ABSTRACT Business analytics enhances managerial decision‐making. To this end, previous research guides firms to combine their tangible business analytics resources, such as data and technology, with intangible knowledge‐based business analytics resources to enable that business analytics supports decision‐making. While the recent literature has recognised the importance of knowledge‐based resources, there remains a lack of comprehensive understanding of the types of these resources and how they are incorporated in leveraging business analytics. Applying the knowledge‐based view of the firm and a systematic literature review approach, this study analysed 105 peer‐reviewed academic papers and synthesised the current discussion on the role of knowledge‐based resources in leveraging business analytics for managerial decision‐making. The results highlight five types of knowledge‐based resources: human skills, human perceptions, organisational culture, organisational capabilities and organisational practices that are integrated into the processes of BA adoption, use and value creation. Firms are likely to realise the expected benefits of their business analytics investments by focusing on developing these vital resources.

In the context of an increasingly accelerated process of globalization and digitization, enterprises face an ever more complex and dynamic market environment, making business analysis a key factor for maintaining competitive advantage. To help enterprises gain a deeper understanding of the essence of business analysis, this paper adopts a literature review approach to explore the origins, development, key turning points, and practical applications of business analysis across different industries. Through the organization of literature, this study finds that business analysis has evolved from early, simple data recording to the embryonic form of market research after the Industrial Revolution, and then to a systematic and quantitative development in the information age, driven by theoretical models and technological tools. Its evolution has been profoundly influenced by technological changes such as the Internet, big data, and artificial intelligence. In the future, business analytics is expected to evolve toward deep integration with artificial intelligence and cross-industry innovative applications.

The fast spread of artificial intelligence (AI) in the United States organizations has radically altered the managerial decision-making process, but on the other hand, it has augmented the complexity and uncertainty in decision, and the accountability stresses. Despite the high-level predictive and prescriptive potentials of AI-based analytics, most organizations have difficulties converting algorithmic results into sustainable managerial decisions. Low levels of trust, lack of explanation, and poor integration between AI systems and human judgment have been caused by over reliance on automation, weak explain ability, and poor organizational outcomes. Current literature has majorly focused on automation-based views of decision support, with a severe lack of insight into the coordinated manner in which human experience and AI intelligence can be systematically integrated with the assistance of analytics. This paper fills this gap by outlining a Human-AI Collaborative Decision Analytics Framework that could be beneficial to improve managerial decisions and organizational performance. Following a conceptual research design, the study integrates interdisciplinary literature in the field of managerial decision-making theory, business analytics, and governance of AI in its attempt to establish an integrative framework where analytics becomes the focal interpretive intercession between AI outputs and human decision-makers. The framework has five overlapping layers such as data, AI analytics, business analytics interpretation, human judgment, and feedback learning that combine to facilitate transparency, accountability, and contextual decision-making. The framework is depicted in the most important areas of the organization with the main focus on the strategic management and workforce decision-making and the secondary focus on the finance, operations, and marketing. The framework minimizes the effects of the algorithmic bias, automation bias, and enhances workforce confidence through embedding managerial control and ethical reasoning and contextual evaluation frameworks into the workflows of AI-assisted decision-making. The contribution of the study to the theory is that it develops human-grounded decision analytics and to practice by providing practical advice to executives and analytics leaders. The presented framework contributes to the responsible use of AI, productivity, and economic competitiveness in the United States in the long term.

ABSTRACT Self-Service Business Intelligence & Analytics (SSBI&A) is a modern approach to data analysis in which tasks for decision-oriented data usage are transferred from specialised experts (so-called power users) to casual business users. However, this self-empowerment of casual users also creates challenges for an organisation. A typical problem with this self-service approach is that casual users may receive more responsibility than is appropriate, given their individual skills in using and analysing data. The resulting consequences of improper use of SSBI&A include the generation of incorrect analyses and reports, as well as inadequate distribution and maintenance of reports. Therefore, clarity is needed in differentiating responsibilities in the SSBI&A context. This article addresses the corresponding issue of governing SSBI&A: It is necessary to determine which SSBI&A use cases are appropriate for pure ‘self-service’, which should at least be supported by experienced power users, and which clearly fall outside the competence and responsibility of casual users. To address this issue, this study proposes an ‘SSBI&A Responsibility Framework’. This framework implements two criteria-based dimensions of SSBI&A use case evaluation: ‘Difficulty’ and ‘Relevance’. In a matrix, four scenarios for governing, i.e. allocating responsibilities, can be derived. An evaluation based on real-life application scenarios demonstrates its utility.

