Adoption Of Big Data Research Articles

The role of big data in transforming financial management in U.S. healthcare: A conceptual framework

This paper explores the transformative role of big data in financial management within the U.S. healthcare sector, highlighting its capacity to address long-standing financial inefficiencies, enhance decision-making, and improve regulatory compliance. Big data technologies, such as predictive analytics and real-time financial reporting, provide healthcare institutions with the tools to forecast financial trends, optimize budgeting, and mitigate risks. Despite the significant benefits, the adoption of big data in healthcare finance faces challenges, including data privacy concerns, regulatory complexities, and technological limitations. The paper examines these barriers and enablers, offering strategic recommendations for healthcare institutions and policymakers to optimize the use of big data. By improving data governance, upgrading infrastructure, and fostering collaboration across departments, healthcare organizations can maximize the impact of big data on their financial operations. The review concludes with a call for continued policy support and financial incentives to encourage the broader adoption of big data in healthcare finance, ultimately promoting cost efficiency and operational resilience in the sector. Keywords: Big Data, Healthcare Financial Management, Predictive Analytics, Data Governance, Regulatory Compliance, Financial Decision-Making.

Antecedents and Outcomes of Big Data Adoption in Supply Chain: A Meta-Analytic Investigation

This paper aims to provide a comprehensive understanding of the big data–performance relationship based on the existing empirical evidence. Using a meta-analysis approach, big data adoption (BDA) related 446 effect sizes reported in 133 prior empirical studies were gathered from 118848 informants in more than 30 countries. Results confirm ten significant antecedents and eight outcomes of BDA based on identified literature. We further estimate the heterogeneity based on subgroup analysis by considering two types of moderators as (a) economic regions (developed vs developing), and (b) type of industry. We find that organizations in developed countries adopt big data largely due to environmental and organizational factors. Further, developed countries can harness the potential of big data for better performance (e.g., supply chain integration, collaboration, customer relationship management, and innovation). This study provides multifaceted insights for practitioners and academia alike regarding the use of big data.

Research on Recruitment Management Innovation of Chinese Budget Hotel Chain Enterprises under the Big Data Environment

Abstract: This study delves into the transformative impacts of big data on recruitment management within China's economy hotel chains, emphasizing the urgency and relevance of this research theme in light of the swift digitization witnessed in the hospitality sector. By scrutinizing the current scenario of China's economy hotel industry, the paper underscores the infusion of big data to enhance recruitment processes, thereby addressing prevalent challenges such as inefficiencies and skill mismatches. A thorough analysis reveals that the prevailing recruitment framework within economy hotel chains fails to adequately align with evolving market dynamics and technological progressions. Consequently, innovative strategies leveraging big data analytics and artificial intelligence are proposed to refine recruitment practices, aiming for heightened precision in talent acquisition and process optimization. The research concludes by highlighting the substantial business value and societal ramifications associated with the adoption of big data in recruitment, emphasizing enhancements in operational efficiency, competitive positioning, and contributions to the broader economic landscape of China's hospitality industry.

The AI and Big Data Equitable Transformation Theory (ABETT): A Pathway to Inclusive Development and Technological Empowerment

The rapid advancement of artificial intelligence (AI) and big data technology has created new potential for economic growth, social development, and creativity. However, their incorporation into local economies presents issues regarding ethical governance, skill development, access to infrastructure, and equitable benefit sharing. This paper presents the AI and Big Data Equitable Transformation Theory (ABETT), a comprehensive framework for guiding the responsible and inclusive use of AI and big data in various scenarios. The ABETT model has six basic constructs: policy and ethical governance, skills development and education, scalable and equitable infrastructure access, collaborative innovation and knowledge exchange, sustainable AI and data for development, and economic growth and competitiveness. The ABETT framework was created, validated, and enhanced using a qualitative research design that included a systematic literature assessment, expert interviews, and case study analysis. The results support the model's relevance and applicability, illustrating how its elements work together to generate ethical and sustainable technological innovation. Comparisons with previous theories show that ABETT adds value by proposing a comprehensive and customizable pathway for AI and big data adoption that emphasizes collaboration, equity, and sustainability. The paper discusses the model's policy and practical implications, limitations, and future research recommendations. ABETT provides a practical roadmap for stakeholders who want to use AI and big data to promote inclusive growth, sustainable development, and equitable technical advancement.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

