Search Results (866)

As small–medium-sized enterprises (SMEs) increasingly adopt cloud technologies, understanding the factors influencing this shift is crucial as it helps to optimize cloud integration strategies, enabling SMEs to thrive in today’s digital economy. A cross-sectional, quantitative survey was conducted in February 2022 on 626 employees of SMEs in the USA, based on the TAM-2, TAM-3, and UTAUT-2 models. The questionnaire presented satisfactory reliability, as well as factorial and convergent validity. Employees presented positive behavioral intentions to use cloud technologies, particularly during the COVID-19 period. SMEs were satisfied with the use of Software as a Service (SaaS), Infrastructure as a Service (IaaS), and the public cloud development model in the wake of the COVID-19 period. Behavioral intention to use cloud technologies was linked with higher performance and effort expectancy, price, perceived enjoyment, computer self-efficacy, and social influence. A higher behavioral intention was observed in employees (a) with a mid–top-level role; (b) who worked in finance and insurance, information services data, construction, or software and in an SME with 26–500 employees; (c) who had a master’s degree; (d) were 35–44 years old; and (e) had family obligations. Higher experience with the use of cloud technologies enhanced the positive impacts of effort expectancy, computer self-efficacy, and perceived enjoyment on behavioral intention. Full article

The aim of this study was to identify and explore the most significant barriers in implementing smart manufacturing (SM) in terms of small and medium enterprises (SMEs). A two-round Delphi method was used to uncover them in this regard. To assess the reliability of the obtained results, Cronbach’s alpha, Intraclass correlation coefficient, and a statistical F-test were performed for both rounds. Cronbach’s alpha for round 1 was 0.729, and 0.816 for round 2. On this basis, good inter-rater reliability was demonstrated in round 2. At the same time, the Intraclass correlation coefficient from round 1 was 0.54, and from round 2, it was 0.74, indicating a significant improvement in panel consensus. The comparison of the equality of variances within the two rounds using the F-test confirmed that a third round of the survey was not necessary. Moreover, the coefficient of variation and relative interquartile range were applied to assess internal consistency among the involved experts to come to a more comprehensive and cohesive understanding of the issue at hand. A total of 30 barriers/limitations or shortages were identified in the preparatory phase of the research, which, in some sense, do not allow or slow down the implementation of the SM. The Delphi survey found that financial problems, lack of government support, and technological constraints can be considered as the most serious barriers to the implementation of SM in an SME environment. Finally, the obstacles/constraints or shortcomings that proved to be the most critical were analyzed in terms of their impact on the ability of small and medium-sized enterprises to embrace the challenges of smart manufacturing. Full article

As digitalization and environmental sustainability advance globally, small and medium-sized enterprises (SMEs) are facing transformative pressures as well as emerging opportunities. Rapid digital innovation promotes intelligent production, cost reduction, efficiency gains, and improved management practices, while green development mandates emphasize energy conservation, emissions reduction, and sustainable supply chains. Amid concurrent digital and green transformations, SMEs are leveraging digital technologies to bolster green learning and enhance sustainable product development. This study investigates the digital–green learning orientation (DGLO) and its influence on ambidextrous research and development (R&D) capabilities, which in turn shape sustainable product development performance (SPDP). Drawing on survey data from 306 SMEs in eastern and southern China, multiple regression analysis was employed to assess the relationships between DGLO, ambidextrous R&D capabilities, and SPDP. The findings reveal that DGLO significantly enhances SPDP. Moreover, DGLO promotes SPDP through both exploitative and exploratory R&D capabilities, with each playing a complementary role. Full article

Serbian SMEs face mounting pressure to stay competitive, agile, and aligned with sustainability goals amid rapid digital change. This mixed-method study—12 qualitative case studies and a survey of 200 firms—examines how AI adoption supports flexible and adaptive strategic transformation. We examine how organizational context and AI readiness translate into the strategic application of AI and, in turn, sustainable development and strategic performance outcomes among Serbian SMEs. Through the AI-Driven Strategic Transformation Framework (AISTF-SME), three adoption types were identified —Traditionalists, Experimenters, and Strategic Adopters—distinguished by digital maturity, strategic integration, and sustainability orientation. While AI is primarily deployed for operational efficiency, firms with higher AI maturity and tighter strategic alignment report stronger gains in agility, innovation, and customer experience; sustainability-oriented use cases remain limited. Key barriers include shortages of technical talent, financial constraints, and insufficient institutional support. We recommend a multi-stakeholder policy approach emphasizing sector-specific AI readiness programs, better access to funding, and stronger university–industry collaboration. The findings enrich digital transformation and sustainability research and offer practical guidance for accelerating AI adoption in transitional economies. Full article

