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Artificial Intelligence (AI) for Economics and Business Management
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Artificial Intelligence (AI) for Economics and Business Management

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 65094

Special Issue Editors

Prof. Dr. Polona Tominc

E-Mail Website
Guest Editor
Department of Quantitative Economic Analysis, Faculty of Economics and Business, University of Maribor, 2000 Maribor, Slovenia
Interests: quantitative methods in economics and business; entrepreneurship research; quality in higher education
Dr. Maja Rožman

E-Mail Website
Guest Editor
Department of Quantitative Economic Analysis, Faculty of Economics and Business, University of Maribor, 2000 Maribor, Slovenia
Interests: quantitative methods in economics and business; contemporary management problems in organizations; digital transformation in HR

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a transformative force in the fields of economics and business management, revolutionizing the way organizations operate and make decisions. AI-powered technologies have enabled businesses to enhance their efficiency, optimize processes, and gain valuable insights from vast amounts of data.

In economics, AI has opened up new avenues for research and analysis. Machine learning algorithms have the capability to analyze complex economic data and identify patterns, leading to more accurate predictions and forecasts. AI models can be employed to analyze market trends, consumer behavior, and macroeconomic indicators, aiding policymakers, investors, and businesses in making informed decisions. AI-powered algorithms also contribute to the development of automated trading systems and financial risk management tools.

In the realm of business management, AI plays a vital role in optimizing operations and enhancing productivity. AI algorithms can streamline supply chain management, inventory control, and demand forecasting, leading to cost savings and improved customer satisfaction. Natural language processing (NLP) enables AI-powered chatbots and virtual assistants to handle customer inquiries and provide personalized recommendations, enhancing customer service experiences. Moreover, AI techniques are utilized for sentiment analysis of social media data, allowing businesses to gauge public opinion and tailor their marketing strategies accordingly.

However, the increasing integration of AI in economics and business management also presents challenges. Ethical considerations, privacy concerns, and potential biases embedded in AI algorithms must be addressed to ensure fair and responsible usage. Additionally, the evolving nature of AI demands a continuous upgrade of skills and knowledge for professionals in these fields, as well as a diligent and sustained effort toward the modernization of the curriculum in higher education institutions.

Prof. Dr. Polona Tominc
Dr. Maja Rožman
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

  • digital transformation
  • predictive analytics
  • natural language processing
  • AI-assisted strategy planning
  • business process automation
  • AI in HR and recruitment
  • AI for strategic decision-making
  • AI-supported management

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Published Papers (17 papers)

