Jul 2026· Journal of Information & Knowledge Management· 0 citations
TL;DR
Findings indicate that librarians are more inclined to adopt BDA as they find it useful, easy to use and consistent with institutional priorities; strong leadership support and adequate digital infrastructure were also significant contributors to adoption.
Abstract
The adoption of Big Data Analytics (BDA) is a critical process for academic libraries to enhance decision-making processes based on evidence-based indicators. BDA techniques enable libraries to analyse vast amounts of structured, unstructured and semi-structured data that is growing at an exponential rate over time. In order to effectively leverage the power of BDA capabilities, policymakers and management have to understand the complex interaction between various personal and institutional factors involved in the adoption decision-making process. The purpose of this study is to explore the factors that affect the adoption of BDA tools in academic libraries in selected universities in the Arab region. To this end, this study combines the extended Unified Theory of Acceptance and Use of Technology, the Technology-Organization-Environment framework, and the Diffusion of Innovations theory. Multinomial logistic regression analysis is performed on survey data collected from 416 librarians, and the results reveal that performance expectancy, effort expectancy, social influence, compatibility, top management support, institutional readiness, knowledge stock, and hedonic motivation are the significant drivers of BDA adoption. These findings indicate that librarians are more inclined to adopt BDA as they find it useful, easy to use and consistent with institutional priorities; strong leadership support and adequate digital infrastructure were also significant contributors to adoption. In contrast, no statistically significant effect was found for observability and regulatory compliance factors. The findings of this study offer practical insights for library managers and decision-makers, assisting them in developing effective implementation strategies to increase BDA adoption and support data-driven library decision-making.
Purpose – This study examines how the four canonical dimensions of big data capabilities—volume, variety, velocity, and veracity—influence the transformation of management accounting practices within Cameroon economy. Despite the proliferation of digital analytics in developed markets, empirical evidence regarding their differential effects in resource-constrained institutional contexts remains fragmented.
Design/methodology/approach – Anchored in the Resource-Based View, Decision Usefulness Theory, and Dynamic Capabilities Theory, the study employs an ex-post facto cross-sectional design. Primary data were collected via structured questionnaires from 87 management accountants employed in firms operating digital data infrastructures across Douala and Yaoundé, Cameroon’s principal commercial and industrial centres. Multiple regression analysis, supplemented by diagnostic tests for multicollinearity, heteroscedasticity, and normality, was used to test the hypothesised relationships.
Findings – The findings reveal that data velocity (β = 0.336, p < 0.001) and veracity (β = 0.291, p = 0.003) exert the strongest positive influence on management accounting transformation, followed by variety (β = 0.278, p = 0.005) and volume (β = 0.214, p = 0.019). The model explains 64.1% of the variance in management accounting practices (Adjusted R² = 0.598; F = 14.782, p < 0.001), suggesting that real-time processing capability and data reliability are more consequential than data magnitude per se in emerging economy settings.
Originality/value – The study contributes to the digital accounting literature by providing rare quantitative evidence from a Sub-Saharan African context, where infrastructural deficits, institutional voids, and capability constraints shape technology adoption trajectories differently than in developed economies. The findings challenge the assumption that greater data volume automatically yields superior accounting outcomes, highlighting instead the primacy of processing speed and information integrity.
Ejole Glenn Akume, Ibrahim Ayouba· Account and Financial Manage...· 0 citations
Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector.
James O. B. Rotimi, Upuli Rasanjani Kaluarachchi Kaluarachchillage· Buildings· 0 citations
The rapid evolution of new technologies has revolutionized management practices in organizations. Decision-making, once based on traditional methods such as intuition and experience, has shifted towards more modern methods grounded in big data analytics (BDA). This study is part of a systematic review of research. By consolidating recent literature, particularly from 2021 to 2025, the study aims to highlight the strategic role played by big data (BD) and BDA in decision-making (D-M) and business performance. Following a meticulous methodological search, articles were extracted from three databases: Google Scholar, DOAJ, Scopus and Web of Science. Applying the PRISMA 2020 methodology yielded 35 articles based on exclusion and inclusion criteria. Reviewing the selected articles highlighted the importance of Big Data-driven D-M across six domains: finance, accounting, supply chain, marketing, human resources, and the environment. The study shows that the integration of big data is not limited to business growth but also contributes to sustainable development. The results demonstrate the undeniable advantage of D-M and improving organizational performance. However, addressing challenges such as cultural and ethical barriers, data privacy and security, algorithmic bias, infrastructure complexity, and resource constraints would lead to the effective and efficient use of this tool. Furthermore, future research should focus on integrating AI into analytical D-M processes and exploring mechanisms for improving real-time D-M.
