Business Analytics: Mining, Analysis, Optimization and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 16359

Special Issue Editors

Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
Interests: business analytics; AI; deep learning
Faculty of Business Administration, University of Macau, E22 Avenida da Universidade, Taipa, Macau, China
Interests: business analytics; evolutionary computing; genetic algorithm

Special Issue Information

Dear Colleagues,

Business analytics is attracting increasing attention from researchers and practitioners. It involves the use of various methods and techniques, including, but not limited to, data science, statistical methods, data mining, machine learning and AI, to analyze and solve problems in business. In the current business environment, many new business models and business needs are emerging, which urgently require new methods, algorithms, mathematic models and applications in business. On the other hand, the development of information technologies and AI techniques, such as deep learning, has great potential to meet these needs.

The proposed Special Issue aims to publish review papers, research articles, and communications that present original methods, algorithms, applications, data analyses, case studies, and other results in the field of business analytics. The topics will be focused on, but are not limited to, data mining, machine learning, optimization, prediction methods, data analytics, and business applications.

Dr. Ou Liu
Dr. Heng Tang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • business analytics
  • business applications
  • data mining
  • machine learning
  • optimization

Published Papers (8 papers)

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Research

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21 pages, 4146 KiB  
Article
Integrating Business Analytics in Educational Decision-Making: A Multifaceted Approach to Enhance Learning Outcomes in EFL Contexts
by Minsu Cho, Jiyeon Kim, Juhyeon Kim and Kyudong Park
Mathematics 2024, 12(5), 620; https://doi.org/10.3390/math12050620 - 20 Feb 2024
Viewed by 538
Abstract
This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper [...] Read more.
This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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16 pages, 2974 KiB  
Article
Multivariate SVR Demand Forecasting for Beauty Products Based on Online Reviews
by Yanliang Wang and Yanzhuo Zhang
Mathematics 2023, 11(21), 4420; https://doi.org/10.3390/math11214420 - 25 Oct 2023
Cited by 1 | Viewed by 852
Abstract
Owing to changes in consumer attitudes, the beauty consumer population is growing rapidly and the demands of beauty consumers are variable. With a wide range of beauty products and exaggerated product promotions, consumers rely more on online reviews to perceive product information. In [...] Read more.
Owing to changes in consumer attitudes, the beauty consumer population is growing rapidly and the demands of beauty consumers are variable. With a wide range of beauty products and exaggerated product promotions, consumers rely more on online reviews to perceive product information. In this paper, we propose a demand forecasting model that takes into account both product features and product emotional needs based on online reviews to help companies better develop production and sales plans. Firstly, a Word2vec model and sentiment analysis method based on a sentiment dictionary are used to extract product features and factors influencing product sentiment; secondly, a multivariate Support Vector Regression (SVR) demand prediction model is constructed and the model parameters are optimized using particle swarm optimization; and finally, an example analysis is conducted with beauty product Z. The results show that compared with the univariate SVR model and the multivariate SVR model with only product feature demand as the influencing factor, the multivariate SVR model with both product feature and product sentiment demand as influencing factors has a smaller prediction error, which can enable beauty retail enterprises to better grasp consumer demand dynamics, make flexible production and sales plans, and effectively reduce production costs. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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39 pages, 964 KiB  
Article
Event Log Data Quality Issues and Solutions
by Dusanka Dakic, Darko Stefanovic, Teodora Vuckovic, Marina Zizakov and Branislav Stevanov
Mathematics 2023, 11(13), 2858; https://doi.org/10.3390/math11132858 - 26 Jun 2023
Viewed by 1354
Abstract
Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly [...] Read more.
Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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20 pages, 2425 KiB  
Article
Optimizing Distribution Routes for Chain Supermarket Considering Carbon Emission Cost
by Changlu Zhang, Liqian Tang, Jian Zhang and Liming Gou
Mathematics 2023, 11(12), 2734; https://doi.org/10.3390/math11122734 - 16 Jun 2023
Cited by 3 | Viewed by 1195
Abstract
The low-carbon economy and sustainable development have become a widespread consensus. Chain supermarkets should pay attention to path optimization in the process of distribution to reduce carbon emissions. This study takes chain supermarkets as the research object, focusing on the optimization of the [...] Read more.
The low-carbon economy and sustainable development have become a widespread consensus. Chain supermarkets should pay attention to path optimization in the process of distribution to reduce carbon emissions. This study takes chain supermarkets as the research object, focusing on the optimization of the vehicle routing problem (VRP) in supermarket store distribution. Firstly, based on the concept of cost-effectiveness, we constructed a green and low-carbon distribution route optimization model with the lowest cost. With cost minimization as the objective function, the total distribution cost in the vehicle delivery process includes fixed cost, transportation cost, and carbon emission cost. The carbon emission cost is calculated using the carbon tax mechanism. Secondly, through integrating the Floyd algorithm, the nearest neighbor algorithm, and the insertion algorithm, a fusion heuristic algorithm was proposed for model solving, and an empirical study was conducted using the W chain supermarket in Wuhan as an example. The experimental results show that optimizing distribution routes considering carbon emission cost can effectively reduce carbon emissions. At the same time, it can also reduce the total costs of enterprises and society, thereby achieving greater social benefits at lower costs. The research results provide effective suggestions for chain supermarkets to control carbon emissions during the distribution process. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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18 pages, 2173 KiB  
Article
Community Evolution Analysis Driven by Tag Events: The Special Perspective of New Tags
by Jing Yang, Jun Wang and Mengyang Gao
Mathematics 2023, 11(6), 1361; https://doi.org/10.3390/math11061361 - 10 Mar 2023
Cited by 1 | Viewed by 886
Abstract
