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28 pages, 2191 KiB

Open AccessArticle

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

Viewed by 417

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

Open AccessArticle

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

Viewed by 456

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

Open AccessArticle

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 - 04 Apr 2024

Viewed by 706

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

Open AccessArticle

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 - 02 Apr 2024

Viewed by 741

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

Open AccessArticle

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

Viewed by 1236

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

Open AccessArticle

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

Viewed by 2172

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

Open AccessArticle

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 - 06 Feb 2024

Cited by 1 | Viewed by 1559

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

Open AccessArticle

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

Viewed by 1387

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

Open AccessArticle

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

Viewed by 3832

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

Open AccessArticle

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 - 09 Nov 2023

Viewed by 4048

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

Open AccessArticle

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 - 06 Nov 2023

Viewed by 2321

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

Open AccessArticle

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 1584

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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