Purpose This paper investigates spatial interdependencies among economic sectors in the USA using Multivariate Moran’s I applied to county-level employment data classified according to the North American Industry Classification System (NAICS). The study aims to identify patterns of sectoral co-location, spatial clustering and regional economic interdependence, providing an exploratory framework for understanding the spatial organization of the US economy. Design/methodology/approach The study uses a spatial econometric framework based on queen contiguity matrices and Multivariate Moran’s I. County-level employment data across 14 major NAICS sectors are analysed to detect spatial autocorrelation and intersectoral relationships. The analysis is complemented by a network-based interpretation in which sectors are represented as interconnected nodes linked through significant spatial correlations. Findings The results reveal distinct spatial economic clusters, including industrial agglomerations, consumer-oriented urban economies and financial and managerial hubs. The analysis also identifies negative spatial relationships between metropolitan knowledge-intensive sectors and territorially dispersed population-serving activities. Furthermore, some sectors occupy structurally central or bridging positions within the spatial network, suggesting differentiated roles in regional integration and specialization across the US economy. Originality/value This paper contributes to the literature by integrating Multivariate Moran’s I with a network-based interpretation of sectoral spatial relationships. Unlike traditional spatial autocorrelation studies focused on single sectors or variables, the analysis examines interdependencies across multiple economic sectors simultaneously. The study provides an exploratory perspective on how sectoral co-location, divergence and structural centrality shape regional economic organization in the USA. By combining multivariate spatial econometrics with network analysis, the paper offers a novel framework for interpreting spatial economic structures and intersectoral connectivity.
PurposeThis study aims to critically examine the integration of big data analytics (BDA) into sustainability accounting, identifying thematic developments, methodological patterns and gaps that shape future research and practice. Design/methodology/approachA systematic literature review was conducted on 70 peer-reviewed articles published between 2017 and 2024. The study uses a structured analytical framework, text mining techniques and thematic coding to synthesize findings and identify research gaps. FindingsThe review reveals five key thematic clusters: supply chain and circular economy, artificial intelligence-enabled sustainability practices, climate change and sustainability accounting standards, stock returns and corporate transformation and environmental, social and governance (ESG) interactions. Significant research gaps are identified, with implications for academic inquiry, professional practice and regulatory policy. The study highlights the need to address fragmented reporting standards and technological barriers, emphasizing the urgency of aligned and data-driven ESG policies, robust assurance mechanisms and adaptive regulation. Originality/valueThis research seeks to provide methodological insights for interdisciplinary studies in sustainability accounting, integrating BDA. It explores the transformative potential of BDA to reshape sustainability reporting, assurance and policy development.
Purpose This study aims to examine the feasibility of blockchain adoption during investment banks’ Know Your Customer (KYC) validation processes. It studies the role played by government in regulating the blockchain-based KYC process. Design/methodology/approach A framework based on the extended technology acceptance model (TAM) was conceptualised to formulate six hypotheses. Based on this, a structured questionnaire was developed and administered among the employees of investment banks through a multi-stage sampling technique. The final sample, comprising 605 responses, was analysed using a covariance-based structural equation modelling (Mediation Analysis) on JASP V.19. Findings The present research explains that the government, as a mediating variable, has a 45.7% direct impact and 54.3% indirect effect on investment banks in the adoption and actual usage of blockchain technology for KYC validation. The perceived ease of use, perceived usefulness and attitude to use technology are key factors that influence its adoption for front-office operations. Perceived ease of use is a dominant indicator within the model. Research limitations/implications This study contributes theoretically by extending the existing TAM model with its practical application in the KYC process during validation of customer documentation in the banking industry, adding practical relevance to the regulatory framework. Originality/value The research derives its originality from the mediating role of government regulation in implementing KYC through blockchain. It proposes a blueprint of a working model that can be internalised to optimise the processes, extending the existing theory and its application with practical relevance.
