Search Results (3,571)

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

In the field of human–robot interaction (HRI), researchers have extensively examined the role of social robot characteristics and how these can influence human–robot relationships. In particular, the robot’s voice is one of the most studied aspects, with numerous studies focusing on specific features such as tone, frequency, pitch, and gender. The robot’s voice represents a powerful social signal, whose design can influence people’s affective evaluations and acceptance of robots. With regard to language, however, relatively few studies have investigated the role of a robot’s accent (native or foreign). This experimental study therefore explores the influence of native accent on trust in robots. The study was conducted on two different samples: 60 Italian participants and 37 Arabic participants. Participants listened to two robot presentations in their native language: one delivered with a native accent and the other with a foreign accent. After listening to both presentations, participants were asked to indicate which robot they trusted. The results showed a 77.3% preference for the robot speaking with a native accent, compared to 22.7% for the robot with foreign accent. These findings demonstrate that, regardless of the language (Italian or Arabic), accent significantly influences the choice to invest trust in the robot, supporting the similarity-attraction effect. Accent calibration thus emerges as a low-cost, high-impact parameter in socially assistive and commercial robotics. Since accent influences trust-based delegation, voice design should be strategically adapted in service, healthcare, education, and customer-facing contexts. Full article

Embedded cryptography stands or falls on entropy quality, yet small devices have few trustworthy sources and little tolerance for heavyweight protocols. We build a Quantum Entropy as a Service (QEaaS) system that moves QRNG-derived entropy from a Quantis device to ESP32-class clients over post-quantum-secured channels. On the server side, the design exposes two paths: direct quantum entropy through a custom OpenSSL provider and mixed entropy through the Linux system pool. On the client side, we extend libcoap’s Zephyr support, integrate wolfSSL-based DTLS 1.3 into the CoAP stack, and add a BLAKE2s entropy pool that preserves the standard Zephyr extraction interface while introducing an injection API for server-provided entropy. Benchmarks on ESP32 hardware, targeting 100 iterations per configuration, show that ML-KEM-512 completes a DTLS 1.3 handshake in 313 ms on average without certificate verification, 35% faster than ECDHE P-256. Pairing ML-KEM-512 with ML-DSA-44 lowers the mean to 225 ms. Certificate verification adds roughly 194 ms for ECDSA but only 17 ms for ML-DSA-44, so the fully post-quantum configuration remains 63% faster than classical ECDHE P-256 with ECDSA even under full verification. Local BLAKE2s pool operations stay below 0.1 ms combined. On this platform, post-quantum key exchange and authentication are not only feasible; they are faster than the classical baseline. Full article

E-commerce web content is dynamic and diverse, necessitating continuous monitoring and adaptation. This presents researchers with the challenge of discovering methods to improve delivered services. Hence, integrating natural language processing (NLP), Machine Learning (ML), Deep Learning (DL), and sentiment analysis (SA) provides businesses with robust frameworks to utilize customer feedback and enhance decision-making. Therefore, we introduce a novel dataset collection methodology that captures the dynamic relationships between e-commerce web content and consumer sentiment. Additionally, we introduce a novel, real-consumer-based quality metric on product content through FG-CSrP, extending SA into a new Fine-Grained Consumer Sentiment related to the Product. We evaluated our dataset using baseline models: Deep Neural Network (DNN), Long Short-Term Memory (LSTM), DistilBERT, and twelve automatically optimized models created by AutoGluon-Tabular across three scenarios, each with varying feature inputs (numerical, textual, and both). We then applied Explainable Artificial Intelligence (XAI) to the DNN model to explain feature importance in prediction. Our findings showed that LightGBMXT outperformed the other models in two out of three scenarios, and XAI interpretations highlighted the significant role of vendor-provided web content details in consumer sentiment. Overall, our approach provides actionable insights that can help vendors improve e-commerce strategies and strengthen customer engagement. Full article

Inventory policy design in modern supply chains must balance cost efficiency, service reliability, and responsible resource utilization under significant demand and supply uncertainty. In many real-world supply chains, both customer demand and replenishment lead time exhibit substantial variability, making the design of continuous-review (s, S) inventory policies challenging. Although stochastic inventory models have been widely studied, many existing approaches rely on simplified assumptions or single-objective formulations, which may limit their applicability under simultaneous demand and lead-time uncertainty. This study proposes a data-driven multi-objective simulation–optimization framework for designing sustainable (s, S) inventory policies under dual uncertainty. The framework integrates empirical stochastic modeling, Monte Carlo simulation, and evolutionary multi-objective optimization to evaluate trade-offs between expected inventory cost and service performance. To enhance methodological rigor, statistical reliability control is incorporated into the simulation-based evaluation process to ensure that Pareto dominance relationships are not distorted by simulation noise. Historical operational data are used to estimate probability distributions for demand and lead time, which are incorporated into a stochastic simulation model representing inventory system dynamics. A multi-objective evolutionary algorithm (NSGA-II) is employed to identify Pareto-efficient policy parameters. An empirical case study from a health supplement supply chain demonstrates how the framework identifies efficient replenishment policies under realistic uncertainty conditions. The results reveal structural trade-offs between inventory cost and service level and show that data-driven policy design can improve decision transparency compared with heuristic replenishment rules. The proposed approach provides a structured decision-support tool for selecting replenishment policies that balance service continuity and inventory sustainability in shelf-life-constrained supply chains. Full article

