Exploring the digital tourism ecosystem: unveiling user behavior through social network analysis

Abstract

The global tourism industry’s digital transformation necessitates systematic understanding of how users navigate interconnected platform ecosystems, particularly in emerging markets where unique institutional constraints shape user behavior patterns. This study addresses the critical gap in comprehending structural dynamics within digital tourism ecosystems by applying Social Network Analysis to examine platform interdependencies and user transition flows. The research employs a four-phase methodological framework encompassing data collection, network formation, community detection, and structural analysis to investigate Iran’s digital tourism ecosystem. Using Alexa traffic data and expert validation, 162 tourism-related websites were identified and analyzed through directed network modeling. The Louvain modularity optimization algorithm was applied to detect community structures, while weighted edge analysis quantified inter-platform user transitions. The analysis revealed eight distinct service communities representing functionally cohesive clusters including ticket booking, accommodation services, location-based platforms, and culinary offerings. Community detection achieved a modularity score of 0.634, indicating robust structural segmentation. Inter-community flow patterns demonstrated that ticket and tour booking platforms function as central orchestration hubs, facilitating 31.9% of ecosystem activity and exhibiting strong directional flows to complementary services. Conversely, specialized services such as online taxi platforms and culinary websites showed minimal integration with core planning communities, representing structural gaps in ecosystem connectivity. These findings provide actionable insights for service providers seeking enhanced platform interoperability, investors identifying innovation opportunities, and policymakers designing supportive regulatory frameworks. The study contributes a replicable analytical framework for mapping tourism ecosystems globally while advancing Service Ecosystem Theory through empirical demonstration of platform-mediated value co-creation in digitally constrained environments.

Introduction

The global tourism industry is undergoing a structural transformation catalyzed by rapid digitalization, shifting consumer expectations, and the widespread adoption of online platforms that mediate nearly every aspect of travel planning and service (Büyüközkan and Ergün 2011; Christou et al. 2023; Pencarelli 2020). As digital interfaces increasingly shape traveler interactions—from personalized itinerary development to real-time booking confirmations—understanding how users navigate, engage with, and transition across platforms has become a key determinant of strategic success and operational resilience (Ozdemir et al. 2023b; Panda and Khatua 2025). This digital evolution calls for nuanced, system-level insights into user behavior, as travel decisions are no longer made in isolation but are embedded in dynamic, multi-platform interaction sequences (Kozak and Buhalis 2019). Despite this growing complexity, academic and industry research on user behavior across tourism platforms remains fragmented—particularly in contexts characterized by localized constraints in infrastructure, regulation, and digital maturity (Pasca et al. 2021; Yanes et al. 2019).

While existing scholarship has made significant strides through the use of artificial intelligence, big data analytics, and machine learning to model user preferences and predict travel behavior (Pasca et al. 2021; Yanes et al. 2019), such approaches often treat platforms as isolated systems rather than components of an interconnected digital ecosystem. As a result, they frequently neglect the underlying relational structures and interdependencies that shape user flows. Social Network Analysis (SNA) addresses this gap by conceptualizing platforms as nodes and user transitions as directed edges, enabling the identification of emergent communities, central actors, and directional value flows (Camacho et al. 2020; HabibAgahi et al. 2022; Maghsoudi et al. 2023). This perspective offers critical leverage for optimizing platform interoperability, service integration, and user experience design. Nonetheless, the application of SNA in tourism research remains underdeveloped, particularly in emerging digital markets where platform ecosystems are still forming and are strongly influenced by socio-cultural, economic, and infrastructural particularities.

This study integrates Service Ecosystem Theory, derived from the Service-Dominant Logic (SDL) paradigm, which views value as co-created through dynamic interactions and resource integration rather than embedded in isolated outputs (Vargo and Lusch 2008, 2014). Within this framework, each digital tourism platform acts as a service provider, a resource integrator, and a co-creator of value (Lusch and Nambisan 2015). Adner (2017) Innovation Ecosystem framework complements this view by focusing on the interdependencies among service actors—such as transport, lodging, and local experience providers—whose coordinated interactions yield integrated, personalized travel experiences. Through this combined lens, websites and user flows are analyzed not as static entities, but as components of a dynamic ecosystem in which communities emerge, evolve, and shape value-in-use.

To address the gap in understanding how these ecosystems form and function, this study pursues three interrelated research questions grounded in SNA and ecosystem theory:

• 1. RQ1: What distinct communities emerge within a tourism website ecosystem when analyzed through the lens of user navigation patterns, and how do these communities reflect functional service synergies?

• 2. RQ2: How do inter-community interactions—quantified through edge weights and directional flows—reveal latent user preferences and decision-making hierarchies?

• 3. RQ3: What strategic interventions can stakeholders prioritize to enhance ecosystem cohesion, service integration, and user satisfaction, informed by network centrality and community structure?

The methodological framework applies Social Network Analysis to a network of tourism websites drawn from a digitally maturing market. While empirical data originate from Iran—where unique factors such as regulatory processes, internet infrastructure variability, and culturally driven travel norms influence user behavior—the SNA approach and the resulting community structures and value-flow hierarchies remain applicable to other national contexts. By explicitly accounting for these context-specific dynamics, the analytical insights become scalable for global tourism ecosystems facing similar patterns of platform interdependence and digital transformation.

