Search Results (2,208)

In contemporary Information Technology (IT) enterprises, workforce role configuration decisions sit at the intersection of leadership strategy, corporate Artificial Intelligence (AI) transformation and sustainable operational governance. This study investigates how heterogeneous workforce role configurations affect Service Level Agreement (SLA) compliance in SLA-aware Business Process Model and Notation (BPMN) IT business processes, framing this as a people management and leadership decision problem with explicit Environmental, Social and Governance (ESG)-oriented trade-offs. A validated MATLAB Simulink simulation tool is employed to conduct structured scenario testing across combinations of human role configurations and AI maturity levels, measuring their impact on key Service Level Objectives (SLOs) and Key Performance Indicators (KPIs). To support leadership decision making, a Workforce Sustainability Index (WSI) is introduced that integrates SLA compliance with workforce cost and AI dependency risk, where AI dependency risk captures the social dimension of ESG by emphasizing workforce skill development, upskilling and reskilling pathways, human oversight and responsible AI adoption. The simulation results reveal that no universally optimal configuration exists; the best-performing workforce design depends on organizational context and leadership priorities, as captured through scenario-based weight configurations representing performance-driven, cost-driven, human-centric and balanced governance orientations. These findings provide IT leaders and organizational decision-makers with actionable, evidence-based guidance for sustainable workforce design in the context of corporate AI transformation, contributing to the broader discourse on people management, merit-based organizational performance, responsible AI governance and sustainable digital operations management. Full article

Peer firm identification provides a basis for financial decision-making. Text-based approaches increasingly utilize 10-K annual reports for peer selection because they describe firms’ business activities and strategic positioning. However, most existing methods construct firm representations using full-document embeddings that uniformly utilize the entire textual content. This study proposes a sentence-level industry-discriminative reconstruction framework for peer selection using 10-K reports. The framework trains a sentence-level classification model to predict each firm’s GICS Sector label and uses the predicted probabilities to prioritize sentences reflecting its industry identity and business characteristics. The classifier was trained using 2021 Item 1. Business sections from 4150 non-S&P 500 firms. Reconstruction ratios were validated using 2021 reports from 466 S&P 500 firms and their corresponding 2022 GICS, stock-return, and financial data. The determined ratios were then applied to the same firms’ 2022 reports, with final performance evaluated using 2023 outcomes. Based on the selected sentences, 10-K business descriptions were reconstructed before generating document embeddings for peer selection. Experiments across five embedding models and three peer group settings demonstrate that the proposed framework generally achieves higher GICS alignment, stock-return correlation, and financial homogeneity than full-text, length-matched random-deletion, alternative sentence-selection baselines and benchmark methods. Full article

The study addresses the design and development of a unified corporate information system for multimodal transport and logistics companies engaged in maritime and railway transportation. Analysis of the existing literature revealed the absence of a coherent methodological framework for the design of corporate information systems tailored to the specific operational characteristics of multimodal transport and logistics enterprises. To bridge this gap, a design methodology for corporate information system databases is proposed, intended for subsequent deployment in companies operating multimodal supply chains. The development of the unified corporate information system was guided by the principle of “total costs,” which requires that the decision-maker—when selecting transport modes, methods of transportation, carriers, routing, and auxiliary intermediaries (insurer, stevedore, bank, and customs broker)—address the problem as an integrated whole rather than optimizing individual components in isolation. The study encompasses information modeling of the business processes of multimodal transport and logistics companies, construction of an optimal model of the transport process for maritime and railway transportation using integrated computer automated manufacturing definition (IDEF) and structured analysis and design technique (SADT) modeling, and the design of a multilevel unified database structure for the coordination of different transport modes. A decision-making and support system has been developed for managing the operational activities of a multimodal transport and logistics company engaged in maritime and railway transportation. The proposed unified corporate information system enables the replacement of task resolution by local optimization criteria applied separately to each transport mode—such as freight cost and delivery time—with a single global optimization criterion for the multimodal supply chain. Full article

