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.

Nowadays, urban ecosystems require major transformations aimed at addressing the current challenges of urbanization. In recent decades, policy makers have increasingly turned their attention to the smart city paradigm, recognizing its potential to promote positive changes. The smart city, through the conscious use of technologies and sustainability principles, allows for urban development. The scientific literature on smart cities as catalysts of public value continues to develop rapidly and there is a need to systematize its knowledge structure. Through a three-phase methodological approach, combining bibliometric, network and content analyses, this study provides a systematic review of the scientific literature in this field. The bibliometric results showed that public value is experiencing an evolutionary trend in smart cities, representing a challenging research topic for scholars. Network analysis of keyword co-occurrences identified five different clusters of related topics in the analyzed field. Content analysis revealed a strong focus on stakeholder engagement as a lever to co-create public value and a greater emphasis on social equity over technological innovation and environmental protection. Furthermore, it was observed that although environmental concerns were prioritized during the policy planning phase, their importance steadily decreased as the operational phases progressed.

This research systematically reviews the relationship between populism and economic policies, analyzing their impact on state development and growth. It is the first study to comprehensively examine the interaction between these two concepts through a systematic literature review. The review process adhered to the PRISMA protocol, utilizing the Scopus, EBSCO, and Web of Science databases, covering the period from 2012 to 2024. The findings reveal a deep interconnection between populism and economic policies, with significant implications for governance and socioeconomic well-being. The review identifies that neoliberal populism combines pro-corporate elements with populist rhetoric, favoring economic elites while presenting itself as beneficial for the “people.” Additionally, it underscores that neoliberal globalization has facilitated market liberalization but also increased inequality and undermined national sovereignty. The review concludes that while populism may offer quick fixes to immediate economic issues, its simplistic and polarizing approaches can be counterproductive in the long term. Thus, there is a critical need to reevaluate and reformulate economic and governance policies to balance global economic integration with the protection of citizens’ rights and well-being.

This paper is the third in a series focused on bridging the gap between secondary and higher education. Our primary objective is to develop a robust theoretical framework for an innovative e-business model called the Undergraduate Study Programme Search System (USPSS). This system considers multiple criteria to reduce the likelihood of exam failure or the need for multiple retakes, while maximizing the chances of successful program completion. Testing of the proposed algorithm demonstrated that the Stochastic Gradient Boosted Regression Trees method outperforms the current method used in Lithuania for admitting applicants to 47 educational programs. Specifically, it is more accurate than the Probabilistic Neural Network for 25 programs, the Ensemble of Regression Trees for 24 programs, the Single Regression Tree for 18 programs, the Random Forest Regression for 16 programs, the Bayesian Additive Regression Trees for 13 programs, and the Regression by Discretization for 10 programs.

This study provides a comparative analysis of Environmental, Social, and Governance (ESG) ratings methodologies and explores the potential of eXtensible Business Reporting Language (XBRL) to enhance transparency and comparability in ESG reporting. Evaluating ratings from different agencies, the research identifies significant methodological inconsistencies that lead to conflicting information for investors and stakeholders. Statistical tests and adjusted rating scales confirm substantial divergence in ESG scores, primarily due to differing data categories and indicators used by rating firms. Using a sample of 265 European companies, the study demonstrates that individual ESG agencies report markedly different ratings for the same firms, which can mislead stakeholders. It proposes that XBRL based reporting can mitigate these inconsistencies by providing a standardized framework for data collection and reporting. XBRL enables accurate and efficient data collection, reducing human error and enhancing the transparency of ESG reports. The findings advocate for integrating XBRL in ESG reporting to achieve higher levels of comparability and reliability. The study calls for greater regulatory oversight and the adoption of standardized taxonomies in ESG reporting to ensure consistent and comparable data across sectors and jurisdictions. Despite challenges like the lack of a standardized taxonomy and inconsistent adoption, the research contends that XBRL can significantly improve the reliability of ESG ratings. In conclusion, this study suggests that standardizing ESG data through XBRL could provide a viable solution to the unreliability of current ESG rating scales, supporting sustainable business practices and informed decision making by investors.