Search Results (1,869)

Under a dual-carbon background, promoting substantive green innovation in manufacturing enterprises has become a central topic in green transition research. This paper constructs an evolutionary game model involving manufacturing enterprises and consumers under market mechanisms and government intervention to analyze the evolutionary patterns and stability conditions of their strategic choices. Using case data and numerical simulations, it explores the role of government guidance in addressing market failures and fostering green innovation in manufacturing. The findings reveal the following: (1) Under market mechanisms, system evolution is influenced by multiple factors. If enterprises prioritize short-term gains by accelerating symbolic green innovation, consumer trust erodes, leading to a shift toward traditional consumption and ultimately driving the system toward market failure. (2) Under government intervention, incentive subsidies must reach a specific threshold to effectively guide manufacturers toward substantive green innovation. Such subsidies also lower the marginal cost of low-carbon consumption, enhancing consumer willingness to purchase green products. Furthermore, government regulation demonstrates positive promoting effects on the green behaviors of both manufacturers and consumers, with a more pronounced impact on the former. (3) The policy combination of incentive subsidies and government supervision significantly shapes evolutionary trajectories through a synergistic mechanism of “reward incentives and regulatory rigidity.” Policy mismatches may trap the system in market failure. Only when subsidy intensity sufficiently compensates for innovation costs and regulatory capacity exceeds enforcement efficiency thresholds can the system stably evolve toward a substantive green innovation, low-carbon consumption state, fostering a virtuous cycle of supply–demand synergy. Full article

In a duopoly market comprising an e-tailer and a physical retailer, we develop analytical models to explore the e-tailer’s strategy of introducing a virtual showroom to alleviate consumers’ fit uncertainty. While a virtual showroom can increase online consumer traffic, consumers may instead purchase from physical stores (i.e., webrooming) or reduce offline browsing before buying online (i.e., showrooming). Our findings indicate that consumers with a moderate online hassle cost tend to showroom when offline travel cost is not high, whereas those with a high online hassle cost rely on the virtual showroom for webrooming. Therefore, we identify the conditions under which a virtual showroom should be introduced. Specifically, when the cost of travel to the store is relatively high, the e-tailer can profit from introducing a virtual showroom if its return-handling cost is not too low. Under moderate travel cost, the e-tailer can leverage a virtual showroom to weaken competition if the return-handling cost is not too high, enabling both retailers to benefit. Notably, the impact of product fit probability on virtual showroom strategy decisions reverses between high and moderate travel costs. Under specific conditions, the virtual showroom can achieve a “win-win-win” situation for the e-tailer, the physical retailer, and consumers. Full article

This study aims to investigate the antecedents to ‘green personal care product’ (GPCP) awareness and purchase intention. Based on the theory of planned behavior (TPB) and sustainable consumption (SC), a conceptual model is developed and tested. Data collected from 455 Gen Z consumers from India were analyzed using structural equation modeling. The results confirmed a positive association of subjective norms, perceived behavioral control (PBC), and social media influence with attitude towards GPCPs. The findings indicate that attitude and health consciousness are positively associated with awareness, which in turn leads to purchase intention. Further, the findings support the positive role of environmental consciousness in driving consumers to engage in pro-environmental behavior. This study underscores the importance of health consciousness and environmental consciousness in driving individuals to engage in green purchase behavior. The findings from this study provide valuable insights into an individual’s attitude towards GPCPs and awareness of GPCPs, especially in the context of environmental sustainability. This study recommends actionable strategies to policymakers and marketers to advertise the benefits of consuming healthy products. The implications for theory and practice are discussed. Full article

