This study addresses the vast amount of information held by the banking sector, especially regarding opportunities in tourism development, production, and large residential projects. With advancements in information technology and databases, data mining has become essential for banks to...

This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a "default" in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk...

In today's context of escalating environmental pressure, traditional methods of natural resource conservation face numerous challenges. The use of big data analytics to support the formulation of environmental policies has become a crucial approach for enhancing the efficiency and scientific...

Most of peer-to-peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from renrendai.com, which is...

This study aims to identify the common indices having an impact on the SPV of BRICS countries during crises. To address this, the monthly data retrieved from the database of the Global Economic Monitor (GEM), World Bank, IMF International Financial Statistics data, and OECD in the period of...

The purpose of this study is to identify airline asymmetric attributes that affect customer satisfaction based on the three-factor theory and to build an Asymmetric Impact-Sentiment Analysis (AISA) for resource allocation prioritization. We identified food and beverage service as a basic factor;...

Urban eco-environmental management is a key focus in the modernization of national governance systems and governance capacity. Advanced information technology should be used to identify ecological problems in urban areas accurately while enhancing the environmental management capacity to promote...

Recent developments in big data analytics (BDA) enable financial institutions to optimize their decision-making processes. However, many BDA projects fail due to a lack of understanding of the value related to the realisation process. Guided by affordance actualisation theory, this article uses...

Big data are known to improve operational efficiency, competitiveness, and performance. Despite these unprecedented benefits, the understanding of how big data transform organizational processes remains limited. To address this gap, this research empirically investigates how big data analytics...

This paper presents a framework for collecting large datasets of hotel reviews (e.g., from Booking.com and TripAdvisor) and performing useful analytics from the data collected. This approach automates data collection, reduces manual effort, enhances data cleaning, and standardizes data...

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities,...

Predicting the outcome of a sports game is a favourite pastime for sports fans and researchers. The interest has intensified in recent years due to data availability, the development and successful implementation of machine learning algorithms, and the proliferation of internet gaming. This...

Online health consultation services are crucial for mental health support, particularly in densely populated areas. However, the heavy reliance on human expertise often leads to delays, necessitating more efficient and automated solutions. This study developed a machine learning framework to...

Association rules can offer a human-interpretable insight extracted from data. The lift measures used for evaluating association rules in classical Association Rule Mining (ARM) contexts are mainly based on traditional and well-known ones but suffer from interpretation inadequacy when dealing...

This systematic review investigates sentiment analysis of news headlines in the banking sector, a field susceptible to public sentiment, as demonstrated by phenomena like bank runs leading to rapid deposit withdrawals. We trace the evolution of analytic methods from traditional machine learning...

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the...

Purpose: Big Data Analytics Capabilities (BDAC) facilitate the generation of critical insights required for competitive performance. This study evaluates the relationship between Big Data Analytics Capabilities and competitive performance and argues the effect is mediated by Business Model...

This paper explores regional planning as an applied field of practice in Africa, where it is observed as a failed project or a flawed endeavour. In theory, regional planning (the practice) is based on regional science (the field of study) from which regional policy and regional development flow....

This study investigates the increasingly important issue of sustainable tourism development through the lens of Big Data analytics, economic considerations, and policy frameworks. Guided by the Theory of Planned Behavior, we formulated seven hypotheses to explore how these variables impact...

The Internet of Things (IoT) has attracted the attention of researchers and practitioners in supply chains and logistics (LSCs). IoT improves the monitoring, controlling, optimizing, and planning of LSCs. Several researchers have reviewed the IoT-based LSCs publications indexed by academic...

Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the core components of data-driven technologies of Industry 4.0, attracting much attention in digital supply chains and leading to a growing tide of academic publications. This study conducts a bibliometric analysis of data-driven...