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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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

This document is a compilation of various sources that discuss the integration of Artificial Intelligence (AI) into business operations and its impact on various sectors. It explores the history and advancements of AI, provides case studies of successful AI implementations, and highlights the benefits of AI in improving efficiency and decision-making processes. The document also addresses challenges such as ethical considerations and biases in AI algorithms, and factors influencing the adoption of AI in businesses. It offers a wide range of perspectives and insights into the current state and future directions of AI. [Extracted from the article]

Huge demand on business intelligence applications over large volume of enterprise data has resulted in rapid adoption of High performance data analytics. Hadoop based high performance computing environment are optimized for large files. Data centric execution with localization of computing proximal to data provided higher performance for large files. But for small files, the performance reduced and overhead increased due to the way Hadoop handles files. Resource allocation and scheduling policies of Hadoop has to be improvised to handle small files. This work proposes an integrated data, task, and resource management technique to speed up processing of small files in Hadoop based high performance computing environment. As part of data management, the data placement is made dynamic to access frequency and inherent data semantics. As part of task management, tasks were grouped based on fine grained data semantics correlation they process. As part of resource management, data blocks or storage nodes are replicated to improve the access latency with minimal cost overheads. With this integrated management, the proposed solution is able to increase the speedup of handling small files by at least 8.7% compared to most recent works on individual management of data, task and resources. [ABSTRACT FROM AUTHOR]

This study explores the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs), on organizational decision-making in the stock market. Utilizing CNN architectures like VGG16, ResNet50, and InceptionV3, the research emphasizes the significance of leveraging deep learning for improved business intelligence and management. It highlights the superiority of CNN models over traditional algorithms, with VGG16 achieving an accuracy rate of 90.45%. The study underscores the potential of deep learning in extracting valuable insights from complex data, leading to a shift in optimizing organizational processes. Additionally, it stresses the importance of investing in infrastructure and expertise for successful CNN integration, alongside addressing ethical and privacy concerns. Through a dive into real-time mathematical concepts, the study provides insights into CNN functionality and offers comparisons between different architectures, aiding in specialized applications such as stock market trends. [ABSTRACT FROM AUTHOR]

Objective: The existing literature on artificial intelligence is reviewed with the aim of identifying interesting and novel research avenues. This leads to a focus on experiences, customisation, diversity and strategy as the main issues requiring further investigation. Research Design & Methods: A systematic literature review was conducted on articles related to artificial intelligence and business using various databases including Scopus, Web of Science and Google Scholar. Findings: Artificial intelligence is a hot topic particularly for international business managers who are incorporating technological innovation for competitive reasons. This article focuses on the link between artificial intelligence and international business in terms of future research opportunities. Implications & Recommendations: The results of this study will help business managers and practitioners understand the main trending areas related to artificial intelligence in a business setting. It is recommended that academic researchers and policy makers consider the future research suggestions stated in the article in order to help them derive new research directions. Contribution & Value Added: Potential research topics and questions are stated that help international business researchers and practitioners focus on key important areas. Thereby helping to consolidate the existing research but also offering new possibilities regarding the use of artificial intelligence in international business. [ABSTRACT FROM AUTHOR]

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]

Artificial intelligence is a dynamic and emerging form of technological innovation that has numerous ramifications for international business managers. The aim of this article is to obtain commentary from researchers about the role artificial intelligence will play in the global arena. This includes asking questions about how it will affect internationalization processes and whether it will lead to more international collaboration. Well‐known researchers provide advice on what international business managers should do in terms of staying competitive but also how they can integrate learning from artificial intelligence into their business operations. Lastly, suggestions for future research regarding the interplay between international business and artificial intelligence are provided. [ABSTRACT FROM AUTHOR]

Artificial intelligence is poised to transform international business through the adoption of new digital technology practices as part of the fourth industrial revolution. The how, when, and why of artificial intelligence implementation in international business is still somewhat unknown but will likely significantly influence performance outcomes, market interpretation, risk prediction, consumer interactions, and technology‐based metrics. With greater emphasis on artificial intelligence‐based tools, the future practice of international business will require digital literacy. This means business managers need to improve the way they analyze artificial intelligence in terms of appreciating its usefulness while maintaining privacy and mitigation strategies. In this editorial, we discuss the current developments regarding artificial intelligence in international business with the goal of identifying future research areas of importance. [ABSTRACT FROM AUTHOR]

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]

