Data Analytics Research Articles

Data-driven digital marketing and battery supply chain optimization in the battery-powered aircraft industry through case studies of Rolls-Royce’s ACCEL and Airbus's E-Fan X Projects

This paper investigates the interplay between data-driven digital marketing strategies and battery supply chain optimization in the battery-powered aircraft industry, with a focus on Rolls-Royce's ACCEL and Airbus's E-Fan X projects. As the electric aircraft market emerges, these companies face the dual challenge of promoting technological advancements to a skeptical audience while navigating complex supply chain requirements. The study explores how data analytics and targeted content marketing are employed to enhance stakeholder engagement, build consumer trust, and communicate the benefits of electric aviation. By leveraging digital platforms, Rolls-Royce and Airbus effectively highlight key aspects such as battery performance, sustainability, and the safety of electric propulsion, addressing both technical and consumer concerns. Concurrently, the research delves into the intricacies of battery supply chain optimization, examining the processes involved in sourcing high-energy-density batteries, ensuring quality control, and adhering to regulatory standards. Advanced supply chain management techniques, including predictive analytics and real-time monitoring, are analyzed for their role in streamlining procurement and manufacturing processes. The integration of these strategies within digital marketing campaigns demonstrates how each company positions itself as a leader in the industry, emphasizing technological innovation and reliability. This study aims to provide a comprehensive understanding of how digital marketing and supply chain optimization are not only critical for promoting the adoption of electric aircraft but also for overcoming market skepticism and ensuring operational efficiency. By presenting case studies of two pioneering projects, ACCEL and E-Fan X, the research highlights best practices and strategic approaches that can guide other companies in the industry. The findings underscore the importance of a synergistic approach where digital marketing is informed by supply chain realities, creating a transparent and trustworthy narrative for consumers and stakeholders. Ultimately, this paper contributes to the discourse on how emerging technologies in aviation can be successfully marketed and operationalized, offering insights into the effective use of digital marketing and supply chain management in the rapidly evolving electric aircraft sector.

Reimagining crisis management with an organizational learning framework

AbstractTechnological advancements have altered the landscape of crisis management, making many crises potentially preventable and controllable. Moreover, these technological advancements have provided opportunities for organizations to implement an informed learning approach in crisis responses. The interconnectedness and complementary aspects of three theories within crisis management, including the situational crisis communication theory's base crisis response strategies, image repair theory's corrective action, and discourse of renewal theory's organizational learning, were assessed and integrated into a proposed framework for understanding a comprehensive approach to crisis management. In this research, technology is regarded as both an asset for facilitating organizational learning (e.g., informed decision‐making through data analytics), and a potential source of challenges in crisis communication (e.g., Zoombombing and negative social amplification). The proposed framework aims to bridge proactive risk and reactive crisis management efforts through organizational learning. Findings from this 4 × 2 between‐subjects experimental design study explore the effectiveness of organizational learning to address crisis responsibility in an evolving technological landscape.

Cultural profiles evaluation and their impact on the performance of production systems in Colombian companies using data analytics

Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.

Conceptual framework for enhancing decision-making in higher education through data-driven governance

In an increasingly complex and competitive landscape, higher education institutions must leverage data-driven governance to enhance decision-making processes. This paper presents a conceptual framework aimed at integrating data analytics into the governance structures of higher education, facilitating informed decision-making, and improving institutional effectiveness. The framework emphasizes the importance of collecting, analyzing, and utilizing data across various dimensions, including academic performance, student engagement, financial management, and operational efficiency. By adopting a data-driven approach, institutions can identify trends, forecast outcomes, and make strategic decisions that align with their mission and goals. The study outlines key components of the framework, including data governance, analytics capabilities, stakeholder engagement, and institutional culture. Data governance ensures that data quality, integrity, and security are maintained, while analytics capabilities empower decision-makers with actionable insights. Engaging stakeholders—faculty, administrators, students, and external partners—fosters a collaborative environment where data-driven decisions are embraced and supported. Furthermore, the framework addresses the challenges institutions face when implementing data-driven governance, such as resistance to change, data silos, and inadequate technological infrastructure. Strategies for overcoming these challenges include investing in professional development, enhancing data literacy, and promoting a culture of continuous improvement. The framework also highlights the significance of ethical considerations and transparency in data usage, ensuring that decision-making processes are equitable and accountable. The findings indicate that by embracing data-driven governance, higher education institutions can enhance their responsiveness to changing demands, optimize resource allocation, and improve overall institutional performance. Ultimately, the conceptual framework serves as a guide for higher education leaders seeking to harness the power of data in their decision-making processes, fostering a proactive and adaptive institutional environment.

