Search Results (9,661)

We estimate Oregon’s mass timber-related market value and economic contribution using two complementary valuation strategies and two IMPLAN implementations. Although mass timber includes CLT, glulam, nail-laminated timber, dowel-laminated timber, mass plywood panels, and structural composite lumber products, the empirical market-value estimates are centered primarily on CLT- and MPP-related evidence because these products have the most consistently available Oregon-specific data. Market value is inferred from production-based approaches, including facility capacity, Oregon’s share of U.S. output, and tracer-product scaling, and from demand-based approaches, including harvest routing, construction floor area, and U.S. demand allocation. These direct values are then entered into industry contribution analysis (ICA) for Oregon’s Engineered Wood Member and Truss Manufacturing sector and into analysis-by-parts (ABP) using a custom mass timber spending pattern. During 2018–2023, production-based estimates were larger and more variable than demand-based estimates, bracketing a plausible scenario range rather than providing a single point estimate. In 2022 price scenarios, all price-exposed cases scale proportionally with assumed panel prices. When identical direct values are used, ABP produces larger total employment and output effects than ICA because it routes more activity through upstream supplier industries. Output-per-worker sensitivity affects only direct employment in ABP. Forward scenarios for 2030 and 2035 indicate substantially larger total effects under ABP than ICA, but these estimates are conditional scenarios rather than forecasts. The framework provides a transparent basis for policy, investment, supplier-development, and workforce-planning discussions in an emerging industry with incomplete product-level data. Full article

Small and Medium-sized Enterprises (SMEs) are central to the industrial fabric of Sub-Saharan Africa (SSA). Yet, they confront increasing demands to implement sustainability practices originating from institutional contexts markedly different from their own. Existing research has tended to neglect the cultural and institutional negotiations inherent in this process, often framing sustainability adoption as a technical or compliance-oriented exercise rather than as a multifaceted cultural adaptation. This study proposes and empirically examines the concept of sustainability acculturation—the process by which firms align global sustainability norms with local business cultures. Drawing on Institutional Theory, the Resource-Based View, and Berry’s Acculturation Model, we present a context-specific framework, tested using a sequential explanatory mixed-methods approach: survey data from 284 manufacturing SMEs across six SSA countries, followed by 24 semi-structured interviews. Structural equation modeling reveals that international market pressure and owner–manager values are direct drivers, whereas local regulatory pressure exhibits only a weak association with deep cultural integration. Managerial commitment and organizational learning mediate these relationships, while Ubuntu values enhance social sustainability integration, and institutional voids diminish regulatory effectiveness. The model accounts for 57% of the variance in sustainability acculturation. Findings show that SSA SMEs employ distinct acculturation strategies—Integration, Assimilation, Resilient Adaptation, and Decoupling—shaped by the interplay of external pressures, internal capabilities, and contextual conditions. The study underscores the importance of culturally attuned, context-specific interventions for sustainable industrial development in SSA. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ε_i/(1−γ), which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

This study develops a lifecycle economic comparison framework for shared energy storage, in which multiple users share a common storage asset through capacity leasing. A multi-service revenue structure, including capacity leasing, spot-market arbitrage, auxiliary frequency regulation, peak shaving, and capacity compensation, is established for comparative evaluation. Case studies are conducted for lithium iron phosphate (LFP) and vanadium redox flow (VRF) batteries across six representative Chinese electricity markets and six standardized revenue-combination scenarios. The results show that, among the scenarios that more closely reflect current operating practices, P3 (capacity compensation + spot market + auxiliary frequency regulation) delivers the highest net present value (NPV). P6 combines all five revenue streams without explicitly modeling service-coupling dispatch constraints, and is therefore treated as a theoretical benchmark rather than an immediately deployable operating mode. Under this benchmark assumption, its calculated NPV is 21.1% and 41.7% higher than that of P3 for the two battery types, respectively. The study also shows that power-related services are more sensitive to rated power, while spot-market and peak-shaving revenues are more dependent on rated capacity. Full article

