Search Results (460)

Service failures remain inevitable in tourism and hospitality, yet complaint behavior is often suppressed, particularly in non-routine, time-bound travel contexts. The Tourist Complaint Constraints (TCC) framework explains this silence through five tourism-specific constraints. However, it does not explicitly account for how platform-based and AI-mediated service environments reshape post-failure behavior. This paper revisits TCC and introduces TCC 2.0, a conceptual extension that reframes complaint constraints as structurally generated within platform-mediated recovery architectures. Drawing on justice theory and emerging research on AI-enabled service systems, the framework positions distributive, procedural, and interactional justice as central mediators linking complaint constraints to behavioral outcomes. It further incorporates platform/AI process constraints and algorithmic trust constraints as additional structural dimensions, while identifying recovery channel and failure magnitude as boundary conditions. A key contribution is the concept of platform-mediated silence, defined as a structurally induced form of non-complaining behavior shaped by constrained agency and recovery system design rather than satisfaction. The paper advances a set of propositions to guide empirical testing and future scale development in AI-mediated tourism contexts. By extending complaint behavior theory into digitally mediated service environments, TCC 2.0 offers a foundation for understanding how platform architectures shape customer voice, silence, and post-failure responses. Full article

Sustainable transport management (STM) has become an increasingly important issue in recent years, as cities have faced growing traffic congestion, air pollution, and other transport-related challenges. Travel time (TT) represents one of the critical determinants of Quality of Service (QoS) and user satisfaction in public passenger transport (PPT). TT extends beyond in-vehicle duration and encompasses a sequence of temporal components, including access, waiting, transfer, and egress times. TT reflects the complexity of an integrated passenger transport system (IPTS), where users experience transport services as a door-to-door journey rather than isolated trips. This article analyses the TT within IPTSs through the lens of European quality standards EN 13816 and EN 15140 for PPT. Standard EN 13816 provides a normative framework for defining TT as a key QoS criterion reflecting user expectations and a user-oriented perspective, while standard EN 15140 operationalises this framework by specifying methodological requirements for the measurement and evaluation of the delivered TT quality at system-level performance objectives. This research highlights a structural gap between the conceptualisation of TT as a door-to-door journey, a user-oriented phenomenon, and its measurement through fragmented, mode-specific performance metrics. It limits the ability of transport authorities and operators to accurately evaluate the QoS and to design efficient urban mobility (UM) systems. Full article

Theme park tourism has become a trend and brings huge profits; however, how Fantawild, a representative theme park in China, can attract tourists to return remains to be explored. Given the substantial economic value of repeat visitors for theme parks, this study focuses on revisit intention rather than general tourist behavior. Drawing on the Stimulus–Organism–Response (S-O-R) framework as the theoretical foundation, this study examines how narrative-based experiences influence visitors’ revisit intention to Fantawild as a theme park tourism destination. Data were collected from 573 visitors to Fantawild in China and analyzed using structural equation modeling. The results show that storytelling does not directly influence revisit intention but instead operates through emotional resonance and satisfaction, indicating a fully mediated mechanism. Emotional resonance also indirectly affects revisit intention through satisfaction. In addition, marketing activities, fond travel memories, and IP appeal significantly enhance emotional and evaluative responses, which in turn drive revisit intention. These findings provide a clearer understanding of the affective and cognitive mechanisms underlying narrative immersion and offer practical implications for improving visitor engagement, repeat visitation, and retention in theme park tourism. Full article

