Search Results (1,014)

Agile teams often struggle to translate business requirements into maintainable, high-quality software due to the persistent ambiguity in the roles and relationships of behavior-driven development (BDD), Acceptance Test-driven Development (ATDD), and Test-driven Development (TDD). These approaches are frequently misunderstood, inconsistently applied, and only loosely connected within a unified delivery lifecycle. This article introduces BLOW (Behavior-Driven Development in a Layered Organization of Work-Centers), a systematic approach that establishes BDD as the coordinating methodology between ATDD (business-focused) and TDD (technology-focused). BLOW structures scenario-driven development across layered domains of accountability with clearly defined roles and responsibilities, organizing delivery through nested work-centers that transform user stories into executable specifications and production code. This approach integrates two complementary collaboration practices: the Three Amigos for discovering and formulating business scenarios, and the proposed Technical Three Amigos for linking those scenarios to Technical Domain Contexts, identifying required Enablers, and deriving technical scenarios when additional architectural support is needed. The proposed operating model emphasizes observability through executable scenarios as first-class artifacts, introducing native, test-anchored metrics that support reasoning about progress, technical effort, and value delivery within scenario-driven development. An exploratory longitudinal case study, consisting of a single-sprint proof of concept followed by an 18-month production deployment, reports patterns in which technical enablement precedes business value delivery and reusable infrastructure supports sustained growth of business scenarios over time. The findings also indicate that changes in the applied operating model are associated with measurable shifts in scenario evolution and internal quality indicators. Overall, BLOW provides a governance-compatible, end-to-end approach for organizing scenario driven development and improving alignment between stakeholder intent and technical implementation in complex software systems. Full article

Digital business models often emerge rapidly and attract users, yet only some become durable over time. This paper develops the concept of business model establishment to capture the process through which a digital business model becomes organizationally robust, transactionally governable, and economically viable beyond initial growth. Combining the business model literature with Transaction Cost Economics, the paper argues that establishment depends on the co-evolution of governance and monetization. Governance matters because scaling increases the need for measurement, adaptation, and safeguarding mechanisms. Monetization matters because these mechanisms require sustained investment supported by stable, diversified, and economically adequate value capture. The paper applies this framework to fintech, a domain in which digital business models face particular demands around transaction frequency, uncertainty, regulation, and trust. Through qualitative case analysis of Revolut, Klarna, Robinhood, and N26, it illustrates four configurations of establishment defined by varying levels of governance and monetization maturity, contributing to the business model literature by distinguishing establishment from innovation, adaptation, and scaling. Full article

Industrial Symbiosis (IS) is a key Circular Economy strategy that promotes resource efficiency through collaboration among companies. While previous research has largely focused on established IS business models, increasing attention has been given to sector-specific implementation and the contextual factors that influence its success. This study develops a Multi-Criteria Decision Analysis (MCDA) framework based on the Deck of Cards Method (DCM) to support the sectoral implementation of IS. A key innovation of this study is the incorporation of IS enablers and barriers into a sector-specific MCDA framework, providing a structured approach to support decision-making and implementation. By incorporating stakeholder preferences and prioritizing implementation opportunities, the framework provides a structured basis for decision-making, being replicated and adaptable to other industrial sectors. The framework was applied to the Portuguese cement sector through consultations with experts representing five stakeholder groups, furtherer allowing its validation. The analysis combines the importance assigned by stakeholders to the criteria and the performance of the sector across those criteria. Results for the sector perspective indicate that policy, economic, and technological criteria are perceived as the most important for advancing IS, whereas geographical, social, and management-related aspects receive lower priority. In Portugal, this sector demonstrates stronger performance in economic (17.49–3.04), technological (13.80–5.99), and environmental (14.58–3.19) criteria, while challenges remain in geographical coordination (1.16–7.95), social engagement social (0.79–6.81), and intermediary support (1.15–8.44). These findings highlight the importance of aligning policy, technological development, and organizational mechanisms to facilitate industrial collaboration and resource exchange. The study demonstrates the potential of MCDA as a practical and effective decision-support tool for IS implementation and provides insights for designing targeted strategies to strengthen sectoral Industrial Symbiosis. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

