In the context of rapid digital tourism development and platform-based competition, online travel platforms (OTPs) increasingly exhibit coexisting functional convergence and intensified rivalry. This makes it critical for platforms to identify sources of user-perceived differentiation embedded in user-generated content (UGC) and translate them into actionable competitive diagnostics. Focusing on mobile OTP applications, this study analyzes 95,960 online consumer reviews (OCR) collected from Apple App Store and develops a human-in-the-loop, multi-algorithm text-mining framework. By integrating information-entropy filtering, semantic representation and keyword extraction, and deep-learning-based sentiment computation, we identify and quantify consumers’ feature salience and sentiment feedback across five decision-relevant dimensions: price, service, travel activities, accommodation & transportation, and software interaction. Building on these outputs, we bridge social proof theory and the resource-based view by proposing a value–scarcity dual-criterion competitiveness matrix that converts consumer perceptions into diagnostic cues for differentiation-oriented resource configuration. This study contributes a replicable pathway from large-scale UGC to quantified strategic insights, extends social proof from individual decision-making to platform competition and strategic diagnosis, and offers data-driven implications for improving platform mechanisms and experience design under homogenized competition.

Travel companion matching presents unique challenges compared with conventional recommendation domains, as it involves real-world interpersonal interaction, perceived safety risks, and limited historical user data under cold-start conditions. Existing platforms often lack structured multi-factor matching and transparent integration of trust and safety constraints. This study makes three contributions. First, it introduces a methodology for deriving interpretable compatibility weights from user preference data under cold-start conditions. Second, it presents a four-algorithm comparative evaluation framework that identifies user-preferred matching strategies through controlled real-user testing. Third, it proposes a safety-enhanced empirical hybrid algorithm that integrates a hard trust gate (T ≥ 0.7), safety-oriented components (51.3% normalised weight), and empirically derived preference personalisation (48.7%) within a single scoring framework. A three-phase empirical methodology is adopted: Phase 1 (n = 26 survey) derives compatibility weights, revealing safety (69.2%), travel pace (76.9%), and budget (73.1%) as dominant factors; Phase 2 (n = 15) compares four algorithms, with safety-first matching receiving the highest acceptance rate (60.0%, 95% Wilson CI: 35.7–80.2%); Phase 3 (n = 13 journeys) evaluates the hybrid algorithm, achieving an 84.6% selection rate with Precision@6 = 0.333, MRR@6 = 0.554, and NDCG@6 = 0.597. These results provide preliminary evidence that trust-aware constraints can be integrated with empirically derived preference modelling to produce actionable recommendations under cold-start conditions, offering a reproducible approach for peer-to-peer travel platforms prioritising user safety.

The Internet has become a quick significant information and shopping resource, especially for the tourism sector. With the development of secure information systems, travel plan and booking have become increasingly dependent on technology. As a result, travel platforms today have a significant impact on tourists’ choices and experiences. Based on the stimulus-organism-response (SOR) theory, this study examines the factors influencing consumers’ continuance use intention toward travel-related purchase platforms. Specifically, it aims to investigate on how platform features affect relationship quality and continuance use. Data was collected from users with prior experience using travel platforms through a structured questionnaire, employing purposive and convenience sampling methods. The analysis was conducted using AMOS software to test both direct and indirect effects. The findings reveal that informativeness and privacy & security significantly impact relationship quality but do not influence continuance use. In contrast, customer support affects continuance use but does not have a significant impact on relationship quality. Moreover, relationship quality has a mediating effect between travel platform attributes and the continued use intention. The findings provide insights for enhancing long-term user engagement on travel platforms and offer key theoretical and practical implications. This study contributes to the body of literature on online travel by enhancing the knowledge on the continued utilization of online travel websites based on relationship quality . The findings also provide platform developers and tourism stakeholders to formulate credible and user-friendly web-based travel platforms that will necessitate frequent usage and facilitate long term competitiveness.

