Search Results (2,175)

Physical manufacturing and research infrastructures are essential for advanced innovation but often remain inaccessible to SMEs, start-ups, and research institutions that cannot justify ownership of capital-intensive assets. This study examines whether platform-mediated infrastructure sharing can function as a sustainable open-innovation mechanism in advanced manufacturing. Using the SCIP/SYNPRO platform developed in the SYNERGY and IDEATION projects, an exploratory case-study design combines descriptive analysis of a registry of 290 infrastructure items across 11 countries with qualitative analysis of 23 documented access requests, interaction records, and pilot reports. The results show that the Provider–Taker model facilitates observable access-enabling interactions, including infrastructure publication, request submission, provider–taker communication, negotiation, and selected documented use, although it does not measure population-wide access outcomes. Sharing potential is uneven: modular and emerging technologies, especially VR/AR infrastructures, attract higher request intensity than production-integrated assets. Users and providers favour negotiated access, flexible pricing, operator support, and contractual clarification rather than standardised rental models. Qualitative evidence shows that value is created through access to otherwise unavailable equipment, postponed investment, experimentation, technology familiarisation, student training, capability development, and new inter-organisational research links. The findings indicate that infrastructure sharing can support more resource-efficient innovation but depends on discoverability, governance, trust, and support mechanisms to scale. Full article

The growing incorporation of environmental, social, and governance (ESG) considerations into global financial systems has significantly influenced investment decision-making. Previous studies have mainly concentrated on ESG performance and their associated implications for businesses and have failed to examine the role of ESG risks in shaping barriers to cross-border investment. In this regard, this paper attempts to analyze the effects of ESG risks on foreign direct investment (FDI) inflows based on an unbalanced panel dataset for up to 250 countries spanning the years 2000 to 2024, coupled with cross-sectional data for 2020. This study uses a two-dimensional approach, whereby structural ESG risks are evaluated using panel FMOLS regression, while ESG risk exposures are assessed using cross-sectional models. This research also considers moderating factors such as economic development, industrial composition, and innovation capabilities. Based on the use of the national-level ESG risk, it is evident that ESG risks considerably reduce inward foreign direct investment. Full article

Transitioning to a circular economy (CE) is now a strategic priority for countries to decouple economic growth from environmental degradation. However, in developing contexts, the readiness of environmental resource sectors to adopt CE principles is unknown due to a lack of data and uneven institutional capacity. This study presents the first regional baseline assessment of circularity readiness in Vietnam’s environmental resource sectors, focusing on land, mining, water and waste. A five-dimensional readiness framework (policy, resource management, innovation, business, awareness) was developed and applied across Vietnam’s six ecological–economic regions. A Delphi process with 12 experts was conducted in three rounds to capture and refine expert judgments, supplemented by triangulated proxy indicators (e.g., plastic recycling rates, wastewater treatment coverage). Readiness scores were aggregated at dimension and regional levels and analyzed using radar charts, heatmaps and hierarchical clustering. Results showed significant regional disparities. The Southeast (SE) and Red River Delta (RRD) have high readiness due to clearer policy frameworks, stronger institutions and more dynamic business ecosystems. The Northern Midlands and Mountains (NMM) and Central Highlands (CH) have low readiness due to infrastructural gaps, weak innovation and limited public engagement. The Mekong Delta (MD) and North Central Coast (NCC) have medium readiness, reflecting partial progress but uneven implementation. The study made three contributions: (1) a new context-specific framework for CE readiness in environmental resource sectors; (2) the value of expert-based, proxy-informed methods in data-scarce contexts; and (3) a policy roadmap for different regional readiness levels. Findings suggest that the CE should be integrated into resource planning, regional observatories should be established and CE-related research and development (R&D) should receive investment. Future research should move towards standardized quantitative indicators and predictive models to track how readiness changes under policy interventions. Full article

In a global business environment marked by digital disruption, Small and Medium-sized Enterprises (SMEs) must integrate digital transformation with strategic agility and organizational resilience. This study addresses the fragmentation of the current management literature by developing an AI-driven meta-theory through a high-performance computational synthesis of 4811 academic publications from the OpenAlex database. Utilizing a theoretically grounded hybrid framework of lexical filtering (TF-IDF), semantic embedding (SciBERT), and a diverse ensemble of five Large Language Models (LLMs), we move beyond descriptive mapping to identify the ontological and integrative mechanisms of SME adaptation. The methodology is validated through a multi-stage expert audit of model reasoning traces to ensure theoretical alignment. Results reveal a clear dominance of Contingency Theory (20.5%) and Resource-Based View (14.1%), which are re-conceptualized here as Regulatory–Technical Brokerage and Internal Fortification. Through Social Network Analysis (SNA) and Aggregate Constraint metrics, the study identifies Innovation Frontiers that are operationally challenging to synthesize through traditional manual reviews at this scale. The research concludes by formulating four meta-theoretical propositions and an integrative synergetic mechanism, explaining how SME resilience emerges as an emergent property of cross-layer alignment between technical, cognitive, and structural logics. By providing this causal roadmap, the study establishes a robust, AI-augmented blueprint for SMEs to function as intelligent, self-regulating nodes within a Post-Normal digital ecosystem. Full article