Cooperatives play a significant role within organizational models by providing essential services such as technical support, advocacy, information, knowledge, and guidance, which contribute to the production of high-quality animal products in a safe, efficient, and responsible manner. Furthermore, cooperatives aim to enhance the livelihoods of marginalized populations and address consumer needs. In this context, a study focusing on the status of women’s cooperatives in the Eastern Mediterranean offers valuable insights into women’s participation in economic and social life, as well as their challenges and expectations. This research aims to evaluate the status, perspectives, participation, activities, and expectations of women’s cooperatives in Lebanon and Turkey. The findings indicate that 90% of respondents in Lebanon and 45.5% in Turkey expressed satisfaction with their respective cooperatives. Additionally, 90% of Lebanese respondents and 59.1% of Turkish respondents would recommend that women establish their own cooperatives. The most common motivation for forming cooperatives in both countries was the belief that women are stronger when they collaborate. Furthermore, 75% of respondents in Lebanon and 45.4% in Turkey believe that cooperatives are suitable for conducting business, while those who disagreed emphasized the need for specialized traders to address specific business requirements. Respondents who expressed dissatisfaction with cooperative collaboration often mentioned difficulties in making joint decisions and challenges in group cohesion. These findings underline the importance of cooperatives in enhancing women’s roles in economic activities and the challenges they face in both Lebanon and Turkey. Despite these challenges, women’s cooperatives continue to be perceived as a valuable means of empowerment and a key strategy for fostering collaboration and economic growth. Full article

This paper analyzes the evolution of sustainability in the wine industry, integrating environmental practices, climate change adaptation, governance mechanisms, and marketing dynamics into a unified perspective. The aim of the research is to identify the main thematic directions and innovative contributions that shape the sustainable development of the wine sector. The methodology consists of a systematic review of the literature conducted and reported in accordance with the PRISMA 2020 guidelines, based on relevant scientific studies addressing the following eight thematic dimensions: sustainable value chain and wine production, environmental practices and ecological management, climate change adaptation and viticultural resilience, governance, policies and SDG integration, wine marketing, positioning and competitiveness, consumer behavior and willingness to pay, wine tourism and regional development, and green innovation and sustainable business models. The broad thematic scope of the review was established on the basis of a preliminary scoping of the existing literature, while the specific themes and analytical patterns were derived inductively through systematic content analysis of the 175 included studies. The results highlight the predominance of the environmental dimension, particularly through the use of life-cycle assessment, climate adaptation strategies, and resource optimization. At the same time, there is a growing interest in digitalization, sustainable governance, and the consumer’s role in market orientation, while the social dimension remains insufficiently explored. The analysis reveals a transition toward integrated and systemic approaches, in which sustainability, innovation, and competitiveness are interdependent throughout the entire wine value chain. The conclusions highlight the need for an integrated approach, based on innovation, collaborative governance, and consumer orientation, to support the transition toward a sustainable model in the wine industry. Full article

