布拉格经济大学

Higher temperatures are expected to impact globally on poverty and inequality, yet little cross-country analysis exists to quantify the effects. Here we analyse a panel dataset of subnational poverty in 130 countries covering the past decade and find that a 1 degrees C increase in temperature causes headcount poverty increases of 0.63-1.18 percentage points, using the daily poverty lines of US$2.15 (corresponding to 8.3% and 15.6% increases), and increases in the Gini inequality index of 1.3-1.9%. These poverty estimates equal a projected increase of global poor by 62.3-98.7 million people by 2030 compared with a scenario without climate change. Poorer countries-particularly those in Sub-Saharan Africa-are more vulnerable, as are countries with higher agriculture shares in the economy. Estimates at the subnational level are larger than those using the country-level data, indicating that aggregated analysis may underestimate climate change risks.

This study examines how customer satisfaction and their repurchase intentions are shaped by chatbot interactions in online shopping experience. Three human-like chatbot attributes, i.e., responsiveness, anthropomorphism, and social presence, are examined for their influence on customer satisfaction and their repurchase intentions. The moderating influence of e-shopping enjoyment over the relationship between customer satisfaction and their repurchase intentions is assessed. Flow theory is used as an overarching theory to build the research model and explain the results. The data was collected through questionnaire survey from 344 Chinese online shopping participants. We used Structural Equation Modeling (SEM) to analyze the collected data. Findings state that chatbots with responsiveness, anthropomorphism, and social presence are much better equipped to satisfy customer needs. In addition, results show that customer satisfaction affects repurchase intentions. Besides, our findings show that e-shopping enjoyment makes satisfaction even more influential on repurchase intentions. It implies that the affective and attention-demanding aspects of e-commerce play their part in defining a customer's intentions toward repeat purchases. This study will serve as a useful reference for e-commerce platforms that intend to use chatbots to increase customer engagement and loyalty, and marketers who could benefit from their activities.

In Vietnam, SMEs (small and medium-sized enterprises) or MSMEs (micro, small and medium-sized enterprises) usually struggle to predict the effectiveness of advertising on e-commerce platforms using machine learning. When the input data on successful campaigns only accounts for a small fraction, the dataset is imbalanced, that easily leading to biased results based on the majority class. This study uses a real-world dataset from a cosmetics store on Shopee. We evaluate the dataset across eight machine learning (ML) models combined with advanced oversampling techniques such as Borderline-SMOTE, ADASYN, and SMOTEENN. Experimental results indicate that combining Borderline-SMOTE with neural networks provides balanced performance, achieving a recall accuracy of 0.4545 and an F1 score of 0.2143. Research confirms that addressing data imbalances is crucial for SMEs when applying machine learning-based ad prediction. When SMEs adopt this method, they can optimize marketing operations and improve return on investment.

In the era of digitalization, data can be considered as an asset with significant importance. Its quality is crucial, especially when conducting various types of analyses that examine trends. Even minor inaccuracies in the input stream can lead to deviations in results and subsequent misinterpretations. This study explores the impact of noisy data on the effectiveness of sentiment analysis models. A use case was conducted, demonstrating two different approaches: one is a lexicon-based method Valence Aware Dictionary and Sentiment Reasoner, while the other algorithm is based on deep neural networks and is trained on large datasets called Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach. The analysis is performed over a dataset of user opinions from the social media platform Twitter. Additionally, the potential for combining the outcomes of both models to achieve a more comprehensive and reliable interpretation is assessed. The study highlights the need of data preprocessing and quality control measures to ensure higher accuracy and reliability in sentiment analysis. A conceptual model and a prototype module for improving data quality are proposed, designed to assist in the initial processing of input data. This research provides guidelines for enhancing the productivity of sentiment analysis in social media and lays the foundation for future studies focused on optimizing natural language process models.

The study employed a bibliometric approach to systematically map the trends in public administration and economic growth during the 2001-2024 period. The analysis was based on 621 documents retrieved from the Scopus database and analyzed using VOSviewer and Microsoft Excel software. The results highlighted prominent authors in the public administration and economic growth literature, including Acemoglu, Glaeser, Kaufmann, and Dollar & Kraay, which reflected the critical importance of public administration studies for economic growth. The United Kingdom and the United States served were the two dominant research centers, possessing superior total link strength. Meanwhile, China, Malaysia, and Vietnam were emerging with increasing contributions. Vietnam has achieved 22 publications and 607 citations, demonstrating its developing position within the regional academic network. Additionally, several new research trends were emerging in this field, such as smart public administration and technology, significantly impacting economic growth.