Data-driven diabetes management: a statistical assessment information system leveraging big data

ABSTRACT Amidst the escalating global burden of diabetes and its multifaceted complications, there arises an urgent need for robust healthcare information systems tailored to address the complexities of diabetic patient management. This paper introduces a novel statistical assessment healthcare information system engineered to meticulously analyze diabetes-related data by harnessing the power of big data technologies. Diabetes, affecting millions worldwide, exerts substantial strain on healthcare infrastructures, necessitating innovative solutions to optimize patient outcomes and treatment efficacy. Leveraging advancements in data processing and analytics alongside vast medical datasets, this study pioneers a statistical assessment model explicitly crafted for diabetes analysis within a big data paradigm. The model’s efficacy is rigorously evaluated using key performance metrics such as accuracy and F-measure, within the Hadoop framework. Remarkably, the results demonstrate a significant enhancement in performance compared to conventional methodologies, affirming the model’s potential to revolutionize the accuracy and efficiency of diabetes analysis within a big data milieu. Moving forward, further research avenues include refining the model’s algorithms to accommodate diverse data sources and expanding its applicability to encompass a broader spectrum of diabetes-related factors.

A conceptual framework for digital health marketing strategies to enhance public health outcomes in underserved communities

This paper proposes a conceptual framework for integrating digital health marketing strategies with public health initiatives to enhance healthcare access and outcomes in underserved communities. The framework focuses on leveraging digital marketing tools such as social media, mobile applications, email campaigns, and search engine optimization to disseminate critical health information, promote preventive measures, and engage communities in healthier behaviors. It addresses the persistent challenges faced by underserved populations, including limited healthcare access, low health literacy, and cultural barriers, by utilizing the accessibility and broad reach of digital channels. The proposed framework incorporates three core components: (1) Health Information Dissemination, which explores the use of targeted digital campaigns to provide culturally relevant and accessible health education; (2) Community Engagement, which leverages interactive platforms such as social media and mobile apps to foster participation in preventive healthcare initiatives and healthy lifestyle practices; and (3) Behavioral Change Promotion, which emphasizes using digital tools to influence health behaviors through personalized messaging, reminders, and virtual support groups. By integrating digital marketing strategies into public health programs, the framework aims to improve healthcare outcomes by increasing awareness of preventive measures, enhancing early diagnosis, and promoting disease management. It also underscores the role of data analytics in refining outreach efforts by identifying specific health needs, tracking engagement, and measuring the effectiveness of interventions. This conceptual framework holds promise for healthcare providers, policymakers, and digital marketers seeking to enhance public health outcomes in underserved communities. It provides a structured approach to combining the strengths of digital marketing with public health goals, creating more inclusive, accessible, and effective health interventions. Future research and case studies can build on this model to refine digital health marketing strategies and assess their long-term impact on public health in underserved populations.

Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns

This study explores innovative approaches to risk management in clinical research, focusing on the intricate balance between ethical standards, regulatory compliance, and intellectual property (IP) concerns. As clinical trials become increasingly complex and globalized, managing risks effectively while maintaining participant safety and upholding legal and ethical principles has become paramount. The study adopts a comprehensive review of emerging technologies, such as artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics, to assess their role in transforming risk management strategies. Key findings reveal that AI and predictive analytics significantly enhance real-time monitoring and the early identification of risks, while adaptive trial designs offer flexibility in managing unforeseen challenges during trials. Blockchain technology improves transparency and data integrity, ensuring secure and verifiable data transactions. However, the intersection of IP protections and equitable access to treatments presents ongoing ethical challenges, particularly in low-income regions. The study concludes that while technological innovations provide robust tools for mitigating risks, a balanced approach to regulatory frameworks and IP protections is essential. The study recommends increased collaboration between regulatory bodies, research institutions, and technology providers to harmonize global regulatory standards, ensuring both innovation and accessibility. Furthermore, it advocates for more flexible IP frameworks that address the tension between protecting innovations and ensuring equitable access to life-saving treatments.

Integrating FinOps in healthcare for optimized financial efficiency and enhanced care

The integration of Financial Operations (FinOps) in healthcare is increasingly recognized as a critical strategy for optimizing financial efficiency and enhancing patient care. This paper explores the principles and practices of FinOps, emphasizing its role in improving cost management, fostering financial transparency, and ensuring better resource allocation in healthcare organizations. By addressing traditional inefficiencies in healthcare financial management, FinOps promotes collaboration between finance and operations, enabling real-time financial decision-making that aligns with both operational goals and patient outcomes. Additionally, the paper outlines the challenges healthcare organizations face in managing rising costs and complex billing systems, and provides practical strategies for the successful implementation of FinOps, including the use of advanced technologies such as cloud-based platforms and data analytics tools. Ultimately, the adoption of FinOps not only enhances short-term financial efficiency but also contributes to the long-term sustainability and scalability of healthcare services.

A Systematic Literature Review on Knowledge Management in Healthcare: Best Practices and Future Directions

The role of knowledge management (KM) in improving the standard and effectiveness of healthcare is gaining more attention. However, because of the complexity and the sensitive nature of medical information, the use of knowledge management approaches in this industry faces unique challenges. In order to identify best practices, summarize current research on knowledge management (KM) in healthcare, and suggest future research paths, a comprehensive literature review was conducted. A detailed search was carried out across many important databases, including PubMed, Scopus, IEEE Xplore and Web of Science, in order to gather the relevant studies for the review. The review focused on studies published between 2010 and 2024 that specifically addressed KM practices within healthcare settings and adhered to the PRISMA guidelines for systematic reviews. The review found 50 papers that satisfied the requirements for inclusion. Significant findings illustrate how important organizational culture, leadership backing, and technology infrastructure are, to the effective use of knowledge management. Some of the most significant techniques in Knowledge Management that are emphasized are creating communities of practice, fusing KM systems with medical guidelines, and employing data analytics for knowledge creation. On the other side, challenges include hesitation to change and interoperability issues

The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions

Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex information and assess the potential benefits they may reap from successful implementation. The introduction defines big data and predictive analytics in the context of healthcare and outlines the paper's objectives. It further discusses the evolution of big data and analytics in healthcare, its key applications, and the challenges it presents. Additionally, we explore potential solutions and ethical considerations surrounding big data analytics in healthcare. Overall, this paper underscores the transformative power of big data and predictive analytics in revolutionizing U.S. healthcare delivery and improving patient outcomes.

Optimizing food and FMCG supply chains: A dual approach leveraging behavioral finance insights and big data analytics for strategic decision-making

Optimizing food and FMCG supply chains: A dual approach leveraging behavioral finance insights and big data analytics for strategic decision-making

Enhancing solar energy systems: The role of computational and automation techniques in achieving net zero by 2050

This review aims to show how computational and automation can be applied to optimize the solar power system toward net-zero emissions in 2050. It emphasizes the power of data analytics, machine learning and automated systems for optimizing business models and measuring the performance of solar technology. The role of solar power in decarbonizing our energy needs and lowering carbon emissions is discussed along the global climate agenda. It also discusses novel uses such as self-teaching robots for solar panel inspection and automated manufacturing to increase the efficiency and sustainability of solar power generation. Additionally, it addresses the need for sophisticated research tools such as computational fluid dynamics (CFD) and finite element analysis (FEA) to create new technologies in the solar market and ultimately meet the ambitious renewable energy targets of 2050.

