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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: This paper aims to examine the direct and indirect impact of competitive intelligence (CI) practices on sustainable competitiveness (SC) and firm performance (FP) mediated by strategic design collaboration (SDC). Design/methodology/approach: This empirical study is based on a survey of 179 respondents comprising senior managers from pharmaceutical companies operating in Egypt across three sectors: public, private and multinational corporations (MNCs). Findings: The results show that CI has a positive and significant impact on SDC, which, in turn, positively impacts SC and FP. The study also shows that SDC mediates the relationship between CI and both SC and FP. Originality/value: This study sheds light on the significant and mediating role of SDC in explaining the relationship between CI and both SC and FP. [ABSTRACT FROM AUTHOR]

Today, the ever-increasing growth of technology and data has undeniably affected various industries. Due to the importance of the factor of comprehensive quality management (QM), big data, and information technology (IT) in the industry and the increasing attention to it, different attitudes regarding the ways and means of reaching it have been presented by these sources. As a result, the purpose of this research is to study new IT-based technologies related to the success of the QMS. The statistical population of this research is the managers and employees of the technology sectors, which is equal to 400 people. The sample size was equal to 196 people, and a simple random sampling method was used to obtain the sample size. This research is applied in terms of its nature and purpose, and in terms of the data collection method, it is a descriptive and survey type. PLS structural equation modeling and SPSS software were used for data analysis. The findings showed that the internet of things (IoT), business intelligence (BI), cloud computing, and distributed systems have a significant impact on the success of the QMS, while near-field communication and radio frequency identification have an impact on the success of the QMS through the intermediary variable of the IoT. Finally, the results revealed that the expert systems also affect the success of the QMS through the mediator variable of BI. [ABSTRACT FROM AUTHOR]

Data center architecture plays an important role in effective server management network systems. Load balancing is one such data architecture used to efficiently distribute network traffic to the server. In this paper, we incorporated the load-balancing technique used in cloud computing with power business intelligence (BI) and cloud load based on the queueing theoretic approach. This model examines a bulk arrival and batch service queueing system, incorporating server overloading and underloading based on the queue length. In a batch service system, customers are served in groups following a general bulk service rule with the server operating between the minimum value ' a ' and the maximum value ' b ' . But in certain situations, maintaining the same extreme values of the server is difficult, and it needs to be changed according to the service request. In this paper, server load balancing is introduced for a batch service queueing model, which is the capacity of the server that can be adjusted, either increased or decreased, based upon the service request by the customer. On service completion, if the service request is not enough to start any of the services, the server will be assigned to perform a secondary job (vacation). After vacation completion based upon the service request, the server will start regular service, overload or underload. Cloud computing using power BI can be analyzed based on server load balancing. The function that determines the probability of the queue size at any given time is derived for the specified queueing model using the supplementary variable technique with the remaining time as the supplementary variable. Additionally, various system characteristics are calculated and illustrated with suitable numerical examples. [ABSTRACT FROM AUTHOR]

Graphical user interface (GUI) browsing and retrieval tools are becoming essential to interaction design research and practice. These tools allow GUI designers to browse large amounts of data and recover inspiring or relevant designs for their task. Unfortunately, data-driven market analysis or business intelligence (BI) applied to GUIs have mostly been left aside. To address this research gap, we elicit designers' needs and responses regarding the development of Enricommender, a high-fidelity prototype of a market analysis recommender and reporting system. We identify and discuss key design challenges, as reported by more than 200 real-world designers, as well as their workflows and overall expectations towards such a BI system. Ultimately, this article sets the foundation for developing future GUI-oriented BI applications. RESEARCH HIGHLIGHTS Insights about ideation and design of graphical user interfaces. Elicited preferences and current practices from 200 real-world designers. A business intelligence prototype for comparative analysis and design recommendations. [ABSTRACT FROM AUTHOR]

The annual conference of the dcif e.V. and Steilvorlagen for business success took place on October 29, 2024 in Frankfurt am Main. Approximately 50 experts from the fields of market research, competitive intelligence, and information economy gathered to discuss Artificial Intelligence, digital tools, and best practices for Market and Competitive Intelligence. Topics such as Open Source Intelligence, patent analysis with AI, and AI-driven MI/CI methods were presented to shape the future with AI. [Extracted from the article]

