Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis

Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study clarifies the definition of business analytics combined with its functionality and the relation between business analytics and business intelligence. Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed.Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Scopus and Web of Science. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information.Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system that enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills.Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

This study systematically reviews the impact of business analytics on achieving competitive advantage in emerging markets, focusing on the integration of advanced analytical tools and strategies within organizational processes. Employing a systematic literature review and content analysis methodology, this research scrutinizes peer-reviewed articles, conference papers, and grey literature from 2018 to 2023, sourced from databases such as Web of Science, Scopus, and IEEE Xplore. The inclusion criteria targeted studies that explore the application, outcomes, and strategic implications of business analytics, while exclusion criteria filtered out non-peer-reviewed sources, non-English literature, and studies outside the scope of emerging markets or published before 2018. Key findings reveal that business analytics significantly enhances strategic decision-making, operational efficiency, and innovation, providing firms in emerging markets with a robust foundation for sustaining competitive advantage. The study identifies the critical role of data-driven insights in transforming organizational strategies and operations. However, challenges such as data privacy, security, and the digital skills gap pose potential barriers to the full realization of business analytics benefits. The study concludes with strategic recommendations for practitioners and policymakers to foster a culture of data literacy and develop supportive frameworks for data privacy and security. Future research directions are proposed to bridge existing gaps in literature, particularly focusing on the long-term impacts of business analytics, ethical considerations, and the integration of emerging technologies. This research contributes to the understanding of how business analytics can be leveraged to navigate the complexities of emerging markets, offering a roadmap for organizations seeking to harness data for competitive advantage. Keywords: Business Analytics, Competitive Advantage, Emerging Markets, Data-Driven Decision-Making.

Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in todays world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.

The application of business analytics has become a critical success factor for improving the performance and economic impact of SMEs in competitive markets. With advancements in digital transformation, SMEs can now leverage analytical tools to enhance decision-making, understand complex markets, and compete effectively with larger organizations. This study employed an exploratory and quantitative research approach, collecting primary data from SME owners and managers and secondary data on financial performance, market dynamics, and economic impact. Statistical and thematic analyses revealed a strong positive relationship between business analytics adoption and improved financial performance, operational efficiency, and economic contribution. Key gains were observed in inventory management, customer relationship management, and strategic decision-making, with businesses adopting analytics achieving higher revenue growth and resilience during economic volatility. The findings underscore the importance of organizational readiness, data quality, and strategic alignment in ensuring successful analytics implementation. This research highlights the managerial and policy implications of supporting SMEs in building analytics capacity to drive economic growth. Further studies are recommended to explore specific implementation models and address barriers to analytics adoption, emphasizing the pivotal role of business analytics in enhancing the competitiveness and sustainability of SMEs.

Research into the relationship between education and information systems (IS) success (use, satisfaction, and impact) has produced mixed results. Such results seem counterintuitive, given the many benefits that education brings to the workplace. However, workplace research from Human Resources (HR) has similarly found that education has little direct effect on job performance. Instead, education has indirect effects on job performance through job expertise, which is what drives behavior and job performance. The present research integrated the Delone & McLean IS Success Model with the Job Performance Model, and found similar results: in a survey of 465 professionals working in business analytics (BA), user education level had no direct effect on IS success (BA tool use, satisfaction, and impact). Instead, education level had a positive effect on expertise with the BA tool, which in turn positively affected BA tool use. These results build upon those from HR, and suggest that education has an indirect effect on IS success, rather than a direct effect.