The type, quantity, and scale of social-tagging systems have grown constantly in recent years as users’ interest increases. Tags have important reference value in the study of networked communities since they typically represent user preference. This paper aims to examine how a tagging [...] Read more.
The type, quantity, and scale of social-tagging systems have grown constantly in recent years as users’ interest increases. Tags have important reference value in the study of networked communities since they typically represent user preference. This paper aims to examine how a tagging community evolves and to check the impact of new tags on evolution. Therefore, we proposed an improved evolution model for tag communities where tags constantly accumulate without withdrawal. Based on the model, we conducted an evolution analysis on three different tag communities with the datasets generated from the Delicious bookmarking system, CiteULike, and Douban. The results from Delicious emphasized that new individuals have an enormous influence on the community evolution, for they dominate the Form event, lead the early Split event, indirectly have a hand in the Merge event, and affect existing tags’ transfer when they flood into the system. Moreover, new tags are proved to be more influential in tagging relation data of CiteULike and Douban, where new tags dominate the Split event. The in-depth and detailed depiction of community evolution helps us understand the evolution process of tag communities and the crucial role of new tags. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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18 pages, 1038 KiB  
Article
An Efficient Algorithm for the Joint Replenishment Problem with Quantity Discounts, Minimum Order Quantity and Transport Capacity Constraints
by Shiyu Liu, Ou Liu and Xiaoming Jiang
Mathematics 2023, 11(4), 1012; https://doi.org/10.3390/math11041012 - 16 Feb 2023
Viewed by 1363
Abstract
The joint replenishment problem has been extensively studied and the joint replenishment strategy has been adopted by a large variety of retailers in recent years. However, the joint replenishment problem under minimum order quantity and other constraints does not receive sufficient attention. This [...] Read more.
The joint replenishment problem has been extensively studied and the joint replenishment strategy has been adopted by a large variety of retailers in recent years. However, the joint replenishment problem under minimum order quantity and other constraints does not receive sufficient attention. This paper analyzes a retailing supply chain involving a supplier that provides quantity discount schedules and limits the order quantity. The order quantity constraints include minimum order requirements for each item and as to the total quantity; additionally, the latter cannot exceed the transport capacity constraint. These are common constraints in the retail industry today and create greater complexity and difficulty in the retailer’s decision-making. To analyze the problem, an integer nonlinear programming model is set up to maximize retailers’ profit with all practical constraints. A two-layer efficient algorithm named the Marginal and Cumulative Profit-Based Algorithm (MCPB) is then proposed to find whether to order and the optimal order quantity for each item. The results of computational experiments show that the proposed algorithm can find near-optimal solutions to the problem efficiently and is a reference for retailers to solve practical joint replenishment problems. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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19 pages, 2460 KiB  
Article
Research into the Relationship between Personality and Behavior in Video Games, Based on Mining Association Rules
by Mengyang Gao, Jun Wang and Jing Yang
Mathematics 2023, 11(3), 772; https://doi.org/10.3390/math11030772 - 03 Feb 2023
Cited by 1 | Viewed by 3905
Abstract
Nowadays, people have started to spend more and more time using the Internet, which has a crucial impact on people’s lives. Individual personality type is often the main factor dictating the various behaviors that people carry out, and it dominates their activities when [...] Read more.
Nowadays, people have started to spend more and more time using the Internet, which has a crucial impact on people’s lives. Individual personality type is often the main factor dictating the various behaviors that people carry out, and it dominates their activities when socializing, communicating, and making choices in the virtual world. This study is dedicated to uncovering how the six dimensions of personality traits relate to players’ in-game behavior. This research is divided into two studies. Study 1 uses the K-means method to classify players in “Clash of Kings”, an online strategy video game, according to their activities. Using apriori algorithm, this research analyzes the correlation between in-game behavior and personality. In Study 2, the correlations are validated. In conclusion, not all personality traits are related to in-game behaviors. Players with high extraversion demonstrate more killings and attacks in games. Conscientiousness is negatively related to deaths. Emotionality shows strong extremes. The highest or lowest emotionality scores are associated with killings and attacks, while players with moderate emotionality will behave irregularly. Honesty/humility, agreeableness, and openness to experience are not predictive of in-game behaviors. For game manufacturers, players’ personality traits can be inferred through their corresponding in-game behaviors, to use in order to carry out targeted promotions. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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Review

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20 pages, 1629 KiB  
Review
A Review on Business Analytics: Definitions, Techniques, Applications and Challenges
by Shiyu Liu, Ou Liu and Junyang Chen
Mathematics 2023, 11(4), 899; https://doi.org/10.3390/math11040899 - 10 Feb 2023
Cited by 7 | Viewed by 5311
Abstract
Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the Big Data era, business analytics techniques have been changing and evolving [...] Read more.
Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the Big Data era, business analytics techniques have been changing and evolving rapidly. Therefore, this paper reviews the latest techniques and applications of business analytics based on the existing literature. Meanwhile, many problems and challenges are inevitable in the progress of business analytics. Therefore, this review also presents the current challenges faced by business analytics and open research directions that need further consideration. All the research papers were obtained from the Web of Science and Google Scholar databases and were filtered with several selection rules. This paper will help to provide important insights for researchers in the field of business analytics, as it presents the latest techniques, various applications and several directions for future research. Full article
(This article belongs to the Special Issue Business Analytics: Mining, Analysis, Optimization and Applications)
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