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

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

This study investigates how AI-driven personalization shapes traveler satisfaction in a post-adoption tourism context, with particular attention to the mechanisms and boundary conditions through which personalization is associated with experiential outcomes. Using an integrated post-adoption framework, the study conceptualizes AI-driven personalization as an experiential input influencing satisfaction through trust formation, perceived value, and individual readiness to engage with technology. Survey data were collected from 347 tourists with direct experience of AI-enabled tourism services in Kosovo. The relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that AI-driven personalization is positively associated with traveler satisfaction. It enhances trust in AI-powered systems, and trust is positively associated with perceived value. Perceived value mediates the relationship between trust in AI-powered systems and traveler satisfaction, highlighting value appraisal as a central post-adoption mechanism. AI-driven personalization is also indirectly associated with traveler satisfaction through a sequential mechanism, in which trust precedes perceived value in the experiential evaluation process. Technology readiness moderates the relationship between perceived value and traveler satisfaction, indicating heterogeneous experiential responses to AI-enabled tourism services. The study contributes to tourism and hospitality research by demonstrating a sequential relational–evaluative mechanism through which AI-driven personalization is associated with traveler satisfaction, shifting the focus from adoption-based explanations toward post-adoption experiential pathways. It further clarifies the role of trust as a relational mechanism preceding value formation and identifies technology readiness as a boundary condition shaping satisfaction outcomes in an emerging destination context. The findings also offer practical guidance for designing AI-enabled services that strengthen trust, enhance value perceptions, and align personalization strategies with varying levels of traveler technology readiness. Full article

This study examines accessibility barriers experienced by individuals with disabilities in urban public transportation and analyzes how these barriers influence their travel behavior. Survey data were collected from 450 participants with different disability types in Alanya, Turkey, a tourism-oriented city characterized by pronounced seasonal mobility fluctuations. To ensure internal consistency and analytical robustness, the Analytic Hierarchy Process (AHP) was applied to prioritize seven accessibility criteria, and the consistency of pairwise comparisons was verified prior to analysis. Based on the AHP-derived weights, a composite accessibility-based Problem Severity Index (PSI) was constructed and integrated into regression models to quantify behavioral effects. The results show that the Problem Severity Index (PSI) is strongly associated with satisfaction (R² = 0.895), frequency of public transport use (R² = 0.924), and perceived travel difficulty (R² = 0.924), reflecting constrained mobility conditions and limited modal alternatives rather than improved service quality. Deficiencies in bus stop design and vehicle accessibility equipment were identified as the most influential barriers affecting public transport experience. Beyond the case study context, the proposed AHP–regression framework provides a structured analytical approach for evaluating accessibility performance and generating empirical evidence to inform inclusive and sustainable urban mobility planning. The findings offer empirical evidence on the relative importance of accessibility barriers and highlight critical infrastructure and service deficiencies. Rather than constituting a decision-support tool themselves, these results provide structured information that, when appropriately contextualized, can inform and guide transport authorities and urban planners in prioritizing accessibility improvements and enhancing inclusive public transport performance over time. Full article

Demand-responsive transit (DRT) requires real-time vehicle assignment under dynamically arriving requests, where each decision may alter multi-stop routes and affect both onboard and newly arriving passengers. However, DRT simulations often face three key limitations: rapidly increasing computational complexity as fleet size and demand grow, insufficient integration of traffic congestion into routing decisions, and limited consideration of passenger-oriented service quality in final vehicle assignment. To address these issues, this study proposes an integrated DRT simulation incorporating three core algorithms: Fréchet Distance-based Candidate Vehicle Selection (FD-CVS), Congestion-Aware Path Planning (CA-PP), and Satisfaction-Aware Vehicle Assignment (SA-VA). FD-CVS reduces computational burden by filtering candidate vehicles based on route similarity. CA-PP extends conventional path planning by incorporating congestion-adjusted travel costs derived from public transportation data. SA-VA determines the final vehicle assignment by jointly evaluating passenger waiting time, in-vehicle travel time, and capacity constraints. The algorithms are implemented within a discrete-event simulation environment using real-world data. Experimental results demonstrate that FD-CVS significantly reduces execution time under high-demand conditions, while SA-VA improves passenger waiting time and acceptance rates. Overall, the proposed three-algorithm framework enables more realistic and computationally efficient DRT system evaluation. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

