Search Results (289)

The purpose of this study is to characterize, through an exploratory empirical diagnosis, the degree of development and preliminary association between circular economy capabilities and sustainability-oriented digital transformation capabilities in Chamber-affiliated SMEs in Lambayeque, Peru. Guided by three exploratory working hypotheses, the study expected intermediate levels of development, heterogeneous performance across dimensions, and a positive but non-confirmatory coupling between both capability families. A self-administered questionnaire with thirty Likert-type items measured four circular economy dimensions—circular design and eco-design, resource optimization, circular waste management, and circular business models—and four sustainability-oriented digital transformation dimensions—digital technology infrastructure, dynamic digital capabilities, sustainable digital strategy, and digital innovation culture. The initial database contained 111 complete Chamber-affiliated responses; however, seven large Chamber-affiliated firms were retained only as contextual comparators and were excluded from all statistical processing. Consequently, all descriptive, psychometric, and SEM results were calculated using the final analytical sample of 104 micro-, small-, and medium-sized enterprises. The findings show intermediate development in both constructs, higher perceived performance in digital innovation culture and resource optimization, and lower performance in digital technology infrastructure, reverse logistics, platforms enabling circularity, and monetization of circular models. The latent association between the two higher-order constructs was very high (β = 0.985, p < 0.001); however, because global fit indices were below conventional thresholds, this coefficient is interpreted as preliminary evidence of empirical overlap and capability co-occurrence rather than confirmatory evidence of a validated structural model or causal integration. Full article

Restaurants combine long opening hours, catering demand, kitchen ventilation, DHW, and mixed-fuel cooking loads, making their decarbonisation different from generic commercial retrofit. For small- and medium-sized enterprise (SME) hospitality premises, this makes the transition to net-zero operation a distinct sustainability challenge because a large, process-driven share of demand lies outside conventional building-fabric and building-services retrofit. This single-case study develops a whole-building utility-calibrated OpenStudio/EnergyPlus model for Beit El Zaytoun, a 655.82 m² restaurant in Park Royal, London. Monthly electricity and gas data for June 2024–May 2025 were used to calibrate the baseline at whole-building level. Standalone and cumulative scenarios tested insulation, low-emissivity double glazing, LED lighting and controls, ASHP service scenarios, and an 11 kWp PV array. Baseline demand was 413,895 kWh/yr, equivalent to 631.1 kWh/m²·yr and 75,020 kgCO₂e/yr. The lowest-net-energy analytical package reduced net imported energy to 314,734 kWh/yr and operational carbon to 56,700 kgCO₂e/yr, a retained 24.0% reduction on the source reporting basis; this package is treated as an analytical bound rather than as a final design recommendation because it excludes cooling. The model-derived residual process load, kitchen and catering gas plus kitchen, and back-of-house electricity remained 233,920 kWh/yr across building-focused scenarios. The Residual-Load Index (RLI) rose from 0.57 to 0.74; with ±15% process-load allocation uncertainty, the optimised RLI range was 0.63–0.85, so the post-retrofit balance remained process-load dominated. The case demonstrates a practical decarbonisation ceiling likely to recur in similar high-process-load hospitality premises: fabric, lighting, heat electrification, and PV are necessary but insufficient without catering-equipment, cooking-fuel, kitchen-ventilation, refrigeration-control, sub-metering, and demand-response strategies. The paper contributes whole-building utility-calibrated quantitative evidence and a transferable RLI metric for sub-sector-specific sustainable retrofit policy, and the net-zero transition of SME food-service premises. Full article

