Earticle

Generative AI refers to computing technology that can generate seemingly novel and meaningful content from training data, including text, images, and audio. With the further development of artificial intelligence, artificial intelligence has entered a stage of rapid growth. This paper reviews the historical evolution of generative AI through the research method of literature review, from the introduction of artificial intelligence as a science in 1956 to the development of key technologies such as GAN, VAE, and Transformer in recent years. At the same time, it also analyzes the current technical status of generative AI, including the development of multimodal generative models and diffusion model technologies. While generative AI is developing rapidly, it has also brought a series of problems. This paper also discusses the challenges brought about by development, such as bias, data privacy, and legal supervision. In order to analyze the life cycle of generative AI, the S curve is used to analyze technology, market, and other factors. The results of the study show that generative AI is currently in a stage of rapid growth and will usher in more breakthroughs in the future. At the same time, it emphasizes the establishment of a technical ethics framework and a legal supervision mechanism to ensure the development of generative AI. The research results of this paper help to deepen the understanding of the development of generative AI technology and the evolution of commercial value, and provide a reference for researchers and practitioners in related fields.

Electronic business (e-business) has become an integral part of global economic activity. This study aims to identify the current stages of e-business evolution in South Korea and Ukraine using a quantitative research design. Data were collected through a survey questionnaire distributed to individuals involved in e-business activities in both countries. The assessment was based on social, economic, political, and technical factors and their corresponding subfactors. The results indicate that South Korea is currently positioned at the Transformation Stage, whereas Ukraine is at the Growth Stage. South Korea demonstrates relatively high performance across all assessed factors, while Ukraine exhibits moderate levels of development. An understanding of the nuances of each stage and the specific results for each country can help government authorities, corporate decision-makers, and interested parties develop targeted strategies to further advance e-business development in Ukraine, using South Korea as a benchmark.

This study delves into the factors shaping consumer loyalty and satisfaction within South Korea’s mobile wallet market, a pivotal area for digital expansion in the industry. We analyzed consumer behavioral data from 2017 and 2024 to track changes in attitudes and usage patterns toward mobile payment applications. This study aims not only to apply the Dual-Factor Model in a novel context but also to contribute to the existing literature by offering both a temporal and a comparative analysis of the dynamics shaping this rapidly evolving market. Utilizing structural equation modeling (SEM) and bootstrapping for mediation analysis, we validated the impact of dedication-based and constraint-based constructs on consumer loyalty and satisfaction. Our findings indicate a shift from pleasure-centric to convenience-centric service usage as the industry matures, accompanied by a decreased correlation between perceived enjoyment and satisfaction. Moreover, we identified an amplified role of switching costs in fostering habitual usage and steadfast loyalty, along with an increased effect of personal innovativeness on consumer engagement over time. This research underscores a shift in South Korea’s mobile wallet market, emphasizing the necessity of continuous innovation to sustain consumer loyalty and commitment within the evolving mobile wallet application landscape.

Agricultural productivity in developing countries is increasingly imperiled by climate variability. Nepal presents a compelling case study: with 66% of its population dependent on agriculture, the country must contend with fragmented landholdings, erratic monsoon patterns, and severe agro-ecological heterogeneity across 1,359 distinct climatic zones. Timely, location-specific weather forecasts are a prerequisite for informed crop management, yet such systems have remained largely inaccessible to smallholder farmers. This study presents a comprehensive, data-driven agricultural decision-support framework that integrates spatio-temporal weather forecasting, soil-aware crop recommendation, and a Retrieval-Augmented Generation (RAG) advisory chatbot within a unified mobile platform, with the aim of enhancing farm-level decision-making across Nepal's diverse agro-ecological landscape. Historical weather data spanning 42 years from 1,359 locations (NASA POWER) were used to train two deep learning architectures: a Transformer-based Graph Neural Network and a Spatio-Temporal Graph Convolutional Network (STGCN). A graph structure encoding geodesic distance and altitudinal similarity (edges within 15 km or 50 m altitude difference) modeled spatial dependencies. A 120-day rolling lookback window captured temporal dynamics. Forecasted weather variables were integrated with static soil attributes via an inverse-distance scoring algorithm to rank crop suitability. A RAG chatbot using hybrid SPLADE and dense embeddings provided natural-language advisory support in Nepali and English. The STGCN achieved superior forecasting performance with a Mean Squared Error (MSE) of 0.011 compared to 0.013 for the Transformer-based model, demonstrating its capacity to capture complex spatial and temporal dependencies. The crop recommendation engine generated ranked suitability indices across all 1,359 locations. The RAG-based advisory system produced contextually relevant, multilingual responses to diverse farmer queries. The mobile deployment received positive qualitative feedback from rural users in terms of accessibility and relevance. This study demonstrates that integrating spatio-temporal deep learning with soil data fusion and conversational AI can deliver scalable, accessible, and accurate agricultural guidance in data-scarce, geographically complex settings. The framework offers a replicable model for precision agriculture in other developing-country contexts vulnerable to climate variability.