The influence of business analytics on business performance: the role of dynamic capabilities

Artificial intelligence (AI) was over; artificial intelligences (AIs) are booming around us. Artificial intelligence is significant for many people as a discipline, whereas artificial intelligences have benefited people, organizations, companies, and nations. This paper examines artificial intelligence and the evolution from AI to AIs. It presents a calculus of artificial intelligences, which covers AIs, AI computing, and AI entities (AIE) and AIE computing. This paper looks at business intelligences (BIs) as examples of AIs. This paper uses ChatGPT to answer how can we develop "Calculus of Intelligences". ChatGPT’s answers and our remarks suggest that we should cooperate with AI to explore many new topics including "Calculus of Intelligences". The proposed approach in this paper might facilitate the research and development of AI, AIs, business intelligences, and business analytics.

This study investigates the role of business analytics in advancing digital inclusion and promoting economic equity among underserved populations in India, with a focus on mitigating persistent socioeconomic challenges. While digital access has expanded rapidly, meaningful inclusion, defined by affordability, digital literacy, relevant content, and the ability to translate digital engagement into tangible outcomes, remains uneven. Drawing on mixed-methods data collected during March to August 2025 from 390 survey respondents and 30 in-depth interviews across urban and peri-urban India, the research tests five hypotheses linking digital inclusion, business analytics adoption, digital literacy, perceived algorithmic fairness, and socioeconomic outcomes. Quantitative data analysis is done using multiple regression analysis, where the bootstrapped mediation and interaction-based moderation are used to test all five hypotheses, and thematic analysis was performed on qualitative data. Findings confirm that higher levels of digital inclusion correlate positively with perceived economic equity, but this relationship is significantly mediated by digital literacy and amplified by institutional use of business analytics. Analytics-driven services enhance accessibility, personalization, and responsiveness, thereby strengthening the equity-enhancing potential of digital tools. Crucially, perceived algorithmic fairness emerges as a foundational element of user trust and sustained engagement. The study underscores that technological interventions alone are insufficient; to effectively address socioeconomic challenges, they must be embedded within ethically designed, transparent, and human-centered ecosystems that prioritize the needs and agency of marginalized communities. These insights carry important implications for policymakers, practitioners, and designers aiming to harness data-driven innovation for inclusive development and overcome the socioeconomic challenges.

The rapid advancement of large language models (LLMs) has opened new possibilities for business analytics and market intelligence. While traditional single-model systems such as ChatGPT can analyze text and summarize insights, they lack collaborative specialization and workflow coordination. This study explores a no-code experimental framework using a multi-agent system built upon GPT to perform automated market analysis. The experiment compares a baseline single-LLM configuration with a three-agent structure composed of a Data Agent, an Analysis Agent, and an Auditor Agent. A dataset of 200 publicly available product reviews was used to evaluate performance across quantitative metrics (accuracy, precision, recall, F1-score) and qualitative metrics (report structure, insight quality, information coverage). Results show that the multi-agent workflow produced clearer, more structured market reports with marginally higher accuracy and significantly improved interpretability (accuracy = 0.86 vs. 0.81; macro-F1 = 0.86 vs. 0.81). This exploratory research highlights the potential of GPT-based agents for business decision support and demonstrates a reproducible no-code approach accessible to non-technical practitioners.

