Business Analytics Applications Research Articles

The Role of Analytics Solutions in Revolutionizing Procurement Efficiency

The Role of Analytics Solutions in Revolutionizing Procurement Efficiency

Problem resolution with business analytics: a task-technology fit perspective

PurposeThe study explores the extent to which business analytics can address business problems using the task-technology fit theory.Design/methodology/approachThe qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.FindingsThe study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.Originality/valueExtant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

“The integration of Business Analytics in Smart Cities”

The integration of business analytics in smart cities represents a paradigm shift in urban development, leveraging data-driven insights to enhance efficiency, sustainability, and quality of life. This paper explores the significance of business analytics in the context of smart cities, aiming to understand its implications for urban planning, resource allocation, and decision-making processes. Through a comprehensive review of existing literature and case studies, the paper examines the diverse applications of business analytics in smart city initiatives, ranging from optimizing transportation systems to improving public safety and infrastructure management. Despite the potential benefits, challenges such as data privacy concerns, skills gaps, and organizational barriers pose significant obstacles to effective implementation. By analysing successful integration strategies and identifying key lessons learned, this paper offers insights and recommendations for policymakers, urban planners, and other stakeholders to harness the full potential of business analytics in shaping the cities of tomorrow.

Retail Platform Analytics: Practice, Literature, and Future Research

The explosive growth of retail platforms over the past decade has resulted in a significant amount of customer and seller data that can be leveraged for advanced business analytics. As a result, the management of retail platforms with business analytics capabilities has garnered increased attention in the field of operations management. Despite the recognition of the importance of business analytics techniques for retail platforms, a systematic study of their operations is lacking in the literature. Based on our observations of the industrial practice and understanding of the academic literature, we attempt to address this gap by proposing a framework that broadly categorizes retail platform management into three key themes: demand-side management, supply-side management, and matching. For each theme, we identify critical topics, discuss the current practices of platforms, and review relevant literature. We also propose future research questions with directions for the initial modeling and solution strategy, together with applicable data sources and potential insights. At last, to facilitate future research, we provide a roadmap and datasets for further exploration of business analytics applications of retail platforms. Overall, this paper lays a strong foundation for researchers to delve deeper into the exciting and constantly evolving field of retail platform analytics.

HARNESSING BUSINESS ANALYTICS FOR GAINING COMPETITIVE ADVANTAGE IN EMERGING MARKETS: A SYSTEMATIC REVIEW OF APPROACHES AND OUTCOMES

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.

Reviewing business analytics in healthcare management: USA and African perspectives

This comprehensive review explores the application and impact of business analytics in healthcare management, focusing on the distinctive perspectives of the United States and Africa. In an era where data-driven decision-making is paramount, healthcare organizations globally are increasingly turning to business analytics to enhance efficiency, improve patient outcomes, and optimize resource allocation. The review begins by examining the state of business analytics in healthcare management within the United States, a pioneer in adopting advanced analytics solutions. The analysis encompasses the utilization of predictive modeling, data mining, and artificial intelligence to streamline operations, enhance clinical decision-making, and improve patient care. Case studies and success stories from leading healthcare institutions in the USA illustrate the transformative power of business analytics in areas such as patient risk stratification, resource optimization, and population health management. Contrastingly, the review delves into the unique challenges and opportunities that characterize the adoption of business analytics in healthcare across the African continent. Despite facing resource constraints and varying levels of technological infrastructure, African nations are increasingly recognizing the potential of analytics to address pressing healthcare challenges. The discussion encompasses initiatives and collaborations aimed at leveraging data analytics to improve healthcare delivery, disease surveillance, and resource management in the African context. The comparative analysis between the USA and Africa highlights the diversity of approaches and contexts in implementing business analytics in healthcare management. Factors such as data accessibility, infrastructure development, and cultural considerations emerge as critical determinants shaping the successful integration of analytics tools in healthcare decision-making. The review underscores the pivotal role of business analytics in reshaping healthcare management practices, while acknowledging the need for tailored strategies that align with the specific challenges and opportunities inherent in the USA and African healthcare landscapes. This study provides valuable insights for policymakers, healthcare administrators, and researchers seeking to navigate the evolving landscape of healthcare analytics on both a global and regional scale.

Business Analytics: Emerging Practice and Research Issues in the Health Insurance Industry

This paper summarizes emerging practice and research issues in the health insurance industry. We provide an industry overview, epitomize business analytics applications, and outline current and emerging problems of interest to key stakeholders and researchers in information systems, operations management, and healthcare management. Specifically, we focus on three areas of research that are of importance to the health insurance industry as well as the healthcare system: (i) management and evaluation of intervention programs, (ii) creation of effective provider networks, and (iii) emerging issues such as the impact of a pandemic, patient communication and steering, and technological innovations in health care. We lay out brief details of how each of these problems can be formulated, where the data can be acquired (public and/or proprietary data sets), and provide guidance for solving them. For several of these problems, we provide models that span causal modeling, predictive modeling, and prescriptive modeling.

