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Artificial intelligence (AI) can enhance, enable, or replace traditional open innovation (OI) practices, changing the scope and efficiency of both outside-in and inside-out OI. This article provides a comprehensive framework to analyze AI's influence on OI, supported by illustrative examples, and outlines the key implications for organizations and researchers. The co-evolutionary relationship between AI and OI will be a central focus in both research and practice moving forward. [ABSTRACT FROM AUTHOR]

Purpose: The COVID-19 pandemic has changed customer social media engagement behavior, which challenges the establishment of effective marketing strategies to strengthen digital communication with customers and leads to new opportunities for social media competitive intelligence analytics. This study presents a new social media competitive intelligence framework that incorporates not only the detection of brand topics before and during the COVID-19 pandemic but also the prediction of customer engagement. Design/Methodology/Approach: A sector-based empirical study is conducted to illustrate the implementation of the proposed framework. We collected tweets generated by 23 leading American catering brands before and during the pandemic. First, we used Amazon Comprehend and Latent Dirichlet allocation (LDA) to extract sentiments and topics behind unstructured text data. Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers. Findings: The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. Pre-pandemic topics primarily included "Food and lifestyle", "Promotion", "Food ordering", "Food time", and "Food delivery". During the pandemic, the topics expanded to include "Social responsibility" and "Contactless ordering". For predicting customer engagement, the performance metrics show that Random Forest and C5.0 (C50) are generally the best-performing models, with Random Forest being particularly strong for "Likes" and "Retweets", while C50 performs best for "Replies". Originality: This framework differentiates itself from existing competitive intelligence frameworks by integrating the influence of external factors, such as the COVID-19 pandemic, and expanding the analysis from topic detection to customer engagement prediction. This dual focus provides a more comprehensive approach to social media competitive intelligence. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 pandemic caused dashboards to become widely used by the public and decision-makers. Nevertheless, dashboard interfaces have been related to business intelligence since their origins, and the search for improvements in their design is not new. This article's objective is to conduct a user evaluation of COVID-19 dashboards that contain geospatial information. This is done through a formative study to identify problematic aspects of user/dashboard interaction. This is enhanced by comparing two self-developed dashboards that, according to previous tests, have functionalities with different appearances. User evaluation is performed through mixed research that combines objective (eye-tracking) and subjective (a questionnaire and an interview) methods. The results generate recommendations for better-designed dashboard interfaces that can transfer information appropriately. The vital elements needed to achieve this are interactivity, the option to choose the metrics, and the distribution of the elements in the layout, all playing a role in a more user-friendly interaction between the user and the dashboard. [ABSTRACT FROM AUTHOR]

Business Intelligence is the key technologies that ensures effective decision making through extracting relevant information and providing adapted systems as the Data Warehouses. To access decisional information, the decision maker should express his requirements in Natural Language interfaces without any technical skills, avoiding the IT-Designer intervention. Often, the decision maker's requirements are expressed as WH-questions (What, Who, Where, etc.) or Keyword-like questions. In this paper, we emphasize on a Why-Question asked in Business Intelligence context. This question has not been well dealt in the literature in terms of produced answers. Indeed, to respond this type of question, it is necessary to provide explanations. These explanations are determined by identifying causal relationships between the phenomenon highlighted in the Why-Question and factors that can influence this phenomenon. In this context, we propose an approach on which a system can address a causality problem related to answering a decisional Why-Question. To validate our approach a tool called BI Why Q/A is developed. In order evaluate our proposal in terms of efficiency and relevance, a set of experimental studies is carried out and presented. [ABSTRACT FROM AUTHOR]