Historic streets, as living heritage environments, preserve everyday cultural practices while facing increasing digital mediation in tourism and daily life. This study examines how a digital sense of place is constructed online in the Ming–Qing Old Street of Songyang, China. User-generated text and image data were collected primarily from Weibo, supplemented by user reviews from major travel platforms, including Dianping, Fliggy, Mafengwo, and Ctrip, and analysed through a multimodal framework. BERTopic was applied to identify thematic narratives in textual content, and ResNet-50 was used to classify visual scene elements in shared images, enabling an integrated interpretation of textual and visual representations. The results reveal four dominant dimensions of digital place perception: local food culture, living handicrafts, historic spatial fabric, and everyday atmosphere. Textual narratives emphasise emotional attachment and experiential interpretation, while visual representations highlight photogenic, performative, and shareable street scenes. The integration of these modalities forms a layered digital sense of place grounded in cultural continuity and daily life. The study demonstrates the value of multimodal social media analysis in understanding how living heritage streets are digitally represented and perceived, offering implications for sustainable heritage conservation, community-centred revitalisation, and data-informed cultural tourism management.

AI travel assistants are increasingly designating hotels as “eco”, yet when the evidence is not independently verifiable, these recommendations may serve as persuasive cues or credible decision support. We present a preregistered 2 × 2 between-subject laboratory experiment (n = 63) that manipulates autonomy framing (Recommend vs. Plan) and evidence verifiability (verifiable vs. non-verifiable) in a realistic hotel-booking workflow with a standardized “Verify eco-claim” drawer. Phasic arousal was recorded at recommendation onset (E1) and verification initiation (E3), employing eye-tracking indexed verification behavior (verify clicks, time-to-verify, verification depth) and event-locked galvanic skin response (GSR). Verifiability did not directly speed up or deepen verification (H1 not supported), but verification was common (74.6% clicked Verify). Rather, autonomy influenced checking: Plan slowed verification and altered verification depth. E1 SCR revealed an Evidence × Autonomy interaction, which is consistent with an autonomy-boundary account (H4), rather than credibility stress emerging as a simple evidence main effect at E1 (H2 not supported as stated). Verification served as a repair moment: depending on the availability of diagnostic cues, arousal dynamics from E1 to E3 supported differential “repair” (H3). SCR dynamics explained incremental variance in perceived manipulation/greenwashing concern beyond condition and eye-tracking indices (H5b supported), but verification depth did not mediate effects on trust or delegation (H5a not supported). Overall, users’ interpretation of AI sustainability advice is influenced by autonomy, and multimodal process measures offer useful signals for auditing eco-recommendation designs in travel platforms.

The rapid growth of digital travel platforms has intensified competition in the online ticketing industry, requiring companies to understand the key factors influencing consumer satisfaction. However, empirical evidence on how trust and price affect consumer satisfaction through purchase decisions remains limited, particularly in online train ticket purchases. Therefore, this study aims to analyze the influence of trust and price on consumer satisfaction, with purchase decisions as an intervening variable, in purchasing train tickets through the Traveloka application in Jakarta. This study employs a quantitative, survey-based approach. Data were collected through an online questionnaire distributed to 100 respondents who had purchased train tickets via Traveloka at least twice in the last six months. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results show that trust has a positive and significant effect on both purchase decisions and consumer satisfaction. Price has a positive and significant effect on purchase decisions but does not significantly affect consumer satisfaction. Furthermore, purchase decisions significantly influence consumer satisfaction, indicating a mediating role. These findings highlight the importance of trust and purchasing decisions in shaping consumer satisfaction on digital travel platforms.

