Search Results (545)

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

Taste strongly influences food acceptance and purchase intention. Beyond the five basic tastes, oral sensations such as astringency or koku modulate overall taste perception. Both umami and koku act as taste enhancers, increasing mouthfeel and savoriness. While the taste of most proteogenic amino acids is well established, non-proteogenic amino acids and related protein derivatives remain insufficiently characterized. This study analyzes the taste profile of seventeen underexplored amino acids and protein derivatives using the PredMol in silico tool and quantitative descriptive analysis (QDA), with particular emphasis on their umami and koku potential. In silico evaluation identified bitterness and sweetness as the predominant tastes and predicted carnosine, theanine, citrulline, and ornithine to have koku potential with values higher than 0.44. Principal Component Analysis of the QDA revealed that sweetness, bitterness, and sourness were the main drivers of sample differentiation. Ornithine, glutamine, citrulline, pyroglutamic acid, and theanine exhibited a positive dose–response in umami perception, with potential synergistic effects observed in the presence of 0.5 mmol/L IMP. Additionally, theanine, citrulline, and ornithine enhanced koku-related attributes, particularly aftertaste and continuity, in aqueous model solutions. Overall, these findings suggest that these compounds can have a taste influence in food products and potential to be used as taste enhancers. Full article

Understanding the emotional drivers of consumer choice is critical for effective food packaging design. This study proposes a novel ‘Emotion–AI Framework’ to decode consumer responses to ten processed fish product labels using the circumplex model of emotion. Explicit emotional responses and purchase intentions were collected from 100 participants, and unsupervised machine learning (K-Means clustering) successfully classified consumers into three distinct segments (Enthusiasts, Passives, and Rejectors) strictly based on their multidimensional emotional profiles. Furthermore, a supervised Random Forest regression model, coupled with permutation feature importance, revealed that aggregated emotional states (specifically the low-arousal/pleasant and high-arousal/unpleasant quadrants) are the dominant drivers of purchase intention. Crucially, these emotional states significantly outperformed the direct impact of physical label attributes. The findings demonstrate that integrating theoretical emotional models with predictive machine learning provides robust, data-driven insights for the food industry, enabling the optimization of product labels to evoke targeted affective states and maximize consumer acceptance. Full article

Food waste fashion—garments produced from agricultural and food industry by-products, such as fruit peels, coffee grounds, and grape marc—represents a radical yet understudied innovation within the circular economy. This study proposes the Fashion Innovation Adoption Model, a novel framework that organizes consumer adoption of fashion innovations across three hierarchical levels: a distal level comprising sociodemographic characteristics, an intermediate cognitive–evaluative level comprising consumer decision-making styles and functional product attribute evaluations, and a proximal psychosocial level comprising attitudes, static and dynamic social norms, and past fashion purchasing behavior. The model is applied for the first time to food waste fashion as a paradigmatic case of radical circular innovation in the textile sector. Hypotheses were tested via structural equation modeling on a sample of 396 Italian consumers. Purchase intention was directly predicted by attitudes, static and dynamic norms, and general fashion purchasing, whereas sustainable fashion purchasing showed no effect. Among product attributes, only sustainability information influenced both attitudes and intentions. Perfectionism and hedonism were positively associated with intention through sustainability information, while impulsivity and habit were negatively associated with intention. Sociodemographics influenced intention only indirectly, via cognitive and normative mechanisms. These findings reveal complex pathways linking psychological profiles and perceived product attributes to circular fashion adoption, with implications for communication strategies emphasizing sustainability information and targeting heterogeneous consumer motivations. Full article