Sales forecasting plays an important role in business operations as it impacts decisions on inventory management, allocation of resources, and financial planning. Accurate sales predictions are essential for optimizing cash flow management, adapting marketing and sales strategies, and facilitating strategic planning. This study presents a computational framework for predicting business sales using transformers, which are considered one of the most powerful deep learning architectures. The design of our model is specifically tailored to accommodate tabular data with low dimensions. The experimental results demonstrated that our proposed method surpasses conventional machine learning models, achieving reduced mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), as well as higher R2 values of nearly 0.95. The results confirmed that the model is applicable not only to this research but also to similar studies that use low-dimensional tabular data. The improved accuracy and stability of our model demonstrate its potential as a useful tool for enhancing sales prediction, therefore facilitating more informed decision-making and strategic planning in corporate operations. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is a presentation of a case study describing the stages of Business Intelligence (BI) system implementation in healthcare organisations and the reasons for this implementation. The implementation practices used are described and the benefits of the project are demonstrated. Design/methodology/approach: The article presents the process of designing a BI system, which took place in subsequent stages such as: defining business goals, identifying data sources and developing a data flow structure diagram, defining functional needs and the form of presentation of management data. Findings: The implementation of the BI environment in a healthcare facility brings the following benefits: Support for decision-making processes in the hospital management area, ensuring monitoring and analysis of the use of hospital resources, identifying the causes affecting the service time, risk management and data management. Practical implications: The paper presents the process of implementing a BI system in healthcare facilities, indicates the validation and testing process, and demonstrates numerous advantages of implementing a BI system. Originality/value: The study is a valuable material that can be used by both theoreticians and practitioners. The presented procedures can be successfully used in other industries to build BI systems. [ABSTRACT FROM AUTHOR]

Developing one's entrepreneurial mindset is important for all students, regardless of discipline. Evidence‐based decision‐making (which has the potential to lower costs and improve quality of life) is one approach for applying entrepreneurially minded learning in the undergraduate classroom. This approach allows students to understand trends related to data, in general, and big data, specifically. Furthermore, it better prepares graduates to evaluate and identify effective data science‐based solutions. The purpose of this study is to report on one pedagogical approach to developing the entrepreneurial mindset through integrating evidence‐based decision‐making into the engineering and technology classroom using Microsoft Power BI Desktop (a freely available tool released by Microsoft in September 2013, where "BI" implies Business Intelligence). A mixed methods assessment was conducted including a rubric to measure students' effectiveness in applying the entrepreneurial mindset and a metacognitive reflection to better understand student motivation, awareness of learning, and engagement. First, the rubric was applied, and students were categorized by performance group (e.g., high, mid, low). Second, each performance group was analyzed to identify themes within the reflections. Our findings suggest that students in the high‐performing group communicated overall high levels of motivation, while students in the low‐performing group shared overall moderate levels of motivation. The relationship between performance and motivation among students in the mid‐performing group was inconclusive. Findings from our study suggest that there may be a relationship between students' performance and motivation. The key study implications relate to the use of new literacies, such as technological literacy, data literacy, and human literacy, as practices for promoting the development of an entrepreneurial mindset. Our findings suggest that our approach was effective in accomplishing this goal, but there is also room for improvement. Lessons learned and recommendations are provided. [ABSTRACT FROM AUTHOR]

The purpose of this qualitative case study comprising 18 thematic interviews was to explore social learning and provide an internal focus on the development path of an international company coping in a crisis. EC is an international engineering and design agency that grew from a small business to a medium-sized enterprise during the crisis era. Since it began operations in 2011, there has been a suspicion of industrial espionage, directed at the founders of the company, including the CEO. Managing internal and external reputations in these circumstances is challenging, as the company builds its legitimacy. The data were content analysed and the results of the study concluded that social learning is crucial to surviving crises. The abductive content analysis and findings of this study are threefold. First, it is suggested that social learning enhances organisational coping in the midst of a crisis. Next, to manage business development and organisational reputation during the crisis, it is necessary to use distributed learning, thinking and doing simultaneously. Thus, the core business and the need for ongoing espionage crime detection create potential harm to the company's external reputation. Nevertheless, EC's revenue continuously increased and the trust of clients was maintained. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving business landscape, data analytics has become a critical tool for organizations striving to remain competitive and make informed decisions. Although technology facilitates data analysis, many decision-makers need more technical knowledge to use these tools. This project focuses on developing a business intelligence system in Power BI to analyze agricultural exports from Mexico as part of the Agro-logistics Observatory, aiming to bridge this gap and make data more accessible for non-technical users. The objective is to create an interactive panel that would allow users to visualize and compare export and import data, thereby supporting decision-making for both business and academic purposes. The methodology involved collecting and cleaning data from various sources, such as public institutions, and a modeling process that integrates geospatial data and economic classifications. Key performance indicators (KPIs), such as trade balance and the export share by-product, were designed and calculated to identify critical products and trade patterns. Although similar approaches have been presented in the literature, the novelty of this research lies in the detailed presentation of its methodology, which provides a step-by-step guide for its replication that is often lacking in other studies. In addition, it offers a dashboard that integrates data from various sources, presenting them through easy-to-interpret visualizations so that stakeholders can quickly and efficiently access valuable information beyond traditional data visualization. Furthermore, the project highlights opportunities for improvements in data visualization, including integrating more specific product data and enhancing the dashboard's capabilities through predictive analysis. [ABSTRACT FROM AUTHOR]

