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...

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...

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...

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...

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;...

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...

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...

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...

Purpose: This study aims to provide insight into the contemporary food waste issue for the better management of food waste in large quantities in Incheon, one specific region in Korea, based on the analysis of the Naver Blog corpus, employing analysis techniques including text mining and LDA...

Association rule mining is a widely used data mining technique in various domains. It enables the identification of trends, frequent patterns, and relationships among the data. This study introduced a new method for mining association rules independently from multiple data sources. It combined...

Breast cancer is a disease characterised by abnormal cell growth in the breast, with various types and unique characteristics depending on the malignant cells involved. It predominantly affects women, and researchers in the field of clinical sciences are increasingly interested in leveraging...