Purpose The purpose of the research is to evaluate how effective information technology companies are in Big Data Analytics (BDA) Management due to the developments in the world of technology, and at the same time by investigating the mediating role of Environmental Dynamism (ED), it is aimed to offer new perspectives on the interaction between BDA adoption, environmental factors and organizational outcomes in this dynamic sector. Design/methodology/approach In the research, data was collected from companies in the field of information technologies. Since there are many companies in the field of information technologies in Turkey, firstly, companies in the Marmara region, which is the most developed region of Turkey in the field of industry, were selected by the convenience sampling method. 940 e-mails were sent to each of the companies' expert personnel and 607 responses were received (response rate 65%) and the survey form was closed for answering as it was deemed sufficient. The answers of 23 participants were excluded from the analysis. A total of 584 data points were analyzed in the SmartPLS program. Findings The analysis results show that the mediating effect of ED is significant. In particular, mediation analysis results states that the importance of environmental compatibility in increasing organizational effectiveness. Research limitations/implications Since the data in the research was collected from information technology companies in the Marmara region, it may not be correct to make full generalizations about the analysis results. Analysis results may differ between sectors depending on regions and competitive conditions. For this reason, it is recommended that the research model be developed by taking into account the limited situation in future research. Practical implications The research emphasizes that information technology companies need to analyze ED correctly in order to be successful in an intense competitive environment, and also how important big data management is to be successful in competition. By evaluating the different effects of BDA on various organizational outcomes mediated by ED, companies can adapt their strategies to optimize performance and sustainability. Originality/value The research contributes to the literature by providing a nuanced perspective on the impact of BDA adoption in the context of IT companies. The identification of differential mediation effects highlights the need for specific strategies to exploit the full potential of BDA tools in dynamic environments and thus increase the competitiveness and sustainability of IT organizations.

The epitome of sustainable public procurement practices aligns with the principles of transparency, integrity, competitiveness, and efficiency, underpinned by the growing adoption of big data analytics capabilities. Big data analytics (BDA) enhances supply chain visibility, operational efficiency, and risk management; however, it has become evident that public supply chains in developing nations require robust digital infrastructure to effectively leverage the potential benefits of BDA. The research objective is to establish the effective role of Big Data Analytics (BDA) in enhancing sustainable public procurement practices (SPPP), aligning with the objectives of the Namibian Procurement Act 2015. The research further evaluates the factors impacting the technological, organisational and environmental context of public procurement principles through data-driven insights on the view of economic constraints and inequalities. The descriptive quantitative research design is rooted in a positivist perspective, focusing on an empirical case study of Namibia to assess the impact of BDA within the Technology-Organisation-Environment (TOE) framework. The research design employs a descriptive case study, enabling the exploration of factorial interrelationships. Diverse perspectives are integrated via stratified sampling, resulting in a sample size of 270 administered for data collection. Inferential statistics, including multivariate analysis, were used to analyse the data. The study revealed enhanced public procurement practices following the enactment of the Public Procurement Act 2015. ICT infrastructure, data quality, and system scalability are pivotal for BDA integration, despite hindrances such as leadership commitment, procurement capacity, procedural complexities, and resource constraints, which impede the shift towards sustainable procurement. These findings culminate in a TOE framework, which suggests that BDA's adoption offers significant strategic value, including improved decision-making, increased efficiency, enhanced transparency, and improved supplier performance in procurement. BDA's strategic effective role/value influences its adoption, leading to the transformation of sustainable public procurement practices in Namibia. This model systematically outlines the impact of BDA adoption on public procurement, showcasing how the TOE factors shape BDA's success in achieving SPPP. This research provides policymakers, public procurement professionals, and technology providers with practical insights to enhance the strategic value of BDA for sustainable procurement. This study contextualises the interplay of TOE factors for BDA adoption in public procurement for developing nations.

Digital transformation has significantly increased data complexity and risk exposure in public companies, demanding internal audit functions that are adaptive, innovative, and technology-oriented. This study aims to analyze the implementation of Big Data Analytics (BDA) in internal auditing and examine its impact on audit effectiveness. The research adopts a qualitative approach using a case study method, focusing on PT Telkom Indonesia (Persero) Tbk through an in-depth analysis of its 2024 corporate reports and related governance documents. The findings reveal that the adoption of BDA facilitates a transition toward data-driven auditing practices, enabling more comprehensive analysis of large datasets in real time. Furthermore, BDA enhances the detection of risks, irregularities, and potential anomalies, while also improving the accuracy and efficiency of audit procedures. The implementation of analytics-based auditing strengthens the strategic role of internal audit in supporting transparency, accountability, and effective corporate governance. Overall, these results emphasize the growing importance of big data analytics in enhancing internal audit effectiveness within the rapidly evolving digital business environment.