Purpose This study aims to critically analyse the landscape of Micro, Small and Medium Enterprises (MSMEs) literatures to identify direction, constrains and knowledge gap in performance analysis and sustainability. Design/methodology/approach This comprehensive review applies Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol to select systematically publication source listed in Australian Business Deans Council (ABDC) from “Scopus” and “Web of Science” (WoS) databases. Findings The finding shows that strategic enablers and managers maturity significantly affect MSMEs performance. Government support facilitates the adoption of sustainability, while environmental knowledge and practices and management compliance and systems enable MSMEs to evaluate their environmental strength and weaknesses. Government and top management support, collaborating skills, training and innovation determine the effective coordination between performance and sustainability. Originality/value Prior studies predominantly focus on performance analysis and sustainability of MSMEs separately. A comprehensive and integrated review of both dimensions has been considered rather than individual focus to bridge the gap by offering systematic synthesis.
Purpose This study aims to address two key managerial decisions in marketing: allocating the promotional budget and selecting customer groups for each promotion.Design/methodology/approach The authors propose a linear programming model that optimizes these decisions jointly to maximize campaign returns. Rather than assigning promotions to broad, manually defined customer segments, the framework applies machine learning to form data-driven micro-clusters based on customers' estimated propensities for each promotion type.Findings Computational results show that the mini-batch k-means algorithm can generate up to 20,000 high-quality clusters with limited computational effort, enabling precise and efficient targeting. Moreover, the proposed linear programming model can be solved within reasonable time even at this scale, demonstrating the practical applicability of the solution approach.Practical implications The system aims to improve marketing campaign performance while reducing manual effort and minimizing reliance on user expertise. The system is also designed to accommodate extensive customization. Marketing managers may adjust promotion parameters and specify objectives - such as profit maximization, inventory reduction or customer acquisition - according to current strategic needs.Originality/value Integrating linear optimization with machine learning techniques provides a unified and analytically robust solution to marketing budget allocation, improving promotional effectiveness and resource utilization.
Purpose This paper aims to develop an artificial intelligence (AI)-driven Bayesian framework for valuing country-level HR technology readiness under uncertainty and volatile financial environments. Traditional rankings provide point estimates that create a false sense of precision, leading to suboptimal investment decisions. The authors' integrate AI-powered uncertainty quantification into managerial decision models based on real options theory and portfolio optimization, demonstrating how machine learning (ML) enhances strategic decision intelligence.Design/methodology/approach The authors' construct the Global HR Technology Readiness Index (GHRTI) using Bayesian ML - specifically, probabilistic factor analysis implemented through advanced Monte Carlo algorithms (MCMC with no-U-turn sampling). This AI methodology processes data from 217 countries (2010-2025), handling missing data through intelligent Bayesian imputation. The framework integrates AI-generated uncertainty-aware outputs into real options analysis for market entry and modern portfolio theory for capital allocation, creating an end-to-end AI decision support system.Findings Switzerland ranks first (96.2, 95% CI: 94.8-97.4). Developed countries exhibit narrow confidence bands (+/- 1.2 points), while developing nations display wider ranges (+/- 3.8 points). Incorporating AI-generated uncertainty distributions into decision models leads to substantially different investment choices. The AI-enhanced uncertainty-aware approach outperforms traditional point-estimate strategies by 18-25% in high-uncertainty scenarios. Predictive validity is strong: GDP growth (r = 0.76), patents (r = 0.82), FDI (r = 0.71). The framework demonstrates how AI transforms static rankings into dynamic decision intelligence.Research limitations/implications The contribution is primarily theoretical and methodological, demonstrating AI's potential to revolutionize business intelligence. Empirical validation requires controlled experiments or case studies of actual managerial decisions. Country-level data may mask sub-national variations. Their analysis acknowledges data quality challenges in emerging economies, where missing data and measurement inconsistencies result in wider credible intervals that accurately reflect genuine uncertainty rather than false precision. The Bayesian framework explicitly propagates data quality uncertainty through the entire analysis, with credible interval widths serving as transparent indicators of measurement reliability. Future research should explore deep learning extensions and real-time AI updating mechanisms.Practical implications The AI-driven framework enables managers to value strategic flexibility, optimize capital allocation across country portfolios and implement sophisticated risk management. It provides decision-relevant intelligence beyond simple rankings, helping counteract overconfidence bias through ML-powered uncertainty quantification. Organizations can implement this as an intelligent decision support system.Originality/value To the best of the authors' knowledge, they were the first to formally integrate AI/ML methodologies (Bayesian inference, probabilistic programming) into country-level technology indices within an economic framework of decision-making under uncertainty. Bridges AI/ML, composite index methodology and managerial decision theory (real options and information economics), demonstrating how AI revolutionizes business intelligence from descriptive rankings to prescriptive decision support.
