Search Results (571)

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

Background: An individual’s health status is determined by the interaction of genetic, environmental, social, and lifestyle variables. Nutrition plays a fundamental role in disease prevention, a notion widespread among the informed public, which is keen on participating in initiatives for personalized recommendations informed by advanced biological data. Objectives: To evaluate whether a personalized nutrition service can produce measurable changes in nutritional behavior and biological outcomes, we established the GENIE digital platform. This platform delivers personalized online food shopping and recipe recommendation informed by integrated data from (i) biochemical blood markers, (ii) nutrigenetic profiles, (iii) gut microbiota composition, and (iv) consumer preferences. Methods: We conducted a single-arm study in a Spanish cohort that used a specific online retailer for food shopping, totaling 1177 participants. Group 1 (n = 620) had recommendations based only on the biochemical blood test; Group 2 (n = 357) included the nutrigenetic test; and Group 3 had the gut microbiome test (first batch, n = 200; second batch, n = 97). After one month of informed, tailored dietary advice, a quantitative evaluation of the experience was conducted. Results: The GENIE platform led to strong engagement (mean session time 7.07 min; +154% e-commerce use), with 71% of participants following at least part of the recommendations. This was associated with an increase in microbiome diversity in about 70% of participants, after just one month of guided recommendations. Conclusions: The GENIE platform represents a pragmatic model for translating nutrigenetic and microbiome data into actionable dietary recommendations, bridging the gap between scientific evidence and consumer behavior. Full article

This study explains cryptocurrency investment decisions by integrating personality traits, influencer credibility, and social influence within the Stimulus–Organism–Response (SOR) framework. Openness, extraversion, conscientiousness, influencer credibility, and social influence are positioned as stimuli; heuristic bias and herding behaviour as organism states; and cryptocurrency investment decision as the response, with risk tolerance acting as a serial mediating mechanism. Data were collected from 367 Indonesian retail cryptocurrency investors through an online survey and analysed using SEM-PLS. The measurement model demonstrates adequate reliability and convergent validity, while discriminant validity is supported by HTMT values below the recommended threshold. The results indicate that personality traits significantly influence heuristic bias, while influencer credibility and social influence increase herding behaviour. Heuristic bias and herding behaviour both positively affect risk tolerance and cryptocurrency investment decisions, with heuristic bias showing the stronger effect. Risk tolerance also positively influences investment decisions and mediates the effects of heuristic bias and herding behaviour. The model explains a substantial portion of the variance in cryptocurrency investment decisions (Adjusted R² = 0.623). These findings extend the SOR framework to cryptocurrency markets by highlighting how psychological traits and social cues shape risk tolerance and ultimately influence investment behaviour in volatile digital asset environments. Full article

The transition from cash to digital payment instruments is reshaping retail commerce across Europe unevenly, with Central and Eastern European (CEE) countries exhibiting both some of the fastest growth and some of the lowest baseline levels in online shopping participation. This study examines whether the development of digital payment infrastructure proxied by the share of individuals using internet banking (NetBank) is associated with e-commerce adoption across eleven CEE EU member states over the period 2014–2023, yielding a balanced panel of 110 country-year observations. Drawing on harmonised data from Eurostat, the World Bank, and the ITU, we estimate a two-way fixed-effects model with kernel-robust standard errors and a dynamic specification with a lagged dependent variable. The results indicate that a one-standard-deviation improvement in internet banking penetration is associated with a 6.2 percentage point increase in the share of online shoppers once country and year fixed effects are controlled for, a finding that is precisely estimated under kernel standard errors (p < 0.001). Income-group heterogeneity analysis suggests that this association may be substantially larger in lower-income CEE countries (β = 6.9, p = 0.006) compared to higher-income ones (β = 2.3, p = 0.554), consistent with the hypothesis that payment infrastructure improvements generate the highest marginal returns where baseline access is lowest. Romania, despite recording the steepest absolute growth in online shopping in the EU over the sample period (+33 percentage points), remains persistently below the CEE median, illustrating how payment infrastructure constraints can slow convergence even during periods of rapid digitisation. The findings should be interpreted as robust conditional associations rather than causal effects, given the limitations of macro-panel identification. Full article