Nowadays, closed-circuit television (CCTV) cameras are deployed worldwide to monitor movements of humans and other objects to improve the efficiency and safety of societies. Therefore, their proper placement is crucial for achieving effective surveillance coverage. Additionally, their proper placement is significantly important for maximizing visual coverage while reducing installation/management costs. For this task, digital twin is a useful technology, since it can simulate coverage and blind spots while freely changing camera locations. To implement digital twin, 3D modelling of a structure including a complex room is a key issue. In this paper, we propose a 3D indoor modelling method using 360° panoramic images and show its application to a CCTV camera placement optimization. This method constructs a structured 3D model of a target room from captured 360° panoramic images using a 3D Gaussian Splatting reconstruction method based on a visual simultaneous localization and mapping (VSLAM) framework. The Inertial Measurement Unit (IMU) is used together to improve the camera position estimation accuracy. The model construction is anchored using a GNSS/GPS reference to establish global spatial coordinates. As an application of the generated 3D model, optimal locations of a given number of CCTV cameras are determined by combining ray-casting visibility analysis and a greedy optimization algorithm in the virtual environment, maximizing visual coverage while minimizing blind spots and avoiding excessive overlap between camera views. For evaluations, we applied the proposed method to three rooms in Okayama University, Japan, and seven rooms in the Indonesian Institute of Business and Technology, Indonesia. After optimizing camera locations in the virtual environment, the cameras were actually installed in the rooms according to the recommended positions. The performance was evaluated using visibility coverage, blind spot reduction, and Root Mean Squared Error (RMSE) between the estimated and actual camera positions, where promising results were achieved. Full article

Cultivating and strengthening agricultural new quality productive forces (ANQPF) has become a key pathway for driving high-quality agricultural development (HQAD), ensuring food security, and advancing agricultural modernization. To investigate the spatiotemporal evolution and underlying mechanisms linking ANQPF and HQAD in the Tuojiang River Basin, this study employs panel data from 37 counties in the Tuojiang River Basin over the period 2011–2020. The entropy method and the DEA-BCC model are used to measure the levels of ANQPF and HQAD, while the fixed effects model, the mediating effect model, and the spatial econometric model are applied to examine the mechanisms through which ANQPF affect HQAD. The findings indicate the following: (1) The level of ANQPF is relatively low but has shown continuous growth, whereas the level of HQAD is relatively high but exhibits considerable fluctuation. Moreover, both dimensions display uneven development across regions. (2) ANQPF significantly promote HQAD at the 1% level, with agricultural technology development and green development serving as mediating mechanisms. However, a suppression effect is identified in agricultural technology development, and ANQPF exhibit a short-term inhibitory effect on agricultural green development. (3) Heterogeneity analysis indicates that the promoting effect of ANQPF is statistically significant in upstream and downstream regions but is not yet significant in the midstream region. (4) Further analysis reveals that ANQPF have a significant positive effect on HQAD within local regions. However, the spatial spillover effect across regions has not been fully realized. Therefore, to better leverage ANQPF in advancing HQAD, efforts should focus on addressing the challenges of agricultural technology extension and implementation, and on fostering new agricultural business entities. A special subsidy mechanism should be established with green transition as the guiding orientation to alleviate ecological protection pressure in the early stages of agricultural development. With regional coordination as the overarching goal, differentiated support policies should be implemented to promote overall basin development. Full article

Background/Objectives: Breast cancer remains a leading cause of cancer-related mortality among women worldwide. This study presents a systematically justified multi-modal breast cancer classification pipeline that combines established, physically motivated preprocessing operations, modality-specific deep learning models, late-fusion inference, and a deployment-aware federated learning evaluation. Rather than introducing new image restoration or federated optimization algorithms, this work formalizes how standard preprocessing methods can be organized according to the dominant degradation characteristics of ultrasound, MRI, and mammography, and evaluates their contribution under centralized and simulated federated learning settings. Methods: Patient-wise stratified five-fold cross-validation was applied across ultrasound (BUSI, $n=780$), dynamic contrast-enhanced MRI (DUKE, $n=922$), and mammography (CBIS-DDSM, $n=400$). A five-algorithm federated learning comparison, including FedAvg, FedProx, SCAFFOLD, FedNova, and FP16-FedAvg, was conducted under IID and non-IID conditions using a Dirichlet distribution with $\alpha =0.5$. The evaluation reports diagnostic performance together with per-round training time, communication time, latency-related measurements, and cumulative bandwidth. Ablation experiments, McNemar’s test, Cohen’s h effect sizes, and confidence intervals were used to support the analysis. Results: Per-modality models achieved 92.50 ± 1.2%, 90.63 ± 1.5%, and 92.00 ± 1.3% accuracy for ultrasound, MRI, and mammography, respectively, with statistically significant improvements over the corresponding baselines according to McNemar’s test ($p<0.05$). Weighted late fusion achieved 93.10 ± 1.1% accuracy and improved performance compared with the best individual modality ($p=0.031$). FP16 transmission reduced cumulative bandwidth from 8.14 GB to 1.23 GB ($-84.9\%$) without a statistically significant performance difference compared with FP32 transmission ($p=0.74$), while SCAFFOLD achieved the highest non-IID accuracy (90.50%). Conclusions: The findings demonstrate internal technical validity and deployment-relevant trade-offs, but they should be interpreted cautiously because the federated evaluation is simulation-based, key-slice extraction may require annotation-assisted assumptions, and external multi-center validation remains necessary before clinical deployment. Reported improvements are statistically significant in several comparisons, but corresponding Cohen’s h effect sizes are small, and clinical meaningfulness requires independent validation rather than inference from p-values alone. Full article

