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.
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
This study investigates how big data analytics capability appears to support competitive strategy development in the Palestinian dairy industry, focusing on the role of data utilization in developing organizational capabilities, creating value and positioning the firm strategically.
This study adopts an exploratory multiple-case study approach supported by descriptive survey evidence and Visualization-Based Pattern Analysis. Data were collected through unstructured interviews with managers and employees from leading Palestinian dairy companies, alongside descriptive questionnaire-based evidence. The qualitative findings were analyzed using thematic and VRIO-based analysis to identify patterns related to big data practices, capability development, organizational maturity and competitive strategy dimensions.
The findings indicate that participating firms recognize the value of data for customer understanding, product development and operational decision-making. However, analytics practices remain fragmented and rely largely on conventional or manual processes. Limited system integration, analytical expertise, data governance and real-time processing restrict the incorporation of analytics into organizational routines and strategic decision-making. These conditions reflect a gap between awareness of data's potential value and the development of integrated analytics capabilities.
Managers should prioritize the gradual integration of customer, operational and market data, supported by appropriate infrastructure, analytical skills and governance procedures. These measures may improve the consistency of data-informed decision-making, operational coordination, customer responsiveness and market positioning.
This study provides context-specific empirical evidence on data utilization and analytics capability development within the Palestinian dairy industry, an underexplored resource-constrained setting. Its primary contribution is contextual rather than the development of a new theoretical construct. The study uses the term data maturity gap as an interpretive lens to organize evidence on the disconnect between firms' recognition of data value and their ability to integrate analytical, technological and organizational capabilities. The findings illustrate how mechanisms already identified by the Resource-Based View, Dynamic Capabilities Theory, absorptive capacity and analytics maturity research operate within this particular industrial context.
Ibrahim M. Awad, A. Awad, M. El-Jafari et al.· Journal of Enterprise Inform...· 0 citations
The proposed study explores the impact of Big Data-driven business analytics on management accounting information systems (MAIS) and managerial decision-making in Vietnamese firms. Despite research worldwide pointing to the revolutionary nature of analytics in forecasting, performance measurement, and risk management, empirical studies remain scarce in emerging economies. A quantitative method was used to collect data (n=81 Vietnamese firms) in a structured Likert-scale questionnaire that was structured. Cronbach's Alpha, Exploratory Factor Analysis (EFA), and One-Sample t-tests were used to test the reliability, validity, and hypothesis testing. The finding suggests that sophisticated analytical tools, definite decision-making needs, and quality data assurance are the key to the enhanced effectiveness of MAIS. Additionally, Big Data analytics has a positive influence on the decision accuracy, efficiency, IT integration, risk management, and data security. Both the null hypotheses (H01 and H02) were rejected which proved that there is a strong relation between the adoption of Big Data and the enhancement of managerial decisions. Furthermore, the study shows that integrating digital infrastructures, such as cloud data systems, automated accounting tools, and AI-driven analytics, significantly enhances MAIS performance. Although these results are obtained, the researchers also mention such limitations as a small sample, the use of self-reports, and the lack of causal process analysis. The study has some practical implications for SMEs and highlights the necessity of increasing analytical competencies. The findings also emphasize the importance of strengthening IT governance, cybersecurity protocols, and data management standards to support analytics-driven MAIS. The future research must investigate certain ways in which analytics enhance the performance of organizations in different markets.
Thuc Duy Tran, Bang Hai Truong, M. Bańka et al.· American Journal of Internat...· 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
The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning, and predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions.
I. Yusuf, Grace Ndlovu· International Journal of Com...· 0 citations
This study investigates the adoption of BI practices in small and medium-sized enterprises (SMEs) to examine the extent of BI integration and its impact on decision-making, competitiveness, and the critical success factors influencing effective BI implementation.
Mirano Jansen, K. Ohei, Sam Lubbe· Veredas do Direito· 0 citations
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