovation performance using a convenient sampling technique. We used partial least square structural equations modeling through PLS-SEM statistics. Results indicated that open innovation has a direct, positive and significant connection with innovation performance. Similarly, the current research serves as a pioneering exploration into mediation analysis, highlighting the mediating role of knowledge management capability that influences the relationship between open innovation and innovation performance. Empirical studies offer valuable insights for leaders of high-tech firms, guiding them to identify effective knowledge management practices and determine the ideal extent of open innovation to boost innovation performance. The current study reveals novel insights into the benefits of knowledge management capability in enhancing open innovation efforts within firms. This research provides valuable implications and future research directions.

Problem statement: An environmentally conscious consumer’s perspective can shift as they look for things that are gentler on the planet. Conversely, businesses engage in greenwashing when they try to cover up their lacklustre environmental initiatives. The current research was used the theory of rational choice behaviour to examine a model that connects corporate green washing and consumers’ green purchase intentions via the mediating roles of perceived risk, green trust and green confusion about food and beverage brands in Saudi Arabia. Research motivation: Sustainable business practices have been developed and adopted by corporations in response to the growing interest in environmentally friendly lifestyles and green products. However, green washing has become increasingly common as a means for businesses to give off the impression that they care about the environment when they really don’t. Research methodology: The online survey was used to obtain data directly from consumers about their views on green washing by corporations. Primary data was analysed using appropriate statistical tools and techniques in SPSS, AMOS and SmartPLS software, such as Correlation, Regression, Structural Equation Modelling (SEM), etc. Results: In terms of perceived greenness and confusion, the results showed that green wash mediates the relationship between green purchasing intention and greenness. There is a two-way correlation between consumers’ intentions to buy environmentally friendly products and their levels of green perception, and green confusion. The findings of this study were broadening our understanding of the consequences of green washing. Conclusions: All things considered, the study was encouraging more research on the subject and be a useful tool for academics, corporate managers, and students interested in environmental sustainability, product innovation, and green branding. According to the results, businesses can improve their green purchasing intentions by cutting down on green washing and focusing instead on building a positive reputation for their brand and encouraging customer loyalty. Corporate performance and social environment sustainability can both benefit greatly from this paper’s expansion of knowledge regarding the processes of individual customer psychological effects after perceptions of corporate greenwashing behaviour.

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.