As AI-powered e-commerce platforms grow more capable of predicting customer wants, a critical question remains unexplored: what makes customers perceive these experiences positively? The rapid integration of artificial intelligence (AI) into e-commerce platforms is reshaping how customers search for, evaluate, and experience digital services. However, empirical research has not kept pace with clarifying which platform-level factors most effectively shape customer experience (CX) in AI-driven environments. This study validated the Trust, Autonomy, Personalization, and Customer Engagement (TAPE) framework as a comprehensive set of CX drivers in intelligent commerce. Using survey data from 400 active e-commerce users, we employed a multi-stage approach combining exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling (SEM) with bootstrapped mediation testing. All four TAPE drivers demonstrated significant positive reflective associations with CX, with personalization and engagement emerging as the strongest contributors. CX was strongly associated with customer satisfaction, loyalty, and brand equity, and mediated the effects of all four dimensions on these strategic outcomes, with model comparison evidence supporting full mediation. The study contributes theoretically by integrating and empirically validating four established CX dimensions within the AI-enabled e-commerce context, and by demonstrating the central mediating role of CX in converting intelligent platform features into user-perceived strategic value. Managerially, the TAPE framework provides actionable guidance for designing transparent, adaptive, and engaging AI-driven customer journeys that enhance both experience quality and long-term brand outcomes. Full article

Purpose: This study provides a critical examination of the literature on applying artificial intelligence (AI) and Extended Reality (XR) in restaurant settings and related foodservice operations. It focuses on how AI and XE influence consumer nutrition awareness and decision-making about food choices, and their implications for customer satisfaction, loyalty, and service delivery in foodservice environments. Design/methodology/approach: The study adopts a systematic literature review (SLR) approach following the PRISMA method. An initial search identified over 3900 academic papers published between 2016 and 2025. Studies were selected on the basis of predetermined inclusion and exclusion criteria, and 26 peer-reviewed articles were analyzed. The review provides a conceptual synthesis and develops propositions for practical applications and future research directions. Findings: The review reveals a shift from static systems that rely on optimization, toward adaptive and user-centered solutions that are behavior-oriented. AI applications predominate in the case of calorie tracking, personalized recommendations, and menu planning. Though deployment of XR technologies (e.g., AR and VR) is less prevalent, they offer potential for immersive, and real-time interventions. A key distinction emerges between studies demonstrating empirical effectiveness (e.g., improved understanding and healthier choices) and those focused on technical and/or conceptual developments. To date, there has been limited validation of behavioral impacts in foodservice settings. Originality: This study offers a theory-informed conceptualization of AI and XR applications in restaurant and foodservice contexts by integrating three perspectives: hospitality (menus and dining experience), nutrition (dietary awareness and healthier choices), and human–technology interaction (technology acceptance and user engagement). The study reconceptualizes AI- and XR-enabled systems as behavioral intervention tools and outlines a focused research agenda for advancing nutritional communication in foodservice environments. Full article

Increasing pressure for innovation-driven competitiveness requires hospitality firms to integrate technological capability, market intelligence, and customer-focused innovation into coherent strategic configurations. However, prior research has largely examined these capabilities separately, limiting understanding of how their integration influences restaurant marketing performance in emerging markets. This study develops and empirically tests an integrated capability framework linking techno-resonance innovation capability, competitor orientation, consumer-centric innovation strategy, and new service development to restaurant marketing performance using survey data from 300 restaurant managers in Java and Bali, Indonesia. The results of PLS-SEM analysis indicate that techno-resonance innovation capability significantly strengthens consumer-centric innovation strategy and new service development, which subsequently improves marketing performance outcomes. The findings extend dynamic capabilities theory by demonstrating that capability integration—rather than isolated strategic actions—supports innovation-driven competitiveness in emerging hospitality markets and provides practical guidance for restaurant managers seeking to enhance performance under resource-constrained conditions. Full article