Worldwide, breast cancer is the leading cause of death in women. This emphasizes the significance of an accurate breast cancer detection system. This study presents a unified framework for segmentation of breast cancer using multimodal imaging, such as histopathology, MRI, mammogram, and ultrasound. This framework integrates the CNN with Transformer modules and has three core technical innovations. First, features are extracted using an encoder–decoder design. The encoder has Residual Blocks with a base channel of 32, following feature extraction, which are progressively mapped and downsampled into four stages (32 → 64 → 128 → 256) of channels. The spatial channel is reduced using MaxPool2d operations from 256 × 256 to 128 × 128, 64 × 64, 32 × 32, and 16 × 16. After further convolutional refinement, a Transformer encoder is used on the 16 × 16 feature maps in the bottleneck. The Transformer comprises four encoders with multi-head self-attention (eight heads) and a 4.0 MLP ratio, enabling the model to capture local and global contextual dependencies at the lowest resolution. The proposed framework is trained with a learning rate of 1 × 10⁻⁴, up to 50 epochs with early stopping (patience = 12), using a combined Dice and binary cross-entropy loss that balances pixel-wise accuracy and overlap-based learning. Gradient clipping with a maximum norm of 5.0 is used to ensure training stability; ReduceLROnPlateau (factor = 0.5, patience = 5) is used to dynamically adjust the learning rate; and early stopping is used to prevent overfitting. To improve generalization and enhance robustness to data variability, data augmentation techniques such as random horizontal and vertical flips, intensity variations, and small rotations (±15°) are applied. Incremental learning was implemented in this study as a warm-start fine-tuning strategy, where the model was initialized based on learned weights from a previously trained model instead of training from scratch. This is done by loading saved checkpoints of the best-performing model and continuing training on a new dataset. The performance of the proposed framework is evaluated on four publicly available datasets and one local dataset, such as BUS-UCLM, BUSI, BreastDM, TNBC NucleiSegmentation, and BCSD-2024. The impressive results are achieved with Dice scores of 0.974 on ULCM, 0.975 on BUSI, 0.971 on BreastDM, 0.904 on TNBC nuclei segmentation, and 0.982 on BCSD-2024. The proposed model consistently performed better than classical U-Net models. Full article

Social entrepreneurship has emerged as a strategic approach to addressing collective needs through innovative and sustainable solutions. While many instruments have traditionally focused on business and organizational metrics, a significant gap remains in tools specifically validated for the professional and cultural context of health sciences in Latin America. Grounded in a multidimensional theoretical framework of social entrepreneurship traits, this study aimed to validate a questionnaire designed to measure social entrepreneurship among 1129 Mexican health science students. Following exploratory factor analysis using principal axis factoring and confirmatory factor analysis, a refined 17-item three-dimensional structure was identified, comprising innovative, execution, and social traits. The model demonstrated moderate fit (χ² = 580.468, p <0.001; CFI = 0.831; RMSEA = 0.084) and preliminary internal consistency. Measurement invariance across sex and semester was also supported. These findings provide a context-specific tool for exploring initial entrepreneurial potential, enabling higher education institutions to design targeted programs that foster proactive innovation and social commitment toward sustainable development. Full article

The accelerating convergence of geopolitical volatility, technological disruption, environmental stress, and societal transformation has rendered traditional strategic management frameworks insufficient. Organizations now operate in environments defined not only by disruptions with existential implications but by wickedness—conditions in which problems are ambiguous, stakeholders disagree, and solutions reshape the challenge itself. Building on the premise that strategy itself is a wicked problem, this article advances a central claim: organizational resilience is best understood as an architectural capability largely grounded in humanity-based identity. Unlike organizational structure, mission, or even current strategy, each of which may be transient in turbulent environments, organizational identity, which is a construct that derives from individuals and humanity, provides an enduring basis for harmonizing the organization and its environment. Utilizing the lens of “humanity”—in its two dimensions of humankind and humaneness—we synthesize research on wicked problems, organizational identity, dynamic capabilities, modular design, alliances and smart power, and hybrid intelligence. We then propose an integrative model linking humanity-driven identity to resilience through three vectors—Inspirational Transformative Ambition, Innovative Value Networks, and Hybrid Intelligence Ecosystems—operationalized via a recently developed diagnostic tool. Finally, we offer corroborative evidence for the “Business of Humanity” logic, arguing that aligning humankind (opportunity across the full market spectrum) with humaneness (values-based evaluation) strengthens resilience by expanding opportunity sets while enhancing legitimacy, trust, and stakeholder alignment. Full article