ABSTRACT Small and medium‐sized enterprises (SMEs) often face unique challenges in integrating advanced digital technologies due to limited resources and organizational constraints. While much attention has been given to large firms' adoption of big data analytics (BDA) and artificial intelligence (AI), less is known about how SMEs can leverage these tools effectively. This study addresses the research question of how SMEs, especially SaaS B2B, can transform marketing processes through advanced data analytics, large language models (LLMs), and generative AI. Drawing on a qualitative case study of a SaaS company, this research proposes a framework for integrating web mining and LLMs into a semantic analysis process to improve client acquisition by better targeting potential clients, i.e., leads. It optimizes lead qualification and facilitates the replication of successful cases by automating data collection, generating tailored marketing content, and creating customized commercial proposals. Based on the business analytics success model (BASM) framework, our study investigates the organizational changes necessary to embed innovative data‐driven business processes into existing workflows through process mapping. Key stakeholders evaluate the proposal via interviews, which discuss the barriers to aligning and opportunities associated with adopting AI‐driven solutions and emphasize the importance of technological capabilities with business goals. The findings reveal that for SMEs, adopting advanced analytics is contingent upon overcoming resource constraints through targeted organizational adjustments, such as incorporating dominant logic identification into strategic planning. This research contributes theoretically by customizing the BASM for SaaS SMEs and offering a roadmap for implementing AI‐driven marketing solutions, demonstrating how SMEs can enhance operational efficiency and achieve competitive advantages, even in resource‐constrained environments.

This study examines how big data analytics (BDA) enhances supply chain performance by analyzing two key process pathways: decision-making and forecasting. Using data collected from 400 supply chain managers across various industries, the study applies structural equation modelling (SEM) using SmartPLS to test the relationships among BDA capabilities, supply chain efficiency (SCE), and firm-level performance outcomes. The results show that while BDA improves both forecasting and decision-making processes (DMP), only decision-making consistently drives higher SCE. Forecasting, though technically enhanced by BDA, fails to translate into efficiency gains due to volatility and uncertainty inherent in supply chain environments. SCE is found to fully mediate the relationship between BDA-enabled decision-making and operational and financial performance. This study extends and contributes to Chatterjee et al. (2023) by highlighting process-specific mechanisms in BDA adoption. The findings suggest that organizations should adopt a decision-centric BDA strategy, emphasizing real-time, data-driven choices over predictive modelling alone. This study contributes theoretically by highlighting process-specific mechanisms and offers practical guidance for optimizing BDA investments.

Research background: Digital transformation has become a strategic imperative for companies worldwide, yet the heterogeneity in adoption patterns remains poorly understood, particularly in transitional economies. Existing research predominantly focuses on large economies, whilst small European countries face distinct institutional environments. The interplay between digital maturity, strategic orientation, and economic performance represents a critical knowledge gap. Purpose of the article: The study aims to identify distinct clusters of Slovak companies based on their attitudes towards artificial intelligence, sustainability orientation, and strategic resilience. The research determines how these clusters differ in technology adoption, perceived barriers, and economic performance outcomes. Methods: The study employs quantitative research based on a structured survey of 402 Slovak companies conducted between February and July 2025. The methodological framework integrates k-means cluster analysis using twelve indicators. The optimal two-cluster solution was validated through the elbow method and silhouette analysis, with principal component analysis providing visualization. Chi-square tests examined differences between clusters across technology adoption, barriers, and performance indicators. Findings & Value added: The analysis reveals a fundamental structural divide within Slovak companies, identifying digitally advanced and traditional companies with significantly different profiles. Advanced companies demonstrate substantially higher adoption of Big Data analytics, ERP systems, and e-commerce platforms. Paradoxically, these companies perceive stronger barriers to transformation, including legal and regulatory uncertainty, implementation costs, system integration issues, and financial and network constraints, revealing a paradox of digital advancement. Most significantly, advanced companies report superior economic performance in turnover and EBIT changes. The research contributes novel evidence from a transitional economy, addressing gaps in comparative digital transformation research. Findings provide actionable insights for policymakers and managers navigating transformation complexities.

