Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12, 1-13 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 26 September 2025; Revised: 6 November 2025; Accepted: 11 November 2025; Published: 28 November 2025 * Correspondence: lidm@sxu.edu.cn Users’ continuance intention toward online travel platforms: A bibliometric and systematic review of key drivers, boundary conditions, and future directions Dongmei Lee1*, Shiyu Wang2, Tianyu Liu3 1School of Economics and Management, Shanxi University, Taiyuan, 030006, China; lidm@sxu.edu.cn (D.L.) wsy15238255230@163.com (S.W.) lty4753@163.com (T.L.). Abstract: 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. Keywords: Bibliometric analysis, Continuance intention, Network externality, Online travel platforms, Technology adoption, User trust; Perceived value. 1. Introduction The global travel industry has undergone a profound digital transformation, with online travel platforms emerging as the backbone of modern trip planning and booking. The online travel market was valued at $2.3 trillion in 2023 and is projected to grow at a compound annual growth rate of 10.3% in the coming years, driven by rising smartphone penetration, post-pandemic travel resurgence, and evolving consumer preferences for digital convenience. A striking post-pandemic trend is the dominance of digital booking channels: approximately 82% of travelers now book via websites or mobile apps, with 70% relying on smartphones for travel research. This shift has intensified competition among platforms, including industry leaders like Ctrip, Expedia Group, and TripAdvisor, who increasingly prioritize user retention to capture long-term value in a crowded market. Against this backdrop, users’ continuance adoption intention, defined as the willingness to sustain long-term engagement with a platform, has become a critical metric for platform success. Unlike initial adoption, which reflects short-term curiosity, continuance intention directly correlates with revenue stability, user-generated content volume, and competitive advantage in the travel sector [1, 2]. For online travel platforms, which rely on repeat bookings and sustained user activity, understanding the drivers of continuance intention is not merely academic but strategically imperative. 2 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate A growing body of literature identifies consumer trust as a foundational antecedent of continuance intention across digital services, including online travel [3, 4]. In travel contexts where bookings involve financial transactions, time-sensitive arrangements, and reliance on third-party service providers, trust mitigates perceived risks Hashim and Tan [5] and Jiang and Lau [6]. Agag and El- Masry [7] confirmed this link in their longitudinal study of mobile commerce users, finding that trust indirectly strengthens continuance intention by reducing uncertainty. Similarly, Kim et al. [8] noted in their study that trust interacts with perceived usefulness to shape long-term platform engagement, particularly in information-intensive sectors like travel. Agag and Eid [9] further reinforced this by demonstrating that trust acts as a mediating mechanism between platform quality and continuance intention in online travel services. While existing research has advanced the understanding of continuance intention in diverse digital contexts, such as short-video apps [10], mobile learning platforms [11], mobile shopping platforms [12], and general mobile applications [13], the online travel domain presents unique complexities, especially as platforms evolve beyond single-function models. A defining trend in the industry is the rise of hybrid online travel platforms, which integrate transactional capabilities (e.g., flight/hotel bookings) with review-based social features (e.g., user-generated content, interactive feedback) [14]. This dual- function model responds to consumer demand for “the best of both worlds”: streamlined digital transactions paired with the social proof and personalized insights of peer reviews [15, 16]. For example, platforms like TripAdvisor now enable seamless booking directly within review interfaces, while Ctrip integrates user ratings into every search result to inform decision-making. Despite the market prevalence of hybrid platforms, scholarly attention to their unique dynamics remains limited. Current research on online travel platforms tends to focus on single-function models: either transactional platforms (examining factors like booking efficiency) or review platforms (studying constructs like review usefulness) [15, 17]. This gap is problematic because hybrid platforms create distinct user experiences, such as deriving hedonic pleasure from multimedia reviews [18, 19] or building trust through both transactional reliability and peer interaction [20], that single-function models do not