Search Results (884)

Manufacturing SMEs in Ecuador operate under macroeconomic volatility and limited financing; improvements in processes and management are key mechanisms for sustaining productivity and competitiveness. In contexts where conventional innovation indicators are unavailable, financial ratios constitute replicable signals that close a measurement gap in emerging economies. This study constructs the Structure-Based Innovation Index (SBII) as the mean of within-sample percentile ranks of capital efficiency (EBIT/Assets) and sales per employee, using financial statements from the SCVS, sectoral indicators from ENESEM, and size classification from REEM. The sample includes 58 formal manufacturing SMEs in Ecuador in 2023, stratified by province and size. Performance is measured through labor productivity and operating profitability (EBIT/Sales). Tercile comparisons reveal clear performance differentiation: the high-SBII group exhibits substantially higher median sales per employee (USD 129,552 vs. USD 40,176 in the low group) and higher operating profitability. Signals are more strongly reflected in productivity than in margins, indicating that operational gains materialize earlier. A robustness check using SBIIalt confirms that gradients are not index artifacts. High-performing SMEs are distinguished by institutionalized operational discipline: asset utilization, throughput stability, and cost control. The SBII is a replicable proxy for structure-based innovation in data-constrained environments. The findings align with SDGs 8, 9, and 12. Full article

Efficiency refers to the performance level corresponding to using minimal inputs to achieve the maximum possible outputs. Despite its importance to the Canadian economy, such performance assessments has rarely been undertaken in the hardware retail industry in recent years. We present the results of a recent study of the relative efficiencies for three major chains of hardware and renovation retail stores operating in Canada (Home Depot, Lowe’s and Rona). We use the classic and bootstrap data envelopment analysis (DEA) models to measure performance levels over the 22 years from 2000 to 2021. Overall, the firms exhibited high efficiency during this period, and operations management was the primary source of inefficiency. However, an analysis of trends over the 22 years shows that all three companies experienced periods of declining efficiency at the beginning of the study period, followed by a phase of recovery that appears to have accelerated towards the end of the study period. Our longitudinal analysis also indicates that recent shocks and crises have impacted the firms. The succession of crises at the end of the 2000s, the 2007 forestry crisis in Canada, and the 2008 global financial crisis led to the lowest period of efficiency for all the firms, from which they started rebounding in 2011. The specific impact on Rona can explain Lowe’s acquisition of Rona in 2015. However, such a move did not seem to have had a significant improvement beyond accelerating a recovery that had started a few years earlier. This may explain Lowe’s sale of all its Canadian operations in 2022, leading to a new firm called Rona+. Finally, the COVID-19 pandemic also seems to have had a similar effect: accelerating the recovery from the 2008 financial crisis that the firms had started in 2011. Full article

The increasing pressure on natural resources and the environment has intensified the need for sustainable cascade utilization in closed-loop supply chains (CLSCs). This study develops a game-theoretic framework to examine cascade utilization under both constant and heterogeneous quality restoration costs across three collection structures: centralized, manufacturer-led, and third-party collection. The results show that the relative performance of different structures depends on key economic conditions, including material recycling revenue and the comparative advantage of remanufacturing. No single structure dominates across all dimensions: a manufacturer-led collection tends to promote new product sales, while a third-party collection enhances remanufacturing and recovery levels, particularly under cost heterogeneity. Environmental performance, evaluated through collection quantity, cascade utilization efficiency, and an environmental impact indicator, also varies across structures, with cost heterogeneity shifting advantages toward the third-party collection. Policy analysis further indicates that both collection and remanufacturing subsidies increase recovery volumes but operate through distinct mechanisms. The collection subsidy expands return flows but may reduce cascade utilization efficiency by directing more low-quality products to recycling, whereas remanufacturing subsidy promotes higher-value reuse pathways and improves environmental performance. These findings highlight the importance of aligning collection structures and policy instruments under different cost conditions to enhance resource efficiency and support the circular economy and sustainable consumption and production objectives. Full article