Steel production contributes significantly to global resource consumption and accounts for nearly 8% of worldwide CO₂ emissions. As the construction industry moves toward decarbonization, the reuse of structural steel has emerged as a highly impactful strategy for reducing embodied carbon and extending material life cycles. However, most end-of-life structures are still demolished using conventional methods that prioritize recycling for scrap value rather than recovering reusable steel components, resulting in the loss of high-quality material and reduced environmental and economic benefits. Despite growing interest in circular construction, systematic analyses of real-world structural steel reuse projects remain limited. This restricts the understanding of the practical conditions required for scalable implementation. Moreover, there is a lack of structured approaches for identifying the factors influencing deconstruction and large-scale structural steel reuse across reverse supply chains. This study addresses these gaps by developing a framework of factors affecting efficient deconstruction and structural steel reuse, identified through a thematic analysis of the literature and structured around structure attributes, business attributes, value chain activities, project management, and regulations. Fifteen case studies were analyzed: two from North America, twelve from Europe, and one from Asia. A structural steel reuse classification model is introduced to support cross-case comparison of coordination demands, implementation barriers, and scalability potential within reverse supply chains. The results demonstrate that reuse pathways differ substantially in coordination requirements, implementation complexity, and scalability outcomes. They further indicate that the dominant challenges to structural steel reuse are no longer primarily technical, but instead stem from system-level coordination gaps, limited information availability, and insufficient integration across reverse supply chains. Based on these findings, targeted solutions are identified, including improved dismantling strategies, enhanced documentation systems, dedicated storage infrastructure, and stronger regulatory alignment. This study provides a structured analytical approach and practical recommendations to support decision-making, stakeholder coordination, and the scalable implementation of structural steel reuse. Full article

Green digital technologies are increasingly used to improve environmental visibility, resource efficiency, and sustainability-oriented decision-making. However, their association with sustainable business performance may depend on whether firms convert digital resources into organizational capabilities and implemented circular practices. Drawing on the Natural Resource-Based View, Dynamic Capabilities Theory, and Institutional Theory, this study examines a moderated serial mediation model in which eco-innovation and circular economy adoption sequentially mediate the relationship between green digital technologies and sustainable business performance, while institutional support moderates the relationship between eco-innovation and circular economy adoption. Survey data were collected from managers and professionals in Saudi industrial and manufacturing firms. Of the 450 questionnaires distributed, 420 were returned, and 386 usable responses remained after data-quality screening and were analyzed using partial least squares structural equation modeling in SmartPLS 4. The results show that green digital technologies are positively associated with eco-innovation (β = 0.662, p < 0.001) and circular economy adoption (β = 0.263, p < 0.001). Eco-innovation is positively associated with circular economy adoption (β = 0.487, p < 0.001), which, in turn, is strongly associated with sustainable business performance (β = 0.726, p < 0.001). The serial indirect association through eco-innovation and circular economy adoption is significant (β = 0.234, p < 0.001). Institutional support also strengthens the eco-innovation–circular economy adoption relationship, although the interaction is comparatively modest (β = 0.118, p = 0.002). The study contributes by distinguishing eco-innovation as a resource-conversion capability from circular economy adoption as an implemented organizational practice and by identifying institutional support as a boundary condition within Saudi industrial firms. Full article