The primary objective of this article is to examine the organizational, economic, and sustainability-related implications of implementing artificial intelligence (AI) systems in small and medium-sized enterprises (SMEs) in Poland. The study combines a survey of 112 SMEs in the Kuyavian–Pomeranian region, including 70 AI-using firms, with 13 in-depth interviews with managers. The quantitative analysis applies logit models to identify determinants of perceived AI effects on internal processes: working time and workload reduction, automation, cost effects, and creativity. The qualitative component explains how AI is adopted and embedded in business practice. The results show that AI adoption in SMEs is increasingly common but remains uneven and mostly operational. The strongest effects concern workload reduction and time efficiency, particularly in service firms and where AI is used intensively. Advanced AI adoption increases the probability of perceiving workload and cost-related effects. However, these effects should not be interpreted simply as direct cost reduction. Rather, AI improves productivity and work capacity while creating new costs related to paid tools, data preparation, integration, output verification, and governance. The interviews show that AI implementation follows a staged path: from curiosity-driven experimentation, through cognitive work augmentation, to workflow integration and, in selected cases, AI-enabled business model innovation. The transition from ad hoc use to strategic implementation depends less on firm size alone and more on process maturity, capabilities, and data readiness. Barriers also change with maturity: early-stage firms face a lack of knowledge, time, and clear use cases, whereas advanced users encounter data quality, hallucinations, security, integration, and governance problems. The study finds that sustainability considerations, particularly environmental impacts and ESG-related implications of AI, remain largely unperceived in SME decision-making. Entrepreneurs primarily interpret sustainability through the lenses of organizational resilience, long-term competitiveness, adaptability, and responsible digital transformation rather than through formal environmental metrics. The findings suggest that SME managers should implement AI gradually, link adoption to measurable process-level outcomes, and invest in AI literacy and governance. They should also integrate responsible AI principles into organizational strategy to support sustainable digital transformation. The study contributes to the literature by showing that AI adoption in SMEs should be understood not only as a productivity-enhancing process but also as a broader organizational transition shaping long-term sustainability and resilience. Full article

Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article

As micro, small and medium-sized enterprises (MSMEs) compete with limited resources, lightweight systems are needed to secure their digital assets. Fuzzy vaults (FVs) are useful for protecting secrets and, when applied to biometric systems, provide error-tolerance and privacy to enrolled biometric features. Combining multiple biometric traits also improves performance against attacks like spoofing in multimodal (MM) authentication systems. However, the design of the FV and the biometric-fusion method applied can limit the system’s effectiveness. This study systematically evaluates recent studies on FVs and MM systems and presents an up-to-date review to identify gaps, give directions for future studies, and, ultimately, improve the design of these systems. The research targeting MSMEs was carried out in two parts, with the first search focused on MM systems and the second on FVs, following the PRISMA guidelines. The main findings include the need to optimise the resource intensity of FV systems for the authentication of large numbers of individuals. It also found the need to make the model compatible with other biometric modalities as greater focus is on minutiae features. By reviewing these systems, we aim to foster the development of lightweight MM FV models to provide privacy and security in MSMEs. Full article

This study examines how Ukrainian enterprises of different size classes adapted their trade activity under the compounded shocks of COVID-19 and the full-scale war. The article addresses national economic resilience and sustainable recovery by examining how export and import dynamics changed among micro-, small-, medium-, and large-sized firms during 2015–2023. The methodology combines the logarithmic decomposition of intensive and extensive trade margins with a strategic positioning matrix based on labour productivity and the net-export coefficient. The results reveal marked size-based differences in aggregate trade-adaptation patterns. During the pandemic, microbusinesses shifted toward a quantity-led compensatory pattern, whereas during the war, medium-sized and large enterprises showed a stronger efficiency-led export pattern. Micro- and small firms displayed characteristics associated with technology-oriented adaptation, combining rapid labour productivity growth with negative trade balances, whereas large enterprises were positioned closer to the niche-exporter profile, supporting the balance of payments but showing signs of slower productivity growth. Medium-sized firms occupied a transformation zone, indicating unresolved adjustment pressure and continued dependence on trade restructuring. These findings suggest that enterprise-size heterogeneity can serve as an analytical basis for differentiated recovery policy. The results are relevant for trade-dependent sectors, including agri-food and food-processing systems, where recovery depends on technological upgrading, export capacity building, and the more effective conversion of imports into future export potential. Full article