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Research

37 pages, 4792 KiB  
Article
Is the Taiwan Stock Market (Swarm) Intelligent?
by Ren-Raw Chen
Information 2024, 15(11), 707; https://doi.org/10.3390/info15110707 - 5 Nov 2024
Viewed by 419
Abstract
It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data [...] Read more.
It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data (as in the literature). In this paper, we obtain a proprietary data set that contains detailed trading information, and as a result, for the first time it allows us to validate this hypothesis. The data set contains all trades transacted in 2019 by all the brokers/dealers across all locations in Taiwan of all the equities (stocks, warrants, and ETFs). Given such data, in this paper, we use swarm intelligence to identify such herding behavior. In particular, we use two versions of swarm intelligence—Boids and PSO (particle swarm optimization)—to study the herding behavior. Our results indicate weak swarm among brokers/dealers. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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15 pages, 753 KiB  
Article
Balancing Risk and Profit: Predicting the Performance of Potential New Customers in the Insurance Industry
by Raquel Soriano-Gonzalez, Veronika Tsertsvadze, Celia Osorio, Noelia Fuster, Angel A. Juan and Elena Perez-Bernabeu
Information 2024, 15(9), 546; https://doi.org/10.3390/info15090546 - 6 Sep 2024
Viewed by 712
Abstract
In the financial sector, insurance companies generate large volumes of data, including policy transactions, customer interactions, and risk assessments. These historical data on established customers provide opportunities to enhance decision-making processes and offer more customized services. However, data on potential new customers are [...] Read more.
In the financial sector, insurance companies generate large volumes of data, including policy transactions, customer interactions, and risk assessments. These historical data on established customers provide opportunities to enhance decision-making processes and offer more customized services. However, data on potential new customers are often limited, due to a lack of historical records and to legal constraints on personal data collection. Despite these limitations, accurately predicting whether a potential new customer will generate benefits (high-performance) or incur losses (low-performance) is crucial for many service companies. This study used a real-world dataset of existing car insurance customers and introduced advanced machine learning models, to predict the performance of potential new customers for whom available data are limited. We developed and evaluated approaches based on traditional binary classification models and on more advanced boosting classification models. Our computational experiments show that accurately predicting the performance of potential new customers can significantly reduce operation costs and improve the customization of services for insurance companies. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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33 pages, 1996 KiB  
Article
Streamlining Tax and Administrative Document Management with AI-Powered Intelligent Document Management System
by Giovanna Di Marzo Serugendo, Maria Assunta Cappelli, Gilles Falquet, Claudine Métral, Assane Wade, Sami Ghadfi, Anne-Françoise Cutting-Decelle, Ashley Caselli and Graham Cutting
Information 2024, 15(8), 461; https://doi.org/10.3390/info15080461 - 2 Aug 2024
Viewed by 1769
Abstract
Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in [...] Read more.
Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in the landscape of practical tools in the field of DMS and advances the state of the art. This system represents a complex process of integrated AI-powered technologies that creates an ontology, extracts information from documents, defines profiles, maps the extracted data in RDF format, and applies inference through a reasoning engine. The DMS was designed to help all those companies that manage their clients’ tax and administrative documents daily. Automation speeds up the management process so that companies can focus more on value-added services. The system was tested in a case study that focused on the preparation of tax returns. The results demonstrated the efficacy of the system in providing document management service. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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16 pages, 865 KiB  
Article
Applying Machine Learning in Marketing: An Analysis Using the NMF and k-Means Algorithms
by Victor Gallego, Jessica Lingan, Alfons Freixes, Angel A. Juan and Celia Osorio
Information 2024, 15(7), 368; https://doi.org/10.3390/info15070368 - 22 Jun 2024
Cited by 2 | Viewed by 1611
Abstract
The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic [...] Read more.
The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms “machine learning” and “marketing” in their titles, which yields a pool of papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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15 pages, 1403 KiB  
Article
BERTopic for Enhanced Idea Management and Topic Generation in Brainstorming Sessions
by Asma Cheddak, Tarek Ait Baha, Youssef Es-Saady, Mohamed El Hajji and Mohamed Baslam
Information 2024, 15(6), 365; https://doi.org/10.3390/info15060365 - 20 Jun 2024
Cited by 1 | Viewed by 1831
Abstract
Brainstorming is an important part of the design thinking process since it encourages creativity and innovation through bringing together diverse viewpoints. However, traditional brainstorming practices face challenges such as the management of large volumes of ideas. To address this issue, this paper introduces [...] Read more.