Geographical origin identification of fragrant pears is crucial for ensuring fruit quality, protecting regional brand value, and maintaining market order. However, pears from different origins often exhibit highly similar appearance and physicochemical properties, making rapid and nondestructive identification challenging for traditional methods. This study proposes a hyperspectral origin identification method based on an enhanced one-dimensional convolutional neural network (ECNN-1D) incorporating an Efficient Channel Attention (ECA) mechanism, using visible–near-infrared (Vis–NIR) and short-wave infrared (SWIR) spectral data. To address the technical challenges of highly similar spectra, redundant features, and complex information distribution, ECNN-1D enhances discriminative spectral feature representation, overcoming limitations of conventional machine learning and standard deep learning models in feature extraction and classification stability. Systematic comparisons with machine learning models (LDA, RF, KNN, SVM) and deep learning models (VGG-1D, ResNet-1D, CNN-1D) showed that while all models performed well on Vis–NIR spectra, ECNN-1D achieved the highest test accuracy of 98.94% and F1 score of 98.95% on the more challenging SWIR spectra, outperforming other approaches. These results indicate that ECNN-1D enables high-precision, nondestructive origin identification of fragrant pears, with potential cost advantages, providing a reliable technical solution for fruit traceability and quality supervision. Full article

Growing consumer awareness of environmental and health issues has increased demand for sustainable products, yet a persistent gap remains between positive attitudes and actual purchasing behavior. This study addresses inconsistent findings in prior literature regarding the effects of psychological drivers on willingness to pay a premium for green products. Drawing on the Theory of Planned Behavior and value-based perspectives, this study examines how environmental concern, health consciousness, and consumer innovativeness influence purchase intention and willingness to pay a premium (WTP) for green cosmetics. Data were collected from 872 respondents in Indonesia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with multi-group analysis (MGA) to capture demographic heterogeneity. The results show that all three drivers significantly influence purchase intention, which in turn affects WTP and acts as a partial mediator. Demographic differences further moderate several relationships, highlighting heterogeneity in green consumer behavior. This study contributes by integrating psychological drivers, behavioral mechanisms, and demographic heterogeneity into a unified framework to explain willingness to pay for green cosmetics. The findings offer practical insights for developing targeted strategies to promote sustainable consumption in emerging markets. Full article

This paper measures China’s role as an “external market provider” by quantifying, for 168 source countries during 2001–2022, the share of each country’s exports absorbed by China and decomposing that share into extensive (product coverage), quantity, and price margins using the Hummels–Klenow framework. To characterize destination-market concentration, we construct an HHI-based network diversification indicator from export-destination shares and interpret it from a complementary information-theoretic perspective, where higher concentration corresponds to lower diversification and stronger dependence. We document the dynamics of China’s market-provision role and estimate an extended gravity-type model with country- and year-fixed effects. The results show that China’s external market-provider role expanded markedly after WTO accession, with growth driven mainly by the quantity margin and, after 2018, increasingly supported by the price margin. Economic proximity and similarity in global value-chain position are associated with stronger China-absorption shares, while greater destination concentration relative to China is associated with lower China-absorption shares. Free trade agreements are linked to stronger, more extensive, and larger margins. Robustness checks based on lagged covariates, additional controls, higher-dimensional fixed effects, Tobit estimation, and winsorization support the main findings. Overall, the paper provides a replicable framework for measuring destination-market pull and shows how China’s import-side role varies across products, regions, and development groups, while using the information-theoretic perspective as a supplementary interpretation of diversification patterns rather than as a separate empirical tool. Full article