Corporate Social Responsibility (CSR) is increasingly shaping the pathways of Small Medium-sized Enterprises (SMEs). This study presents an entity-relationship diagram (ERD) approach to 184 determinants of SME internationalization success, in order to provide structured inputs for Deep Learning (DL) Recommenders that can support CSR-aligned internationalization strategies. Employing Visual Paradigm 17.2 Professional software for modeling, the research synthesizes state-of-the-art findings on foreign market entry, and export performance, into ERDs. Then the market adoption drivers for such a DL tool are explored through semi-structured interviews with twelve stakeholders. The results reveal a propensity to adopt the DL recommender, with experts highlighting essential features for engagement, pricing, and implementation. The discussion contextualizes these findings, while the conclusion addresses gaps and future directions. The study’s focus in Portugal/Germany may limit worldwide extrapolation, yet it advances knowledge by consolidating success determinants, validating platform requirements, exposing gaps, and suggesting research in both CSR, AI and SME internationalization. Full article

Small and medium-sized enterprises (SMEs) are critical contributors to economic growth, innovation, and employment. However, they often struggle in securing external financing. This financial gap mainly arises from perceived risks and information asymmetries creating barriers between SMEs and potential investors. To address this issue, our study proposes a machine learning (ML) framework for predicting the investment readiness (IR) of SMEs. All the models involved in this study are trained using data provided by the European Central Bank’s Survey on Access to Finance of Enterprises (SAFE). We train, evaluate, and compare the predictive performance of nine (9) machine learning algorithms and various ensemble methods. The results provide evidence on the ability of ML algorithms in identifying investment-ready SMEs in a heavily imbalanced and noisy dataset. In particular, the Gradient Boosting algorithm achieves a balanced accuracy of 75.4% and the highest ROC AUC score at 0.815. Employing a relevant cost function economically enhances these results. The approach can offer specific inference to policymakers seeking to design targeted interventions and can provide investors with data-driven methods for identifying promising SMEs. Full article

The main objective of this research was to evaluate the effect of extrusion temperature (ET), feed moisture content (FMC), and the proportion of huitlacoche relative to corn starch (HCP/Starch) on the physicochemical, techno-functional, and color properties of an extruded snack, using response surface methodology to optimize processing conditions and product quality. A Box–Behnken design and response surface methodology were used to model and optimize the process. The responses analyzed included residence time (RT), specific mechanical energy (SME), expansion index (EI), bulk density (BD), texture (Tex), water absorption index (WAI), water solubility index (WSI), pH, and color parameters (L*, a*, b*, C*, h°, and ΔE). Results showed that the huitlacoche proportion significantly affected BD, Tex, WSI, and color, while ET and FMC mainly influenced EI, SME, and other techno-functional traits. Multi-response optimization indicated that 150.4 °C, 15.8 g/100 g FMC, and 10–20 g/100 g HCP/Starch maximized EI (2.27) and minimized BD (0.40 g/cm³), Tex (17.5 N), and SME (347.6 J/g). The overall performance was summarized by global desirability (0.83–0.88), a metric that combines all responses into a single scale (0 = poor; 1 = is the most desired goal). The optimized conditions produced snacks with acceptable hydration capacity, pH, and color, supporting huitlacoche as a viable functional ingredient. These findings demonstrate the potential of this traditional resource for developing sustainable, value-added, and health-oriented extruded foods. Full article

In this paper, we introduce a novel, self-hosted Syslog collection platform designed specifically to address the challenges that small and medium enterprises (SMEs) face in implementing comprehensive syslog monitoring solutions. Our analysis begins with an assessment of current network observability practices, evaluating enterprise solutions, on-premises systems, and Software as a Service (SaaS) offerings to identify features crucial for SME environments. The proposed platform represents an advancement in the field through the incorporation of modern practices, including GitOps and continuous integration and continuous delivery/deployment (CI/CD), and its implementation onto a self-managed Kubernetes platform, which is an approach not commonly explored in SME-focused solutions. We will explore its scalability by leveraging dynamic templates, which allow us to select the number and type of nodes when deploying networks of various sizes. This architecture ensures organisations can deploy a pre-designed, scalable network monitoring solution without extensive external support. The resilience of the proposed platform is assessed by providing empirical evidence of the scaling performance and reliability under various failure scenarios, including node failure and high network throughput stress. Full article