This research reviews nine years of literature on how artificial intelligence (AI) can be used for sustainable practices through business intelligence and data analytics. The study employs bibliometric and content analyses, examining 329 English-language articles from journals indexed in the Web of Science (WOS) database. The United States stands out as the leading contributor and recipient of citations, while the Indian Institute of Technology System (IIT System) and King Abdulaziz University are the most prolific institutions in this field. Sustainability and The International Journal of Environmental Research and Public Health are the most frequently published and cited sources, respectively, with Kumar A. and Mehmood R. emerging as prominent authors. The analysis reveals a growing interest in topics like machine learning, big data analytics, and sustainability, particularly between 2020 and 2022. While the content analysis highlights the positive impact of these emerging technologies, it's important to acknowledge that the study's focus on the WOS core collection might limit its generalizability beyond this specific dataset. [ABSTRACT FROM AUTHOR]

Recently, social media has gradually become an important topic in public opinion analysis field. Among social media, Microblog is one of the most important platforms because it is short, convenient, mobile and instantaneous. Social media microblog recognition well reflects the attitudes from an enormous big colony to a specific incident, either positive or negative, which can be used for deriving competitive intelligence, marketing strategies, detecting depression and so on. However, the existing methods usually use only text or image from internet but not take advantages of their complementary information to finalize the recognition, it limits the performance and robustness of the algorithms. In this paper, we present a collaborate decision network (CDN) based on cross-modal attention to exploit the discriminative attributes of multi modalities by data- and knowledge joint driven strategy in depth, and further improve the recognition performance. In addition, we collect and construct a visual-text microblog recognition dataset with 2854 samples to support the subsequent research of related fields. Finally, experimental reuslts on the collected dataset show the effectiveness and superiority of the proposed CDN. [ABSTRACT FROM AUTHOR]

ABSTRACT The development of new energy and the application of intelligent technologies are crucial for ensuring energy security and promoting high‐quality economic growth. This article utilises a sample of A‐share listed enterprises in China from 2008 to 2020, employing a difference‐in‐differences (DID) approach to explore the impact of new energy demonstration cities (NEDC) on innovation quality and focuses on the role of intelligent development. It is found that the construction of NEDC contributes to the improvement of enterprise innovation quality. Corporate intelligence transformation further enhances the effect of the NEDC policy, demonstrating a synergistic relationship with new energy transition. Additionally, compared with collaborative innovation, the improvement effect of NEDC policy on enterprise self‐innovation quality is more significant. Non‐state‐owned enterprises, those in the growth stage, with a solid technological foundation and located in non‐resource‐based cities are more likely to achieve improvements in innovation quality during the energy transition. Our work provides empirical evidence and valuable insights for ensuring energy security and facilitating intelligent innovation development. [ABSTRACT FROM AUTHOR]

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.)