KEY DETERMINANTS OF GOVERNMENT AUDITOR’S BEHAVIOUR TO ADOPT BIG DATA ANALYTICS IN AUDIT PRACTICE

<p><strong>Background</strong>: Despite the increasing adoption of big data and Big Data Analytics (BDA) in the business world, the accounting and audit profession is considered slow in taking advantage of these innovative developments. Government auditors play an important role indirectly in improving good governance. The use of BDA in government audits has been carried out in Indonesia since 2020. However, because its use is not mandatory, not all government auditors use the application.</p><p><strong>Objective</strong>: This research investigates the key determinants that drive government auditors' behaviour to adopt BDA technology in audit practice, especially in the Indonesian context, with a focus on the Indonesian Supreme Audit Agency (SAA).</p><p><strong>Research Method: </strong>This study was conducted through a questionnaire survey involving 126 government auditors in Indonesia. Meanwhile, research data was analyzed using the Structural Equation Modelling - Partial Least Square method (SEM-PLS).</p><p><strong>Research Results</strong>: Research findings show that performance expectancy and effort expectancy have a direct influence on auditors' intentions to use BDA techniques at SAA. Meanwhile, the auditor's intention to use BDA techniques has a significant effect on Actual Use.</p><p><strong>Originality/Novelty of Research: </strong>This research addresses the gap about<strong> </strong>factors that encourage auditors' behaviour to adopt BDA technology in audit practice, especially in public sector context.</p><p> </p>

Big Data Adoption: Perspectives, Realities, and Myths - An Outer View in Academic Libraries

It is clear that the deluge of information has forced the current civilization into the era of Big Data adoption given the information's quick development and the widespread use of information gadgets. It has been noted that technology is becoming more and more essential to many facets of human existence, which makes this dependence relevant and important. A university's academic library is one of its three pillars. It performs vital functions such as teaching and research services and serves as the hub for auxiliary instruction, information sharing, academic activities, and cultural inheritance. All of these functions depend heavily on the ever-growing amount of data, which is sometimes referred to as "Big Data adoption." This study addresses the concerning connection between the use of Big Data and operations in many areas of human existence and offers a comprehensive explanation of Big Data and its significance in academic libraries. Aside from this, the study clarifies the general ethics of Big Data, its features, advantages, tools, the role of librarians, and the causes of the important problems related to big data adoption and the perspectives in academic libraries. Thus, the adoption of big data continues to be an intriguing new area in both science and technology. Therefore, greater familiarization and demystification of Big Data adoption is still necessary, even with the development of technology and availability of free knowledge. It will require more advanced computing abilities to comprehend and utilize it to its full potential.

BIG DATA, ARTIFICIAL INTELLIGENCE, AND MANAGEMENT ACCOUNTANT: A GLOBAL PERSPECTIVE

This study aims to objectively explored the relevance of big data issues that have developed in the professional world to the best practices of the management accounting profession. The conceptual framework was developed to become the frame for consideration of making structured designs on artificial intelligence issues. Using data sources derived from literature studies and conducting various reviews of articles related to this interesting topic, conclusions are generated that refer to the implications of management accountant best practices. This study finds that the concept of management accountants is strongly influenced by the adoption of Big Data in the companies. Furthermore, we specifically define and present strategic steps that can suggest management accountants can carry out best practices in accordance with professional programs that have become an important part of practice. This study contributes to the development of the best practice of management accountants where Big Data is the center of attention that cannot be separated from their professional practice so that it is possible to adjust the practice of management accountants that generate added value. To the best authors knowledge, this is the first study to seeks and explores the Big Data and Artificial Intellegence in the management accountant profession from global perspectives. The study provides some deep insight to the accountant management global more take care for their sustainable profession in the long wave of digitalisation.

Digital Transformation in HRM: Leveraging AI and Big Data for Employee Engagement and Retention

The Jordanian banking sector is now seeing a significant change in the use of artificial intelligence (AI) and big data in human resource management (HRM). To assess the influence of this era on employee engagement and retention, 300 employees of Jordanian bank were the sample of the study. Using partial least squares (PLS) structural equation modeling, we have a look at investigating how pride mediates and organizational culture modifies those effects. The study found a favorable correlation between AI adoption (r = 0.65, p < 0.01) and Big Data use (r = 0.58, p < 0.05) and work satisfaction, organizational culture, savings technology, staff engagement, and positive connections with Jordanian savings banks. Furthermore, regression research demonstrates that AI adoption and usage of Big Data are predictive. AI and big data can optimize HRM practices and provide insights, leading to higher employee satisfaction (r = 0.72, p < 0.01). This can enhance competitive advantage and strategy in the digital age.