Business intelligence and analytics (BI&A) have recently emerged as a strategic approach to managerial tasks, providing opportunities to improve work performance. Despite the growing interest in evaluating cases of BI&A adoption, to the best of our knowledge, few studies have addressed the influence of data-driven culture and the effects of BI&A adoption specifically on the work performance of managers. The aim of this study is to assess whether a data-driven culture predicts the adoption of BI&A in companies and its impact on decision-making effectiveness and managerial performance. This novel research model was tested with 180 managers from Romanian companies that work with BI&A tools. Based on PLS-SEM data analysis, our findings suggest that a data-oriented culture is a strong predictor of BI&A adoption and decision-making effectiveness. The results also confirm that BI&A utilization positively impacts decision-making effectiveness and individual work performance. The primary implication drawn from empirical evidence is that executives should prioritize the cultivation of a data-driven culture within their organizations, as this is essential for enhancing managerial performance through the adoption of business intelligence and analytics. [ABSTRACT FROM AUTHOR]

Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management. [ABSTRACT FROM AUTHOR]

Seaports are experiencing several challenges due to the explosive growth of the maritime shipping business, which has led to the need for digitalized operations and more effective solutions. This article provides a comprehensive exploration of the process used to create a reliable business intelligence solution by analyzing the container delivery and pick-up services flow in one of Portugal's largest maritime container ports, using the CRISP-DM methodology. The solution, built with Microsoft Power BI®, provides the capability to identify and address data anomalies and present key performance indicators in visually dynamic dashboards. This solution empowers stakeholders to gain invaluable insights into the current and future operational status, thereby facilitating well-informed and adaptable decision-making, representing the main practical contributions. As a theoretical contribution, this study advances research by covering a gap in the literature and establishing the foundations for future business intelligence applications within the maritime industry, with a focus on addressing data dispersion challenges, enhancing logistics flow analysis, and reducing port congestion. The manuscript is structured into seven sections: introduction, literature review, port challenges, methodology, tool development, SWOT analysis, and conclusion. [ABSTRACT FROM AUTHOR]

This study examines the evolving landscape of Business Intelligence (BI) research and its impact on organizational decision-making through a comprehensive bibliometric analysis. Analyzing 2442 BI-related articles from the Scopus database published between 2014 and 2024, we identified key trends, influential authors, leading institutions, and significant research gaps. Despite substantial literature, our findings reveal a declining trend in BI publications and significant under-exploration in integrating BI with competitive intelligence, decision-making processes, data mining, and human resources management. The study highlights geographical disparities in research output, with the United States and China leading in publications and citations. Our analysis underscores the need for more interdisciplinary research approaches and increased collaboration among researchers and institutions to address these gaps. This study provides valuable insights for researchers and practitioners, offering direction for future research to enhance the application of BI in organizational decision-making and strategic development. [ABSTRACT FROM AUTHOR]

This study explores intellectual capital's impact on social media analytics adoption in the banking industry. It also examines the impact of social media analytics on competitive intelligence and banking entrepreneurship. Furthermore, this study explores the mediating role of competitive intelligence in the impact of social media analytics on banking entrepreneurship. Data were collected from 320 bank managers and specialists and analyzed using Smart PLS, Version 4. The findings indicated that human, structural, and relational capital significantly impact the adoption of social media analytics. This study also revealed that social media analytics significantly impacts competitive intelligence and banking entrepreneurship. Furthermore, the results showed a significant mediating role of competitive intelligence in the impact of social media analytics on banking entrepreneurship. This study provides invaluable contributions for both academic discourse and industry professionals. It thoroughly investigates the interrelationships among intellectual capital, social media analytics, competitive intelligence, and banking entrepreneurship. This study advances our understanding of how these capabilities operate within the ever‐changing banking realm. Moreover, this study provides novel practical insights for bank managers and policymakers, emphasizing the importance of intellectual capital as well as the contributions of social media analytics and competitive intelligence in driving banking entrepreneurship. [ABSTRACT FROM AUTHOR]

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]