By synthesizing network theory with behavioral analytics and embedding the analysis within a service ecosystem perspective, this study contributes to both theory and practice. Academically, it formalizes a replicable methodology to analyze platform interactions and identify emergent service structures. Practically, it equips providers, investors, and policymakers with a decision-making toolkit to optimize service design, strengthen value co-creation, and foster innovation within increasingly interconnected digital tourism ecosystems.

The remainder of this article is structured as follows: Section 2 reviews the existing literature on Social Network Analysis (SNA) and the digitization of tourism, including the Service Ecosystem Theory that underpins this study. Section 3 outlines the methodology employed, including data collection, network construction, and the analytical techniques used. Section 4 presents the findings, focusing on community structures and inter-cluster dynamics within the tourism ecosystem. Section 5 provides a comprehensive discussion, addressing the research questions, management implications, limitations, and future research directions. Section 6 concludes by summarizing the key contributions and broader implications for tourism industry stakeholders.

Literature Review

Advancing an understanding of the structural and behavioral dimensions of tourism in the digital age requires more than descriptive accounts of industry practices; it calls for an integrative framework that situates tourism within the broader service ecosystem paradigm and links sector-specific transformations to underlying theoretical constructs. Accordingly, this section develops the conceptual foundations of the study along three interrelated strands. First, tourism is introduced as a complex service ecosystem in which diverse stakeholders interact to co-create value across accommodation, transportation, gastronomy, and entertainment services. Second, Service Ecosystem Theory and its extensions are examined to explain how institutional arrangements, resource integration logics, and multi-level dynamics shape value co-creation processes beyond dyadic encounters. Third, the analytical potential of Social Network Analysis (SNA) is reviewed as a methodological lens for mapping inter-platform interdependencies, identifying emergent sub-ecosystems, and uncovering structural hierarchies within digital tourism environments. Together, these perspectives establish a theoretically grounded basis for investigating the Iranian tourism website network as an emergent, digitally mediated service ecosystem.

The Tourism Service Ecosystem

The tourism sector constitutes a complex, service-centric ecosystem that spans accommodation, transportation, food and beverage, entertainment, and specialized services, collectively driving significant global economic value through output, employment, and tax revenues (Adekuajo et al. 2023) (Rwigema 2021). Its integrated nature means travelers co-create their experiences by navigating seamlessly between lodging, transit, and local activities (Prodinger and Neuhofer 2023), while high service quality remains essential for satisfaction and loyalty (Harkison 2022). Competitive pressures compel continuous innovation in business models and digital offerings (Xu et al. 2020), and a diverse stakeholder network—including providers, intermediaries, investors, policymakers, and tourists—shapes ecosystem dynamics (Morant-Martínez et al. 2019).

Analyzing user behavior within this ecosystem is strategically vital: it uncovers key touchpoints and unmet needs for service customization (Gursoy et al. 2022; Holloway and Humphreys 2022), informs investment by highlighting growth segments (Gupta 2019), and supports policy design for sustainable and inclusive tourism development (Chen et al. 2021; Kwok 2023). By framing our research in this service ecosystem context, we emphasize the importance of mapping user flows and community structures to enhance value co-creation and foster a competitive, resilient digital tourism landscape.

Service Ecosystem Theory and Value Co-creation

Service Ecosystem Theory emerges from the Service-Dominant Logic (SDL) paradigm, which reconceptualizes economic exchange as fundamentally service-oriented rather than goods-centered. According to Vargo and Lusch (2016), service is “the application of competencies (knowledge and skills) for the benefit of another actor” and value is not embedded in outputs but co-created through dynamic, actor-to-actor interactions within an ecosystem of resources and institutions (Vargo and Lusch 2008, 2014). This shift foregrounds several core concepts (Fig. 1):

• Resource Integration: Actors (individuals, firms, platforms) combine operant resources (skills, knowledge) and operand resources (tangible assets) to co-produce value.

• Value-in-Use: Value emerges only during actual service usage, shaped by context and interaction dynamics.

• Institutional Arrangements: Norms, rules, and governance structures guide how resources are accessed, exchanged, and recombined across service networks.

Fig. 1

The Service Ecosystem Cycle illustrating resource integration, value-in-use, and institutional arrangements.

Digital platforms—such as tourism websites—instantiate this ecosystemic view by enabling multi-actor resource integration at scale. Lusch and Nambisan (2015) extend SDL to Digital Service Ecosystems, highlighting how information technology mediates resource exchange, flattens traditional value chains, and fosters new forms of stakeholder collaboration (Lusch and Nambisan 2015). In a digital tourism context, each website acts simultaneously as (a) a service provider, offering booking, information, or community-based features; (b) a resource integrator, aggregating content from third parties; and (c) a value co-creator, as travelers interact with and between platforms to shape their unique experiences.

Adner (2017) Innovation Ecosystem framework further sharpens our understanding by focusing on the interdependencies among actors whose combined activity yields an innovation outcome. In tourism, innovation emerges when transport, accommodation, local-experience, and ancillary services orchestrate their offerings—often mediated by digital platforms—to deliver seamless, personalized journeys. Key insights from these theoretical perspectives include:

• 1. Sub-Ecosystem Identification: Communities detected via Social Network Analysis (SNA) correspond to sub-ecosystems of service co-creation (e.g., ticketing networks, accommodation clusters). Each cluster represents a tightly coupled group of actors whose resource exchanges generate distinct value propositions for users.

