Search Results (722)

This paper investigates the energy-efficient hybrid flow shop scheduling problem considering deteriorating maintenance and transportation times (EHFSP-DMT), which is an NP-hard combinatorial optimization problem in complex manufacturing systems. To formulate this problem, a mixed-integer programming mathematical model is established. A computational intelligence approach, named the adaptive feedback multi-start variable neighborhood search (AFMS-VNS) algorithm, is proposed to simultaneously minimize the makespan and total energy consumption. In AFMS-VNS, a decoding strategy integrating conflict detection is designed to satisfy the mathematical constraints of equipment maintenance. A reinforcement learning (Q-learning) mechanism evaluates and adaptively selects search operators to enhance search efficiency. A search strategy based on the feedback of population distribution status is proposed to balance the optimization directions between the two conflicting objectives. An iterated greedy reconstruction strategy, guided by an elite external archive, is adopted to replace stagnant individuals. Experiments on 44 instances and Wilcoxon signed-rank tests show that AFMS-VNS outperforms five comparison algorithms in terms of inverted generational distance, hypervolume, and set coverage when solving the EHFSP-DMT. Full article

Although customer experience consistency contributes to omnichannel business success, few studies have investigated the influence of omnichannel customer experience consistency from the perspective of the customer journey. Based on analysis of data from two samples comprising product-based (N = 592) and service-based (N = 579) omnichannel customers, this study found that pre-purchase, purchase, and post-purchase customer experience consistency had significant and positive effects on the development of an omnichannel shopping habit, which, in turn, promoted face-to-face word of mouth (WOM) and electronic WOM. In addition, in the product-based omnichannel, pre-purchase customer experience consistency had a greater impact on the development of an omnichannel shopping habit than did purchase and post-purchase customer experience consistency. However, in the service-based omnichannel, no significant differences were observed between the effects of pre-purchase, purchase, and post-purchase customer experience consistency on the development of an omnichannel shopping habit. This study presents new insights into omnichannel consumer experience consistency and provides suggestions for managing omnichannel customer experience effectively. 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

Shopping malls have become major retail and lifestyle destinations that influence travel demand, mode choice, traffic congestion, private vehicle dependence, and transport-related environmental impacts. However, research connecting shopping mall visitation with sustainable urban mobility remains fragmented across transport, urban planning, retail, consumer behaviour, and environmental sustainability. This study uses bibliometric analysis to investigate publication patterns, intellectual structures, international collaboration, thematic clusters, and emerging trends in this interdisciplinary field. A total of 567 English-language articles and reviews published between 1 January 2000 and 7 May 2026 were retrieved from Scopus and analysed using descriptive indicators and VOSviewer-based document co-citation, country-level co-authorship, and keyword co-occurrence analyses. The findings indicate that publication activity has grown considerably since 2017, with China emerging as the most productive contributor and as a central hub for international research collaboration. Document co-citation analysis revealed six intellectual clusters related to parking and traffic congestion, online and in-store shopping behaviour, the built environment and land use, consumer value and retail experience, retail attractiveness and sustainable community planning, and retail revitalisation and urban regeneration. The keyword analysis revealed five main themes: urban transport, carbon emission, travel behaviour, shopping mall, and shopping activity. More recently, carbon emissions, sustainable development, online shopping, energy efficiency, and data-driven solutions have attracted increasing research attention. This study synthesises the fragmented knowledge base and suggests future research directions for low-carbon shopping mobility and sustainable access to retail destinations. 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

This study addresses a distributed flexible job shop scheduling problem with machine degradation and state-driven imperfect preventive maintenance, denoted as MD-SDIPM-DFJSP. A bi-objective model is developed to minimize makespan and total energy consumption by jointly optimizing job assignment, operation sequencing, machine selection, and processing speed. The model links processing speed with processing time, power consumption, and degradation increment, and uses a unified degradation bound to represent both degradation and reliability constraints. Preventive maintenance is treated as an imperfect recovery action and is generated according to machine states and idle-window conditions. To solve the problem, a degradation-aware multi-objective memetic algorithm (DMA) is proposed, incorporating four-layer encoding, state-driven decoding, hybrid initialization, knowledge-guided neighborhood search, and a speed-based adjustment operator. Numerical experiments show that Gurobi solved the small instance to optimality with a 0% optimality gap, and the resulting schedule satisfied the modeled production, maintenance, degradation, reliability, and energy accounting requirements. Across 84 combinations of instances and factory sizes, DMA achieved the highest HV in 72 cases and the lowest IGD in 62 cases. The Wilcoxon tests further confirmed its overall advantages over the three comparison algorithms in terms of both HV and IGD. 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

