Nowadays, customer service in telecommunications companies is often characterized by long waiting times and impersonal responses, leading to customer dissatisfaction, increased complaints, and higher operational costs. This study aims to optimize the customer service process through the implementation of a Generative AI Voicebot, developed using the SCRUMBAN methodology, which comprises seven phases: Objectives, To-Do Tasks, Analysis, Development, Testing, Deployment, and Completion. An experimental design was used with an experimental group and a control group, selecting a representative sample of 30 customer service processes for each evaluated indicator. The results showed a 34.72% reduction in the average time to resolve issues, a 33.12% decrease in service cancellation rates, and a 97% increase in customer satisfaction. The implications of this research suggest that the use of Generative AI In Voicebots can transform support strategies in service companies. In conclusion, the implementation of the Generative AI Voicebot has proven effective in significantly reducing resolution time and markedly increasing customer satisfaction. Future research is recommended to further explore the SCRUMBAN methodology and extend the use of Generative AI Voicebots in various business contexts.

This study aims to evaluate theories and ideas about social values and determine the high quality of virtues that potentially change social practices, thinking, self-awareness, and behavior of the individual and society. The relevance of the study of value components is determined by the fact that such values as “spirituality and morality”, “responsibility”, “justice”, “rationality”, and “security” are capable of capturing the greatest value of many interests, which allows for the integration of society. An experimental study was conducted using sociological research methods based on developed questionnaires with questions touching on the parameters of sustainable development of society, determining the high quality of virtues and behavior of the individual and society. The study was conducted from May to June 2023 (N = 1387). Based on Demoethical values, special attention is paid to global problems related to climate change and inefficient use of energy and water resources, thereby achieving the Sustainable Development Goals. As a result of the study, Demoethical values are revealed in interaction with the economic components of demography, democracy, and demoeconomics as a tool for social transformation, as they shape the harmonious vision of the world, human behavior, decisions, and relationships with other people.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Rapid global warming and continuous climate change threaten the construction industry and human existence, especially in developing countries. Many developed countries are engaging their professional stakeholders on innovation and technology to mitigate climate change on humanity. Studies concerning inclusive efforts by developing countries’ stakeholders, including Nigeria, are scarce. Thus, this study investigates the construction industry’s practitioners’ preparedness to mitigate climate change through pre- and post-planning. Also, the study appraises climate change’s impact on construction activities and proffered measures to mitigate them. The research employed face-to-face data collection via a qualitative approach. The researchers engaged 33 knowledgeable participants. The study covered Abuja, Benin City, Owerri, and Lagos and achieved saturation at the 30th participant. The research employed a thematic approach to analyse the collected data. Findings reveal that Nigerian construction practitioners cannot cope with climate change impacts because of lax planning and inadequate technology to mitigate the issues. Also, the government’s attitude towards climate change has not helped matters. Also, the study suggested measures to mitigate the impact of climate change on construction activities in Nigeria. Therefore, as part of the research contributions, all-inclusive and integrated regulatory policies and programmes should be tailored toward mitigating climate change. This includes integrated stakeholder sensitisation, investment in infrastructure that supports anti-climate change, prioritising practices in the industry to achieve sustainable project transformation, and integration of climate change interventions into pre- and post-contract administration.

The global shortage of nurses has resulted in the demand for their services across different jurisdictions causing migration from developing to developed regions. This study aimed to review the literature on drivers of nurses’ migration intentions from source countries and offer future research directions. A search strategy was applied to ScienceDirect, Web of Science, and Scopus academic databases to find literature. The search was limited to peer-reviewed, empirical studies published in English from 2013–2023 resulting in 841 papers. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a systematic review of 35 studies after thorough inclusion and exclusion criteria. In addition, the VOSviewer software was utilized to map network visualization of keywords, geographic and author cooperation for bibliometric understanding. The findings revealed various socio-economic, organizational, and national factors driving nurses’ migration intentions. However, limited studies have been conducted on family income, organizational culture, leadership style, infrastructure development, social benefits, emergency service delivery, specialized training, and bilateral agreements as potential drivers for informing nurses’ migration intentions. Moreover, a few studies were examined from a theoretical perspective, mainly the push and pull theory of migration. This paper contributes to the health human resources literature and shows the need for future studies to consider the gaps identified in the management and policy direction of nurse labor migration.