This study examines the effect of corporate sustainability and management information systems (MIS) on business performance within the Libyan banks and investigates the mediatory role of information quality along with the moderating role of organizational culture. While the importance of the interaction between sustainability and digital capability is becoming increasingly acknowledged, there is yet to be an explicit agreement on the combined impact of both aspects on performance in developing banking organizations. To overcome this problem, a quantitative methodology was applied in the course of the research, which involved collecting data using a survey from 387 respondents from public and private Libyan banks. Structural Equation Modeling (SEM) was employed to validate the suggested associations. According to the results obtained, corporate sustainability and management information systems have a substantial positive influence on business performance in the case under investigation. At the same time, information quality works as an important mediating variable implying that the efficacy of any sustainability- and technology-related strategy relies on the quality of generated information. Moreover, organizational culture is shown to have a strong moderating influence on the link between information quality and performance since cultures supportive of innovation make the use of information even more valuable for performance. In conclusion, it could be stated that business performance in Libyan banks is achieved through the integration of socio-technical processes. Full article

To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing model is constructed to accurately characterize the two-stage evolution of software price: diminishing marginal utility during the mature technology accumulation stage and exponential growth during the technical bottleneck breakthrough stage. To ensure the consistency of pricing logic between hardware and software, a penalty function is innovatively designed to modify the standard likelihood function, effectively transforming practical business logic into a model regularization term. Parameter estimation is achieved by employing a Bayesian inference framework integrated with operational constraints, utilizing Markov Chain Monte Carlo (MCMC) sampling to realize robust posterior inference under small-sample constraints. Empirical analysis demonstrates that the proposed method achieves superior cross-domain data transfer performance compared to traditional baseline models, with a Leave-One-Out Cross-Validation (LOOCV) Mean Absolute Percentage Error (MAPE) of 21.2%. This research provides a practical value-oriented price estimation method for embedded equipment software pricing. Full article

Construction firms in emerging markets operate in highly volatile environments that threaten business continuity and sector-wide resilience. This study provides a novel, integrated framework that links multiple strategic survival models to construction business resilience and development in Ghana’s construction industry, with particular emphasis on the evolving role of digitalisation. Four survival models are conceptualised as strategic portfolios: Innovation and Digital Transformation, Diversification and Growth, Lean and Resilience, and Strategic Risk and Partnerships. A quantitative research design was employed, using structured questionnaires administered to 128 construction industry stakeholders. Data were analysed using Partial Least Squares Structural Equation Modelling to assess direct, indirect, and mediating effects among survival models, construction business resilience, and construction business development. All four survival models have significant positive effects on construction business resilience, with Diversification and Growth (β = 0.404) and Innovation and Digital Transformation (β = 0.377) exerting the strongest influence, followed by Strategic Risk and Partnerships (β = 0.265) and Lean and Resilience (β = 0.207). The structural model explains 55.7% of the variance in construction business resilience, while construction business resilience is positively and strongly related to construction business development (β = 0.439), accounting for 19.3% of its variance. The findings show, for the first time in this context, that construction business resilience systematically mediates the relationship between distinct strategic survival portfolios and business growth in an emerging-market construction sector. This study advances the resilience and construction management literature by empirically demonstrating the hierarchical effectiveness of different survival models and by positioning construction business resilience as both a defensive capability and a strategic engine of sustainable development for construction firms in volatile markets. This paper recommends that firms develop composite resilience portfolios that integrate these strategies, while policymakers foster enabling regulations, digitalisation incentives, and joint risk-sharing arrangements that amplify sector-wide resilience. It offers a portfolio-based perspective on how to combine diversification, digital transformation, lean management, and strategic partnerships to build resilient, growth-oriented construction businesses. Convenience sampling and a cross-sectional design in a single national context highlight the need for longitudinal and cross-country research to validate and extend the proposed framework. Full article

