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Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
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In the context of the global shift toward a low-carbon economy, carbon disclosure has emerged as a crucial tool for facilitating the low-carbon transition of firms and addressing climate change. As a result, it has become an increasingly prominent focus in academic research and policy making. This paper reviews the existing literature on carbon disclosure, examining the methods, standards, motivations, and impacts associated with current research in this area. Based on this analysis, the paper identifies key gaps in the existing literature and suggests directions for future research, aiming to contribute to the advancement of theoretical understanding and provide a valuable reference for future studies.
Understanding consumer purchasing behavior is crucial for businesses aiming to enhance customer engagement and optimize marketing strategies. In today’s digital economy, traditional marketing approaches are becoming less effective due to evolving consumer behaviors, the rise of online communities, and the widespread use of ad-blocking software. To remain competitive, businesses must adopt data-driven strategies to analyze consumer preferences and tailor their marketing efforts accordingly. Machine learning provides a powerful tool for predicting consumer purchasing behavior, enabling businesses to anticipate customer needs and implement targeted marketing campaigns. Previous studies have demonstrated the effectiveness of machine learning in consumer analysis, particularly in customer segmentation and purchase prediction. However, while much research focuses on technical model optimization, relatively few studies have applied machine learning specifically for marketing prediction and strategic decision-making. This study addresses that gap by leveraging machine learning to analyze consumer purchasing behavior and generate practical insights for marketing strategies and business applications. Using a dataset of 4680 transactions, we employ Generalized Linear Models (GLM), Logistic Regression, Random Forest, and XGBoost to predict repurchase behavior within a specified timeframe. Our objective is to provide practical implications for businesses, such as improving targeted promotions, refining customer segmentation, and enhancing demand forecasting.
Sustainable leadership with authority span on the development of the United Arab Emirates’ oil and gas sector
Article ID: 3103
Vol 2, Issue 1, 2025
DOI: https://doi.org/10.54517/bmtp3103
Vol 2, Issue 1, 2025
Received: 26 November 2024; Accepted: 17 January 2025; Available online: 24 January 2025; Issue release: 31 March 2025
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Abstract
This study examines sustainable leadership and span of control in the United Arab Emirates (UAE)’s oil and gas industry. Sustainable leadership may boost strategic efficiency, creativity, and operational resilience in geopolitically complex and unstable environments. An optimum span of control permits close supervision without slowing decision-making, notably in its subsidiaries. Results show that sustainable leadership boosts employee engagement and innovation. The review paper shows that the UAE’s gas and oil business faces several obstacles due to political instability and global market fluctuations. The UAE must implement sustainable leadership practices to foster industrial innovation, development, and growth.
Keywords
sustainable leadership; span of control; organizational development
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2025-04-02
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We would like to inform all of our readers of the formal release of the first issue of Business and Management Theory and Practice (eISSN: 3082-8155) in 2025. This issue consists of three research articles, five review articles, and one editorial from our respected editorial board member, Dr. Jian Xu, who has been recently selected into the 2024 Elsevier "Highly Cited Chinese Researchers" list.
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