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Strategy monitoring is essential for business management and for administrators, including managers and executives, to build a data-driven organization. Having a tool that is able to visualize strategic data is significant for business intelligence. Unfortunately, there are gaps between business users and information technology departments or business intelligence experts that need to be filled to meet user requirements. For example, business users want to be self-reliant when using business intelligence systems, but they are too inexperienced to deal with the technical difficulties of the business intelligence systems. This research aims to create an automatic matching framework between the key performance indicators (KPI) formula and the data in database systems, based on ontology concepts, in the case study of Prince of Songkla University. The mapping data schema with ontology (MapDS-Onto) framework is created through knowledge adaptation from the literature review and is evaluated using sample data from the case study. String similarity methods are compared to find the best fit for this framework. The research results reveal that the “fuzz.token_set_ratio” method is suitable for this study, with a 91.50 similarity score. The two main algorithms, database schema mapping and domain schema mapping, present the process of the MapDS-Onto framework using the “fuzz.token_set_ratio” method and database structure ontology to match the correct data of each factor in the KPI formula. The MapDS-Onto framework contributes to increasing self-reliance by reducing the amount of database knowledge that business users need to use semantic business intelligence.

Knowing that nowadays information is one of the strongest weapons a company can have, it has a relevant role in the decision-making part of the company. Building the right structures and using the right tools for these structures is essential for a certain company or business to be a leader in the field where it operates. Building IT infrastructure in business is a must for businesses today, one of the most innovative areas today is undoubtedly Business Intelligence. BI has restructured the company's decision-making processes, becoming the main pillar on which decision-making is based. This paper sheds light on the main concepts related to BI, also it focuses on the main techniques of Business Intelligence, BI techniques, and analysis of the current situation of use and involvement of BI in Albanian companies.

This research introduces a novel approach to enhance knowledge discovery through the construction of classification rules for improved event prediction within the domain of business intelligence. One of the applications of the solution can be the improvement of large-scale advertising campaign forecasting. In order to solve the existing problems of data imbalance and high dimensionality in practical applications, we present a new approach of quantum-inspired genetic algorithm (QIGA). Our methodology employs a two-phase strategy: the first phase utilizes QIGA for fast attribute selection while the second phase enhances the manner in which categorical data is represented to enhance rule interpretability. This combined approach is very efficient in producing accurate and usable classification rules without requiring a lot of data pre-processing. Experimental evaluation on benchmark datasets demonstrates the superiority of our proposed method in terms of precision, reliability, and efficiency when compared to traditional approaches. Thus, the proposed algorithm enhances the state-of-art in knowledge discovery by providing a practical and scalable solution to the problem of handling imbalanced data and reducing the effect of high dimensionality, which allows for improving the accuracy of event prediction. Thus, the findings of this research can be applied to various fields other than advertising including fraud detection, customer churn, and medical diagnosis.

The rising popularity of artificial intelligence has led to an increasing amount of research on deep learning models. Many current studies have focused on topics such as model structures, model optimization techniques, fine-tuning, and transfer learning, aiming to create novel models that have greater predictability in one or more fields of interest. However, while model development is important, it should not be limited to the topics mentioned above. Instead, the scope of research can be broadened to encompass the holistic design of an end-to-end pipeline for deep learning model development, which includes data storage, extract, transform, and load (ETL), business intelligence, model training and testing, and incremental learning. This paper therefore aims to underscore the importance of this data pipeline and provide a paradigm that delineates each aspect of this pipeline in detail through a practical case study centered on the end-to-end development of recommender system models. Compared to the conventional model development process, the novel data pipeline provides a more organized and efficient data storage and data preparation, an easier and more manageable visualization solutions, and a more comprehensive way for model evaluation and model selection through the usage of databases, business intelligence tools, and incremental learning.