The article discusses the history of the Majarski family, an Armenian family known for manufacturing kontusz sashes in the Polish-Lithuanian Commonwealth. The sashes were a symbol of the common cultural heritage of several nations and were highly sought after for their quality and design. The authors, historians Denis V. Lisejčikov and Evgenij S. Glinskij, provide a detailed analysis of the family's history and the manufacturing process of the sashes, shedding light on the multicultural and multinational nature of the region during that time. The article also touches on the complexities of national identities and historical interpretations related to the sashes, emphasizing the need to view them in a broader cultural context. [Extracted from the article]

This study aims to analyze pharmaceutical sales performance by leveraging big data and advanced information technology tools, providing insights for improved business strategies. Using the Exploratory Data Analysis (EDA) method, the study processes raw sales data through spreadsheet applications to identify key patterns and trends. The findings are then visualized with Looker Studio on an intelligent dashboard tailored to the needs of the marketing team. The dashboard enables quick, data-driven decision-making by displaying performance metrics and trends relevant to sales and marketing strategies. The results reveal critical insights into sales behavior, including high-performing products, regional sales variations, and temporal sales trends. The analysis equips the marketing team with actionable data to refine their strategies, prioritize product focus, and adjust marketing efforts in response to identified patterns. In conclusion, the use of EDA and data visualization provides a structured approach to big data interpretation, allowing the company to optimize business and marketing performance. The study underscores the potential of data-driven dashboards to enhance strategic planning, suggesting broader applications in various data-intensive business environments. [ABSTRACT FROM AUTHOR]

Background: Business intelligence (BI) is an area in which data analytics is applied to generate crucial information supporting business decision-making and has been a significant domain for over three decades. However, there is uncertainty regarding whether investments can effectively improve organizational outcomes. Objective: This study aimed to provide a comprehensive overview of the knowledge generated and disseminated in previous investigations related to the intricate relationship between BI and decision-making performance (DMP) over the past 20 years. Methods: An R-tool namely bibliometrix, which supports suggested workflow for conducting bibliometrics and includes descriptive as well as knowledge structure analysis was used on a dataset containing 1,484 English-language articles published between 2003 and 2023 and indexed in Web of Science databases. Results: The results showed that field study has stabilized over the past three years, signaling a shift in the focus of scholars. However, only a few studies use decision theory and further investigations are required to fully understand how BI impacts DMP both inside and outside organizational boundaries. Conclusion: Based on the results, BI studies tend to be more application-oriented and there is a need to change the emphasis from focusing only on tools to variables such as the role of effective use and competencies that might improve decision quality. [ABSTRACT FROM AUTHOR]

Competitive Intelligence (CI) is an important element in a company's strategic decision-making in the midst of increasingly fierce business competition. In business literature, CI has been recognized as a vital tool for understanding a company's external environment and analyzing competitors' strategies. However, there is still debate regarding the role and effectiveness of CI in strategic decision-making. This study aims to explore the role of CI in strategic decision-making through a comprehensive literature review. The research method used is a qualitative method by analyzing various articles and journals related to CI and strategic decision making. The discussion in this study includes the importance of CI in providing relevant information, the methods used in CI implementation, and its impact on the company's strategic decisions. The results show that a deeper understanding of the role of CI in the context of strategic decision making, as well as providing recommendations to improve the effectiveness of CI in supporting corporate strategic decisions. The implication of this research is to provide valuable insights for business practitioners and researchers in understanding the contribution of CI to the strategic decision-making process. This research is also expected to be a reference for companies in optimizing CI implementation to strengthen competitive advantage in the market. [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. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to investigate the interplay between individual self-efficacy (SE) and the adoption of business intelligence (BI) tools, and their combined effects on organizational agility and performance. This research offers a novel perspective by examining the relationship between individual SE and BI tools together, which was neglected in the previous research, shedding light on how these factors collectively influence organizational performance and agility. The importance of this study addresses the crucial need for understanding the role of individual capabilities in leveraging BI tools, especially in the context of rapidly changing environments. The study employs a quantitative approach to examine the proposed model. A survey was conducted with 174 respondents from private and public organizations in Jordan. The findings reveal significant and positive impacts of individual experiences, vicarious experiences (VE), and psychological feedback (PS) on SE. Moreover, the study demonstrates that SE significantly and positively influences the utilization of BI tools, consequently affecting organizational agility and performance. The significance of the study findings lies in its ability to bridge the gap between individual capabilities and the effective utilization of BI tools to equip businesses with invaluable insights for enhancing their decision-making processes. [ABSTRACT FROM AUTHOR]