The subject of the study is theoretical and methodological approaches to the formation of innovative models of management of software companies in the digital economy. The purpose of the study is to identify the key components of management mechanisms that ensure the efficiency of IT companies, generalize their content, determine the role and tasks in the complex system of digital transformation. The components of the organizational structure of management mechanisms that contribute to the implementation of analytics, flexible methodologies and adaptation to market changes are studied. The study was conducted using a set of methods: general methods - generalization, abstraction, system-structural analysis; special methods of economic and statistical analysis, in particular comparative analysis to assess the dynamics of growth of the IT sector of Ukraine, as well as content analysis to generalize scientific approaches to management. The views of scientists on the concept of software company management are summarized, the key role of data analytics (big data, business analytics tools such as Power BI and Tableau), flexible methodologies (Agile, SCRUM, DevOps) and modern technologies (artificial intelligence, cloud solutions) in ensuring competitiveness in the digital economy is identified. Based on comparison and analysis methods, the dynamics of growth in the number of IT specialists and company productivity in Ukraine for the period 2018–2024 are assessed, as well as the impact of martial law on the adaptation of the IT sector. The components of management mechanisms are structurally defined, covering analytical tools for forecasting market trends, process automation through CI/CD, investments in human capital through training programs (upskilling, reskilling) and partnerships, including participation in open-source projects and ecosystems with startups and academic institutions. The goals and objectives of these components are described, which form the basis for creating an integrated management system for software companies capable of responding promptly to the challenges of the digital economy. It is substantiated that the optimal combination and mutual consistency of structural components, such as analytics, innovative methodologies, artificial intelligence technologies, cloud solutions and adaptability to change, allow for the formation of effective management policies. This ensures the competitiveness of IT companies, contributing to their sustainable development in the context of globalization and technological transformations. The study emphasizes the features of the Ukrainian IT sector, which demonstrates resilience due to high adaptability and innovative potential, despite the challenges of wartime, such as staff shortages and cyber threats. The results of the study can be used to improve management strategies for IT companies in Ukraine and abroad.

The article is devoted to analyzing the peculiarities of using the framework methodology and management philosophy approaches from the perspective of defining the strategic goal of sustainable development based on minimizing losses in all business processes, focusing on consumer needsand reducing the negative impact on the environment. The article examines a range of aspects of the project approach to integrating sustainable development business analytics requirements into the process of business process strategizing and developing practical recommendations for economic entities in the context of security measurement based on the competitiveness of activities. The theoretical foundations and current practices of applying an integrated approach to business process strategizing using financial equilibrium sustainability frameworks in the context of business analytics of competitiveness in the security dimension and sustainable development have been identified. Approaches to our own vision of the peculiarities of business analytics development in the context of this study for the needs of business security and competitiveness have been formed based on the framework of forms of financial equilibrium manifestation in time and space. An analysis of scientific publications has been carried out to understand the research question of the project approach to business process strategizing using frameworks in the security dimension and competitiveness in the context of the need for sustainable development business analytics on the approaches of multifacetedness and diversity of views.

Predictive Analytics is a crucial branch for data-driving analysis in business, and could be used in cutomer behavior prediction and evaluation. The selection of an appropriate model involves a trade-off between prediction accuracy, interpretability and computational costs. This study aims to empirically compare the predictive performance of Decision Tree (DT) and Random Forest (RF) in identifying potential customer type and bahavior for a new travel package, addressing a challenge of model selection in realbusiness context. Using a customer dataset from a tourism company (“Visit with us”), it implemented and tuned both DT and RF models. Model performance was evaluated on key metrics including accuracy, precision, recall, and F1-score. The analysis found that tuned Random Forest Model is found to be the superior model with the highest test accuracy and F1 score, indicating a balance between precision and recall. While tuned Decision Tree Model ranked at the second. The study confirms the established superiority of ensemble methods like Random Forest for prediction tasks but provides a nauced business insight: the choice between DT and RF might depend on the strategic goal—maximizing customer reach versus optimizing marketing efficiency. The findings offer actionable strategies for targeted marketing and demonstrate the significant value of predictive analytics in formulating business strategy.

In the digital era, the rapid growth of social media and online platforms has led to an explosion of unstructured textual data that holds significant business value. Traditional marketing strategies, once reliant on structured data such as demographics and purchase history, now benefit from insights derived from text analytics and sentiment analysis. This paper explores the integration of structured and unstructured data to strengthen marketing intelligence and customer segmentation. By utilizing text mining techniques and Natural Language Processing (NLP), unstructured data such as customer reviews and comments can be analyzed to extract sentiments, identify emerging trends, and refine customer relationship strategies. The study proposes an integrated framework that combines data extraction, transformation, and loading (ETL) processes with a data warehouse system for unified analysis. Using clustering algorithms such as K-Means and visualization tools, insights into customer behavior, preferences, and market segmentation are revealed. The paper also discusses the challenges of handling multilingual and context-dependent text, ethical and privacy considerations, and the technical architecture necessary for business intelligence implementation. Findings suggest that effective integration of textual analytics with structured data can lead to more informed decision-making, improved marketing strategies, and stronger customer engagement.

