The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

In Emerging economies, MNCs (Multinational corporations) encounter several issues while devising Strategies to penetrate foreign markets, examining these SMEs’ performance in present times and assessing their internationalisation process is crucial. The purpose of this research is to investigate how international entrepreneurial orientation affects SMEs’ international performance during internationalization, as well as how organizational culture in the Kingdom influences the international performance of these MNCs. To attain this objective (n = 206) MNCs in the Kingdom have adopted internationalisation strategies. Questionnaires were administered as part of a survey approach for this study. To forecast and estimate relationships, partial least squares structural equation modelling (PLS-SEM) was employed. This study indicates that improving internationalization performance, mainly through active participation in foreign markets, is one of the SMEs’ strategies during the internationalization process. The empirical findings demonstrate that international entrepreneurial orientation influences the internationalisation performance of SMEs largely influenced by organisational culture. Previous research shows that the success of SMEs’ internationalization, however, is not directly impacted by their international entrepreneurial orientation. This study supports the significance of organisational culture during internationalisation. This study offers insightful information that motivates policymakers and owner-managers in developing nations, especially in KSA, to build organizational cultures and dynamic capacities that meet the demands of globalization in today’s business scenario.

Unmanned Aerial Vehicles (UAVs) have gained spotlighted attention in the recent past and has experienced exponential advancements. This research focuses on UAV-based data acquisition and processing to generate highly accurate outputs pertaining to orthomosaic imagery, elevation, surface and terrain models. The study addresses the challenges inherent in the generation and analysis of orthomosaic images, particularly the critical need for correction and enhancement to ensure precise application in fields like detailed mapping and continuous monitoring. To achieve superior image quality and precision, the study applies advanced image processing techniques encompassing Fuzzy Logic and edge-detection techniques. The study emphasizes on the necessity of an approach for countering the loss of information while mapping the UAV deliverables. By offering insights into both the challenges and solutions related to orthomosaic image processing, this research lays the groundwork for future applications that promise to further increase the efficiency and effectiveness of UAV-based methods in geomatics, as well as in broader fields such as engineering and environmental management.

Recently, Agile project management has received significant academic and industry attention from due to its advantages, such as decreased costs and time, increased effectiveness, and adaptiveness towards challenging business environments. This study primarily aims to investigate the relationship between the success factors and Agile project management methodology adoption and examine the moderating effect of perceived compatibility. The technology-organization-environment (TOE) framework and technology acceptance theories (UTAUT, IDT, and TAM) were applied as the theoretical foundation of the current study. A survey questionnaire method was employed to achieve the study objectives, while quantitative primary data were gathered using a carefully designed methodological approach focusing on Omani oil and gas industry. The PLS-SEM technique and SmartPLS software were used for hypotheses testing and data analysis. Resultantly, readiness, technology utilization, organizational factors, and perceived compatibility were the significant factors that promoted Agile methodology adoption in the oil and gas industry. Perceived compatibility moderated the relationship between success factors and Agile methodology. The findings suggested that people, technology, and organizational factors facilitate the Agile methodology under the technology acceptance theories and frameworks. Relevant stakeholders should adopt the study outcomes to improve Agile methodology adoption.