This paper examines whether broad enterprise AI adoption in Europe is best understood as an isolated technology decision or as the outcome of a wider bundle of digital capabilities. Using harmonized Eurostat data for European enterprises, the analysis builds a repeated cross-section at the country–size-class–year level and models high AI adoption with a combination of random forest and elastic-net estimation. The dependent variable captures enterprises using at least one AI technology, while the explanatory set focuses on cloud adoption, cloud CRM, cloud ERP, cloud database hosting, cloud security, cloud software use, e-sales intensity, and enterprise size. The findings reveal a stable predictive structure and consistent classification performance across specifications. Across models, cloud CRM and e-sales emerge as the strongest predictors of high AI adoption, followed by general cloud use and selected data-related cloud capabilities. This ordering remains largely stable in threshold-sensitivity checks based on alternative definitions of high adoption. The pattern also remains visible when country controls are removed, which suggests that the result is not merely a reflection of national heterogeneity. The paper contributes by shifting attention from broad claims about “digital readiness” to a narrower and more operational notion of capability complementarity: AI uptake tends to cluster where firms already possess customer-facing, cloud-based, and commercially digital infrastructures. In that sense, the paper offers a transparent, reproducible, and policy-relevant account of the digital foundations of enterprise AI adoption in Europe. Full article

This study investigated the forecasting performance of the South African gold sales series using the seasonal autoregressive integrated moving average (SARIMA), generalised autoregressive conditionally heteroscedastic (GARCH), general regression neural network (GRNN) and artificial neural network (ANN)-based extreme learning machine (ELM). This study employed traditional methods and a recently developed ML method for single hidden-layer feed-forward neural networks (SLFNs). The findings revealed that $\mathrm{SARIMA} \left(0,1,1\right){\left(2,1,2\right)}_{12}$ was considered the best model for the gold sales series. The empirical findings demonstrated that the SARIMA model outperforms neural network-based models, providing the South African government and its lenders with a more reliable and cost-effective tool for predicting foreign exchange earnings from gold. This study contributes to the literature by providing one of the first comparative evaluations of traditional time-series models and advanced neural network methods for forecasting South African gold sales. This study is novel as it is a first-of-its-kind comparative application of traditional SARIMA and GARCH models alongside GRNN and ANN-based ELM methods to South African gold sales, revealing the superior forecasting performance of a traditional SARIMA model over advanced ML approaches. Future research should explore the development and application of hybrid models that integrate the strengths of linear SARIMA frameworks with the pattern-recognition capabilities of nonlinear ANN-based ELM models. Full article

A method for integrated brand analysis and strategy is developed in this work. The foundation of this method is market research, through which the relevance of brand attributes, their evaluation for competing brands and the market performance of these brands on the steps of the buying process are determined. On this basis, the overall evaluation of brands and their number of brand attributes with the best evaluation are calculated so that strategic decision guidelines for overall brand positioning can be deduced. These strategic decision guidelines are securing the brand based on the existing identity/image, developing the brand based on the existing identity/image, developing (pivoting to) a new brand identity/image, whilst securing the strengths of the existing identity/image, and developing a new brand identity/image. On the level of brand attributes, the weighted relevance of attributes and their evaluation difference to the best competitor are calculated so that, again, strategic decision guidelines can be deduced. The strategic decision guidelines on brand attribute level are securing the attributes as the core brand identity (first priority), selecting and developing the attributes to the core brand identity (second priority), securing the attributes as the extended brand identity (third priority), and selecting and developing the attributes as the extended brand identity (fourth priority). Based on the market performance of brands across the stages of the buying process, the conversions between these steps are determined. On this basis, strategic decision guidelines for market cultivation are deduced, i.e., awareness, image, sales, and loyalty strategies. To gain first indications of the validity of the method for integrated brand analysis and strategy, it is applied to food retail and chocolate brands in the German market. Future research should focus on further validating the method and enhancing it by integrating segmenting and targeting processes and, potentially, marketing measures on an operational level. Full article

