Search Results (183)

Artificial intelligence (AI) is transforming requirements elicitation: machine learning, natural language processing (NLP), and large language models (LLMs) now identify software requirements automatically from the textual data that surrounds every project—user feedback, specifications, regulations, and stakeholder transcripts. This paper presents a systematic mapping study of 74 peer-reviewed primary studies on AI-based automated requirements elicitation published between 2021 and 2025, identified from five databases following PRISMA 2020 and classified by AI technique, textual source, elicitation activity, and application domain. The evidence is divided into two equally sized source families—user feedback and agile artefacts versus formal documentation—each coupled to the AI techniques that suit its signal profile. Fine-tuned transformer encoders set the performance ceiling and, task-for-task, still outperform far larger generative models, while LLMs extend elicitation to long regulatory documents, multilingual feedback, and structured outputs. The central finding concerns automation depth. AI identifies requirements with consistently high accuracy (routinely F1 0.8 and above), but automation thins at every subsequent step: 51% of approaches structure what they identify, 23% consolidate them, and only 8% engineer stakeholder validation into the loop. This leaves the steps that turn candidates into agreed requirements largely manual. Benchmark fragmentation (77% custom datasets), thin industrial validation (14%), and skewed non-functional coverage compound this gap. The resulting map gives researchers an evidence-derived agenda for deepening automation, and practitioners guidance on which techniques the evidence supports for each elicitation task and textual source. Full article

As airlines increasingly deploy an AI-Driven Chatbot (ADC) to enhance service efficiency, this study aims to investigate how the agility capability of ADC drives perceived usefulness and satisfaction, ultimately enhancing customer loyalty in a competitive aviation industry. Drawing on a survey of 303 respondents in South Korea, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were employed for empirical analysis. The structural model examines how the agility capability of airline ADC—modeled as a second-order construct comprising competence, responsiveness, speed, and flexibility—shapes the consumer experience. The findings reveal that agility capability positively influences perceived usefulness, which in turn enhances satisfaction and ultimately fosters customer loyalty. Notably, neither agility capability nor perceived usefulness exerts a direct effect on loyalty; their influence is fully channeled through satisfaction, highlighting satisfaction as the pivotal mechanism that transforms agility capability into customer loyalty. These results offer meaningful insights into customer behavior, contributing to the strategic utilization of ADC for better customer experience. Full article

This study sought to examine how micro and small customer-facing businesses sustain continuity of serving customers under conditions of disruption. Drawing on data collected from 230 owners of micro and small enterprises operating across various sectors, the study focuses on the roles of entrepreneurial bricolage and local supply network strength, as well as on the role of service agility as a connecting mechanism. The results showed that entrepreneurial bricolage is closely associated with both service agility and the ability of businesses to maintain continuity of serving customers under challenging conditions. Likewise, local supply network strength appeared to support service agility, although it did not show a direct relationship with continuity of serving customers. Significantly, service agility emerged as a central mechanism that helped turn available resources and network access into sustained continuity of serving customers. These findings highlight that, in resource-constrained and fragile contexts, maintaining continuity of serving customers depends less on resource availability alone and more on how businesses adapt and respond to changing conditions. The study contributes by offering initial insights into the mechanisms through which owners of micro and small enterprises can navigate disruption, emphasizing the importance of adaptive capabilities in sustaining continuity of serving customers. Full article

Entrepreneurial marketing (EM) has emerged as a critical strategic orientation for small and medium-sized enterprises (SMEs) navigating volatile markets, yet the mechanisms through which EM is associated with environmental performance of SMEs remain theoretically underdeveloped. Drawing on entrepreneurial marketing theory, dynamic capabilities theory, the resource-based view, the natural resource-based view, and contingency theory, this study proposes and tests a two-stage capability model in which EM is linked to market agility and customer agility as parallel dynamic mechanisms that subsequently build marketing capability, which in turn is associated with environmental performance of SMEs. Market turbulence is examined as a boundary condition moderating the agility-to-capability pathways. Data were collected from 402 SME owners and managers across manufacturing and service sectors in the United Arab Emirates and analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. Results confirm that EM is positively associated with market agility, customer agility, and marketing capability, and that both agility constructs partially mediate the EM-to-marketing capability relationship. Marketing capability shows a strong positive association with environmental performance of SMEs. Market turbulence significantly strengthens the market agility-to-marketing capability and customer agility-to-marketing capability relationships, while its moderating role in the direct EM-to-marketing capability path is not significant. These findings contribute to the entrepreneurial marketing and dynamic capability literature by specifying the organizational mechanisms linking EM to environmental performance and by identifying market turbulence as a selective boundary condition that strengthens agility-driven, but not orientation-driven, capability development. Practical implications for SME managers in emerging market contexts are discussed. Full article