The role of data as a valuable resource has caused significant transformations in various areas of life. Data Science (DS) aims to extract knowledge from data and thus, has gained attraction from organizations aiming to optimize existing processes and uncover previously unknown potentials. DS can be beneficially integrated into the business processes of Original Equipment Manufacturers (OEMs). Therefore, in this study, a structured literature review is conducted to assess the current state-of-the-art of DS in OEMs, especially in the automotive industry and in procurement, offering valuable insights for both researchers and practitioners in DS and OEMs. Several financial, operative, and strategic potentials of DS in the context of OEMs are identified and described. Examples are operational cost reduction, supplier selection and evaluation, forecasts of product demand, and promoted collaboration between stakeholders. Nevertheless, the literature also suggests several challenges in the execution of DS projects. It was observed that OEMs face both technological and procedural obstacles in this area, including the lack of data-driven work culture, inappropriate systems, and deficits with data collection and integration. Mitigating these challenges will be valuable in improving the success rates of DS projects. Further measures to enrich the results of this article are provided. Due to the rapidly evolving character of DS, the application possibilities and challenges might change in the future.

The emergence of business intelligence (BI) from advancements in information technology transforms vast amounts of company data into valuable information crucial for strategic decision-making. Numerous studies have shown that very little data is actually used in making strategic decisions, and their quality and comparability are often questionable. Questions of the usefulness of data and their analytical processing occupy an important area in business intelligence. For the purposes of this paper, the use of BI systems for analysis and reporting in public company Croatian Post was analysed. The aim of this research is to examine whether the application of the BI system in the sales network increases the efficiency of sales workers as a result of database management and analytical processing. The paper utilized data collected from the Sales Efficiency System (SES) regarding realized sales and sales efficiency of sales workers over a five-year period from 2018 to 2022, comparing them with data from 2017, i.e., the year preceding the introduction of the SES system. Sales realization per worker and per customer were used as indicators of sales efficiency. The findings reveal a significant increase in sales realization per worker and per customer following the implementation of the BI system in the sales network. Interpretations and implications of results are further discussed in the study. By shedding light on the benefits of BI system adoption in optimizing sales efficiency, this research contributes to the growing body of knowledge on the utilization of information technology in enhancing organizational performance.

AutoML offers significant benefits in solving real-life problems because it accelerates the development of machine learning models. In contexts involving real scenarios like analyzing companies’ environmental, social and governance (ESG), where the dataset presents some challenges, AutoML is anticipated as a promising solution to address these complexities. Although researchers have shown significant interest in exploring Genetic Programming (GP) in AutoML for handling complex datasets, a critical issue that remains unresolved is the comprehensive understanding of GP hyper-parameters that influence machine learning performance. While GP-based AutoML excels in automating many aspects of the modelling, there has been a scarcity of research that provides insight into the significance of individual features and GP population size within the models of GP-based AutoML. This paper presents a comprehensive analysis of the models’ performance evaluation from multiple facets, including feature selection, GP population sizes, and different machine learning algorithms. Furthermore, this study provides insights into the association between Pearson correlations, machine learning performance, and the importance of machine learning features. The findings demonstrate that incorporating all the determinants as features in GP-based AutoML or relying solely on firm characteristics led to superior performance with an excellent trade-off between True Positive Rate and False Positive Rate. Thus, higher accuracy results exceeding 0.9 of Area Under the Curve (AUC) are presented by the proposed models. The novelty of this study lies in its empirical evaluation of different approaches to GP-based AutoML implementation. These findings provide alternative solutions for business investors to identify companies with strong sustainability practices.

The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.

This study aims to determine the problem of implementing business intelligence in the outsourcing industry with complex IOIS integration. The data sources come from the Tcompany business processes (internal) and the business processes from partner companies (external) to obtain relevant information for the business Intelligence system. Soft System Methodology is used for system analysis and modeling to integrate technology (complex) and human (soft) systems. Using the view of soft systems thinking and rooted in the paradigm of situational complexity, the capacity of all-around real-world systems that are messy and unstructured by looking at the success factors of implementing business intelligence system managerially through organizational, process, and technology aspects. The final result expected by the researcher is in the form of suggestions for improvements that the company can make with a systematic approach and corporate culture. Implementing business intelligence in the outsourcing industry faces issues due to conflicts of interest between business and technology. CEOs focus more on growth and profits, while CIOs are more concerned with technological infrastructure and operational efficiency. Addressing this challenge requires initiating conversations between CEOs and CIOs and a comprehensive assessment of both business and technology strategies to ensure enhanced alignment.