The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation cost advantages due to the Pearl River Delta’s short haul distance characteristics. In recent business practice, a novel, environment-friendly, and competitiveness-enhanced inland waterway transportation mode has emerged in the area, called the leftover-cargo mode in this paper. This mode is composed of first-tier (big companies) and second-tier (small companies) inland barge companies, which establish a cooperative relationship and jointly meet the needs of shippers and can lead to a modal shift from inland truck to inland waterway transportation. In real practice, the pricing methods of this novel mode still rely on experience. We propose four pricing game theory models based on channel leadership in order to investigate how decision-making impacts the pricing and income of the two-tier companies. We find that, if the market price ceiling is low, second-tier inland barge companies always benefit more than first-tier companies, which is very interesting and counter to the existing literature. These findings offer pricing insights into economically viable leftover-cargo cooperation and its role in supporting sustainable road-to-waterway freight modal shift in the Pearl River Delta. Full article

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

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

Artificial intelligence (AI) has transformed how we engage with visual data, particularly within the context of enterprises. Multi-modal codification systems enable the creation of semantic connections between text and visual data using AI models. This opens new markets for businesses by enabling visual search engines, recommendation systems, and automatic tagging of visual data. However, implementing these systems presents significant technical challenges. The typical workflow involves encoding images using an AI model, converting these representations into semantic vectors, and inserting them into databases optimized for fast searches. This not only affects technical efficiency but also impacts the ability of companies to scale these systems to a commercial level. This paper presents a comprehensive comparative analysis of communication architectures for large-scale image encoding systems, evaluating gRPC, RabbitMQ, serverless Lambda, and SageMaker approaches across performance and resource efficiency dimensions. Through controlled experiments processing up to 18,000 images using the SigLIP model, we establish clear performance–architecture relationships that inform system design decisions for visual content-based search applications. Full article

Offshore oil and gas exploitation is one of the riskiest businesses to invest in and is dominated by various uncertainties: high deepwater pressure, low temperatures, remote operation, long-distance tiebacks and transportation, as well as environmental factors such as wind, waves and ocean currents. Serving as a profitability threshold, the minimum economic field size is defined as the economic recoverable reserve level that an oilfield must exceed to achieve economic returns. This paper develops an approach for determining the minimum economic field size of offshore oil and gas reservoirs. It categorizes the capital expenditure into four major components: drilling and completion costs, platform costs, pipeline costs, and subsea production system costs. The regression models of drilling costs and subsea production costs are developed respectively, with water depth and recoverable reserves as key influencing factors. The pipeline costs are estimated using the unit pipeline cost per mile and pipeline length. A profit model for the offshore field is established under the constraints of the contract, which allocates the oilfield’s production profits between the contractor and the government according to the contractual fiscal terms. Finally, taking the Lucius oilfield in the Gulf of Mexico as a case study, the paper simulates its investment, operating costs, and oilfield revenues. The minimum economic field size is calculated, accompanied by the derivation of the sensitivity boundaries for the primary parameters. Full article