Aug 2026· The International Journal of Organizational Analysis· pp. 1-25· 0 citations· 30 references
TL;DR
Results indicate that data quality, analytical capabilities and alignment between analytics and decision-making are the strongest determinants of LIP, and machine learning models outperform linear regression in predicting LIP.
Abstract
Although data analytics and artificial intelligence are increasingly reshaping logistics decision-making, limited empirical evidence explains how organisations transform these technologies into tangible intelligence-driven performance, particularly in emerging economies. This study aims to develop and empirically examine the concept of logistics intelligence performance (LIP), defined as a multidimensional organisational capability that captures a firm’s capacity to transform logistics data and AI tools into timely, accurate and forward-looking decisions.
The study is based on survey data collected from 2,752 Moroccan firms. A quantitative approach is adopted, combining confirmatory factor analysis (CFA) to validate the measurement model of LIP with descriptive statistical analysis and a comparative machine learning framework. Multiple predictive models are implemented and evaluated, including linear regression, decision trees, ensemble methods (Random Forest, Gradient Boosting, XGBoost), support vector machines, k-nearest neighbors and artificial neural networks, to compare their ability to explain variations in LIP.
The CFA confirms the validity and reliability of the LIP construct, showing strong model fit and convergent validity. Machine learning models outperform linear regression in predicting LIP, with artificial neural networks achieving the highest accuracy (R² up to 0.74). Results indicate that data quality, analytical capabilities and alignment between analytics and decision-making are the strongest determinants of LIP. AI adoption alone has limited impact without complementary organisational capabilities. Findings also reveal significant non-linear relationships, threshold effects, and capability complementarities shaping the development of logistics intelligence across firms.
This study contributes to the literature by conceptualising and empirically validating LIP as a distinct organisational capability. By integrating organisational capability theory with machine learning methods in the context of an emerging economy, the research moves beyond technology adoption perspectives and provides new insights into how firms convert data and AI investments into intelligence-driven logistics performance.
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
The increasing complexity of logistics operations owing to e-commerce development, supply chains, and rising customer expectations has driven organizations to employ decision support systems driven by analytics and machine learning technologies. This study presents a systematic review of peer-reviewed articles that apply analytics and machine learning to obtain better logistics operational performance. Using established systematic review guidelines, papers on analytics and supply chain logistics published between 2007 and 2025 were identified and categorized by logistics functions and analytical techniques. The results indicate that predictive analytics is the most widely used technique, in demand forecasting and transportation, whereas prescriptive analytics suffers from system integration challenges and computational intensity. In addition to improved performance, issues such as data quality, model explainability, and implementation in practice still require further attention. This survey thus brings together research findings and identifies gaps, ultimately guiding future research and analytics-based decision-making in the field of logistics.
V. Raveendran· European Journal of Artifici...· 0 citations
It is concluded that AI-driven business intelligence frameworks represent a transformative approach to enterprise management by enabling organizations to anticipate future challenges, optimize strategic decisions, improve resource utilization, and create resilient business ecosystems capable of adapting effectively to rapidly evolving economic and technological environments.
Shi-Hu Gan· International journal of com...· 0 citations
Purpose: To examine how Competitive Intelligence (CI) can be operationalized within financial software to support environmental scanning, strategic signal identification, and sustainable decision-making under volatile conditions.
Methodology/Approach: A quantitative, design-oriented approach was applied to OpenFinData, comprising 1,500 records across 19 financial tasks mapped into 12 CI dimensions. The analysis combined data preprocessing, TF-IDF, dimensionality reduction, clustering, and exploratory indicators of information density, responsiveness, and strategic fit.
Originality/Relevance: The study integrates Competitive Intelligence, Financial Analytics, and Dynamic Capabilities, positioning financial software as enabling infrastructure for organizational intelligence processes while distinguishing computational capability from organizational CI capability.
Key Findings: The findings reveal different levels of coverage and alignment between analytical workloads and CI dimensions, with greater concentration in investment intelligence and risk and compliance intelligence. Workload structure shows potential to support organizational sensing and strategic decision support but should not be interpreted as a direct measure of organizational CI maturity.
Theoretical/Methodological Contributions: The study extends the interface among CI, Environmental Scanning, Financial Analytics, and Dynamic Capabilities and proposes a reproducible procedure for mapping analytical demands into CI dimensions and assessing their strategic alignment.
Pengchong Chen, Tao Huang· Journal of Sustainable Compe...· 0 citations
The rapid growth of Environmental, Social, and Governance (ESG) investing has exposed limitations in traditional
data collection, scoring, and risk assessment methods. This study examines how Artificial Intelligence (AI) is transforming ESG
and sustainable finance using secondary data from institutional reports, academic literature, and market databases from 2020-
2025. Through systematic review and thematic analysis of secondary sources including Bloomberg ESG data, MSCI ESG
Ratings methodology, World Bank sustainable finance reports, and peer-reviewed articles, this paper identifies key AI
applications in ESG data aggregation, greenwashing detection, climate risk modeling, and portfolio optimization. Findings
indicate that AI technologies, particularly Natural Language Processing (NLP) and Machine Learning (ML), improve ESG data
coverage by up to 40% and enhance predictive accuracy for climate-related financial risks. However, challenges related to data
bias, model transparency, and regulatory fragmentation persist. The paper proposes a conceptual framework for responsible AI
adoption in sustainable finance and outlines future research directions.
D. R, Santhosh Kumar A G· International Journal for Re...· 0 citations
The built environment (BE) sector faces growing pressure to improve sustainability performance and artificial intelligence (AI) is increasingly used to support sustainability decisions. Beyond model performance, the decision-support capacity of AI also depends on underlying data characteristics. However, existing research mainly focused on AI techniques and model performance, with limited attention to the data characteristics underpinning AI applications. This study examines how data characteristics shape AI-driven decision-making in the BE sector.
Following PRISMA methodology, this study systematically reviews 125 studies to analyse patterns of AI-driven sustainability decision-making and the underlying data characteristics in the BE sector.
AI-driven sustainability decision-making contexts concentrate in energy management and multi-dimensional sustainability trade-offs. Prediction and optimisation are dominant decision focuses, while monitoring and control remain comparatively limited. Most AI workflows rely on secondary, structured and historical datasets, as unstructured and real-time data require more extensive preprocessing. Hybrid AI approaches and diverse preprocessing techniques are adopted to address fragmented data characteristics. Recurring data limitations highlight the importance of data governance in supporting AI-driven sustainability decision-making.
The review advances current knowledge by synthesising AI-driven sustainability decision-making alongside the underlying data characteristics. Findings show that data characteristics and preprocessing requirements influence the decision-making contexts, decision focuses and AI techniques that can be supported. The study further indicates that AI maturity depends not only on AI technique advancement, but also effective data governance, AI skills and data literacy.
S. J. Teoh, Z. N. Maaz, M. Hanid et al.· Engineering Construction and...· 0 citations
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