The integration of systems for Adaptive Business Intelligence (ABI) in the healthcare industry has the potential to revolutionize and reform the way organizations approach data analysis and decision-making. By providing real-time actionable insights and enabling organizations to continuously adapt and evolve, ABI has the potential to drive better outcomes, reduce costs, and improve the overall quality of patient care. The ABI Interoperability System was designed to facilitate the usage and integration of ABI systems in healthcare environments through interoperability resources like Health Level 7 (HL7) or Fast Healthcare Interoperability Resources (FHIR). The present article briefly describes both versions of this software, learning about their differences and improvements, and how they affect the solution. The changes introduced in the new version of the system will tackle code quality with automated tests, development workflow, and developer experience, with the introduction of Continuous Integration and Delivery pipelines in the development workflow, new support for the FHIR pattern, and address a few security concerns about the architecture. The second revision of the system features a more refined, modern, and secure architecture and has proven to be more performant and efficient than its predecessor. As it stands, the Interoperability System poses a significant step forward toward interoperability and ease of integration in the healthcare ecosystem. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the efforts of a public healthcare business intelligence unit to implement and disseminate their data products and thus make the healthcare organization more data-driven. The paper is based on ethnographic fieldwork in a healthcare business intelligence unit (the BIU) whose mission is to improve healthcare efficiency and quality by making data and data analyses available to healthcare managers and staff. Their primary products consist of a data warehouse and Data Reports, both providing curated and daily updated data for healthcare staff to analyze and visualize. We conceptualize these Data Reports and the data warehouse as boundary objects through which cooperation around data between various users is achieved. Our focus is on the BIU's efforts to introduce and promote the use of boundary objects to healthcare staff while providing them with the competencies to use them in practice. Efforts that we conceptualize as collaborative boundary work through which a new joint field of working with data is created between the BIU and healthcare staff. Based on the analysis of the ethnographic fieldwork, we point to three important aspects in creating this new joint field: Mobilizing interest, building local capabilities, and propagating data locally. The paper makes three contributions: It adds to our understanding of how new joint fields can be cultivated through collaborative boundary work to make healthcare data-driven; it contributes to the emergent field of data work studies; and finally, it adds to the largely normative literature on business intelligence and self-service business intelligence through an ethnographic analysis of its efforts to make healthcare data-driven. [ABSTRACT FROM AUTHOR]

This study examines corporate strategy development through the lens of military strategic practices, highlighting the parallels between military and business approaches. It draws on data from a survey conducted among experienced Czech managers and management students, analysing their use of strategic tools, data preferences, and operational methodologies. Advanced statistical techniques, including Principal Component Analysis (PCA) and cluster analysis, reveal differences in the emphasis placed on macroeconomic indicators and internal processes by managers versus the preference for tools like SWOT and PESTLE among students. Key findings underscore the significance of protecting competitive intelligence and adopting adaptive methodologies to enhance decision-making. The study bridges theory and practice, offering practical recommendations for integrating military-inspired strategies into business education and corporate frameworks. [ABSTRACT FROM AUTHOR]

Copyright of Materia Arquitectura is the property of Universidad San Sebastian 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 large volume of data in systems in the collection area leads to the lack of adequate management of information, as well as dissatisfaction on the part of the user. The purpose of the study is to implement business intelligence (BI) technology to improve the effectiveness of the information and the satisfaction of the attention of the users of a municipality of Lima in the area of collection; therefore, the phases of the Ralph Kimball method with the following phases: project planning; definition of requirements; design of technological architecture; in the dimensional modeling a snowflake scheme was made with 9 dimensions and 1 table made, in the physical design it was implemented in the MySQL management system and in the extract, transform, and load (ETL) development the migration, transformation and cleaning of the data from the online transaction processing (OLTP) database to online analytical processing (OLAP) was executed; obtaining as results that BI managed to increase the level of information efficiency by 53.32%, as well as the level of user satisfaction (LUS) by 1.90%, concluding that BI allows to meet the needs of the user since it maintains a clean, secure and reliable data structure. [ABSTRACT FROM AUTHOR]

Copyright of Journal de Ciencia e Ingeniería is the property of Corporacion Universitaria Autonoma del Cauca 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 learning objectives of introductory database courses were changed about twenty years ago to incorporate the changing role of data that was occurring in the world. Data were being viewed in new ways for analysis and techniques were being developed to better store, retrieve, and analyze the data. The structure known as the Data Warehouse was developed throughout the 1990s as a method of modifying the data and storing it permanently. Techniques were developed to access the data residing in the warehouses in ways that led to easy analysis of the data. Online Analytical Processing (OLAP) tools and data mining techniques became standards. IT professionals developed structures to capture and maintain very large amounts of data and allow for relatively stress-free retrieval. This paper provides a statistical study of the outcomes of learning objectives of business intelligence and big data analytics in an introductory database course at a large urban public university. Since business intelligence and big data analytics are relatively new, the time period under consideration covers the years 2017 through 2024. The study considers different outcomes in sections that are delivered in the instructor-led format verses the online format. The study also considers the varying outcomes of the learning objectives during spring and fall semesters and the shortened summer session. A factorial ANOVA model is used to measure the significance of the differences in the means of outcomes of learning objectives in categorized sections of the database course. There is not enough evidence to conclude that differences exist in the semester in which the course is taken. However, there are significant differences in the means of the outcomes of learning objectives between instructor-led sections and online sections. [ABSTRACT FROM AUTHOR]