Purpose This paper aims to examine how online reviews and electronic word-of-mouth (eWOM) convert homestay service encounters in South Asian Association for Regional Cooperation countries into platform-visible signals that shape discoverability and demand, and distinguishes optimisable drives from non-negotiable threshold conditions. Design/methodology/approach The authors analyse 47,186 online reviews and a SERVQUAL-based guest survey. The structural equation modelling (SEM) estimates the path from SERVQUAL to satisfaction to positive eWOM, testing an asymmetric penalty for poor responsiveness, while the necessary condition analysis identifies minimum performance levels required for high satisfaction; the authors also examine moderation by digital trust cues and management response behaviour. Findings Empathy is the strongest driver of satisfaction, followed by reliability and assurance. Responsiveness shows an asymmetric penalty. Slow or curt replies below expectation hurt satisfaction far more than above-average responsiveness helps it. Tangibles, reliability and responsiveness form non-compensatory floors, below which high Satisfaction is unlikely. Satisfaction does not automatically produce positive eWOM. Instead, the satisfaction-advocacy link is contingent on strong digital trust cues and visible, timely management response behaviour. The direct path from satisfaction to positive eWOM is non-significant; advocacy emerges only when satisfied guests also perceive high platform-mediated trust and prompt host engagement in responses. Research limitations/implications The review corpus is primarily English-language content from online travel platforms, which may under-represent perspectives expressed only in regional languages. Multilingual and longitudinal extensions linking platform signals to bookings are needed. Originality/value The authors extend SERVQUAL to platform-mediated homestays, reconcile SEM (drivers) with necessary condition analysis (NCA) (floors) and formalise digital trust as cues and public response behaviour as boundary conditions for converting satisfaction into public advocacy. Owners should manage tangibles, reliability and responsiveness as minimum standards, such as cleanliness, accurate listings, timely and polite replies and script Empathy in communication through pre-arrival messages, host bios and review responses, so that warmth and accountability are visible to future guests.

The sustainable development of online travel platforms relies on consumer trust and a healthy ecosystem. However, challenging cancellation policies have become a significant issue, threatening the industry’s sustainability. Existing research often analyzes cancellation policies from a short-term profit perspective, lacking a dynamic evolutionary analysis. This study employs evolutionary game theory and the Hotelling model, introducing heterogeneity in reputation loss sensitivity to explore how platforms evolve optimal cancellation policies between strict and lenient policies. We find that in markets with low reliance on reputation, platforms tend to adopt differentiated policies, making it difficult for the equilibrium to be unified and lenient. As reputation becomes more important, the market exhibits policy imitation or differentiation, both of which are significantly influenced by user loyalty. In highly competitive environments, reputation becomes central, and even reputation-insensitive platforms may adopt lenient policies to gain market share. Notably, increased user loyalty drives the market toward more lenient cancellation policies. This research provides a theoretical basis for platforms to formulate sustainable policies in dynamic competition.

The Jeju Olle Trail has evolved from a grassroots initiative into a contested space where post-pandemic growth intersects with environmental limits and fragmented governance. Moving beyond environment-centric models, this study examines the trail as a transcultural walking tourism system. The authors triangulated 900 user-generated content (UGC) narratives from major travel platforms (Korean, Chinese, and English) with semi-structured interviews from three key institutional informants (NTO, RTO, and NPO). The analysis explores how sustainable experiences are negotiated in practice. Findings suggest that Self-Determination Theory (SDT) constructs like autonomy are not universal constants but are culturally mediated through Western “digital detox,” Korean “collective healing,” and Chinese chūxīn (original heart) narratives. Institutional and narrative data indicate that these experiences appear linked to managing governance tensions between national mandates and localized stewardship. The study concludes that experiential sustainability involves navigating trade-offs regarding narratively signaled environmental impacts and community capacity. By framing walking tourism as a governance-dependent practice, this research demonstrates how culturally embedded mechanisms shape destination viability.

The rapid growth of digital platforms has transformed consumer decision-making processes in the hospitality industry, particularly through the increasing reliance on online customer reviews. This study aims to examine the effect of online customer reviews on booking intention, with trust and perceived value serving as mediating variables. Drawing upon signaling theory and consumer behavior theory, this research proposes that online reviews function as informational cues that shape consumers’ cognitive and affective evaluations before making reservation decisions. A quantitative approach was employed using survey data collected from hotel customers who actively use online travel platforms such as Traveloka, Booking.com, and Tripadvisor. The data were analyzed using SPSS to test both direct and indirect relationships among variables. The findings indicate that online customer reviews significantly influence booking intention, both directly and indirectly through trust and perceived value. Trust and perceived value are found to partially mediate the relationship, suggesting that consumers are more likely to make hotel reservations when reviews enhance their confidence and perceived benefits. This study contributes to the hospitality marketing literature by providing empirical evidence on the psychological mechanisms underlying digital review influence and offers managerial implications for hotel marketers in optimizing online reputation strategies.

