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...

Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME...

Background: The growing complexity of production processes in the metal-mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and...

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...

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...

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,...

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...

Investing in the stock market has always been an exciting topic for people. Many specialists have tried to develop tools to predict future stock prices in order to make high profits and avoid big losses. However, predicting prices based on the dynamic characteristics of stocks seems to be a...

Background: Logistics challenges, such as driver shortages, are a major global issue, with many countries struggling to find effective solutions. YouTube, as a social networking platform, has a growing user base and is increasingly used not only for entertainment but also for social interaction,...

Small and medium enterprises around the world, and especially in emerging markets, face challenges when it comes to market segmentation. They have limited knowledge of the importance of big data customer insights for making concrete market segmentation decisions. The purpose of this study was to...

This study explores the skills and competencies required by modern supply chain management professionals, focusing on the shift toward advanced technological capabilities. We analyze job advertisements from a prominent Canadian employment platform using web scraping, natural language processing,...

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...

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...

In the health field, longitudinal studies involve the recording of clinical observations of the same sample of patients over successive periods, referred to as waves. This type of database serves as a valuable source of information and insights, particularly when examining the temporal aspect,...

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...

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...

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;...

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...

Global supply chains become increasingly complex due to various uncertainties associated with the constant flux. With more firms engaging in global trade, reaping the benefits of offshoring and outsourcing a multitude of business functions worldwide, the increasing complexity continuously tests...

The financial sector faces mounting challenges in processing vast volumes of high-velocity data to support intelligent, real-time decision-making. Traditional machine learning models often fall short in accuracy, scalability, and responsiveness when dealing with large, dynamic financial...

While big data analytics capabilities (BDAC) have been pivotal to retail companies, their effects on innovation capabilities and sustainable performance have not been investigated systematically thus far. Therefore, this study explores the influence of BDAC on the innovation capabilities and...