Purpose This paper aims to make a thorough bibliometric analysis of blockchain technology in document verification in the education sector to trace the intellectual organization of the subject matter and suggest future research lines. Design/methodology/approach The data set comprises 1,054 papers located in the Scopus database. The performance analysis was applied to identify the best authors, best institutions, best countries, best journals and best publications. Moreover, VOSviewer was used as science - mapping methods to analyse the collaboration networks, keywords co-occurrence patterns, thematic clusters and citation patterns in the research field. Findings The analysis reveal that there are four significant thematic groups: (1) blockchain and related technologies; (2) integration with modern technologies such as artificial intelligence (AI), Internet of Things (IOT), machine learning and cybersecurity; (3) major security issues such as privacy, authentication, secure access control mechanisms; and (4) blockchain uses in virtual environments and protection of network infrastructure. The findings show that there is a great rise in interdisciplinary studies and international cooperation, as well as reveal research gaps belonging to multi-database studies and diversified use. Research limitations/implications The research is constrained to the papers that were included in the Scopus database, and this might be missing other studies that are relevant. Future research can use several databases and mixed research techniques to expand the range and level of analysis. Practical implications This study is innovative in that it advances the understanding of the conceptual and social structure of blockchain applications for document verification in the education sector by analysing the body of previous literature and offering insightful recommendations. Originality/value The research will offer a systematic and up-to-date bibliometric mapping of blockchain-based document verification in education research. It can provide useful knowledge to researchers, practitioners and policymakers and aid in the creation of verbal, intellectual and social infrastructure of the discipline, as well as facilitate the construction of secure and scalable verification systems in the educational ecosystem.
Purpose This research addresses the critical need for a comprehensive framework to assess transportation disruptions and their cascading effects on supply chain performance. This study aims to overcome the limitations of traditional risk management techniques that handle disruptions in isolation, ignoring their systemic, interdependent and uncertain nature. Design/methodology/approach This research uses a two-phase integrated decision-support approach combining Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II) and fuzzy cognitive maps (FCMs). In Phase 1, transportation disruption factors are identified through systematic literature review and expert consultation, then prioritized using PROMETHEE-II based on transportation cost, time and service reliability impacts. Phase 2 develops an FCM where nodes represent risks and performance indicators, with weighted edges capturing causal influence strength and direction. MATLAB-based FCM simulations enable dynamic scenario analysis and sensitivity testing until system convergence. Findings The analysis identifies nine critical transportation disruption factors, with political tensions, labour strikes, logistics provider failures and natural disasters emerging as the most significant sources of disruption. FCM simulations demonstrate how these factors propagate through the system and critically impact supply chain performance metrics (cost, lead time and inventory stability). Freight damage and political tension show the highest impact on cost (20.81% and 17.09%, respectively), while labour strikes and freight damage most significantly affect lead time (18.71% and 17.14%). Natural disasters and political tension have the greatest influence on inventory performance (20.16% and 18.88%). Originality/value This hybrid approach presents the first scalable, interpretable and technically sound solution that integrates multi-criteria decision-making, semantic modelling and fuzzy systems analysis for understanding transportation disruption dynamics. The model’s convergence after nine iterations demonstrates its stability and predictive power, providing supply chain practitioners with an effective means for proactive risk reduction and resilience maximization in dynamic logistics environments. The framework bridges the gap between qualitative expert knowledge and quantitative analysis, enabling real-time, context-aware decision-making for transportation risk management.
