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.

Background: Globally, unpaid carers face economic and societal pressures. Unpaid carers’ support is valued at £132 billion a year in the United Kingdom (UK) alone. However, this care comes at a high cost for the carers themselves. Carers providing round the clock care are more than twice as likely to be in bad health than non-carers. These carers are therefore proportionately more likely to need statutory services such as health care provision. It is critical that carers are better supported to be involved in the shaping, delivery and evaluation of the services they receive. Unfortunately, qualitative evidence on how carer organisations can do this better is scarce. Methods: Working collaboratively with a community-based carers organization, we undertook a qualitative study. Purposive sampling was used to recruit 23 participants. Online, semi-structured, one-to-one interviews were conducted with carers, community organization staff and stakeholders to ascertain their experience and views on the involvement service. Results: Firstly, there are a range of benefits resulting from the involvement service. The carers see the service as an opportunity to connect with other carers and share their views and ideas. Secondly, staff and service providers also reported how involvement gave a platform for carers and was of value in helping them shape needs-led services. Thirdly, we found that barriers to good involvement include the lack of a clearly understood, shared definition of involvement as well as the lack of a diverse pool of carer representatives available for involvement activities. Conclusion: The findings from our study provide important insights into how carers, staff and service stakeholders view barriers and enablers to good involvement. The findings will be of interest to a range of community-based organizations interested in further involving members of their community in shaping the services they receive.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.

This study aims to assess the efficacy of speech-to-text (STT) technology in improving the writing abilities of special education pupils in Saudi Arabia. A deliberate sample of 150 special education college students was selected, with participants randomly allocated to either an experimental group employing STT technology or a control group using traditional writing methods. The study utilized a comprehensive approach, which included standardized writing assessments, questionnaires, and statistical analyses such as t-tests, correlation, regression, ANOVA, and ANCOVA. The results demonstrate a substantial enhancement in writing skills among the experimental group utilizing Speech-to-Text (STT) technology. The findings contribute to the discussion on assistive technology in special education and offer practical recommendations for educators and policymakers.

Climate change is forcing countries to take strategic measures to reduce the negative impact on future generations. In this context, sustainable finance has played a key role in sustainable development since the establishment of environmental, social and governance principles. The underlying market has developed rapidly since its inception, with green bonds being the most prominent instrument. This article aims to study the impact of green bond issues on the abnormal stock returns of stocks listed on the main Euronext indices. The sample includes 58 issues carried out between 2014 and 2022 by 21 different firms listed on the AEX (Netherlands), BEL 20 (Belgium), CAC 40 (France), ISEQ 20 (Ireland), OBX (Norway) and PSI (Portugal) indices. The methodology follows the procedures of the event study using the market model. The results show significant positive stock price reaction on the issue date. After the abnormal losses just before the issues, suggesting the reserves of this consolidating market, abnormal gains persisted for over a week, providing evidence against the weak efficiency Euronext’s financial markets. The findings are useful for policy makers and entrepreneurs to promote innovative initiatives that encourage the financing and development of environmentally sustainable infrastructures.

Adult obesity is a significant health problem, with nearly a quarter of Hungarian citizens aged 15 years and older being obese in 2019 (KSH, 2019a). The use of mobile devices for health purposes is increasing, and many m-health apps target weight-related behaviours. This study uniquely examines the effectiveness and user satisfaction of health-oriented apps among Hungarian adults, with a focus on health improvement. Using a mixed-methods approach, the study identifies six key determinants of health improvement and refines measurement tools by modifying existing parameters and introducing new constructs. The principal objective was to develop a measurement instrument for the usability of nutrition, relaxation and health promotion applications. The research comprised three phases: (1) qualitative content analysis of 13 app reviews conducted in June 2022; (2) focus group interviews involving 32 students from the fields of business, economics and health management; and (3) an online survey (n = 348 users) conducted in December 2023 that included Strava (105 users), Yazio (109 users) and Calm (134 users). Six factors were identified as determinants of health improvement: physical activity, diet, weight loss, general well-being, progress, and body knowledge. The LAUQ (Lifestyle Application Usability Questionnaire) scale was validated, including ‘ease of use’ (5 items), ‘interface and satisfaction’ (7 items) and ‘modified usefulness and effectiveness’ (9 items), with modifications based on qualitative findings. This research offers valuable insights into the factors influencing health improvement and user satisfaction with healthy lifestyle-oriented applications. It also contributes to the refinement of measurement tools such as the LAUQ, which will inform future studies in health psychology, digital health, and behavioural economics.