Brainstorming is an important part of the design thinking process since it encourages creativity and innovation through bringing together diverse viewpoints. However, traditional brainstorming practices face challenges such as the management of large volumes of ideas. To address this issue, this paper introduces a decision support system that employs the BERTopic model to automate the brainstorming process, which enhances the categorization of ideas and the generation of coherent topics from textual data. The dataset for our study was assembled from a brainstorming session on “scholar dropouts”, where ideas were captured on Post-it notes, digitized through an optical character recognition (OCR) model, and enhanced using data augmentation with a language model, GPT-3.5, to ensure robustness. To assess the performance of our system, we employed both quantitative and qualitative analyses. Quantitative evaluations were conducted independently across various parameters, while qualitative assessments focused on the relevance and alignment of keywords with human-classified topics during brainstorming sessions. Our findings demonstrate that BERTopic outperforms traditional LDA models in generating semantically coherent topics. These results demonstrate the usefulness of our system in managing the complex nature of Arabic language data and improving the efficiency of brainstorming sessions. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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28 pages, 2191 KiB  
Article
Chatbot Design and Implementation: Towards an Operational Model for Chatbots
by Alexander Skuridin and Martin Wynn
Information 2024, 15(4), 226; https://doi.org/10.3390/info15040226 - 17 Apr 2024
Cited by 2 | Viewed by 3893
Abstract
The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are [...] Read more.
The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and a model is developed and validated to support the planning and implementation of chatbot projects. The presented model can serve as an exemplary guide for researchers and practitioners working in this field. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience in chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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23 pages, 706 KiB  
Article
Using ML to Predict User Satisfaction with ICT Technology for Educational Institution Administration
by Hamad Almaghrabi, Ben Soh and Alice Li
Information 2024, 15(4), 218; https://doi.org/10.3390/info15040218 - 12 Apr 2024
Cited by 1 | Viewed by 1698
Abstract
Effective and efficient use of information and communication technology (ICT) systems in the administration of educational organisations is crucial to optimise their performance. Earlier research on the identification and analysis of ICT users’ satisfaction with administration tasks in education is limited and inconclusive, [...] Read more.
Effective and efficient use of information and communication technology (ICT) systems in the administration of educational organisations is crucial to optimise their performance. Earlier research on the identification and analysis of ICT users’ satisfaction with administration tasks in education is limited and inconclusive, as they focus on using ICT for nonadministrative tasks. To address this gap, this study employs Artificial Intelligence (AI) and machine learning (ML) in conjunction with a survey technique to predict the satisfaction of ICT users. In doing so, it provides an insight into the key factors that impact users’ satisfaction with the ICT administrative systems. The results reveal that AI and ML models predict ICT user satisfaction with an accuracy of 94%, and identify the specific ICT features, such as usability, privacy, security, and Information Technology (IT) support as key determinants of satisfaction. The ability to predict user satisfaction is important as it allows organisations to make data-driven decisions on improving their ICT systems to better meet the needs and expectations of users, maximising labour effort while minimising resources, and identifying potential issues earlier. The findings of this study have important implications for the use of ML in improving the administration of educational institutions and providing valuable insights for decision-makers and developers. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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18 pages, 981 KiB  
Article
From Typing to Talking: Unveiling AI’s Role in the Evolution of Voice Assistant Integration in Online Shopping
by Guillermo Calahorra-Candao and María José Martín-de Hoyos
Information 2024, 15(4), 202; https://doi.org/10.3390/info15040202 - 4 Apr 2024
Cited by 2 | Viewed by 2491
Abstract
This study develops a theoretical framework integrating the Technology Acceptance Model (TAM) and Uses and Gratifications Theory (UGT) to predict and understand the acceptance of voice shopping intentions, particularly through AI-driven voice assistants. This research delves into the dual aspects of AI voice [...] Read more.
This study develops a theoretical framework integrating the Technology Acceptance Model (TAM) and Uses and Gratifications Theory (UGT) to predict and understand the acceptance of voice shopping intentions, particularly through AI-driven voice assistants. This research delves into the dual aspects of AI voice shopping platforms: the functional attributes outlined by the TAM and personal gratifications highlighted by the UGT, such as enjoyment, performance expectancy, and perceived safety. It uncovers a favorable user attitude towards voice shopping, emphasizing the significant role of performance expectancy and perceived utility on behavioral intentions. Key insights include the critical importance of security and privacy for user trust and the acceptance of new AI technologies, and the necessity of a balanced approach that merges functional, emotional, and security aspects for successful AI integration in daily technology use. Contrary to expectations, this study reveals a weak relationship between social norms and perceived usefulness, suggesting a misalignment with societal expectations. This research enriches the understanding of voice shopping using virtual assistants, offering valuable insights into consumer behavior and AI technology acceptance. It highlights practical implications for AI research, the development of voice-based software, and AI-driven advertising strategies, emphasizing the communication of benefits and emotional resonance in voice-enabled AI assistants for consumer purchases. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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19 pages, 4609 KiB  
Article
AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning
