Search Results (4,025)

Implementing the principles of sustainability in business practices evokes complex changes in the way companies operate and behave. The purpose of this paper is to reveal the concept of corporate sustainability from the corporate governance perspective. Based on the integrative review method, a multilevel conceptual model of sustainable corporate governance practice is designed. The model connects the global, macro and micro levels of sustainability paradigm paramount impacts on the corporate governance mechanisms, thus highlighting the path for creating and implementing more sustainability-related governance practices. The applicative contribution of the paper stems from the improved understanding of the corporate sustainability paradigm taken from the corporate governance perspective, while the model contributes to the reduction in tensions that corporate sustainability induces in the processes of corporate decision-making and management. To corroborate the model operability, we have inspected the current state of the adopted corporate sustainability practices in Croatian companies. The research results show that neither strategic nor operational integration of sustainability into business models has yet been achieved. Sustainability is primarily considered as compliance-based, without strategic approach to sustainability management that would nominate clear objectives and link them to annual top management goals and performance. Recommendations for the future research are provided. Full article

Hyperspectral imaging is a well-established remote sensing technique for material detection and classification, relying on hundreds of reflectance bands to exploit the spectral signatures of surface materials. Although hyperspectral imagery has demonstrated excellent capabilities for material identification, its operational implementation may be constrained in some applications due to data volume and processing requirements. Consequently, there is growing interest in evaluating the capability of lower-dimensional multispectral imagery for material detection tasks. In this sense, this article proposes as its contribution the comparative evaluation of machine learning models and neural networks for asbestos–cement detection on VNIR imagery. For the development of this research, the CRISP-DM methodology was adapted into four phases: P1. Business and data understanding; P2. Data preparation; P3. Modelling and evaluation; P4. Model deployment. At the results level, three datasets with different numbers of bands were constructed, which were structured by adding to the original dataset an additional layer with the NDVI and two additional layers with the PCA components of the original image. Across the three datasets, four machine learning models and one neural network model were tuned and evaluated, yielding as a result that in all three datasets the KNN and neural network models achieved the best performance. Likewise, it was found that the detection capability of the models improved with the inclusion of the additional bands. The proposed approach serves as a reference to be extrapolated by research centres and universities for the detection of asbestos and other materials in VNIR images, with a view toward integration into resource-constrained systems and specifically into environmental monitoring systems. Full article

The Markets in Crypto-Assets Regulation (MiCA) harmonizes market rules across the European Union, but it does not itself determine how entities should classify, measure, document, and tax crypto-asset transactions. This study examines whether Slovakia’s recent implementation measures have translated formal harmonization into operational accounting clarity. An anonymous online survey of 34 accounting, tax, finance, and business professionals recruited through purposive and convenience sampling was analyzed using counts, percentages, a descriptive cross-tabulation with Cramér’s V, and a documented but limited coding of two open-ended items. Because the sample is small and non-probability, all results are interpreted as indicative of the observed respondents rather than as population estimates. In the sample, 52.9% assessed the direction of legislative development positively, 72.7% of valid respondents considered current valuation rules inadequate, 60.6% reported that the reforms had not increased accounting clarity, and 60.6% perceived greater uncertainty. Tax obligations (50.0%) and record-keeping and documentation (35.3%) were selected more often than bookkeeping mechanics (14.7%). Practical experience co-varied with legislative monitoring in the realized sample (Cramér’s V = 0.62), although no population-inferential p-values or confidence intervals are reported. The pattern suggests a regulatory–operational clarity gap: legal taxonomy and market supervision have advanced faster than implementable valuation and documentation guidance. The proposed implementation framework combines survey indications with regulatory analysis, prior literature, and the authors’ professional judgment; it is a non-ranked proposal for consultation and further testing rather than a set of empirically validated policy priorities. Although derived from Slovakia, the framework is relevant to other EU jurisdictions translating MiCA’s common market rules into national accounting and tax practice. Full article

This study examines how blockchain technology is associated with institutional governance and operational effectiveness in blockchain-enabled plastic recovery, with particular attention to resource-constrained developing regions and Less Developed Countries (LDCs). Although prior research highlights blockchain’s technical capabilities, less attention has been given to the institutional, socio-technical, financial, and data governance conditions that shape practical adoption. To address this gap, the paper develops a literature-derived Blockchain-Enabled Waste Management Framework (B-WMF) and evaluates it through an interpretivist single-case study of Plastic Bank. The findings suggest that blockchain’s primary value in this case lies less in technological novelty than in its capacity to support verified recovery records, incentive-linked participation, auditability, and multi-stakeholder coordination. At the same time, the case shows that traceability, tokenized incentives, interoperability, and decentralized verification remain conditional on data quality at the source, institutional oversight, regulatory alignment, and sustainable business models. The paper contributes by reframing blockchain as a socio-technical governance infrastructure for blockchain-enabled plastic recovery, while offering practical guidance for circular economy initiatives operating in resource-constrained environments. Full article