capture this aspect. Additionally, existing studies often overlook platform-level characteristics inherent to hybrid models, such as network externality (the value of the platform increasing with user and service provider participation), which is a defining feature of two-sided travel marketplaces [21]. To address these gaps, this review synthesizes existing literature on users’ continuance adoption intention toward online travel platforms, with a focus on trust and hybrid platform dynamics. Employing a dual-method approach: bibliometric analysis (to map the field’s intellectual structure) and systematic literature review (to distill key influencing factors), the study aims to: (1) characterize the evolution of research on continuance intention in online travel from 2014 to 2024; (2) identify core drivers and boundary conditions of continuance intention, with emphasis on hybrid platform-specific constructs; (3) highlight gaps in understanding the interplay between transactional and review-based functionalities; and (4) propose theoretically grounded directions for future research. 2. Bibliometric Analysis Based on CiteSpace 2.1. Data Collection, Screening, and Parameter Setting To ensure the comprehensiveness and rigor of the literature sample, this study selected the Web of Science Core Collection (WoSCC) as the primary retrieval database, which is consistent with established practices in tourism and information systems research for accessing high-impact, peer-reviewed studies [22]. The retrieval process followed a stepwise, iterative approach to refine the sample and minimize bias, as detailed below: (1) Theme-based initial retrieval. To address potential omissions caused by synonymy in key constructs, this study simultaneously searched for literature using four core thematic terms: “continued use intention,” “continuance intention,” “continuance usage intention,” and “usage continuance https://booking.com/ 3 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate willingness.” This initial search yielded 6,923 records, capturing a broad range of studies on long-term user engagement with digital platforms, including online travel services [11]. (2) Time period restriction. To focus on recent research trends (2014–2024) and align with the rapid evolution of online travel platforms (e.g., rise of hybrid models, post-pandemic digital adoption), the retrieval time frame was limited to this decade. As of April 2024, this step reduced the sample to 5,353 records, ensuring relevance to contemporary platform dynamics [14, 23]. (3) Language filtering. Given the global scope of online travel research and to maintain consistency in analysis, only English-language literature was retained. This step excluded non-English records, resulting in 5,324 eligible records. (4) Discipline category refinement. To narrow the sample to literature closely aligned with the study’s focus (consumer trust and continuance intention in online travel), retrieval categories were restricted to four core disciplines: “Information Science & Library Science,” “Hospitality, Leisure, Sport, Tourism,” “Computer Science & Information Systems,” and “Management.” This refinement yielded 1,569 records, excluding irrelevant fields and ensuring alignment with key theoretical bases [21]. To prioritize high-influence studies and reduce noise from low-impact work, this study selected literature with a citation frequency of ≥50 times consistent with criteria for identifying seminal or widely recognized research in bibliometric analyses [24]. This step yielded 310 records. Additionally, the 311th record was retained despite a citation frequency of 49 times, as it was marked as a “hot paper” by WoS (indicating recent high download and citation momentum), ensuring coverage of emerging trends (e.g., hybrid platform dynamics) [14]. In total, the screening process resulted in 311 valid retrieval records, forming the core sample for subsequent bibliometric analysis. To systematically map the knowledge structure, evolutionary trends, and research hotspots of literature on consumer trust in online travel platforms (websites and mobile apps), this study employed CiteSpace 6.1.R6 (64-bit) a widely validated tool for visualizing academic networks, identifying core authors/institutions, and tracking emerging frontiers in fields like tourism technology and information systems [3, 25]. The analysis integrated data from the 311 core records and supplementary high- quality studies retrieved from WoSCC and Scopus, ensuring coverage of both seminal works [1] and recent contributions [1, 6]. CiteSpace parameters were set to balance granularity and interpretability, as follows: (1) Time slicing: 2014–2024, with a 2-year time slice. This interval captures evolutionary shifts (e.g., from single- function to hybrid travel platforms) without excessive fragmentation of the network; (2) Node