Thrust vector control (TVC) technology significantly enhances the attitude control capability of aircraft. However, conventional multi-axis calibration is inherently cost-prohibitive and time-consuming, whereas static ground-bench calibration completely neglects the significant aerodynamic interference from external freestreams during actual flight. To resolve this operational bottleneck in engineering applications and calibration workflows, this paper proposes an agile, cost-effective hybrid modeling and process optimization methodology that seamlessly integrates low-cost ground experiments with high-fidelity numerical investigations. First, a customized one-axis force sensor test bench was developed to calibrate the baseline thrust vector performance under static ground conditions. Subsequently, computational fluid dynamics (CFD) simulations were conducted across the typical cruise speed range (30–60 m/s) of TVC aircraft to investigate the nonlinear interactions between the external freestream and the vectorized jet. The numerical results reveal that the interaction between the deflected jet exhaust and the external flow field significantly changes the flow structure and pressure distribution around the control surface. The momentum exchange, primarily governed by mass entrainment and cross-shear-layer mixing, broadens the control surface’s impact scope and enhances the injection effect, resulting in nonlinear variations in the control force under different operating conditions. Considering the influence of external flow and deflected jet, a dynamic control force model was established by integrating the ground static baseline model with simulation data. The fitting results demonstrate that this control model limits match error to within 3% across both experimental and numerical datasets, effectively suppressing loop uncertainties across the entire flight envelope while maintaining a minimalist sensing architecture. From a process engineering perspective, this “Hybrid Modeling”-driven approach substitutes expensive multi-degree-of-freedom testing hardware with an integrated simulation-experimental workflow, offering a highly scalable, simplified, and analyzable process method for TVC research and application. Full article

Background: As supply chains undergo rapid transformation, the concept of supply chain viability (SCV) has gained increasing attention among scholars and industry practitioners. Despite this growing interest, the existing literature lacks a comprehensive and integrated understanding of readiness factors across SCV dimensions. Methods: To address this gap, this study conducted a systematic review of the literature. A total of 86 peer-reviewed articles published between 2015 and 2025 were included, synthesizing evidence across four SCV dimensions: agility, resilience, sustainability, and digitalization. Two-mode network representation was used to analyze cross-dimensional connections between readiness factors and SCV dimensions via degree centrality. Results: The analysis identified 36 readiness factors, 9 of which are cross-dimensional and connected to all four SCV dimensions: leadership and decision-making, stakeholder integration, customer relationship and engagement, partner collaboration and readiness, advanced analytics, technological/digital capabilities, real-time data visibility, information and communication technology infrastructure availability, and digital transformation/smart technologies in supply chain management. Conclusions: Readiness factors vary across SCV dimensions, with some spanning multiple dimensions and others remaining specific. SCV strategies should account for these differences rather than treating factors as uniform. Full article

Digital transformation has become a critical pathway for manufacturing firms seeking to improve responsiveness, resource efficiency, and long-term sustainability. However, existing studies have paid limited attention to how digital transformation strategies generate organizational agility across different stages of sustainable manufacturing transformation. Drawing on dynamic capability theory, this study develops a stage-contingent Strategy–Ambidexterity–Agility framework and conducts a longitudinal single-case study of Contemporary Amperex Technology Co., Limited (CATL) from 2011 to 2023. The findings show that organizational agility develops cumulatively through three transformation stages. In the initial stage, a lean-oriented strategy supports balanced ambidexterity and cultivates customer agility through production optimization. In the development stage, an enhancement-oriented strategy enables exploitation-dominant combined ambidexterity and builds market agility through cross-functional integration and closed-loop business logic. In the industry-leading stage, a leap-oriented strategy supports exploration-dominant combined ambidexterity and fosters value chain agility through ecosystem orchestration, intelligent operations, and circular value creation. This study contributes to the literature on digital transformation and sustainable manufacturing by showing how stage-contingent digital strategies shape ambidexterity configurations, generate layered agility capabilities, and support sustainability-oriented manufacturing outcomes. Full article