Labuan Bajo has been designated as one of Indonesia’s Super Priority Tourism Destinations, possessing strong competitive advantages derived from its unique natural attractions, particularly Komodo National Park, marine tourism, and distinctive island landscapes. This study aims to analyze marketing strategies in enhancing the competitiveness of Labuan Bajo as a super priority destination amid increasing competition and sustainability challenges. Using a qualitative approach, data were collected through in-depth interviews, observations, and documentation involving key stakeholders, government representatives, local communities, and domestic and international tourists. The findings indicate that marketing strategies play a crucial role in strengthening destination competitiveness, with digital marketing through social media, official websites, and online travel platforms proving effective in building destination image and attracting visitors. However, marketing efforts remain concentrated on iconic attractions and are not yet fully supported by equitable infrastructure development, consistent service quality, and inclusive tourist information, particularly for international visitors. Therefore, an integrated and adaptive marketing strategy supported by service improvement, infrastructure strengthening, destination diversification, and sustainable management is essential to ensure long-term competitiveness and sustainable tourism growth in Labuan Bajo.

As an emerging marketing model, travel livestreaming has gained popularity among tourist destinations, online travel agencies, and social media platforms. Currently, major travel platforms have launched live streaming rooms through apps like TikTok and Xiaohongshu, attracting users to discover attractions, take photos for check-ins, and ultimately complete transactions. Research shows that the content attributes of travel livestreaming significantly influence consumer purchase intent, helping to boost conversion rates on e-commerce platforms. This paper explores how content attributes in travel livestreaming affect purchasing intentions and examines the mediating role of product involvement. A survey of 402 consumers who watched travel livestreams revealed: The content attributes of travel livestreaming (richness, entertainment value, and emotional engagement) positively impact purchase intention; product involvement moderates the relationship between emotional content and purchase intention, meaning higher product involvement amplifies the positive effect of emotional content on purchase intention.

Shopping is a key element of urban tourists’ consumer behavior. Today, social media and travel-specific online platforms provide plentiful information on urban retail opportunities, enhancing tourists’ shopping experiences. TripAdvisor, one of the most popular online travel platforms, enables tourist shoppers to gather information about the quality of retail experiences, shopping locations, and merchandise, thereby helping them identify “good places.” Despite the theoretical and managerial potential of the “good place” concept derived from Oldenburg’s third place theory, it remains underutilized in both retail and tourism research. This exploratory study addresses this gap by examining the occurrence of the term “good place” in the context of retail consumption on TripAdvisor, offering insights into how urban shopping tourists interpret this concept. A qualitative content analysis was carried out using tourist-generated communications on the TripAdvisor forum platform concerning 18 European cities. The findings reveal that shopping tourists on this platform interpret a “good place” as a retail environment where their needs are met in alignment with their expectations. The study contributes to extending Ray Oldenburg’s third place theory into the realm of tourism, promoting its conceptual integration into tourism studies.

The development of digital technology is driving a shift in consumer behavior in planning and booking trips online. Traveloka as one of the largest online travel platforms in Indonesia is widely chosen because of its complete features and is considered to be easy for users. However, various negative reviews on the app store related to price discrepancies at checkout, limited payment methods, and customer service that uses AI show that it causes problems with Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and user satisfaction that can reduce repurchase intentions. This study analyzes the influence of PEOU and PU on the satisfaction and repurchase intention of Traveloka users in Indonesia using the Technology Acceptance Model (TAM) framework, as well as testing satisfaction as a mediator. The methods used were quantitative explanatory online questionnaires on the Likert scale and Partial Least Squares (PLS) analysis. The results are expected to make a theoretical contribution to the implementation of TAM and practical input for Traveloka management to improve convenience, transparency, and service quality.