This study proposes a prototype (Business Intelligence) for monitoring the continuing education of indigenous health professionals in the Secretariat of Indigenous Health (SESAI) of Brazil's Ministry of Health (MS). The prototype builds on SESAI/MS staff knowledge and takes into account the characteristics and demands of the 34 Special Indigenous Health Districts (DSEIs). The prototype provides information for managing SESAI/MS continuing education in order to meet strategic goals. By improving the effectiveness and efficiency of the continuing education of professionals who serve Brazil's indigenous population, we will be acting directly on access issues and improving the quality of life of the indigenous population. [ABSTRACT FROM AUTHOR]

The invoicing process is critical to the financial management of organizations. However, modeling this process presents challenges such as data updating, information availability, and aligning planned activities with the actual execution of the process. One difficulty is that designing the invoicing process requires extensive knowledge of the activities involved, and process representations based on organizational repositories are not necessarily aligned with the actual invoicing processes in the organization. Process Mining is complemented by the use of dashboards, which are inherent to business intelligence and allow for visual tracking of process behavior. This paper explores how the combination of process mining and business intelligence can enable a new level of process modeling that addresses specific issues in constructing processes that are aligned with real-world activities. To accomplish this, we first propose the Design Science Research (DSR) methodology, which outlines how a researcher or practitioner should approach the task of modeling a specific process using Process Mining augmented with dashboard resources. The research strategy was to identify the most appropriate methodology to construct the actual billing process, which led to the identification of the DSR methodology. This methodology, with its 12-step plan, allowed the construction of an artifact representing the actual invoicing process. Ultimately, the objective of constructing a real invoicing process in the Colombian pharmaceutical sector is achieved through the development of an artifact, complemented by business intelligence dashboards that ensure the alignment of the execution of activities within the process. [ABSTRACT FROM AUTHOR]

Featured Application: Applications can be found in industries, services, and other areas of human activity where products are designed to be brought out to the market. The proposed methodology requires information technologies because the methods are data-driven. This paper describes procedures that can be adopted during product development, so that there is a higher chance that new products are properly designed before being placed on the market. To implement the suggested procedures, market data are first obtained, then processed in a specified way for the new products to reflect relevant market information. This means putting into use suitable data-processing algorithms and information technologies, based on the type of collected information. The text deals with two information scenarios in this regard, the first assuming that not much is known about customers' desires and the second working with larger amounts of more specific customer data. While the former requires a more sophisticated approach to product design, theoretically speaking, the latter is simpler to handle from the theoretical point of view, but potentially less so from a practical perspective, given the amounts of data. These theoretical and practical problems are discussed in what follows, and their possible solutions are presented. [ABSTRACT FROM AUTHOR]

This empirical study examines how chief knowledge officers (CKOs) interpret measurements performed in knowledge management systems (KMS) and reflected in business intelligence dashboards. Specifically, it investigates CKOs' perceptions of common KMS indicators and their relationship to knowledge management (KM) success. Adopting a constructivist inductive approach, the study relies on qualitative data from interviews, focus groups, and cyber‐ethnography. The findings reveal that usage statistics, like system logins, do not necessarily signify the value of KM initiatives and that organizations avoid linking KMS metrics to business performance. By contributing vital insights to KMS performance literature, we indicate the limitations inherent in current evaluation approaches focused narrowly on usage quantification. Practical implications suggest combining quantitative monitoring of access frequency and patterns with KMS benefits qualitative assessments. Overall, the juxtaposition of usage data against perceived value provides an important perspective on developing more meaningful and effective KMS performance measurements. [ABSTRACT FROM AUTHOR]