Driving Factors Affecting the Business Analytics Application from the Cross-Cultural Perspective: An Empirical Study

ABSTRACT Despite the growing recognition of business analytics (BA)‘s role in boosting the firm’s intellectual capital and subsequent competitiveness, some firms are still hesitant to adopt BA. To have a clear picture of what will lead to the serious consideration of BA as a business intelligence tool, we conducted confirmation factor analyses and used the structural equation model to determine what drives BA applications. Through a series of hypothesis testing, we discovered that security, ease of use, perceived risk, and IT capability became important determinants for BA applications. On the other hand, various costs associated with BA use and organizational resistance against BA use turned out to be non-deciding factors for BA applications. This paper is one of the first attempts to develop practical guidelines for the successful application of BA based on the empirical study of BA practices among US and Korean firms from a cross-cultural perspective.

The Application of Business Analytics for Performance Management: The Results of Some Exploratory Case Studies

The Application of Business Analytics for Performance Management: The Results of Some Exploratory Case Studies

Research on Barriers to Implementing Business Analytics in Organizations

Business Analytics has been widely applied to multiple aspects with a data-explosive background. People utilize data mining tools and models to guide marketing strategy, organization management, evaluate financial budgets, etc. However, what comes after its popularity is various implemental obstacles, this is why only a few companies have the capacity to run their own business analytics teams and adopt conclusions stemming from data and models into their organizations operation and decision-making. After reading and organizing previous related papers, and distilling and comparing the findings of different researchers this paper divides the barriers to the use of Business Analytics into six parts and summarizes the causes and possible effects of these barriers. In this context, barriers to implementing business analytics in organizations are introduced in dimensions of data quality, security and accessibility, analytical tools and knowledge, financial investment, human resources, analytics procedure, and organizational barriers. This article provides a basic overview of the barriers and may be useful to guide and improve the practical application of Business Analytics.

Leveraging HR Analytics using Business Intelligence and Analytics

Organizations are becoming more and more interested in human resources (HR) analytics. Businesses now have new perspectives on their employees thanks to the application of business intelligence (BI) and analytics in HR, which helps them make wiser decisions. The use of BI and analytics tools to leverage HR analytics is covered in this article. We look at how HR analytics may help in identifying talent, lowering employee turnover, and boosting engagement. We also go over the difficulties with HR analytics, including issues with data quality and privacy, and how to overcome them. Lastly, we give examples of companies who successfully applied HR analytics through utilizing BI and analytics tools, along with the effects it had on their business operations. Overall, this paper offers insights into the potential value of HR analytics as a tool for businesses aiming to enhance both their HR operations and overall business performance

A Review on Business Analytics: Definitions, Techniques, Applications and Challenges

Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the Big Data era, business analytics techniques have been changing and evolving rapidly. Therefore, this paper reviews the latest techniques and applications of business analytics based on the existing literature. Meanwhile, many problems and challenges are inevitable in the progress of business analytics. Therefore, this review also presents the current challenges faced by business analytics and open research directions that need further consideration. All the research papers were obtained from the Web of Science and Google Scholar databases and were filtered with several selection rules. This paper will help to provide important insights for researchers in the field of business analytics, as it presents the latest techniques, various applications and several directions for future research.

The application of business analytics in personalized customer experience

Purpose: The purpose of this publication is to present the applications of usage of business analytics in personalized customer experience. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The applications of business analytics in personalized customer experiences span data collection, segmentation, predictive analytics, and multi-channel personalization, delivering greater customer satisfaction, revenue, and competitive advantage. Achieving this transformation requires the use of various software solutions, including CRM systems, marketing automation tools, analytics platforms, predictive analytics software, and recommendation engines. Harnessing the power of data and analytics is the key to gaining a competitive edge in this customer-centric era. Businesses that tailor their offerings to each customer's unique preferences and needs can not only meet but exceed customer expectations, ensuring growth and long-term success. Ultimately, personalized customer experiences, driven by business analytics, are not merely a response to customer demands; they are the path to unlocking the full potential of customer relationships and establishing a strong position in the ever-evolving marketplace. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; customization, personalization. Category of the paper: literature review.