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured. Full article

Sustainable hydrogen policy and national strategy documents are rich in qualitative information whose systematic evaluation still relies largely on subjective SWOT frameworks. This study proposes a reproducible hybrid methodology that couples expert-supervised Natural Language Processing (NLP) with a stochastic techno-economic model of the Levelized Cost of Hydrogen (LCOH) to convert policy discourse into quantitative, evidence-based recommendations. TF-IDF vectorization, K-Means clustering, Shannon entropy and Correspondence Analysis (CA) are applied to a manually annotated corpus of 107 sentences drawn from Türkiye’s national hydrogen strategy documents (Cohen’s κ = 0.81, substantial agreement). CA positions Regulation/Legislation and Financing near the Weakness quadrant, Renewable Resource Potential in the Strength quadrant, and Export/Demand Risk near Opportunity—revealing structural bottlenecks that challenge the sustainable energy transition. These qualitative findings are subjected to a quantitative consistency check via a Monte Carlo simulation (N = 10,000 iterations) propagating joint uncertainty in CAPEX, electricity price, electrolyzer efficiency, annual operating hours, discount rate and plant lifetime. The deterministic 2025 LCOH baseline of 4.89 €/kg H₂ carries a P10–P90 interval of [3.95; 5.92] €/kg. Global Sobol sensitivity analysis identifies electricity price as the dominant driver (S₁ ≈ 0.52), suggesting that financing is discursively surfaced by the textual layer. Under business-as-usual technological learning, the probability of reaching a globally competitive LCOH (≤2 €/kg H₂) by 2050 is only 17.8%; a stylized proactive policy intervention (carbon pricing + subsidies) raises this probability to 78.4%. The framework is adaptable to other countries and languages (though the current implementation is Turkish-specific), providing a scalable, open methodology for evidence-based sustainable clean energy planning. Full article

Rapidly developing, mid-sized Canadian municipalities often suffer from a deficit in dedicated modernization capacity, leaving public infrastructure lagging behind growth and reliant on historically “grandfathered” legacy solutions. To overcome the lack of empirical, data-backed planning in these regions, this paper proposes an agile, data-driven smart city framework centered around a localized digital twin (DT) environment. The framework is evaluated through a case study of a proposed new public transit route in Abbotsford, British Columbia, a rapidly expanding city grappling with decentralized commercial zones and low-density sprawl. Our approach synthesizes heterogeneous, multi-source spatial data, including regional commuter trajectories, real-time Abbotsford International Airport (YXX) flight schedules, and points of interest (POI) business densities, to map high-resolution hourly temporal variations in traffic conditions. These streams feed into a virtual simulation framework that evaluates operational cost–benefit trade-offs for proposed transit routes. Crucially, this framework serves as a living, continuously updated system that enables resource-constrained cities to dynamically simulate transit networks as commercial footprints and transit volumes evolve. Finally, we discuss the roadmap for this framework, detailing how integrating predictive AI models and gamified interfaces can democratize urban planning, enabling municipal stakeholders and non-technical operators to interactively co-design public transit systems. Full article

As organizations lean more heavily on their IT systems, managing cyber risk is gaining increasing importance. Organizations are often challenged to determine which cybersecurity risk framework they should adopt. Choosing the right framework can have a significant impact on the quality of governance, operational resilience, and assurance in risk reporting. However, most prevalent cybersecurity risk frameworks vary significantly in their intent, design, and analytical approach. This makes it difficult for organizations to understand how each framework may meet their business needs. This study presents an AI-enhanced multi-criteria decision support approach for evaluating cybersecurity risk frameworks. The model incorporates machine learning-driven risk scoring as a conceptual input layer, enhancing the objectivity and analytical rigor of the comparison without executing new predictive algorithms. The methodology includes a hybrid approach of literature review, document analysis, and multi-criteria decision analysis (MCDA) to compare and rank NIST CSF, ISO 27001, FAIR, OCTAVE, and CRAMM based on eight criteria that are designed to represent modern requirements for risk frameworks, including governance, scalability, quantitative focus, and interoperability. These criteria also reflect differences in security metrics supported by each framework to provide an organized means to compare qualitative versus quantitative measurement methodologies. The results indicate that NIST CSF performs the best overall in agility, business alignment, and interoperability. ISO 27001 outperforms all others in established governance and compliance. FAIR outperforms all others in quantitative risk analysis and provides superior analytical depth that other frameworks do not offer. OCTAVE and CRAMM function well in legacy systems but lack scalability and are not well-suited for modern distributed systems. Robustness analysis shows that the ranking of NIST CSF, ISO 27001, and FAIR is consistent under different weighting combinations and industry types. The result of this research demonstrates that a combined or hybrid approach to cybersecurity risk framework selection, such as using NIST CSF with FAIR, can give organizations a more well-rounded foundation for applying machine learning-enabled risk analytics with cyber controls. This research also offers a reusable decision support tool that organizations can leverage when aligning their risk priorities to the features of cybersecurity risk frameworks. Full article