• 2. Value-Flow Dynamics: Directed edges and edge weights in the network map value-in-use pathways, illustrating how users migrate from one service offering to another—reflecting preferences, complementarities, and sequential resource integration.

• 3. Centrality and Structural Holes: Central platforms function as orchestration hubs, coordinating resource flows and governing user experiences. Conversely, platforms bridging unconnected communities occupy structural holes, representing strategic opportunities for innovation and value expansion.

By embedding our research within Service Ecosystem Theory and its digital and innovation extensions, we achieve four scholarly benefits:

• Conceptual Clarity: We frame RQ1 as the identification of service sub-ecosystems, RQ2 as the analysis of value-flow hierarchies, and RQ3 as the design of interventions to strengthen co-creation capacity.

• Methodological Rationale: SNA emerges as a natural tool to operationalize resource integration and value flows, translating abstract SDL constructs into measurable network metrics (e.g., modularity, centrality).

• Practical Relevance: Insights into sub-ecosystems inform providers where to focus integration efforts, while structural-hole analysis guides investors toward high-leverage innovation nodes.

• Theory Advancement: Our empirical mapping of a national tourism service ecosystem contributes to SDL and digital ecosystem literature by demonstrating how community detection reveals latent co-creation structures and informing theoretical models of platform orchestration.

While the foregoing discussion outlines tourism as a service-centric ecosystem shaped by diverse stakeholders and dynamic market forces, a deeper theoretical lens is required to capture the institutional, structural, and behavioral mechanisms that underpin value co-creation in such environments. Accordingly, the following section introduces Service Ecosystem Theory as the conceptual foundation of this study, elaborating on institutional arrangements, resource integration logics, and multi-level dynamics that extend beyond descriptive industry characteristics to explain the systemic organization of digital tourism ecosystems.

Theoretical Foundations of Digital Tourism Service Ecosystems

Service Ecosystem Theory, grounded in Service-Dominant Logic (SDL), transcends traditional linear value chain conceptualizations by emphasizing dynamic, multi-actor resource integration within institutionally governed environments (Vargo and Lusch 2016). While previous tourism research has predominantly focused on dyadic service encounters, ecosystem theory recognizes that value emerges through complex webs of interconnected actors whose interactions are mediated by institutional arrangements, technological infrastructures, and shared meaning systems (Akaka et al. 2013; Edvardsson et al. 2011).

The theoretical foundation of service ecosystems rests on three fundamental pillars that extend beyond mere structural mapping: institutional arrangements as governance mechanisms, resource integration logics as value-creation processes, and multi-level dynamics that connect individual behaviors to ecosystem-wide outcomes (Vargo and Akaka 2012). These dimensions collectively shape how digital platforms function not merely as isolated service providers, but as interconnected nodes within broader value-creation networks.

Institutional Arrangements and Ecosystem Governance

Institutional arrangements represent the rules, norms, and governance structures that enable or constrain actor participation within service ecosystems (Vargo and Lusch 2016). In digital tourism contexts, these arrangements manifest through multiple layers of governance that influence platform interactions and user navigation patterns. Regulatory frameworks establish the boundaries within which platforms can operate, while technological standards determine interoperability possibilities (Breidbach and Brodie 2017).

The institutional lens reveals that platform centrality and community formation are not merely emergent properties of user behavior, but are fundamentally shaped by underlying governance mechanisms. Dominant platforms achieve their orchestrative capacity through institutional legitimacy—their ability to establish and enforce norms that guide ecosystem-wide resource flows (Koskela-Huotari et al. 2016; Koskela-Huotari and Vargo 2016). This theoretical insight explains why certain platforms become central nodes while others remain peripheral, moving beyond descriptive network metrics to uncover the institutional logics that drive ecosystem architecture.

Technological infrastructures serve as institutional arrangements by establishing the technical possibilities for resource integration (Lusch and Nambisan 2015). Application programming interfaces (APIs), data sharing protocols, and platform design choices create structural constraints that influence how users can navigate between services and how platforms can integrate their offerings (Barrett et al. 2015). These technological institutions are particularly critical in understanding the isolation of certain service communities, as they may reflect institutional barriers rather than mere user preferences.

Resource Integration Logics and Value Co-Creation Mechanisms

Resource integration represents the core process through which actors combine their operant resources (knowledge, skills, capabilities) and operand resources (tangible assets, data) to create value-in-use (Vargo and Lusch 2014). However, the mechanisms of resource integration in digital ecosystems operate through complex feedback loops that involve multiple stakeholder groups beyond the traditional producer-consumer dyad (Storbacka et al. 2016).

In digital tourism ecosystems, resource integration occurs through three primary mechanisms that extend beyond simple user navigation patterns. First, platform orchestration involves dominant actors coordinating resource flows across multiple service domains, acting as institutional entrepreneurs who establish the rules and standards that guide ecosystem behavior (Helfat and Raubitschek 2018). Second, complementary specialization emerges as different platform communities develop specialized capabilities that enhance the value propositions of other ecosystem participants (Jacobides et al. 2018). Third, recursive value creation occurs as user interactions generate data and feedback that become operant resources for continuous service improvement and innovation (Grover and Kohli 2012).