Financial performance is a subjective assessment of a firm's ability to use assets from its core business operations to create income. The phrase is utilized as a comprehensive indicator of a company's financial well-being throughout a certain duration. Strategic management practices play a crucial role in determining the financial performance of organizations. This paper aims to critically analyzed the impact of strategic management practices on financial performance by examining various scholarly articles and studies in the field. Moreover, the influence of business intelligence as a moderator on the relationship between strategic management practices and the financial performance of SMEs in the northern region of Malaysia was also investigated. This study amalgamated the resource-based view (RBV) to elucidate the impact of the factors on effective strategy execution. Questionnaires were administered to 378 SMEs in the northern region of Malaysia. The above sample was utilized in the analysis employing the Structural Equation Modelling (SEM) - Partial Least Squares (PLS) approach. The study's results indicated a substantial impact of strategic management practices on financial performance. The research indicated that the moderating influence of business intelligence on strategic management practices and financial performance was significant. This study emphasized the research ramifications, recommendations for further investigations, and its limits. [ABSTRACT FROM AUTHOR]

Objective: Organizational maturity refers to the level of development and capabilities of an organization in which the organization can manage its processes in a systematic and appropriate manner to achieve its strategic goals. The food industry is one of the vital industries for the health and well-being of society. The aim of the current research is to provide a model of factors affecting organizational maturity in knowledge-based companies of the food industry in Iran with the approach of the fourth-generation industry. The fourth-generation industry has created a revolution in the decision-making processes of companies. Technologies such as the Internet of Things, blockchain, and big data are mainly data-driven and use huge amounts of data for analysis. These technologies play an important role in intelligence, automation and integration of processes and decisions of organizations. Many functional areas of organizations such as human resources and marketing have been affected by this technology. The research consists of two qualitative and quantitative parts, in the first part, the main and secondary factors of the model were obtained through metacomposition. In the second part, the relationships between the main factors were determined through cross impact analysis. Method: The current research is applied in terms of orientation and is a mixed study in terms of methodology. Also, the current research is a pragmatic study in terms of philosophical foundations and exploratory research in terms of the goal. Due to the use of qualitative and quantitative methods together, the research is a mixed study from a methodological point of view, and its philosophical foundation is pragmatism. The theoretical population of the research is the experts of the food industry and the sampling was mainly done in a judgmental and purposeful way. In the judgmental sampling method, cases and samples are selected based on the expertise of experts (Maturation of food industry and fourth generation technologies). In this research, the meta-synthesis method was used to extract factors and the cross-impact analysis was used to determine the relationships between the main factors. Meta- synthesis is a qualitative method based on the analytical review of articles, which is used to extract the main and secondary factors related to a phenomenon. The Cross Impact Analysis method is based on structural analysis and examines and evaluates the relationships between factors affecting a phenomenon. Validation of articles was done by CASP tool. The extracted articles were screened step by step to identify the most qualitative articles for study. In this research, meta-synthesis method was used to extract factors and cross impact analysis technique was used to determine the relationships between the main factors. In order to get experts' opinions about the relationships between the research factors, influence evaluation questionnaires were used. The experts were asked to express their opinions on a scale of 0 to 3. Also, to measure the validity of the meta-synthesis part, the Lawshe content coefficient was used, and the Kappa coefficient was used to measure the reliability of the codings. Validity and reliability values in this research were appropriate. Findings: This research was done in two steps. In the first step, factors affecting organizational maturity were extracted using a systematic background review and meta-combination method. The output was 6 main factors and 27 secondary factors. The main factors affecting organizational maturity were: structural factors, training and learning, technological factors, factors related to the nature of governance, factors related to innovation, and factors related to decision-making. For example, the components of the technological factors are: the nature of the organization's information systems, the technology infrastructure in the organization, the amount of use of smart machines and equipment in the company, the knowledge management system in the company, and the amount of use of cloud platforms in the organization. Also, the components of decision-making factors are: data-driven decision- making culture governance, methods of risk identification and analysis in the organization, decision-making methods of managers and experts in the company, methods of collecting and analyzing data related to customers, reporting methods in the organization and structuring and problem-solving style in organization. In the following, these factors were evaluated with the approach of structural analysis of cross impact analysis. The results of this technique showed that decision-making, structural and governance factors are effeinfluential variables. Also, technological and innovative factors were evaluated as impressionable variables. Results: Practical suggestions of the research were developed based on the influencing factors. Some suggestions were: the use of data-driven technologies such as business intelligence and big data to strengthen data-driven decision-making, paying attention to the compatibility and integration of technologies and information systems in knowledge-based companies in the food industry, and the use of technologies such as blockchain to improve governance and improve the transparency of knowledge-based companies in the food industry. By using technologies such as big data, a large amount of data can be collected and analyzed, and based on that, many risks and threats can be identified and managed. Business intelligence can be used to classify and predict key variables. Blockchain is also a key technology to enhance transparency and develop smart contracts. It should not be forgotten that the fourth-generation technologies are mainly integrated and systemic, so before the introduction of new technology, attention should be paid to system adaptation. In this research, only the factors and indicators and the relationships between them have been obtained using the structural approach. Testing this model can greatly increase its level of generalizability. Also, from the point of view of method, in the future researches, it is possible to mention things such as the statistical test of the present research model with the structural equation modeling approach and the comparative comparison of the model results in various industries. [ABSTRACT FROM AUTHOR]