The increasing competitiveness among online travel agencies (OTAs) has encouraged companies to innovate through user‐engagement strategies such as mobile gamification. Gamification refers to the integration of game elements into non‐game contexts to enhance users’ enjoyment, motivation, and behavioral intentions. This study investigates the effect of gaming affordances on purchase intention, mediated by enjoyment, among users of the Traveloka mobile application in Indonesia. Using an explanatory quantitative design, data were collected from 115 respondents who had previously interacted with Traveloka’s gamified features. The analysis employed path analysis using SPSS version 29. Results indicate that gaming affordances particularly achievement, identity, and competition affordances significantly influence enjoyment, while self‐expression affordance shows a positive but nonsignificant relationship. Furthermore, enjoyment significantly predicts purchase intention, confirming its mediating role between gaming affordances and user behavioral intention. These findings demonstrate that well‐designed gamification elements can enhance user experience and drive purchase decisions in mobile travel platforms. Implications for OTA developers and future research directions are also discussed.

Ecotourism-oriented homestays are a significant driving force for rural revitalization. The landscape is instrumental in distinguishing homestays from one another and enhancing their allure, contributing to cultural heritage and sustainable economic development. However, the current homestay development landscape is marred by several challenges, including a lack of diversity in landscapes, a disconnect from local cultural roots, overdevelopment, ecological protection imbalances, a dearth of interactive experiences, and unregulated service standards. This study uses six representative cases in Zhejiang Province, China, integrating publicly accessible government environmental performance data, tourist review texts sourced from online travel platforms, and insights gleaned from semi-structured expert interviews. A multi-dimensional evaluation framework is established based on Environmental, Social, and Governance (ESG) principles. Quantitative text semantic analysis techniques were applied to parse the review texts, subsequently utilizing statistical methods to ascertain the relative importance of dimensions and indicators and explore their relationships. A significant correlation was observed between the prevalence of ecologically favorable language in user reviews and official environmental assessment scores. The frequency of culturally specific references in guest reviews exhibited a statistically significant positive correlation with tourist ratings. Drawing upon the preceding analysis, the study formulates landscape design guidelines that emphasize green technology, cultural empowerment, and social identity.

Purpose Through the lens of tourists’ information search behavior framework, this study aims to investigate Chinese outbound tourists’ information search strategies and behavioral outcomes. The relationships among sources of information search (SISs); attitudes and temporal visit intention across short-, medium-, and long-term periods; and the mediating role of attitudes on the relationship were investigated. Design/methodology/approach A list of 11 SISs often used by Chinese travelers including tourism product booking platforms (e.g. Ctrip and Tongcheng) and travel forums (e.g. Mafengwo and Qyer.com) was covered. The self-administered questionnaire was created and distributed to Chinese travelers from October to December 2023. In total, 966 usable samples were collected. Exploratory factor analysis (EFA) and multiple regressions were used to assess the relationships. Findings EFA results classified the 11 SISs into 3 factors, namely, traditional official sources, social media (TikTok, Red and WeChat moments) and online travel platforms (tourism product booking platforms and travel forums). Choosing Hong Kong as the destination, the findings showed significant impacts of traditional official sources and online travel platforms on attitudes. Social media affected short- and medium-term intentions. For long-term intentions, travelers relied on other sources. As a mediator, attitudes stimulated travelers’ visit intentions across all time frames. Originality/value Existing research scarcely focuses on the temporal dimension of information search. This study enriches research on tourists’ information search strategies by considering the impacts of different SISs on tourists’ temporal travel intentions across different time frames. It also adds to the existing tourists’ travel behavior research by explaining the influence of SISs on attitudes, attitudes’ direct and mediating effect in the relationship.