Dedy Christelle Sekadjie, Charles Mbohwa· International Journal of Sus...· 0 citations
This paper presents a PRISMA-compliant systematic literature review of the adoption, implementation, and measurable impact of Financial Data Analytics (FDA) in India's Micro, Small, and Medium Enterprises (MSMEs) and Small and Medium Enterprises (SMEs). Drawing on 61 peer-reviewed studies published between 2010 and 2025, identified through searches of Scopus, Web of Science, and Google Scholar, the review synthesises findings across six thematic clusters: adoption drivers, adoption barriers, fintech and AI/ML integration, firm performance effects, regulatory context, and sectoral variation.
The theoretical contribution is an integrated three-stage adoption model sequencing the Technology Acceptance Model and Diffusion of Innovation theory at the initiation stage, UTAUT and the Resource-Based View at the institutionalisation stage, and Dynamic Capabilities Theory at the capability-building stage. This sequencing resolves a persistent problem: the tendency to apply a single framework to a process that evolves significantly as firms mature analytically.
Core findings: perceived usefulness driven by GST reconciliation pain points and affordable cloud delivery is the dominant predictor of initial adoption; the cost of skilled analysts — not software licensing — is the primary barrier to deeper capability development; fintech-enabled ML credit scoring is improving loan access for MSMEs with digital footprints, but field-trial evidence in Indian contexts remains absent; and manufacturing firms in industrial clusters show a 7-14% ROA premium among analytics adopters, though the performance literature is hampered by reverse-causality and non-standardised outcome measures. The Account Aggregator framework and GST data ecosystem represent institutional infrastructure changes that the reviewed literature has barely begun to theorise.
Methodological gaps dominate: cross-sectional surveys, urban sampling bias, and concentration in four Indian states limit generalisability. Future priorities include longitudinal panel studies, AI/ML lending field trials, ESG analytics integration studies, and algorithmic fairness audits of fintech credit models serving MSME populations.
Dinesh Junghare, Sachin Lad· Journal of Intelligent Decis...· 0 citations
This investigation investigates the manner in which the use of Big Data Analytics Capabilities (BDAC) impacts organisational effectiveness in the information technology (IT) services industry, considering into thought the mediation responsibilities during talent capability, innovation, ambidexterity, and agility in addition to the modifying responsibilities of information circulation, alignment with strategy, along with fluctuations in the environment. This investigation expands further isolated analysis of relationships through providing an extensive as well as contextual dependant comprehension of BDAC-driven organisational achievement. To evaluate the effect of BDAC findings on organisational efficiency as well as evaluate the moderating and mediation functions of specific organisation components within the Indian information technology services sector. Utilising data collected from 650 employees in information technology in the nation of India, a conceptual framework that utilises information concepts as well as the changing abilities perspective was developed as well as experimentally verified with Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that BDAC significantly improves talent capacity, innovation, ambidexterity, and agility, among other things which enhance organisational effectiveness when considering of all the operational, financial, as well as environmental factors. Knowledge flow positively moderates the relationship between innovation and performance, while strategic alignment strengthens the link between dynamic talent capabilities and performance. Environmental turbulence intensifies the relationship between agility and performance.
Prachi Sharma, Lokesh Vijayvargy, Srikant Gupta et al.· International journal of com...· 0 citations
Digital transformation has increased organizational reliance on big data analytics (BDA) to support strategic decisions and improve business performance. This study synthesizes evidence on how BDA contributes to strategic decision-making, organizational performance, and innovation through a systematic literature review. The review followed the PRISMA 2020 framework and searched Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language journal and conference publications from 2021 to 2026. After identification, screening, and full-text eligibility assessment, 33 studies were included and examined using thematic analysis. The findings show that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights. BDA is also associated with operational efficiency, project success, organizational agility, customer personalization, competitive advantage, sustainability, and innovation capability. The dominant themes were strategic decision-making, business performance, sustainability and innovation, and artificial intelligence with predictive analytics. However, the literature provides limited evidence on explainable and ethical artificial intelligence, human-AI collaboration, real-time analytics, and BDA adoption among small and medium-sized enterprises and organizations in developing economies. The review contributes an integrated view of BDA as a socio-technical and strategic capability and recommends transparent, scalable, and human-centered analytics governance.
Amelia Contesa, Ilzi Adrolis, Wenni Syafitri et al.· Business System & Innova...· 0 citations
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