The collaboration between artificial intelligence (AI) and humans is reshaping international business (IB) management dynamics, aiming to achieve global sustainable development. Recent IB literature indicates that managing AI brings benefits such as better resource reconfiguration, reduced transaction costs, and global sustainable development. However, existing IB literature provides only meager knowledge about the characteristics of AI and how these characteristics can be employed for international expansion at the intersection of sustainable development. In response, our aim is to construct these characteristics by employing directed qualitative content analysis of empirical AI research. Based on our three constructed characteristics of AI, we contribute to current IB literature by providing a framework to balance economic and social goals and utilizing AI for global sustainable development. Further, we provide future IB research themes to guide IB and AI research toward achieving a sustainable production and consumption agenda. [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]

Objective: In many companies, it is possible to see the low alignment of marketing and business strategies. This defect leads to problems in business development, product sales, and failure to achieve the designed goals and plans. Based on the importance of the subject, the current research seeks to identify the antecedents of the alignment of marketing strategies and business strategies at the business-to-business level in knowledge-based businesses. Method: In this research, a qualitative analysis of the interviews of 10 experts and executive managers in the field of knowledge-based businesses was done. These people were selected purposefully and collected data through in-depth interviews. After data analysis, 215 initial codes were extracted and 34 organizing themes and 10 comprehensive themes were obtained from these codes. Findings: The findings of this research indicate that the antecedents of the alignment of marketing and business strategies at the interenterprise level in knowledge-based businesses include 10 comprehensive themes including communication-information integration, efficiency, and human capital empowerment. Strategic pronunciation, strategic thinking, customer value management, structural-management efficiency and coherence, strategic leadership, processoperational agility, competitive intelligence, and supporting resources. Conclusion: Based on the results of the research, it was determined that knowledge-based companies should know that before anything and any kind of action to align the stated antecedents, the conditions of the company should be examined and the competitors and the environment should be fully analyzed and understood, and the necessary preparations for Provide alignment. [ABSTRACT FROM AUTHOR]

This study examines the impact of AI intelligence levels on consumer decision delegation through three experimental studies. Study 1 manipulates AI intelligence levels (low vs. high) to establish the main effect. Study 2 explores the mediating role of trust and the moderating effect of task types (subjective vs. objective) on the relationship between AI intelligence and trust. Study 3 investigates the role of AI anthropomorphism in AI delegation. The findings reveal that increasing AI intelligence enhances consumer decision delegation for both subjective and objective consumption tasks, with trust serving as a mediating factor. However, objective tasks are more likely to be delegated to AI. Moreover, anthropomorphic appearance positively influences trust but does not moderate the relationship between intelligence and trust. This research expands the literature on AI decision delegation, shedding light on the psychological processes related to AI‐powered product design, consumer perceptions, and consumption situations, while also offering practical implications. [ABSTRACT FROM AUTHOR]

AI‐based voice assistants (AIVA) are capable of interpreting human speech and responding with useful information, aiding with tasks, and controlling other devices. The usage of these AIVAs has grown significantly worldwide. Despite this growth, studies on user behavior related to continued usage intention of AIVAs and effects on the long‐term commercial sustainability of brands, remain low. What is less understood is the potential of AI instrumentality attributes and brand credibility components in provoking shifts in post‐use behavior of AIVAs. This study proposes a model which expands on the Expectation‐Confirmation Model for user continuance behavior of AIVAs, by integrating user's technology related traits, AI instrumentality attributes and brand credibility. To verify the research hypotheses, the study employed partial least square—structural equation modelling, based on 281 validated responses of a survey. This study highlights the significance of Optimism, Innovativeness, and Discomfort for post‐adoption confirmation. Higher post‐adoption confirmation is strongly associated with perceived intelligence, anthropomorphism, information quality, and system quality. Anthropomorphism and information quality are key factors for brand expertise, while anthropomorphism and system quality are significant for brand trustworthiness. The study confirms that brand expertise and trustworthiness lead to post‐use satisfaction and use continuance intention. Understanding the antecedents of satisfaction and continuance intention extends the existing literature on AIVAs and provides valuable insights for academics and practitioners alike. Some implications for researchers and managers are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Driven by the fierce competition in the airline industry, carriers strive to increase their customer satisfaction by understanding their expectations and tailoring their service offerings. Due to the explosive growth of social media usage, airlines have the opportunity to capitalize on the abundantly available online customer reviews (OCR) to extract key insights about their services and competitors. However, the analysis of such unstructured textual data is complex and time-consuming. This research aims to automatically and efficiently extract airline-specific intelligence (i.e., passenger-perceived strengths and weaknesses) from OCR. Topic modeling algorithms are employed to discover the prominent service quality aspects discussed in the OCR. Likewise, sentiment analysis methods and collocation analysis are used to classify review sentence sentiment and ascertain the major reasons for passenger satisfaction/dissatisfaction, respectively. Subsequently, an ensemble-assisted topic model (EA-TM) and sentiment analyzer (E-SA) is proposed to classify each review sentence to the most representative aspect and sentiment. A case study involving 398,571 airline review sentences of a US-based target carrier and four of its competitors is used to validate the proposed framework. The proposed EA-TM and E-SA achieved 17–23% and 9–20% higher classification accuracy over individual benchmark models, respectively. The results reveal 11 different aspects of airline service quality from the OCR, airline-specific sentiment summary towards each aspect, and root causes for passenger satisfaction/dissatisfaction for each identified topic. Finally, several theoretical and managerial implications for improving airline services are derived based on the results. [ABSTRACT FROM AUTHOR]

