Search Results (4,583)

This study proposes a hybrid machine learning framework that integrates structured financial indicators and unstructured textual strategy disclosures to improve firm-level management performance prediction. Using corporate business reports from South Korean listed firms, strategic text was extracted and categorized under the Balanced Scorecard (BSC) framework into financial, customer, internal process, and learning and growth dimensions. Various machine learning and deep learning models—including k-nearest neighbors (KNNs), support vector machine (SVM), light gradient boosting machine (LightGBM), convolutional neural network (CNN), long short-term memory (LSTM), autoencoder, and transformer—were evaluated, with results showing that the inclusion of strategic textual data significantly enhanced prediction accuracy, precision, recall, area under the curve (AUC), and F1-score. Among individual models, the transformer architecture demonstrated superior performance in extracting context-rich semantic features. A soft-voting ensemble model combining autoencoder, LSTM, and transformer achieved the best overall performance, leading in accuracy and AUC, while the best single deep learning model (transformer) obtained a marginally higher F1 score, confirming the value of hybrid learning. Furthermore, analysis revealed that customer-oriented strategy disclosures were the most predictive among BSC dimensions. These findings highlight the value of integrating financial and narrative data using advanced NLP and artificial intelligence (AI) techniques to develop interpretable and robust corporate performance forecasting models. In addition, we operationalize information security narratives using a reproducible cybersecurity lexicon and derive security disclosure intensity and weight share features that are jointly evaluated with BSC-based strategic vectors. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

The proposed research aims to analyse the effects of the relationship between Sustainability Governance (SG) and Climate Impact (CI), taking into consideration Green Finance (GF). Furthermore, it examines how Institutional Support (IS) enhances the governance systems governing these variables. The research provides a holistic approach for analysing the effects of financial dynamics on climate impacts. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed in this research study. The data were collected from various industries using a standardised questionnaire. The structural model examined the direct and indirect relationships between variables such as GF, SG, and CI. IS emerged as the moderated variable. The outcomes of the study confirmed that “GF has an important and direct as well as indirect (through SG as the mediator) impact on CI. IS significantly increases SG and thus exerts an overall enhancing effect on the impact of GF on the climate.” The study has supported the research objectives and aims. The limitations of this study comprised constraints related to both time and cost. The researchers encountered limitations in accessing senior managers and directors of various organisations for the study. IS emerged as an important intermediate factor that can significantly link various actions and activities that impact the climate. This study supports both global and local research objectives. The study offers significant insights, underscoring the critical role of SG within Green Business (GB). Additionally, IS emerges as a vital enabling tool that strengthens the overall governance framework. The study contributes significantly to the development of integrated frameworks for institutions seeking to effectively address environmental challenges. The implications for action indicate that furthering entrenched institutional structures and instilling good governance practices can add tremendous value to the transformation potential of GF and usher in accelerated efforts to achieve national and international objectives on climate change. Full article

Background: In an increasingly volatile global business environment, supply chain resilience has become a strategic imperative, particularly for firms operating in developing economies. Guided by Dynamic Capabilities Theory (DCT), this study examines how AI-powered tools foster an innovation culture comprising communication, creativity, and learning, and how these dimensions enhance supply chain resilience measured through flexibility, efficiency, and velocity. Methods: A quantitative research design was employed using survey data collected from 270 supply chain and operations managers in Jordanian manufacturing firms. Twelve direct hypotheses were tested using Partial Least Squares Structural Equation Modeling. Results: The findings indicate that AI-powered tools significantly influence communication, creativity, and learning. Communication and creativity positively affect all three dimensions of supply chain resilience. Learning significantly improves efficiency but shows no significant effect on flexibility or velocity, indicating that learning is mainly utilized for process improvement rather than rapid adaptation. Conclusions: The study demonstrates that AI adoption alone is insufficient to build resilient supply chains unless supported by innovation-oriented cultural capabilities. The findings extend DCT by clarifying the differentiated role of learning in resilience building and provide actionable guidance for managers seeking to align AI investments with cultural development in resource-constrained manufacturing contexts and long-term competitive advantage. Full article