Background/Objectives: The dark green leaf color trait in bunching onion (Allium fistulosum L.) is an important agronomic trait closely associated with market value; however, its genetic basis remains poorly understood. This study aimed to identify quantitative trait loci (QTLs) associated with leaf color using SPAD values as a phenotypic indicator. Methods: An ${\mathrm{F}}_2$ population derived from a cross between the dark green line YSG1go and the light green line Asagikei-KUJYO was used. A linkage map was constructed based on RNA-seq-derived SNP markers, and SPAD values were measured for QTL analysis. Results: The linkage map consisted of eight linkage groups with a total length of 2103.0 cM and 765 mapped markers. SPAD values showed significant differences between the parental lines, with high broad-sense heritability ($H^2$ = 0.76), indicating a strong genetic contribution to this trait. Multiple significant QTLs were detected on chromosomes 4 and 5, each explaining 27.4–38.1% of the phenotypic variance. The direction of allelic effects differed among QTLs, suggesting that favorable alleles are distributed between the parental lines. In addition, genes related to chloroplast protein translation were identified within the QTL regions. Conclusions: SPAD values are a suitable indicator for genetic analysis of leaf color in bunching onion, and the QTLs identified in this study provide valuable information for molecular breeding aimed at improving dark green leaf color. Full article

This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period of heightened political uncertainty, namely the cancellation and re-run of the 2019 Istanbul local elections. This setting provides a unique opportunity to analyze how political events and news-driven information flows influence financial market dynamics. The empirical analysis is based on a comprehensive dataset that includes daily price indicators (opening, closing, high, and low values), technical indicators, selected macroeconomic variables, and Turkish-language news headlines. Textual data are processed using topic modeling techniques to extract latent information embedded in financial news, allowing for the incorporation of qualitative signals into the forecasting framework. From a methodological perspective, this study employs a feedforward deep neural network model designed to capture nonlinear relationships across heterogeneous and contemporaneous features. Feature selection is conducted using the Boruta algorithm, while hyperparameters are optimized via grid search. The model structure reflects a deliberate design choice aimed at capturing short-term, news-driven shocks and cross-feature interactions, which are particularly relevant during periods of political uncertainty. The results indicate that incorporating textual information significantly improves forecasting performance and that news topics related to political decisions, central bank policies, and geopolitical developments have a measurable impact on the XBANK index. Furthermore, the findings suggest that the political uncertainty surrounding the 2019 local elections led to increased market sensitivity and volatility, highlighting the role of information shocks in emerging financial markets. Overall, this study contributes to the literature by combining financial and textual data in an emerging market context, utilizing Turkish-language news sources, and providing empirical evidence on the impact of political uncertainty on the BIST bank index. Full article

In the highly competitive e-commerce landscape, platforms must strategically balance complex operational and marketing parameters. These real-world systems inherently involve high-dimensional nonlinear interactions and strongly coupled variables, leading to complex consumer response behaviors and highly non-convex optimization landscapes. Traditional optimization approaches usually suffer from high computational costs in business environments, while conventional surrogate models are prone to premature convergence during hyperparameter estimation. To address these management and operational challenges, this study proposes a Chaos-initialized Quantum-behaved Particle Swarm Optimization Kriging (CQPSO–Kriging) framework. Chaotic mapping is introduced to enhance population diversity, while quantum-behaved particle dynamics improve global exploration capability. Utilizing large-scale real-world transaction data from the Brazilian e-commerce industry, high-fidelity surrogate response surfaces are constructed for three core business indicators: profitability, customer loyalty, and value density. Experimental results show that the proposed CQPSO–Kriging model significantly outperforms conventional approaches, such as support vector regression and radial basis function networks, achieving an exceptional coefficient of determination of R² = 0.9586 in profit prediction. Furthermore, Sobol variance-based global sensitivity analysis is employed to extract critical managerial insights, revealing that financial variables act as interaction-driven utility multipliers in consumer decision-making. Multi-objective Pareto analysis further demonstrates that profit maximization naturally converges toward a balanced operational configuration, providing a robust quantitative tool for e-commerce precision marketing. Full article