Purpose This study aims to investigate the intellectual structure of research on artificial intelligence (AI) within the sales domain through a co-word analysis approach. By examining patterns of keyword co-occurrence in scholarly publications, the study seeks to uncover the major research themes, trends and knowledge clusters shaping the field. Furthermore, the study evaluates Iran’s contribution and positioning within this global research landscape. In addition, this research explicitly highlights its academic contribution by systematically mapping the intellectual structure of AI-sales studies and situation of Iran’s position within the global landscape. Design/methodology/approach A total of 5,399 scientific articles published between 1984 and 2024 were retrieved from the Scopus database. VOSviewer software was used to analyze the titles and abstracts of these publications, enabling the visualization of co-occurring terms and the identification of thematic clusters within the literature. Findings The results reveal that “sales” and “artificial intelligence” are the most prominent and frequently co-occurring terms, indicating their central role in the field. The co-word analysis identified six major research clusters: sales forecasting and data analytics; personalization and recommendation systems; natural language processing and chatbots; intelligent order and inventory management; market analysis and dynamic pricing; and smart sales security and regulatory compliance. Originality/value This study provides a structured framework that can guide future research on AI applications in sales. It also offers practical insights for developing intelligent sales strategies and aligning marketing practices with technological advancements. The long-term significance and methodological novelty of this scientometric approach further strengthen the study’s contribution to both academic research and practice.
Purpose This study aims to systematically review the application of machine learning (ML) techniques in predicting two major categories of financial risk: consumer loan default risk (CLDR) and systemic risk (SR) arising from institutional interconnectedness. The purpose is to identify the dominant models and methodological trends that define the current research landscape. Design/methodology/approach Using the preferred reporting items for systematic reviews and meta-analyses framework, this review initially retrieved 130 journal articles from the Scopus database, of which 50 met the inclusion criteria. The selected studies were comparatively analyzed to identify common prevailing ML techniques, data sets and evaluation metrics used for predicting CLDR and SR. Findings The review finds that models such as decision trees, support vector machines, naïve Bayes, random forest, LightGBM, XGBoost and artificial neural networks dominate CLDR prediction research. For SR, complex network embeddings methods, graph neural networks, recurrent neural networks (RNN), convolutional neural networks and network centrality measures are prevalent. Research limitations/implications The review also identifies contextual limitations and recommends incorporating hybrid models that combine deep learning with tree-based algorithms and explainable artificial intelligence interpretability for CLDR prediction. For SR, multivariate RNN-based models are recommended, and network-based analyses should be extended to include nonbank intermediaries, incorporating macroeconomic factors, policy environments and broader data sets in future research. Practical implications The findings offer valuable insights for implementing accurate, interpretable ML models to improve loan approval processes, credit risk monitoring and SR mitigation strategies. Social implications Enhanced financial risk prediction can strengthen economic stability, protect consumers from default and mitigate systemic crises, while interpretable ML models promote transparency and trust in automated financial decisions. Originality/value This study offers a comprehensive synthesis of ML approaches to financial risk prediction, emphasizing hybrid models, interpretability and broader systemic analysis. It serves as a guide for researchers and practitioners to develop more robust and context-aware predictive frameworks in financial institutions.
