Search Results (710)

High shrinkage-related defect rates in aluminum die casting reduce effective production capacity. They also create energy-intensive re-melting loops, which weaken production planning reliability and environmental performance. This study examines how Green Lean Six Sigma can stabilize a resource-constrained Small and Medium-Sized Enterprise (SME) die-casting process and translate quality improvement into measurable capacity recovery and Scope 2 electricity-related carbon savings. Based on a 10-month case study, the Define–Measure–Analyze–Improve–Control (DMAIC) framework was integrated with factorial ANOVA, the Response Surface Methodology (RSM), one-way analysis of variance (ANOVA) and statistical process control (SPC). These methods supported process parameter identification, operating-window development and shop-floor process stabilization. The analysis identified the filling speed and mold temperature as significant shrinkage drivers, developed a mold temperature control map, and determined the standardized filling speed at 800 mm/s. The intervention reduced the shrinkage defect rate from 7.19% to 1.46%, reduced the overall scrap rate from 7.60% to 2.71%, and improved the overall sigma level from 2.93 to 3.42. This yield improvement generated a 4.89 percentage-point yield-equivalent capacity gain, avoided 4401 kWh of re-melting electricity, reduced Scope 2 emissions by 2.36 t CO₂e, and generated gross annualized savings of RMB 249,214 (USD 35,783). Considering a one-time implementation cost of RMB 31,445 (USD 4515), the first-year net saving was RMB 217,769 (USD 31,268). The findings show that accessible statistical process control methods can provide SMEs with a resource-efficient pathway to improve process stability, capacity utilization and electricity-related environmental performance before investing in advanced digital technologies. Full article

Young adults are often treated as a single digital consumer segment, although adjacent age cohorts may differ in e-commerce use and attitudes toward circular consumption. This study compares respondents aged 18–24 and 25–34, and examines whether age cohort or sustainability orientation is more closely associated with e-commerce behaviors and circular product acceptance. The analysis uses a respondent-level dataset harmonized ex post from five separate convenience-sample questionnaire surveys (n = 482 young adults). Respondents were not linked across sources; each hypothesis was tested on the subsample with the relevant variables observed, and the sustainability and circular-acceptance tests draw on n = 179 respondents from two of the five sources. Analyses used Mann–Whitney U tests, Spearman correlations, Benjamini–Hochberg false discovery rate correction, robust ordinary least squares models, and selected ordinal logistic models. Respondents aged 25–34 reported more frequent online shopping, whereas respondents aged 18–24 showed more positive personalization attitudes only before controls were introduced. No meaningful age differences were found in sustainability orientation, ecological purchase willingness, or willingness to buy refurbished products, while sustainability orientation was consistently associated with both circular-acceptance outcomes. The Age-Differentiated Circular Acceptance in Retail E-commerce framework is proposed as a conceptual synthesis: age relates to selected digital routines, while sustainability orientation is a cross-cohort correlate of circular product acceptance. The findings suggest that retailers should prioritize sustainability orientation, product-condition transparency, and risk-reducing information over broad age-based segmentation. Full article

Generative AI shopping assistants are becoming a primary touchpoint for online consumers, yet they often produce convincing but inaccurate content, a phenomenon known as AI hallucination that poses an under-studied risk to consumer trust in e-commerce. This study explains that phenomenon by building and testing a moderated mediation model grounded in Expectation Violation Theory, Epistemic Vigilance Theory, and Algorithmic Trust Repair Theory. A sequential, exploratory mixed-methods design was used: a qualitative phase identified the dimensions and configurational pathways of consumer trust withdrawal using the Gioia methodology and fuzzy-set Qualitative Comparative Analysis, and a subsequent large-scale quantitative phase tested and refined the resulting model across a multi-country European sample using partial least squares structural equation modeling and Necessary Condition Analysis. The results show that exposure to hallucinations triggers expectation violation, activating epistemic vigilance and reducing perceived AI competence; this sequence drives trust recalibration, reflected in lower continued-use and purchase intentions and greater negative word-of-mouth. AI literacy, prior trust, and transparency cues significantly moderate these relationships, and structural trust repair mechanisms, namely retrieval-augmented generation and uncertainty disclosure, prove more effective than purely communicative repair strategies. Theoretically, this study advances a dynamic account of trust recalibration in AI-mediated commerce; practically, it offers concrete guidance for platform design and regulatory policy under the EU AI Act. Full article