In recent years, the rise in the utilization of business intelligence (BI) as a pivotal tool for enhancing economic performance has spurred considerable interest. This study, anchored in resource‐based theory, delves into the intricate relationships among BI, environmental performance (EP), financial performance (FP), green accounting (GA), and energy efficiency (EE) within manufacturing firms in Bangladesh. Employing a multistage stratified random sampling technique, a structured questionnaire was administered to 368 participants over 5 months starting in June 2023. The study employs a comprehensive analytical method that includes disjoint two‐stage PLS‐based structural equation modeling, artificial neural networks, and fuzzy set qualitative comparative analysis to ensure robust and insightful results. The findings highlight the positive and significant impact of BI on EP, FP, GA, and EE for manufacturing firms, demonstrating the efficacy of BI in driving improvements across environmental and financial metrics. Notably, the study unveils the mediating effects of EE between GA and FP, as well as between BI and FP, emphasizing the crucial role of EE in bolstering financial outcomes. Moreover, EP emerges as a mediator between BI and GA, underscoring the connectedness of these constructs within the organizational frame. Pioneering in offering a BI‐integrated concepts for environmental and FP, exploring mediating effects, and applying the integrated modeling approach, this research provides theoretical insights and practical implications, offering valuable guidance for stakeholders aiming to leverage BI strategies effectively. [ABSTRACT FROM AUTHOR]

Social media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework. [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.)

Abstract Today, the ever-increasing growth of technology and data has undeniably affected various industries. Due to the importance of the factor of comprehensive quality management (QM), big data, and information technology (IT) in the industry and the increasing attention to it, different attitudes regarding the ways and means of reaching it have been presented by these sources. As a result, the purpose of this research is to study new IT-based technologies related to the success of the QMS. The statistical population of this research is the managers and employees of the technology sectors, which is equal to 400 people. The sample size was equal to 196 people, and a simple random sampling method was used to obtain the sample size. This research is applied in terms of its nature and purpose, and in terms of the data collection method, it is a descriptive and survey type. PLS structural equation modeling and SPSS software were used for data analysis. The findings showed that the internet of things (IoT), business intelligence (BI), cloud computing, and distributed systems have a significant impact on the success of the QMS, while near-field communication and radio frequency identification have an impact on the success of the QMS through the intermediary variable of the IoT. Finally, the results revealed that the expert systems also affect the success of the QMS through the mediator variable of BI.

The present paper explores the role of Competitive Intelligence (CI) within the Shipping industry, focusing on strategic information gathering and analysis for decision-making and strategic planning. The study synthesizes insights from scholarly works and academic perspectives to highlight the transformative impact of CI practices on operational efficiency and competitiveness in shipping operations. Key themes include the integration of CI technology, such as Business Intelligence (BI) tools and data analytics, to optimize decision-making processes and enhance service quality in a dynamic global market. The research also underscores the significance of diverse intelligence sources, including industry associations, government agencies, financial data providers, and academic research, in shaping CI strategies for Shipping companies. Furthermore, the discussion emphasizes the convergence of CI with geopolitical and macroeconomic analyses to inform strategic decision-making and mitigate operational risks. Overall, the paper contributes to a comprehensive understanding of CI practices and their implications for sustainable growth and resilience within the Shipping industry.