Climate risk has become a defining challenge for businesses, influencing strategic decision-making, organisational resilience, and long-term performance. Despite the rapid growth of research in this area, the intellectual development and thematic evolution of climate-related corporate studies remain fragmented. This study provides a comprehensive synthesis of the literature through a bibliometric analysis and systematic review of 643 Scopus-indexed, peer-reviewed articles published between 1993 and 2025, with a systematic thematic synthesis of 23 empirical studies. Using Biblioshiny and VOSviewer, science-mapping techniques, including co-citation analysis and bibliographic coupling, were employed to examine publication trends, intellectual foundations, and major research themes. The findings indicate a shift from environmental measurement and compliance toward climate-risk management, carbon disclosure, and sustainable finance. Financial outcomes are heterogeneous, and context-dependent carbon exposure is generally associated with valuation penalties and downside risk, while the relevance of disclosure and climate strategies depends on credibility, substantive implementation, and organisational and institutional conditions. The integrated review shows that the financial implications of climate-related corporate actions are contingent upon climate-risk exposure, disclosure credibility, organisational capabilities, and institutional context. It further explains the coexistence of mixed empirical findings and identifies priorities for future research and policy. The findings offer valuable implications for researchers, corporate managers, investors, and policymakers seeking to strengthen sustainable business practices under an evolving climate risk landscape. Full article

Digital transformation is now one of the key engines of entrepreneurial behavior and company development, but there is still a lack of empirical evidence on the creation of digital ecosystems, especially in the Middle East and North African (MENA) region. This paper examines how digital capabilities affect the sustainable development of entrepreneurial ventures and how the relationship changes depending on the three emerging digital ecosystems of Lebanon, Iraq, and Egypt. Data were collected through a structured online questionnaire developed using Google Forms and distributed via email, WhatsApp, and the Prolific research platform to owners, managers, and key decision-makers of small and medium-sized enterprises (SMEs). A total of 250 questionnaires were distributed, yielding 131 responses, of which 22 were excluded after screening for completeness and eligibility. The final analytical sample comprised 109 valid firm-level responses from Egypt (n = 51), Lebanon (n = 34), and Iraq (n = 24). The study quantitatively assesses digital capabilities on several dimensions, including digital infrastructure adoption, data and analytics capabilities, digital product development, and digital revenue integration, and attributes those capabilities to sustainable business growth indicators (the revenue stability, scalability, profitability, and long-term resilience). The results showed that more robust digital capabilities have more significant impacts on sustainable business growth outcomes, whereas basic digitalization has no significant effect. Further, the analysis found no statistically significant moderating effect of ecosystem contextual factors, including institutional quality and digital maturity, on the relationship between digital capabilities and sustainable business growth across Lebanon, Iraq, and Egypt. This study contributes to the body of knowledge of entrepreneurship and sustainability in emerging markets by integrating Resource-Based View, Dynamic Capabilities Theory, and Digital Ecosystem Theory. The findings have practical implications for entrepreneurs and policymakers who aim to use digital transformation to attain sustainable entrepreneurship in the vulnerable and dynamic digital ecosystems. Full article

Integrating Corporate Social Responsibility (CSR) into business strategy requires organizational practices that convert sustainability- and stakeholder-related intentions into measurable and managerially usable information. This study examines whether distinct Business Performance Measurement (BPM) maturity profiles can be identified among hospitality SMEs and how these profiles are associated with sustainability- and stakeholder-oriented measurement practices. BPM maturity is defined as a multidimensional organizational capability reflected in the breadth, integration, monitoring, interpretation, and use of performance information, rather than merely in the possession of a formal measurement system. Using cross-sectional survey data from 600 decision-makers in Hungarian and Romanian accommodation and food service SMEs, the study applies Principal Component Analysis and Two-Step Cluster Analysis. Three profiles emerge: Integrated, Transitional, and Low BPM Maturity. Firms in the Integrated profile report broader use of financial, customer-related, employee-related, sector-specific, and sustainability-oriented performance information, whereas firms in the Low profile report more limited measurement practices. Sustainability- and development-related measurement practices provide the strongest relative contribution to profile differentiation in both national models. The findings demonstrate systematic associations between BPM maturity profiles and sustainability- and stakeholder-oriented measurement practices. They are consistent with the theoretical interpretation that BPM maturity may provide organizational conditions supporting the operationalization of responsible management intentions. However, the cross-sectional design does not establish causal direction, and the study does not directly measure formal CSR implementation or sustainability outcomes. Full article