ABSTRAC The rapid advancement of digital technology has encouraged the adoption of Big Data Analytics (BDA) in various fields, including accounting and finance. This study aims to examine the utilization of BDA in fraud detection and financial performance prediction based on recent literature reviews. The research was conducted by analyzing academic articles and prior studies published since 2021. The findings indicate that BDA significantly contributes to detecting potential fraud by identifying complex data patterns that traditional methods often fail to capture. Moreover, BDA has proven effective in improving the accuracy of financial performance predictions by incorporating broader and real-time variables. This study concludes that BDA is a relevant and adaptive solution to address the challenges of fraud detection and financial forecasting in the big data era, while also providing opportunities for further research in accounting and information systems. Keywords : Big Data Analytics, fraud, financial performance

Healthcare is one of the most important sectors, and the potential for improvement through the application of data and analytics is tremendous. The study aimed to identify the current approaches, innovations, and future directions in the healthcare industry based on big data analytics. A systematic literature review was applied to address the study’s objectives. Required studies were explored through ten digital databases that included Scopus, Web of Science, PubMed, Ovid Medline, Medline, PLOS, Global Health, Emerald, Wiley Inter Science, and Pro Quest. Thirty-five peer-reviewed research papers published in key digital databases from 2014 to 2025 were selected to conduct the study. This study has addressed a specific gap in the literature that is the lack of a consolidated and up-to-date synthesis of technological advancements and implementation challenges related to big data analytics in healthcare. While various studies have examined individual aspects of big data in healthcare, there is a critical need for a holistic and integrative review that maps out current practices, emerging innovations, and persistent barriers. This research addresses that gap through a comprehensive overview of big data analytics across various dimensions of the healthcare sector. Findings of the study revealed that big data analytics assisted in clinical decision support, population health management, healthcare industry improvement, and electronic health data transformation. The study revealed that machine learning and artificial intelligence, blockchain technology, and cloud computing are the most important technological innovations in health information analysis tools. It also indicated that data privacy, technical complications, and expertise and resources caused challenges for the adoption of big data analytics in the healthcare industry. Robust privacy and security measures need to be developed for ensuring confidentiality of sensitive medical data. Sufficient financial resources should be provided for the implementation of big data analytics in healthcare organizations to ensure compatibility. Staff training should be ensured to cultivate required skills to efficiently manage health systems based on big data analytics. Data standards should be established to address challenges related to complex variables and differences in data entry preferences. Documented policies, procedures, and clear guidelines should be developed to manage sensitive patients’ information. The study’s original contributions include: (1) a categorized synthesis of enabling technologies (AI/ML, blockchain, cloud computing); (2) a structured classification of implementation challenges (data privacy, technical complexity, lack of expertise and resources); and (3) strategic recommendations for addressing these challenges through workforce training, policy frameworks, data governance, and collaborative ecosystems. These contributions are grounded in a comprehensive synthesis of existing literature and offer clear direction for future research, implementation efforts, and policy development. This work has offered a comprehensive synthesis of the state-of-the-art in big data analytics for healthcare through key approaches, innovations, and prospective developments.

Research on big data analytics (BDA) and supply chains often inventories “capabilities” but rarely explains how firms progress through adoption—or how governance over data and related resources shapes resilience outcomes. Drawing on 16 semi-structured interviews with senior managers in the manufacturing sector, we analyze organizational practices around data, analytics, and decision-making and synthesize a governed-adoption process framework. The framework specifies how five governance levers—ownership, standards, stewardship, access, lineage—operate differentially across four adoption gates (data plumbing—descriptive monitoring—predictive alerting—prescriptive decisioning). To move beyond staged descriptions, we make the underlying generative mechanisms explicit—Comparability, Explainability, Authorization, Fidelity, Executability—and link them to dynamic-capability micro foundations (sensing, seizing, reconfiguring) via decision-latency outcomes (“resilience timers”: Time-to-Detect, Time-to-Decide, Time-to-Reconfigure, Time-to-Recover). Brief deviant-case contrasts (e.g., notification without action; dashboards without owners) clarify boundary conditions under which governance enables or impedes resilient action. We also state concise, testable propositions (e.g., standards+lineage as a necessary condition for improving Time-to-Detect; ownership+access as necessary for improving Time-to-Decide) and provide gate exit-criteria to support evaluation and future comparative tests. Claims are bounded to analytic generalization from a single-country, manufacturing-sector qualitative sample; we make no assertion of statistical validation. Practically, the framework prioritizes governance work ahead of tool spend, helping organizations convert dashboards into repeatable decisions at speed.