This study investigates the acceptance of an AI-powered decision-support chatbot among professionals in a marketing and sales context, addressing a gap in technology acceptance research by examining data-intensive decision environments that remain underexplored. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes an extended model incorporating Behavioral Intention, Performance Expectancy, Effort Expectancy, Social Influence, Output Quality, Time Saving, Source Trustworthiness, Cognitive Load, and Chatbot Self-Efficacy. An experimental study was conducted with 106 professionals using a chatbot-enhanced business analytics platform to complete marketing KPI analysis tasks. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that Behavioral Intention to use decision-support chatbots is significantly influenced by Performance Expectancy, Effort Expectancy, and Social Influence. Performance Expectancy is strongly driven by Output Quality, Time Saving, and Source Trustworthiness, while Effort Expectancy is significantly shaped by reduced Cognitive Load and higher Chatbot Self-Efficacy. The findings suggest that chatbot acceptance in professional decision-making depends not only on usability and performance beliefs but also on cognitive relief, trust in information sources, and efficiency gains, highlighting important implications for both theory and the design of AI-based decision-support systems. Full article

To enhance the sustainability of temporary office buildings, energy-saving and emissions-reduction technologies, as well as the optimization of photovoltaic (PV) energy storage systems in such structures, are of great importance. In this study, a distributed energy storage system was developed for a temporary office building in Jincheng, China. Measurements showed climatic factors had the greatest effect on building energy consumption due to the building envelope’s low thermal performance and airtightness. The air conditioning system accounted for the highest proportion (87%) of building energy consumption. The PV system’s peak output occurred in the morning due to illumination conditions and module orientation. On this basis, a time-of-use (TOU)- and state-of-charge (SOC)-aware scheduling strategy was developed for the PV-ESS of the temporary office building to improve renewable-energy utilization and reduce user-end electricity cost. Unlike purely theoretical optimization studies, this work focuses on the practical application and validation of the scheduling framework in a real temporary office building using monitored data. The electricity cost decreased by 0.3 RMB/kWh, and the revenue from electricity sales during the scheduling period increased by 0.03 RMB/kWh after model optimization. The optimized scheduling strategy resulted in significantly fewer charge–discharge cycles of the storage battery, substantially decreasing the battery’s storage capacity and the system’s investment costs. Full article

To reduce renewable output volatility and improve system integration efficiency, this study constructs a coordinated wind–solar–storage–hydrogen framework. The proposed MILP model innovatively integrates electrolyzer power-dependent efficiency and start-up dynamics into a coupled capacity-sizing and dispatch framework and differs from existing MILP models in refined dynamic constraint construction, multi-energy flow coupling, and practical engineering logic constraints. Refined mathematical models are formulated for core components, including wind and photovoltaic units, battery energy storage systems (BESS), and electrolyzers with power-dependent hydrogen production efficiency and operational dynamics. The electrolyzer efficiency peak at 0.25 p.u. input power is calibrated by industrial test data, and the optimization results show strong robustness to the slight deviation of this peak point. Independent control strategies are designed for each electrolyzer, and a capacity optimization model is formulated to maximize system performance. Simulation tests using wind and solar profiles from Northwest China show that the optimized system achieves a renewable energy utilization rate of 96.7%, a BESS capacity of 7 MWh, and a hydrogen storage tank of 3500 kg. Adopting a time-of-use (TOU) electricity pricing mechanism combined with hydrogen sales significantly enhances system efficiency, while expanding power and hydrogen transmission capacities further improves renewable energy integration. These results demonstrate the practical potential of the proposed integrated system for large-scale renewable energy deployment. Full article