There is a significant change in operational approaches, especially in business intelligence solutions, due to the application of modern technologies at Jordan's universities. However, further research is required. Faculty and staff are often reluctant to accept these technologies, as they are used to traditional teaching approaches. This highlights the importance of examining these change dynamics. To gain more insight into this issue, a questionnaire was created based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model and distributed to 587 participants in 12 prominent Jordan universities. Structural equation modeling was deployed to assess the fit of the model to the data and determine whether the hypothesized relationships are supported. The findings indicated that there was a strong behavioral intention among staff and faculty members to embrace business intelligence solutions in their professional roles. This study discusses the positive and negative factors that affect the adoption of business intelligence in Jordanian higher education institutions. It is widely believed that these variables are critical in university management as they help relevant individuals make informed decisions about future investments and implement new technologies into institutional practices. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been used in various industries to provide innovative and intelligent features in micro, small and medium enterprises (MSMEs). Big industries started adopting AI for their HR and workforce management (WFM) activities, however, past literature suggests a lack of AI adoption in MSMEs. In the ongoing pandemic, a large number of job loss is reported in the literature. Thus, artificial intelligence-powered intelligent workforce management (WFM) may be critical during or post-pandemic to manage the huge employment in MSMEs. In this study, we develop and test a conceptual framework based on three areas where AI-powered WFM adoption for MSMEs revenue development has been highlighted. These are (a) risk management (workforce), (b) business and marketing, and (c) information exchange. Six hypotheses have been proposed and tested using structural equation modelling (SEM) with responses from 307 employees. According to the research findings, all of the offered hypotheses are significant. The findings suggest to MSME decision-makers that AI-powered WFM may help revenue growth, workforce risk reduction, intelligent business and marketing, and thoughtful, innovative, and safe information exchange. MSMEs are required to use AI in the information sharing that help in workforce risk management, business and marketing, and intelligent workforce management that scale-up the economic growth. [ABSTRACT FROM AUTHOR]

Over the past years, business intelligence and competitive intelligence practices achieved a significant importance in organizations, due to their capability of providing the necessary knowledge for the decision-makers, providing a significant advantage against market competitors or internal and external threats and vulnerabilities. Also, business intelligence and competitive intelligence have been under researcher’s scope, the literature in those domains being continuously updated with new findings. Also, knowledge sharing is a process that helps organizations increase their employees understanding and expertise in certain fields, being highly recognized by both practitioners and researchers for its importance and efficiency. Therefore, the purpose of this research is to analyze the state of the literature in business intelligence and competitive intelligence, in correlation with knowledge sharing concept, in order to identify the connections between these domains. Also, this article aims to present an analysis that could provide a better understanding about the research interest in integrating knowledge sharing in business intelligence and competitive intelligence practices. In order to achieve the proposed objective, this research performs a bibliometric analysis using VOSViewer, a specialized software designed for creating visualizing maps that present networks between specific items. The information used for conducting this research was retrieved from Web of Science core collection and Scopus, world’s largest index databases.

Abstract Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results.

In Jordan’s banking sector, organizations face the dual challenge of stability and innovation. Achieving this balance requires a deep grasp of leveraging capabilities for existing opportunities and exploring new ones. The key lies at the intersection of business intelligence and organizational ambidexterity. Therefore, this study has investigated the relationship between business intelligence and organizational ambidexterity within the Jordanian banking sector. It examined the impact of managerial, technical, and cultural competencies of business intelligence on organizational ambidexterity in Jordanian commercial banks listed on the Amman Stock Exchange. The quantitative study involved 449 bank employees. The data were gathered via a questionnaire, and the responses were analyzed using R language version 4.2.2. The results indicated that managerial competencies (z = 8.26, p = 0.000) and technical competencies (z = 4.09, p = 0.000) exhibit statistically significant positive direct effects on organizational ambidexterity. Additionally, cultural competencies were found to have a statistically significant direct effect on organizational ambidexterity (z = 2.083, p = 0.037). Managerial, technical, and cultural business intelligence competencies play crucial roles in the adoption of organizational ambidexterity among Jordanian commercial banks. Achieving organizational ambidexterity necessitates integrating these competencies with organizational capabilities.