Commercial determinants of health (CDoHs) describe how corporate practices influence population health. This scoping review aimed to characterize the extant evidence base regarding how CDoH in the sugar-sweetened beverage (SSB) industry affects health and health-related outcomes among Latinx populations in the United States of America (USA). The present study was conducted in accordance with the JBI methodology for scoping reviews. Overall, 1236 references were identified and imported for screening. After duplicate removal, screening, and full-text eligibility assessment, 33 studies met all inclusion criteria. SSB marketing and advertising was the most frequently examined CDoH (61%), including advertising exposure, messaging strategies, and warning label interventions. SSB taxation studies projected reductions in consumption and obesity prevalence. Outcomes associated with health focused primarily on perceptions of marketing and purchasing intentions (94%). Additional studies examined the impact on knowledge, attitudes, beliefs, and behaviors (e.g., purchasing and consumption of SSBs) (66%), while a few studies included chronic disease (27%) or healthcare outcomes (6%). Evidence highlights several gaps in CDoH research associated with SSBs, with 94% of the included studies focused on understanding marketing exposure, signaling a need to examine other domains of CDoH, SSB industry practices, and impacts on health disparities. Findings suggest that structural policy interventions such as taxation and stronger regulation of commercial practices are necessary to address higher exposure to marketing and consumption of SSBs among Latinx populations in the USA. Full article

This study aims to provide insight into consumer purchasing decisions by integrating eye-tracking data with forecasting techniques. First, the study investigates how consumption motives (hedonic vs. utilitarian) and purchasing purposes (for oneself vs. for others) influence visual attention and decision-making processes. An experimental design was conducted with 128 participants in a simulated online shopping environment, where eye-tracking data were collected based on fixation counts and durations across defined Areas of Interest (AOIs). Second, a total of 20 input features were collected, comprising fixation counts and fixation durations for 10 review-related Areas of Interest (AOIs), and these features were evaluated across the experimental scenarios, while the binary output variable represented the participant’s purchase decision. These biometric features, together with scenario information, were used to forecast purchasing decisions using six machine-learning methods, including Artificial Neural Networks, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Logistic Regression. The results indicate that consumers’ visual attention aligns with their consumption motives and purchasing purposes, revealing distinct gaze patterns across different scenarios. In the forecasting phase, the accuracy of different methods for predicting purchasing decisions using review-related eye-tracking data is evaluated. Support Vector Machines achieved the highest overall accuracy, approximately 59–60% across the evaluated datasets, compared with a validation-specific majority-class baseline of 53.85%. This corresponds to a modest improvement of approximately 5.15–6.15 percentage points over the naive benchmark. Overall, the findings suggest that objectively recorded review-related eye-tracking data can be operationalized as behavioral input features in a machine-learning-based purchase-decision classification framework, highlighting the methodological value of integrating eye-tracking insights with consumer behavior forecasting. Full article

Behavioral interventions are widely used to promote sustainable consumption, but their effectiveness under high-friction real-world conditions remains uncertain, especially when multiple tools are combined. We report a quasi-experimental natural field study conducted in a busy urban café in Tel Aviv, Israel, examining the isolated and combined effects of a localized identity-based social-norm cue and a small financial incentive on reusable cup adoption. Across four consecutive weeks and 9414 hot-beverage transactions, a baseline week was followed by a norm condition, a 1 NIS discount condition, and a combined condition. Reusable cup use increased from 3.33% at baseline to 3.59% in the norm week, 4.19% in the incentive week, and 3.72% in the combined week, but none of these changes reached statistical significance. The financial incentive produced the largest descriptive increase, whereas the combined intervention did not outperform the incentive alone. Across the intervention period, reusable cup use exceeded the number expected under the baseline rate by approximately 35 purchases. These bounded null findings suggest that low-cost behavioral tools may yield only modest gains in convenience-driven consumption settings and that combining policy tools does not necessarily generate additive effects. The study contributes ecologically grounded evidence on the boundary conditions of sustainable behavior change and highlights the importance of testing behavioral policies under realistic implementation constraints. Full article