The cooperation and integration of channels has promoted the evolution of the traditional dual-channel supply chain into an omni-channel supply chain. The omni-retailing modes can be divided into M mode and R mode depending on the difference in the online channel openers (which are the manufacturer and the retailer, respectively). This paper explores the inter-chain competition equilibrium and intra-chain coordination strategy of two different omni-channel supply chains (which adopt M mode and R mode, respectively). First, the EPEC (Equilibrium Problems with Equilibrium Constraints), MPEC (Mathematical Program with Equilibrium Constraints), and Nash decision models are used to depict the competition equilibrium of the two omni-channel supply chains for three cases: Both supply chains are decentralized, one is decentralized, and the other is integrated, and both are integrated, respectively. Then, the supply chain contracts are designed to coordinate the different kinds of omni-retailing modes. Last, this study verifies and extends the relevant conclusions by using numerical analysis. The results show that: service cooperation is the dominant strategy of omni-channel supply chains when there exists inter-chain competition; R mode is gradually dominating with the deepening of the service cooperation; there exists the parameter interval to make it so that M mode and R mode each becomes the dominant mode in supply chain competition; supply chain coordination is the dominant strategy of inter-chain competition; and the wholesale price contract with subsidy designed in this paper can realize the coordination of the omni-channel supply chain, effectively. But when the two omni-channel supply chains all adopt the coordinative strategy, the supply chain system may fall into the “prisoner’s dilemma”. Full article

Buying local food can support local economies, but the factors that drive these purchases in specific retail settings, such as local shops, are still not well understood. Research that considers sustainability alongside factors such as trust and perceived retail access remains limited. This study examines how dimensions of sustainability, trust, and perceived retail access influence purchase intention, using the Theory of Planned Behaviour (TPB). Data were collected via an online survey in Međimurje County, Croatia (n = 303), and analysed using partial least squares structural equation modelling (PLS-SEM), which explained 55% of the variance in purchase intention. The results show that, in addition to attitude, subjective norm, and perceived behavioural control, only the environmental dimension of sustainability significantly influences purchase intention. These findings suggest that consumer decision-making in local shops is more strongly shaped by internal evaluations and perceived environmental benefits than by trust or access. The study provides channel-specific evidence from an intermediated short food supply chain (SFSC) format and shows that the relevance of extended TPB predictors varies across retail contexts. Full article

Continuous customer activity generated by modern digital platforms drives the evolution of behavioral segments over time. Traditional customer segmentation methods typically rely on periodic batch analysis of historical data, producing static snapshots that may quickly become outdated and fail to capture emerging behavioral patterns. This paper presents a monitoring-oriented framework for detecting customer segment evolution and generating timely notifications about meaningful structural changes in the customer population. The proposed system continuously ingests user activity events, incrementally updates customer profiles, and periodically recomputes behavioral segments using fixed-k KMeans clustering over standardized recency, frequency, and monetary (RFM) features. To improve robustness and interpretability, the framework incorporates adaptive event scoring, stability-aware segment validation, drift-aware centroid matching, and persistence-based filtering of transient changes. These mechanisms reduce noisy alerts caused by repeated clustering updates while preserving meaningful signals about evolving customer behavior. The framework is evaluated on the Online Retail II and Instacart datasets under streaming simulation conditions. Experimental results show that the proposed approach maintains stable clustering structures, identifies persistent segment changes, and uncovers economically meaningful customer groups. Compared with static segmentation and periodic clustering baselines, the framework improves clustering quality while enabling continuous monitoring of segment evolution. Overall, the results suggest that adaptive monitoring can extend traditional customer segmentation into a practical continuous analytics process for moderate-scale dynamic environments. Full article

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often suffer from texture squeezing and boundary artifacts in challenging scenarios, such as long sleeves and tucked-in garments, especially under high-resolution settings. In this work, we propose StageAttn-VTON (Stage-wise Attentive Virtual Try-On), an appearance flow-based framework that improves structural coherence and visual fidelity through stage-wise deformation modeling. Specifically, garment warping is decomposed into three stages—coarse alignment, local refinement, and non-target region removal—which mitigates the coupling between competing objectives, such as smooth texture preservation and accurate structural alignment. Furthermore, we introduce a self-attention module in the image synthesis stage to enhance global dependency modeling and capture long-range garment–body interactions. Experiments on VITON-HD and the upper-body subset of DressCode demonstrate that StageAttn-VTON achieves consistently strong performance against representative warping-based and diffusion-based baselines. In addition, qualitative comparisons show that the proposed method better alleviates deformation artifacts in challenging regions such as sleeves and waist areas. Full article