Against the backdrop of global warming and China’s Carbon Peaking and Carbon Neutrality Goals, this study focuses on carbon footprint rating information on packaging, addressing the gap between consumer cognition and behavior in food consumption caused by invisibility of carbon emissions. This study integrates the Theory of Planned Behavior (TPB) with the Technology Acceptance Model (TAM), designed a “letter-plus-color” dual-coded carbon footprint label as the experimental stimulus, and conducted a survey of 581 respondents across China’s four major regions using structural equation modeling. Results indicate that perceived ease of use, perceived usefulness, subjective norms, and perceived behavioral control all positively predict purchase attitudes. Among these, perceived behavioral control also independently predicts actual purchase behavior. Multigroup analysis further reveals that household purchasing responsibility moderates the “attitude → intention” relationship: purchasing decision-makers engage in realistic trade-offs, whereas non-purchasing decision-makers are driven by value congruence.Theoretically, this study deepens our understanding of cognitive intervention mechanisms of carbon footprint labels and expands explanatory power of the TPB-TAM model in low-carbon contexts. From a practical perspective, this study provides guidance for governments in designing targeted labeling policies and for companies in developing packaging that aligns with cognitive principles. Full article

University cafeterias generate solid waste as a result of high user turnover and routine food service operations. While waste characterization studies are common in higher education institutions, data-driven predictive modeling remains limited, particularly in Latin American contexts. This study addresses this gap by integrating physical waste generation with behavioral surveys to develop predictive tools for operational decision-making. The findings should be interpreted as a single-site operational demonstration; broader generalization requires replication and local recalibration in cafeterias with different operational and social characteristics. Waste generation was characterized in a Panamanian university cafeteria by shift over 20 consecutive working days, separating organic and inorganic fractions, and collecting 705 user surveys on consumption habits. Two complementary predictive approaches were developed: a rule-based classification model and a Monte Carlo simulation framework. Organic waste exhibited a stable pattern throughout the study period, with clear concentration during lunch hours and a strong dependence on user volume. In contrast, inorganic waste showed higher day-to-day variability and increased during evening service, reflecting changes in service practices rather than attendance alone. Statistical analysis indicated that waste generation was more closely associated with food type purchased and faculty affiliation than with self-reported environmental awareness. Overall, the results demonstrate that straightforward predictive approaches can support shift-level planning and operational waste management decisions in university cafeterias. Full article

Predicting garlic prices is difficult because the crop behaves as both an agricultural commodity and a speculative asset. Unlike staple grains, which follow more predictable seasonal supply cycles, garlic can be stored for over a year, its production is geographically concentrated, and its demand remains inelastic. This industry structure makes it susceptible to speculative hoarding, where even minor harvest deficits may trigger sharp price spikes. A typical example is the “Suan Ni Hen” (crazy garlic) phenomenon in the Chinese market: during the 2009–2010 and 2016 periods, speculative capital repeatedly exploited expectations of harvest reduction to engage in large-scale hoarding. According to data released by China’s National Development and Reform Commission (NDRC) at the end of October 2016, national wholesale garlic prices surged by 90% year-on-year, with purchase prices in some major producing areas doubling or multiplying within a short period. Such short-term price bubbles, together with severe volatility and abrupt regime shifts, can make standard forecasting models unreliable in this uncertain environment. Existing methods, ranging from traditional seasonal algorithms to deep learning networks, often overlook the need to decouple the local trend-weekly-seasonal baseline from the dynamic effects of multi-source external signals. This paper proposes GPF-EVMoLE, a compositional multi-step forecasting framework built on an explicit division of labor. The framework first extracts an interpretable local trend and weekly-seasonal baseline through an ETS decomposition module. Two specialized components then process the residual signal: a temporal fusion Transformer-style variable selection network (VSN) uses multi-source external features to identify informative macroeconomic and environmental signals at each forecasting step, while a Mixture of Linear Experts (MoLE) models phase-wise regime shifts within the residual series. Together, these modules adaptively integrate heterogeneous information. This study evaluates the framework on a custom daily evaluation dataset containing 17,685 records across six major producing regions in three provinces. At 7-day and 14-day forecasting horizons, GPF-EVMoLE consistently outperforms eight representative statistical, machine learning, and deep learning baselines across MAE, RMSE, and MAPE metrics. Ablation studies verify the necessity of each component, showing that structural separation of the forecasting tasks helps overcome the limitations of monolithic models and provides an accurate and interpretable solution for complex agricultural markets. Full article