Background: Recreational mini-football is associated with a high incidence of lower-limb injuries, largely driven by neuromuscular deficits and insufficient exposure to structured preventive training. This study aimed to evaluate the effects of a hybrid injury-prevention program combining the FIFA 11+ protocol with customized neuromuscular interventions on functional performance and injury-related risk factors. Methods: Forty male recreational mini-football players were included in a retrospective analysis of data collected during the routine implementation of a 12-week hybrid preventive training program. Participants were allocated to an intervention group (n = 20) or control group (n = 20) according to routine training practices rather than randomization. The intervention was performed twice weekly. Outcome measures included lower-limb strength (Kineo system), dynamic balance (Y-Balance Test), functional hop performance (Single Hop and Side Hop tests), and agility/change-of-direction ability (Illinois and 505 tests). Results: The intervention group demonstrated significantly greater improvements in lower-limb peak force across all muscle groups (all adjusted p < 0.001), as well as in single-leg hop, side hop, and agility/change-of-direction performance (all adjusted p < 0.001) compared to controls. No significant changes were observed in dynamic balance outcomes. Conclusions: A hybrid neuromuscular training program combining FIFA 11+ with customized exercises was associated with improvements in lower-limb strength, hop performance, and agility/change-of-direction ability in recreational mini-football players. These findings suggest that integrating customized exercises into standardized training programs may enhance functional performance and positively influence modifiable factors associated with injury risk. Full article

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

The advent of digital technologies, increasing competition, market globalization, and the fourth industrial revolution compel organizations to rethink their operating models to sustain competitive advantage. At the same time, increasingly informed consumers expect higher levels of personalization, responsiveness, and cost efficiency. In this context, mass customization has emerged as a strategic response enabling firms to deliver tailored products while maintaining acceptable levels of cost, lead time, and operational efficiency. However, operationalizing mass customization remains particularly challenging for small- and medium-sized enterprises (SMEs), especially within normative environments characterized by regulatory and compliance requirements affecting product architectures and manufacturing processes. Although the literature highlights modular product design and product configuration as key enablers, it lacks a structured strategy for their implementation in such contexts. This article aims to develop and validate an operational strategy for mass customization based on these two levers. The methodology adopts an action research approach structured through a hybrid Agile–Stage-Gate framework and validated through its application to a representative portion of the product architecture within a case study. The results highlight the structured integration of variability analysis, modular product design, and configuration logic into an operational process, supporting the management of complexity and the implementation of mass customization in manufacturing SMEs. Full article

Business-to-consumer (B2C) e-commerce firms operate in fast-changing digital markets, where timely interpretation of external signals may strengthen organisational agility. This study examines how four dimensions of competitive intelligence—market, technological, social, and competitor intelligence—relate to organisational agility in Croatian B2C e-commerce firms. The study adopted a pragmatic explanatory sequential mixed-methods design. Quantitative data were collected through an online survey, and 208 valid responses were analysed using reliability testing, construct-validity assessment, correlation analysis, and multiple regression. Qualitative follow-up evidence was used to support the interpretation of the quantitative results. The findings show that the effects of competitive intelligence dimensions on organisational agility are not uniform. In the final validated model, social intelligence emerged as the only significant positive predictor of organisational agility, while market intelligence, technological intelligence, and competitor intelligence did not show statistically significant effects. The study therefore suggests that, in this context, systematic attention to customer conversations, online feedback, and socially visible market signals may play a more decisive role in supporting agile organisational responses than other intelligence domains. The study contributes to the competitive intelligence and agility literature by showing that intelligence dimensions should be examined separately rather than treated as a single undifferentiated capability in digital commerce settings. Full article