types: “author,” “institution,” “keyword,” and “reference” were selected. This multi-node approach enabled analysis of three key dimensions: collaboration networks (authors and institutions) to identify research clusters (e.g., Swansea University’s work on travel technology continuance), knowledge sources (references) to trace theoretical foundations (e.g., TAM, ISS model), and research focus (keywords) to map hotspots [21]; (3) Pruning: “Pruning sliced networks” and “Pruning merged networks” functions were enabled. This simplified visualizations by removing redundant connections while retaining core links, which is critical for interpreting large-scale citation networks [26]. (4) Threshold: Top 50 nodes per time slice were retained. This focus on high-impact nodes ensured the analysis prioritized influential literature, authors, and institutions. 2.2. Author and Institution Collaboration Network To obtain the core author collaboration network in the field of continuance adoption intention research, this study conducted a visual analysis of the author collaboration network of the sample literature (see Figure 1). As shown in Figure 1, most scholars complete their research through collaborative efforts. Among them, Rana et al. [27] have close collaborations with other scholars, forming relatively large research teams. The network density is 0.0041, indicating that the overall collaborative relationships in the field of continuance adoption intention research are currently relatively loose, often appearing in the form of small groups. In addition, there are a large number of collaborations in this research field, with some research groups showing high collaboration density, but 4 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate such collaborations are not widespread. Except for fixed research teams, most scholars have little academic exchange, and no large-scale research teams have been formed yet. Overall, the collaborative relationships among scholars in the field of continuance adoption intention research from 2014 to 2024 present the characteristic of "small clusters and large dispersion." Figure 1. Collaborative network mapping of authors (2014-2024). This study conducted a visual analysis of the institutional collaboration network based on the sample literature (see Figure 2). As shown in Figure 2, over the past decade, Swansea University has published the largest number of papers in the field of continuance adoption intention research, with 11 papers published since 2015. It is followed by Harbin Institute of Technology, Huazhong University of Science and Technology, Kyung Hee University, and City University of Hong Kong. Scholars from these institutions began to focus on the field of continuance adoption intention research, mainly around 2014-2015. Additionally, over the past decade, collaborations in this field have primarily occurred between universities, with Huazhong University of Science and Technology collaborating more frequently with other institutions (with a connection count of 20). 5 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate Figure 2. Collaborative network mapping of institutions (2014-2024). 2.3. Keyword Co-Occurrence Analysis Keywords are a highly concise summary of the themes of literature. Analyzing high-frequency keywords in literature can reveal research hotspots, trends, and relationships between different research themes in the field. In this study, CiteSpace 6.3.1 was used for data conversion, resulting in 311 converted sample data entries. The time slicing was set to 1 year, and thus the knowledge map of keyword co-occurrence in continuance adoption intention research from 2014 to 2024 was obtained (see Figure 3). As shown in Figure 3, the most frequently occurring keyword is "continuance intention," followed by "Acceptance" and "Satisfaction". 6 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate Figure 3. Keyword co-occurrence graph (2014-2024). Table 1 presents the top 10 high-frequency keywords ranked by centrality. These keywords are closely connected to other keywords and can represent the core research threads of the field. Additionally, each of these keywords appears more than 30 times, ensuring they effectively reflect the research hotspots in the domain of continuance adoption intention. Keyword centrality denotes its mediating capacity within the entire knowledge map: higher centrality indicates more connections with other keywords. We found that "Continuance intention" has the closest links to other keywords, with a centrality of 0.2, and it also ranks highest in occurrence frequency (176 times between 2014 and 2024). Other keywords, such as "Acceptance," "Satisfaction," and "Model," follow in terms of centrality. Most of these high-centrality keywords first emerged prominently around 2014. As shown in Table 1, research in the field of continuance