This study examines the impact of emotional intelligence (EI) and employee motivation on employee performance within the telecommunication industry in the Sultanate of Oman. The target population consisted of 4344 non-managerial employees across nine telecommunication companies, including Omantel, Ooredoo, Vodafone, Oman Broadband Company, Awasr Oman & Co, TEO, Oman Tower Company L.L.C, Helios Tower, and Connect Arabia International. Employing a deductive research approach, finally data were collected via an online survey from 354 respondents. The hypotheses were tested using multiple regression analysis. The results indicate that all dimensions of EI self-awareness, self-regulation, empathy, and social skills positively and significantly influence employee performance, with social skills having the strongest effect. Furthermore, both intrinsic motivation factors, such as work itself and career development, and extrinsic motivation factors, including wages, rewards, working environment, and co-worker relationships, significantly enhance employee performance. The interaction between EI and employee motivation was found to amplify these positive effects. Among control variables, age and education level showed significant impacts, while gender did not. These findings underscore the critical role of both emotional intelligence and motivation in driving employee performance. The study suggests that managers and policymakers should adopt integrated strategies that develop EI competencies and enhance motivational factors to optimize employee performance, thereby contributing to the success of organizations in the telecommunication sector.

In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

This research investigates the determinants of digital transformation among Vietnamese logistics service providers (LSPs). Employing the Technological-Organizational-Environmental framework and Resource Fit theory, the study identifies key factors influencing this process across different three stages: digitization, digitalization, digital transformation. Data from in-depth interviews with industry experts and a survey of 390 LSPs were analyzed using covariance-based structural equation modeling (CB-SEM). The findings reveal that the factors influencing the digital transformation of Vietnamese LSPs evolve across different stages. In the initial phase, information technology infrastructure, financial resources, employee capabilities, external pressures, and support services are key determinants. As digitalization progresses, leadership emerges as a crucial factor alongside the existing ones. In the final stage, the impact of these factors persists, with leadership and employee capabilities becoming increasingly important.

This study analyzes the perception of university students regarding the use of virtual reality (VR) in higher education, focusing on their level of knowledge, usage, perceived advantages and disadvantages, as well as their willingness to use this technology in the future. Using a mixed-methods approach that combines questionnaires and semi-structured interviews, both quantitative and qualitative data were collected to provide a comprehensive view of the subject. The results indicate that while students have a basic understanding of VR, its use in the educational context is limited. A considerable number of students recognize VR’s potential to enhance the learning experience, particularly in terms of immersion and engagement. However, significant barriers to adoption were identified, such as technical issues, the high cost of equipment, and inadequate access to technological infrastructure. Additionally, there is a need for broader training for both students and faculty to ensure the effective use of this technology in academic environments. The semi-structured interviews confirmed that perceptions of VR vary depending on prior exposure to the technology and access to resources. Despite the challenges, most students appreciate VR’s potential to enrich learning, although its effective adoption will depend on overcoming the identified barriers. The study concludes that strategies must be implemented to facilitate the integration of VR into higher education, thus optimizing its impact on the teaching-learning process.