Effective wildfire management demands seamless integration of real-time detection and long-term spread forecasting. This paper proposes a novel power-efficient UAV–satellite hybrid pipeline that synergizes the agility of UAVs with the scale of satellite intelligence. The system begins with a dashboard-guided, multi-UAV detection module that scores fire likelihood from historical satellite data and enables scalable, energy-efficient deployment with low-latency onboard processing. This aerial component ensures persistent surveillance and reliable ignition detection, supported by a Dual LoRa (Long Range) communication scheme for robust and low-power connectivity. It achieves an F1-score of 97.4% while minimizing power consumption to extend operational flight times. Following detection, the pipeline transitions to a dynamic perimeter-prediction phase utilizing a custom Canadian boreal dataset. We employ a Squeeze-and-Excitation Residual U-Net (SE-ResUNet) to model spatiotemporal fire propagation based on static terrain and dynamic environmental features. The model was validated using a dynamic simulation framework that evaluates temporal consistency and convergence behavior against final cumulative burned-area masks, effectively addressing the absence of daily ground truth. Under these conditions, the model achieves a recall of 84% and an AUC of 0.97, demonstrating a strong capability to delineate active fire fronts. By coupling dashboard-driven UAV sensing with satellite-based predictive modeling, this work establishes a modular, foundational framework to support data-scarce forecasting in modern wildfire management. Full article

The tourism industry faces increasing pressure for agile, personalized services, yet travel agencies struggle with fragmented knowledge scattered across isolated systems and legacy formats. While Large Language Models (LLMs) are widely applied in customer-facing roles, their potential to enhance internal operational efficiency remains largely underexplored. This study presents the design and evaluation of an intelligent assistant specifically for travel agency operations, built upon a Retrieval-Augmented Generation (RAG) architecture using Gemini 2.0 Flash. The system integrates heterogeneous data sources, including structured product catalogs and unstructured documentation processed via Optical Character Recognition (OCR), into a unified interface comprising work assistance, interactive training, and evaluation modules. Results demonstrate information retrieval times not greater than 45 s, ensuring its daily usability, while maintaining 95% accuracy. Furthermore, the system democratizes tacit senior expertise and accelerates new employee onboarding. This research validates RAG architectures as a powerful solution to knowledge fragmentation, shifting the strategic AI focus from customer automation to employee empowerment and operational optimization. Full article

We propose a centralized Activity Retrieval and Detection Intelligence (ActivityRDI) solution framework, demonstrate its application performance in network threat detection in detail, and show its generalization to other domains. Network threat detection is challenging owing to the complex nature of attack activities and the limited historically revealed threat data from which to learn. To help enhance the existing methods (e.g., analytics, machine learning, and artificial intelligence) to detect the network threats, we propose a multi-agent AI solution for agile threat detection. In this solution, a knowledge graph is used to analyze changes in user activity patterns and calculate the risk of unknown threats. Then, an imbalanced learning model is used to prune and weight the knowledge graph and to calculate the risk of known threats. Finally, a large language model (LLM) is used to retrieve and interpret the risk associated with user activities from the knowledge graph and the imbalanced learning model. The preliminary results show that the solution improves the threat capture rate by 3–4% and adds natural language interpretations of the risk predictions based on user activities with 95% accuracy. Furthermore, a demonstration application has been built to show how the proposed solution framework can be deployed and used. The generalizability of the proposed solution in other domains is also shown through an application to customer engagement, with 97% accuracy. Full article

In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process method extended with triangular fuzzy sets (FAHP) was used to determine the criteria weights applied for ranking the specific competencies within the most important groups. For ranking competencies within these key groups, the Technique for Order Preference by Similarity to Ideal Solution method extended with triangular fuzzy sets (FTOPSIS) was applied. The same algorithmic procedure was carried out for both traditional and agile project management approaches, in a case study conducted across four companies in the automotive industry. The study showed that, in traditional project management, the most important competency group is related to organizational and managerial skills and competencies. On the other hand, in agile project management, the most important competency group refers to contextual skills and competencies. Furthermore, within the traditional approach, the most significant specific competency is project goal orientation, while in the agile approach, the most significant specific competency is customer and stakeholder orientation. Full article