Purpose This study examines the adoption of big data analytics (BDA) in public sector auditing through a modified technology acceptance model (TAM) framework, focusing on system quality, complexity, self-efficacy, ease of use and usefulness. It also analyzes the impact of BDA usage on audit performance and quality, with auditor certification assessed as a moderating factor. Design/methodology/approach This study employs a survey questionnaire method and involves government auditors at Indonesia’s Supreme Audit Agency as samples. The data are analyzed using partial least squares techniques. Findings The findings show that system quality positively influences both perceived ease of use and usefulness, while complexity has no significant effect. Self-efficacy enhances ease of use but not usefulness. Both perceived ease of use and usefulness significantly drive the intention to adopt BDA, which in turn leads to actual usage. BDA usage improves audit performance and quality; however, auditor certification only moderates the relationship between BDA usage and audit performance, not audit quality. Certification also does not moderate the effect of perceived ease of use or usefulness on adoption intention. Originality/value This study offers a significant contribution by extending the TAM framework to the context of public sector auditing in developing countries, a setting often overlooked in existing literature. Uniquely, it introduces auditor certification as a moderating variable, providing fresh insights into how professional credentials shape the relationship between BDA adoption and audit outcomes.

Digital transformation has become a crucial engine for economic growth, especially amid the structural shifts of the Fourth Industrial Revolution. However, comparative insights into the progress of Central and Eastern European (CEE) Member States remain underexplored in the literature, particularly regarding Romania’s positioning within broader EU digitalization trends. This study addresses this gap by evaluating Romania’s digital development relative to other CEE countries, based on summarized indicators from the Digital Economy and Society Index (DESI). We focus specifically on the digital transformation of the business sector, assessing four key indicators: the share of SMEs with at least a basic level of digital intensity; use of electronic information sharing through ERP systems; adoption of big data analytics; and integration of artificial intelligence technologies. Using official European Commission reports from 2014 to 2023, our comparative case study offers both a temporal and regional perspective on digital performance. The methodology involves a quantitative assessment of DESI components relevant to enterprise digitalization, emphasizing cross-country comparison and trend analysis. Findings show that although Romania has registered consistent improvements since EU accession, it continues to lag behind the CEE and EU averages in the digital readiness of its business environment. The results underline the need for targeted policy measures and increased investment in R&D and emerging technologies to accelerate convergence. One limitation of the study is the partial availability of data on AI adoption, which is only provided for the 2021–2023 period.

Big data analytics (BDA) has become a transformative driver of competitiveness in supply chain management (SCM). It offers opportunities for efficiency, cost reduction, and enhanced customer satisfaction. Yet, adoption in developing economies remains limited due to infrastructural, financial, and skills-related challenges. This study examines the experiences, strategies, barriers, and outcomes of BDA adoption in Ghanaian supply chains. Using a survey of 205 managers and executives in the Greater Accra Region, the research employed descriptive statistics, chi-square tests, correspondence analysis, and logistic regression to analyze adoption patterns and predictors. Findings reveal that large firms are significantly more likely to adopt BDA, implement enterprise-wide strategies, and derive greater benefits compared to small and medium-sized enterprises (SMEs). Barriers include high investment costs, limited executive support, and concerns over data security. Professional capacity and operational experience emerged as significant predictors of future adoption. The study contributes to the literature by providing empirical evidence from Ghana, offering insights into the unique challenges of digital transformation in Sub-Saharan Africa. Policy recommendations emphasize investment in infrastructure, capacity building, and targeted support for SMEs to bridge the digital divide.

