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AI Tools for Business and Economics
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AI Tools for Business and Economics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 5091

Special Issue Editors

Dr. Pejman Ebrahimi

E-Mail Website
Guest Editor
Department of Information Systems & Computer Science, University of Liechtenstein, Fürst-Franz-Josef-Strasse, 9490 Vaduz, Liechtenstein
Interests: data science; machine learning; deep learning; Artificial Intelligence
Prof. Dr. Mihai Dascalu

E-Mail Website
Guest Editor
Computer Science and Engineering Department, National University of Science and Technology Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: natural language processing; discourse analysis; educational data mining; intelligent tutoring systems

Special Issue Information

Dear Colleagues,

Aim and Scope

The rapid advancements in AI, machine learning, deep learning, and large language models (LLMs) have generated a significant impact on various industries, particularly in business and economics. These technologies offer transformative tools for data analysis, automation, and strategic decision-making, which are increasingly critical in a world driven by complex economic dynamics and data-intensive processes. However, the integration of these technologies within business and economic frameworks remains challenging and requires a more nuanced understanding of their practical applications, ethical implications, and potential limitations. This Special Issue addresses the urgent need for a consolidated body of research that not only showcases the latest developments in the field, but also explores the diverse and often complex ways AI technologies are reshaping the landscape of business and economics.

By fostering interdisciplinary contributions, this Special Issue aims to bring together scholars, practitioners, and industry experts to advance both theoretical understanding and practical knowledge. Our ultimate goal is to offer insights that can inform responsible and innovative use of AI technologies, promoting efficient practices, economic growth, and ethical standards across the field.

Specific Objectives

The specific objectives of this Special Issue are as follows:

  1. To examine the latest developments in AI, LLMs, machine learning, and deep learning as they apply to business and economics, providing a platform for cutting-edge research and case studies that highlight current capabilities and challenges.
  2. To explore the practical applications of AI technologies in solving real-world business problems, such as predictive analytics, supply chain optimization, personalized marketing, risk management, and customer service automation.
  3. To assess the economic impacts of AI technology adoption across various sectors and global markets, offering insights into how these technologies drive economic innovation, growth, and competitive advantage.
  4. To address ethical considerations and regulatory frameworks relevant to AI in economic contexts, promoting responsible AI use and highlighting the importance of compliance with emerging regulations.
  5. To evaluate the unique challenges and opportunities faced by small- and medium-sized enterprises (SMEs) and developing economies in implementing AI technologies, aiming to identify strategies for inclusive and sustainable AI integration.
  6. To analyze AI-driven approaches for economic forecasting, competitive market analysis, and data-informed policy recommendations, with an emphasis on understanding the role of AI in shaping future economic policies.

By meeting these objectives, this Special Issue aims to provide a comprehensive and multidisciplinary perspective on the potential, challenges, and ethical dimensions of AI in business and economics.

Dr. Pejman Ebrahimi
Prof. Dr. Mihai Dascalu
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. Information is an international peer-reviewed open access monthly 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 1600 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 (AI)
  • Large Language Models (LLMs)
  • machine learning
  • deep learning
  • business intelligence
  • economic forecasting
  • predictive analytics
  • supply chain optimization
  • personalized marketing
  • customer behavior analysis
  • fraud detection
  • risk management
  • automated decision-making
  • human resource management
  • revenue optimization
  • sentiment analysis
  • AI, innovation, and entrepreneurship
  • ethical AI
  • regulatory compliance
  • AI and international trade
  • global economic policy
  • competitive market analysis
  • AI and small and medium-sized enterprises (SMEs)
  • real-time data analysis
  • economic policy recommendations