In the era of ever-greater data production and collection, public health research is often limited by the scarcity of data. To improve this, we propose data sharing in the form of Data Spaces, which provide technical, business, and legal conditions for an easier and trustworthy data exchange for all the participants. The data must be described in a commonly understandable way, which can be assured by machine-readable ontologies. We compared the semantic interoperability technologies used in the European Data Spaces initiatives and adopted them in our use case of physical development in children and youth. We propose an ontology describing data from the Analysis of Children’s Development in Slovenia (ACDSi) study in the Resource Description Framework (RDF) format and a corresponding Next Generation Systems Interface-Linked Data (NGSI-LD) data model. For this purpose, we have developed a tool to generate an NGSI-LD data model using information from an ontology in RDF format. The tool builds on the declaration from the standard that the NGSI-LD information model follows the graph structure of RDF, so that such translation is feasible. The source RDF ontology is analyzed using the standardized SPARQL Protocol and RDF Query Language (SPARQL), specifically using Property Path queries. The NGSI-LD data model is generated from the definitions collected in the analysis. The translation has been verified on Smart Applications REFerence (SAREF) ontology SAREF4BLDG and its corresponding Smart Data Models (52 models at the time). The generated artifacts have been tested on a Context Broker reference implementation. The tool supports basic ontology structures, and for it to translate more complex structures, further development is needed. Full article

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business management systems, this paper proposes a fuzzy stable matching method for three-sided agents under a framework of combinatorial preference relations, integrating network and decision theory. First, we construct a membership function to measure the degree of preference satisfaction between elements of different agents, and then define the concept of fuzzy stability. By incorporating preference satisfaction, we introduce the notion of fuzzy blocking strength and derive the generation conditions for blocking triples and fuzzy stability under the fuzzy stable criterion. Furthermore, we abstract the three-sided matching problem with combined preference relations into a shortest path problem. Second, we prove the equivalence between the shortest path solution and the stable matching outcome. We adopt Dijkstra’s algorithm for problem-solving and derive the time complexity of the algorithm under the pruning strategy. Finally, we apply the proposed model and algorithm to a case study of project assignment in software companies, thereby verifying the feasibility and effectiveness of this three-sided matching method. Compared with existing approaches, the fuzzy stable matching method developed in this study demonstrates distinct advantages in handling preference uncertainty and system complexity. It provides a more universal theoretical tool and computational approach for solving flexible resource allocation problems prevalent in real-world scenarios. Full article

Organizational learning refers to the systematic development, exchange and dissemination of knowledge throughout the organization. Organizational learning processes in construction are disrupted by the decentralized flow of information and the temporary, short-term nature of project teams. The emergence of Building Information Modelling (BIM) has significantly enhanced the ability to capture and disseminate construction project knowledge within the architecture, engineering, construction, and facilities management (AEC-FM) sector. Despite this progress, existing research has predominantly focused on the technical aspects of BIM, with limited evidence on its effects on organizational learning capabilities. This study addresses this gap by examining how BIM shapes organizational learning mechanisms within AEC-FM contexts. Employing a systematic literature review (SLR) approach, 104 articles from the Scopus database were analyzed using scientometric and thematic analyses. The systematic review of the literature was carried out following the PRISMA guidelines. The SLR provided a comprehensive examination of BIM’s contribution to strengthening the three core organizational learning mechanisms: experience accumulation, knowledge articulation, and knowledge codification. The thematic analysis revealed seven BIM-enabled organizational learning factors that are expected to strengthen learning mechanisms in AEC-FM organizations: agility of thinking and reasoning skills; enhanced decision-making; interconnected stakeholders’ relationships; integrated business processes; BIM-facilitated project knowledge sharing; BIM-supported project knowledge retention; and BIM-supported project knowledge extraction. Findings suggest that BIM significantly facilitates learning mechanisms within AEC-FM firms. A conceptual model of BIM-supported learning mechanisms was developed to highlight opportunities for enhancing organizational learning capabilities in the BIM environment. Full article

Compared to traditional marine productivity, marine new-quality productivity (MNQP) is composed of advanced productive forces driven by the deepening application of new technologies, is characterized by the rapid emergence of new industries, new business models, and new modes of operation, and is marked by a substantial increase in total factor productivity in the marine economy. It has, therefore, become a new engine and pathway for China’s development into a maritime power. The main research approaches and conclusions of this paper are as follows: ① Using a combined order relation analysis method–Entropy Weight Method (G1-EWM) weighting method that integrates subjective and objective factors, we measured the development level of China’s MNQP from 2006 to 2021 across two dimensions: “factor structure” and “quality and efficiency”. The findings indicate that China’s MNQP is developing robustly and still holds considerable potential for improvement. ② Utilizing Gaussian Kernel Density Estimation and Spatial Markov Chain analysis to examine the dynamic evolution of China’s MNQP, the study identifies breaking the low-end lock-in of MNQP as crucial for accelerating balanced development. Spatial imbalances in China’s MNQP may exist both at the national level and within the three major marine economic zones. ③ To further examine potential spatial imbalances, Dagum Gini decomposition was employed to assess regional disparities in China’s MNQP. The DER polarization index and EGR polarization index were used to analyze spatial polarization levels, revealing an intensifying spatial imbalance in China’s MNQP. ④ Finally, geographic detectors were employed to identify the factors influencing spatial imbalances in China’s MNQP. Results indicate that these imbalances result from the combined effects of multiple factors, with marine economic development emerging as the core determinant exerting a dominant influence. The core conclusions of this study provide theoretical support and practical evidence for advancing the enhancement of China’s MNQP, thereby contributing to the realization of the goal of building a maritime power. Full article

