Search Results (4,209)

The high-value utilization of construction and demolition waste is critical for sustainable development in the building sector. However, in construction and demolition waste (CDW) recycling supply chains, existing studies lack a systematic analysis of pricing mechanisms for such recycled CDW as substrate products, particularly regarding interest coordination and the quantification of green value. To reveal the bargaining mechanism between farmers as recyclers and processors and supermarkets as retailers under an asymmetric bargaining structure, this study applies Nash bargaining theory to construct a dynamic game model. The study revealed that (1) when the green degree of a product reaches a certain level, it can obtain a sustainable market premium and create a stable income space for both parties. (2) The relative strength of the bargaining power between the two sides significantly affects the impact of market base scale changes on profit distribution. When the bargaining power of the supermarket is lower than the threshold and the bargaining power of the farmers is higher than the threshold, the difference in profit between the farmers and the supermarket is negatively correlated with the market base scale of the CDW as a substrate. (3) The green sensitivity level of consumers affects the difference in profit of the main body with the government subsidy to farmers. This level is determined by the value of the green sensitivity coefficient of consumers and presents a differentiated adjustment effect in different value ranges, which in turn affects the transmission direction of government subsidies to profit distribution. (4) When the green sensitivity coefficient and the green communication intensity of farmers and the investment level are lower than the corresponding critical values, the difference in social welfare with or without subsidies is positively correlated with the amount of the subsidy. This study provides decision support for farmers and supermarkets in designing rational bargaining strategies and offers insights for improving coordination and sustainability in construction and demolition waste recycling supply chains. Full article

When finished with an electronic device, consumers choose between storing, recycling, giving away, trading-in, reselling, or throwing it away. This choice has environmental and data privacy implications, e.g., reuse of devices is generally environmentally preferable to recycling, which is better than throwing away in the trash. Through a survey of 4000 U.S. consumers and regression analysis, this study analyzes how stated attitude and knowledge connect to consumers’ previous and planned disposition choices. The binomial regression model (pseudo-$R^2=13\%$) models the decision to store or not store a device. Important factors leading to increased likelihood of storing are data security concerns when recycling (+14%) or reselling (+9%), lack of knowledge of recycling (+10%), and wanting a backup of data (+11%). Notably, data security concerns when recycling or reselling were significant for past behavior, but not for intended behavior. This suggests consumers take data security more seriously when faced with the actual disposition decision. Multinomial regression (pseudo-$R^2=15\%$) is used to model which non-storage option is chosen. Knowledge of (+47%) and perceived convenience (+9%) of recycling programs were important in consumers choosing to recycle, reselling of devices was strongly influenced by knowledge of reuse markets. Full article

Generation Z, a cohort defined by digital connectivity, sensitivity to social influence, and environmental awareness, has attracted considerable scholarly attention in sustainable consumption research. Yet a persistent gap between their expressed pro-sustainability attitudes and actual purchasing decisions remains well-documented. This study examines whether Gen Z characteristics help bridge that gap by directly influencing sustainable purchase behavior and by moderating the role of purchase intention in that process. A quantitative design was employed using survey responses from 302 Gen Z consumers. The findings suggest that while Gen Z characteristics significantly predicted actual sustainable purchasing and purchase intention exerted a positive direct effect, the interaction between the two was negative and statistically significant. Conditional effects analysis further revealed that the influence of generational characteristics on purchasing behavior is stronger at lower levels of purchase intention and progressively weaker as intention increases. These results suggest that traits such as digital responsiveness, social embeddedness, and environmental orientation do not merely reinforce existing intentions but appear to compensate for their absence, activating sustainability-aligned behavior even when motivational commitment is limited. The study repositions the intention–behavior gap among Gen Z as something modulated by generational characteristics that drive purchasing behavior when intention alone falls short. Full article

This paper examines drip pricing related to compulsory charges—a situation where firms intentionally make it costly for consumers to discover mandatory fees or surcharges that “drip” into the full (total) price, which is only revealed after incurring the hassle cost of completing a purchase. We show that drip pricing can arise as an equilibrium phenomenon with fully rational consumers and profit-maximizing firms. We also show that when consumers and firms are rational (a) situations where drip pricing raises prices and harms consumers are unlikely to arise from unilateral business decisions and (b) the most likely avenue by which drip pricing harms consumers is through the coordinated adoption of drip pricing. Full article