The revolutionary impact of business analytics on corporate strategy in modern firms is investigated in this research paper. Integrating analytics into strategic planning has become essential for gaining a competitive edge in a time of rapid technological innovation and data abundance. The significance of this study is in its capacity to clarify how data-driven decision-making can improve organizational performance and flexibility in a changing marketplace. The main goal of this study is to investigate the effects of employing business analytics in the development of corporate strategies, with a particular emphasis on how these processes affect decision-making and the overall efficacy of strategic initiatives. By analyzing these dynamics, the study hopes to offer firms looking to use analytics for strategic success practical insights. The scope of the study includes a thorough investigation of how business analytics impacts many aspects of corporate strategy, such as risk management, customer relationship management, and operational efficiency. The main issue discussed is the discrepancy between conventional strategic planning techniques and the requirement for a more data-centric strategy that can adapt to changing market conditions. The idea that better strategic results can result from the efficient application of business analytics forms the basis of the theoretical framework that directs this study. Using secondary data sources including case studies, industry reports, and scholarly literature, a qualitative research methodology is used to accomplish these goals. This methodology makes it easier to conduct a comprehensive analysis of current business analytics methods and knowledge in strategic situations. Organizations that successfully incorporate analytics into their strategic planning procedures report better performance metrics and increased decision-making capabilities, according to the main findings of the analysis. Potential biases in secondary data and the difficulty of extrapolating results across several businesses are drawbacks, though. In terms of theory and practice, this study has consequences. Theorists can better grasp how business analytics may transform strategic frameworks, and practitioners can learn about the most effective ways to put data-driven plans into reality. The findings of this study ultimately highlight how important it is for businesses to develop a strong data culture that values analytics as a fundamental part of their strategic decision-making procedures.

This study aims to examine the application of Business Analytics (BA) in organizations and explore how BA contributes to firms' productivity, along with the challenges faced during its implementation. The study employed a qualitative approach, collecting data from journal articles and semi-structured interviews with senior executives to explore how organizations incorporate BA into their strategies to enhance performance. To understand the best practices in BA, the study uses thematic analysis to identify recurring patterns. The study established that organizations with high maturity levels in BA adoption experience higher benefits in terms of competition, especially in cases where analytics are in tandem with strategic plans. However, challenges persist, such as data privacy concerns, integration problems, and algorithm issues, which continue to limit efficient use. Finally, the study offers recommendations to contribute to the body of knowledge and provides guidance to organizations on how best to increase the strategic application of BA.

Tourism is one of the most impacted sectors of the economy caused by the COVID-19 pandemic consequences. The transformation of the tourism industry has conditioned new tourism trends and influenced the development of nature-based tourism. The research of the tourism market shows changes in consumer behaviour and motives of tourism movements. The meeting with the destination value chain where they travel is extremely important for modern tourists. The destination as a ʻvalue chainʼ consists of related activities making it recognizable and more competitive in the tourism market. The value chain assessment is an indicator of tourists’ satisfaction degree with the provided and used service, i.e. the tourism destination ʻfinalʼ product. The application of information technologies has a key role in the tourists’ assessment of the value chain and the destination selection. The future tourism research will be based on the application of BD, BD analytics and business analytics.

Data Science and Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications.

The objective of the research was to verify the existence of the mediating role of Transaction Cost Management (TCM) in the relationship between Business Analytics (BA) and Collaborative Advantage (CA). Based on a quantitative approach, it was found that there is a partial mediation exercised by the TCM in the relationship between BA and CA. In addition, the results showed that the tested model was able to explain 69,30% of the CA variation. The article goes on to deepen the theoretical relationships between the variables that make up the research model, cooperating both for the evolution of the emerging field in BA, and for a reflection on the critical success factors of the organizational alliances. Furthermore, based on the proposed model, it is possible to revisit the Transaction Costs Theory, discussing its assumptions in the current scenario marked by the high production of data and information in corporations, suggesting that business analytics is a directly influencing element in management of transaction costs.