Efficient wood logistics is essential for a sustainable wood supply chain. In Poland, mounting symptoms of inefficiency highlight the need for a systematic and comprehensive performance assessment across the entire supply chain. Previous studies have predominantly focused on individual supply chain segments or specific stakeholder perspectives, but a comprehensive evaluation of wood supply chain efficiency in Poland remains unrealized. The study aimed to assess the efficiency of the timber supply chain at the Regional Directorate of State Forests level. The analysis encompasses the entire process from harvest planning to delivery to wood processing plants. The business-as-usual scenario derived from historical data is compared with improvement scenarios using a simulation–based optimization approach implemented in ArcGIS Pro 3.4.0. The scenarios are structured according to their decision-making impact: strategic logistics, tactical infrastructure, and operational routines. In total, 18 scenarios are developed to assess total operational costs, including environmental costs per m³ of timber. This covers harvesting, extraction, and transport across all feasible equipment combinations in Poland. The model’s performance was verified for timber harvesting and skidding. Full article

Corporate social responsibility (CSR) is shifting from compliance-driven to strategically embedded practice, yet large-scale identification of strategic CSR (SCSR) remains methodologically challenging due to reliance on surveys or ratings. This study proposes a hybrid identification framework combining pre-trained language models (PLMs) and Latent Dirichlet Allocation to conduct paragraph-level semantic analysis on 3022 CSR reports from Chinese manufacturing firms. The approach effectively identifies and extracts the topics of both strategic and non-strategic CSR. Findings show that only 25.6% of CSR behaviors simultaneously possess strategic intent, business synergy, resource investment, and value output. SCSR practices are highly concentrated on value chain optimization and R&D collaboration, with significant variations across ownership types and industry environments. This study contributes a novel PLM-based methodology for SCSR identification and offers empirical evidence and practical tools for CSR quality assessment, regulatory oversight, and strategic decision-making in manufacturing sectors. Full article

An ethnographic study employing 72 qualitative interviews with agricultural producers and associated water decision-makers concerning farm climate change adaptation in Saskatchewan, Canada concludes that farm size matters. Inductively we established a distinction of 2025 hectares as the differentiation between small and large agricultural farms; very small farms (less than 810 hectares) and one very large farm (51,030 hectares) were most vulnerable. Our research deviates from prior research that has concluded either large or small farms are more adaptive. In Saskatchewan, large farms mitigate climate impact risk of drought or flood over a bigger area, and have institutional and financial capacity to undertake adaptations. However, small farmers embrace more adaptation through environmental farm practices. Unlike other areas of the world where farms are becoming smaller and fragmenting, the trend is toward larger farms. Farmers thought of their farms as businesses; however, their multi-generational connection to the land was strong, and the concept of sustainability was rejected as farmers wanted to leave the land in a better state for future generations. The most important adaptive strategy identified, regardless of size, was ensuring the financial health of a farm through cashflow and the ability to pay expenses and debt. Full article

The internal audit function provides consulting services and recommendations for management on performance quality, business environment security, or risk management. Internal auditors are expected to monitor the senior managers’ behaviors and support them in operational and strategic decision making. They should recognize and reduce the possibility of an employee’s opportunistic behavior, improve internal control, and prevent financial fraud. Therefore, they should be highly qualified, and their competencies are to be constantly updated. Hence, they are encouraged to learn and participate in certification processes organized by various institutions, e.g., the Institute of Internal Auditors (IIA). Although there are many strategies of learning in the certification process, this study focuses on learning-by-testing approach. This method emphasizes the use of testing to acquire the internal audit knowledge needed to successfully pass the professional certified exams. Beyond that, the authors present the use of large language models (LLMs) in internal auditing. However, the research goal is to how the large language models (LLMs) can be used to answer questions included in the Certified Internal Auditor (CIA) exam directed by the Institute of Internal Auditors (IIA). In the research section, fourteen LLMs were evaluated using English and Polish-language CIA test sets comprising 184 and 183 multiple-choice questions, respectively, with accuracy as the primary performance metric. The best-performing individual model achieved approximately 91% accuracy, while the Friedman test identified statistically significant overall differences among the models for both datasets. The findings indicate that a multi-agent system can achieve better performance than an individual LLM. The learning-by-testing method can be supported not only by the use of professional association materials but also by the opportunity learners have to enrich their knowledge through the use of LLMs in the learning process. Full article