This study examines the effects of sustainability-related data requests—spurred by the EU Corporate Sustainability Reporting Directive (CSRD)—on small and medium-sized enterprises (SMEs) in the Netherlands. Using a representative survey of 431 SMEs and 48 qualitative interviews with SME representatives and business stakeholders, the research provides a comprehensive overview of their experiences in late 2024. A key finding is that most Dutch SMEs (72%) have not yet received sustainability data requests. However, SMEs embedded in international value chains report more frequent and complex data demands, particularly concerning environmental indicators like CO₂ emissions and material use. Ratings of perceived relevance reveal a disconnect between external data requests and SMEs’ internal priorities, with many SMEs prioritizing health and safety over climate metrics. While some SMEs see data requests as opportunities for improved sustainability performance and market positioning, many also experience challenges, including limited resources, fragmented IT systems, and regulatory uncertainty. The implementation of CSRD highlights the urgency of supporting SMEs in building data management capacities and standardized processes. The study recommends clearer communication of data relevance, targeted support measures, and further research into cross-national and longitudinal dynamics to foster an effective sustainability transition across value chains. Full article

Small and medium-sized enterprises (SMEs) often face severe resource constraints and operational fragility during crises. However, little is known about how managerial resilience (MR) translates into performance through time-related psychological resources and innovation—two capabilities that are both scarce and critical under such conditions. Drawing on Temporal Motivation Theory (TMT), this study develops and tests a dual-mediation model in which employee temporal psychological capital (TPC) and employee innovative behavior (EIB) transmit the effects of MR on performance. As a core methodological innovation, we adopt a multi-method analytical strategy to provide robust and complementary evidence rather than a hierarchy of results: Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to examine sufficiency-based causal pathways and quantify the mediating mechanisms; Support Vector Machine (SVM) classification offers a non-parametric predictive validation of how MR and its mediators distinguish high- and low-performance cases; and Necessary Condition Analysis (NCA) identifies non-compensatory conditions that must be present for high performance to occur. These three methods address different research questions—sufficiency, classification robustness, and necessity—therefore serving as parallel, equally important components of the analysis. A total of 455 SME managers and employees were surveyed, and results show that MR significantly enhances all three dimensions of TPC (temporal control, temporal fit, time pressure resilience) and EIB (idea generation, idea promotion, idea realization), which in turn improve employee performance. SVM classification confirms that high MR, strong TPC, and active innovation align with high performance, while NCA reveals temporal control, idea generation, and idea realization as necessary bottleneck conditions. By integrating sufficiency–necessity logic with predictive classification, our findings suggest that SMEs should prioritize leadership resilience training to strengthen managers’ adaptive capacity, while simultaneously implementing time management interventions—such as temporal control workshops, workload balancing, and innovation pipeline support—to enhance employees’ ability to align tasks with organizational timelines, execute ideas effectively, and sustain performance during crises. Full article

This study investigates the nexus between leverage and financial performance in a sample of 54 Moroccan agricultural small- and medium-sized enterprises (SMEs) over the period of 2017–2022. Drawing on trade-off, pecking order, and agency theories, this analysis examines whether different levels of indebtedness influence performance, as measured by return on assets (ROA). Using panel data regression models, both linear and nonlinear specifications were tested to explore the potential curvature of the leverage–performance relationship. The empirical results reveal a significant and negative linear relationship between both short-term and long-term leverage and ROA, suggesting that increased indebtedness impairs financial performance. A quadratic specification reveals a persistently negative effect of short-term leverage and a U-shaped relationship between long-term leverage and ROA, indicating that performance may improve beyond certain debt thresholds. To address endogeneity concerns and validate the findings, dynamic panel estimation using the generalized method of moments (GMM) was employed, confirming the leverage’s adverse effects on performance. Thus, this study provides policy-relevant insights into optimal capital structure decisions for small agribusinesses and underscores the need for tailored financial strategies to support their sustainable development. Full article