New heuristics for the RCPSP with multiple overlapping modes

Healthcare systems worldwide are buckling under the pressures of rising costs, increases in patient demand and the complexity of chronic disease management. It is in this context that data-driven decision-making (DDM) backed by business analytics (BA) have become a critical enabling factor for efficiency, precision, and better patient outcomes. This paper investigates the role of BA in improving healthcare outcomes by means of a systematic review of the recent empirical studies and a secondary analysis of quantitative datasets from sources worldwide, such as the World Health Organization (WHO), the Organization for Economic Co-operation and Development (OECD) and the Centers for Medicare & Medicaid Services (CMS). The study combines descriptive analytics, predictive analytics, and prescriptive analytics models to determine their contribution to operational efficiency and cost reduction and clinical effectiveness. Findings include that predictive analytics can lead to a saving of up to 20% in hospital readmission, descriptive dashboards can enhance resource allocation and increase staff productivity, and prescriptive analytics can be used to optimize treatment pathways, leading to measurable improvements in patient satisfaction and clinical outcomes. Unlike previous studies that tend to separate the clinical/operational benefits, this paper introduces a holistic framework of the linkages between BA adoption and strategic decision-making at organizational and policy levels. The novelty of this study is the cross-functional nature of the approach, which emphasizes the synergy of clinical, managerial, and policy decisions which are grounded in analytics. Ultimately, the results highlight the importance of healthcare organizations investing in the capabilities of BA and aligning the data strategy with patient-centered care goals, all while achieving sustainable improvements in both outcomes and efficiency.

PurposeThis study develops a comprehensive taxonomy of the business analytics (BA) discipline, uncovering its intellectual core and revealing its evolution over the past 12 years.Design/methodology/approachUsing stakeholder-driven identity formation theory, this study explores how organizational identity emerges through stakeholder negotiations. It investigates how top scholarly journals shape the BA discipline’s image and influence perceptions. High-quality articles from top journals listed by the Australian Business Deans Council are analyzed using latent Dirichlet allocation (LDA), a natural language processing and topic modeling method.FindingsThe study outlines key research areas identified as analytics methods, marketing, finance, operations and decision support analytics, along with 12 subareas. An analysis of the top 100 topics reveals prevalent research themes, showcasing the breadth of BA. A 12-year time-series review shows initial growth followed by maturation across most areas, except for decision support analytics, which maintained steady growth. These findings provide empirical evidence of BA’s development as a distinct discipline, highlighting its interdisciplinary nature and evolving research focus.Originality/valueThis study presents the first comprehensive, data-driven taxonomy of BA research, distilling the intellectual core into five key areas and 12 subareas, while identifying 100 supporting themes. It extends the stakeholders’ approach to identity development theory in the context of BA, providing empirical support for discussions on the field’s identity and diversity. The findings offer valuable insights for scholars, industries, managers and professionals, guiding curriculum development, research directions and practical applications of BA.

The intersection of the Internet of Things (IoT) and wearable devices is transforming patient monitoring because they allow the provision of data-driven, uninterrupted, and remote healthcare services. The paper examines real-time health management and decision-making clinical and operational situations on how these technologies, combined with business analytics frameworks, can improve real-time health management and decision-making. Carrying out a synthesis of the current breakthroughs and large-scale deployments throughout the worldwide health system, the paper explores the operational synergy of smart medical devices and analytical platforms in care outcome optimization, response time decrease, and resource utilization. This study employs a data-driven observational study design to analyze high-frequency physiological measurements recorded by wearable sensors and connected medical devices in a range of chronic and acute care conditions. business analytics tools are applied to the collected data in order to isolate actionable business insights, spot anomalies, and enable predictive risk modelling. The study methodology focuses on real-time data capture, patient stratification and cross-sectional system performance measures assessment. The results indicate a considerable positive shift in early intervention abilities, patient adherence, and operational effectiveness, proving that real-time analytics based on IoT-connected wearables can decrease the number of hospitalizations and fine-tune treatment plans. Another influential obstacle pointed out in the study is data privacy, device interoperability, and the digital divide. The study has its contribution to the emerging domain of healthcare informatics, as it presented a scalable and replicable model of implementing IoT and analytics in patient monitoring. It meets the existing literature gaps by uniting the technological, clinical, and business standpoints and offering practical insights that can be used by health IT leaders, policymakers, and clinicians who want to transform the care delivery models using smart and data-driven solutions.