Background and Aim: The author provides a foundational understanding of Big Data, describing its characteristics and the inadequacy of traditional management methods. This serves as a general introduction to the topic. However, this section could more explicitly articulate the specific research problem or gap concerning Big Data Analytics (BDA) adoption within Small and Medium-Sized Enterprises (SMEs) in the Eastern Economic Corridor (EEC). While the importance of BDA for strategic decision-making and competitive advantage is mentioned, a more direct linkage to the challenges or unique circumstances of SMEs in this particular region would strengthen the problem statement. The research aims to investigate BDA adoption among SMEs in the EEC, as clearly stated. This section effectively introduces the theoretical underpinnings by referencing the Technology-Organization-Environment (TOE) framework and the Technology Acceptance Model (TAM). The author correctly identifies key components of these frameworks—technological attributes, organizational conditions, external environmental factors from TOE, and perceived usefulness and perceived ease of use from TAM. To further enhance this, a concise justification for selecting these specific frameworks, perhaps highlighting their relevance to understanding technology adoption in an SME context or their complementary nature, would add depth. The objectives are well-defined, aiming to identify critical influencing components and analyze relationships among variables, which guide the study. Material and Methods: The author clearly outlines the methodological approach. The sample group of 340 SME entrepreneurs in the EEC, selected via purposive sampling, is appropriately described. This section precisely details the primary data collection instrument as a questionnaire, structured into six sections, differentiating between open-ended and closed-ended Likert scale questions. The inclusion of specific validation metrics - a content validity index (IOC) of 0.874 and an overall reliability (Cronbach’s alpha) of 0.936 - is commendable. These values indicate a robust instrument, which is essential for the credibility of quantitative research findings. This level of detail in the abstract is suitable, providing confidence in the data collection process. Results: The author presents key findings from the statistical analyses with precision. The result from Confirmatory Factor Analysis (CFA) highlights organizational leadership capability as the most influential observed variable, with a beta coefficient of 0.915. This emphasizes its importance in fostering technology acceptance and implementation, especially for SMEs facing competitive pressures and rapid technological changes in the EEC. Furthermore, the Structural Equation Modeling (SEM) analysis is reported to show a strong and statistically significant relationship between technological factors and perceived usefulness (β = 0.653, p < 0.01). The inclusion of specific fit indices (Chi-square = 686, df = 350, p-value = 0.001, and RMSEA = 0.032) confirms a good model fit. These findings provide concrete evidence of the variables influencing BDA adoption, aligning well with the study's objectives. Conclusions: The analysis concludes that a supportive organizational system significantly influences employees’ confidence and perceived ease of use with information technology. This directly connects to the findings regarding organizational leadership and perceived ease of use. The author emphasizes that when organizational structures align with technological goals, employees are more likely to integrate technology into their work. Based on these insights, the author provides actionable recommendations for SMEs in the EEC. These include prioritizing leadership development through continuous training and knowledge enhancement, enabling leaders to drive technology adoption. Additionally, the author suggests improving internal systems for flexibility and responsiveness to technological changes, fostering an innovation-friendly culture, and providing staff training in BDA. This section effectively summarizes the study's implications, highlighting how these efforts can lead to sustainable growth, market competitiveness, and effective adaptation in the digital era. However, the terms "Organization" and "Technology" are very broad. To enhance specificity and improve the discoverability of this research, it is recommended to use more precise terms. For instance, "Organizational Factors," "Organizational Readiness," or "Organizational Culture" would better capture the nuanced aspects of the organization's role in BDA adoption as discussed in the abstract, particularly the emphasis on leadership and internal systems. Similarly, "Technological Factors," "Technological Readiness," or "Technology Adoption" would be more specific than simply "Technology," aligning more closely with the study's focus on adoption. Including "Small and Medium-Sized Enterprises" (SMEs) and "Eastern Economic Corridor (EEC)" as keywords would also be beneficial, as these are critical contextual elements of the research, ensuring the study is easily found by those interested in this specific sector and geographic region.

Big Data Analytics (BDA) offers transformative potential for Small and Medium Enterprises (SMEs), enabling enhanced performance, improved decision-making, innovation and business growth. Yet, manufacturing SMEs often face considerable constraints that hinder effective BDA implementation. This study adopts Resource Orchestration Theory (ROT) to explore how managers in manufacturing SMEs structure, bundle, and leverage resources to overcome these challenges and deploy BDA effectively. Using semi-structured interviews with 17 SMEs managers, we examine BDA deployment across supply chain operations guided by the SCOR model. The findings reveal key managerial roles and strategies, including approaches to selecting, configuring, and operationalising BDA solutions. This study contributes to theory by applying ROT to the underexplored context of BDA implementation in SMEs, highlighting the dynamic capabilities managers must develop to succeed. Practically, it provides actionable insights for SMEs managers navigating digital transformation in resource-constrained settings. The study proposes a roadmap to guide BDA adoption in manufacturing SMEs.

Exploring big data analytics adoption for sustainable manufacturing supply Chains: Insights from a TOE-guided systematic review

This study investigates the determinants of Big Data Analytics (BDA) adoption among hospitality firms in Abuja, Nigeria. Anchored on the Technology–Organization–Environment (TOE) framework, the study focuses on three key internal factors: perceived benefits, top management support, and IT infrastructure. A quantitative research approach was adopted, utilizing survey and correlational designs. Primary data were collected from 100 hospitality firms using a structured questionnaire, and the results were analyzed using Ordinary Least Squares (OLS) regression. The findings reveal that while top management support and IT infrastructure significantly and positively influence BDA adoption, perceived benefits, though positively related, have an insignificant effect. These results suggest that internal capacity and leadership commitment are more critical than mere awareness of BDA advantages. The study recommends stronger executive involvement, investment in IT capabilities, and strategies to bridge the gap between perceived benefits and implementation. Limitations and suggestions for future research are also provided to guide further exploration in this area.