Now a days online shopping has become popular and E-commerce are a usual concept. However, companies still have little knowledge about their customers. It is important to merge e-commerce with business intelligence, because this would enable to obtain knowledge about e-commerce platforms, customers, discovering purchasing patterns, better financial performance and so on. Now a days it became more important to know the need and preference of customers and provide the required products to them. This paper provides a review on how business intelligence helps in the development of E-commerce and helps the businessmen to take decisions about their products. [ABSTRACT FROM AUTHOR]

In recent years, calls have increased for adherence to standards that ensure sustainability, including the global initiative presented by the United Nations with 17 Sustainable Development Goals (SDGs) to ensure a more sustainable future. Achieving these goals is extremely important, as institutions have sought to integrate technology, especially business intelligence, into their operations to ensure their achievement. This study aims to provide a systematic literature review of the intersection of information systems and sustainability in business intelligence. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilized to select high-quality studies from various databases, including ScienceDirect, IEEE Xplore, and Scopus, to be included in this review. The methodology resulted in 32 studies taxonomized into four main categories covering different aspects of the intersection of information systems and sustainability. This study discusses integrating information systems and sustainability in various sectors, such as tourism, health, urban, and other sectors, with different technologies, such as Blockchain, IoT, Industry 4.0, and other innovations. Moreover, the information system types implemented to support sustainability practices in different domains are highlighted.

The benefits that organizations accrue from information systems depend on how effectively the systems are used. Yet despite the importance of knowing what it takes to use information systems effectively, little theory on the topic exists. One recent and largely untested exception is the theory of effective use (TEU). We report on a contextualization, extension, and test of TEU in the business intelligence (BI) context, a context of considerable importance in which researchers have called for such studies. We used a mixed methods, three-phase approach involving instrument development (n = 218), a two-wave cross-sectional survey (n = 437), and three sets of follow-up interviews (n = 33). The paper contributes by (1) showing how TEU can be contextualized, operationalized, and extended, (2) demonstrating that many of TEU's predictions hold in the BI context while also revealing ways to improve the theory, and (3) offering practical insights that executives can draw on to improve the use of BI in their organizations. [ABSTRACT FROM AUTHOR]

While market research has been the cornerstone of the intelligence ecosystem, the emergence of 'insights' vendors is re-shaping the market. Adjacent practices, ranging from competitive intelligence, social listening and data science, could relegate market research to legacy status in firms. This investigation explores how expert market researchers respond to the commoditisation of market research techniques and their diminishing access to the client's organisation to address this issue. The findings show that market researchers are adapting – effectively reinventing themselves as 'insights' professionals – through the following four initiatives: (1) offering solution services, (2) creating architectures that integrate organic and designed data, (3) making heroes in the client's organisation and (4) forging performative relationships based on strategic guidance. These initiatives shift market research from ostensive (descriptive or declarative) to performative (effectual or actionable). Theoretically, the article conceptualises the changes in the market research industry through the performativity lens. [ABSTRACT FROM AUTHOR]

This paper examines how business users can leverage machine learning and data analytics through dashboards to optimize their decision making in demand-side supply chain management. We present a case study of an Austrian B2B hygiene product retailer that needed to provide its top management, sales representatives, and marketing managers with more relevant information to improve business intelligence and to enhance customer acquisition and retention. To generate this information, we utilized various data analysis and machine learning methods, including RFM analysis, market basket analysis, TURF analysis, and demand forecasting, using real-life transaction data. To provide business users with easy access to this information, we developed dashboards that integrate these methods providing an interactive and visual tool for data exploration and understanding. We conclude that dashboards can enable, business users to make better informed and effective decisions on the demand side of supply chains leading to improved sales performance and increased customer satisfaction. [ABSTRACT FROM AUTHOR]