The theoretical significance of these mechanisms lies in their explanation of why certain inter-platform transitions occur while others do not. Strong transition flows between ticket booking and accommodation platforms reflect complementary resource integration logics, where transportation decisions create information resources (travel dates, destinations, preferences) that enhance accommodation value propositions (Chandler and Vargo 2011). Conversely, weak integration between core planning platforms and experiential services (food, local attractions) suggests institutional or technological barriers that prevent effective resource complementarity.

Multi-level dynamics: from micro-interactions to macro-structures

Service ecosystem theory emphasizes the recursive relationship between micro-level individual behaviors and macro-level institutional structures, mediated by meso-level organizational arrangements (Vargo and Akaka 2012). This multi-level perspective is essential for understanding how individual user navigation choices aggregate into ecosystem-wide community structures and flow patterns.

At the micro-level, individual users engage in resource integration activities as they navigate between platforms, combining their personal knowledge, preferences, and circumstances with platform-provided information and services (Helkkula et al. 2012). These micro-interactions are guided by individual service logic—the mental models that users employ to make sense of service offerings and their potential value-in-use (Heinonen et al. 2010).

The meso-level encompasses the organizational arrangements and platform strategies that shape how micro-interactions can occur. Platform design decisions, partnership agreements, and business model choices create the structural possibilities within which users can integrate resources (Cusumano et al. 2019). Meso-level arrangements also include the communities of practice that emerge around specific service domains, creating shared understanding and behavioral norms that influence user decision-making (Wenger 1999).

At the macro-level, institutional logics and technological infrastructures establish the broader context within which both micro-interactions and meso-arrangements operate (Thornton et al. 2012). Regulatory environments, cultural norms, and technological standards create the institutional field that shapes ecosystem evolution and governance (Koskela-Huotari and Vargo 2016).

The theoretical contribution of this multi-level perspective lies in its explanation of ecosystem stability and change. Strong community structures emerge when micro-level user behaviors align with meso-level platform arrangements and macro-level institutional logics, creating reinforcing feedback loops that stabilize particular configuration patterns (Vargo and Akaka 2012). Conversely, weak inter-community connections may signal misalignment between these levels, suggesting opportunities for institutional entrepreneurship and ecosystem reconfiguration.

Theoretical synthesis: toward dynamic ecosystem understanding

The integration of institutional arrangements, resource integration mechanisms, and multi-level dynamics provides a comprehensive theoretical lens for understanding digital tourism ecosystems that moves beyond descriptive network mapping. This framework reveals that observed network structures are not simply emergent properties of user behavior, but are fundamentally shaped by the institutional contexts, technological infrastructures, and governance mechanisms that enable or constrain actor participation and resource integration.

The theoretical implications extend beyond tourism to broader understanding of digital service ecosystems. Platform centrality reflects not merely popularity or user preference, but institutional legitimacy and orchestrative capability within governance structures (Gawer 2014). Community formation represents not just functional similarity, but shared institutional logics and complementary resource integration possibilities (Adner 2017). Inter-community flows indicate not merely user transitions, but the presence or absence of institutional arrangements that enable cross-domain resource integration.

This theoretical synthesis provides the conceptual foundation for interpreting empirical network patterns as manifestations of deeper ecosystem dynamics, enabling researchers and practitioners to move beyond descriptive observations toward predictive and prescriptive insights about ecosystem development and governance.

Related Work

The increasing complexity and dynamism of the tourism industry in the digital era have prompted a paradigm shift from linear value chains to interconnected service ecosystems (Vargo and Lusch 2016). Within these ecosystems, value is co-created through multidimensional interactions among diverse stakeholders—including tourists, platforms, destination management organizations (DMOs), and local communities—mediated by digital technologies (Gretzel 2022; Trunfio and Della Lucia 2019). While significant progress has been made in understanding individual actors and bilateral relationships, a more holistic ecosystem-level perspective that integrates network structures, platform interdependencies, and institutional logics remains underexplored (Lucas 2019; Werthner et al. 2015).

Digital Service Ecosystems in Tourism

Drawing on service-dominant (S-D) logic, the concept of digital service ecosystems (DSEs) has gained traction as a theoretical lens to examine how value is co-created in complex, digitally mediated environments (Vargo and Lusch 2016). In tourism, DSEs encompass not only technological platforms but also the socio-institutional arrangements and resource integration mechanisms that facilitate mutual value creation (Bhuiyan et al. 2022; Borges-Tiago and Avelar 2025). However, much of the current literature focuses narrowly on individual digital innovations (e.g., mobile apps, AI chatbots) rather than the relational architecture that binds stakeholders into cohesive yet evolving ecosystems (Jaakkola et al. 2015; Neuhofer et al. 2015).

Studies such as Esmaeili Mahyari et al. (2024) provide systematic reviews of digital transformations in rural tourism, highlighting the emergence of multi-platform environments. Nevertheless, these reviews rarely interrogate how digital platforms interoperate and compete/cooperate within multi-actor systems, nor how governance and orchestration occur across platforms and institutional boundaries.