Liquidity distress can propagate through trade credit, contractual obligations, and financing dependencies in production networks. Existing network-contagion studies largely ask how distress spreads, but say much less about how scarce stabilization resources should be allocated across heterogeneous firms. This paper develops a heterogeneous network model in which firms are divided into structurally central large firms and small- and medium-sized enterprises (SMEs) with lower recovery capacity in the benchmark setting. The model incorporates recurrent healthy–distressed transitions, asymmetric contagion across firm types and network directions, decaying support stocks, preventive and curative support channels, and expectation-driven feedback linking aggregate distress to effective contagion and recovery probabilities. A reduced two-block approximation is used to characterize a local persistence threshold defined by the spectral radius of the Jacobian at the low-distress equilibrium. The analysis shows that targeted support need not dominate uniform support: its value depends on whether allocation priorities match the firm groups or network positions that generate the largest marginal reduction in persistent distress. Simulations on directed scale-free networks show that policy scale mainly determines peak containment, whereas allocation architecture primarily affects post-peak adjustment and long-run distress. Recovery-enhancing support plays a larger role in post-peak stabilization, and combined preventive–curative support yields stronger resilience than either channel alone. The framework provides a tractable basis for analyzing stabilization rules under limited support capacity in heterogeneous production networks. 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

The rapid growth of publicly available digital services increases the need for scalable security assessment. This is particularly important for software directly used by end users, such as Android applications. Due to staff shortages and financial constraints, small and medium-sized enterprises are often unable to test their applications for vulnerabilities. One possible support mechanism is the use of large language models (LLMs) to assist testers during such assessments. The aim of this study was to investigate the possibility of using an LLM as an interactive guide for dynamic application security testing (DAST) of Android applications. For this purpose, five LLM-based systems were compared: Gemini 2.5 Flash, GPT-oss 120B, Llama 3.3 70B, Qwen 3 32B, and Trinity Large Preview accessed via OpenRouter. The models were evaluated on intentionally vulnerable Android applications using weighted step-level scoring and three selected exploit guidance scenarios. In the main guidance experiment, Gemini achieved the highest combined Fully Discovered and Partially Discovered (FD + PD) detection rate of 79.1% in the representative run, while repeated runs for selected models showed limited aggregate variability. The results also indicate that more detailed prompts improve the quality of generated guidance. The findings suggest that LLMs can serve as interactive guides for DAST testing of Android applications, although they should be treated as supporting tools rather than standalone security-testing systems. Full article

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems. Full article

Background: Small- and medium-sized enterprises (SMEs) constitute a large portion of the industrial energy demand in the emerging economies, but their shift to renewable energy is not well comprehended at the firm level. Bangladesh is a special case, since the country has adopted national commitments to Sustainable Development Goal 7 on clean energy, but the uptake of renewable energy by SMEs remains minimal due to complex socio-economic factors. Most of the literature has concentrated on household access to energy or national policy models, leaving a gap in empirically validated models of firm-level adoption in the manufacturing sector. Method: Based on the diffusion of innovation theory, institutional theory, and the resource-based view, this research paper formulates and empirically verifies a combined socio-economic model of renewable energy adoption. Partial least squares structural equation modeling (PLS-SEM) was used to analyze a cross-sectional survey of 426 owners and managers of manufacturing SMEs in Bangladesh’s textile and food processing sub-sectors. Findings: Four out of five hypothesized direct relationships were supported. The most important drivers were environmental orientation (β = 0.467, p < 0.001, f² = 0.413), market competitiveness (β = 0.287, p < 0.001, f² = 0.413), policy and institutional factors (β = 0.211, p < 0.001, f² = 0.413), and access to finance (β = 0.096, p = 0.004). Perceptions of cost did not become significant (β= −0.036, p = 0.279). Top management support significantly and negatively moderated the relationship between environmental orientation and adoption (β = −0.093, p = 0.003), possibly because it moderates the substitution mechanism in SME decision-making, which is highly centralized. The model accounted for 64.5% of the variation in renewable energy adoption (R² = 0.645). Conclusion: The results show that attitudinal and institutional factors tend to be more important than financial barriers in determining SMEs’ energy transitions. Environmental consciousness, market incentives, and streamlined institutional access should be the focus of policy interventions to hasten inclusive low-carbon transitions in emerging manufacturing economies. Full article