As emerging economies expedite their integration into global capital markets, comprehending the implications of voluntary International Financial Reporting Standards (IFRS) adoption has become increasingly critical for regulators, investors, and corporations. This study examines the influence of voluntary IFRS adoption on the quality of financial reporting and the value of firms in Vietnam, a transitional economy characterized by a unique code-law legal tradition, a substantial disparity between domestic accounting standards and IFRS, and a government-mandated adoption roadmap that establishes a distinctive quasi-voluntary adoption phase. The study utilizes a panel dataset of 562 firms listed on the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) from 2019 to 2022, employing a fixed-effects regression model with robust standard errors to account for unobservable firm heterogeneity. Utilizing agency theory and signaling theory, the research anticipates and validates that voluntary IFRS adoption correlates positively with diminished discretionary accruals (serving as an indicator of financial reporting quality) and elevated Tobin’s Q (acting as a measure of firm value). The estimated effect corresponds to a 10.7% reduction in discretionary accruals and a 13.1% increase in Tobin’s Q relative to sample means—magnitudes that are both statistically and economically significant. Unlike prior studies that rely exclusively on archival data, this study employs a survey-based measure of voluntary IFRS adoption activity to capture preparatory behaviors that are not yet observable in public financial disclosures, representing a methodological contribution to the literature. The results have useful implications for policymakers in Vietnam and other developing countries that are considering adopting IFRS on either a voluntary or mandatory basis. They show that taking the initiative to follow international reporting standards makes reports more trustworthy and the market more valuable. Full article

Value co-creation among diverse actors in digital innovation ecosystems (DIEs) exhibits characteristics of high complexity and dynamic evolution. Grounded in the Quadruple Helix Theory, this study develops a conceptual model that interlinks “supervisory guides, knowledge providers, technology transformers, and user demand parties.” This model is defined by organizational oversight as its nexus, knowledge and technology as its foundation, outcome transformation as its core, and user needs as its orientation. Building upon this conceptual foundation, we establish a four-party evolutionary game model involving “innovation regulators (government), innovation producers (academic/research institutions), innovation decomposers (enterprises), and innovation consumers (users).” This analytical framework is then applied to systematically investigate the dynamic evolutionary mechanisms and collaborative pathways for value co-creation in DIEs. We construct the payoff matrix and replicator dynamics to derive the system’s Evolutionarily Stable Strategies (ESSs). Numerical simulations via MATLAB R2023b identify the stability conditions for each party’s strategic choices and unravel the influence mechanisms of key parameters. The results demonstrate nine distinct ESSs, categorized into three types: low-level stability, regulation-dominated transitional stability, and high-level cooperative stability. While the agents’ initial strategies do not alter the system’s final equilibrium state, they significantly impact the speed of evolutionary convergence. Critical factors—including regulators’ intervention costs, subsidy and penalty mechanisms, producers’ and decomposers’ cooperation and default costs, and consumer feedback behaviors—collectively drive the system toward the ideal (1, 1, 1, 1) equilibrium. Theoretically, this study enriches the perspective on multi-agent collaboration in value co-creation by introducing a dynamic quantitative analytical framework, thereby addressing a notable gap in the literature. Practically, it provides actionable insights for mechanism design and a solid foundation for policy optimization, aiming to foster a synergistic governance system that integrates “regulatory guidance, market incentives, and social feedback.” Full article

The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50% of agricultural GDP and supporting more than 60% of rural households. Yet climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000–2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Local strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30%, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socioeconomic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socioeconomic contexts (e.g., market access, cultural practices). Full article

Paeonia lactiflora Pall. (herbaceous peony) is a high-value ornamental and medicinal plant in China with considerable market potential. However, conventional propagation methods are limited by low multiplication rates and long production cycles, making it difficult to meet the demand for large-scale planting materials. As a key approach for rapid propagation, tissue culture techniques for P. lactiflora have achieved significant progress in recent years. This review summarizes advances in the tissue culture system for P. lactiflora over the past decade, focusing on major in vitro regeneration pathways (organogenesis and somatic embryogenesis) and crucial technical stages, including explant selection and culture environment optimization. Distinct from previous reviews that only introduce partial technical aspects of P. lactiflora tissue culture, this review comprehensively outlines the overall tissue culture system, analyses the current species-specific bottlenecks (browning, vitrification, rooting and acclimatization) with their underlying causes and proposes targeted strategies. Furthermore, future development trends are prospected by integrating emerging research directions, including molecular regulatory mechanisms and eco-adaptive breeding. This review aims to provide a theoretical foundation and technical support for obtaining propagule for commercial plantations and achieving multi-functional utilization of P. lactiflora. Full article