adoption intention primarily focuses on information systems as the research object, with satisfaction identified as a key antecedent of continuance adoption intention. Furthermore, the Technology Acceptance Model (TAM) is the most widely adopted theoretical framework. Table 1. Keyword frequency and centrality (2014-2024). Keywords Centrality Frequency Year Continuance intention 0.2 176 2014 Acceptance 0.15 139 2014 Satisfaction 0.14 103 2014 Model 0.12 72 2014 Adoption 0.12 69 2014 Information technology 0.12 53 2014 Intention 0.11 46 2014 Antecedents 0.11 43 2014 Technology 0.1 39 2014 Technology acceptance model 0.1 35 2014 7 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate (2) Keyword burst analysis. Building on the keyword co-occurrence map, this study extracted the top 20 burst keywords (see Table 2). Burst keywords refer to terms with a high frequency change rate identified through analyzing the temporal distribution characteristics of keywords. Keyword burst analysis can clearly illustrate the evolution of research themes in the field. As shown in Table 2, "Artificial intelligence" exhibited a burst period from 2021 to 2022, indicating it emerged as an academic research hotspot during this timeframe. "Mobile payment" had a burst period spanning 2020 to 2024, suggesting it has been a prominent research focus in academia over this period and is likely to remain so in the near future. Keywords such as "Perceived value," "Intention," "Students," "Customer Engagement," and "Moderating role" demonstrated burst strengths of 2.91, 2.83, 2.79, 2.59, and 2.56, respectively, signifying they became research hotspots during their specific burst periods. Additionally, "Structural equation models" gradually gained prominence as a research focus between 2018 and 2020. Table 2. Keyword burst statistics (2014-2024). Keywords Year of appearance Burst intensity Burst beginning year Burst ending year 2014-2024 Artificial intelligence 2021 3.05 2021 2022 ▂▂▂▂▂▂▂▃▃▂▂ Perceived value 2015 2.91 2019 2021 ▂▂▂▂▂▃▃▃▂▂▂ Intention 2014 2.83 2014 2015 ▃▃▂▂▂▂▂▂▂▂▂ Students 2018 2.79 2018 2019 ▂▂▂▂▃▃▂▂▂▂▂ Customer engagement 2020 2.59 2020 2021 ▂▂▂▂▂▂▃▃▂▂▂ Moderating role 2016 2.56 2020 2022 ▂▂▂▂▂▂▃▃▃▂▂ Unified theory 2015 2.5 2020 2022 ▂▂▂▂▂▂▃▃▃▂▂ Intentions 2014 2.4 2014 2016 ▃▃▃▂▂▂▂▂▂▂▂ Social media 2014 2.36 2020 2021 ▂▂▂▂▂▂▃▃▂▂▂ Community 2014 2.34 2014 2015 ▃▃▂▂▂▂▂▂▂▂▂ Empirical examination 2014 2.25 2018 2020 ▂▂▂▂▃▃▃▂▂▂▂ User satisfaction 2018 2.23 2018 2020 ▂▂▂▂▃▃▃▂▂▂▂ Information technology usage 2019 2.13 2019 2020 ▂▂▂▂▂▃▃▂▂▂▂ Consequences 2014 2.08 2014 2017 ▃▃▃▃▂▂▂▂▂▂▂ Performance 2016 2.06 2016 2019 ▂▂▃▃▃▃▂▂▂▂▂ Mobile payment 2017 2.05 2020 2024 ▂▂▂▂▂▂▃▃▃▃▃ Continued use 2014 2.05 2014 2016 ▃▃▃▂▂▂▂▂▂▂▂ Planned behavior 2016 2.01 2016 2018 ▂▂▃▃▃▂▂▂▂▂▂ Structural equation models 2018 1.95 2018 2020 ▂▂▂▂▃▃▃▂▂▂▂ Systems 2014 1.94 2018 2020 ▃▃▂▂▂▂▂▂▂▂▂ (3) Keyword clustering analysis. Keyword clustering was performed using CiteSpace 6.3.1, and the keyword clustering map for user continuance adoption intention is presented in Figure 4. As shown in Figure 4, 10 cluster themes were identified, namely: flow experience, teaching/learning strategies, UTAUT, online learning, social media, organizational commitment, technology continuance, self- determination theory, literature review, and mobile payment services. Table 3 lists the top 5 focus keywords for each cluster theme. Based on the keyword clustering results, this study categorizes the cluster themes into two major types: (1) Methodological and theoretical themes, which include clusters #2 (Teaching/Learning strategies), #6 (Organizational commitment), #7 (Technology continuance), and #8 (Self-determination theory). These clusters summarize the core research methods and theoretical frameworks employed in the field of continuance adoption intention; and (2) Research context themes, which include clusters #0 (Flow experience), #1 (UTAUT), #3 (Online learning), #4 (Social media), #5 (Literature review), and #9 (Mobile payment services). This indicates that in recent 8 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate years, scholars’ research on continuance adoption intention has primarily concentrated on contexts such as online learning, social media, and flow experience. Figure 4. Knowledge graph for keyword clustering (2014-2024). Table 3. Keyword cluster analysis. Serial No. Cluster Label Silhouette Value S Number of Keywords Research Focus（Top 5 keywords） #0 flow experience 0.828 39 technology acceptance model; technology adoption; context- specific model; general model; theory development #1 teaching/learning strategies 0.835 37 planned behavior; social networking sites; flow theory; learning strategies; distance education #2 UAUT 0.865 34 continuance intention; customer value perspectives; social apps; smart-shopping perception; artificial intelligence #3 online