The two-spot cotton leafhopper (TSCL), Amrasca biguttula (Hemiptera: Cicadellidae), is an emerging invasive pest in the southeastern United States. Although TSCL has historically been associated with cotton and vegetable crops, recent detections on ornamental hibiscus have raised regulatory concern, including “Stop Sale and Hold” orders and an emergency quarantine in Texas. Despite increasing pressure on hibiscus, no insecticide efficacy data exist for ornamental systems. We evaluated the acute (0–24 h) and residual (24–96 h) toxicity of bifenthrin, flupyradifurone, and tolfenpyrad against adult and immature TSCL using a sequential-cohort leaf-disc bioassay. New insects were introduced at 24 h and 72 h to isolate residue-based mortality from prolonged exposure effects. Bifenthrin caused the highest acute mortality at 24 h, whereas flupyradifurone and tolfenpyrad exhibited slower initial activity but strong residual performance. Immatures were more susceptible than adults across all doses. By 72 h, all three insecticides produced near-complete mortality, with significant treatment and dose effects confirmed by ANOVA and binomial GLM analyses. Dose–response curves showed steep concentration-dependent mortality for bifenthrin and tolfenpyrad and a time-dependent response for flupyradifurone. These results provide the first insecticide efficacy data for TSCL on ornamental hibiscus and offer immediate guidance for nursery producers and regulatory agencies. The findings establish a foundation for whole-plant and greenhouse evaluations to support integrated management and interstate plant-movement compliance. Full article

Background: Modern supply chains (SC) are increasingly difficult to manage as they become more complex and interconnected. This encourages companies to rely more on real-time data analysis and analytical tools on operational processes. This study aims to develop and evaluate a Supply Chain Wave Report for a non-food retail that represents goods movement across logistics stages as a continuous analytical flow. Methods: Proposed framework integrates multiple operational phases—Booked Orders, Main Transit, On-Carriage, Warehouse Operations, Store Delivery, and Sales—into a unified monitoring structure. This model can combine operational data with advanced analytics, including Artificial Intelligence-, cloud computing-, and Internet of Things-based technologies. Through cloud-based data infrastructures, System enables data integration and near real-time visibility across organizational functions, allowing continuous monitoring through key performance indicators and predictive simulations. Results: This framework enables dynamic performance of supply chain management and generates real-time signals as goods move across logistics network. This enables managers to detect irregularities earlier and respond before operational deviations propagate further along the chain. Wave-based monitoring approach highlights interdependence between SC stages and illustrates how small disruptions may propagate over time, potentially contributing to effects like bullwhip effect. Conclusions: Findings suggest that a cloud-enabled wave analytics framework can enhance coordination, reduce information gaps, and support informed decision-making in retail. Full article

The rapid integration of AI customer service in e-commerce raises an important managerial question: Can AI effectively reduce product-related information asymmetry and improve sales performance across different product types? While prior research highlights both the uncertainty-reducing benefits of information and the risks of algorithm aversion, little is known about how AI customer service performs under varying levels of product uncertainty and information asymmetry. Using a difference-in-differences design with fixed effects across time, products, shops, and categories, we examine the impact of replacing customer service with AI on sales outcomes, distinguishing between search and experience goods. We further test how the depth and breadth of product information moderate these effects. Our findings indicate that AI customer service reduces sales for experience goods but not for search goods, unless accompanied by sufficient informational depth and breadth. We argue that this effect arises because AI technically inherits and amplifies the information asymmetry inherent in experience products, while greater informational depth and breadth of product information can mitigate this amplified asymmetry. Additionally, we find that this mitigating effect is more pronounced among products with high return rate. These findings clarify when AI-generated information mitigates product uncertainty and when it exacerbates it. Our results provide actionable guidance for firms seeking to deploy AI strategically in digital commerce environments. Full article