Artificial intelligence (AI)-powered online shopping is profoundly reshaping the way in which consumers engage with sustainable clothing. However, the relationships between digital multisensory cues and perceived quality with sustainable consumption intention still need to be explored. Based on the Stimulus–Organism–Response (S-O-R) framework, the present study explores the relationships of digital multisensory cues and perceived quality with consumers’ flow experience, pleasure, and responses (e.g., engagement, loyalty, and sustainable consumption intention) in the context of AI-powered online shopping using a cross-sectional, scenario-based, assisted recall survey. A total of 571 valid responses were retained for analysis. Partial least squares structural equation modelling (PLS-SEM) was adopted for data analysis. The findings indicate that digital multisensory cues showed a positive relationship with pleasure, whereas perceived quality was positively associated with both flow experience and pleasure. Flow experience was positively linked to pleasure, which was further associated with engagement and sustainable consumption intention. Statistically significant indirect effects on sustainable consumption intention through pleasure were observed for flow experience, digital multisensory cues, and perceived quality. The results of this study offer practical implications for retailers seeking to design emotionally engaging AI-powered shopping experiences that may support sustainable consumption intention. Full article

Large language models are increasingly embedded in e-commerce assistants that recommend links to shopping destinations. When the candidate set contains adversarially crafted phishing URLs, a fluent model can recommend a malicious link with the same confidence it recommends a legitimate one, turning a helpful assistant into a delivery channel for fraud. Rather than treating phishing defense as per-link URL classification, this work frames the problem as recommendation-level exposure minimization: the output set itself must be secured, and a recommendation is counted as useful and safe only when it surfaces a benign destination and exposes no phishing URL. We first organize phishing URL constructions into four families spanning brand padding, typosquatting, homograph substitution, and subdomain impersonation, and use them to build candidate pools that mix benign links with plausible distractors. Evaluating four widely used open models under prompt-only defenses reveals a persistent gap: the strongest prompt baseline reaches only a 47.4 percent four-model average useful-safe rate, and weaker models fall below 20 percent even after careful prompting. We then present SAFER, a Security-Adaptive Filtering framework for Exposure-Minimized URL Recommendation. SAFER separates contextual selection from safety admission by coupling a deterministic lexical and structural pre-filter, an evidence-augmented single reasoning pass, and a deny-by-default post-verification stage with a deterministic fallback. The same deterministic URL evidence is injected before generation to condition the model’s reasoning and reused after generation to constrain which model-selected URLs may reach the user. SAFER issues exactly one model call per query, matching the prompt baselines, so its gains come from structure rather than additional inference. Across the four models SAFER raises the average useful-safe rate to 87.0 percent, a 39.6-point improvement over the best prompt baseline, and the deny-by-default stage yields a positive net gain for every model, largest where the model is weakest. Shrinking the deterministic layer’s brand coverage in a held-out analysis degrades the pipeline gracefully rather than collapsing it, indicating that within the range we tested its robustness does not rest solely on memorizing a fixed brand list. Additional robustness experiments confirm that SAFER transfers to real phishing URLs from the OpenPhish feed, generalizes to unseen attack families, maintains zero exposure on hard benign negatives, outperforms supervised URL classifiers as an admission gate, and maintains exposure minimization under realistic pool structures within the evaluated threat model. Full article

This study investigates how social crowding (e.g., overcrowded environments) influences individuals’ preferences for different modes of shared consumption (sharing-in vs. sharing-out). While existing research has predominantly explored shared consumption through the perspective of non-psychosocial environmental factors (e.g., time scarcity), this study shifts the focus to psychosocial environmental factors by analyzing the underlying mechanism through which social crowding shapes preferences for these modes of shared consumption. We conducted one field experiment and three scenario-based laboratory experiments to manipulate social crowding (vs. non-social crowding) across three distinct contexts: (1) restaurant-based social scenarios, (2) shopping mall consumption contexts, and (3) beach tourism settings. Across these experiments, diverse categories of shared goods were employed to examine participants’ preferences for sharing-in versus sharing-out. The findings demonstrate that social crowding increases individuals’ preference for sharing with close friends (i.e., sharing-in), whereas non-social crowding enhances their preference for sharing with strangers (i.e., sharing-out). This relationship is mediated by psychological distance. Furthermore, resource mindset (scarcity vs. abundance) moderates this effect. Specifically, under a scarcity mindset, social crowding strengthens preferences for sharing-in, whereas under an abundance mindset, it promotes preferences for sharing-out. By conceptualizing shared consumption from a psychosocial environmental perspective, this research extends current understanding of how environmental social cues shape interpersonal consumption decisions and provides practical implications for collaborative consumption platforms, hospitality services, retail environments, tourism destinations, and public service design. Specifically, platform managers and service designers can strategically optimize spatial layouts and perceived crowding levels to strengthen social connectedness among close friends or foster positive interactions between strangers. Full article