Culinary tourism is increasingly conceptualized as a strategic domain of destination competitiveness, in which gastronomic experiences serve as mechanisms for cultural representation and localized value creation. However, existing research remains fragmented in explaining how multidimensional culinary motivations translate into specific consumption behaviors, particularly in emerging destinations. Addressing this gap, this study develops and tests a motivation–behavior linkage framework grounded in push–pull motivation theory, conceptualizing culinary motivations as heterogeneous drivers with differential effects on intention to purchase local food. A sequential mixed-methods design was employed, beginning with an initial qualitative phase to refine measurement constructs, followed by a quantitative survey of 396 domestic tourists with prior culinary experience in Vietnam’s Mekong Delta. Data were analyzed using reliability assessment, exploratory factor analysis, and multiple regression modeling. The findings reveal a differentiated structure of influence: cultural experience, sensory appeal, and health concerns significantly enhance purchase intention, with cultural experience and sensory appeal emerging as the most influential predictors. In contrast, interpersonal and prestige motivations are non-significant, indicating that experiential and functional values outweigh social-symbolic drivers in this context. The study contributes by advancing push–pull theory through a behavior-specific, mechanism-based linkage; identifying context-dependent boundary conditions in an emerging destination; and refining culinary motivation by distinguishing experiential–functional from social-symbolic drivers. These insights inform more targeted strategies for promoting local food consumption and sustainable culinary tourism development. Full article

Greenwashing in food marketing undermines consumer trust and impedes Sustainable Development Goal 12 (SDG 12). While prior research has established linkages between greenwashing perception, green skepticism, and purchase intention, no publicly deployed decision support tool has been developed for practitioner use. This study applies Design Science Research (DSR) methodology to translate validated behavioral models into a deployable decision support system rather than re-testing established relationships. We present the development, deployment, and evaluation of the Greenwashing Advertising Message Assessment (GWAMA), a web-based DSR artifact grounded in a validated Stimulus–Organism–Response (S-O-R) structural equation model. GWAMA integrates factor-loading-weighted composite scoring with SEM-derived parameters to generate real-time greenwashing risk diagnostics for food advertising messages. Usability was evaluated with 150 Thai food industry professionals using a Technology Acceptance Model (TAM) instrument applied to the live system. Results provide indicative evidence of stakeholder acceptance, with high perceived usefulness, ease of use, and intention to use. This study contributes by demonstrating how validated behavioral models can be translated into a publicly deployable decision support artifact, with practical implications for sustainable marketing governance and SDG 12 implementation in emerging economies. Full article

Background/Objectives: To promote salt reduction before health problems arise, it is important to understand factors associated with reduced-salt product purchasing among consumers with low interest in such products. This study examined psychological determinants of intention to purchase reduced-salt products among Japanese adults who were not actively purchasing them. Methods: An exploratory sequential mixed-methods design was used, including qualitative interviews followed by a cross-sectional web-based survey of 800 men and women aged 18–59 years in Japan. Participants were categorized into precontemplation and contemplation stages based on the transtheoretical model. Associations between purchase intention and three Integrated Behavioral Model categories—attitude, perceived norm, and personal agency—were examined. Percentage-to-gain values were calculated for each belief item. Results: All three category scores were significantly associated with purchase intention, with attitude showing the strongest association (OR = 12.56, 95% CI: 6.93–22.79). In stratified analysis, attitude showed a stronger association in the precontemplation stage (OR = 18.40, 95% CI: 8.51–39.78), whereas no category score was significantly associated with purchase intention in the contemplation stage. In the precontemplation stage, relatively high percentage-to-gain values were observed for holistic wellness-oriented beliefs and product availability in usual supermarkets. Conclusions: Attitude was most strongly associated with intention to purchase reduced-salt products, particularly in the precontemplation stage. Holistic wellness-oriented beliefs and product availability may represent promising targets for future communication or food environment interventions. These findings may provide a basis for future studies testing strategies to increase actual purchases of reduced-salt products and reduce salt intake. Full article