Value co-creation and stakeholder integration

Value co-creation, a cornerstone of S-D logic, has been extensively studied in tourism contexts, especially in co-producing experiences with tourists (Campos et al. 2018). However, the majority of these studies adopt a dyadic perspective, examining interactions between tourists and providers without accounting for interdependencies among stakeholder networks (Rihova et al. 2015; Timur and Getz 2008). This fragmented view limits our understanding of how value emerges from multi-layered and recursive interactions, particularly in digitally mediated ecosystems where platform owners, local communities, and third-party service providers continuously shape and reshape offerings (Kohtamäki and Rajala 2016).

Emerging work in service research suggests the need to reconceptualize co-creation not as an isolated act but as a systemic phenomenon embedded in networks and guided by institutional norms (Wieland et al. 2017). In tourism, however, empirical studies on such system-level co-creation remain scarce, particularly those that consider the power asymmetries and coordination challenges among heterogeneous actors (Guarda et al. 2022; Lucas 2019).

Social network perspectives and ecosystem structures

Social network theory offers powerful tools for modeling inter-organizational relationships in tourism (Baggio et al. 2010). Yet, applications remain largely confined to destination-level stakeholder mapping or collaboration networks (Baggio et al. 2010). While valuable, these efforts often overlook the broader ecosystemic interconnections across platforms, markets, and regulatory institutions.

Recent advances in tourism research have begun to integrate ecosystem logic with network analysis, exploring, for example, how network centrality affects value creation or how knowledge flows between actors (Del Chiappa and Baggio 2015). However, the literature still lacks studies that bridge digital service ecosystems and network governance, especially in regard to cross-platform interoperability, resource orchestration, and multi-actor coordination in tourism (Frow et al. 2014).

Furthermore, the structural configurations of tourism ecosystems—such as modularity, density, and brokerage roles—remain under-theorized. There is little understanding of how these network attributes influence innovation diffusion, value capture, or ecosystem resilience (Ritala and Almpanopoulou 2017). Addressing this gap is critical in a context where platformization and digital intermediation are reshaping the rules of engagement in tourism markets.

Network structure in digital tourism ecosystems

Recent empirical research has increasingly drawn upon Social Network Analysis (SNA) to uncover structural characteristics within tourism systems, emphasizing spatial, perceptual, and stakeholder network designs. For instance, Yang et al. (2022) analyze the spatial association network of tourism efficiency among Chinese provinces, revealing how network topology (density, centrality) correlates with regional performance indicators. Li et al. (2022) investigate tourists’ perceived images of a destination via online reviews and use SNA to map relationships among perception attributes, showing that certain image constructs cluster tightly and exert influence across network paths. Moreover, De Martino et al. (2024) in their guest editorial underscore a trend in the field towards integrating SNA in hospitality and tourism research—examining not just co‑authorship or stakeholder links, but also structural properties, relational metrics, and network-wide configurations. These studies highlight gaps in quantifying digital platform ecosystems—particularly inter‑website or inter‑platform network structures formed via user transitions or referral links—which the present study aims to address.

Identified research gap

Although tourism research has advanced in theorizing service ecosystems and value co-creation, it remains limited in its engagement with network-level analytics and ecosystem-wide structures that underpin digital transformation. Existing studies tend to conceptualize value co-creation primarily at the dyadic or organizational level, but they rarely examine how the broader configuration of inter-platform relationships shapes collective outcomes. There is a lack of systematic inquiry into how specific structural features of networks—such as density, centrality, and structural holes—mediate co-creation processes and condition the emergence of collaborative or competitive dynamics. Similarly, limited attention has been paid to the ways in which multiple digital platforms interact, compete, or complement each other within the same tourism ecosystem, and how these interdependencies affect user flows, resource allocation, and innovation opportunities.

Another gap concerns governance and orchestration within digitally mediated ecosystems. While service ecosystem theory highlights the role of institutional arrangements in shaping interactions, few empirical studies investigate how governance is enacted across heterogeneous tourism actors—including platforms, intermediaries, policymakers, and local communities—within a networked digital environment. Consequently, little is known about how coordination mechanisms evolve, how power asymmetries are negotiated, or how rules and norms are operationalized to sustain collaboration and innovation.

This study directly addresses these gaps by adopting a network-analytic perspective to examine digital service ecosystems in tourism. By integrating social network theory with ecosystem logic, the research investigates value co-creation at a structural level, focusing on how communities, central actors, and cross-platform linkages collectively shape ecosystem behavior. In doing so, the study advances a more comprehensive and systemic understanding of stakeholder integration, coordination, and innovation within the rapidly evolving digital tourism landscape.

Methodology

This research employs an exploratory Social Network Analysis (SNA) to uncover the structural architecture of Iran’s digital tourism ecosystem, recognizing that existing theory on multi-site user navigation in emerging markets is still evolving (McLevey et al. 2023; Wasserman and Faust 1994). An exploratory approach allows for data-driven pattern discovery that can later ground formal hypothesis testing and is particularly suited for examining under-researched network configurations in digital service ecosystems (Borgatti et al. 2024). The study unfolds in four integrated phases (Fig. 2), each carefully designed to maintain methodological rigor and reproducibility.

Fig. 2

Research methodology of the study, outlining four integrated phases: Data Collection, Network Formation, Community Detection, and Community Analysis.