The rapid increase in residential electricity consumption in the Philippines has underscored the need for accurate metering, predictive forecasting, and improved protection against electrical hazards. We design a residential smart energy metering system with load forecasting using Long Short-Term Memory (LSTM) and integrated overload protection. The system employs non-invasive SCT-013 current sensors manufactured by DFRobot and a ZMPT101B voltage sensor manufactured by Qingxian Zeming Langxi Electronic, both the SCT-013 and ZMPT101B were purchased from Circuit Rocks Philippines. The SCT-013 current sensors and ZMPT101B voltage sensors are interfaced with a Raspberry Pi to measure consumption across four residential branch circuits. Data is transmitted to a cloud-based dashboard for real-time monitoring, while a relay module automatically disconnects loads under overload conditions. To validate accuracy, the prototype was deployed for 24 h and recorded a total of 18.87 kWh compared to the 20 kWh recorded from the actual Manila Electric Company (MERALCO)-provided energy meter. The LSTM model trained on per-minute data with calendar features achieved strong predictive performance across the branches. The LSTM forecasted the load and current for the next 24 h. The forecasted current was used as the dynamic tripping value for the overload protection. The overload protection tests demonstrated reliable tripping behavior within seconds of detecting overload currents. Results confirm that the system provides accurate energy monitoring, reliable overload protection, and robust short-term load forecasting. The prototype demonstrates a cost-effective and scalable approach for enhancing residential energy management, safety, and forecasting in Philippine Households. Full article

This paper examines how the predictive content of insider trading varies across industries. Using U.S. insider transaction data from 2005 to 2025 and firm-month level measures of insider trading and forward returns, we compare technology, banking, and utility firms within a unified framework. The results show that insider purchases in banking firms contain the strongest information about future returns, while the signal is substantially weaker in technology firms and moderate in utilities. We also document a clear asymmetry between buying and selling. Insider purchases are more informative than sales, while sales reflect more heterogeneous motives and are therefore harder to interpret. This buy–sell gap varies across industries and is most pronounced in banking and utilities. Finally, we compare insider-trading informativeness before and after the 2022 amendments to Rule 10b5-1. The results show that sell-side informativeness appears weaker in the post-2023 period, while the predictive content of purchases remains largely unchanged. This evidence is descriptive and does not imply a causal effect of the reform. Overall, the findings highlight the importance of industry-specific information environments and regulatory conditions in shaping the relation between insider trading and future stock returns. Full article

Food waste poses a serious threat to global food sustainability, and consumer rejection of suboptimal food due to perceived risks is a significant factor exacerbating this issue—a phenomenon particularly pronounced in the Chinese context. Using survey data from 1022 Chinese consumers, this study investigates how multidimensional perceived risk and demographic characteristics jointly influence purchase intention toward suboptimal food. The results indicate that perceived quality risk, perceived health risk, and perceived social risk exert significant negative effects on purchase intention, whereas perceived psychological risk shows no significant effect. Moreover, the effect of perceived risk varies significantly across key demographic dimensions. Perceived health risk mediates the relationship between perceived quality risk and purchase intention. A significant interaction also emerges between perceived quality risk and perceived social risk: under conditions of high perceived social risk, high perceived quality risk substantially reduces purchase intention; under low perceived social risk, this negative effect persists but is attenuated. By delineating the differential effects and underlying mechanisms through which distinct risk dimensions shape purchase intention, this study not only advances the theoretical understanding of the interplay between multiple risk perceptions in consumer decision-making but also provides empirical evidence for reducing food waste from the consumption side, offering important implications for promoting sustainable consumption practices. Full article