في البلدان النامية، لا يزال دور ذكاء وتحليلات الأعمال (BI&A) في تحسين الأداء التنظيمي غير مستكشف بشكل كافٍ، وخاصة في قطاع التعليم العالي. لذلك، هدفت هذه الدراسة إلى تحليل تأثير ذكاء وتحليلات الأعمال على الأداء التنظيمي من خلال القدرات الديناميكية والابتكار في الجامعات الأهلية اليمنية. اقترحت هذه الدراسة نموذجًا مفاهيميًا يتكون من ثلاث متغيرات مستقلة (البنية التحتية لتكنولوجيا المعلومات، وخبرة موظفي ذكاء وتحليلات الأعمال، ودعم الإدارة العليا)، ومتغيرين وسيطين (القدرات الديناميكية والابتكار)، ومتغير تابع واحد (الأداء التنظيمي). تم جمع البيانات من خلال استبيان شمل 269 موظفًا أكاديميًا وإداريًا في خمس جامعات أهلية (جامعة العلوم والتكنولوجيا، وجامعة الملكة أروى، وجامعة الناصر، وجامعة الرازي، وجامعة الرشيد). تم تحليل البيانات باستخدام تحليل الانحدار، وتحليل الارتباط، وتحليل العوامل. كشفت النتائج أن خبرة موظفي ذكاء وتحليلات الأعمال ودعم الإدارة العليا يؤثران بشكل كبير على الأداء التنظيمي بشكل غير مباشر من خلال القدرات الديناميكية والابتكار. مع ذلك، لم تُظهر البنية التحتية لتكنولوجيا المعلومات أي تأثير يُذكر على الأداء التنظيمي. تشير النتائج إلى ضرورة قيام قادة الجامعات بتعزيز القدرات المتعلقة بذكاء وتحليلات الأعمال لتحسين أداء المنظمة من خلال الابتكار والقدرات الديناميكية.

Business intelligence and analytics (BI&A) competencies are presented as a strategic factor in managing sustainable competitive advantage. Similarly, dominant logic is presented as a set of beliefs and practices within organizational culture, and strategic diversification is the degree of development for market diversification. This research explored whether BI&A competencies mediate the relationship between dominant logic and strategic diversification in a Chilean SME. This was a non-experimental study with a cross-sectional design. A non-probabilistic sample of 244 employees from the SME was collected. Three instruments were used to measure the variables: the Organizational Dominant Logic Scale, the Organizational Strategic Diversification Scale, and an instrument that measures BI&A competencies. The results identified a proactive dominant logic in the organization, moderate strategic diversification, and moderate consolidation in BI&A competencies. Likewise, in relation to the mediation analysis, it was found that the indirect effects were not statistically significant at the 0.05 level, while the direct effects were, therefore, proactive dominant logic is related to strategic diversification. In conclusion, BI&A competencies could not mediate the relationship between proactive dominant logic and strategic diversification.

Integrating sustainability principles into higher education curricula is a global imperative, yet it poses significant challenges for faculty development, particularly across diverse disciplinary and cultural contexts. This paper explores how the process of embedding sustainability into university courses acts as a catalyst for educator transformation, influencing faculty identity, pedagogical method, and professional agency. Drawing on a qualitative multiple case study conducted at two international universities in the United Arab Emirates and Qatar, this study analyzes teaching artefacts, course materials, and reflective journals from courses spanning information systems, business analytics, digital marketing, and media and communication. The CoDesignS Framework served as both a design and analytical scaffold to align teaching practices with key sustainability competencies and transformative pedagogies. Findings demonstrate that sustainability integration encourages not only deeper student engagement but also meaningful professional growth for educators, shifting their roles from content experts to co-designers of learning. This paper contributes a practitioner-led, contextually grounded model for embedding Education for Sustainable Development (ESD) and argues that empowering faculty through flexible, reflective frameworks such as CoDesignS may be more effective than top-down compliance approaches in driving institutional change.

