Search Results (13,385)

Despite increased investment in information and communication technologies (ICT), many micro, small, and medium-sized enterprises (MSMEs) struggle to translate digital adoption into improved business performance, particularly in post-pandemic contexts. This study addresses this gap by examining the mediating role of knowledge management (KM) in the relationship between ICT integration and business performance in MSMEs in Michoacán, Mexico. A quantitative, construct associate research framework was employed using survey data from 200 randomly selected MSMEs. Structural equation modeling was applied to test the hypothesized relationships. The results reveal that ICT integration does not have a direct significant effect on business performance. However, it significantly enhances KM processes, which in turn positively influence performance outcomes. Furthermore, KM fully mediates the relationship between ICT integration and business performance, indicating that the value of ICT is realized through effective knowledge processes rather than technology alone. These findings underscore the importance of aligning ICT investments with KM strategies to achieve superior performance. The study contributes to the integration of the resource-based view (RBV) and knowledge-based view (KBV), offering empirical evidence from an emerging economy context. Full article

The European Union’s Carbon Border Adjustment Mechanism (CBAM) introduces significant implications for sustainable trade and industrial decarbonization for non-EU industrial exporters. As a major cement supplier to the EU, Turkey faces key economic and environmental transition risks under this framework. This study quantifies the compliance exposure of Turkey’s cement exports to the EU by 2030 within an industrial sustainability framework across multiple carbon pricing, domestic policy, and decarbonization scenarios. Incorporating time-series forecasting, benchmark emissions intensities, and the operational 2026 CBAM regulatory framework alongside Turkey’s Climate Law No. 7552, the study evaluates compliance costs relative to export revenues. Results indicate that under business-as-usual conditions, without decarbonization or domestic carbon pricing, CBAM surcharges would equal 154% of total export revenue by 2030. Accounting for the 2030 CBAM phase-in factor (48.5%), compliance costs remain substantial at 75% of revenue. Establishing a national Emissions Trading System (TR ETS) under Climate Law No. 7552, with a domestic carbon price of €20/tCO₂e, reduces residual CBAM border payments to 44% of revenue, while a €50/tCO₂e domestic price offsets border surcharges entirely, redirecting carbon revenues to an industrial green transition fund. Furthermore, achieving a 41% reduction in direct cement emissions intensity lowers total carbon compliance costs to 21–26% of revenue. These findings demonstrate that domestic carbon pricing paired with dedicated resource allocation for low-carbon technologies is essential to advance long-term industrial sustainability, preserve export competitiveness, and prevent pass-through risks to downstream construction sectors. Full article

Although uncertainties regarding natural hydrogen (H₂) reserves remain high, exploration activity is expanding across several countries. To date, the only active production site uses this resource locally for electricity generation; however, operating companies could target industrial markets where H₂ can serve as both an energy source and a chemical feedstock. Surface constraints and proximity to commercial markets will dictate distribution and utilization strategies when alternative options are available. This study introduces a novel tool that integrates surface and subsurface constraints to rank prospective exploration areas, enabling companies to identify opportunities aligned with their specific corporate profiles or business plans. The methodology is applied to western Colombia, a region characterized by many surface constraints and known H₂-generating source rocks; H₂ surface emanations have also been documented. Western Colombia is structurally divided by north–south mountain ranges (Cordilleras) that severely hinder east–west transportation and contribute to regional energy insecurity. Using a comprehensive data compilation, this study demonstrates how applying different weighting schemes to diverse geographical and geological parameters can optimize site selection. The Medellín area ranks highest if the target is the industrial market. Full article

The sustainability of buildings and the built environment extends beyond energy and environmental performance to circular resource use, health and safety, digital infrastructure, and stakeholder collaboration. This exploratory integrative literature review examines how these established but insufficiently connected domains reshape business models in the built environment. Since the built environment is a major source of global carbon emissions and waste, a primary research stream concerns the transition toward circular economy principles beyond traditional profit-maximization logics. The literature also highlights the potential of health- and safety-oriented innovations to reduce risks and improve indoor environments. Other studies highlight the potential of digital innovations to improve life-cycle management, resource efficiency, and risk mitigation. However, their widespread adoption faces systemic barriers, including data interoperability and cybersecurity issues, implementation costs, skills shortages, organizational resistance, and regulatory and governance challenges. The literature often emphasizes technical potential while paying less attention to value-capture mechanisms and the allocation of costs, risks, benefits, and responsibilities among the actors involved. Integrating sustainable practices, digital infrastructures, circular-economy principles, and collaborative governance is therefore essential to develop economically viable, organizationally feasible, and ethically responsible business models for the built environment, across the building life cycle and among public and private stakeholders. Full article