Application of Internet of Things (IoT) on Monitoring EC and pH Values for Fertigation System

This paper introduces a novel Internet of Things (IoT) based fertigation control system designed to enhance traditional agricultural practices in Malaysia, particularly in rural areas. The system integrates a fertigation approach, combining fertilization and irrigation, with smart devices and a NodeMCU ESP32 microcontroller to automate the tracking of data from liquid fertilizer mixtures. By utilizing EC and pH sensors, the system accurately monitors the nutrient concentration and pH levels of the fertilizer mix, with data displayed on an IoT platform for real-time observation and control. This approach mitigates common issues such as overfertilization and inefficient nutrient use, offering significant improvements over manual data collection methods. The system's performance was validated, showing successful monitoring and control of the fertilization process, leading to enhanced plant health and crop quality. Key findings indicate that the system's integration with IoT technology provides a more efficient, automated solution for managing fertilizer applications, reducing labor and time while ensuring optimal plant nutrient levels. The study also highlights the potential for future development, including the integration of additional sensors and advanced data analytics to optimize fertigation practices further. The implementation of this IoT-based system represents a significant advancement towards sustainable agriculture, offering practical benefits for Malaysian farmers and setting a foundation for further innovation in fertigation technology.

Advanced Technologies as a Solution for Transportation Challenges in Developing Megacities: The Positive Case of China

Rapid urbanization in developing megacities like Beijing has led to pressing transportation challenges, including severe traffic congestion, increased emissions, and poor road safety. This paper examines how advanced software systems, new technologies, and innovative engineering solutions adopted in Beijing can serve as models for sustainable transportation improvements in other emerging megacities. Intelligent transportation systems, advanced traffic control platforms, autonomous vehicle networks, smart mobility apps, big data analytics, and cloud computing have positively impacted congestion, emissions, and safety in Beijing. Developing cities must adopt and localize such software-driven tech innovations within collaborative frameworks to make measurable progress on intricate transportation issues.

Analyzing financial analysts' role in business optimization and advanced data analytics

This paper explores the evolving role of financial analysts in business optimization, focusing on their increasing reliance on advanced data analytics to drive strategic decision-making. It examines the historical shift from traditional financial analysis to more data-centric responsibilities, highlighting how modern financial analysts contribute to operational efficiency, cost reduction, and revenue growth through data-driven insights. Integrating advanced tools such as artificial intelligence, machine learning, and predictive analytics has enabled financial analysts to interpret complex datasets, optimize business processes, and improve market positioning. However, adopting these technologies comes with challenges, including a steep learning curve, data integration difficulties, and ethical concerns related to data privacy and bias in AI algorithms. This paper also discusses future trends in financial analysis, emphasizing the growing importance of real-time data analysis and automation in business optimization. The paper concludes by stressing the need for financial analysts to continually adapt to the rapidly evolving technological landscape to effectively shape corporate strategy.

AI and blockchain technology for secure and transparent financial transactions

The integration of Artificial Intelligence (AI) and blockchain technology presents a transformative approach to enhancing the security and transparency of financial transactions. Blockchain’s decentralized and immutable ledger system ensures that transaction records are tamper-proof and transparent, significantly reducing the risk of fraud and errors. AI, with its advanced data analytics and machine learning capabilities, further augments this ecosystem by providing real-time monitoring, predictive analytics, and automated decision-making processes. Together, these technologies enable more efficient and secure financial operations, from cross-border payments to trade finance and asset management. This paper explores the synergistic potential of AI and blockchain in revolutionizing the financial sector, highlighting key applications, benefits, and challenges. By leveraging the strengths of both technologies, financial institutions can achieve unprecedented levels of security, efficiency, and trust in their operations.