This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures. [ABSTRACT FROM AUTHOR]

Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution and direction of data and assists them in making correct decisions. The core of this feature is visual analysis charts, which need to be pre-created and integrated into the dashboard by the chart makers, so there are situations where user needs cannot be accurately grasped. At the same time, there may be omissions in the work of users, and a method is needed to remind them. Introducing recommendation models into data visualization is a good solution; therefore, this paper proposes a recommendation model suitable for this type of scenario, which recommends high-scoring items (charts) to users. This model consists of a inductive heterogeneous graph recommendation algorithm with user preferences and a slow-acting collaborative filtering method. The experimental results in two datasets showed an improvement of 0.020/0.045, 0.083/0.019 and 0.076/0.023 in Hit, F-score, and NDCG compared to baselines, which proves that it is more suitable for data visualization requirements and other similar scenarios that require inductive recommendations based on user preferences. [ABSTRACT FROM AUTHOR]

The adoption of Business intelligence (BI) has become increasingly prominent among organizations for attracting customers and gaining a competitive edge. This becomes even more important for small and medium enterprises (SMEs) which face immense pressure to retain existing customers and attract more customers for survival. This study presents a systematic literature review of studies investigating the impact of BI on customer relations with exclusive focus on Indonesia, which has a growing number of SMEs (also called ‘UMKMs’). By examining 15 papers published between 2013 to 2024, the findings of the study highlight that BI contributes to building better connections with customers and making them feel more engaged and satisfied. This study provides valuable insights for SMEs in Indonesia to adopt BI to attract more customers. The findings of the study also lay the foundation for future research, especially those wanting to perform an empirical investigation. [ABSTRACT FROM AUTHOR]

Social media platforms like X and Facebook generate vast amounts of image content daily, necessitating automated methods for classification and analysis. Integrating Business Intelligence (BI) with Recurrent Neural Network (RNN) techniques presents a promising approach to extract valuable insights from this data. This study proposes a methodology for social media image content classification using a hybrid architecture combining Convolutional Neural Networks (CNNs) for feature extraction and RNNs for capturing temporal dependencies. The model is trained on labeled image datasets from X and Facebook, leveraging transfer learning and data augmentation techniques. The contribution lies in the fusion of BI and deep learning techniques, offering a scalable solution for real-time image content classification on social media platforms. This approach enables businesses to streamline marketing analysis, trend detection, and content moderation tasks efficiently. Experimental results demonstrate the effectiveness of the proposed methodology, achieving high accuracy in classifying diverse image content. The model's performance is validated through comprehensive evaluation metrics, showcasing its robustness and applicability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management. Design/methodology/approach: The research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations. Findings: The findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics. Research limitations/implications: The study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture. Practical implications: The study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management. Originality/value: The study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process. [ABSTRACT FROM AUTHOR]

