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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.
The application of big data analytics in sports as a tool for personalized fan experience, operations efficiency, and fan engagement strategy
Article ID: 3075
Vol 2, Issue 1, 2025
DOI: https://doi.org/10.54517/bmtp3075
Vol 2, Issue 1, 2025
Received: 18 November 2024; Accepted: 17 January 2025; Available online: 25 January 2025; Issue release: 31 March 2025
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Abstract
In the contemporary sports industry, big data analytics [BDA] has become a cornerstone of marketing, fundamentally reshaping how sports organizations engage with their audiences by providing unprecedented opportunities for personalization and deeper fan connections. Sports organizations, by utilizing a diverse array of data sources, ranging from ticket sales and social media interactions to in-venue sensor data, can construct detailed profiles of their fanbase, facilitating highly targeted marketing strategies and personalized content that align closely with individual preferences and behaviors. This paper delves into the strategic deployment of BDA across the sports sector, emphasizing its role in customizing fan experiences, optimizing operational processes, and crafting immersive interactions that elevate fan engagement and loyalty. Adopting a theoretical approach, the research seeks to illuminate how BDA can be harnessed not only to boost fan engagement but also to streamline operational efficiencies. It further addresses the challenges and considerations that come with implementing these cutting-edge strategies and introduces a set of recommendations to successfully navigate the challenges. Through this exploration, the paper highlights the transformative impact of BDA on redefining fan interactions and engagement within the sports landscape. Ultimately, the paper underscores BDA’s transformative role in redefining fan interactions and engagement in sports, providing strategic insights for practitioners and suggesting paths for future research to further capitalize on this dynamic digital landscape.
Keywords
sports marketing; big data analytics [BDA]; personalized experience; operations efficiency; fan engagement strategies marketing strategies
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