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

15 pages, 801 KiB  
Article
Pricing in the Sharing Economy—A Hybrid Approach Leveraging Econometrics, Machine Learning, and Artificial Intelligence
by Kornilios Vezyroglou and Fotios Siokis
Information 2025, 16(6), 450; https://doi.org/10.3390/info16060450 - 27 May 2025
Viewed by 201
Abstract
This study investigates the determinants of Airbnb prices in Athens and Thessaloniki, Greece, employing a hybrid approach combining econometric analysis, machine learning techniques, and artificial intelligence tools. Our findings highlight the significance of location, property type, host responsiveness, listing quality, and photograph quality [...] Read more.
This study investigates the determinants of Airbnb prices in Athens and Thessaloniki, Greece, employing a hybrid approach combining econometric analysis, machine learning techniques, and artificial intelligence tools. Our findings highlight the significance of location, property type, host responsiveness, listing quality, and photograph quality in influencing rental prices. Notably, we leverage a publicly available AI tool to assess the esthetic and technical quality of listing photos, demonstrating its positive impact on rental prices. This underscores the increasing importance of visual marketing in the sharing economy and the democratization of AI tools for optimizing pricing strategies. We also conduct machine learning analysis, employing algorithms like Random Forest, k-Nearest Neighbors, Support Vector Machine, Neural Network, Gradient Boosting, and AdaBoost. Both AdaBoost and Gradient Boosting demonstrate strong performance across various metrics, with AdaBoost showing an advantage. The study offers valuable insights for Airbnb hosts, platform developers, and policymakers in understanding and optimizing pricing strategies within the short-term rental market. Full article
(This article belongs to the Special Issue AI Tools for Business and Economics)
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24 pages, 1203 KiB  
Article
Artificial Intelligence in Ecuadorian SMEs: Drivers and Obstacles to Adoption
by Reyner Pérez-Campdesuñer, Alexander Sánchez-Rodríguez, Gelmar García-Vidal, Rodobaldo Martínez-Vivar and Margarita De Miguel-Guzmán
Information 2025, 16(6), 443; https://doi.org/10.3390/info16060443 - 27 May 2025
Viewed by 469
Abstract
This study analyzes the current state of artificial intelligence (AI) adoption among micro-, small-, and medium-sized enterprises (MSMEs) in Ecuador, with a focus on its application across core business functions. Using a stratified random sample of 385 firms from the most representative economic [...] Read more.
This study analyzes the current state of artificial intelligence (AI) adoption among micro-, small-, and medium-sized enterprises (MSMEs) in Ecuador, with a focus on its application across core business functions. Using a stratified random sample of 385 firms from the most representative economic sectors, a survey instrument was designed to assess three dimensions: access to AI-enabling conditions, degree of AI utilization, and organizational characteristics. The results reveal that AI adoption remains limited and highly concentrated in marketing-related functions, particularly in content generation and social media automation, with minimal implementation in finance, logistics, and human resource management. The study also identifies the main barriers hindering AI adoption. The lack of qualified professionals and the unavailability of structured databases emerged as the most critical obstacles, followed by limited financial capacity. One-way ANOVA and Kruskal–Wallis tests confirmed significant differences in AI adoption levels based on company size and sector, especially in areas such as inventory optimization and design prototyping. These findings highlight a gap between the potential of AI technologies and their real-world implementation in Ecuadorian MSMEs. They underscore the need for targeted strategies focused on workforce training, digital infrastructure development, and institutional support to promote broader and more effective AI integration. Full article
(This article belongs to the Special Issue AI Tools for Business and Economics)
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21 pages, 1529 KiB  
Article
High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study
by Fátima Rodrigues and Miguel Machado
Information 2025, 16(4), 300; https://doi.org/10.3390/info16040300 - 9 Apr 2025
Viewed by 3283
Abstract
The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. Accurate price forecasting is crucial for informed investment decisions, enabling investors to optimize portfolio allocation, mitigate risk, and potentially maximize returns. Existing forecasting methods often struggle with the inherent [...] Read more.
The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. Accurate price forecasting is crucial for informed investment decisions, enabling investors to optimize portfolio allocation, mitigate risk, and potentially maximize returns. Existing forecasting methods often struggle with the inherent non-stationarity and complexity of cryptocurrency price dynamics. This study addresses this challenge by developing a system for high-frequency forecasting of the closing prices of ten leading cryptocurrencies. We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. Our findings demonstrate that the GRU neural network exhibits superior predictive accuracy (MAPE = 0.09%, MSE = 5954.89, RMSE = 77.17, MAE = 60.20), outperforming other models considered. This improved forecasting accuracy contributes to the existing literature by providing empirical evidence for GRU’s effectiveness in the volatile cryptocurrency market and offers practical insights for investment strategies. A web application integrating the best-performing model further facilitates real-time price prediction for multiple cryptocurrencies. Full article
(This article belongs to the Special Issue AI Tools for Business and Economics)
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22 pages, 1613 KiB  
Article
Detecting Potential Investors in Crypto Assets: Insights from Machine Learning Models and Explainable AI
by Timotej Jagrič, Davor Luetić, Damijan Mumel and Aljaž Herman
Information 2025, 16(4), 269; https://doi.org/10.3390/info16040269 - 27 Mar 2025
Viewed by 511
Abstract
This study explores the characteristics of individual investors in crypto asset markets using machine learning and explainable artificial intelligence (XAI) methods. The primary objective was to identify the most effective model for predicting the likelihood of an individual investing in crypto assets in [...] Read more.
This study explores the characteristics of individual investors in crypto asset markets using machine learning and explainable artificial intelligence (XAI) methods. The primary objective was to identify the most effective model for predicting the likelihood of an individual investing in crypto assets in the future based on demographic, behavioral, and financial factors. Data were collected through an online questionnaire distributed via social media and personal networks, yielding a limited but informative sample. Among the tested models, Efficient Linear SVM and Kernel Naïve Bayes emerged as the most optimal, balancing accuracy and interpretability. XAI techniques, including SHAP and Partial Dependence Plots, revealed that crypto understanding, perceived crypto risks, and perceived crypto benefits were the most influential factors. For individuals with a high likelihood of investing, these factors had a strong positive impact, while they negatively influenced those with a low likelihood. However, for those with a moderate investment likelihood, the effects were mixed, highlighting the transitional nature of this group. The study’s findings provide actionable insights for financial institutions to refine their strategies and improve investor engagement. Furthermore, it underscores the importance of interpretable machine learning in financial behavior analysis and highlights key factors shaping engagement in the evolving crypto market. Full article
(This article belongs to the Special Issue AI Tools for Business and Economics)
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