This study develops and applies a three-factor matrix model for assessing counterparty risk within Anti-Money Laundering (AML) measures. The research problem arises from the need to transform qualitative features related to ownership, effective control, economic logic of operations, geographical links, documents and review behavior into a comparable and documented risk score. The model uses three factors: counterparty exposure, transaction impact and control vulnerability. Each factor is assessed on a five-point ordered scale, and the overall risk score is obtained by multiplying the three values. The methodology includes scoring rules, rating interpretation, an application algorithm and a link between the profile, due diligence, ongoing monitoring and updating. The model was applied in 2026 to 25 anonymized counterparty profiles selected for methodological demonstration across different risk configurations. Identifying data, personal information and confidential business information were removed, while the relevant risk characteristics were preserved. The results demonstrate how the model distinguishes between low, moderate, increased, high and critical risk and how it creates a traceable link between established facts, numerical assessment and follow-up control actions. The study does not claim predictive accuracy, detection of money laundering or empirical validation against external AML outcomes. Its contribution is methodological and applied: it provides a transparent and explainable framework for structured, documented and proportionate management of counterparty AML risk, including potential use in AI-assisted monitoring under human oversight. Full article

Data Analytics methodologies provide structured approaches for transforming organizational data into actionable knowledge. However, many existing methodologies emphasize technical activities while offering limited support for stakeholder participation, user-centered validation, and organizational adoption. This study presents the construction of MIDA5, a Data Analytics methodology for Business Process Improvement developed using the Design Science Research paradigm. The research combined a comparative analysis of existing Data Analytics methodologies with an empirical experimentation process conducted in an organizational environment. The experimentation involved the execution and analytical deconstruction of a previously implemented Data Analytics methodology to identify operational limitations, stakeholder-related challenges, and methodological gaps. The findings were synthesized into design requirements, methodological components, and design needs that guided the construction of MIDA5. The resulting artifact incorporates principles of Business Process Analytics, User-Centered Design, and User Engagement and Gamification Dynamics through a five-phase structure supported by activities, artifacts, and stakeholder validation procedures. The study contributes a traceable Design Science-based development process that connects empirical findings with design decisions and provides a methodological foundation for the complete methodological specification and empirical evaluation of MIDA5. Accordingly, this study focuses on artifact construction rather than on demonstrating the effectiveness of the resulting methodology. Full article

Community-based forest ecotourism is an important livelihood pathway for communities around protected forests, but household agritourism operators often face ecological regulations, seasonal demand and heterogeneous tourist expectations. These conditions increase information asymmetry and make service matching difficult. This study examines how two-way information disclosure affects business performance in community-based forest ecotourism. Drawing on social penetration theory, we test a chained mechanism linking two-way information disclosure to operational performance. Based on 780 valid questionnaires from agritourism operators and core family managers around Giant Panda National Park, China, this study measures information disclosure practices, service responsiveness, operator-side perceptions of tourist responses and self-reported business performance. The results show that seller- and buyer-side information disclosure significantly enhance perceived identification. Seller information disclosure has relatively stable effects on customer orientation, perceived quality and operational performance, whereas buyer information disclosure mainly works through perceived identification. Perceived quality, perceived value and satisfaction act as partial transmission channels. Heterogeneity analysis shows stronger effects among low-income and newly established operators. The findings provide micro-level evidence for reducing expectation mismatch and strengthening sustainable livelihood capacity in forest protected-area communities. Full article

The purpose of this paper is to present and evaluate some compliance-oriented approaches designed and patented by the company Elabordati of Matera, which operates in the business services sector with particular reference to administrative, accounting and tax services, together with a comparison with approaches generated by the generative artificial intelligence ChatGPT, with the aim of making a comparison and providing insights to the academic world in terms of data analysis. The two approaches highlighted concern the assessment of the mandatory nature of a DPO, presented in the first part of the paper, and the assessment of enterprise value, presented in the same way in the second part of this paper. Finally, for each approach presented, a subjective comparison is made by the generative artificial intelligence itself. Full article

