In the digital era, technology innovation and adoption trigger economic growth and enhance CO₂ emissions through productivity, which places it in the mainstream policy debate. For BRICS economies, this paper uses the first method proposed in the literature to quantify their information and communication technology (ICT) and innovatively links each country to their information technology adoption rate, as a surrogate indicator for measuring information and communication technology. Environmental Kuznets curve evidence is also examined, using technology innovation, technology adoption, and trade openness as the control variables for sustainable development. The results show that two out of three technology innovation instruments, fixed telephone, and broadband subscriptions increase CO₂ emissions. Simultaneously, mobile cellular subscriptions have a lowering effect on CO₂ emission in BRICS. The technology adoption indicators, high-technology exports, and electric power consumption also cause an upsurge in CO₂ emission. Moreover, trade openness also enriches the level of CO₂ emission in the BRICS regions. There is a need to devise technology innovation and adoption policies to better use technology and to ensure a green environment.

In this study, we attempt to evaluate the user preferences for web design attributes (i.e., typography, color, content quality, interactivity, and navigation) to determine the trust, satisfaction, and loyalty for uncertainty avoidance cultures. Content quality and navigation have been observed as strong factors in building user trust with e-commerce websites. In contrast, interactivity, color, and typography have been observed as strong determinants of user satisfaction. The most relevant and interesting finding is related to typography, which has been rarely discussed in e-commerce literature. A questionnaire was designed to collect data to corroborate the proposed model and hypotheses. Furthermore, the partial least-squares method was adopted to analyze the collected data from the students who participated in the test (n = 558). Finally, the results of this study provide strong support to the proposed model and hypotheses. Therefore, all the web design attributes were observed as important design features to develop user trust and satisfaction for uncertainty avoidance cultures. Although both factors seem to be relevant, the relationship between trust and loyalty was observed to be stronger than between satisfaction and loyalty; thus, trust seems to be a stronger determinant of loyalty for risk/high uncertainty avoidance cultures.

The analysis aims to examine the impact of eco-innovation and financial efficiency on CO₂ emissions and renewable energy consumption in highly polluted Asian economies, including China, India, Russia, and Japan. For empirical analysis, we have applied the ARDL pooled mean group (ARDL-PMG) model. The long-run estimated coefficient of environmental innovations is positively significant in both renewable energy models and negatively significant in the CO₂ emissions model. These results imply that environmental innovations help facilitate renewable energy consumption and reduce CO₂ emissions. On the other side, the estimates of financial development are insignificant in both renewable energy and CO₂ emissions models. However, the estimates of financial institution efficiency and financial markets are positively significant in both renewable energy and CO₂ emissions models, implying that financial institutions and market efficiency increase renewable energy consumption and decrease CO₂ emissions.

Purpose: This study examines the effect of design quality (i.e. appearance, navigation, information and interactivity) on cognitive and affective involvement leading to continued intention to use the online learning application.Design/methodology/approach: We assume that design quality potentially contributes to enhance the individual's involvement and excitement. An experimental prototype is developed for collecting data used to verify and validate the proposed research model and hypotheses. A partial-least-squares approach is used to analyze the data collected from the participants (n = 662).Findings: Communication, aesthetic and information quality revealed to be strong determinants of both cognitive and affective involvement. However, font quality and user control positively influence cognitive involvement, while navigation quality and responsiveness were observed as significant indicators of affective involvement. Lastly, cognitive and affective involvement equally contribute to determining the continued intention to use.Research limitations/implications: This study will draw the attention of designers and practitioners towards the perception of users for providing appropriate and engaging learning resources.Originality/value: Prevalent research in the online context is focused primarily on cognitive and utilization behavior. However, these works overlook the implication of design quality on cognitive and affective involvement.

The widespread use of online social media has enabled users to express their thoughts, feelings, opinions, and sentiments in their preferred languages. These diverse perspectives offer valuable insights for data-driven decision-making. While extensive sentiment analysis approaches have been developed for resource-rich languages like English and Chinese, low-resource languages such as Roman Urdu and Roman Punjabi, especially in code-mixed contexts, have been largely neglected due to the lack of datasets and limited research on their unique morphological structures and grammatical complexities. This study aims to present a novel approach for multiclass sentiment analysis of low-resource, code-mixed datasets using multilingual transformers. Specifically, a dataset comprising Roman Urdu, Roman Punjabi, and English comments was collected. After applying traditional natural language preprocessing techniques, transformer-based libraries were used for tokenization and embedding. Subsequently, the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) model was optimized and trained for multiclass sentiment analysis on the code-mixed data. The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. These findings highlight the potential of the proposed approach for sentiment analysis in low-resource, code-mixed languages.

Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.