This study examines corporate strategy development through the lens of military strategic practices, highlighting the parallels between military and business approaches. It draws on data from a survey conducted among experienced Czech managers and management students, analysing their use of strategic tools, data preferences, and operational methodologies. Advanced statistical techniques, including Principal Component Analysis (PCA) and cluster analysis, reveal differences in the emphasis placed on macroeconomic indicators and internal processes by managers versus the preference for tools like SWOT and PESTLE among students. Key findings underscore the significance of protecting competitive intelligence and adopting adaptive methodologies to enhance decision-making. The study bridges theory and practice, offering practical recommendations for integrating military-inspired strategies into business education and corporate frameworks.

Abstract This study examines the evolving landscape of Business Intelligence (BI) research and its impact on organizational decision-making through a comprehensive bibliometric analysis. Analyzing 2442 BI-related articles from the Scopus database published between 2014 and 2024, we identified key trends, influential authors, leading institutions, and significant research gaps. Despite substantial literature, our findings reveal a declining trend in BI publications and significant under-exploration in integrating BI with competitive intelligence, decision-making processes, data mining, and human resources management. The study highlights geographical disparities in research output, with the United States and China leading in publications and citations. Our analysis underscores the need for more interdisciplinary research approaches and increased collaboration among researchers and institutions to address these gaps. This study provides valuable insights for researchers and practitioners, offering direction for future research to enhance the application of BI in organizational decision-making and strategic development.

Background and Aim: The increase in the amount of information and the need for their daily monitoring have led to the development of tools called management dashboards that have the ability to analyze graphical data. In addition to preparing quick reports in different time frames and user-specific format, the dashboard can be useful for providing dynamic updated information for accurate decision-making and quick response to changes. Materials and Methods: The current research was carried out in the form of qualitative studies and participatory action research method in 9 steps. In this applied and developmental research that was conducted cross-sectionally using the data of the first half of 2022, 11 members of the leadership team of a super-specialized children’s hospital were selected as research participants. The data collection tool was an interview using a questionnaire to determine the importance of indicators and a usability questionnaire (usability) of the dashboard based on three independent variables (usefulness, ease of use and satisfaction). The Excel file of data needed for the dashboard was collected from the HIS system of the hospital, and the dashboard was designed with Power BI software, and the capabilities and access levels of users were determined based on their duties. Data analysis was done using descriptive statistics and Excel software version 2016. Results: In the stage of determining key performance indicators, out of 39 indicators selected by the research team, 22 indicators scored an average score of 4 or higher (from 5 points) and 21 indicators were able to be implemented. The data repository in Excel format was used as an intermediate environment. The dashboard was displayed on six pages (indicators related to the performance of inpatient beds, mortality, emergency and other indicators) and the capabilities of each page were determined. After implementing the dashboard and determining the access levels of users, obtaining a high score from the questions of the usability questionnaire (5 out of 7 points) and obtaining an average score of 71.8 out of 5 questions related to usefulness variables, 70.5 out of 8 questions related to ease of use. And 71 out of 3 questions related to the satisfaction variable showed that the dashboard designed for the hospital had high usability. Conclusion: Hospital management dashboard information can be a basis for informed decision-making to achieve benefits such as identifying the best performance, improving performance quality, making faster decisions, reducing errors, improving capacity management and work flow, allocating resources and planning for growth and development.

The performance of XYZ, a Government Higher Educational Institution (GHEI) in Indonesia is assessed through two unintegrated applications. The 2023 target performance was missed due to miscalculations outside applications while transforming large data amounts. Thus, business intelligence (BI) serves as a knowledge management (KM) tool to integrate those applications to achieve XYZ's target. Because BI is costly and has a 70% failure rate of development plans, a research model was evaluated to look at the current XYZ innovation capability for successful BI adoption from the KM foundation and KM solution implementation. This study used a quantitative method, employing a questionnaire for 94 civil servants and the partial least squares-structural equation model (PLS-SEM) for data analysis. Results indicate in the KM foundation, organizational (O) negatively influences KM process application (KMP) (β = -0.292, Pv = 0.010) while KM infrastructure (I) and process (P) positively influence KMP, but KM technology (T) does not. In KM solutions, KMP is proven to be linked to innovation capability when KM systems are lacking. Hence, several activities are suggested to activate T through T, O, P, and I. The model validated 80% of the hypotheses, laying the groundwork for future studies into which aspects of T strengthen innovation capabilities in GHEI.