This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible–Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170–870 nm (UV/Vis) and 900–2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900–2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms—League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)—was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety. Full article

Online reviews are increasingly pivotal in consumer decision-making, with platforms employing mechanisms such as badges to denote reviewer credibility. Although prior research has examined the influence of expert reviewers, it has typically treated badge holders as a homogeneous group, overlooking how variation in tenure within expert tiers shapes the way readers process review content. This article examines how Yelp Elite badge tenure, operationalized as Red (1–4 years), Gold (5–9 years), and Black (10+ years) tiers and treated as a proxy for accumulated platform-recognized expertise, moderates the effects of peripheral cues (Extremity, Length) and central cues (Readability, Subjectivity, and Plutchik’s eight emotions) on perceived helpfulness within an Elaboration Likelihood Model (ELM) framework. The analysis draws on the full population of 324,426 restaurant reviews authored by Yelp Elite badge holders between 2019 and 2021, using a pooled count-model specification with badge tier as a categorical moderator. The primary specification is estimated using Poisson quasi-maximum likelihood with HC1-robust standard errors, and full negative binomial estimation is reported as a robustness check. Wald tests indicate that badge tenure significantly moderates eight of twelve cue–helpfulness relationships (${\chi }^2\left(24\right)=4938$, $p<0.001$). The effect of readability is monotonically positive and increases sharply with tenure, while the effect of joy varies across tenure groups. These findings suggest that reviewer expertise signals are not monolithic, refining theoretical insights on how tenure-based credibility cues moderate cue processing and offering practical implications for review platform management. The findings also indicate that platforms applying uniform ranking or surfacing rules across all Elite reviewers risk misallocating visibility, and that tenure-conditional weighting of textual cues warrants consideration. Full article

Clinical trials suggest that daily vinegar ingestion improves fasting blood glucose concentrations, postprandial glucose excursions, and hemoglobin A1c levels in patients with prediabetes and type 2 diabetes. With the recent commercialization of continuous glucose monitoring (CGM) technologies, diabetes patients as well as other health-conscious individuals can evaluate the impact of food choices in real-time and make data-driven decisions to improve dietary behaviors. This 9-day, randomized crossover study documented CGM-derived glycemic patterns during vinegar ingestion in adults with prediabetes. Participants consumed two tablespoons of vinegar twice daily with meals for four days or a control tablet each morning for four days in random order. For each phase, fasting blood glucose on day four, average blood glucose across three days, and peak glucose excursion across three days were calculated. Fasting glucose concentrations of participants (n = 10 women; 36.6 ± 15.6 y; 33.9 ± 6.5 kg/m²) averaged 105.8 ± 20.6 mg/dL at baseline. Vinegar ingestion was associated with significant reductions in the mean glucose concentration (−4.4 mg/dL) and the frequency of blood glucose excursions > 140 mg/dL (−10%) in comparison to the control treatment, but fasting glucose concentrations were unaffected. These data suggest that vinegar-induced improvements in blood glucose can be observed in real-time using a CGM device in adults with prediabetes. Full article

Against the backdrop of the ongoing advancement of the “dual carbon” goals and the carbon emission trading system, green innovation in small and medium-sized manufacturing enterprises faces multiple practical constraints, including financing constraints, technological commercialization risk, and market recognition costs. To examine the mechanism through which government regulation affects firms’ green innovation behavior, this study develops a four-party evolutionary game model involving government, small and medium-sized manufacturing enterprises, consumers, and investment institutions, and analyzes the strategic interactions and dynamic evolution of these actors. The results show that regulatory intensity, consumer green preference, and financial support from investment institutions all exert significant effects on green innovation decisions in small and medium-sized manufacturing enterprises. Whether firms choose substantive green innovation depends primarily on such key factors as financing uncertainty, technological commercialization risk, the intensity of government penalties, and the level of policy incentives. Further stability analysis and numerical simulations indicate that stronger administrative penalties significantly increase the likelihood that firms adopt substantive green innovation and also promote green consumption among consumers. This effect becomes more pronounced when financing uncertainty declines. At the same time, stronger policy incentives for green investment enhance the willingness of investment institutions to participate in green projects, and this effect is further reinforced when technological commercialization risk is reduced. The findings suggest that green innovation in small and medium-sized manufacturing enterprises is characterized by strong multi-actor interdependence. Its evolutionary outcome is shaped not only by regulatory pressure, but also by green financial support, the conditions for technological commercialization, and market demand. Accordingly, sustained green innovation in small and medium-sized manufacturing enterprises requires coordinated efforts to improve regulatory arrangements, strengthen green finance support systems, reduce the cost of technological commercialization, and cultivate green consumer markets. Full article