Small and medium-sized enterprises (SMEs) are critical actors in promoting environmentally sustainable supply chains, particularly in emerging economies where their collective environmental footprint is substantial. Despite growing attention to green supply chain management (GSCM), research has predominantly focused on large firms, leaving the motivational drivers shaping GSCM implementation in SMEs underexplored. Addressing this gap, the present study develops and empirically tests a motivation-based framework to examine how four organizational motives, cost, market, ethical, and legitimacy, drive the depth of GSCM implementation in SMEs. In addition, environmental uncertainty is conceptualized as a key contextual contingency moderating the effectiveness of these motives. Drawing on survey data from Vietnamese SMEs, the findings reveal that all four motives positively influence implementation depth, with ethical motives exerting the strongest effect. Furthermore, environmental uncertainty significantly amplifies these relationships. By integrating multiple theoretical perspectives and emphasizing the contingent role of environmental uncertainty, this study advances GSCM research by providing a nuanced, context-sensitive understanding of how SMEs operationalize sustainability practices in dynamic and resource-constrained environments. Full article

This study presents a comparative analysis of five gender equality certification schemes alongside the ISO 53800 standard with the aim of distilling shared conceptual foundations and design principles that can inform progress toward Sustainable Development Goal (SDG) 5 on gender equality. The comparative analysis reveals marked heterogeneity in scope, design architecture, indicators, and transparency. Methodologically, the study draws on the relevant literature, documentary evidence, and semi-structured consultations with five experts in gender equality, diversity management, auditing, and ESG reporting. Building on the most effective and robust features across gender equality schemes, the study proposes a universal framework for gender equality certification. Under this framework, an ideal universal certification model should apply the same core requirements to both public and private organizations, while including simplified procedures tailored to small- and medium-sized enterprises (SMEs). Moreover, the model should rely on a limited set of key performance indicators (KPIs), focusing on the most material dimensions and prioritizing quantitative measures. It should also strengthen employee feedback mechanisms and enhance accountability in corporate governance. The framework should also pay attention to intersectional dimensions, extend responsibility across the value chain, and address the gender-related implications of artificial intelligence (AI). Importantly, an ideal universal gender equality certification should ensure a high level of transparency through the public disclosure of certified organizations, assessment criteria, KPIs, and levels or scores achieved. Furthermore, it should be supported by a free digital self-assessment tool and robust auditing arrangements, underpinned by a sufficiently large pool of accredited certification bodies and gender-balanced audit teams. Finally, it should undergo periodic review and align with Environmental, Social, and Governance (ESG) principles and other related SDGs. Full article

Sheep and goat milk are mainly used for cheese manufacture in small livestock farms, giving rise to a large volume of whey. Sheep and goat cheese whey possess excellent and specific functional and nutritional characteristics. The valorization of these valuable by-products through physicochemical or biotechnological processes compatible with artisanal production are important in terms of sustainability, i.e., economic, social, and environmental impacts. The main challenges for whey processing in small and medium-size enterprises (SMEs) are the lack of equipment, construction and information as well as the small amounts of cheese whey generated from these plants. Membrane technology can be convenient to produce valuable by-products for small dairy plants in the presence of enough investment cost and whey amount. Biotechnological treatments covering anaerobic digestion systems and fermentation processes are advantageous for SMEs over physicochemical methods on investment cost. In these processes, efficient microorganisms are able to produce high-value natural products, biofuels, and biopolymers. Anaerobic digestion is a suitable method for goat and sheep cheese whey valorization in SMEs due to the small volumes. Additionally, bioconversion into fermented beverages is a good choice for cheese whey valorization in SMEs because of its low operational and equipment cost. Full article

Small- and medium-sized commercial banks constitute a fundamental component of the financial system, and their corporate governance plays a critical role in the modernization of financial governance. Over the past two decades, these banks have largely established a modern enterprise framework, typically structured around shareholders’ meetings, boards of directors, supervisory boards, and senior management (SBSS). This governance arrangement has supported sustained institutional growth; however, persistent challenges have emerged, including the accumulation of non-performing assets and the increasing frequency of risk events. These problems cannot be attributed solely to market or operational factors, but are also closely related to limitations in the top-level design and practical functioning of the SBSS governance structure. In particular, a notable gap exists between the original design objectives of the modern enterprise system and its actual governance outcomes in practice. This study adopts an institutional and analytical approach, supported by descriptive regulatory statistics, to examine governance deficiencies in small- and medium-sized commercial banks. By introducing French state-led governance culture as an institutional reference, the paper conceptualizes non-shareholder-centered governance arrangements under strong public involvement and proposes an embedded governance framework emphasizing accountability, supervision, and information integration. Full article