City information systems are steadily relying on various stakeholders, heterogeneous resources, and interrelated digital services. Nevertheless, many initiatives still face difficulties in aligning actors, business processes, data, and technological resources within a unified information systems architecture. Accordingly, this study presents and evaluates a federated enterprise architecture-based model for value co-creation in city information systems. The studied model investigates how strategy and capture, relationship management, and community context contribute to federated enterprise architecture, and how the architecture structuring supports service value co-creation, continuous improvement, and global impact. To validate the research model, data were gathered using a structured questionnaire that was administered to actors involved in information system initiatives. The studied hypotheses were tested using partial least squares structural equation modeling. The main findings demonstrate that community context has a positive influence on enterprise architecture structuring, which, in turn, supports service value co-creation through the alignment of stakeholders, processes, data, and digital services. Service value co-creation then contributes to continuous improvement and ecosystem global impact, acting as a mediating mechanism between information system to city service findings. Thus, the research contributes to the information systems literature by highlighting how federated architecture contributes positively to digital service in a multi-actor ecosystem and supports value co-creation in the city information system. Full article

As cities accelerate climate adaptation and decarbonisation, municipally owned companies (MOCs) play an important role in delivering sustainable urban infrastructure, accessing transition finance, and supporting Positive Energy Districts (PEDs). However, the expansion of sustainability reporting requirements under the European Union’s Corporate Sustainability Reporting Directive (CSRD) raises questions about the preparedness of public infrastructure providers to meet evolving sustainability-information demands. This study examines ESG reporting readiness among Polish MOCs, focusing on current reporting activity, reporting intentions, regulatory awareness, indirect ESG information pressures, sustainable-finance and investment plans, and potential transition-risk exposure. The analysis is based on a Computer-Assisted Web Interviewing survey of 226 municipal enterprises conducted in August 2025. The results indicate substantial reporting gaps. Only 8% of surveyed MOCs had already prepared or planned to prepare a non-financial report. When companies planning EU Taxonomy reporting only were also included, 14% of the sample had some form of current or planned reporting, while the remaining 86% had neither current nor planned non-financial or EU Taxonomy reporting. Reporting readiness was lower among smaller companies, while indirect ESG information pressures arising from business relationships, stakeholder requests, and financing plans were also evident across the surveyed sample. The findings suggest that limited reporting preparedness, when combined with external sustainability-information demands, may contribute to potential transition-risk exposure relevant to municipal investment and financing processes. More broadly, ESG reporting readiness represents an organisational capability relevant to sustainable finance, municipal climate investment, and the development of PEDs and smart cities. Full article

Loss models of fixed capacity constitute a fundamental tool in teletraffic theory, with the classical Erlang loss model being widely used for dimensioning purposes. In practical communication systems, however, server failures and repairs introduce time-varying capacity, significantly affecting call blocking probabilities (CBPs). This paper studies an Erlang loss system where busy servers may fail. Failed servers are repaired by either a shared or a non-shared repair facility, while in-service calls are lost upon server failure. Three approaches for determining CBP in the shared and non-shared repair cases are examined. The first provides exact results by solving a 2D Markov chain but becomes computationally demanding for large systems. The second, known as the performability method, offers a simple approximation but allows failures of idle servers. The third approximate approach employs state aggregation while restricting failures to busy servers. These approximate solutions offer computational efficiency, but they cannot ensure consistently accurate performance. To circumvent this limitation, we propose a novel method for the exact and efficient determination of CBPs. Analytical comparisons show that: (1) the third approach consistently outperforms the performability method and (2) the proposed method outperforms the approximate methods in both the shared and the non-shared repair cases. 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

Climate risks drive dynamic adjustments to global climate policies, creating significant climate policy uncertainty (CPU). This uncertainty profoundly affects enterprises’ survival and sustainable development. A key question emerges: how do enterprises’ perception of CPU influence their resilience? To answer this, this study adopts a capability perspective and empirically examines the impact of climate policy uncertainty perception (CPUP) on enterprise resilience (RESI) and the underlying mechanism. Using panel data on Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2023, the study defines CPUP as the interaction between a news-based provincial CPU index and the frequency of climate risk words in annual report texts, and measures RESI with the entropy weight method across four dimensions (business volatility, long-term growth, short-term performance, and enterprise survival). Panel regression with fixed effects indicates that CPUP significantly enhances RESI. A one-standard-deviation increase in CPUP raises RESI by approximately 0.0019 index units, equivalent to about 2.2% of the standard deviation of RESI. This effect is more pronounced for enterprises in the eastern and central regions and in high-carbon industries. Mechanism tests confirm that CPUP boosts RESI by optimizing management capabilities and strengthening development capabilities, revealing a capability-driven path between CPUP and RESI. This study enriches the theoretical understanding of CPU’s economic consequences and RESI antecedents from a capability perspective. It also provides empirical references for enterprises to build resilience amid policy fluctuations and for policymakers to formulate regionally differentiated climate policies. 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