learning 0.78 33 online learning; success model; transformational leadership; balance theory; cultural heritage #4 social media 0.82 33 social media; consumer motivations; high street fashion retailers; consumer behavior; customer loyalty #5 organizational commitment 0.792 31 continuance intention; social media marketing activities; social identification; purchase intention; participation intention #6 technology continuance 0.864 31 continuance intention; technology acceptance model; expectations confirmation theory; purchase behavior; online games #7 self- determination theory 0.787 29 continuance intention; mobile payment platform; adult learning; digital literacy; continuing professional development #8 literature review 0.872 22 interface; interactivity; model; moderating role; knowledge #9 mobile payment services 0.77 20 mobile payment services; continued usage; mobile wallets; developing countries; engagement 9 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate 3. Analysis of Influencing Factors on Users' Continuance Adoption Intention Based on Systematic Literature Review Through a systematic literature review, this study systematically sorts out and summarizes the influencing factors of users' continuance adoption intention. Existing research indicates that the decision-making process of users' continuance adoption behavior is jointly shaped by multi-dimensional factors, which can be categorized into core influencing factors and boundary moderating factors. 3.1. Core Influencing Factors 3.1.1. Perceived Value Perceived value is regarded as the core driver of users' continuous usage behavior, as it forms an overall evaluation of products or services by integrating functional benefits and emotional satisfaction. Studies have shown that perceived value directly and positively affects continuance adoption intention, particularly in scenarios such as mobile payment and online learning [20, 28]. For example, Leung et al. [20] found in their research on travel applications that each unit increase in users' perceived value of service efficiency and personalized experience is associated with a 0.37-unit increase in continuance usage intention. This effect is further reinforced in digital service contexts, where users’ trade-offs between perceived benefits and costs directly determine their long-term engagement [19]. 3.1.2. User Trust Trust is a critical factor in reducing users' perceived risk and promoting long-term usage, especially in the initial stage of technology acceptance. Agag and El-Masry [7] confirmed through a longitudinal study on mobile commerce users that trust indirectly strengthens continuance adoption intention by reducing uncertainty, and this effect is more pronounced in social media contexts. Additionally, research by Jiang and Lau [6] highlighted that platform security and information transparency are core antecedents of user trust, noting that trust acts as a "bridge" between technical attributes and sustained usage behavior in sharing economy platforms. 3.1.3. Technology Characteristics The ease of use and usefulness of technology, as core variables in the Technology Acceptance Model (TAM), significantly influence continuance adoption intention. Kim et al. [8] conducted a meta- analysis in the field of information systems and found that the effect size of perceived ease of use on continuous usage is slightly lower than that of perceived usefulness, but the former plays a more prominent role among elderly user groups. The UTAUT model further expands this field, and Lim et al. [29] confirmed that the interaction between technological characteristics (e.g., performance expectancy) and social influence can enhance continuance adoption intention, particularly in educational technology contexts where peer recommendations amplify the impact of perceived usefulness. 3.2. Boundary and Moderating Factors 3.2.1. Individual Differences Individual characteristics such as age, technical proficiency, and innovativeness moderate the strength of core factors. For example, Lim et al. [29] found that younger users are significantly more sensitive to technological characteristics than older users, while highly innovative groups are more likely to be driven by social influence to continue using a product. Moreover, Liu et al. [23] noted that among student groups, continuance adoption intention toward online learning tools is more susceptible to the moderation of perceived enjoyment, as educational scenarios inherently emphasize both instrumental and hedonic needs. 3.2.2. Contextual Factors The stability and complexity of usage scenarios alter the influence paths of core factors. In mobile payment scenarios, policy support in the external environment can strengthen the positive effect of trust 10 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate on continuance adoption [30] as regulatory frameworks reduce institutional uncertainty. Conversely, in social media contexts, information overload weakens the role of perceived value, as users’ cognitive resources become depleted when processing excessive content [24]. 