Purpose The purpose of this study is to assess the operational efficiency of commercial casinos across US states and examine the relationship between efficiency and broader economic productivity, particularly in relation to community well-being. Design/methodology/approach A quantitative approach using data envelopment analysis was adopted to assess state-level casino operations. The output-oriented Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) models were applied to secondary data from the American Gaming Association’s annual reports. Findings Of the 21 US states with legalized commercial casinos, 13 demonstrated operational efficiency. The analysis revealed that underperforming states could enhance their socioeconomic contributions by improving employment-related inputs, highlighting opportunities for more impactful resource allocation. Originality/value This research contributes to the benchmarking literature by applying data envelopment analysis to the casino industry, a sector that is frequently overlooked in performance analysis. This study offers a novel perspective on how operational efficiency in hospitality-based entertainment can inform regional productivity strategies and policy development.
Purpose The franchising sector has emerged as a strategic investment avenue, offering scalability, standardization and risk mitigation. However, franchise chains differ significantly in their ability to create value. This study aims to investigate how different franchise profiles generate value, considering financial, operational and institutional attributes.Design/methodology/approach A quantitative methodology was used using unsupervised machine learning techniques. Initially, the K-Means algorithm served as an exploratory segmentation tool. Subsequently, principal component analysis (PCA) was applied for dimensionality reduction, followed by hierarchical density-based spatial clustering of applications with noise (HDBSCAN) as the primary clustering technique. This approach enabled the identification of dense cluster structures and the detection of atypical franchise chains. Variables such as franchise fee, payback period, franchisee support and franchise chain satisfaction were analyzed.Findings HDBSCAN outperformed K-Means by revealing clusters that more accurately represented the underlying data structure. Six distinct strategic profiles were identified, including an outlier cluster (Cluster -1), comprising highly differentiated franchise chains. The clusters exhibited patterns linked to institutional reputation, organizational maturity and sector engagement. The results indicate that value creation extends beyond initial investment and relies on a combination of structured support, operational standardization and symbolic positioning.Research limitations/implications Although the applied clustering techniques effectively captured structural nuances, the study was limited to a specific set of financial and operational variables. Future research could benefit from incorporating behavioral, digital and regional dimensions to enrich the understanding of value creation in franchise chains.Originality/value This study contributes to franchising literature by integrating advanced unsupervised learning techniques to segment franchise chains and uncover strategic profiles. The findings provide valuable guidance for franchisors and investors, emphasizing the centrality of institutional reputation and support mechanisms in sustaining franchise success. The use of HDBSCAN represents a methodological contribution by enhancing the accuracy of cluster detection in complex data environments.
Purpose This study aims to identify, structure and model the key factors influencing the adoption of green food supply chain (GFSC) practices in Iran’s pasta industry. Given the growing need to reduce the environmental footprint of food production and distribution, the research seeks to provide a clear analytical framework for understanding the drivers and outcomes of sustainability integration in this sector. Design/methodology/approach A systematic literature review is conducted to extract relevant indicators associated with the GFSC implementation. These findings are complemented by expert consultation involving 13 specialists from academia and the pasta manufacturing industry. Fifteen influential factors are identified and evaluated through pairwise comparisons. Interpretive structural modeling (ISM) is then applied to determine the hierarchical relationships among the variables, followed by matrix impact cross-reference multiplication applied to a classification (MICMAC) analysis to assess their driving and dependence power. Findings Results reveal a four-level hierarchical structure in which environmental regulations, managerial commitment, technological capability and social responsibility function as foundational drivers of green supply chain adoption. Mid-level organizational enablers such as culture, clean technology investment, procurement practices and financial incentives link strategic goals to operational change. Outcome-oriented factors, including transparency, consumer awareness and corporate responsibility, emerge as dependent variables shaped by the broader system. Originality/value This study offers a structured multilevel model that integrates ISM and MICMAC to examine sustainability adoption in a key segment of Iran’s food industry.