Understanding the reliability and health of critical business assets is essential for every organization. Digital transformation changes users' behaviours and imposes several challenges to organizations that need to collect and use more diversified data to improve digital services, drive decision-making, and spur innovation. Coupled with data growth and diversification, new approaches for analysing data rely on the schema-on-read approach, prioritizing data collection over data organization. Thus, knowing where data is stored, and how can be used, improves the capability to respond to business changes and thus helps to take better actions. To enable the creation, verification, and upkeep of dashboards in the analytical system, this work proposes a measure data catalog used to support the management and validation of metadata. [ABSTRACT FROM AUTHOR]

Educational Data Mining has been growing and many institutions have started to use it to become more competitive. In recent years, many studies have been done on Educational Data Mining, applied to different study topics, and using distinct methods and algorithms. With the growing popularity of Educational Data Mining, it would be beneficial to have a summary of the most used techniques and approaches. To this end, a review was conducted to identify the most important methods, and algorithms in the context of Educational Data Mining. Fifteen studies were analysed, and the results showed the most used topics, methods, algorithms, and the relationship between them. These results can help new studies define their techniques and approaches. [ABSTRACT FROM AUTHOR]

Despite the promising outcomes achieved over time in Asset Management, data accessibility, correlation, analysis, and visualization still represent challenges. The integration, readability, and interpretation of heterogeneous information by different stakeholders is a further concern, especially at the urban scale, where spatial data integration is required to correlate virtual information with the real world. The Geographic Information System (GIS) allows these connections, representing and digitizing extensive areas with significant benefits for asset analysis, management, and decision-making processes. Such benefits are central for managing large and widespread university campuses as they are comparable to small cities, covering a wide urban region and including resources highly integrated into the urban context. The paper presents how GIS integrated into Business Intelligence (BI) tools can support university Asset Management System (AMS) creation for the optimal use of resources, illustrating the University of Turin case study. The results discussion considers the relationship between the different elements of the assets and their synergy with the city. It focuses on four themes, dealing with the asset identification of buildings and resources, especially the educational ones, asset spatiotemporal evolution, and buildings' distances for proximity analysis. The benefits achievable through the AMS, related challenges, and possible future developments are highlighted. [ABSTRACT FROM AUTHOR]

This study takes stock of business and economics research on Artificial Intelligence (AI) and provides a dynamic panorama of the overall knowledge structure of this ever-growing body of work ever since its inception in 1966. Our bibliometric analysis based on the full archive of 1024 studies identifies the main trends of and the major intellectual contributors to the extant knowledge of AI in business and economics research. Specifically, our results show that (1) AI-focused business and economics research wintnessed growth over three stages, particularly with a sharp increase after 2017. (2) While this body of research has gained tremendous momentum across the globe, the United States is by far the center of knowledge generation. (3) Research collaborations are still limited in this area. (4) Research topics flourished, ranging from early decision support systems, neural networks, and scheduling methods to more recent machine learning, automation, and big data. This study also identifies fruitful avenues for further business and economics research with an AI focus. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to identify the drivers and future scenarios of Iran's Social Security Organization. Design/methodology/approach: The research is applied in terms of orientation and mixed in terms of methodology. In this research, the methods of theme analysis, root definitions, fuzzy Delphi and Cocoso were used. The theoretical population is the managers and senior experts of the social security organization, and the sampling method was done in a judgmental way. The tools of data collection were interviews and questionnaires. The interview tool was used to extract the main and subdrivers of the research and develop the scenarios. Findings: Through theme analysis, 35 subdrivers were extracted in the form of economic, sociocultural, financial and investment, policy, marketing, environmental and legal themes. Due to the large number of subdrivers, these factors were screened with fuzzy Delphi. Eleven drivers had defuzzied coefficient higher than 0.7 and were selected for final prioritization. The final drivers were prioritized with the CoCoSo technique, and the two drivers of social security holdings governance and state of government revenues had the highest priority. Based on these two drivers, four scenarios of prosperity, resilient social security, unstable development and collapse have been developed. Originality/value: Some of the suggestions of the research are: using the capacity of FinTechs and financial startups to invest the government revenues of the organization, using digital technologies such as business intelligence for more efficient decisions and developing corporate governance in the organization. [ABSTRACT FROM AUTHOR]