This study investigated consumer expectations and perceived quality of strawberries across different geographical contexts to identify the main drivers of purchasing behavior within a cross-country framework. An online survey was conducted among consumers in Italy, Germany, and Turkey to explore consumption habits, purchasing channels, sensory expectations, product perceptions, and willingness to pay (WTP) for specific product attributes. Results confirmed a high level of consumer appreciation for strawberries across all countries, primarily driven by their sensory characteristics. However, purchasing behavior and consumption patterns were strongly influenced by cultural and market-related factors. Visual attributes were confirmed to be key cues guiding product choice; however, label indications related to sensory traits and functional properties exerted a greater influence. Flavor, firmness, and overall taste balance represented critical determinants of consumer satisfaction. Differences across demographic groups were also observed, with younger and male consumers reporting lower levels of satisfaction with key sensory attributes, including juiciness, aroma, and freshness. Cross-country comparisons revealed heterogeneous WTP patterns, with Turkish consumers showing a greater propensity to pay premium prices for quality-related, local, organic, and environmentally friendly attributes compared with German and Italian consumers. Overall, the findings highlight the combined influence of sensory quality, cultural context, and sociodemographic characteristics in shaping strawberry perception and purchasing behavior. These insights may support breeders, producers, and retailers in developing targeted product strategies and market positioning across different geographical areas and consumer segments. Full article

Context. The grocery sector is undergoing a massive shift in consumer behavior, with global chatbot usage projected to reach 8.4 billion units by 2024—surpassing the total human population—and online grocery revenue per shopper expected to hit USD 449.00 by 2023. In this competitive landscape, small grocery stores must adopt AI-driven tools to modernize their operations. However, these businesses often face significant inefficiencies in manual inventory management, resulting in errors and reduced competitiveness. Objective. This research aims to develop and validate a chatbot application using Large Language Models and Retrieval-Augmented Generation (RAG) for operational management of grocery stores. Method. The method employed a quantitative experimental approach with a five-component system architecture: a web interface, a FastAPI API, a Mistral-7B-Instruct-v0.2 model, a dynamic SQL generator, and a custom RAG application with an FAISS vector database, all integrated through SQLAlchemy 2.0.40. Results. The results demonstrate that a chatbot achieves an average response time of 0.08 s with 80% overall accuracy, showing a 96.2% improvement in information query time and a 92.9% reduction in operational errors. Conclusions. Major conclusions suggest that the chatbot system is effective for retail environments and has the potential to enhance the operational efficiency of grocery stores, serving as a foundation for future research in applied conversational assistance. Full article

The rapid integration of AI customer service in e-commerce raises an important managerial question: Can AI effectively reduce product-related information asymmetry and improve sales performance across different product types? While prior research highlights both the uncertainty-reducing benefits of information and the risks of algorithm aversion, little is known about how AI customer service performs under varying levels of product uncertainty and information asymmetry. Using a difference-in-differences design with fixed effects across time, products, shops, and categories, we examine the impact of replacing customer service with AI on sales outcomes, distinguishing between search and experience goods. We further test how the depth and breadth of product information moderate these effects. Our findings indicate that AI customer service reduces sales for experience goods but not for search goods, unless accompanied by sufficient informational depth and breadth. We argue that this effect arises because AI technically inherits and amplifies the information asymmetry inherent in experience products, while greater informational depth and breadth of product information can mitigate this amplified asymmetry. Additionally, we find that this mitigating effect is more pronounced among products with high return rate. These findings clarify when AI-generated information mitigates product uncertainty and when it exacerbates it. Our results provide actionable guidance for firms seeking to deploy AI strategically in digital commerce environments. Full article