Housing stock is observed to be associated with high carbon emissions, high fuel poverty and low comfort levels in the UK. Retrofitting the housing stock is one of the best solutions to address these problems. This paper directly corresponds with human and planetary health in terms of climate change, human health and mental health by addressing the challenges of housing retrofit at scale. Retrofitting houses can also contribute to social equity, reduced use of planetary resources and better financial and physical comfort. Despite the availability of the right technology, government grants and the potential to acquire supply chain and skilled labour, the progress of retrofit is extremely poor. Importantly, the UK is off track to achieve net zero by 2050, and the housing stock contributes 18.72% of the total emissions. The problem is further exacerbated by the 30.4 million units of housing stock. Robust strategies are required to retrofit the housing stock at scale. The study uses a qualitative modelling method under the diffusion of innovations theory to formulate a retrofit-at-scale strategy for the UK. Findings recommend focusing on skill development, show homes, research and innovation, supply chain development, business models, government grants and regulatory tools in a trajectory from 2025 to 2050. The proposed strategy is aligned with the segments of the diffusion of innovation theory. Although the analysis was performed with reference to the UK, the findings are transferable, considering the broader and urgent concerns related to human and planetary health. Full article

This paper introduces flourishing circularity as a transformative approach to resource assessment that transcends both traditional Resource-Based View (RBV) theory and conventional circular economy concepts. We demonstrate RBV’s fundamental limitations in addressing the polycrisis of breached planetary boundaries and social inequities. Similarly, while the circular economy focuses on resource reuse and recycling, it often merely delays environmental degradation rather than reversing it. Flourishing circularity addresses these shortcomings by reconceptualizing natural and social capital not as externalities but as foundational sources of all value creation. We develop a comprehensive framework for assessing resources within an open systems perspective, where competitive advantage increasingly derives from a firm’s ability to regenerate the systems upon which all business depends. The paper introduces novel assessment tools that capture the dynamic interplay between organizational activities and coevolving social and ecological systems. We outline the core competencies required for flourishing circularity: regenerative approaches to social and natural capital, and systems thinking with cross-boundary collaboration capabilities. These competencies translate into competitive advantage as stakeholders increasingly favor organizations that enhance system health. The framework provides practical guidance for transforming resource assessment from extraction to regeneration, enabling business models that create value through system enhancement rather than depletion. Full article

The growing dominance of low-cost carriers (LCCs) in global air transport has intensified the need for airport terminal facilities that reflect their simplified, efficiency-driven operating principles. Traditional Level of Service (LOS) standards, based on International Air Transport Association’s Airport Development Reference Manual (IATA ADRM), were primarily designed for traditional air carriers or full-service network carriers (FSNCs) and may lead to over-dimensioned or misaligned airport terminal facilities when applied to airports with dominance of LCCs. This study presents the first newly developed computational tool called Airport Terminal Facilities Software (ATFS) as a methodological and conceptual advance in airport terminal planning, that integrates LOS guidelines differentiated by airline business models. The methodology integrates spatial–temporal LOS parameters, specific facility capacity formulas, and peak-hour demand calculations of airport terminal facilities. Results from the case study conducted at Pula Airport show substantial differences between IATA and LCC LOS outcomes, i.e., applying LCC LOS guidelines can significantly reduce required areas for the several airport terminal facilities. Findings confirm that new LCC LOS guidelines and the ATFS tool can optimize airport terminal facilities, reduce or reconfigure excessive or empty space, and improve passenger flow efficiency at LCC-dominant airports. Full article