The increasing availability of Big Data is changing the way data exploration for Business Intelligence is performed, due to the volume, velocity and uncontrolled variety of data on which exploration relies. In particular, data exploration is required in Data Lakes that have been proposed to host heterogeneous data sources, given their flexibility to cope with cumbersome properties of Big Data. However, as data grows, new methods and techniques are required for extracting value and knowledge from data stored within Data Lakes, aggregating data into indicators according to multiple analysis dimensions, to enable a large number of users with different roles and competencies to capitalise on available information. In this paper, we propose PERSEUS (PERSonalised Exploration by User Support), a computer-aided approach for data exploration on top of a Data Lake, structured over three phases: (1) the construction of a semantic metadata catalog on top of the Data Lake, leveraging tools and metrics to ease the annotation of the Data Lake metadata; (2) modelling of indicators and analysis dimensions, guided by an openly available Multi-Dimensional Ontology to enable conformance checking of indicators and let users explore Data Lake contents; (3) enrichment of the definition of indicators with personalisation aspects, based on users' profiles and preferences, to make easier and more usable the exploration of data for a large number of users. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel author profiling method specially aimed at classifying social network users into the multidimensional perspectives for social business intelligence (SBI) applications. In this scenario, being the user profiles defined on demand for each particular SBI application, we cannot assume the existence of labelled datasets for training purposes. Thus, we propose an unsupervised method to obtain the required labelled datasets for training the profile classifiers. Contrary to other author profiling approaches in the literature, we only make use of the users' descriptions, which are usually part of the metadata posts. We exhaustively evaluated the proposed method under four different tasks for multidimensional author profiling along with state-of-the-art text classifiers. We achieved performances around 88% and 98% of F1 score for a gold standard and a silver standard datasets respectively. Additionally, we compare our results to other supervised approaches previously proposed for two of our tasks, getting very close performances despite using an unsupervised method. To the best of our knowledge, this is the first method designed to label user profiles in an unsupervised way for training profile classifiers with a similar performance to fully supervised ones. [ABSTRACT FROM AUTHOR]

This research explores the dynamic interplay among intellectual capital, the intention to adopt business intelligence (BI) technology, and the decision to innovate within the industrial landscape of Jordan. Using a quantitative approach, the study employs bootstrapping and Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from participants familiar with their companies' technological and innovation orientations. The findings reveal a noteworthy positive correlation between human capital and structural capital with the intention to adopt BI technology. Additionally, human capital demonstrates a significant positive association with the decision to innovate. The research further validates a positive relationship between the intention to adopt BI technology and the decision to innovate. The practical implications of these findings extend to decision-makers and managers in Jordan's industrial sector, underscoring the pivotal role of adopting business intelligence technology in fostering innovation. Significantly, by concentrating on innovation orientation in the Jordanian context, this paper contributes to the expanding body of research in developing countries. [ABSTRACT FROM AUTHOR]

The new age of digital growth has marked all fields. This technological evolution has impacted data flows which have witnessed a rapid expansion over the last decade that makes the data traditional processing unable to catch up with the rapid flow of massive data. In this context, the implementation of a big data analytics system becomes crucial to make big data more relevant and valuable. Therefore, with these new opportunities appear new issues of processing very high data volumes requiring companies to look for big data-specialized solutions. These solutions are based on techniques to process these masses of information to facilitate decision-making. Among these solutions, we find data visualization which makes big data more intelligible allowing accurate illustrations that have become accessible to all. This paper examines the big data visualization project based on its characteristics, benefits, challenges and issues. The project, also, resulted in the provision of tools surging for beginners as well as well as experienced users. [ABSTRACT FROM AUTHOR]

This article explores the concept of discovery projects, which involve analyzing project scope, goals, and complexities before committing resources to a larger project. It provides case studies of both successful and failed discovery projects, highlighting the importance of stakeholder involvement and proper project management. The article emphasizes the need to define clear goals, conduct thorough research, and involve various experts in the discovery process. It concludes by offering recommendations for building project scope, conducting cost/benefit analysis, and managing stakeholders. [Extracted from the article]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, it can be noticed that academics are still looking at the concept of CSR from a different perspective. This review focuses on the most relevant academic publications and historical events that have influenced the evolution of CSR as a conceptual paradigm. The literature review shows that the future of CSR must also consider recent technological advancements and their role as part of new business frameworks and strategies. The adoption and adaptation of new digitization processes and tools, as well as the incorporation of Artificial Intelligence into the business environment is a relevant challenge not only for the CSR debate, but for companies in general. The main contribution of this article is a structured historical review accompanied by the evolution of CSR and future research opportunities [ABSTRACT FROM AUTHOR]