Recommender systems have been a kind of powerful tool to improve e-commerce benefits. Existing recommender systems mostly employ explicit features of products, such as description and attributes. However, visual characteristics contain fruitful implicit and intuitive information, and are always ignored by existing works. To deal with this issue, this paper proposes a novel intelligent recommender system via visual Transformer (ViT) model and convolutional neural network (CNN) structure. First, the ViT part is utilized to extract visual feature representation. By learning the visual similarity among products, it is expected to obtain a better scene understanding. Then, an improved CNN part is utilized to extract hidden association information from historical behaviors of users. It is expected to better perceive user preference and purchasing characteristics. The combination of two parts constructs the proposed recommender system. Finally, we make performance evaluation for the proposal on real-world e-commerce dataset. The results indicate that the proposal exhibits high recommendation accuracy and efficiency. Compared with other typical algorithms, our proposal can better understand product images and user behaviors, providing more personalized recommendation results. [ABSTRACT FROM AUTHOR]

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization's internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0037). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Copyright of Transformations in Business & Economics is the property of Vilnius University 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 today's fast-paced financial industry, knowledge management and data-driven decision making have become essential for the success of financial technology (FinTech) companies. Big data (BD) is a prevalent phenomenon that can be found across many industries, including finance. Despite its complexity and difficulty to comprehend, big data is a critical component of financial services enterprises and technology architectures. We examine BD from various aspects, considering data science (DS) techniques and methodologies that can be applied during the operation of an enterprise. Our aim is to provide an overview of knowledge management (KM) practices and data analysis (DA) strategies and techniques in the daily operations of financial companies. We address the role of knowledge management, data analytics in a financial institution. The paper demonstrates financial institutions' enablement for new services resulting from technological advancements. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data scenario involving 200 older adults with various measurements, including health beliefs, social support, self-efficacy, and disease duration, was used to establish a medication adherence prediction model in a BI system. A regression model, logistic regression model, tree model, and score-based prediction model were used to predict medication adherence among older adults. The developed BI-based prediction model has visualization, real-time feedback, and data updating functionality. These features enhanced the effectiveness of prediction models in clinical practice. Healthcare professionals can incorporate the proposed system into their care practice for health assessments and management, and patients can use the system to manage themselves. The developed BI-based care system can also be used to achieve effective communication and shared decision-making between care managers and patients. Further empirical studies integrating prediction models into the proposed BI system for assessment, management, and decision-making in healthcare practice are warranted. [ABSTRACT FROM AUTHOR]

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In today's digital environment, business intelligence advances make it difficult to stay competitive and up to date on business trends. Decision-making in the financial industry is increasingly being powered by big data and machine learning. A decision-making process may be thought of as any sequence of processes that an individual goes through in order to select the option or course of action that is most suitable to meet their needs and necessities. The ability to anticipate the onset of a financial crisis is a significant economic phenomenon. A nation's economic development and strength can be gauged by its capacity to provide an accurate assessment of the number of failed firms and the frequency with which they fail. The economics of the globe have been ravaged by recent global crises like as the COVID-19 pandemic and other recent environmental, financial, and economic disasters, which have marginalized efforts to construct a maintainable economy and civilization. The health and growth of a nation's economy can be determined by precisely estimating the number of enterprises that will fail and the number that will succeed. Historically, there have been numerous strategies for constructing a successful financial crisis prediction (FPC) method. Effectively predicting business failures is a gauge of a country's economic health. Several strategies are available for effective FCP. Classification performance, forecast accuracy, and legality are insufficient for practical use. Several of the suggested methods work for some issues. The specific dataset is not expandable. To improve classification, design a good prediction model adaptable to several datasets. An effective financial crisis prediction method (FPC) requires the right qualities. ML models can also be used to classify a company's financial health. This research presents political optimizer-based feature selection (POFS) with optimal cascaded deep forest (OCDF) for FCP in big data environments. Hadoop Map Reduce handles huge datasets. POFS reduces computing complexity by handling feature selection. POFS is an original FCP algorithm categorization using OCDF. SFO is used to optimize CDF model parameters. A thorough simulation study was performed to evaluate POFS performance on benchmark datasets OCDFs. The results confirmed the POFS-OCDF method's superiority over state-of-the-art approaches. With an outstanding sensitivity of 0.912, specificity of 0.953, accuracy of 0.944, F-score of 0.930, and Matthews correlation coefficient (MCC) of 0.912, the proposed POFS-OCDF technique has shown optimum results. The experimental results demonstrated that the POFS-OCDF technique outperformed other recently developed strategies on a variety of criteria. As previously stated, Sunflower optimization (SFO) is also used to tune the Cascaded Deep Forest (CDF) parameters. A detailed simulation analysis is performed based on the benchmark dataset to evaluate the higher classification efficiency of the POFS-OCDF technique. The invention of the POFS algorithm for FCP exemplifies the work's originality. [ABSTRACT FROM AUTHOR]