This study explores how Artificial Intelligence (AI) shapes Green Purchasing Behavior through cognitive and attitudinal mechanisms by implementing the Stimulus–Organism–Response (SOR Model) Theory. It analyzes AI as an external stimulus that influences Environmental Knowledge and Green Truth, which, in turn, affects Environmental Attitudes and Green Purchasing Behavior. A cross-sectional quantitative design was employed using survey data collected from 412 consumers in the province of Guayas (Ecuador). The data were analyzed using partial least-squares structural equation modeling (PLS-SEM). The results indicate that AI exerts a weak influence on Green Purchasing Behavior; instead, its impact operates primarily through indirect pathways. Specifically, AI significantly enhances Environmental Knowledge and promotes Green Truth, subsequently shaping consumers’ Environmental Attitudes. Furthermore, Environmental Attitude emerged as the strongest predictor of Green Purchasing Behavior, confirming its central role in translating internal evaluations into consumption decisions. These findings contribute to the literature by integrating AI into sustainable consumption models and demonstrate that its effectiveness depends on its ability to generate credible and meaningful internal responses rather than directly influencing behavior. Full article

This study considers the digital transformation of Kazakhstan’s agro-industrial complex, which has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. This study proposes a hybrid decision-support framework integrating a modified NSGA-III algorithm with machine learning techniques for optimizing digital marketing strategies in the agro-industrial complex of Kazakhstan. The model considers three objectives: maximizing channel efficiency and audience reach while minimizing marketing costs. Experimental results based on a dataset of N = 1200 observations demonstrate that the proposed approach improves the composite performance indicator by 12.4% compared to baseline single-objective optimization methods. Pareto front analysis reveals three distinct clusters of strategies, corresponding to (1) high-impact integrated digital TV strategies, (2) cost-efficient traditional channel strategies, and (3) high-risk high-return allocations. The clustering validity is confirmed by a silhouette score of 0.624, indicating strong separation between strategy groups. The results highlight the practical significance of adaptive budget allocation and demonstrate the effectiveness of combining evolutionary optimization with machine learning for decision support in complex marketing environments. Full article

Vulnerability management (VM) is becoming increasingly important as organizations face growing cybersecurity threats. This study examines how organizations adapt their vulnerability management routines in response to evolving vulnerability signals through the integration of artificial intelligence (AI) and automation. Drawing on data from an international fast-moving consumer goods (FMCG) company, we investigate how human expertise and AI interact across the full VM process, from triage to remediation. Using Organizational Routine Theory (ORT), we show that AI does not simply automate tasks but acts as a co-performer, influencing how decisions are made, work is coordinated, and actions are adapted. We develop a three-phase model capturing (1) the integration of AI-enabled automation into strained routines, (2) the manifestation of tensions between human expertise and automation as well as between usability and system complexity, and (3) the stabilization of hybrid routines through iterative adaptation and feedback loops. We identify two key tensions in this process: technology versus human expertise, and usability versus the complexity of multi-vendor tools. These tensions create frictions in practice but also open opportunities for learning and improvement. Rather than treating AI as a technical tool, our findings highlight its role as an active routine participant. Importantly, we show that routine evolution enables organizations to improve how vulnerability signals are interpreted and acted upon, thereby supporting more coordinated and adaptive cybersecurity practices. This has both theoretical implications for understanding how routines evolve with technology and practical relevance for improving adaptive cybersecurity practices. By linking micro-level routine dynamics to broader organizational outcomes, this study contributes to explaining how organizations sustain stable and adaptive operations under conditions of continuous cyber threat exposure. Full article