Purpose Allocating common costs across projects is a challenging problem in management accounting. Analytical techniques for allocating multiple costs are limited in availability. This study aims to propose an analytical method for allocating multiple fixed costs across multiple managerial criteria. Design/methodology/approach This paper investigates different sequential and data envelopment analysis models to identify conditions and propose solution procedures for multi-criteria multi-resource (MCMR) fixed-cost allocation (FCA) problems. Findings The paper shows that certain multi-resource, single-criterion problems can be solved as independent, multi-stage, single-resource, fixed-cost allocation problems. However, for MCMR-FCA problems with conflicting criteria, a three-dimensional genetic algorithm cube data structure is necessary to solve them. Practical implications This paper presents a range of analytical and heuristic methods for allocating multiple costs and resources across projects based on a broad set of managerial criteria. Originality/value To the best of the authors’ knowledge, this study is a pioneering work on allocating multiple fixed costs to multiple projects under multiple managerial criteria.
Purpose Optimization is essential in business, engineering and AI-driven decision-making. However, conventional methods like Differential Evolution (DE) struggle with high-dimensional, dynamic and multi-modal problems due to fixed adaptation mechanisms. This study aims to introduce a hybrid metaheuristic approach, EvoQ-EA (Evolutionary Q-Learning-Based Evolutionary Algorithm), which integrates Covariance Matrix Adaptation - Evolution Strategy (CMA-ES), Evolutionary Game Theory (EGT) and Deep Q-Networks (DQN) to enhance adaptive search and decision-making.Design/methodology/approach EvoQ-EA dynamically selects mutation and crossover strategies using reinforcement learning (DQN), optimizes exploration-exploitation balance via CMA-ES and refines search space reduction through EGT-based historical solution analysis. The framework is evaluated on 13 benchmark test functions for robustness and tested on a real-world supply chain data set, benchmarking its performance against NSGA-II (Non-dominated Sorting Genetic Algorithm II). Additionally, EvoQ-EA is applied to Neural Architecture Search (NAS) and compared with NSGA-II and Random Search in terms of accuracy, computational efficiency and model size.Findings EvoQ-EA demonstrates superior robustness in supply chain optimization. It reduced the expected cost by 30.53% and the worst-case cost by 24.56% compared to NSGA-II. It improved the expected service level by 0.055% and the worst-case service level by 0.20%, highlighting its resilience under uncertainty. In NAS, EvoQ-EA outperforms NSGA-II and Random Search, achieving the highest accuracy (91% vs 88% and 85%), lowest computational cost (3.34 GFLOPs vs 8.11 and 7.89), but with a slightly larger model size (4.78 MB vs 2.47 and 2.92 MB).Originality/value This study bridges evolutionary algorithms with AI-driven decision-making, offering an adaptive, scalable and efficient solution for real-world optimization. By demonstrating superior robustness in supply chain management and efficiency in Neural Architecture Search, EvoQ-EA provides a next-generation approach to complex optimization challenges.
Purpose This study aims to identify and analyse consistent future scenarios in the Execution and Results domains of the European Foundation for Quality Management (EFQM) Model 2025 by using an integrated multi-method framework.Design/methodology/approach An integrated mixed-methods framework was used, with fuzzy cognitive mapping (FCM) modelling causal relationships and fuzzy linguistic Matrice d'Impacts Crois & eacute;s Multiplication Appliqu & eacute;e & agrave; un Classement (FLMICMAC) analysing influence-dependence in parallel to identify the initial structural drivers of the Execution and Results domains. These were refined via pairwise comparisons and the Pareto principle to obtain final strategic drivers. The cross-impact balance (CIB) method generated scenario configurations and identified internally consistent scenarios, while quality function deployment (QFD) quantified the relationships among the cross-domain internally consistent scenarios and was used to analyse the impacts and prioritise them. The study used data from 61 senior managers, experts and scholars within a large Iranian industrial holding.Findings In the Execution domain, four final strategic drivers generated 81 scenario configurations, from which two internally consistent scenarios emerged: sustainable transformation through strong stakeholder relationships and overall experience, and crisis of instability and dissatisfaction. In the Results domain, three final strategic drivers produced 27 scenario configurations, yielding two consistent scenarios: sustainable transformation through branding and crisis of trust and transformation failure. Cross-domain QFD analysis showed an asymmetric pattern: the desirable Execution scenario strongly supported the desirable Results scenario and reduced