Competitor identification is an essential component of corporate strategy. With the rapid development of artificial intelligence, various data-mining methodologies and frameworks have emerged to identify competitors. In general, the competitiveness among companies is determined by both market commonality and resource similarity. However, because resource information is more difficult to obtain than market information, existing studies primarily identify competitors via market commonality. To address this limitation, we introduce multisource company descriptions as well as heterogeneous business relationships, and we propose a novel method for simultaneously mining themarket commonality and resource similarity. First, we use multisource company descriptions to represent companies and transform the heterogeneous business relationships into a heterogeneous business network. Then, we propose a novel multisource data fusion-based heterogeneous graph attention network (MHGAT) to learn the pairwise competitive relationships between companies. Specifically, a graph neural network-based model is proposed to learn the embeddings of companies by preserving their competition, and a multilevel attention framework is designed to integrate the embeddings from neighboring company level, heterogeneous relationship level, and multisource description level. Finally, experiments on a real-world dataset verify the effectiveness of our proposed MHGAT and demonstrate the usefulness of company descriptions and business relationships in competitor identification [ABSTRACT FROM AUTHOR]

The main objective of this article is to implement business intelligence (BI) technology to improve incident management in the control center of a security company; making use of the Ralph Kimball method for the development of the data mart, contemplating the phases: planning of projects, definition of requirements, design of technological architecture, performing a dimensional modeling a star with 9 dimensions and 1 table made, also used the structured query language (MySQL) database, extract, transform, and load (ETL). Development the pentaho data integration, which allowed the migration, transformation and cleaning of the data from the online transaction processing (OLTP) and online analytical processing (OLAP) database, obtaining as a result that the BI managed to increase the level of the service by 28.42% and reducing the frequency index by 25%. [ABSTRACT FROM AUTHOR]

In this in-depth study, we explored the nuanced dynamics of boardroom gender diversity and its consequential impact on sustainability reporting within the U.S. Healthcare sector. Leveraging a comprehensive dataset from Refinitiv Eikon, our analysis spanned a spectrum of 646 observations across 57 healthcare entities listed in the S&P 500, covering the period from 2010 to 2021. Our methodology combined various empirical techniques to dissect correlations, unravel heterogeneity, and account for potentially omitted variables. Central to our findings is the discovery that various metrics of board gender diversity, such as the proportion of female directors and the Blau and Shannon diversity indices, exhibit a robust and positive correlation with the intensity and quality of sustainability reporting. This correlation persists even when controlling for a multitude of factors, including elements of corporate governance (such as board size, independence, and meeting attendance), as well as intrinsic firm characteristics (such as size, profitability, growth potential, and leverage). The presence of female directors appears to not only bolster the breadth and depth of sustainability reporting but also align with a broader perspective that their inclusion in boardrooms significantly influences corporate reporting practices. These insights extend beyond academic discourse by offering tangible and actionable intelligence for policymakers and corporate decision-makers. By elucidating the intrinsic value of gender diversity in governance, our study contributes a compelling argument for bolstering female representation in leadership roles as a catalyst for enhanced corporate responsibility and stakeholder engagement. [ABSTRACT FROM AUTHOR]

Covert Database and Covert Channels can be represented regarding intriguing facet of information security, which had the confidential data within seemingly innocuous data structures. This review paper gives a quite comprehensive idea of Covert Databases and Covert Channels, providing a detailed analysis of its concepts, mechanisms, detection techniques and applications [1]. The initial section of this review paper dives into the theoretical concepts of Covert Databases & Covert Channels, its core principles and terminologies and historical context. It describes how the Covert Databases have developed over the time, from previous analogue communication methods to the modern era. Here we have described the difference between normal and covert databases. The next section describes Covert Databases and Covert Channels, explaining how sensitive data can be included within seemingly benign data warehouse it brings light on topics like steganography, data hiding techniques and the challenges related with covert database detection. We also examined the inspiration behind the utilization of covert channels and databases highlighting their different uses in various fields, including cyber warfare, industrial espionage and intelligence operation. It also describes the risks related with these Covert Communication Methods and the potential risks for individuals and organisations. Identification is a crucial perspective of Covert Channel and Database Research. This review paper gives an overview of already existing identification techniques and overcoming measures, including statistical analysis, intrusion detection systems and AI based approaches. In conclusion, this comprehensive review paper influences as a precious resource for researches, security professionals and policy methods searching to understand the concepts of Covert Database and Covert Channels. Proposed system will be published in upcoming papers with flowcharts and use cases along with results and discussion. We will be using port scanning method in a new way to improve the detection of covert database and covert channels. [ABSTRACT FROM AUTHOR]