This paper explores the interaction strategies of service robots in self-service shops from a user experience perspective in the case of robots with insufficient capabilities. A Yanshee robot and a self-developed localization-rotation system are employed as the experimental platform. A sales return in a self-service shop is employed as the experimental scenario. Two types of robot’s insufficient capabilities, three strategies of robots’ apology and a social interaction cue imitated from a human salesperson are considered in the design of interaction strategy between human and robot in this scenario. The results show that robots’ social insufficiency leads to more negative influence on customer experiences of fluency, comprehensibility, impression, intelligence, willingness for future interaction than robots’ performance insufficiency. An empathetic apology when the robot has insufficient performance is an effective interaction strategy. The interaction cue that the robot turns to face customers is not beneficial to customer experiences but does influence the internal relationship between customer experiences during HRI and after HRI. In the case of robots with social insufficiency in a self-service shop, impression, intelligence and interaction capability have positive impacts on the willingness for future interaction, while they are also positively affected by fluency or comprehensibility. In the case of robots with performance insufficiency, impression has a positive impact on willingness, while it is not directly related to fluency. The findings are valuable for informing the interaction design of service robots deployed in shopping, especially in real environments where performance and cost must be balanced. Full article

Document-level Event Extraction (DEE) requires identifying complex event records and arguments dispersed across unstructured texts. However, applying general Large Language Models (LLMs) to DEE is intrinsically hindered by their lack of inductive bias for rigid structural constraints, often leading to schema violations and suboptimal performance in complex structural prediction tasks. To address this, we propose the S tructure-Aware Lightweight DEE, termed SALE, which leverages the structural reasoning potential of Code-Based LLMs (Code-LLMs) as a favorable inductive preference. We leverage the natural isomorphism between event schemas and programming object definitions, formulating event extraction as a Python 3.9 class instantiation task to bridge the gap between semantic understanding and structural adherence. Specifically, SALE employs a novel two-stage training paradigm: First, a Structure-Aware Fine-tuning stage injects general structural knowledge via diverse code-style instruction tasks derived from broad Information Extraction (IE) datasets; second, an Event Extraction Alignment stage utilizes a reward-based alignment loss—optimized via policy gradient—to adapt this capability to document-level intricacies. The effectiveness of SALE stems from the synergy between its structure-aware prompting and the specialized alignment stage built on a code-oriented backbone. Extensive experiments on established news-domain benchmarks (RAMS and WikiEvents) demonstrate that our approach significantly outperforms representative supervised and general LLM baselines in cross-task zero-shot and few-shot transfer settings (e.g., surpassing supervised baselines by over 7% in F1 score). Furthermore, SALE maintains a highly efficient inference profile and parameter-efficient footprint, offering a practical and scalable solution for vertical domain applications. Full article

We present Hyperbolic Symmetric Hypermodular Neural Operators (ONHSH), a novel operator learning framework for solving partial differential equations (PDEs) in curved, anisotropic, and modularly structured domains. The architecture integrates three components: hyperbolic-symmetric activation kernels that adapt to non-Euclidean geometries, modular spectral smoothing informed by arithmetic regularity, and curvature-sensitive kernels based on anisotropic Besov theory. In its theoretical foundation, the Ramanujan–Santos–Sales Hypermodular Operator Theorem establishes minimax-optimal approximation rates and provides a spectral-topological interpretation through noncommutative Chern characters. These contributions unify harmonic analysis, approximation theory, and arithmetic topology into a single operator learning paradigm. In addition to theoretical advances, ONHSH achieves robust empirical results. Numerical experiments on thermal diffusion problems demonstrate superior accuracy and stability compared to Fourier Neural Operators and Geo-FNO. The method consistently resolves high-frequency modes, preserves geometric fidelity in curved domains, and maintains robust convergence in anisotropic regimes. Error decay rates closely match theoretical minimax predictions, while Voronovskaya-type expansions capture the tradeoffs between bias and spectral variance observed in practice. Notably, ONHSH kernels preserve Lorentz invariance, enabling accurate modeling of relativistic PDE dynamics. Overall, ONHSH combines rigorous theoretical guarantees with practical performance improvements, making it a versatile and geometry-adaptable framework for operator learning. By connecting harmonic analysis, spectral geometry, and machine learning, this work advances both the mathematical foundations and the empirical scope of PDE-based modeling in structured, curved, and arithmetically. Full article