by Waseem Abbas, Zuping Zhang, Muhammad Asim, Junhong Chen and Sadique Ahmad
Information 2024, 15(4), 196; https://doi.org/10.3390/info15040196 - 2 Apr 2024
Cited by 2 | Viewed by 2786
Abstract
In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche [...] Read more.
In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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15 pages, 539 KiB  
Article
Influence Analysis of Real Exchange Rate Fluctuations on Trade Balance Data Using Feature Important Evaluation Methods
by Min-Joon Kim and Thi-Thu-Huong Le
Information 2024, 15(3), 156; https://doi.org/10.3390/info15030156 - 10 Mar 2024
Cited by 2 | Viewed by 3991
Abstract
This study delves into the intricate relationship between fluctuations in the real exchange rate and the trade balance, situated within the framework of a ‘two-country’ trade theory model. Despite a wealth of prior research on the impact of exchange rates on international trade, [...] Read more.
This study delves into the intricate relationship between fluctuations in the real exchange rate and the trade balance, situated within the framework of a ‘two-country’ trade theory model. Despite a wealth of prior research on the impact of exchange rates on international trade, the precise extent of this influence remains a contentious issue. To bridge this gap, our research adopts a pioneering approach, employing three distinct artificial intelligence-based influence measurement methods: Mean Decrease Impurity (MDI), Permutation Importance Measurement (PIM), and Shapley Additive Explanation (SHAP). These sophisticated techniques provide a nuanced and differentiated perspective, enabling specific and quantitative measurements of the real exchange rate’s impact on the trade balance. The outcomes derived from the application of these innovative methods shed light on the substantial contribution of the real exchange rate to the trade balance. Notably, the real exchange rate (RER) emerges as the second most influential factor within the ‘two-country’ trade model. This empirical evidence, drawn from a panel dataset of 78 nations over the period 1992–2021, addresses crucial gaps in the existing literature, offering a finer-grained understanding of how real exchange rates shape international trade dynamics. Importantly, our study implies that policymakers should recognize the pivotal role of the real exchange rate as a key determinant of trade flow. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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17 pages, 1151 KiB  
Article
Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach
by Ive Botunac, Jurica Bosna and Maja Matetić
Information 2024, 15(3), 136; https://doi.org/10.3390/info15030136 - 28 Feb 2024
Cited by 6 | Viewed by 6443
Abstract
Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies [...] Read more.
Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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19 pages, 1023 KiB  
Article
AI Model for Industry Classification Based on Website Data
by Timotej Jagrič and Aljaž Herman
Information 2024, 15(2), 89; https://doi.org/10.3390/info15020089 - 6 Feb 2024
Cited by 3 | Viewed by 2813
Abstract
This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting [...] Read more.
This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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28 pages, 2814 KiB  
Article
Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling
by Nikolaos T. Giannakopoulos, Marina C. Terzi, Damianos P. Sakas, Nikos Kanellos, Kanellos S. Toudas and Stavros P. Migkos
Information 2024, 15(2), 67; https://doi.org/10.3390/info15020067 - 23 Jan 2024
Cited by 2 | Viewed by 2728
Abstract
Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and [...] Read more.
Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms’ digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector’s employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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16 pages, 5211 KiB  
Article
Developing Integrated Performance Dashboards Visualisations Using Power BI as a Platform
by Célia Talma Gonçalves, Maria José Angélico Gonçalves and Maria Inês Campante
Information 2023, 14(11), 614; https://doi.org/10.3390/info14110614 - 15 Nov 2023
Cited by 6 | Viewed by 12501
Abstract
The rapid advance of business technologies in recent years has made knowledge an essential and strategic asset that determines the success or failure of an organisation. Access to the right information in real time and with high selectivity can be a competitive advantage [...] Read more.
The rapid advance of business technologies in recent years has made knowledge an essential and strategic asset that determines the success or failure of an organisation. Access to the right information in real time and with high selectivity can be a competitive advantage in the business environment. Business intelligence systems help corporate executives, business managers, and other operational workers make better and more informed business decisions. This study aimed to assess the impact of using business intelligence tools on the decision-making process in organisations, specifically in sales marketing. The methodology applied to realise the study’s objective was the Vercellis methodology. A set of data available on the sales marketing website SuperDataScience was used to implement a set of pressing KPIs for the business decision-making process in the area. Using these data, a complete business intelligence system solution was implemented. A data warehouse was created using the ETL (extract–transform–load) process, and the data were then explored using a set of dynamics dashboards with a view of the business metrics. The results showed the use of business intelligence systems that allow the integration and transformation of data from various sources stored in data warehouses, where it is possible to implement KPIs and carry out quick, concise, easy-to-interpret graphical analyses. This paper contributes to a better understanding of the importance of data-integrated dashboard visualisation for the decision-making process. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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22 pages, 496 KiB  