This paper discusses the digital transformation journey of a small and medium enterprise (SME) based in the UK. With the proposed digital transformation archetype, the paper highlights the improvement in various key performance indices (KPIs) for the case SME. The core KPIs and operational KPIs show improvement through the technology adoption as part of the digital manufacturing initiative. While embracing technology, such as Industry 4.0, it is important to highlight the importance of the change and other benefits of technological changes. The paper uses socio-technological system principles to achieve a successful transition. An action research approach and a specific soft system thinking methodology known as Process-Oriented Holonic (PrOH) modeling were used in this paper. A digital twin architecture is presented in the paper that showcases the use of integrated technologies for a digital manufacturing roadmap. [ABSTRACT FROM AUTHOR]

The literature on information systems emphasizes the positive impact of information from business intelligence systems (BIS) on decision-making, especially in highly regulated environments. Assessing BIS effectiveness is vital to understanding its value and significance in improving operational performance and management. However, deploying BIS and understanding how BIS dimensions are interrelated and how they affect the decision-making process in organizations in the nuclear field still need to be explored. In order to address this research gap, this article investigates the process of implementing BIS in a Brazilian company from the nuclear industry using an action research methodology. Results suggest that the use of BIS in decision-making routines allowed company managers to expand their perception of previously neglected information, significantly helping in decision-making and prioritizing actions and/or solutions. [ABSTRACT FROM AUTHOR]

Background: The growing need for healthcare services as a result of a consistently rising prevalence of chronic diseases and rapid population aging calls for a new set of activities and practices. Therefore, we developed a program—3S (Simple, Smart, and Speed) Business Intelligence Systems (3S-BIS), which is an ERP software system that helps nursing business to support nursing entrepreneurship —and analyzed its effects on nursing students. Methods: A repeated-measures randomized controlled trial was performed with two groups: experimental (n = 29) and control (n = 30) groups. The former group underwent the five-day 3S-BIS education program. Each session comprised four components: lectures 1 and 2, simulation case study, and debriefing. Post-tests were performed immediately post-intervention and four and eight weeks later. The effectiveness was measured using the following variables: simulation design assessment, evaluation of educational practices in simulation, education satisfaction, self-efficacy for learning, and entrepreneurship. The differences before and after intervention between the experimental and control groups were analyzed using the Friedman test. The Mann–Whitney U test was used for comparisons between groups at each time point, and the Wilcoxon signed-rank test was used for comparisons within groups at each time point. Results: Post-intervention (8 weeks after intervention), the experimental group demonstrated higher simulation design assessment (z = -3.88, p = <.001), evaluation of educational practices in simulation (z = -3.34, p =.001), education satisfaction (z = -3.11, p =.002), self-efficacy for learning (z = -3.04, p =.002), and entrepreneurship (z = -2.15, p =.031) compared to controls. Furthermore, simulation design assessment score in the experimental group significantly differed between T1 (immediately after intervention) and T0 (baseline), and between T3 (8 weeks after intervention) and T0. Evaluation of educational practices in the simulation, education satisfaction, and self-efficacy also significantly differed between T1 and T0, and between T3 and T0. Entrepreneurship significantly differed between T3 and T2 (4 weeks after intervention), and between T3 and T0. Conclusions: The 3S-BIS program contributes to enhancing nursing start-up competency. Subsequent studies should evaluate the effects of the program on nurses who work in home healthcare services. [ABSTRACT FROM AUTHOR]

Small and Medium Enterprises (SMEs) are essential to the global economy, promoting employment, innovation and sustainable development. Effective sales information management is critical to your success, involving the collection, storage, analysis and application of data about customers, products, distribution channels, prices and market trends. Proper management of this data allows SMEs to understand customer demands, identify market opportunities and optimize their sales strategies. However, SMEs face significant challenges in this area, such as technological limitations, budget constraints and data complexity, which can lead to manual processes, lack of visibility in the supply chain and loss of competitiveness. The research carried out in the La Maná canton, in Cotopaxi, Ecuador, supports the implementation of a sales information management system due to its intense commercial activity and the presence of SMEs in sectors such as agriculture and commerce. The study adopted a mixed methodology, which combined literature review and field research, using inductive and deductive approaches. Managers, administrators and workers from two companies were surveyed. The results indicate that this methodology is effective in achieving the research objectives, underscoring the importance of integrating various methodologies to obtain a complete understanding of the topic. [ABSTRACT FROM AUTHOR]