Data collection

The initial phase commenced with the systematic identification of Iranian tourism-related websites through a multi-stage filtering approach grounded in established protocols for web-based network data collection. This process began with extracting the top 500 Iranian websites based on Alexa.com traffic rankings, which provided a comprehensive foundation representing the most frequently accessed websites across all categories at the national level.

To narrow this general dataset to the tourism domain, a two-step filtering approach was employed. First, an automated keyword screening was applied using the terms “travel,” “hotel,” and “cultural heritage.” These keywords were deliberately selected to reflect the conceptual scope of the study, focusing on tourism services and user navigation within tourism ecosystems. Their selection was informed by preliminary scoping of tourism-related terminologies in the literature and by expert consultations to ensure contextual validity (Baggio et al. 2010; Pencarelli 2020).

Subsequently, these candidate websites were subjected to an independent validation stage conducted by a panel of five domain experts with extensive professional experience in digital tourism, travel management, and hospitality services. Each expert evaluated the websites individually and identified those that could be credibly categorized as part of the tourism ecosystem. Only websites endorsed by at least three of the five experts were retained for inclusion in subsequent analysis stages. This expert-driven validation ensured both accuracy and contextual alignment, thereby mitigating the limitations of relying solely on automated keyword filtering (Del Chiappa and Baggio 2015).

Through this combined process of traffic-based selection, keyword-driven scoping, and expert validation, the dataset was refined from a general list of the 500 most visited Iranian websites into a targeted subset of platforms directly associated with tourism services and user engagement (see Fig. 3).

Fig. 3

Communities in the network.

Network formation

The second phase involved the construction of a comprehensive network structure based on user transition patterns between tourism-related websites following established principles of relational network construction (Borgatti and Halgin 2011). This process utilized Alexa’s “Similar Sites by Audience Overlap” feature, which identifies websites sharing similar user bases, visitation behaviors, and keyword engagement patterns. For each initially selected tourism-related website, Alexa provided up to five comparable websites, each accompanied by an “overlap score” quantifying the degree of similarity between sites.

To ensure relevance and minimize noise, an inclusion threshold based on overlap score was applied: only websites with a score of 50% or higher were retained for further analysis. This threshold was empirically derived through pilot testing and expert validation, as preliminary observations showed that websites with scores below 50% often lacked direct relevance to tourism, whereas those meeting or exceeding the threshold demonstrated consistent topical and functional alignment (Christou et al. 2023).

Using Python programming language and the BeautifulSoup library, the identified similar websites were systematically extracted and processed¹. This procedure was recursively repeated up to six iterations, with each round generating a refined set of additional relevant sites. The process terminated at the sixth iteration, as subsequent stages either produced duplicates or suggested websites that did not meet the minimum similarity threshold.

To further enrich the dataset, Alexa’s “Referral Sites” feature was utilized to identify the five most common websites that users visit immediately before and after each focal site. These referral links reflect actual user transition patterns and were cross-validated wherever possible. The referral data captured both inbound transitions (websites users accessed prior to the focal tourism site) and outbound transitions (where users typically navigated afterward), providing directional flow information essential for network construction (Vargo and Lusch 2016).

During the network refinement stage, irrelevant nodes were systematically removed to improve analytical clarity and maintain focus on the tourism ecosystem. Specifically, e-commerce platforms such as Digikala.com and payment-related services were excluded, as these websites typically served as transaction completion points rather than tourism service providers. Similarly, social media platforms (e.g., Instagram, Telegram, Twitter) were excluded, as they function as external communication environments rather than internal components of the tourism service ecosystem (Lusch and Nambisan 2015).

The network construction process employed graph theory principles consistent with digital ecosystem modeling approaches (Adner 2017; Jacobides et al. 2018), modeling the tourism ecosystem as a directed, weighted graph where nodes represent individual websites and edges denote user transition relationships. Edge weights reflect the magnitude of user transitions between respective sites, with directional properties capturing the sequential nature of user navigation behavior (Baggio et al. 2010).

Community detection

The third phase implemented community detection analysis to identify functionally cohesive subgroups within the tourism network. Community detection represents a fundamental application of Social Network Analysis, involving the identification of clusters of nodes that exhibit higher internal connectivity relative to external connections (Newman 2006).

The Louvain modularity optimization algorithm was selected for community detection due to its efficiency and performance in large networks and widespread validation in digital network research (Blondel et al. 2008; Lambiotte et al. 2015). This algorithm operates through a two-phase iterative process: initial assignment and community aggregation. In the initial assignment phase, each node is treated as its own community, and the algorithm examines whether moving a node to a neighboring community increases overall modularity. If modularity improvement is achieved, the reassignment is implemented; otherwise, the node remains in place. This process continues until no further improvements are possible.

The community aggregation phase treats newly formed communities as super-nodes in a simplified network, and the optimization process is repeated. The algorithm iterates through these phases until modularity reaches a peak and community structure stabilizes (Fig. 4).

Fig. 4

Stages of community detection based on the Louvain algorithm, showing modular optimization in two phases.

Implementation was conducted using the modularity optimization module in Gephi software environment, with standard resolution setting of 1.0 to balance detection of both large and small communities while enabling meaningful subgroup distinctions without over-fragmentation (Fortunato 2010; Fortunato and Barthelemy 2007).