This study addresses a pivotal strategic issue in hospitality e-commerce: how hotels can optimize cooperation with heterogeneous online travel agencies (OTAs). Moving beyond the conventional question of whether to cooperate, we investigate the interrelated decisions of which OTA type to partner with (quality-focused vs. price-focused) and which business model to adopt (merchant vs. agency). We develop a game-theoretic model that incorporates key e-commerce factors, including hotel capacity constraints, cross-channel spillover effects, and differential consumer acceptance of OTA types. Our analysis yields a contingent decision framework. We demonstrate that OTA cooperation becomes beneficial only when a hotel’s room capacity exceeds its direct-channel demand. The optimal strategy evolves with capacity: hotels with moderate capacity should partner with a single OTA type—predominantly the quality-focused one—while larger hotels should engage both types to maximize market coverage. In terms of business models, smaller hotels benefit from the risk-shifting merchant model, whereas larger hotels capture higher margins through the agency model. A key finding is the general superiority of a differentiated approach: applying the agency model to quality-focused OTAs and the merchant model to price-focused OTAs. This research provides a structured analytical framework to guide hotel managers in crafting e-commerce platform strategies and offers scholars a foundation for further inquiry into platform competition and contract design in digital marketplaces. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

The aim of this research is to elaborate on the activities of non-formal experiential learning in agritourism developed by an organic family farm in Thessaloniki, Greece. Based on a qualitative approach, in-depth interviews with the owners of the farm and a Business Model Canvas (BMC) approach to this case study, the results indicate that workshops, seminars on nutrition, environmental conservation activities, plant identification, hands-on activities for children and cooking lessons with chefs are some of the non-formal learning tools. The aforementioned activities, on the one hand, raise gate sales for the family and, on the other hand, promote knowledge and awareness towards the contemporary environmental challenges that the rural areas and our food chain are facing. Future development strategies were also identified through the BMC, such as the adoption of digital educational tools, and ‘Do It Yourself’ kits for growing microgreens at home, while the need for official certification and support of multifunctional farms by the Ministry of Rural Development was also highlighted. Full article

The proliferation of Large Language Models (LLMs) has transformed natural language processing (NLP) applications across diverse domains. This paper presents a comprehensive comparative analysis of three state-of-the-art language models—GPT-4o, Claude-3, and Julius AI—evaluating their performance across systematic NLP tasks using standardized datasets and evaluation frameworks. We introduce a reusable evaluation methodology incorporating five distinct prompt engineering techniques (Prefix, Cloze, Anticipatory, Heuristic, and Chain of Thought) applied to three categories of linguistic challenges: data extraction, aggregation, and contextual reasoning. Using a bibliometric analysis use case as our evaluation domain, we demonstrate the framework’s application to structured data processing tasks common in academic research, business intelligence, and data analytics applications. Our experimental design utilized a curated Scopus bibliographic dataset containing 3212 academic publications to ensure reproducible and objective comparisons, representing structured data processing tasks. The results demonstrated significant performance variations across models and tasks, with GPT-4o achieving 89.3% average accuracy, Julius AI reaching 85.7%, and Claude-3 demonstrating 72.1%. The results demonstrated significant performance variations across models and tasks, with Claude-3 showing notably high prompt sensitivity (consistency score: 74.3%, compared with GPT-4o: 91.2% and Julius AI: 86.7%). This study revealed critical insights into prompt sensitivity, contextual understanding limitations, and the effectiveness of different prompting strategies for specific task categories. Statistical analysis using repeated measures ANOVA and pairwise t-tests with Bonferroni’s correction confirmed significant differences between models (F(2, 132) = 142.3, p < 0.001), with effect sizes ranging from 0.51 to 1.33. Response time analysis showed task-dependent latency patterns: for data extraction tasks, Claude-3 averaged 1.9 s (fastest), GPT-4o 2.1 s, and Julius AI 2.8 s; however, for contextual reasoning tasks, latency increased as follows for Claude-3: 3.8 s, GPT-4o: 4.5 s, and Julius AI: 5.8 s. Overall averages were as follows for GPT-4o: 3.2 s, Julius AI: 4.1 s, and Claude-3: 2.8 s. While specific performance metrics reflect current model versions (GPT-4o: gpt-4o-2024-05-13; Claude-3 Opus: 20240229; Julius AI: v2.1.4), the evaluation framework provides a reusable methodology for ongoing LLM assessment as new versions emerge. These findings provide practical guidance for researchers and practitioners in selecting appropriate LLMs for domain-specific applications and highlight areas requiring further development in language model capabilities. While demonstrated on bibliometric data, this evaluation framework is generalizable to other structured data processing domains. Full article