Transitioning cultivated land from a narrowly defined production resource into a coordinated multifunctional asset is a cornerstone of agricultural modernization. Despite this imperative, current land utilization in China remains largely constrained by a singular production focus, resulting in suboptimal multifunctionality. Although digitally driven agricultural new quality productive forces (ANQPFs) are posited as a critical catalyst for functional restructuring, empirical evidence quantifying their relationship with cultivated land multifunctionality (CLM) remains limited. To examine the association between ANQPF and CLM, this study employs panel data from 115 prefecture-level cities across the Yangtze River Basin, China, spanning the period 2013–2023. The empirical results indicate that ANQPF is significantly and positively associated with CLM. These associations are robust to a battery of robustness checks, and endogeneity tests provide additional evidence supportive of a positive association. Transmission pathway analysis suggests that the positive association operates through three pathways: increasing the main business revenue of agricultural product processing enterprises above a designated size, expanding the number of agricultural technology patents, and improving the level of agricultural socialized services. Subgroup analyses, supplemented by Chow tests of coefficient equality, reveal that these associations tend to be larger in non-major grain-producing regions, areas with lower per capita GDP, and the upper and middle reaches of the Yangtze River Basin. Threshold effect analysis further demonstrates that once ANQPF exceeds a certain level, the positive association exhibits diminishing marginal returns; a similar but weaker pattern is observed for leading enterprises. These findings provide policy implications for developing ANQPF in accordance with local conditions and for synergistically optimizing CLM patterns. Full article

The pervasive rise in digital platforms has fundamentally recalibrated the relationship between urban communities and their cultural landscapes, reconfiguring not only the sensory experience of these environments but also their interpretive frames and the everyday contestations that surround them. Using coffee spaces in Beijing as an empirical lens, this study integrates 630 validated POI locations, 287,632 online reviews from Dianping, GIS spatial analysis, LDA topic modelling, semiotic coding, and a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) text classifier to examine the convergence between platform-mediated public perceptions and urban spatial differentiation. Our spatial interrogation identifies a statistically significant centre–periphery divide within this platform-curated corpus—a pattern that describes the geography of platform-mediated visibility rather than the total physical distribution of coffee outlets in Beijing, characterised by pronounced high-density agglomerations nesting within historic urban cores, central business districts, nascent knowledge hubs, and gentrifying lifestyle enclaves. Three landscape prototypes are derived from the data—business corridors, cultural experimental fields, and social-media spectacles. Each type exhibits discernibly different patterns of spatial density, adjacent land-use configurations, perceived experiential qualities, and underlying symbolic discourses. While positive sentiments dominate platform discourse, localised anxieties emerge regarding visual homogenization, intense queuing, premium pricing, and loss of place authenticity. The 630 venues analysed here represent a platform-visibility stratum—establishments sufficiently reviewed to feature on digital intermediaries—and the spatial patterns we map describe the geography of this visibility rather than the total physical distribution of coffee outlets. 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

Predicting customer churn in the telecom sector is critical for retaining subscribers, maintaining brand reputation, and staying ahead of competitors. Losing customers not only reduces revenue but can also weaken long-term market position in a highly competitive industry. While machine learning has been widely used to address this challenge, most traditional approaches depend on centralizing customer data. This raises major concerns about user privacy, data ownership, and compliance with strict regulations such as GDPR. These challenges make it difficult for businesses to fully utilize customer data while safeguarding sensitive information. In this paper, we investigate a privacy-preserving approach to churn prediction that combines federated learning (FL) with differential privacy (DP). Rather than collecting all customer data in a single repository, the investigated framework enables multiple clients to collaboratively train a deep neural network while maintaining data locality during the federated training process. To further enhance privacy protection, we employ Differentially Private Stochastic Gradient Descent (DP-SGD) and add controlled noise to model updates, reducing the possibility of inferring individual data contributions. This work systematically evaluates how different privacy levels, expressed through ε and δ, influence model performance under simulated non-IID client distributions. The experiments analyze the privacy–utility trade-off using multiple evaluation metrics and compare the results with centralized and non-private federated-learning approaches. The findings show that the investigated framework maintains competitive predictive performance across a range of privacy budgets while demonstrating a clear privacy–utility trade-off. Very strict privacy budgets result in substantial performance degradation, particularly for smaller and more imbalanced datasets, whereas moderate privacy budgets maintain competitive predictive performance with limited degradation. This study highlights the potential of privacy-preserving federated learning for practical distributed analytics applications where protecting sensitive data is essential. Full article

This research investigated the relationship between agility and educational service quality in higher education. The prior literature has mainly studied such relations in production organizations, while service organizations, especially higher education organizations, have been examined less often. We extend prior knowledge by developing and analyzing a comprehensive model that explains how key internal and external organizational factors affect overall agility and, in turn, how overall agility influences perceived educational service quality in higher education institutions. The study draws on management, education, and quality theories and analyzes responses from final-year students in business and management studies. The results show that the main agility factors—namely, market forces, internal practices and processes, and human resource management—significantly affect the agility of higher education institutions. The study’s findings also confirm that the agility of higher education institutions significantly affects the dimensions of Higher Education PERFormance (HEdPERF), the chosen model for assessing the quality of educational services. Considering the importance of agility in achieving objectives in an era of uncertainty and crises, we propose a model that may help improve the current higher education system by enhancing service provision to students and other stakeholders. Full article