Background: Tourists often travel within their own country or abroad for business, leisure or to receive planned healthcare. However, they are often not prepared for unexpected medical emergencies that occur far from home. Seeking emergency healthcare during travel may pose various barriers and challenges to tourists. Aims: This systematic review aimed to identify the challenges and barriers tourists face while seeking emergency healthcare during travel. Methods: A comprehensive search was performed in PubMed, Scopus, Web of Science and ScienceDirect from 1st January 1995 to 31 October 2025. The review included studies focusing on tourists who sought emergency healthcare abroad. Due to the methodological heterogeneity of the studies making meta-analysis impossible, a narrative synthesis of the results was conducted. The review protocol was registered with PROSPERO (ID CRD420251156975). Results: From 608 initial titles (603 from database searches and 5 additional from similar articles), 10 studies were selected—5 cross-sectional and 5 retrospective. Most (7/10) were conducted in Asian countries, while others were conducted in Europe (1), the U.S.A. (1) and multiple countries (1). The participant number ranged from 37 to 2333. All studies included both genders, apart from one that focused exclusively on pregnant women. The most common challenges identified were language and cultural barriers, limited access to healthcare services in terms of appropriateness and timeliness of care and financial and insurance coverage issues. Conclusions: The findings underscore that tourists face multiple barriers when seeking emergency healthcare abroad, resulting in negative tourist travel experiences. Once identified, specific strategies should be adopted to improve accessibility and the overall quality of care for tourists. Full article

Gender equity is a research topic in the field of urban mobility, but it varies across countries, according to their social and cultural features, including levels of development. There is far less understanding of how gender influences access and mobility patterns in developing countries, where specific cultural and socioeconomic factors play a significant role. Within urban mobility research, there is a relevant gap in transport equity and accessibility. This paper follows a gender-sensitive approach to examine and compare travel behaviour and profiles of bus users in two cities: one in a developed country, Oviedo (Spain), and a second in a less developed one, Tangier (Morocco). For this purpose, two tailored surveys were conducted among bus users, collecting socio-economic, trip-related, and satisfaction variables. The analysis was structured in two steps. The first step involved a gender-informed comparison between the two cities. The second step focused on Tangier and applied a Cluster Analysis to identify distinct user profiles and gender-related travel patterns. The results from the first step reveal that gender mobility differences are more pronounced and very different aspects in Tangier than in Oviedo. In Tangier, men use buses for work, while women have lower employment rates and travel for a broader range of purposes, including shopping, studying, and leisure. In contrast, in Oviedo, men and women use the bus for similar purposes, but women use public transport more frequently. Cluster Analysis in Tangier identified four groups of bus users with varying proportions of women. Three correspond to daily travellers; among them, only one cluster shows no relevant gender differences, while the other two, composed of workers performing trips of different lengths, reveal gender differences in employment and trip purposes. The fourth cluster corresponds to occasional travellers, mainly men going to work and women going shopping. Overall, results highlight persistent gendered disparities in Tangier, while Oviedo presents more balanced mobility patterns Full article

Public transport is a critical instrument for mitigating traffic congestion, reducing environmental pollution, and promoting social inclusion in urban areas. This study presents the results of a quantitative survey conducted among 406 residents of Łódź, Poland, aimed at identifying the determinants of public transport use and the factors influencing modal choices. The findings indicate that 89% of respondents had used public transport within the past three years, with over half reporting the use of both buses and trams. However, public transport is predominantly chosen out of necessity rather than preference, driven by limited access to private vehicles, absence of a driver’s license, or the high costs of car ownership. Environmental considerations and service quality factors play a comparatively minor role. User satisfaction with public transport services in Łódź is moderate, and current users express limited intention to increase their usage or actively recommend the system, suggesting constrained potential for demand growth. In contrast, non-users declare a willingness to shift to public transport if travel costs are reduced and service quality is improved. Measures aimed at restricting private car use demonstrate limited motivational impact, whereas enhancing the reliability, accessibility, and affordability of public transport emerges as the most effective strategy. Methodologically, the study contributes by combining bibliometric mapping with quantitative survey analysis, providing a replicable framework for assessing urban mobility determinants in other cities with similar socio-economic and transport contexts. Full article