The application of business analytics in cost reduction

The application of business analytics in cost reduction

The application of business analytics in product customization and personalization

The application of business analytics in product customization and personalization

The application of business analytics in cost reduction

The application of business analytics in cost reduction

Consumer retail industry and profitability: The role of analytics

As the retail industry gets extensively competitive, the ability to optimize business processes while gratifying customer expectations has never been more essential. Consequently, managing and channelizing info to work to client delight & generate whole revenue is crucial to survive prosperously. In the circumstance of big show players globally and in America, info or perhaps rather big data analytics is now used for every phase of the show progression, tracking emerging items which are common, forecasting potential sales and demand through predictive simulation, optimizing product placements and offers by customer heat mapping, and several others. Alongside this specific, determining the customers are extremely prone to be sharp on certain merchandise sorts based on their prior actions, working out the easiest technique to target them through certain marketing initiatives, and finally working out what to advertise them following will be the point that forms the middle of data analytics. This particular statement will be the product of a descriptive exploration of yesteryear, present and future of the retail industry, and the application of business analytics in shaping suitable marketing methods.

Maintenance resource allocation – the business analytics usage in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the applications of usage of business analytics in maintenance resources allocation. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics with condition monitoring technologies heralds a transformative shift in maintenance resource allocation. Through the utilization of real-time data from sensors and monitoring devices, organizations can craft dynamic and adaptive maintenance strategies. These strategies, grounded in the current equipment condition, strike a nuanced balance between preventive and corrective actions. Business analytics enriches decision-making by factoring in critical elements such as equipment importance, operational impact, resource availability, and budget constraints. This integration instigates a paradigm shift, fostering proactive, adaptive, and efficient resource allocation, resulting in heightened asset reliability, diminished downtime, and amplified operational performance. The evolving synergy between business analytics and maintenance practices is becoming integral to the future of asset management and industrial operations. The diverse applications of business analytics in maintenance, as outlined in Table 1, underscore its versatility, encompassing predictive maintenance, condition monitoring, asset performance management, work order prioritization, resource optimization, cost-benefit analysis, inventory management, and performance metrics monitoring. Concurrently, the adoption of advanced software solutions in Industry 4.0 conditions, exemplified by IBM Maximo Asset Management, SAP Intelligent Asset Management, and Fiix CMMS, reflects a commitment to efficiency and innovation in maintenance resource allocation. Despite the substantial advantages, addressing challenges outlined in Table 4, including data quality, integration complexities, implementation costs, and skill development, is crucial. These challenges underscore the necessity for a strategic and holistic implementation approach that considers technology, personnel training, and organizational readiness. In essence, the evolution of maintenance resource allocation through business analytics signifies a data-driven revolution poised to optimize current operations and position organizations for sustained success amid rapid technological advancements and the transformations of Industry 4.0. Originality/value: Detailed analysis of business analytics in the case of maintenance resource allocation. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; maintenance resource allocation. Category of the paper: literature review.

PROADAPT: Proactive framework for adaptive partitioning for big data warehouses

Parallel DBMSs have become more and more mature and getting several success stories in the industry. This situation has been reached by powerful data partitioning and data allocation techniques and algorithms. By analyzing these findings closely, we figure out that they are quickly stressed by the workload changes, which represents the usual case of business analytics applications. To deal with this challenge in the context of big data warehouses, several studies proposed to move to another processing paradigm outside the DBMS realm such as Spark by proposing adaptive partitioning solutions to tackle the workload changes. The majority of approaches are offline and those that are online cause significant random disk I/O costs. This is because the correlation that may exist between jobs and data blocks read from the disk is not captured to refine the adaptive partitioning algorithms. This represents one of the major causes of providing high performance of dynamic workloads. To solve such limitations, we propose in this paper a proactive framework for query-aware adaptive partitioning (called PROADAPT) that uses an AI-inspired methodology that can be connected to any query optimizer managing partitioned data. This methodology uses a genetic algorithm to solve our formalized problem that considers the interaction that may exist among workload queries. PROADAPT intensively rewrites queries by exploiting dimension hierarchies to skip irrelevant data and then improves I/O performance. Different technical modules of our framework are discussed. Finally, we conduct intensive experiments on Postgres-XL and a Spark SQL parallel cluster to show the effectiveness and efficiency of our approach.

Business Analytics and Market Adaptation in the E-Commerce Industry in Nigeria

The study examined the impact of business analytics on market adaptation in the eCommerce industry in Nigeria. Using purposive sampling and random sampling techniques, a sample of 40 respondents from various eCommerce organizations was taken. The data collected were analyzed using the quantitative approach. Precisely, both correlation and ordinal regression analyses were applied. The result from the study shows that the most important aspect of business analytics that can have a significant impact on market adaptation is the area of data analysis and application of the result to decision-making in the organization. The study recommends the application of business analytics to eCommerce businesses if they want to improve their market adaptation.