Pemanfaatan Big Data oleh Petani Milenial Kota Bandung: Tinjauan Teori Difusi Inovasi

Utilization of Big Data in agricultural practices has become a crucial topic, particularly for millennial farmers in Bandung City. Modern agriculture requires swift and precise access to information to enhance efficiency and productivity. This research aims to explore how the utilization of Big Data influences millennial agricultural practices in Bandung City and to provide recommendations for improving its usage in the agricultural sector. The study employs a qualitative approach with in-depth interviews and participatory observation of active millennial farmers in Bandung City. The analysis reveals that the adoption of Big Data, viewed through the lens of Innovation Diffusion Theory, strengthens communication among farmers, facilitates the formation of cooperatives based on available data, expedites decision-making processes, and aids in optimizing resource utilization. Strengthening technological infrastructure, enhancing training and education, providing financial support, fostering partnerships, and advocating supportive policies can enhance the utilization of Big Data, thus positively impacting millennial agriculture in Bandung City.

Big Data Adoption in the Chinese Construction Industry: Status Quo, Drivers, Challenges, and Strategies

Big Data Adoption in the Chinese Construction Industry: Status Quo, Drivers, Challenges, and Strategies

Factors influencing the adoption of big data in libraries: a systematic literature review of peer-reviewed articles from 2013 to 2023

PurposeThe purpose of this study are to identify the factors influencing the adoption of big data in libraries, determine the challenges causing the hindrance of big data implementation and reveal the best practices for the efficient adoption of big data in libraries.Design/methodology/approachA systematic literature review was applied to address the objectives of the study. Twenty-two studies published in peer-reviewed journals were selected to conduct the study.FindingsThe findings of the study revealed that decision-making, service enhancement, professional development and preservation factors influenced the adoption of big data technologies in libraries. The study also displayed that challenges of infrastructure, technical skills, data management and legal considerations caused barriers to the adoption of big data in libraries. Results also revealed that training and professional development, guidelines and policies establishment, leadership and strategic planning and resource allocation proved fruitful in the efficient adoption of big data applications in libraries.Originality/valueThe study offers theoretical implications for future investigators through the provision of innovative literature on the factors, challenges and best practices associated with big data in the context of librarianship. The study has also provided practical implications for management bodies by offering guidelines for the successful adoption of big data in libraries.

DATA-DRIVEN TRANSFORMATION: OPTIMIZING ENTERPRISE FINANCIAL MANAGEMENT AND DECISION-MAKING WITH BIG DATA

This study explores the transformative impact of big data on enterprise financial management, highlighting significant improvements in decision-making efficiency, data quality, and risk management capabilities. Through a mixed-methods approach that includes meta-analysis, surveys, interviews, and case studies, the research reveals a substantial increase in decision-making efficiency, better integration of diverse data sources, and more accurate financial reporting. Practical benefits such as reduced data processing times and improved credit risk assessments are identified, alongside challenges like the skill gap in data science, cultural resistance, and technical difficulties in integrating new technologies with legacy systems. Addressing these challenges requires strategic leadership, continuous training, and investment in scalable infrastructure. Despite these hurdles, the long-term advantages of big data adoption, including enhanced financial reporting and resource allocation, underscore its value in driving organizational performance and competitiveness. This study provides empirical evidence and practical insights, offering a valuable foundation for organizations aiming to leverage big data in their financial management practices.

Understanding Big Data Adoption and its Impact on Decision-Making Quality: A Perspective from Public Sector IT Managers in Oman

Understanding Big Data Adoption and its Impact on Decision-Making Quality: A Perspective from Public Sector IT Managers in Oman

Institutional pressure and responsible innovation: how big data analytics adoption drives manufacturing SMEs toward competitiveness