The rapid growth of e-commerce has intensified last-mile delivery challenges in Thailand, where rising parcel volumes contribute to failed deliveries and increased carbon emissions. Smart parcel lockers offered a promising solution, though their adoption depends on consumer acceptance. However, existing research has not sufficiently examined how trialability, performance expectancy, and perceived risk operate alongside core TAM beliefs within an integrated framework, particularly in emerging markets. This study extends the Technology Acceptance Model (TAM) with constructs from Diffusion of Innovation (DOI) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of smart parcel locker adoption, incorporating trialability, performance expectancy, and perceived risk. A quantitative survey was conducted with 397 Thai consumers with prior online shopping and parcel delivery experience. Data were analyzed using covariance-based structural equation modeling (CB-SEM). The model explained 79.3%, 94.3%, and 82.7% of the variance in perceived ease of use, attitude, and intention. Trialability is the strongest predictor, working through perceived ease of use, while attitude and performance expectancy together drove intention. Contrary to traditional TAM, perceived usefulness did not significantly affect attitude, and perceived risk had no significant effect. These findings suggest that, for simple self-service delivery technologies, first-hand experience is more influential than emphasizing usefulness or safety concerns. This contributes to technology acceptance theory and offers practical guidance to increase smart parcel locker usage. Full article

Purpose: Online customer reviews (OCRs) are an essential informational resource in e-commerce, but little is known about the interaction between review type and reader type. This research explores whether consumer’s hedonic or utilitarian personality orientation moderates the relationship between OCR type (hedonic vs. utilitarian) and perceived OCR helpfulness, and whether this moderation is further conditioned by product type. Design/methodology: A 2 (OCR type: hedonic vs. utilitarian) × 2 (product type: bar soap vs. perfume) × 2 (personality type: hedonic vs. utilitarian) mixed factorial experiment was conducted with 586 usable responses collected in South Korea. Review-type and product-type manipulations were validated, personality orientations were measured with established shopping-value scales, and hypotheses were tested using three-way ANOVA with simple-effects decomposition. Findings: Personality orientation significantly moderated the review type–helpfulness relationship (F(1, 586) = 14.67, p < 0.0001), and this moderation was itself qualified by product type (three-way interaction: F(1, 586) = 24.23, p < 0.0001). When the review type was congruent with the product context, personality-mismatched readers—hedonic consumers reading utilitarian reviews and utilitarian consumers reading hedonic reviews—reported the highest helpfulness, whereas reviews that mismatched both the product and the reader received the lowest helpfulness ratings. Implications and originality: The results indicate that review helpfulness is an emergent property of the review–reader–product configuration rather than of review text alone, providing e-commerce platforms with a basis for orientation-aware review ranking, solicitation, and page design. Full article

While e-commerce has transformed consumption patterns, online shopping behavior remains influenced by the physical environment. Using Shanghai as a case study, this research applies machine learning, SHAP analysis, and K-Means clustering to examine the nonlinear impacts and spatial variations in the built environment on e-shopping. The findings reveal that: (1) E-shopping expenditure follows a long-tail distribution and displays a concentric spatial pattern, peaking between the Outer and Suburban Rings while decreasing within the Inner Ring and beyond the Suburban Ring. (2) Built-environment factors exhibit non-linear effects, with local shopping potential and transit distance playing dominant roles. Indicators such as store density and delivery facility coverage show inverted U-shaped threshold effects, indicating a shift from complementarity to substitution between offline and online retail. (3) The urban space can be clustered into three sub-district types—traditional residential, single-function, and mixed-use—each with distinct e-shopping patterns and drivers. This research highlights the spatial mechanisms shaping digital consumption, providing empirical evidence for context-specific retail planning in megacities. Full article