Business intelligence is becoming more essential for supply chain administrators to make good decisions. The globalization of supply chains makes their management and control more challenging. Blockchain is a distributed digital ledger technology that guarantees traceability, transparency, and security and promises to ease global supply chain management issues. This paper proposes the Blockchain-assisted Secure Data Management Framework (BSDMF) for financial data handling for supply chain integrated business intelligence models. Analyzing, collecting, and demonstrating data could be important to a business, its supply chain performance, and sustainability. The blockchain can interrupt supply chain processes for improved finance handling, distributed management, and process automation. The study's experimental result will help organizations deploy blockchain applications with intelligent business strategies to support supply chain management effectively. The simulation outcome has been implemented, and the recommended method achieves a computation time of fewer than 2 hours, an efficiency ratio of 97.4%, an error ratio of 94.1%, data authentication of 92.1%, and a data management ratio of 98.7%. [ABSTRACT FROM AUTHOR]

This article explores the use of social media data to gain insights and improve business intelligence. It introduces a model called the Online Conversation Remark Model (OCAM) that estimates customer perception by analyzing online conversations. The study finds that brand community features have a significant impact on customer perception, providing valuable feedback for decision-making. The article also discusses related work in sentiment analysis and competitive intelligence. The experiments conducted on a dataset from Amazon's Twitter account demonstrate the effectiveness of the model in categorizing conversations and identifying the service provider's influence. The study suggests further research to explore additional factors that shape customer perception. [Extracted from the article]

This study contextualizes the transformative role of Business Intelligence (BI) over the past two decades, emphasizing its impact on business strategy and competitive advantage. Employing a dual-method approach, it integrates a bibliometric analysis using SciMAT with a qualitative examination of six key articles from the Web of Science (WoS), analyzed through the Gioia methodology, focusing on BI and competitiveness. The aim is to examine the metamorphosis of Business Intelligence (BI) and how it has evolved from a traditionally supporting role to a central strategic player in shaping corporate strategy and business competitive advantage over the past two decades. It discusses the overall transformation of BI and provides an in-depth examination of the specific ways in which Business Intelligence tools have redefined the landscape in contemporary business practices. Key findings reveal BI's pivotal role in enhancing knowledge management, innovation, and marketing capabilities. Challenges in BI implementation, such as the necessity for skilled personnel and adaptability to swift technological shifts, are also highlighted. Results advocate for a dynamic BI approach, adaptable to market trends and technological evolutions. The research demonstrates that BI tools, especially when integrated with technologies like AI, IoT, and machine learning, significantly enhances decision making and efficiency in socio–technical and management systems, leading to a paradigm shift in handling complex systems and adapting to changing environments. [ABSTRACT FROM AUTHOR]

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The emergence of artificial intelligence (AI) has transformed global business, aiding operational efficiency and innovation. It utilizes machine learning and big data analytics, driving predictive market trends and strategic decision‐making. However, despite the rising discussion and accessibility of AI tools, understanding its impact on international business remains limited. This article explores AI's potential in international business strategies, practices, and activities. To address this aim, we reviewed 37 articles in the existing literature to critically explore AI within the context of international business. More specifically, we explored how AI can be applied to innovation approaches in international business, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, managing across cultures, and more topics. AI has necessitated changes in workplace configurations and the need for organizational and employee adjustments in response to this technology. As a result of the foregoing issues on AI integration within international business, our analysis provided an exploratory discussion around its use, challenges, managerial implications, and suggested areas requiring future studies. [ABSTRACT FROM AUTHOR]