Article
Small and Medium-Sized Enterprises in the Digital Age: Understanding Characteristics and Essential Demands
by Barbara Bradač Hojnik and Ivona Huđek
Information 2023, 14(11), 606; https://doi.org/10.3390/info14110606 - 9 Nov 2023
Cited by 3 | Viewed by 9663
Abstract
The article explores the implementation of digital technology in small and medium-sized Slovenian enterprises (SMEs), with a focus on understanding existing trends, obstacles, and necessary support measures during their digitalization progress. The surveyed companies mainly rely on conventional technologies like websites and teamwork [...] Read more.
The article explores the implementation of digital technology in small and medium-sized Slovenian enterprises (SMEs), with a focus on understanding existing trends, obstacles, and necessary support measures during their digitalization progress. The surveyed companies mainly rely on conventional technologies like websites and teamwork platforms, emphasizing the significance of strong online communication and presence in the modern business world. The adoption of advanced technologies such as blockchain is limited due to the perceived complexity and relevance to specific sectors. This study uses variance analysis to identify potential differences in the digitalization challenges faced by companies of different sizes. The results indicate that small companies face different financial constraints and require more differentiated support mechanisms than their larger counterparts, with a particular focus on improving digital competencies among employees. Despite obtaining enhancements such as elevated operational standards and uninterrupted telecommuting via digitalization, companies still face challenges of differentiation and organizational culture change. The study emphasizes the importance of recognizing and addressing the different challenges and support needs of different-sized companies to promote comprehensive progress in digital transformation. Our findings provide important insights for policymakers, industry stakeholders, and SMEs to formulate comprehensive strategies and policies that effectively address the diverse needs and challenges of the digital transformation landscape. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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15 pages, 3739 KiB  
Article
Trend Analysis of Large Language Models through a Developer Community: A Focus on Stack Overflow
by Jungha Son and Boyoung Kim
Information 2023, 14(11), 602; https://doi.org/10.3390/info14110602 - 6 Nov 2023
Viewed by 3642
Abstract
In the rapidly advancing field of large language model (LLM) research, platforms like Stack Overflow offer invaluable insights into the developer community’s perceptions, challenges, and interactions. This research aims to analyze LLM research and development trends within the professional community. Through the rigorous [...] Read more.
In the rapidly advancing field of large language model (LLM) research, platforms like Stack Overflow offer invaluable insights into the developer community’s perceptions, challenges, and interactions. This research aims to analyze LLM research and development trends within the professional community. Through the rigorous analysis of Stack Overflow, employing a comprehensive dataset spanning several years, the study identifies the prevailing technologies and frameworks underlining the dominance of models and platforms such as Transformer and Hugging Face. Furthermore, a thematic exploration using Latent Dirichlet Allocation unravels a spectrum of LLM discussion topics. As a result of the analysis, twenty keywords were derived, and a total of five key dimensions, “OpenAI Ecosystem and Challenges”, “LLM Training with Frameworks”, “APIs, File Handling and App Development”, “Programming Constructs and LLM Integration”, and “Data Processing and LLM Functionalities”, were identified through intertopic distance mapping. This research underscores the notable prevalence of specific Tags and technologies within the LLM discourse, particularly highlighting the influential roles of Transformer models and frameworks like Hugging Face. This dominance not only reflects the preferences and inclinations of the developer community but also illuminates the primary tools and technologies they leverage in the continually evolving field of LLMs. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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14 pages, 2596 KiB  
Article
New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market
by Timotej Jagrič, Daniel Zdolšek, Robert Horvat, Iztok Kolar, Niko Erker, Jernej Merhar and Vita Jagrič
Information 2023, 14(10), 565; https://doi.org/10.3390/info14100565 - 14 Oct 2023
Viewed by 2120
Abstract
Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools [...] Read more.
Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools enable them to discover more and to intervene in a timely manner. We propose an artificial intelligence approach using Kohonen’s self-organizing maps. The dataset used for development and testing included yearly financial statements with 4058 observations for European composite insurance companies from 2012 to 2021. In a novel manner, the model investigates the behaviour of insurers, looking for similarities. The model forms a map. For the obtained groupings of companies from different geographical origins, a common characteristic was discovered regarding their future financial deterioration. A threshold defined using the solvency capital requirement (SCR) ratio being below 130% for the next year is applied to the map. On the test sample, the model correctly identified on average 86% of problematic companies and 79% of unproblematic companies. Changing the SCR ratio level enables differentiation into multiple map sections. The model does not rely on traditional methods, or the use of the SCR ratio as a dependent variable but looks for similarities in the actual insurer’s financial behaviour. The proposed approach offers grounds for a Suptech tool of predictive generation to support early detection of the possible future financial distress of an insurance company. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
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