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies. [ABSTRACT FROM AUTHOR]

This article aims to comprehensively analyze the crucial role played by data analytics and business intelligence (BI) strategies in propelling digital transformation within diverse industries. Through an extensive literature review and examination of real-world case studies, the study employs a systematic analysis of scholarly works and industry reports. This approach provides a panoramic view of how organizations utilize data-driven insights for competitive advantages, improved customer experiences, and fostering innovation. The findings underscore the pivotal significance of data analytics and BI strategies in influencing strategic decision-making, enhancing operational efficiency, and ensuring long-term sustainability across various industries. The study stands out in its originality by offering a unique synthesis of insights derived from scholarly works and real-world case studies, contributing to a holistic understanding of the transformative impact of data analytics and BI on contemporary business practices. While the study provides valuable insights, limitations include the scope of available literature and case studies. The implications call for further research to explore emerging trends and evolving challenges in the dynamic landscape of data analytics and BI. The practical implications highlight the tangible benefits organizations can derive from integrating data analytics and BI strategies, emphasizing their role in shaping strategic decisions and fostering operational efficiency. In a broader context, the study delves into the social implications of the symbiotic relationship between data analytics, BI, and digital transformation. It explores how these strategies impact broader societal and economic aspects, influencing innovation and sustainability. [ABSTRACT FROM AUTHOR]

The extraction of webpage information is of paramount importance in the realm of competitive intelligence. This research is dedicated to the design and implementation of a visual webpage information extraction module within a competitive intelligence system, approached through the lens of research and development (R&D) technology and its practical applications. Initially, the study delineates the objectives and requirements for webpage information extraction, emphasizing the practical needs of competitive intelligence systems. By critically assessing the strengths and weaknesses of current theories and methodologies in webpage text information extraction, this paper introduces an innovative visual method for extracting webpage text information. Subsequently, the paper meticulously outlines the comprehensive architecture of the proposed module. Building upon this foundation, the study delves into the specifics of the extraction template, rule generation, optimization techniques, and the extraction algorithm pivotal to the process of visual webpage information extraction. The system's effectiveness and practical utility are substantiated through a series of confirmatory experiments, the results of which are thoroughly analyzed. The findings affirm that the developed system adeptly fulfills the webpage information extraction needs of competitive intelligence systems, contributing significantly to the R&D efforts in which the authors are engaged. [ABSTRACT FROM AUTHOR]

Although Business Intelligence (BI) and Business Analytics (BA) have been widely adopted in the tourism sector, comparative research using BI and BA remains scarce. To fill this gap in the literature, the present study explores how BI and BA contribute to strategic innovation, address operational challenges, and enhance customer engagement. To this end, using a dual-method approach that incorporates both quantitative and qualitative methodologies, we first conduct a bibliometric analysis using SciMAT. This sets the stage for the subsequent application of the Gioia methodology. Specifically, we perform an in-depth qualitative examination of a total of 12 scholarly articles on the tourism sector, evenly split between BI and BA. Upon synthesizing the findings on the roles of BI and BA, we outline distinct pathways through which they influence tourism sector management solutions. Based on the obtained evidence, we argue that, while BI focuses on technological advancement and operational integration, BA is more aligned with predictive analytics and data-driven customer engagement. These insights provide managers with a better understanding of the roles of BI and BA, serving as a guide for their strategic applications, from improving service quality to innovating in customer engagement. The novelty of this approach lies in its use of the Gioia methodology, in a comparative analysis to evaluate the separate yet complementarily roles of BI and BA, and in enhancing tourism industry practices. [ABSTRACT FROM AUTHOR]

Purpose: This project aims to optimise a secondary agricultural company's reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users. Design/methodology/approach: A design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle. Findings: The study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team. Practical implications: This study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings. Social implications: Improving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts. Originality/value: This paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry's entire data and business intelligence lifecycle. [ABSTRACT FROM AUTHOR]