The Controller Area Network (CAN) remains the backbone of in-vehicle communication, but its lack of built-in security exposes safety-critical systems to cyberattacks. This paper presents a real-time, reconfigurable, specification-driven intrusion detection system (IDS) implemented on a custom test bench that emulates an EV powertrain. The CAN traffic captured from the four-ECU setup formed the dataset used in this study. The IDS enforces a compact, reconfigurable ruleset covering timing bounds, jitter envelopes, identifier whitelists, frame format, data length code (DLC) compliance, bus-load thresholds, application-level CRC, and alive-counter verification. The IDS achieves detection times below 2 ms with false positive rates under 1% for injection, denial of service (DoS), and fuzzy attacks, even at CAN bus loads up to 70%, while microcontroller resource usage remains within the constraints of automotive-grade devices, supporting deployment in embedded environments. The main contributions of this study are as follows: (i) a validated and reproducible EV powertrain test bench with millisecond-level timing, (ii) a deployable and easily reconfigurable ruleset with deterministic runtime, and (iii) a latency-oriented evaluation framework that is portable across automotive microcontroller platforms. The EV powertrain dataset v1.0 was released in a public GitHub repository to facilitate reproducible research and enable future benchmarking studies. Full article

High-speed railway services represent complex service environments in which customers evaluate both functional performance and lived experience. Thus, this study investigates how high-speed railway service quality influences customer satisfaction, and further examines whether consumer emotions affect the relationship between them. Data were collected via an online survey of 558 customers with recent travel experience at major high-speed railway stations in Guangzhou. Service quality was captured via reliability, responsiveness, empathy, tangibility, and compensation; emotions were measured as positive and negative affects. Main and interaction effects were estimated using hierarchical regression. Findings suggest a strong positive link between overall service quality and satisfaction. Four of the five dimensions have significant positive effects, whereas compensation is not significant. In addition, positive emotions amplify the effects of all five service quality dimensions on satisfaction, while negative emotions reduce the effects of empathy, tangibility, and compensation on satisfaction but do not significantly affect the effects of reliability or responsiveness. Overall, satisfaction in a high-demand hub depends on dependable operations, timely support, considerate encounters, and well-maintained facilities, alongside emotional experience management to improve service management across the overall journey. Full article

Customer churn refers to the termination of a customer’s business relationship with a bank, representing a direct loss of future revenue. Product downsell manifests as a reduction in the number of financial products held or a downgrade in service tier, often signaling early customer disengagement. Accurately identifying customers at risk of these two behaviors has become a cornerstone of profitable growth in the competitive retail banking industry as downsell frequently serves as a precursor to total churn. However, the existing research typically treats these highly correlated behaviors as independent prediction tasks, overlooking their intrinsic link and failing to address the critical challenges of class imbalance and regulatory demands for model interpretability. To tackle these problems, we propose an enhanced multi-task learning network (EMTL-Net), a deep learning framework specifically designed to capture the nuanced interplay between churn and downsell behaviors. EMTL-Net introduces an explicit feature interaction module to enhance the modeling of high-order feature relationships and utilizes a shared representation layer to extract universal customer risk patterns, enabling the joint prediction of churn and downsell. Furthermore, we employ Focal Loss as the training objective to dynamically adjust sample weights, effectively mitigating the class imbalance problem. Critically, to meet financial compliance requirements, we implement a SHAP-based interpretation mechanism that is compatible with multi-task outputs, providing preliminary insights into feature importance. Formal validation of interpretability claims remains an important direction for future research. The experimental results on a publicly available pedagogical bank customer benchmark dataset demonstrate that EMTL-Net achieves excellent performance on both tasks. For churn prediction, the model achieves an AUC of 0.8259, an accuracy of 0.8361, and an F1-score of 0.6235, significantly outperforming the existing baseline models. For downsell prediction (noting that the downsell label is rule-derived from the number of products held), the model achieves an AUC of 0.8932, an accuracy of 0.8571, and an F1-score of 0.7504. Ablation studies confirm the critical contributions of the explicit feature interaction module, Focal Loss, and the residual structure to model performance. Crucially, the interpretability analysis corroborates business intuition by identifying customer age, account balance, and product holdings as dominant churn drivers—a consistency that reinforces the model’s credibility and practical utility in high-stakes financial environments. Full article