Assessing Fintech Awareness and Knowledge Among Students at Selected Universities in Somalia

This study examines financial technology awareness among students at four Mogadishu universities (Jamhuriya University of Science and Technology, Banadir University, SIMAD University, and the University of Somalia), using a cross-sectional descriptive research design. The study utilized a structured questionnaire to evaluate 231 students’ understanding and familiarity with fintech concepts, focusing on demographic factors and perceived curriculum gaps. The study’s participants were diverse in gender, age, institution, major, and year of study. The majority were male (63%), with 78% aged between 20 and 25. The study reveals that students’ familiarity and understanding of fintech concepts like blockchain, smart contracts, neo- banking, crowdfunding, cloud computing, big data analytics, robo-advisors, and AI are low. This highlights significant gaps in students’ knowledge of critical areas digital finance, e-payments or e-banking. The study shows a moderate correlation between demographic factors and students’ fintech awareness, with 13.9% of variation explained by these factors. The overall model is statistically significant (F = 7.265, P < 0.001), indicating that these factors collectively impact students’ knowledge and awareness of fintech. Gender, age, and university attended do not significantly influence fintech awareness, suggesting they are not major determinants. However, the study reveals that students’ awareness of fintech increases as they progress through their academic years, with certain majors showing higher levels of awareness. The study recommends universities incorporate fintech topics into their courses, develop accessible options, invest in resources, collaborate with fintech companies, strengthen industry partnerships, and offer cross-disciplinary electives.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

A sales force effectiveness framework for enhancing healthcare access through pharmaceutical sales and training programs

This paper introduces a conceptual framework aimed at enhancing sales force effectiveness within the pharmaceutical industry, specifically focusing on improving healthcare access through optimized sales training programs and customer relationship management (CRM) systems. The proposed framework recognizes the critical role that pharmaceutical sales teams play in facilitating healthcare delivery, particularly during public health crises. It emphasizes the integration of effective training strategies and advanced CRM tools to enhance engagement with healthcare professionals, thereby improving product distribution and patient outcomes. The framework comprises three essential components Sales Force Training Enhancement, which focuses on developing targeted training programs that equip sales representatives with the necessary skills and knowledge to effectively communicate the value of pharmaceutical products to healthcare providers. This includes understanding clinical data, addressing healthcare providers’ concerns, and fostering a consultative selling approach. Secondly, CRM System Implementation, which underscores the significance of adopting robust CRM systems that streamline interactions between sales teams and healthcare professionals. By leveraging data analytics and customer insights, pharmaceutical companies can tailor their approach to meet the specific needs of healthcare providers, thereby enhancing relationship-building and communication. Thirdly, engagement and Outcome Measurement, which highlights the importance of measuring engagement levels and patient outcomes resulting from effective sales strategies. This component advocates for the use of key performance indicators (KPIs) to assess the impact of training and CRM initiatives on product distribution and healthcare access. The framework ultimately aims to foster a more collaborative environment between pharmaceutical sales teams and healthcare professionals, ensuring that patients receive timely access to essential medications and services. By focusing on the strategic integration of training and CRM systems, this framework presents a comprehensive approach to improving sales force effectiveness in the pharmaceutical sector. Future research and practical applications of this framework can further explore its implications for enhancing healthcare access, particularly in times of public health challenges.

Research on the Legal Risks of Digital Currency in the Big Data Environment

With the widespread application and growth of blockchain technology, cloud computing, and big data analytics in the financial industry, digital currencies have emerged. However, relevant regulations specifically targeting digital currencies have not been officially promulgated yet. In the process of promoting digital currencies, legal risks such as unclear boundaries of compensation liability, unclear ownership, potential privacy breaches, and financial regulatory challenges still need to be overcome. Therefore, it is essential to prioritize the prevention and management of legal risks associated with digital currencies in the big data environment, ensuring the smooth circulation and use of digital legal currencies across regions and promoting the internationalization and legalization of China’s digital legal currencies.