Purpose: This research aims to investigate the impact of technological challenges, including techno-overload, techno-complexity, and techno-insecurity, on employee job satisfaction within the banking sector of Saudi Arabia. Additionally, the study examines the mediating roles of supervisor support and job clarity in buffering the effects of technological challenges on job satisfaction. Method: The study employs a quantitative research design, utilizing an online questionnaire to collect data from banking employees in Saudi Arabia. The sample size of 135 participants was determined using the rule of thumb technique. Random sampling was utilized to ensure representativeness. Data analysis was conducted using Statistical Package for Social Sciences (SPSS) to explore the relationships between technological challenges, supervisor support, job clarity, and employee job satisfaction. Findings: The findings of the study reveal a significant negative impact of techno-overload, techno-complexity, and techno-insecurity on employee job satisfaction within the banking sector of Saudi Arabia. Moreover, supervisor support and job clarity were found to mediate these relationships, highlighting their importance in mitigating the adverse effects of technological challenges on job satisfaction. Originality/Significance: This research contributes to the existing body of knowledge by providing empirical evidence on the relationships between technological challenges, supervisor support, job clarity, and employee job satisfaction within the specific context of Saudi Arabian banks. The findings have significant implications for organizational leaders and managers in developing evidence-based strategies to manage technological challenges and promote employee well-being in the banking sector of Saudi Arabia.

Ancestral knowledge is essential in the construction of learning to preserve the sense of relevance, transmit and share knowledge according to its cultural context, and maintain a harmonious relationship with nature and sustainability. The objective of this research is to study and analyze the management of ancestral knowledge in the production of the Raicilla to provide elements to rural communities, producers, and facilitators in decision-making to be able to innovate and be more productive, competitive, sustainable, and improve people’s quality of life. The methodological strategy was carried out through Bayesian networks and Fuzzy Logic. To this end, a model was developed to identify and quantify the critical factors that impact optimally managed technology to generate value that translates into innovation and competitive advantages. The evidence shows that the optimal and non-optimal management of knowledge, technology, and innovation management and its factors, through the causality of the variables, permits us to capture the interrelationship more adequately and manage them. The results show that the most relevant factors for adequate management of ancestral knowledge in the Raicilla sector are facilitators, denomination of origin, extraction and fermentation, and government. The proposed model will support these small producers and help them preserve their identity, culture, and customs, contributing greatly to environmental sustainability.

On 17 February 2008, Kosovo declared its independence from Serbia, receiving recognition from over half of the UN member states, the majority of the European Union, Council of Europe and NATO member states, as well as the most industrialized states in the global economic forum. However, Kosovo did not receive recognition from Serbia, China, Russia, India, certain states with diplomatic grievances with the USA, communist dictatorial states like North Korea, and five EU member states, including Romania, Greece, Cyprus, Slovakia, and Spain. This article focuses on Spain’s possibilities and reasons for recognizing Kosovo or not. Using qualitative methodology, five university professors—two from Madrid, one from Barcelona, and two Kosovar professors, one from the University of Pristina and the other from the University of Winchester, England—were interviewed with open-ended questions in November-December 2023. The research identified opportunities and reasons for Spain’s hesitation in recognizing Kosovo, including Spain’s domestic context, historical relations with the Western Balkans and the newly formed countries after the dissolution of Yugoslavia in the early 1990s, as well as the European and international political context. The research results show that Spain has been hesitant to recognize new states quickly, not only in the case of Kosovo, due to the context of autonomist aspirations within Spain and reluctance to draw parallels between Kosovo and Spain’s autonomous regions.

Students from different cultures possess varying levels of skills in learning, remembering, and understanding concepts. Some terms and their explanations may seem easy for one group of students but difficult for another. Therefore, delivering educational content that aligns with student’s learning capabilities is a challenging task based on cultural orientations. This study addresses the learning challenges by developing a Thesaurus Glossary E-learning (TGE) framework method. This study introduces the TGE method which is a multi-language tool with visual associations that adapts to students’ capabilities. It also examines cultural differences and native languages, particularly aiding Arab Native to visualize appropriate terms (thesaurus) and their explanations (glossary) based on students’ learning capabilities. TGE learns from students’ term selection behavior and displays terms at a simple or advanced level that matches their learning ability. Additionally, TGE demonstrated its effectiveness as an e-learning tool, accessible to all students anytime and anywhere. The study analyzed 314 records related to student performance, out of which 114 students were surveyed to evaluate the effectiveness of the TGE method. This work presents TGE as a novel e-learning tool designed to enhance conceptual thinking within the context of modern educational practices during the digital transformation. TGE is based on artificial intelligence algorithms and associative rules that simulate the human brain, establishing logical connections between related key terms and sketching associations among diverse facets of a situation. An experiment was conducted at a private university in the Sultanate of Oman to assess the effectiveness of the proposed TGE tool. TGE was integrated with selected subjects in information systems and used by the students as a resource for e-learning methods and materials. The results show that 85% of students who used TGE improved their performance by 19%. We believe this work could establish a new smart e-learning teaching method and attract modern and digital universities to enhance student learning outcomes linked with conceptual thinking.

This study investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

This study examines the economic feasibility of the environment-friendly farmland use policy to improve water quality. Conventional highland farming, polluting the Han River basin in South Korea, can be converted into environment-friendly farming through land acquisition or application of pesticide-free or organic farming practices. We estimate the welfare measures of improvement in water quality and the costs of policy implementation for economic analysis. To estimate the economic benefit of improvement in water quality experienced by the residents residing in mid-and-downstream areas of the Han River, the choice experiment was employed with a pivot-style experimental design approach. In the empirical analysis, we converted the household perception for water quality grades into scientific water quality measures using Water Quality Standard to estimate the value of changes in water quality. To analyze the costs required to convert conventional highland farmlands into environment-friendly farmlands, we estimated the relevant cost of land acquisition and the subsidy necessary for farm income loss for organic agricultural practice. We find that the agri-environmental policy is economically viable, which suggests that converting conventional highland farming into environment-friendly farming would make the improvement in water quality visible.