A critical methodological consideration addressed the well-documented resolution limit of modularity optimization, which often prevents detection of smaller communities in larger networks. To address this limitation, a dynamic assessment mechanism was implemented where community stability was evaluated across multiple optimization passes. Only clusters that consistently persisted across iterations—indicating structural resilience above the resolution limit—were retained as robust communities (Fortunato and Barthelemy 2007; Lancichinetti and Fortunato 2009).

Following algorithm execution, communities with fewer than five members were excluded from the final structure to maintain analytical focus on meaningful and stable clusters, consistent with established practices in network science for ensuring statistical significance of detected communities (Fortunato & Barthelemy, 2007).

Communities Analysis

The final phase involved comprehensive structural analysis of the identified communities and their interconnection patterns applying multi-level network metrics commonly employed in ecosystem research (Basole and Patel 2018; Gawer and Cusumano 2014). This analysis employed multiple network metrics to characterize both individual community properties and inter-community relationships within the broader ecosystem structure.

For individual communities, internal structural metrics were computed including edge density, clustering coefficients, and centrality measures. Edge density calculations assessed the cohesiveness of relationships within each community, while clustering coefficients measured the tendency of nodes to form tightly-knit groups (Zafarani et al. 2014). Centrality measures, including degree centrality and betweenness centrality, identified key nodes within each community that serve as important connectors or bridges (McLevey et al. 2023).

Inter-community analysis focused on quantifying the strength and directionality of connections between different service clusters. A weighted interaction matrix was constructed where each value represents the average intensity of user transitions from a source community to a target community.

The directional nature of the network enabled examination of asymmetric flow patterns, revealing hierarchical relationships and sequential engagement patterns across service domains. Cross-cluster bridge identification was performed to locate websites that serve as connectors between different communities, potentially representing opportunities for enhanced service integration (Shi et al. 2024).

All computational analyses were implemented using a combination of Gephi’s Data Laboratory for network visualization and metrics calculation, complemented by custom NetworkX scripts for specialized calculations. The analytical workflow maintained transparency and reproducibility through comprehensive documentation of all parameters, algorithms, and computational procedures.

Statistical validation of community structures was performed through modularity score assessment and comparison with null models generated through random network permutation. This approach ensured that identified community structures represented meaningful organizational patterns rather than artifacts of the detection algorithm (Mester et al. 2021; Newman 2006).

The systematic application of this four-phase methodology yielded a comprehensive network representation of Iran’s digital tourism ecosystem, comprising identified communities and their interconnection patterns. The following section presents the empirical findings derived from this analytical framework, detailing the structural characteristics of detected communities and the behavioral patterns revealed through inter-community transition analysis.

Findings

Data collection

To initiate this study, a systematic data collection process was implemented to identify the most prominent Iranian websites related to tourism. The first step involved extracting a list of the top 500 Iranian websites based on Alexa.com rankings. To ensure contextual relevance and increase the accuracy of tourism-related website identification, we adopted an expert-driven manual curation approach in addition to web mining techniques.

Specifically, this list was reviewed by five domain experts actively engaged in various sectors of the Iranian tourism ecosystem. Each expert independently evaluated the list and selected websites they considered to be directly relevant to tourism services and user experiences. Only websites that were selected by at least three out of the five experts were retained for further analysis. This method ensured the inclusion of websites that reflect real-world relevance and sectoral awareness, surpassing the limitations of automated keyword filtering. The demographic details and professional background of these experts are presented in Table 1.

Table 1 Demographic and Professional Profile of Expert Panel.

Following this validation process, 11 websites were identified as directly affiliated with tourism services. These include platforms related to transportation, accommodation, cultural discovery, and travel planning. The final list of selected websites is presented in Table 2.

Table 2 Tourism-related websites among the top 500 Iranian websites ranked by Alexa.

This expert-informed curation stage laid a reliable foundation for the next steps of analysis. After establishing this initial dataset, the research progressed to a similarity analysis phase using Alexa’s “similar sites” feature to identify additional related platforms, as detailed in the next section.

During the second phase of this research, a comprehensive investigation was conducted using the “Similar Sites by Audience Overlap” feature available on the Alexa platform. This tool identifies websites that share similar user bases, visitation behaviors, and keyword engagement patterns. For each of the initially selected tourism-related websites, Alexa provided up to five comparable websites, each accompanied by an “overlap score” that quantifies the degree of similarity between the sites (Fig. 5). Notably, higher scores indicate a stronger resemblance in audience and thematic alignment.

Fig. 5

Website similarity analysis for lastsecond.ir using Alexa’s overlap score feature.

To ensure the relevance of the sites and to minimize noise, we applied an inclusion threshold based on overlap score: only websites with a score of 50% or higher were retained for further analysis. This threshold was empirically derived through pilot testing and expert validation. Preliminary observations showed that websites with scores below 50% often lacked direct relevance to tourism, whereas those meeting or exceeding the threshold demonstrated consistent topical and functional alignment.

Using the Python programming language and the BeautifulSoup library, the identified similar websites were extracted and processed. At this stage, 33 new websites that passed the similarity threshold were added to the initial list from Step 1.