3.2.3. Moderating Variables Studies have confirmed that perceived risk and switching costs are important moderating variables. Bhattacherjee [1] pointed out that when perceived risk is high, the impact of trust on continuance adoption is enhanced, as users rely more on trust to mitigate potential losses. Anaya-Sánchez et al. [31] further supplemented that high switching costs weaken the effect of perceived value, as users may continue using a service out of inertia rather than positive evaluation, especially in subscription-based service models. In summary, the formation of users' continuance adoption intention is the result of dynamic interactions between core factors and boundary conditions. The effect of each factor varies across contexts, providing a multi-dimensional theoretical basis for subsequent model construction. Future research should further explore how these factors co-evolve in emerging scenarios such as artificial intelligence services. 3.3. Limitations of the Current Perspectives Existing studies have explored the influencing factors of user adoption or continuance adoption in the contexts of digital platforms such as short-video applications [10], mobile learning platforms [11], mobile shopping platforms [12], and mobile applications [13]. Their findings confirm that satisfaction, trust, perceived usefulness, and other factors significantly impact users' continuance adoption intention. Meanwhile, existing research has examined user trust [9] and review usefulness [15, 16] in the context of single-type online tourism platforms, whether transactional or review-based; these literatures contribute to understanding users' continuance adoption intention toward online tourism platforms. However, there remain the following research gaps: (1) Research on online tourism platforms is limited to single-function platforms (either transactional or review-based), lacking studies on hybrid online tourism platforms. Currently, most tourism platforms have gradually evolved into hybrid models that integrate both transaction-facilitating and review-publishing functions. However, existing literature mostly focuses on transactional or review-based platforms individually, and the research models established in these studies, which only involve single-function platforms, may not be applicable to hybrid online tourism platforms. (2) Although a few studies have considered the impact of factors such as trust on users' continuance adoption intention toward hybrid online tourism platforms, they still lack the design of relevant influencing factors based on the characteristics of review-based platforms, such as users' perceived hedonicity and perceived interactivity. Since hybrid online tourism platforms integrate transaction and review functions, users can not only complete online transactions but also interact with other users or service providers through the platform, and derive pleasure from reading review texts, images, and videos. Such interactive behaviors and pleasure will affect their overall experience of using the platform, thereby influencing their post-adoption behaviors. However, to our knowledge, existing literature has rarely considered the impact of users' perceived interactivity and perceived hedonicity brought by review-based tourism platforms on their continuance adoption intention. (3) Existing studies on the influencing factors of user adoption of online tourism platforms have mostly focused on psychological and emotional factors, while lacking research on the impact of external platform characteristics on users' perceived value. Hybrid online tourism platforms are typical two-sided platforms, and network externality is a fundamental characteristic of such platforms. Network externality reflects the change in utility that each user obtains from a product or service when the quantity of a service or product, or the number of its users, changes in a two-sided or multi-sided platform. Nevertheless, existing literature has rarely considered the impact of the network externality of hybrid online tourism platforms on users' continuance adoption intention. 11 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate 4. Future Research Agenda Based on the identified research gaps in the field of users’ continuance adoption intention toward hybrid online tourism platforms, this section outlines several promising directions for future research, integrating theoretical extensions and empirical opportunities supported by existing literature. 