Corporate social responsibility (CSR) has grown in importance as a means for companies to engage with customers who are increasingly environmentally and socially conscious. This study examines how CSR affects sustainable consumer buying tendencies, emphasizing the mediating role of consumer happiness and corporate image. Scientists contend that customers are more inclined to support businesses whose values align with CSR programs that foster positive feelings and trust. Therefore, a conceptual model was developed by following cognitive consistency theory. Data from 504 customers in Pakistan, an expanding market where awareness of sustainability issues is continually rising, were gathered to test this. The results demonstrate that CSR has a significant and favorable influence on consumer purchasing preferences, as assessed using partial least squares structural equation modeling (PLS-SEM). Crucially, the proposed relationship is not only direct: CSR improves consumer happiness and corporate image, leading to better purchase decisions. By emphasizing the emotional and perceptual processes involved, these findings provide a better understanding of how CSR influences consumer behavior. The study demonstrates how CSR can encourage more conscientious consumption habits from a sustainability standpoint, supporting Sustainable Development Goal 12 (Responsible Consumption and Production). Findings suggest that well-thought-out CSR programs may truly affect how and why customers make purchase decisions, especially in emerging countries, going beyond reputation-building. Full article

Property taxation serves as a critical instrument for fiscal efficiency and equitable distribution, yet implementation faces significant challenges including valuation inaccuracies, insufficient administrative capacity, and diminished public trust. Indonesia’s Land and Building Tax (PBB-P2) utilizes the Sales Value of Taxable Objects (NJOP) as an administrative proxy for market value, which frequently deviates from actual land prices. These disparities create horizontal inequities, diminish local revenue potential, and generate taxpayer resistance, especially in decentralized regions with constrained technical resources. This research presents the Spatially Adaptive Land Value Determination (SALAD) model as a comprehensive framework for enhancing property tax governance and modernizing fiscal cadastre systems. Unlike conventional mass appraisal methods, SALAD integrates spatial zoning, assessment ratio analysis, land-use characteristics, and the Index of Developing Villages (IDM) with socio-economic indicators including purchasing power and community fiscal behavior. The model incorporates structured social validation to improve public acceptance. Field validation in Lebak Regency employed mixed-methods design with surveys of 75 respondents across 20 villages and interviews with village heads and tax officials. Results demonstrate that transparency, fairness, and visible public benefits are essential for community support. Validation indices vary significantly by IDM category (ANOVA: F = 4.23, p = 0.03 for economic; F = 3.81, p = 0.04 for institutional), confirming that the SALAD model’s adaptive mechanism is empirically grounded. Full article

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems remain a widely deployed defense against automated abuse, but advances in machine learning have reduced the effectiveness of traditional challenge-based designs and exposed limitations in proprietary risk-scoring systems. This paper presents an adaptive, reinforcement learning-based CAPTCHA defense framework for high-security web applications. The proposed system formulates bot detection as a partially observable Markov decision process and uses a Proximal Policy Optimization (PPO) agent with Long Short-Term Memory to analyze streamed behavioral telemetry, including mouse movements, clicks, keystrokes, and scrolling, over sequential interaction windows. During the observation phase, the agent can continue observing or deploy a honeypot as an early-intervention and evidence-gathering action; after sufficient session evidence is accumulated, it can issue graded CAPTCHA challenges, allow a session, or block it. To complement the sequential agent, the framework also includes an XGBoost classifier that produces a session-level human-likelihood score as a supervised benchmark. The accompanying reinforcement learning environment and code base are publicly available, allowing future researchers to train, evaluate, and extend adaptive CAPTCHA policies as bot capabilities evolve. Experiments conducted on a sandbox ticket-purchasing web application demonstrate that the proposed methodology achieves strong preliminary performance on human-generated sessions and real bot sessions produced by scripted, replay-based, and Large Language Model (LLM)-powered agents. Among the evaluated reinforcement learning algorithm variants, Soft PPO achieved the best performance with 97.7% accuracy, 100% precision, and a 97.6% F1 score. Correspondingly, the XGBoost classifier achieved 99.48% accuracy, a 1.000 ROC-AUC (receiver operating characteristic area under the curve), and a 0.9919 F1 score. Our results indicate that sequential reinforcement learning can support accurate and low-friction bot detection, while the accompanying classifier provides a complementary binary benchmark. Compared to proprietary systems, the proposed framework emphasizes transparency, auditability, and explicit sequential decision-making rather than black-box risk scoring. Overall, this work introduces a publicly available, open, and adaptive CAPTCHA defense framework that supports transparent experimentation with behavior-based bot mitigation while also identifying the remaining limits that must be addressed before commercial deployment. Full article