A graph is called strongly Menger-edge connected (SME-connected) if any two vertices are connected by as many edge-disjoint paths as their smaller degree. For positive integers t and r, a graph G is called t-edge-fault-tolerant SME-connected (t-EFT-SME-connected) of order r if $G-F$ is SME-connected for any set F of edges in G with $\left|F\right|\le t$ and $\delta (G-F)\ge r$. We show that the n-dimensional folded crossed cube is $(n-1)$-EFT-SME-connected of order 1 and $(3n-5)$-EFT-SME-connected of order 2. Let $p(G,f)$ and $p^M(G,f)$ be the probabilities that G is connected and SME-connected when f edges are faulted randomly, respectively. We perform a numerical simulation on $p(G,f)$ and $p^M(G,f)$ for a five-dimensional folded crossed cube and folded hypercube. The numerical results show that, in addition to their same edge connectivity and SME connectivity, these two graphs have almost the same values of $p(G,f)$ and $p^M(G,f)$ for every f. This hints that, although the ‘edge-cross’ pattern in a hypercube-based graph can shorten the mean vertex distance, the ‘edge-cross’ is not a necessary pattern for strengthening the connectivity of the graph. Full article

In response to increasing global sustainability demands, this study examines how green dynamic capabilities influence business performance in Bali Island’s tour and travel SMEs, with green service innovation as a mediating mechanism. Drawing on the resource-based view (RBV) and dynamic capability theory, the research adopts a quantitative approach using survey data from 387 SMEs and employs structural equation modeling (SEM) to analyze the relationships among green dynamic capabilities, green service innovation, and business performance. Findings reveal that green dynamic capabilities significantly enhance both green service innovation and business performance. Notably, green service innovation partially mediates this relationship, underscoring its pivotal role in transforming internal sustainability-oriented capabilities into tangible performance outcomes. The key contribution of this study lies in extending RBV by integrating green service innovation as a strategic conduit that links eco-centric capabilities to competitive advantage in a tourism SME context—a perspective that remains underexplored in emerging economies. Practically, the study provides actionable insights for SME owners and policymakers to prioritize innovation in service design and delivery as a pathway to sustainable tourism performance. Full article

The increasing complexity and volume of cybersecurity logs demand advanced analytical techniques capable of accurate threat detection and explainability. This paper investigates the application of Large Language Models (LLMs), specifically qwen2.5:7b, gemma3:4b, llama3.2:3b, qwen3:8b and qwen2.5:32b to cybersecurity log classification, demonstrating their superior performance compared to traditional machine learning models such as XGBoost, Random Forest, and LightGBM. We present a comprehensive evaluation pipeline that integrates domain-specific prompt engineering, robust parsing of free-text LLM outputs, and uncertainty quantification to enable scalable, automated benchmarking. Our experiments on a vulnerability detection task show that the LLM achieves an F1-score of 0.928 ([0.913, 0.942] 95% CI), significantly outperforming XGBoost (0.555 [0.520, 0.590]) and LightGBM (0.432 [0.380, 0.484]). In addition to superior predictive performance, the LLM generates structured, domain-relevant explanations aligned with classical interpretability methods. These findings highlight the potential of LLMs as interpretable, adaptive tools for operational cybersecurity, making advanced threat detection feasible for SMEs and paving the way for their deployment in dynamic threat environments. Full article

Franchising operates as a proven business model that drives substantial growth for small and medium-sized enterprises (SMEs) worldwide. The franchise ecosystem in Saudi Arabia lacks sufficient research, despite established frameworks for success in markets such as the United States, the United Kingdom, and Australia. This research investigates the elements that lead to franchise success in Saudi Arabia through a combination of qualitative and quantitative data. This research evaluates franchise performance through metrics such as Average Revenue Per Unit (ARPU), Return on Investment (ROI), Franchise Success Rate, Time to Break Even, and Market Growth Rate, comparing Saudi Arabia with the U.S., the U.K., and India to identify essential success determinants. The research reveals that franchise success depends on regulatory frameworks, cultural alignment, economic diversification, and supply chain efficiency. The U.S. and U.K. enjoy established legal protections, whereas Saudi Arabia faces regulatory complexities and resource limitations. The research proposes three strategic recommendations: government incentives, locally adapted business models, and carefully selected locations to boost franchise success. The analysis provides essential information to policymakers, franchisors, and entrepreneurs seeking to expand their businesses in Saudi Arabia. The implementation of Vision 2030 growth barrier solutions and market opportunities will enable Saudi Arabia to build up its franchising sector and enhance market performance. This research adds new knowledge to the franchising literature in emerging markets and its impact on sustainable business growth. Full article