A recommender system extracts user preferences from past interactions to suggest items, widely used in platforms like user-generated content, online shopping, and urban services. These systems aim to provide accurate recommendations, reduce user interaction burden, and enhance user experience while improving socio-economic benefits. Current technologies include content-based, behavior-based, and hybrid recommendations. Behavior-based technologies, like collaborative filtering, are mature due to extensive user–item interaction data but focus on behavior over intrinsic features, lacking comprehensive understanding. Advancements in natural language processing (NLP), text vectorization, and large language models (LLMs) have made content-based recommendations based on semantic understanding more prominent. This study presents an exploratory LLM-agent-based hybrid recommender system framework, building upon a multi-armed bandit (MAB) approach, which enables semantic understanding of item content and integrates both short-term and long-term user interaction behaviors for recommendation. Specifically, it optimizes collaborative filtering models to capture long-term and short-term interests, and uses an agentic LLM memory and reasoning component to identify preference shifts and schedule appropriate recommendation modules. It further vectorizes content using NLP and LLMs to generate retrieval terms that enhance candidate recall, and aligns LLM-optimized content recommendations with user behavior to integrate both aspects. This hybrid system achieved a recall rate improvement of up to 26.63% in behavior recommendation and, in ranking-oriented (diversity-focused) recommendation tasks on the MovieLens dataset, raised NDCG@100 from 0.1428 to 0.3541, a relative improvement of approximately 148% (i.e., about 2.48× the baseline value), albeit against a deliberately simple multi-armed bandit baseline and with a corresponding decrease in Recall@k. However, this work represents an initial exploration in this emerging research direction, and more comprehensive comparisons with state-of-the-art methods are needed in future work to fully assess the framework’s effectiveness. Full article

Human–robot collaborative flexible job shop scheduling (HRC-FJSP) must coordinate heterogeneous capabilities, mode-dependent processing times, safety feasibility, and carbon constraints. The problem becomes harder when a collaboration mode that is attractive during planning becomes infeasible after a human enters the robot safety separation zone. Unlike conventional dynamic disturbances such as machine breakdown or order insertion, this event changes the feasible collaboration mode of the unfinished operation remainder rather than only delaying a resource or adding a job. This study formulates a carbon-aware dynamic HRC-FJSP and evaluates a carbon-aware multi-agent deep reinforcement learning scheduler (CA-MADRL) with local recovery after safety-proximity-induced collaboration disruption. The objective combines normalized makespan, carbon emission, and human workload imbalance with carbon accounting based on operation energy and time-varying grid carbon intensity. Across the benchmark cases, CA-MADRL obtains the best average global criterion (0.7235), wins nine of 12 cases, and achieves the lowest average carbon emissions among the compared policies (48.991 kg CO₂e). Sensitivity analysis shows that stronger carbon preference reduces emissions but increases makespan and tardiness, while adaptive collaboration outperforms fixed human–robot, human-only, and robot-only regimes. The results indicate that dynamic mode adaptation and local rescheduling improve carbon-aware collaborative schedules under safety disruption. Full article

Positioned as a node of low-end globalization, the Chungking Mansions in Hong Kong’s Tsim Sha Tsui represents a transnational urban space with many ethnic restaurants, grocery stores, shops and cheap hotels. As a vertical hub of superdiversity, the building features significant number of people from South Asia (e.g., India, Pakistan, Bangladesh and Nepal), Southeast Asia, Africa and beyond. Unlike most ethnic enclaves and multicultural areas that feature horizontal spatial organization, the Chungking Mansions conceptually represents a vertical ‘Little South Asia’ or ‘Little UN’ condensed into one building. The transient and anonymous Chungking Mansions is something of a ‘non-place’ but it also has a sense of community. The building over time gained a bad reputation as a mysterious, dodgy, shady and dangerous ethnic place where violence and crimes take place. This article aims to demystify the Chungking Mansions from under-explored sociolinguistic/multilingual perspectives. Languages and symbols inscribed in the linguistic/semiotic landscape, we argue, represent important anthropogenic impacts on geography and nature, which constitute salient entry points into understanding how migration, low-end globalization and superdiversity leave traces on the urban landscape. This study documents and shows how various multilingual signs and cultural and religious symbols enact ethnic, linguistic, cultural and religious identities and produce and reproduce social meanings alongside the building’s verticality. These materially and symbolically contribute to a vertical geography of superdiversity. Full article