the likelihood of the undesirable one, while the undesirable Execution scenario produced only weak, non-proportional effects, indicating non-linear interactions driven by each scenario's internal causal coherence.Research limitations/implications The study proposes structural interventions, including a "Stakeholder Experience Office" (SXO) to integrate total experience and a "Transformation Steering Committee" to secure governance buy-in for transformational mandates. It also recommends deploying a "Strategic Alignment Radar" to detect trust-performance decoupling. Furthermore, it advocates an "Asymmetric Resource Allocation" strategy, grounded in clear cost-benefit logic, to mitigate strategic hysteresis and secure long-term organisational resilience against competitive erosion.Originality/value This study advances the EFQM literature by reconceptualising the Execution and Results domains as interacting yet analytically distinct foresight systems, rather than assuming a linear enabler-results relationship. Methodologically, it introduces a novel integrated framework that combines fuzzy methods (FCM and FLMICMAC) with consistency-based scenario analysis (CIB) and cross-domain scenario analysis and prioritisation (QFD). This innovative approach transforms the EFQM Model 2025 from a retrospective diagnostic tool into a future-oriented decision-support system.
Purpose This study aims to develop a comprehensive optimization framework for seafood supply chains that simultaneously addresses economic, environmental, social and risk-resilience objectives. The goal is to provide a decision-support tool that enhances sustainability, transparency and operational robustness. Design/methodology/approach A multi-objective mathematical model is proposed for a four-tier seafood supply chain that incorporates forward and reverse logistics. The model integrates blockchain for traceability, machine learning (ML) for demand forecasting and circular economy principles for resource recovery. It is solved using three metaheuristic algorithms, NSGA-II, MOPSO and MOEA-D. Performance is assessed using criteria such as optimality gap, Pareto spread, time efficiency and solution diversity. Findings MOEA-D consistently outperformed NSGA-II and MOPSO, particularly in large-scale scenarios, by offering better balance among conflicting objectives and superior solution diversity. The model effectively reduces costs and emissions, improves customer satisfaction, enhances job creation in underdeveloped regions and mitigates supply chain risks. Sensitivity analyses confirm the model’s robustness across varying demand levels. Originality/value To the best of the authors’ knowledge, this is among the first studies to jointly integrate blockchain, ML and circular economy strategies into a unified multi-objective model for seafood supply chains. The framework advances academic discourse and provides managers with a scalable tool aligned with Sustainable Development Goals (SDGs), particularly SDG 8, SDG 9 and SDG 12.
Purpose This study aims to develop a decision-support framework that assists small- and medium-sized enterprises (SMEs) in navigating the challenges and opportunities presented by the integration of artificial intelligence (AI). The framework is designed to help SME leaders prioritize initiatives that enable them to gain and maintain a sustainable competitive advantage in an increasingly AI-driven business environment. Design/methodology/approach A constructivist research approach is used, facilitating collaborative knowledge exchange among a panel of experts. The study incorporates cognitive mapping, the nominal group technique (NGT), interpretive structural modeling (ISM), the Warshall algorithm and Matrice d'Impacts Croises Multiplication Applique a un Classement (MICMAC) analysis. These methods are used to identify critical factors and explore interrelationships that can empower SMEs in the AI context.Findings The decision-support framework developed is dynamic and iterative, allowing for continuous refinement as new insights emerge. It systematically prioritizes key initiatives that can enhance the ability of SMEs to effectively adopt and leverage AI technologies. These initiatives are organized into six key clusters: (1) Human Resources; (2) Innovation and Technological Infrastructure; (3) Organizational Culture; (4) Operational Efficiency; (5) Security and Privacy; and (6) Strategic Leadership.Originality/value The framework provides a structured approach for SMEs to address the key challenges associated with AI adoption, such as the need for significant financial resources, concerns about data privacy and security and the lack of technical expertise. By following this framework, SMEs can better equip themselves to integrate AI technologies and sustain a competitive edge in the marketplace. It also offers a novel decision-support framework for SMEs, with an emphasis on empowering leaders through a better understanding of AI and its potential to transform business practices. The dynamic, expert-driven methodology makes the framework highly adaptable to the changing AI landscape.