The rapid growth of digital travel platforms has streamlined booking processes, many existing systems still struggle to deliver deep personalization, effective budget planning, contextual safety support, and meaningful community-based features. To overcome these limitations, our team developed Pocket Safar, an AI-driven travel management platform that integrates multisource price comparison, dynamic itinerary generation, emergency-essential detection, and social travel collaboration into one cohesive system. The platform enables users to book flights, hotels, trains, cabs, and cruises while applying hybrid recommendation techniques such as content-based filtering, collaborative filtering, matrix factorization, clustering models, and advanced geolocation analytics.Pocket Safar also strengthens budget planning by incorporating dynamic programming, linear and integer programming, and genetic algorithms to create cost-efficient and personalized travel plans based on user budgets, dates, and preferences. Route optimization methods including TSP, A*, and Dijkstra’s algorithm, supported by spatial indexing, improve navigation accuracy and assist in locating essential nearby services. NLP models enhance traveler matching and community engagement, while predictive modeling assesses price trends and travel behavior.Initial results show that Pocket Safar reduces manual planning effort, increases itinerary relevance, enhances safety awareness, and improves engagement for both solo and group travelers, offering a more personalized and efficient travel experience.

The proliferation of online travel platforms has fundamentally transformed the tourism industry, making users’ continuance intention a critical metric for platform success. This literature review synthesizes existing research on the factors influencing users' continuance adoption intentions toward online travel platforms (websites and mobile apps). Through a dual-method approach of bibliometric analysis using CiteSpace (2014-2024) and a systematic literature review, the study maps the intellectual structure and evolutionary trends of the field. The analysis identifies core influencing factors such as perceived value, user trust, and technology characteristics, and key boundary conditions, including individual differences and contextual factors. Despite significant scholarly attention, the review identifies critical gaps, particularly the lack of focus on hybrid platforms that integrate transactional and review-based functionalities. It highlights the under-explored roles of perceived interactivity, perceived hedonicity, and network externality in shaping continuance intentions within these complex ecosystems. The paper concludes by proposing a future research agenda that calls for investigating the dynamic interplay of dual functionalities, integrating network effects, and adopting advanced methodological approaches to advance the understanding of user retention in hybrid online travel platforms.

Graphical A framework illustrates the RoBERTa-CSS model for continuous sentiment scoring with its motivation, process, and implications. The diagram integrates three sections: research motivation, the R o B E R T a C S S process, and research implications. The left section explains that sentiment has a continuous nature and that existing methods only classify discrete categories, leading to oversimplified interpretations. The central section outlines the model workflow in five steps: constructing the data loader, preparing tokens with embeddings, obtaining the classification token output, building the regressor layer, and standardising the output value from 0 to 1. The right section summarises methodological and practical implications. Methodologically, the model offers improved accuracy and reduced cost for continuous sentiment scoring. Practically, it supports tourism departments in identifying satisfaction triggers, allocating resources, and guiding marketing strategies. Purpose Tourist sentiment is typically measured as discrete categories (e.g. positive, neutral and negative) through lexicon-based or machine-learning-based approaches in extant studies. However, neuroscience and physiology scholars have argued that sentiments are continuous in nature. Treating sentiment as a categorical state may result in an overly simplified understanding of tourists’ sentiments, ultimately hindering the tourism industry’s ability to derive precise and actionable insights. This study aims to construct an AI-driven framework for continuous tourist sentiment scoring. Design/methodology/approach This paper proposed a tool named RoBERTa-CSS (RoBERTa-based Continuous Sentiment Scoring) to calculate tourists’ continuous sentiment scores based on the pre-trained language model RoBERTa. The structure of RoBERTa is refined by adding a fully connected neural network layer, enabling the prediction of continuous sentiment scores. Using Chinese online reviews of a hotel group from multiple travel platforms, 3,500 sentences segmented from 1,000 randomly selected reviews were manually annotated to evaluate the proposed approach. Findings The comparison with the state-of-the-art open-source packages, deep learning models, pre-trained language models and generative artificial intelligence tools on multiple evaluation metrics demonstrated the superiority of the proposed RoBERTa-CSS. The method was also validated on an English dataset, showing good performance. Several empirical analyses, including individual-level sentiment flow analysis, group-level sentiment distribution and longitudinal analysis, were performed using the full dataset. The results further showcased the edge of RoBERTa-CSS, compared to extant polarity categorization-oriented sentiment analysis methods. Originality/value This study expanded the analytical ability beyond simple categorization to facilitate understanding of the complexity and diversity of human sentiment based on an improved pre-trained language model. The relevance of this paper for tourism practitioners, destination management organizations and online travel platforms is discussed.