Shopping cart abandonment remains a persistent challenge in e-commerce, yet evidence regarding the role of perceived risk remains fragmented across shopping contexts. This study aims to clarify when and under what conditions perceived risk is most strongly associated with shopping cart abandonment by quantitatively synthesizing 153 effect sizes from 60 independent studies reported in 49 empirical articles published between 2004 and 2026. A multilevel random-effects meta-regression approach was employed to account for dependent effect sizes and to test risk source, product category, shopping purpose, sample location, and publication year as moderators, together with two theoretically specified interactions. The results show that perceived risk is positively associated with shopping cart abandonment (r = 0.244, 95% CI [0.191, 0.296]). The association was stronger for transaction than product-related risk, for fast-moving consumer goods rather than durable goods, and in hedonic rather than utilitarian shopping contexts. Sample location and publication year were not significant moderators. Risk source also interacted with shopping purpose and product category, indicating that risk effects depend on configurations of contextual conditions. Sensitivity analyses and publication-bias diagnostics did not materially alter the pooled estimate. The findings shift attention from whether perceived risk matters to the contexts in which it is most likely to interrupt purchase completion. Full article

This paper investigates the determinants of consumer autonomy, such as trust, prior experience, and information richness, within the context of online tourist package purchases. The theoretical considerations presented in this study are grounded in an integrated framework that synthesises Self-Determination Theory (SDT), Cognitive Load Theory (CLT), and the Paradox of Choice (PoC), further enhanced by the principles of Prospect Theory to elucidate the complex dynamics of consumer decision-making. Framing the research within the Stimulus-Organism-Response (SOR) model, the study conceptualises e-purchase characteristics as external stimuli that shape the organism’s (consumer’s) perceived autonomy, subsequently influencing decision-making outcomes. Applying a quantitative Computer-Assisted Web Interview (CAWI) methodology with a sample of 581 Polish consumers, the study demonstrates that trust, prior experience, and information accessibility significantly augment perceived consumer autonomy; notably, perceived risk has no statistically significant impact. A particularly salient and unanticipated finding is that enhanced consumer autonomy significantly reduces decision-making difficulty, which in turn correlates with higher levels of post-purchase satisfaction. By addressing the scarcity of empirical research regarding autonomy in digital environments, this study unpacks the intricate decision-making processes inherent in e-tourism. Ultimately, these findings offer critical managerial implications: tourism operators can effectively mitigate cognitive load for prospective tourists and enhance satisfaction by prioritising transparency and implementing effective trust-building mechanisms, thereby fostering consumer empowerment throughout the digital purchasing journey. Full article

Generative artificial intelligence is increasingly used to produce user interface designs, yet the usability and task-performance implications of AI-generated interfaces remain insufficiently quantified. This study proposes a reproducible evaluation framework combining computational visual complexity metrics with human-centered interface assessment. Twelve user interfaces were evaluated across four scenarios: a mobile health dashboard, a learning management system dashboard, an e-commerce shopping cart, and a university student information portal. Each scenario included three design conditions: human-designed reference interfaces, raw AI-generated interfaces, and prompt-optimized AI-generated interfaces. Computational metrics included grayscale Shannon entropy, spatial edge density, RGB color entropy, RMS contrast, robust contrast, and white-space ratio. A within-subject user study with 62 participants measured perceived cognitive load, perceived visual complexity, task ease, interface evaluation time, and task accuracy. Friedman tests revealed significant differences among the interface conditions for all five user-centered outcomes, with very large effect sizes. Raw AI-generated interfaces were associated with the highest perceived cognitive load, highest perceived visual complexity, lowest task ease, and longest interface evaluation times. Within the tested stimulus set, prompt-optimized AI-generated interfaces showed more favorable user-centered outcomes than raw AI interfaces but remained statistically distinct from human-designed references in perceived cognitive load, perceived visual complexity, task ease, and interface evaluation time. Task accuracy reached 100% for both human-designed and prompt-optimized AI interfaces, whereas raw AI interfaces achieved 63.10%. Interface-level correlations between computational visual metrics and user outcomes were weak to moderate, suggesting that pixel-level visual complexity measures capture only part of the perceived usability burden. Overall, these findings support the potential value of prompt-based HCI constraints in AI-generated interface design, while robust evaluation should integrate computational metrics with human-centered testing. Full article