Live commerce, which combines live streaming, real-time interaction, and immediate purchasing, has grown rapidly across East Asian markets. South Korea provides an appropriate context for examining whether platform attributes regarded as strategically important by industry experts align with those consumers perceive as effective. Drawing on IT affordance theory, Uses and Gratifications theory, and signaling theory, this study combines the Analytic Hierarchy Process (AHP), conducted with academic and industry experts, and Importance–Performance Analysis (IPA), conducted with experienced Korean live-commerce consumers, to compare expert priorities with consumer evaluations across nine platform attributes representing benefit, affordance, and seller characteristic dimensions. The AHP results indicate that both expert groups consistently assign the greatest strategic importance to the platform’s visual and interactive affordances, particularly visibility and meta-voicing, despite some differences in their evaluations of secondary attributes. The IPA results show that consumers consider all nine attributes to be more important than their current level of performance, with the largest gaps observed for review trust and shopping guidance, two attributes closely associated with the credibility of platform-provided information. Comparing the two sets of findings reveals a clear gap in alignment. The attributes receiving the highest priority from experts are not those where consumers perceive the greatest performance deficiencies, while review trust emerges as a particularly urgent area for improvement that expert assessment alone does not fully capture. This study interprets these findings as evidence of a gap between expert priorities and consumer experience and discusses their implications for resource allocation and review verification in Korean live-commerce platforms rather than as indicators of corporate growth, structural risk, or sustainability outcomes. Because the analysis is based on cross-sectional, self-reported data collected from a single national market, the applicability of these findings to other contexts and to objective organizational performance should be examined in future research. Full article

A hybrid algorithm combining two metaheuristics is proposed to solve the flow-shop scheduling problem, aiming to minimise the makespan (Cmax). This approach accounts for random machine failures and limited buffer capacity between machines. Since flow-shop scheduling problems are NP-hard, the metaheuristics could be used to solve them effectively. Researchers proved that the hybridisation of metaheuristics would improve the solution quality. Therefore, this study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm. The initial solutions are generated using a dynamic generation technique that relies on a set of constructive heuristics. The optimal solutions from the PIOA serve as input for the ABC algorithm. Various local search and variable neighbourhood search methods are also included to enhance solution quality. Extensive computational experiments, which focus on industrial scheduling scenarios and benchmark problem instances, are conducted to test the performance of the hybrid algorithm. Statistical analysis shows that the proposed algorithm outperforms other algorithms found in the existing literature. Full article

When flexible job shop scheduling faces concurrent disturbances such as machine failures and rush orders, worker-centric constraints emphasized under Industry 5.0 must also be satisfied. Existing deep reinforcement learning methods for the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) seldom treat worker workload balance as an explicit constraint, and most depend on static penalty coefficients that are difficult to tune across different scenarios. In this paper, we suggest ALP-PPO, an adaptive Lagrangian penalty-enhanced proximal policy optimization algorithm, for real-time rescheduling under concurrent machine breakdowns and rush orders. We formulate the scheduling environment as a constrained Markov decision process. Worker skill heterogeneity, fatigue accumulation and workload equity are modeled as coupled constraints alongside classical scheduling objectives. By decoupling operation sequencing, machine allocation and worker assignment into coordinated sub-decisions, a hierarchical action space is constructed. Dual Lagrangian multipliers for workload balance and fatigue are updated adaptively during training, so that manual penalty tuning is no longer required. An event-triggered mechanism selects between right-shift and full rescheduling on the basis of a disruption severity index. We employ weighted-sum scalarization of makespan, energy consumption and workload variance during training, and Pareto solution sets are obtained by systematically varying the weight vectors across independent training runs. On extended Brandimarte benchmarks augmented with worker and dynamic event parameters, ALP-PPO delivers superior scheduling performance across makespan, energy consumption and workload variance when compared with Double DQN, Dueling DQN, standard PPO, NSGA-II and MOEA/D, as measured by Hypervolume (HV) and Inverted Generational Distance (IGD) indicators. Ablation studies indicate that the adaptive Lagrangian mechanism reduces constraint violations by more than 40% relative to fixed-penalty alternatives while keeping the primary objectives competitive. An analysis of computational efficiency shows that ALP-PPO completes online inference in under 20 ms per decision step, making real-time rescheduling practically feasible. Generalization experiments on previously unseen instances further validate the transferability of the learned policy. These findings support human-centric intelligent scheduling in Industry 5.0 manufacturing. Full article

In recent years, numerous mobile platforms have introduced product ranking systems to guide consumer choices. Yet, how such rankings shape purchasing decisions remains insufficiently understood. This paper employed an event-related potential (ERP) experiment to capture the neurophysiological responses of consumers during mobile shopping, thereby uncovering the psychological processes and neural mechanisms underlying the influence of online rankings. Our findings reveal that consumers in mobile shopping environments are equally susceptible to information cascades. When product rankings were presented as decision aids, despite limited cognitive resources, low-ranking cues elicited high attentional engagement, as indicated by a higher P2 amplitude. Subsequently, ranking cues appeared to be associated with evaluative processing, as reflected in P3 amplitude differences. These neural and behavioral patterns reflect the avoidance tendencies toward low-ranking products and relatively greater trust-related evaluation of higher-ranked products, ultimately shaping purchase intentions. This study provides cognitive neuroscience evidence for how online rankings modulate mobile consumers’ decision-making. Full article

This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a single HFS model has received little attention. The problem is motivated by a real-world order sequencing problem in insulation board manufacturing, where all four constraints arise simultaneously from the production process. To formally characterize the problem, we develop a mixed-integer programming formulation that captures all operational constraints. For practical-scale problems, we propose several dispatching heuristics that can obtain sufficiently good solutions within a short computation time. We further develop a genetic algorithm as an independent solution approach to obtain high-quality solutions close to the optimum within a reasonable computation time. Computational experiments on instances generated based on real insulation board production characteristics demonstrate that the proposed algorithms outperform a benchmark greedy rule, and sensitivity analyses reveal the effects of setup time magnitude and the number of parallel machines on scheduling performance. Full article