The digital economy (DE) is increasingly associated with higher carbon productivity (CP) and is widely regarded as an important factor in efforts to achieve the dual-carbon goals. However, the formulation of differentiated policies is constrained by a limited understanding of the multi-factor collaborative mechanisms and their asymmetric configurational pathways. This study combines the GMDH algorithm with the fsQCA approach to explore the multiple sufficient paths for high carbon productivity. Through feature selection and nonlinear modeling, the GMDH algorithm identifies five key variables associated with CP: the industrial robot permeability, software business development, digital innovation input, the usage depth of digital finance, and mobile communication facilities. The fsQCA method reveals that three configurational pathways consistent with higher levels of CP: the “innovation and finance-driven model” represented by Sichuan and Hunan, the “innovation-assisted digital industrialization model” represented by Henan and Hebei, and the “industry digitalization first developing model” represented by Jiangxi, Guangdong, Zhejiang, and Shanghai. Considering the uneven regional development across China, this study further categorizes provinces into four regional development types: innovation and finance-driven, digital industry empowerment, industrial digitalization leadership, and potential cultivation. Correspondingly, tailored policy recommendations are proposed for each region, providing practical insights consistent with the observed configurational patterns for improving CP in the context of DE development. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article

In the context of the digital economy as a driver of economic transformation, digital enterprises have become pivotal actors in value creation and innovation. A conducive business environment is essential for enhancing productivity, competitiveness, and the long-term resilience of entrepreneurial ecosystems. However, the mechanisms through which this environment influences the entry of newborn digital enterprises, a core indicator of sustainable economic activity, remain inadequately explored. This paper develops a government-led business environment index based on three dimensions: the legal environment, the governmental affairs environment, and public services. Using panel data from 35 major Chinese cities spanning 2016 to 2020, we employ a negative binomial regression model to examine how both the overall business environment and its sub-dimensions affect the entry of newborn digital enterprises. The findings reveal that an overall improvement in the urban business environment significantly promotes the entry of newborn digital enterprises and that all three sub-dimensions, namely the legal environment, governmental affairs environment, and public services, collectively facilitate this process. The principal implication is that local governments should focus on the balanced optimization of all business environment elements. Such policies not only stimulate digital startup formation but also contribute to high-quality, resilient, and economically sustainable urban development. Full article

Business schools, and universities more generally, find themselves within a constant tension between different and often conflicting institutional logics. Scholars have identified two primary and coexisting institutional logics in particular—an academic logic and a market logic—that universities must reconcile. To date, however, we still know little about how university leaders navigate persistent frictions between these institutional logics. To fill this gap, we conduct a grounded qualitative study of business schools’ top leaders who have recently introduced new revenue-generating programs. We develop a process model of institutional alignment that explains how leaders navigate and resolve tensions between competing logics over time. Our aim was to explore how these administrative leaders navigated frictions across institutional logics throughout the process of introducing, motivating, developing, and implementing these programs. We find that frictions between the competing institutional logics were indeed prominent in decision-makers’ minds and actions throughout the entire process. These frictions occurred at various levels, both within and outside the business school. To reduce these frictions, leaders describe employing adaptive strategies such as framing, collaboration, and structural adjustments to align the logics and position the new program as consistent with both. That is, the new programs are interpreted as institutional innovations that serve to align the logics. Full article

Peer-to-peer car-sharing (P2PCS) is a mobility solution in which vehicle owners make their cars available through digital platforms that mediate booking, access, and fleet management. Although P2PCS can offer theoretical advantages in terms of resource efficiency and reduction in private ownership, its market scaling is critically dependent on users’ perceived value. In this context, the present study investigates systemic determinants of scalability in the P2PCS market using a Mixed-Methods Research (MMR) approach, combining a literature review, expert survey, focus-group interviews, Interpretive Structural Modeling (ISM), and cross-impact matrix analysis (MICMAC). This combined approach allows for the identification of barriers and their position within a hierarchical structure of interrelationships, influence, and dependence. The most influential determinants are emotional attachment to private car ownership, legal and regulatory uncertainty, stakeholder misalignment, infrastructure constraints, and ambiguous insurance coverage. By analyzing the hierarchical interrelationships between these determinants and their respective driving and dependence powers, experts in a focus group suggested a set of targeted measures to address the P2PCS system of barriers. Proposed measures include regulatory sandboxes to safely test innovative business models, modular and usage-based insurance products, the promotion of public–private partnerships for shared mobility infrastructure, the advancement of digital interoperability across smart mobility services, and communication campaigns to foster sustainable travel behaviors. Collectively, the findings provide a decision-support framework for policymakers, P2P platform operators, and municipalities, illustrating how the Mixed-Methods Research approach can deepen understanding of the socio-technical interdependencies that shape the effectiveness and scalability of emerging mobility solutions such as peer-to-peer car-sharing. Full article