Purpose This study aims to identify and analyze the key challenges organizations face in adopting Generative Artificial Intelligence (Gen-AI) as a strategic enabler for transitioning toward Supply Chain 5.0, a paradigm that emphasizes sustainability, resilience and human-centricity, and to develop a novel AI-ENABLE framework that provides a structured and actionable roadmap for facilitating the responsible integration of Gen-AI to achieve next-generation supply chains.Design/methodology/approach A systematic literature review was undertaken to identify the major challenges associated with Gen-AI adoption in the context of Supply Chain 5.0. Expert validation was conducted to ensure contextual relevance, after which the Neutrosophic Decision-Making Trial and Evaluation Laboratory (N-DEMATEL) technique was applied to prioritize the identified challenges and classify them into cause-and-effect categories. This hybrid approach enabled the study to address uncertainty and subjectivity in expert judgments while uncovering causal relationships between adoption challenges.Findings The results reveal that skill shortages, high investment costs and data privacy concerns are the most influential causal challenges restricting Gen-AI adoption. These, in turn, escalate side-effect challenges, such as resistance to change management, scalability issues and a lack of transparency. The findings not only highlight the interconnected nature of these challenges but also establish skill development and strategic investment as foundational enablers for adoption. To address them, the proposed AI-ENABLE framework provides organizations with a structured mechanism for workforce upskilling, phased implementation, ethical governance, process re-engineering and continuous innovation - thereby ensuring sustainable, resilient and human-centric adoption of Gen-AI.Originality/value While prior studies have predominantly focused on the technical advantages and potential applications of Gen-AI in supply chain management, little attention has been paid to the practical adoption challenges that hinder its transition toward Supply Chain 5.0. This study fills that gap by systematically identifying and prioritizing these challenges using N-DEMATEL and by proposing the AI-ENABLE framework, which uniquely integrates human-centric, ethical, technological and financial considerations. This study contributes to both theory and practice by enriching the knowledge base with a novel decision-support framework that guides managers and policymakers in navigating adoption complexities and unlocking the transformative potential of Gen-AI for Supply Chain 5.0.
Purpose Research is increasingly governed by artificial intelligence (AI) algorithms that operate as “black boxes” within human resource (HR) practices. The purpose of this paper is to examine how human-in-the loop (HITL) models incorporating explainable AI (XAI) strategies, specifically SHAP and LIME, empower human resource managers to effectively supervise and govern AIdriven decisions in critical HR scenarios. Design/methodology/approach It used a mixed methods experimental design, with 240 HR managers of various industries. They constitute retention/no explanation, retention/LIME explanations, retention/SHAP explanations and synthesis retention. Four machine learning models were trained on both simulated and real alternative anonymized HR data (Logistic Regression, Random Forest, XGBoost and Neural Network). These measurable metrics (accuracy, confidence, trust) were evaluated in light of qualitative findings of semi-structured interviews. Findings Findings demonstrate that XAI substantially enhances quality of decisions made, measurement increases accuracy by 12.1% by use of LIME and 14.8% by use of SHAP, as opposed to passing information to AI recommendation explanations. SHAP was most accurate and most managerially confident. When AI systems became explained, trust in them also rose significantly. Qualitative responses have identified that transparency minimized amount of thought required and facilitated sufficient HR decision-making. Originality/value To the best of the authors’ knowledge, the proposed framework is one of first empirically verified HITL governance frameworks that can be applied to HR; it is a blend of technical techniques for XAI-based finding issues and human parties to draw final conclusions. It provides evidence-based practical guidance on foundation for balancing automation efficacy and ethical human care, as well as revitalizing AI governance theory and ethical human resource technology applications.