Digital and intelligent transformation reshapes manufacturing ecosystems, and the synergy between technological innovation and sustainable business upgrading drives high-quality industrial development. Based on knowledge interaction theory, this paper adopts a longitudinal single-case design and the Gioia analytical framework to study BYD covering the period 2003–2025. With data triangulation realized through internal corporate archives, public industrial materials and five semi-structured interviews, this paper explores the staged evolution and value allocation mechanism of sustainable business model innovation driven by firms’ proprietary core technologies. Three sequential phases of technological value circulation are summarized: value creation enabled by single-point core technologies, value addition realized through generic product technologies, and cross-industry value sharing facilitated by industrial technology openness. The traction, utilization and recombination of knowledge generate synergistic advantages of core technologies across the innovation chain, industrial chain and value chain, reconstructing a new value operation logic centered on value creation, value addition and cross-boundary value sharing. The extant literature decouples technological evolution and business model innovation, resulting in prominent theoretical gaps. This study improves relevant theoretical explanations and proposes operable industrial strategies, offering references for manufacturing enterprises to achieve long-term sustainable development relying on core technological capabilities. Full article

As a booming application domain in sharing economy, the crowd logistics services (CLSs) have emerged in recent years to take advantage of under-utilized resources for generating economic value. However, unduly designed CLS system platforms may suffer substantial problems such as sensitive information exposure, excessive commission fees, and trust issues. To mitigate such problems, we propose an approach comprising four initiatives: (1) exploring the applicability of blockchain technology and its affiliated technology, smart contract, in CLSs to manifest blockchain-enabled benefits including service traceability, process transparency, system automation and disintermediation; (2) adopting blockchain and smart contract technologies to design a blockchain application system architecture (BASA) suitable for reengineering current CLSs; (3) demonstrating the blockchain-based crowd logistics services (BCLSs) system design, implementation, and deployment details; and (4) evaluating the functionality and benefit of BCLSs approach to confirm its feasibility and applicability. After presenting the system design and implementation outcomes, this study elaborates the benefits and implications of BCLS systems through four theoretical frameworks: e-Commerce Value Creation Theory (VCT), Innovation Diffusion Theory (IDT), Principal Agent Theory (PAT), and Transaction Cost Analysis (TCA), deriving important findings and implications. Such benefits and implications suggest that BCLSs may help CLSs (1) mitigate PAT frictions and reduce transaction costs; (2) redefine value creation and accelerate innovation diffusion; (3) eliminate platform monopolies and achieve real-time settlement; (4) implement data sovereignty and enhance privacy/security; and (5) reconfigure trust mechanisms and generate digital credit assets. The research results of this study may help the CLS industry clarify the BCLS system’s upgrade path, promote business model innovation, and enhance fair governance and social sharing. Full article

As China’s new energy vehicle (NEV) industry shifts from scale expansion to sustainable competition, enterprise risk is increasingly shaped by price pressure, innovation investment, operational efficiency, and cash-flow quality. Conventional financial early warning models based on static accounting ratios are limited in capturing how such risks emerge and transmit within NEV business ecosystems. This study develops an AI-driven risk governance framework that combines a digital twin-inspired state representation, interpretable machine learning, Shapley additive explanations, and competitive scenario simulation. Using annual data from 2021 to 2025 for twelve listed Chinese NEV automakers, we construct forty-eight enterprise-year observations and predict next-period high-risk status from current-period financial, operational, and competitive state vectors. Logistic regression is used as a transparent benchmark, while XGBoost serves as the main nonlinear learner. The results show that NEV risk identification requires the joint consideration of profitability, R&D intensity, cash-flow quality, asset utilisation, and liquidity, rather than reliance on a single accounting indicator. Logistic regression provides stronger temporal stability, whereas XGBoost achieves higher recall and area under the receiver operating characteristic curve in cross-validation. SHAP results identify return on assets, R&D intensity, operating cash-flow ratio, fixed asset turnover, and current ratio as the leading contributors to model predictions. Scenario simulations reveal asymmetric resilience: low-risk firms can absorb moderate competitive shocks, while high-risk firms remain locked in elevated risk states. This study provides a practical decision-support framework for identifying risk drivers, evaluating competitive shocks, and improving risk governance in sustainable NEV business ecosystems. Full article