State-owned grain enterprises (SOGEs) in China serve dual roles as implementers of policy-oriented stockpiling and as market-oriented operators. Their purchasing and selling behaviors profoundly influence grain price volatility, which in turn affects farmers’ incomes and the stability of agricultural operations. Based on provincial-level monthly panel data from January 2013 to December 2023, this study constructs price volatility indicators for the grain procurement and wholesale market. A fixed-effects model examines the impact of SOGEs’ purchasing and selling behaviors on grain market price volatility, followed by analyses of mechanisms and heterogeneity. The findings reveal that, during the centralized market arrival period, the purchasing behavior of SOGEs generates a significant demand shock, amplifying price volatility in the grain procurement market. By contrast, their selling behavior exhibits counter-cyclical adjustment characteristics in the wholesale market, mitigating price deviations through inventory regulation and guiding price fluctuations back toward supply-and-demand fundamentals. Mechanism tests indicate that market power, price expectation guidance, and the strategic responses of private enterprises are the primary channels through which SOGEs’ behaviors affect price volatility. Heterogeneity analysis shows that external shocks and regional development disparities significantly influence the market stabilization effects of SOGEs’ purchasing and selling behaviors. This study provides mechanism-level empirical evidence for understanding the price influence of SOGEs in grain markets, offering valuable insights for improving counter-cyclical grain regulation policies. Full article

This study formulates a corporate abatement decision problem under carbon price uncertainty as a continuous-time stochastic control model. To this end, the carbon price is modeled as a geometric Brownian motion, while abatement capacity is accumulated through costly effort and depreciates over time. Specifically, the firm chooses its abatement effort to maximize expected discounted profits while accounting for allowance purchasing costs, compliance-related penalties, abatement costs, and potential revenues from surplus allowances. The paper contributes by integrating stochastic carbon prices, endogenous abatement-capacity accumulation, allowance-shortage/allowance-surplus asymmetry, and surplus allowance monetization into a unified corporate abatement framework. Applying the dynamic programming principle, the associated Hamilton–Jacobi–Bellman equation is derived, and the bounded optimal abatement effort is characterized in feedback form. Since the resulting nonlinear HJB equation generally does not admit a closed-form solution, a finite-difference scheme with damped policy iteration is used for numerical analysis. The results show that optimal abatement effort is strongly state-dependent. Higher carbon prices strengthen abatement incentives in the allowance-shortage region, whereas effort declines sharply after reaching allowance neutrality if surplus allowances cannot be monetized. Moreover, partial monetization of surplus allowances significantly increases abatement effort in the surplus region and can shift firms’ behavior from passive compliance to active low-carbon investment. Overall, these findings suggest that surplus allowance monetization plays an important role in sustaining firms’ abatement incentives under carbon price uncertainty. Full article

This study investigates how parasocial interactions with travel influencers shape consumer–brand identification and purchase intention within hospitality-related virtual communities. Grounded in social influence theory and the literature on parasocial interaction, the study examines the mediating roles of perceived information quality and a sense of belonging in influencer-led digital environments. Data were collected from 940 consumers and analyzed using confirmatory factor analysis and structural equation modeling. The findings demonstrate that parasocial interaction significantly enhances perceived information quality and consumers’ sense of belonging to influencer-led virtual communities, thereby strengthening consumer–brand identification and purchase intention. The results further suggest that influencer-led social environments function not only as promotional channels but also as psychologically meaningful communities that shape consumer perceptions and behavioral outcomes. By integrating parasocial interaction, virtual community belonging, and consumer–brand identification within a hospitality context, this study contributes to the growing literature on influencer marketing and digital consumer behavior. Full article