The financial services industry has witnessed a paradigm shift through the adoption of big data analytics, fundamentally transforming investment operations. Contemporary portfolio management now leverages sophisticated cloud infrastructure to process vast data streams, generating actionable insights and competitive advantages. This transformation extends across technological architecture, analytical methodologies, and performance metrics while presenting regulatory and implementation challenges. Investment firms utilizing hybrid cloud architecture demonstrate enhanced risk-adjusted returns, while machine learning algorithms effectively capture non-linear relationships invisible to traditional models. Natural language processing and deep learning neural networks enable unprecedented pattern recognition in both textual and time-series data. The empirical evidence reveals significant performance stratification based on analytical sophistication, with diminishing returns observed beyond certain implementation thresholds. Despite these advancements, data quality inconsistencies, regulatory constraints, alpha signal decay, and increasing analytical homogeneity present ongoing challenges that require thoughtful mitigation strategies.

The rapid evolution of information technologies has driven the exponential growth of big data, creating opportunities to leverage data analytics across sectors. In higher education, Big Data Analytics (BDA) holds promise for improving decision-making, enhancing student outcomes, and driving institutional efficiency. However, its implementation remains limited due to technological, organizational, and environmental challenges. This study examines the readiness and use of BDA within selected Canadian higher education institutions, focusing on Southwest Ontario. Utilizing the Technology-Organization-Environment (TOE) framework, the research adopts a qualitative approach, drawing on semi-structured interviews with 10 academic and administrative staff from selected universities in Southwestern Ontario. The result identifies several barriers to BDA readiness and use, including a fragmented data landscape, integration challenges, and resource constraints. The study emphasizes the need for strategic investments in technological infrastructure, leadership engagement, and updated policies to improve BDA adoption. The study concludes with recommendations addressing barriers within the technological, organizational, and environmental contexts to enhance institutional performance and student outcomes.

External auditing is a profession dependent on data-driven technologies to satisfy the ‎increasing expectations of regulators and stakeholders, and it is knowledge-intensive. The ‎use of Big Data Analytics (BDA) can significantly improve the quality, efficiency, and risk ‎assessment of audits. This study examines the factors that influence the behavioural ‎intentions of external auditors in Jordan using big data analytics (BDA). This study ‎implemented a quantitative and exploratory methodology by collecting and analysing data ‎from 177 external auditors. The research results indicate that perceived usefulness and ease ‎of use emerged as significant predictors. Perceived risk significantly moderated the ‎relationship between perceived usefulness and behavioural intention, but had no significant ‎effect on the relationship between perceived ease of use and intention. This study makes a ‎substantial contribution to theory by extending the TAM by incorporating perceived risk as ‎a moderating variable in the context of BDA adoption. It provides novel insights from an ‎emerging economy, highlighting the role of risk perception in shaping auditors’ ‎behaviour. This study provides essential knowledge for audit firms and BDA providers by ‎identifying the factors that influence auditors’ intentions to adopt data analytics tools‎.

This systematic review examines the impact of Big Data Analytics (BDA) on airline operations and decision-making, with an emphasis on its applications, problems, and future prospects. The aviation sector generates massive and complicated datasets from various sources, including flight operations, maintenance logs, passenger records, and air traffic control systems. BDA allows greater operating efficiency, predictive maintenance, increased safety, and data-driven decision-making in a variety of aviation disciplines. Key applications investigated include route profitability analysis, air traffic flow optimization, supply chain resilience, and real-time monitoring via the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML). A systematic review technique was used to maintain academic rigor. A qualitative analysis was performed on 19 peer-reviewed research chosen from over 200 papers obtained from major academic databases such as Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The inclusion criteria were aviation-specific BDA applications published between 2010 and 2024. Thematic analysis was performed to extract insights and organize the findings into important areas of effect. The findings show that BDA adoption has tremendous benefits, but it also faces continuing challenges such as data complexity, high implementation costs, ethical concerns, and a need for qualified people. This review establishes a platform for future research and practical implementation strategies in aviation analytics.