Global research trends on Over-The-Top (OTT) media services were examined through a bibliometric and systematic review of 176 peer-reviewed documents published between 2010 and 2024, sourced from 117 journals and books. The analysis conducted using Bibliometrix, VOSviewer, and the PRISMA methodology identified a compound annual growth rate of approximately 34% in scientific output from 2010 to 2023, indicating rapid expansion in OTT research. The most prolific contributors by publication count are Chakraborty, Soren, and Sridevi. The leading countries in this field are India, South Korea, and the United States. Key research themes include platform dominance, consumer behaviour, and the impact of COVID-19 on OTT adoption, with “OTT” and “Netflix” as dominant topics in scholarly discussions. Although the data were sourced exclusively from Scopus, the findings offer a comprehensive overview of the evolution of themes, principal research networks, and prevailing trends in the OTT research domain.

Organizational commitment (OC) has gained popularity in recent times. It’s a very crucial determinant of employee retention, productivity, and a contributor to organizational growth and prosperity. The role of a faculty is very important in shaping the careers of students and in the overall growth of higher education institutions. The current study emphasizes how job-related factors like designation and demographic profiles like age, gender, education qualification, marital status, and area of belongingness affect OC of the faculty working in the HEIs of Uttarakhand, India. A simple random technique was used to determine the sample of the study; the sample of the study was 235 faculty members engaged in the higher education institutions (HEIs). 15 items were adapted from the questionnaire on OC developed by Mowday. To collect the data, an online questionnaire was sent to the faculty engaged in the HEIs through email and the WhatsApp application. To check the internal consistency, Cronbach's Alpha test was applied. The data were distributed normally, hence a parametric test was used. The study reveals that age, gender, experience, and marital status influence OC, and designation and area of belongingness have no impact on the OC. The policymakers need to develop strategies and policies keeping in mind both demographic and job-related factors to embrace and foster commitment amongst the employees.

This study develops an integrated model to explain e-commerce adoption among agricultural producers by combining the Technology–Organisation–Environment (TOE) framework with social identity theory (SIT). Drawing on cross-sectional data from 585 farmers in Shanxi Province, China, and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the research assesses both the direct and mediating effects of technological readiness, organisational barriers, and environmental enablers. Results reveal that social identity acts as a critical mediator, transforming indirect contextual drivers into adoption behaviour. Notably, factors such as complexity and digital environmental change influence adoption exclusively through social identity rather than direct paths. These findings advance existing literature by embedding sociopsychological mechanisms into digital adoption models, and offer practical guidance for promoting inclusive e-commerce development in rural and agricultural contexts, particularly in developing regions like rural China.

As the global tourism industry becomes increasingly shaped by digital transformation, the strategic role of Information and Communication Technologies (ICTs) has gained particular significance, especially in emerging economies. Albania, with its growing appeal as a tourist destination and its economic reliance on tourism, presents a compelling context in which to examine the digital maturity of tourism enterprises. This study investigates the adoption of ICT tools—particularly websites and e-marketing practices—among Albanian tourism businesses and assesses the impact of managerial attitudes and digital infrastructure on perceived business performance. Grounded in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the research employs a quantitative survey methodology, collecting responses from 208 enterprises across five key tourism regions. The findings reveal that 81% of these enterprises maintain an active website, with adoption rates significantly higher in coastal areas such as Durrës and Sarandë. Hierarchical regression analysis demonstrates that a favorable perception of ICTs is positively associated with ICT-based marketing strategies (r = 0.243, p < 0.01) and reported profit growth (R² = 0.291). Although high initial website development costs are acknowledged as a barrier, they are also correlated with long-term profitability, reflecting a growing recognition of ICT as a strategic asset rather than a cost burden. The results support the dual theoretical lens: TAM explains the behavioral inclination toward digital tools, while RBV underscores their value as inimitable resources for sustained competitive advantage. The study highlights the need for targeted government policies, including digital upskilling programs, infrastructure investment, and support for small enterprises lagging in digital readiness. Future research could expand this inquiry through longitudinal and cross-country analyses, exploring how emerging technologies—such as AI-driven personalization, immersive media, and data analytics—reshape destination competitiveness in the digital age.

The proliferation of Generative AI (GenAI) tools has introduced new dynamics in user behaviour, environmental perception, and digital sustainability. This study, based on a primary questionnaire survey of 1,005 GenAI users aged 18 and above from India, investigates the frequency of GenAI usage and its relationship with climate change awareness, environmental concern, and willingness to adopt energy-efficient digital practices. Using regression-based models, the research reveals a pattern of indirect dependence: lower GenAI usage is related with a greater inclination toward environmentally responsible behaviours, such as transitioning from non- sustainable platforms and adopting energy-efficient digital services. In contrast, frequent GenAI users tend to perceive climate change as temporally distant and of lower immediate importance.

The study also examines how the frequency and nature of social media usage influence users’ attitudes toward sustainable technology choices. These findings provide valuable insights for policymakers, AI educators, digital strategists, and sustainability advocates aiming to foster environmentally conscious technology adoption in emerging economies like India.

Accurately diagnosing emotional and psychological disorders is essential for prompt mental health interventions, especially in intelligent healthcare systems. This paper proposes a deep learning model that uses convolutional neural networks (CNN) and long short-term memory (LSTM) networks to classify emotional states based on physiological inputs like EEG and ECG. Bayesian optimisation improves the model's learning efficacy and generalisation ability by adjusting hyperparameters. In comparison to conventional machine learning models such as Support Vector Machines (SVM), random forest, and standalone deep learning models (CNN and LSTM), the proposed CNN-LSTM architecture increases classification accuracy by 25%, to 92.1%. Its exceptional performance is demonstrated by its AUC-ROC score of 0.96, accuracy of 0.93, recall of 0.91, and F1-score of 0.92. These results show that the model can distinguish between several emotional states, including neutral, tense, and concerned. A real-time application is used to investigate the potential of wearable EEG-based brain-computer interface (BCI) devices for continuous emotional monitoring. The findings indicate that the proposed framework might be a helpful tool for the early detection and tailored management of mental health conditions in intricate healthcare environments.