4.1. Investigating The Dynamic Interplay of Functional Attributes in Hybrid Platforms Hybrid online tourism platforms uniquely combine transactional and review-based functionalities, creating a complex ecosystem where users switch between purchasing, reviewing, and social interaction. Future studies should explore how these dual functions interact to shape continuance intention. For instance, research could examine whether high-quality user reviews influence user loyalty and platform engagement [15], amplify the positive effect of transaction efficiency on trust, or whether frequent transactional behavior increases users’ willingness to engage in review posting (e.g., through gamification mechanisms). Longitudinal studies, similar to those by Agag and El-Masry [7] on mobile commerce, tracking how users’ priorities shift between functional (transaction) and hedonic (review) needs over time, can provide insights into lifecycle-based retention strategies. 4.2. Exploring Perceived Interactivity and Hedonicity in Review-Driven Contexts As highlighted earlier, perceived interactivity (e.g., real-time responses to reviews) and perceived hedonicity (e.g., enjoyment from multimedia reviews) are understudied in hybrid platforms, despite their potential to influence post-adoption behavior. Future research could operationalize these constructs and test their relationships with continuance intention, drawing on theories of flow experience [32] and technology acceptance. For example, does interactive feedback from service providers on user reviews enhance perceived control, thereby strengthening continuance intention? Additionally, cross-cultural studies might investigate whether hedonic motivations (e.g., sharing travel experiences via videos) vary across regions, as observed in Liu et al. [23] research on Chinese tourists’ app usage, which emphasizes context-specific emotional drivers. 4.3. Examining Network Externality as a Key Platform Characteristic Hybrid online tourism platforms, as two-sided markets connecting users and service providers, are inherently subject to network externalities where the value of the platform increases with the number of participants [21]. Future studies should quantify how network effects (e.g., growing user bases or increasing service provider participation) moderate core relationships in continuance models. For instance, does a larger user network strengthen the impact of perceived value on continuance intention by reducing search costs? Or does excessive user growth diminish perceived interactivity (e.g., through information overload), as noted by Talwar et al. [24] in social media contexts? Integrating network externality into existing frameworks such as UTAUT or ECM could provide a more comprehensive understanding of platform sustainability. 4.4. Addressing Contextual and Demographic Heterogeneity Existing research on technology continuance has highlighted the role of individual differences [29] and contextual factors [30] in shaping user behavior. Future studies could explore how demographic variables (e.g., age, travel experience) and situational factors (e.g., trip type: leisure vs. business) moderate the effects of trust, perceived value, and network externality in hybrid platforms. For example, are younger users more sensitive to hedonic features (e.g., interactive reviews) than older users, who may prioritize transactional reliability? Similarly, research could investigate how external shocks (e.g., health crises or policy changes) alter the relative importance of platform attributes, building on insights from Yan et al. [26] on online technology continuance during disruptions. 12 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 1-13, 2025 DOI: 10.55214/2576-8484.v9i12.11247 © 2025 by the authors; licensee Learning Gate 4.5. Advancing Methodological Approaches To capture the complexity of hybrid platforms, future research could adopt mixed-methods designs. Quantitative studies using structural equation modeling (as in Kim et al. [8]) could test hypothesized relationships, while qualitative approaches (e.g., interviews or netnography) could explore users’ lived experiences with dual functionalities. Additionally, experimental designs could simulate changes in network size or review quality to isolate their causal effects on continuance intention. Big data analytics, such as analyzing user-generated content or clickstream data, could further uncover behavioral patterns unobservable through traditional surveys [31]. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Funding: This research was supported by the Humanities and Social Science Research Project of the Ministry of Education of China (Grant Number: 22YJA630040). Copyright: © 2025 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] A. Bhattacherjee, "Understanding information systems continuance: An expectation-confirmation model," MIS Quarterly, vol. 25, no. 3, pp. 351-370, 2001. https://doi.org/10.2307/3250921 [2] K. Choi, Y. Wang, and B. 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