Despite decades of methodological sophistication, research on sustainable food behavior remains critically limited in predicting actual purchases. This study aims to examine how methodological fragmentation across psychometric, econometric, and behavioral approaches affects the predictive validity of sustainable food choice and purchasing behavior. This integrative systematic review of 62 empirical studies across psychometric validation, discrete choice experiments (DCEs), trust and cognitive biases, and objective behavioral measurement diagnoses the structural disarticulation between these traditions as the primary cause of limited predictive validity. Findings reveal a pronounced inversion of the evidence hierarchy: while self-report studies report moderate attitude–behavior correlations (β ≈ 0.40–0.50, self-report), the only long-term study using objective scanner data demonstrates that this relationship collapses to a virtually null effect (β = 0.022), representing a 95.6% decay in predictive capacity. Psychometric instruments demonstrate strong structural validity but lack ecological validation against actual purchases. DCEs have evolved econometrically (from MNL to GMNL models), yet remain isolated from psychological theory and real-world validation. Critically, no reviewed study integrated validated scales, a DCE, and objective behavioral data within a single design. Key moderators—skepticism, halo effects, and affective heuristics—are systematically underoperationalized. To overcome this impasse, we propose Hybrid Choice Models (HCM) as the central tool to formally articulate latent attitudes, stated preferences, and observed behavior, enabling cumulative evidence to inform policy and market strategies with greater predictive accuracy. These findings indicate that predictive advances depend on integrating measurement paradigms to achieve ecologically valid and policy-relevant models of sustainable consumer behavior. Full article

Influencer marketing research has shown that source-related evaluations matter, yet less is known about how specific influencer cues are translated into consumer responses through differentiated internal psychological states. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study examines how influencer attractiveness and expertise shape consumer responses through parasocial interaction and trust. Attractiveness is conceptualized as a social-affective cue, whereas expertise is conceptualized as a competence-based cue. Parasocial interaction is modeled as a relational organismic state, and trust is modeled as a reliance-oriented organismic state. Survey data were collected from 532 Taiwanese social media users with prior experience following influencers and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that attractiveness positively predicts parasocial interaction, expertise positively predicts trust, and parasocial interaction further contributes to trust. Trust, in turn, positively influences loyalty, purchase intention, and recommendation intention, with the strongest effect observed for recommendation intention. These findings suggest that influencer effectiveness is better understood as a differentiated cue–mechanism–response process rather than as a generalized source-evaluation effect. By distinguishing attractiveness from expertise and by modeling parasocial interaction and trust as conceptually distinct but sequentially connected organismic states, this study provides a more precise S-O-R account of how influencer evaluations are translated into relational, transactional, and advocacy-oriented consumer responses. Full article

Under fault conditions, Photovoltaic-Storage-Charging Integrated Stations (PSCISs) are regarded as a key resource for enhancing distribution network resilience. However, traditional centralized optimization fails to account for conflicts of interest between the distribution network and PSCISs and neglects the actual response behavior of EV users. To address these issues, a coordinated emergency operation strategy for distribution networks and PSCISs based on the master–slave game is proposed. Firstly, a bilevel optimization framework based on the master–slave game is constructed, where the upper level performs system-level coordination and the lower level handles autonomous decision-making. For the upper level, the minimization of distribution network operation cost is set as the optimization objective by the dispatching center to determine power purchase prices and load shedding rates, which serve as guidance signals for lower-level PSCISs. In terms of the lower level, a dual-factor S-shaped response curve is introduced into the lower-level model to precisely characterize EV users’ nonlinear response behavior to price incentives. Furthermore, based on the signals received from the upper level, the maximization of each PSCIS’s profit is set as the optimization objective to determine the PV output, storage dispatch, and V2G incentive prices. Subsequently, Model Predictive Control (MPC) is employed to implement rolling optimization during the fault period, addressing the source-load uncertainties. Finally, an improved IEEE 33-node distribution network is used for case analysis and validation of the proposed operation strategy. The results indicate that the proposed strategy can effectively coordinate the interests of multiple parties, achieving synergistic improvements in both the economy and reliability of the distribution network. Full article