To investigate the Business Analytics Capabilities on Innovative Performance: Mediating role of Dynamics Capabilities. The aim is to explore the significant relationship between business analytical capabilities on innovative performance of the Dynamics Capabilities. Business analytics capabilities positively affect Dynamics Capabilities (firm responsiveness), which in turn positively impacts innovative performance. A structured questionnaire employing the key informant approach was used to collect data as part of the survey method. A structured questionnaire employed the key informant approach to gather data as part of the survey method. We were gathering data on employees in IT project managers and management across all information and technology-related IT projects to create a sample frame for the current study. The respondents in the sample frame are IT project managers. We used a purposive sampling technique to select a sample of 230 respondents from the IT Projects in Pakistan and the UAE. We were using Google Forms for data collection and self-administration. We were utilizing SPSS and PLS-SMART software for data analysis to transform the collected data into meaningful insights. We utilized the measurements and scales of business analytical capabilities, as well as innovative performance and dynamics capabilities. Business Analytics Capabilities do not drive innovation alone, but when combined with adaptive organizational capabilities, they significantly enhance a firm’s innovative performance. This mediation framework aligns with dynamic capabilities theory, offering both theoretical advancements and practical guidance for data-driven innovation in turbulent environments. Business Analytics Capabilities and Innovative Performance: Mediating Effect of Dynamic Capabilities confirmed a positive effect. The practical implication means that managers invest in advanced analytics infrastructure not just for efficiency, but as a driver of innovation readiness.

The integration of generative artificial intelligence (GenAI) in business education is reshaping data analysis and decision-making. However, concerns remain about potential over-reliance on GenAI at the expense of students’ critical thinking. This article presents an assessment designed to develop students’ technical proficiency in AI-supported data analysis while encouraging critical reflection on AI-generated outputs. Using a real-world-inspired case study, students collaborated with GenAI tools to conduct regression, forecasting, and hypothesis testing, and compared AI-generated results with manual analysis in Excel. Thematic analysis, aligned with Kolb’s experiential learning model, reveals stratified engagement: middle and strong performers demonstrated deeper reflection, especially on ethical implications and the importance of human oversight. Strong students further explored GenAI’s adaptability across different contexts. These findings contribute to the growing discourse on GenAI literacy in management education, underscoring the need for balanced pedagogical strategies that cultivate both AI skills and ethical and professional judgment essential for AI-augmented management practice.

Wealth management is entering a new era, where artificial intelligence (AI), data analytics, and business intelligence tools are reshaping how financial decisions are made. This study provides an in-depth examination of how AI-driven data analytics enhances every stage of the wealth management process—from collecting and interpreting complex financial data to predicting market movements and optimizing client portfolios. Unlike traditional advisory approaches that rely heavily on manual judgment, AI systems can process massive volumes of structured and unstructured data, uncover hidden risk patterns, evaluate asset performance in real time, and generate actionable insights with far greater accuracy and speed. The research explores how machine learning models improve long-term forecasting, stress testing, and risk scoring, while advanced analytics tools support personalized asset allocation tailored to client goals, risk tolerance, and market conditions. The study also discusses how AI-powered platforms reduce human bias, identify early warning signals for market disruptions, automate rebalancing strategies, and expand financial access through intelligent robo-advisory services. Furthermore, the paper analyzes the operational efficiencies gained by financial institutions, including real-time monitoring dashboards, automated compliance checks, and predictive client behavior modeling. By combining data-driven intelligence with human expertise, AI-enabled wealth management delivers more stable portfolio performance, improved transparency, enhanced risk mitigation, and better financial outcomes for both advisors and investors. This research demonstrates that AI and advanced analytics are not simply tools—they represent a fundamental shift toward smarter, adaptive, and more resilient wealth management systems.

Translating complex enterprise data architectures into actionable business analytics driving measurable revenue, efficiency, and resilience outcomes

The interplay between business analytics capabilities and decision-making performance in Indonesia's public sector