Rapid urban development has intensified conflicts between land development and ecological conservation, making spatially explicit land-use planning increasingly important for resource-depleted cities. This study develops a geoinformation-based decision-support framework for Huangshi, China, by integrating multi-scenario land-use modeling, production–living–ecological space analysis, landscape pattern assessment, and SDG 15 diagnostics. Four 2035 policy-oriented scenarios were compared: Business-as-Usual (BAU), Ecological Restoration Priority (ERP), Economic Development Priority (EDP), and Sustainable Development (SD). The results show that ERP delivers the strongest ecological performance, with ecological space reaching 46.47%, forest cover increasing from 35.40% to 36.80%, water area rising to 9.65%, net land degradation declining to −2.04%, and mean habitat quality reaching 0.484. SD provides a more balanced pathway, with ecological space of 44.80%, living space of 8.93%, a land-use stability rate of 96.36%, and a relatively low net degradation rate of 1.33%. BAU and EDP show higher ecological risks. The framework demonstrates how multi-source geospatial data and spatially explicit SDG diagnostics can support adaptive planning in resource-depleted cities. Full article

Generative Artificial Intelligence (Gen AI) is reshaping recruitment, with organizations increasingly using it to draft job postings and screen candidates. Research on labor market analytics has concentrated on extracting skills from existing postings; the complementary task of generating postings aligned with a required skill set has received little systematic attention. This paper addresses that gap by benchmarking ten Gen AI models on skill-aligned job posting generation across three ESCO-derived skill domains: Finance, Healthcare, and Craft and related trades workers. Each model produced 100 postings per domain, giving 3000 synthetic postings, evaluated with five complementary metrics—Perplexity, Cosine Similarity, Word Mover’s Distance, LIWC-style divergence, and Explicitness—and verified through inferential statistical testing. No single model dominated across metrics or domains. The models prioritized explicit skills over the broader contextual information found in real postings, and performance showed a marked domain sensitivity, with a significant model-by-domain interaction for four of the five metrics. These findings offer guidance for HR practitioners and business decision-makers selecting a Gen AI tool for recruitment tasks. Recruitment outcomes such as time-to-hire and candidate–job fit were not measured, so such benefits remain potential practical implications rather than empirically demonstrated effects. Full article

This article presents a novel use case of the deployment of agent-based modeling (ABM) to explore developers’ decision-making along a spectrum of reuse in the built environment. This research project used ABM to model multiple pathways to realizing circularity at the community scale through the reuse of existing buildings and adoption of deconstruction as alternatives to demolition in Ithaca, New York. This exploratory analysis demonstrates (1) the use of ABM in scenario planning, (2) the use of ABM to assess how preservation and building material reuse options affect city-wide greenhouse gas emissions in the form of embodied carbon, (3) and the potential to use the model results in policy analysis. The results of the analysis emphasize trade-offs in local government policies. A Business As Usual scenario produces the most CO₂ emissions and the highest total dollars invested and total square footage developed. This is in contrast to the other three scenarios. A Maximize Preservation scenario achieves the lowest total embodied carbon emissions through retention of existing buildings while maintaining the second highest total investment in urban development. The Deconstruction Incentive and Mandatory Material Reuse scenarios, which involve incentivizing building material reuse, lead to strong ROI signals for developers, reducing the search for suitable properties for new construction as deconstruction becomes a viable avenue for generating returns. ABM-generated scenarios exhibit differences in the performance of developers according to their type and scale. This research produced a readily available ABM and graphical interface that could be used by researchers and local government agencies exploring pathways to achieving circular cities through policies that can affect urban development processes and embodied carbon emissions. Full article