Accurate, rapid, and consistent evaluation of pavement condition across large-scale road networks is critical for sustainable maintenance and rehabilitation planning. However, conventional approaches largely rely on manual visual inspections, which are time-consuming, subjective, and difficult to implement at the network level. In this study, a semi-automated pavement distress evaluation framework that integrates field-based assessment with computer vision techniques is proposed. The study was conducted on a 3 km roadway network located within the Yıldız Technical University Davutpaşa Campus. Field-based distress observations were used as reference data, while street-level images obtained from the Mapillary platform were analyzed using a deep learning-based YOLOv8 model trained on the RDD2022 dataset, which was specifically developed for road distress detection. The analysis focuses on crack and pothole distress, which have a dominant influence on PCR and are highly distinguishable in image-based approaches. Correlation analyses between automated detection results and field-based data demonstrate a strong agreement, reaching values of approximately $\rho \approx 0.90$ in some routes. These findings indicate that these distress types are effective in representing variations in pavement condition. The results demonstrate that multi-source image data and deep learning-based detection methods can be reliably used for section-level pavement condition assessment. The proposed approach addresses a key gap in the literature by transforming image-level detections into engineering-based decision-support information. Furthermore, by leveraging publicly available data sources, the framework offers a low-cost and scalable solution that enables rapid preliminary assessment over large road networks, thereby providing significant potential for sustainable infrastructure management and the development of data-driven maintenance strategies. Several practical challenges encountered during the detection process—including sensitivity to contrast enhancement parameters, false positives from shadows and surface reflections, heterogeneous image resolution across crowdsourced imagery, and training distribution gaps for locally prevalent infrastructure features—are discussed, and directions for reducing human intervention through adaptive preprocessing and targeted model refinement are identified. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article

Against the backdrop of the digital economy, digital transformation has increasingly evolved from a firm-level upgrading process into a collaborative decision-making issue among supply chain members. From the perspective of intelligent supply chain management, this study develops a two-echelon game model of a vertical manufacturer–retailer supply chain to examine digital collaborative decision-making under heterogeneous power structures. By comparing a centralized cooperative benchmark with decentralized non-cooperative scenarios, the study investigates how power structures affect firms’ digital transformation efforts, pricing decisions, and system-level outcomes, while also considering the role of knowledge spillovers. The results show that, under the same power structure, cooperation leads to higher digital transformation effort levels and greater total supply chain profit than non-cooperation. Knowledge spillovers further strengthen firms’ incentives to invest in digital transformation and improve market demand, consumer surplus, and social welfare. Compared with asymmetric power structures, a balanced power structure generates lower retail prices, higher market demand, and better overall supply chain performance. Numerical simulations further show that higher digital transformation costs weaken collaborative gains, whereas greater market sensitivity to digitalization strengthens them. Overall, this study suggests that digital collaboration contributes to supply chain sustainability by improving coordination efficiency, enhancing adaptive operations, and promoting system-level value realization under heterogeneous governance structures. Full article

Near-expired food has received increasing attention in recent years as an important way to reduce food waste and promote sustainable consumption. However, although consumers recognize its economic value and environmental significance, they still have concerns about its quality and potential risks. Drawing on social cognitive theory and social exchange theory, this study adopts a benefit–risk trade-off perspective to examine how personal and environmental factors influence purchase intention toward near-expired food through perceived benefits and perceived risks. Based on 547 valid questionnaires collected from Chinese consumers, this study employs PLS-SEM, multi-group analysis (MGA), and fuzzy-set qualitative comparative analysis (fsQCA) for empirical testing. The results show that personal norm and price discount significantly increase perceived benefits, whereas social image concern and product uncertainty significantly increase perceived risks. Perceived benefits have a significant positive effect on purchase intention, whereas perceived risks have a significant negative effect. The MGA results further show that purchase experience and income level lead to significant differences in consumers’ decision paths. The fsQCA results indicate that both high and non-high purchase intention can be formed through multiple distinct but equivalent paths. High purchase intention mainly follows two patterns, benefit-driven and cognitive trade-off. Non-high purchase intention is mainly characterized by benefit deficiency and risk interference. The findings provide implications for the marketing and risk management of near-expired food. Full article