In order to maximize passenger travel satisfaction and enhance the sustainability of the intercity multimodal transportation system, this paper proposes a two-stage model for intercity multimodal timetable coordination optimization under uncertainty. In the first stage, a robust spatio-temporal graph is built to allocate intermodal passenger flows in order to determine passengers’ route selection results to minimize the total travel cost. At the same time, explicit capacity constraints and transfer behaviors are considered in order to be more realistic. In addition, passengers can take multiple transportation modes (High-speed Rail, Ordinary Rail, EMU, and Coach) in a single trip. The outputs of the first stage are subsequently integrated into the second-stage interval multi-objective timetable optimization model to determine departure times and stopping patterns under uncertain dwell and travel times. It is able to achieve the maximum reduction of passenger travelling time and waiting time within the minimum timetable adjustment, which further improves the integration level of transportation services. To ensure the diversity and convergence of model solving on the basis of retaining uncertain information, we propose an integrated algorithm PSO-IMOEA-MC involving Particle Swarm Optimization algorithm (PSO) and Interval Many-objective Evolutionary Algorithm combined with Monte Carlo (IMOEA-MC). Finally, the effectiveness of the proposed two-stage model and algorithm is validated using three intercity networks: Beijing–Zhangjiakou, Chengdu–Chongqing, and Guangzhou–Qingyuan. The results demonstrate the performance of the method in finding high-level solutions that retain more uncertainty. The findings of this study provide technical support for timetable adjustments under diverse operational scenarios. Full article

Researchers typically treat platform loyalty in online travel agency (OTA) settings as a static outcome of satisfaction, even though repeated platform use unfolds over time. However, consumers update evaluative judgments through learning and memory as they move from pre-consumption expectations to post-consumption experiences, gradually stabilizing evaluations rather than continuously revising them. To address this gap, we use a two-wave time-lagged survey capturing pre- and post-consumption evaluations to examine when and how satisfaction-based platform loyalty strengthens in OTA-mediated hotel choice. The results show that the relationship between satisfaction and platform loyalty intentions intensifies after consumption. Satisfaction increasingly functions as a decision-guiding cognitive signal. This strengthening reflects experience-driven reweighting of hotel choice attributes. Consumers reweight existing criteria through experience rather than introducing new ones. Notably, the importance of core attributes, especially room quality and online reviews, increases as experience accumulates. Satisfaction and platform loyalty intentions also display significant carryover effects, indicating that prior evaluations shape subsequent judgments through memory-based continuity. By showing that evaluative judgments stabilize through selective reinforcement of existing criteria, this study explains how satisfaction transforms from an outcome judgment to a cognitive anchor for future decisions and underscores the value of longitudinal approaches for understanding early-stage experience-based decision dynamics. Full article

This study examined the psychology-driven decision-making dynamics of Millennial solo travellers in Asia, with a comparative focus on Thai and other Asian tourists. While the Theory of Planned Behaviour (TPB) is widely applied in tourism research, prior studies may not fully address the attitude-mediated construct–intention gap, stage-based intention–behaviour variation, and post-intention outcomes. To extend this perspective, the study proposes the I-SMART Cognitive TPB Model, integrating temporal bias, loss aversion, narrative-driven information, Social Exchange Theory, the four-stage tourism life cycle, and post-intention marketing behaviours. Survey data from 800 respondents (400 Thai, 400 Asian) were analysed using structural equation modelling. The findings indicate that narrative information may play a stronger role in shaping attitudes among Asian travellers, whereas Thai travellers appear more influenced by time-based motivation. Pre-trip factors emerged as key contributors to intention formation in both groups, while post-intention patterns diverged: intention linked more strongly to satisfaction among Asian travellers and to revisit tendencies among Thai travellers. Theoretically, the study offers an integrated cognitive–behavioural model that complements TPB by incorporating bias-driven and stage-based mechanisms. Practically, the findings provide guidance for designing digital infrastructure, time-sensitive policies, and storytelling-driven marketing strategies tailored to Millennial solo travellers. Full article