Purpose This study aims to examine the influence of regulatory framework (RF), market dynamism (MDY), competitive intensity (CIN) and environmental public concern (EPC) on responsible innovation (RI) in the manufacturing SMEs of a developing country. Furthermore, the study examines the influence of RI on sustainable competitive advantage (SCA). Moreover, big data adoption (BDA) is used as a moderator between RI and SCA. Design/methodology/approach The study’s hypotheses are evaluated using the structural equation modeling methodology. The study collected data from a sample of 487 owners/managers of manufacturing SMEs using simple random sampling technique. Findings The results explain that RF, MDY, CIN and EPC significantly determine RI. Moreover, RI significantly determines SCA. Besides, BDA significantly moderates between RI and SCA. Research limitations/implications For manufacturing SMEs to obtain SCA, RI is essential, and BDA is crucial to meet changing consumer demands for environmentally friendly products. With more environment-friendly initiatives, the natural environment will become more sustainable. Moreover, this research offers a comprehensive perspective to the public regarding the extent to which manufacturing enterprises are adopting RI and BDA. Originality/value The majority of earlier research on RI and BDA has been carried out in the context of large businesses. Institutional theory was used to look into the drivers of RI. Furthermore, BDA is used as moderating variable between RI and SCA.

The Influencing Factor of Big Data Adoption for Indonesia Open Government

The development of digital technology in the industrial revolution 4.0 has made all aspects change, including in the field of government. This is a pressure and challenge for public organizations, as well as an opportunity to reform and adapt through Open Government. Open Government is the starting point of the Big Data era along with the Open Data process as a direct implication of openness in government. Big Data is one of the main elements of Open Government required to be adopted in government business processes. Therefore, the readiness of public organizations in adoption is an important thing to be followed up. This study aims to deliver a literature review the influencing factor of organizations readiness for Big Data adoption with the TOE (Technology, Organization, and Environment) conceptual framework. From the literature review, it is known that for the context of technology, several factors, including advantage, compatibility, complexity, scalability, and information security technology infrastructure influence the readiness in Big Data adoption. From the organizational context, the top management support, financial, human resources are also concluded to have a positive influence. Finally, from the environment point of view, the availability of laws and regulations and the focus on public services are external support systems for Big Data adoption.

Big Data and its Impact on Demand-Driven Material Requirements Planning

Integrating Big Data into Demand-Driven Material Requirements Planning (DDMRP) has become a game- changer in supply chain management. This study explores Big Data’s significant impact in the context of DDMRP. Big Data, sourced from various channels such as online transactions, sensor data, social interactions, and more, can transform demand forecasting, inventory management, and overall supply chain optimization. By allowing real-time data analysis and predictive modeling, organizations can make informed and agile decisions that drive cost reduction, inventory optimization, and improved customer service. However, implementing Big Data in DDMRP has its challenges, including data security and the need for advanced analytics capabilities. This research delves into the application, benefits, challenges, and impact of Big Data implementation in DDMRP. The study provides valuable insights for organizations seeking to leverage their potential. The findings underscore the significance of Big Data in reshaping supply chain strategies and enhancing the responsiveness of modern businesses in a dynamic market environment. The uniqueness of this paper lies in examining how Big Data, sourced from various channels, can revolutionize demand forecasting, inventory management, and overall supply chain optimization. The research also highlights the challenges of Big Data adoption in DDMRP, including data security and the need for advanced analytics capabilities.

Adoption and use factors of artificial intelligence and big data by citizens

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant.

The Integration of Sustainable Technology and Big Data Analytics in Saudi Arabian SMEs: A Path to Improved Business Performance

Big data analytics technology offers significant opportunities for innovation and performance improvement for small- and medium-sized enterprises (SMEs) operating in competitive environments. However, reaping these benefits requires the adoption of such technologies by SMEs. This study investigates the factors influencing the adoption of big data and analytics in Saudi Arabian SMEs in the service and manufacturing sectors, with a particular focus on the role of facilitating sustainable technology in enabling sustainable business performance. Data were collected from managers of SMEs in Saudi Arabia using a quantitative method. The proposed hypotheses were tested using structural equation modeling with SmartPLS 4.0. The findings reveal that big data security and management support significantly influence the perceived ease of use and usefulness of big data analytics in SMEs. Perceived ease of use significantly influences the adoption of big data analytics. Furthermore, facilitating sustainable technology was a significant predictor of sustainable business performance. Additionally, the study revealed that the adoption of big data analytics significantly influenced business performance. The insights obtained from this study can be useful for the service and manufacturing industries operating in Saudi Arabia, particularly regarding the key influencing factor of perceived ease of use that determines the adoption of big data analytics in the Saudi Arabian SME market.