Health and spa tourism is a rapidly growing sector that merges traditional healing with modern innovations to meet increasingly diverse client needs. Understanding professionals’ perspectives is crucial for developing sustainable strategies that enhance service quality, organizational performance, and long-term business viability. Drawing on qualitative narrative analysis and thematic network analysis, this study explores the key factors that spa tourism professionals in Băile Felix—the largest spa resort in Romania—associate with business success, competitive differentiation, and sustainable development. Data were collected through semi-structured interviews with 41 entrepreneurs and managers who provided detailed narratives on their strategic goals and market positioning. Rather than measuring customer psychological constructs directly, this study captures professionals’ expert attributions regarding service quality, staff professionalism, infrastructure investment, and economic objectives, and interprets these as managerial perceptions grounded in operational experience. Five research propositions guided the interpretive analysis: (P1) professionals narratively associate service quality and treatment diversity with perceived business performance and guest retention signals; (P2) staff professionalism and attitude are attributed as the primary drivers of competitive differentiation; (P3) infrastructure investment and innovation are framed as prerequisites for sustaining market positioning; (P4) the identified themes form a structurally interconnected network with key bridging nodes; and (P5) professional narratives reveal tensions between short-term economic objectives and longer-term commitments to service quality and sustainability. Thematic network analysis identified four central constructs—service quality and treatment diversity, staff professionalism and attitude, innovation and infrastructure investment, and economic and development objectives—and mapped 16 interconnected sub-themes, with modularity analysis (Q = 0.42) confirming a moderately cohesive structure. Sustainable innovation was operationalized across environmental efficiency, social value, and economic resilience dimensions, and found to be embedded systemically across multiple thematic clusters rather than treated as an isolated practice. The originality of this study lies in integrating narrative and thematic network analysis to reveal how these constructs co-evolve within a sustainability-oriented system, offering a novel methodological lens for spa tourism research in post-transitional Central and Eastern European contexts. Findings provide actionable insights for spa managers, policymakers, and investors seeking to balance modernization with tradition in resource-constrained destinations. Full article

Customer loyalty is a critical asset for the restaurant industry, yet the mechanisms linking the physical environment, price perception, and satisfaction remain underexplored in emerging Latin American gastronomy markets. This study examines the relationships among three servicescape dimensions—décor and artifacts, spatial layout, and ambient conditions—price perception, customer satisfaction, and loyalty in full-service restaurants in northern Peru (Chiclayo, Trujillo, and Piura). A cross-sectional survey was administered to 310 diners, and the proposed model was tested using partial least squares structural equation modeling (PLS-SEM) with 10,000 bootstrap resamples. Results supported seven of nine direct hypotheses and three of four mediation hypotheses. Décor and artifacts and ambient conditions significantly predicted both price perception and satisfaction, while spatial layout showed no significant effect on any path. Price perception partially mediated the effect of décor and ambient conditions on satisfaction, and satisfaction partially mediated the relationship between price perception and loyalty. The satisfaction–loyalty path yielded the largest effect size (β = 0.708, f² = 0.798). Serial chain analyses revealed that the physical environment shapes diner loyalty through sequential cognitive and evaluative mechanisms. These findings offer actionable insights for hospitality managers seeking to enhance gastronomy destination competitiveness through strategic servicescape investment. Full article

Carbon emission regulations and customers’ green preferences require ports and shipping companies to develop green services, but green investments entail significant costs. Vertical alliance cooperation between ports and shipping companies through sharing costs can address this issue. Most studies use non-cooperative game to analyze the competitive relationship between ports and shipping companies. Although such research can capture price competition, they struggle to address the distribution of cooperative benefits within an alliance. They also fail to simultaneously reflect the coexistence of competition and cooperation. So, we constructed a non-cooperative–cooperative biform game to analyze green investment under vertical alliance. In the non-cooperative stage, the model captures vertical price competition between ports and shipping companies, as well as horizontal competition among supply chains. In the cooperative stage, the Shapley value is used to allocate the coalition profits from green investment cooperation. The results indicate that alliance cooperation can promote the green development of shipping. Moderate green competition can promote the green development of shipping. Route substitution competition will increase service prices and green investment level and reduce the cost-sharing ratio for shipping companies. Port congestion prompts ports to increase green investment level. These findings offer references for the green collaborative development of ports and shipping companies across different countries, thereby enriching the research framework for global sustainable development in shipping. Full article

To ensure high-quality and reliable service provision for customers, advanced optical networks without active elements have been developed to increase operating reliability, network scalability, and resource efficiency. To this end, wavelength division multiplexing-based passive optical networks (WDM-PON) now have a markedly enhanced role. An important aspect of the WDM-PON design is represented by traffic protection schemes, which play a key role in network reliability. Managing the power budget for optical links allows us to achieve a practically sustainable and realizable infrastructure of advanced passive optical networks. In this work, we focused on simulation model development for the power budget calculation for the WDM-PON optical link and the subsequent optical power budget evaluation of presumptive WDM-PON traffic protection schemes. Full article