Reducing food waste and promoting green consumption have emerged as critical priorities in the transition toward a more sustainable food system. Purchasing near-expired food (NEF) offers a pathway to address both issues simultaneously, yet the mechanisms underlying consumers’ intentions toward such products remain underexplored. This research investigates these mechanisms through two complementary studies conducted in China, focusing on near-expired bread as a representative product category. Study 1 (N = 1154) draws on the stimulus–organism–response (SOR) framework to examine how key factors shape consumers’ purchase intentions toward near-expired bread. The results show that price discounts and longer remaining shelf life increase purchase intentions by enhancing perceived value and reducing perceived risk. Moreover, consumers’ normative beliefs with regard to food waste avoidance positively predict purchase intentions through heightened moral satisfaction. Study 2 (N = 746) employs a 2 × 3 between-subjects factorial experiment to test two types of retail interventions for near-expired bread: discount messages (50% vs. 10% off) and information framing (gain-framed vs. loss-framed). Extending Study 1, this experiment introduces two additional dependent variables—product attitudes and perceived environmental external benefits—to capture a broader range of consumer responses. ANCOVA results reveal that consumers with higher environmental concern exhibit stronger purchase intentions, more favorable product attitudes, and greater perceived environmental external benefits. Price discount messages significantly influence purchase intentions and product attitudes, whereas information framing affects purchase intentions and environmental external benefits. Notably, the two interventions interact to shape consumers’ perceptions of environmental external benefits. Together, these studies advance a comprehensive understanding of near-expired bread purchases and offer empirical guidance for designing effective retail communication strategies to promote green consumption and reduce food waste. Full article

Meat quality serves as a pivotal determinant of consumer purchasing behavior and of the economic viability of the livestock industry; as such, research into its regulatory mechanisms is of critical significance for the development of modern agriculture. Traditional investigations into meat quality have predominantly centered on sensory and physicochemical assessments of ultimate phenotypic traits, thereby facing inherent limitations in systematically deciphering the intricate molecular regulatory networks underlying meat quality formation. By contrast, an integrated analysis of the transcriptome and metabolome effectively connects the cascade of “gene transcription—metabolic regulation—phenotypic determination,” which has emerged as a core methodological paradigm in contemporary research on the molecular mechanisms governing meat quality. This review systematically delineates the evolutionary trajectory and principal technological frameworks of meat quality evaluation systems, with a focused synthesis of recent advances achieved through combined transcriptomic and metabolomic analyses in the field of meat quality regulation. The scope of this review encompasses core transcriptional regulatory networks associated with meat quality attributes, pivotal metabolic pathways, signal transduction mechanisms, and protein degradation dynamics. Furthermore, the regulatory impacts exerted by genetic variation among breeds, nutritional modulation, rearing environments, and stress responses on meat quality characteristics are comprehensively elucidated. Integrative analysis reveals that combined transcriptome–metabolome approaches transcend the inherent limitations of single-omics investigations, systematically unraveling the hierarchical regulatory mechanisms governing fundamental meat quality traits, such as muscle fiber type differentiation, postmortem glycolytic progression, intramuscular fat deposition, and flavor compound accumulation. Such integrative strategies have facilitated the identification of functional genes and metabolic biomarkers with potential utility for the early prediction of meat quality outcomes. Concurrently, this review acknowledges persistent challenges confronting the field, including the absence of standardized protocols for multi-omics data integration, insufficient functional causal validation, and a discernible disconnect between research discoveries and practical industrial implementation. Building upon this comprehensive assessment, prospective directions for future multi-omics research in meat quality are proposed, accompanied by the formulation of an integrated end-to-end improvement framework spanning fundamental research, technological innovation, and industrial application. Collectively, this review provides a systematic theoretical foundation for the in-depth elucidation of mechanisms that determine meat quality and the precision-oriented regulation of quality-determining traits in livestock production practices, thereby offering substantial scientific guidance for quality improvement initiatives within the animal husbandry sector. Full article