The integration of chatbots in the financial sector has significantly improved customer service processes, providing efficient solutions for query management and problem resolution. These automated systems have proven to be valuable tools in enhancing operational efficiency and customer satisfaction in financial institutions. This study aims to conduct a systematic literature review on the impact of chatbots in customer service within the financial sector. A review of 61 relevant publications from 2018 to 2024 was conducted. Articles were selected from databases such as Scopus, IEEE Xplore, ARDI, Web of Science, and ProQuest. The findings highlight that efficiency and customer satisfaction are central to the perception of service quality, aligning with the automation of the user experience. The bibliometric analysis reveals a predominance of publications from countries such as India, Germany, and Australia, underscoring the academic and practical relevance of the topic. Additionally, essential thematic terms such as “artificial intelligence” and “advanced automation” were identified, reflecting technological evolution in this field. This study provides significant insights for future theoretical, practical, and managerial developments, offering a framework to optimize chatbot implementation in highly regulated environments.

In marginalized ecosystem-dependent rural communities, access to ecosystem services plays a crucial role in achieving sustainable livelihoods. This study was conducted to find out the influence of various livelihood capital components on the access mechanism for forest-based Provisioning Services (PS) in some selected villages of the Gosaba Block on the fringes of the Sundarban. The contribution of the livelihood capitals to gain access to Provisioning Services (PS) was identified using factor analysis on 160 households, selected through cluster random sampling. The sustainability levels of livelihood capitals were analyzed using the Prescott-Allen method (2001). The natural, financial, social, and physical capitals were significantly below average, while the human capital was close to average. Enhancement of human, physical, financial, and social capital, ease in issuing Biometric Fisherman cards for entering forests, flexibility in borrowing loans, and ecotourism by involving local villagers must be encouraged to enhance forest-based provisioning services in the near future.

This study aimed to analyze government policies in education during the Covid-19 pandemic and how teachers exercised discretion in dealing with limitations in policy implementation. This research work used the desk review method to obtain data on government policies in the field of education during the Covid-19 pandemic. In addition, interviews were conducted to determine the discretion taken in implementing the learning-from-home policy. There were three learning models during the pandemic: face-to-face learning in turns (shifts), online learning, and home visits. Online learning policies did not work well at the pandemic’s beginning due to limited infrastructure and human resources. To overcome various limitations, the government provided internet quota assistance and curriculum adjustments and improved online learning infrastructure. The discretion taken by the teachers in implementing the learning-from-home policy was very dependent on the student’s condition and the availability of the internet network. The practical implication of this research is that street-level bureaucrats need to pay attention to discretionary standards when deciding to provide satisfaction to the people they serve.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.

In our study, we examined 11 designated tourist destinations in Hungary, which can also be interpreted as tourism products including services, infrastructure and attractions. The National Tourism Development Strategy (NTS) also puts a strong emphasis on digitalisation, as it is an unstoppable process with a significant impact on tourism, thanks to globalisation, increasing competition, accelerating information flows and the dominant paradigm shifts on the demand and supply side. We used both qualitative and quantitative methods in our primary research. First, we conducted in-depth interviews with several important tourism service providers in Hungary on the topic of the digitalisation of tourism. A professional questionnaire, addressed to the offices responsible for destination management was distributed in the designated tourist destinations in Hungary in order to get a more comprehensive picture of the attitudes towards digitalisation in the regions under study. In the course of our work, we managed to classify the destinations into three distinctly different clusters. Our hypothesis—that the higher the digitalisation of a tourist destination is, the higher the average length of stay—was partially confirmed by calculating the regional value of the digitalisation, logistic regression analysis, slope and the individual factor categories.