As a key branch of online-to-offline (O2O) retail, the online-to-home (O2H) model enables goods acquisition through instant delivery, fundamentally reorienting urban retail spatial configuration from “accessibility” to “efficiency”. Using Jincheng, the main urban area of Tianjin as a case study, this research formulated a Goods Acquisition Efficiency (GAE) index to quantify the time-based efficiency gain of O2H over the conventional OIS (offline in-store) mode. An integrated XGBoost-SHAP approach was utilized to examine the spatial variations in efficiency gains and their associated factors. The results reveal that: (1) Efficiency gains follow a concentric pattern, increasing from the core to the periphery (Inner: 0.18; Middle: 0.20; Outer: 0.26), suggesting that O2H provides more pronounced benefits in peripheral areas where retail provision remains limited; (2) The dominant factors vary across zones: environmental attributes in the Inner Urban Zone, transportation and economic factors in the Outer Urban Zone; (3) O2H and OIS exhibit a complementary rather than substitutive relationship—physical stores in inner-city areas can maintain their current configuration, while peripheral zones may benefit from enhanced O2H fulfillment or conversion to micro-fulfillment centers. The GAE index and zonal comparison framework offer methodological references for differentiated optimization of urban retail networks. Full article

In the online shopping context, the proliferation of digital platforms has contributed to an increase in impulsive buying behavior (IBB), which can sometimes lead to regret. This study aims to explore the intrinsic and extrinsic stimuli that influence consumers’ online impulsive buying behavior, which subsequently affects their post-purchase cognitive dissonance, with the moderating role of price consideration (PC). The conceptual framework was formulated using the Stimulus–Organism–Response (S-O-R) model. A total of 813 responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived utilitarian value (PUV), perceived enjoyment (PE), fear of missing out (FOM), and green trust (GT) positively impact online impulsive buying behavior (IBB), which, in turn, positively impacts post-purchase cognitive dissonance (PCD). Moreover, a significant moderating role of PC is found in the relationship between IBB and PCD, suggesting that consumers with low price consideration tend to regret their impulsive buying more. The findings provide insights that guide online retail sellers and digital marketers to develop or implement customized strategies based on the intrinsic and extrinsic stimuli that influence customers’ impulsive buying and subsequent post-purchase cognitive dissonance. Full article

In recent years, consumer preference for sustainable food product attributes has increased. This research aims to investigate the moderating influence of consumers’ shopping motives on the interplay among gender, preferences for sustainable attributes, and brand loyalty. An online cross-sectional study was undertaken with 506 food consumers. Structural equation modeling was used to estimate direct, indirect, and moderating effects between the studied constructs and variables. We found that high quality positively moderates the relationships between gender and sustainable attributes and between gender and brand loyalty, while price as a shopping motive (marginally) negatively moderates these relationships. These findings suggest that brand managers aiming to strengthen brand loyalty among sustainability-oriented consumers—particularly where gender-based differences emerge—may benefit most from pairing sustainability positioning with strong-quality retail settings. In contrast, price-focused retail settings are less effective for activating sustainability-based brand loyalty. Full article

In many organizations, business intelligence systems support analytical reporting and operational decision making. As data volumes grow and analytical tasks become more complex, architectures based on centralized processing pipelines increasingly face limitations related to scalability and timely response. These challenges motivate the development of alternative architectural approaches capable of operating efficiently in data-intensive environments. This study presents a modular multi-agent business intelligence framework that distributes analytical tasks across autonomous agents and applies lightweight reinforcement learning at the decision-making stage. The analytical workflow is decomposed into agents responsible for data collection, preprocessing, analytical modeling, and decision execution. Decision adaptation relies on localized policy updates driven by operational feedback, which avoids complex learning coordination and helps preserve system stability and interpretability. The proposed framework is evaluated using real-world transactional data from an electronic commerce setting. Experimental results show that the approach consistently outperforms centralized analytical pipelines and non-agent machine learning baselines in terms of processing efficiency, classification accuracy, and balanced classification performance. Threshold-independent evaluation further confirms stronger discriminative behavior across varying decision thresholds. In addition, stability analysis across repeated experimental runs indicates reduced performance variance and more predictable system behavior. These findings suggest that the proposed multi-agent business intelligence framework provides a practical and scalable alternative to centralized analytical architectures for data-intensive decision-support environments, while maintaining the robustness and transparency required in enterprise systems. The evaluation is limited to a single dataset and a classification task, and results should be interpreted within this scope. Experiments on the Online Retail dataset (UCI Machine Learning Repository) show an average accuracy of 0.89 ± 0.012 (baseline: 0.74 ± 0.029) and decision latency of 94 ± 9 ms (baseline: 137 ± 16 ms) across 10 independent runs, indicating stable behavior under repeated execution. Full article