In Step 3, this process was recursively repeated. The newly added websites—termed second-layer samples—were again subjected to Alexa’s similarity analysis. Only sites with a similarity score of 50% or greater were retained. This procedure was iterated up to six rounds, with each round generating a refined set of additional relevant sites. The process was terminated at the sixth iteration, as the subsequent stages either produced duplicates or suggested websites that did not meet the minimum similarity threshold. At the end of this recursive expansion, a total of 113 unique Iranian tourism-related websites were identified and included in the analysis.

To further enrich the dataset, an additional Alexa feature called “Referral Sites” was utilized. This tool displays the five most common websites that users visit immediately before and after a specific site (Fig. 6). “Earlier sites visited” refer to the platforms users accessed just prior to the focal tourism site, while “subsequent sites” denote where users typically navigated afterward.

Fig. 6

Referral pattern analysis of lastsecond.ir using Alexa’s referral site feature, showing inbound and outbound navigation.

For each website obtained during the second and third phases, we recorded up to five inbound and five outbound referral connections using this feature. These referral links reflect actual user transition patterns. As a result of this process, a broader network of Iranian tourism websites was constructed based on observed user flows, comprising 498 entities and their associated directional connections. This dataset served as the foundational structure for building the tourism ecosystem network analyzed in the subsequent phases.

Constructing the Web of Interconnected Tourism-Related Websites

In the second phase, the process involved using the initial dataset formed in the first step. In this step, websites were treated as nodes within a network, and the interactions between users moving between two sites were considered as connections or edges within this network. Consequently, a network of Iranian websites relevant to tourism was constructed. To analyze the network’s structural features, a quantitative node weight indicator was employed. To achieve this, user movements from one site to other sites, as well as recorded referrals from other sites to the focal site (considered as a node within the network), were taken into account. The weight attributed to each node was determined based on the number of such incoming and outgoing interactions.

In the subsequent part of this step, the initial dataset was refined by removing irrelevant nodes to improve the coherence and analytical clarity of the tourism-related network. These nodes were introduced into the dataset as a result of user transitions from or to non-tourism websites that, while part of users’ broader digital journey, did not directly contribute to the functional structure of the tourism ecosystem.

Specifically, e-commerce platforms such as Digikala.com and payment-related services were removed. These websites were typically accessed by users to complete purchases (e.g., for travel bookings) but do not provide tourism services themselves. Their inclusion would have distorted the structural integrity of the network by introducing external commercial hubs that are not part of the core tourism interaction flow.

In addition, social media platforms (e.g., Instagram, Telegram, Twitter) were excluded despite their role in content dissemination and marketing. These platforms often serve as channels through which tourism websites promote their services, and most tourism websites in our dataset explicitly link to these channels on their pages. However, they act as external communication environments rather than internal components of the tourism service ecosystem. Their presence in the graph introduced high-traffic but low-specificity nodes, which fragmented the core network and reduced interpretability.

The decision to exclude these categories was made to preserve the analytical focus on service-related connections within the tourism domain and ensure a more homogeneous and interpretable network structure. Following this refinement process, the finalized dataset included 162 tourism-related websites that formed the core of the digital tourism ecosystem under study.

Consistent with the research procedural flow (as depicted in Fig. 4), a dataset comprising 162 Iranian websites pertinent to the field of tourism was compiled. This dataset encompasses the websites themselves and the interconnections between them, resulting from user shifts between consecutive sites. Due to the dynamics of user interactions, the social network takes on the form of a directed graph. Furthermore, the weights assigned to the edges (representing connections between two websites) reflect the magnitude of user transitions between those respective sites. The visualization of this interconnected ecosystem of Iranian tourism-related websites can be observed in Fig. 7.

Fig. 7

Visualization of the Iranian tourism ecosystem based on user consumption behavior across 162 websites.

It is also worth noting that the finalized network graph does not include any foreign or cross-border tourism websites. This exclusion did not result from manual filtering but is rather a reflection of the actual user behavior captured in the data. Despite analyzing both inbound and outbound user transitions, no international platforms emerged within the network. This outcome can be attributed to structural constraints in Iran’s digital tourism landscape. Due to long-standing economic sanctions and limited international service availability, globally recognized platforms such as Booking.com, TripAdvisor, Expedia, and Airbnb do not operate in Iran and are either technically blocked or inaccessible for Iranian users. Moreover, even in cases where access is technically possible, these platforms offer no localized content or services, and thus are rarely visited by domestic users. Consequently, their absence in the dataset is an empirical characteristic of the Iranian digital ecosystem, rather than a methodological omission. This point highlights the unique insularity of Iran’s digital tourism environment, shaped by both geopolitical restrictions and infrastructural limitations.

Community Formation within the Ecosystem of Tourism-Related Websites

To gain a deeper and more structured understanding of the Iranian digital tourism ecosystem, the network underwent community detection analysis using Social Network Analysis (SNA) techniques. This step aimed to identify subgroups of websites—referred to as “communities”—that exhibit high internal connectivity and reflect distinct behavioral or functional patterns.

Communities in a network represent clusters of nodes that share strong interconnections, often indicating similar roles, services, or user flows. The Louvain algorithm, widely recognized for its efficiency and performance in large networks, was employed to detect these communities. The implementation was conducted using the modularity optimization module in the Gephi software environment.

Key parameters for this process included the use of a standard resolution setting of 1.0, which balances the detection of both large and small communities, and enables meaningful subgroup distinctions without over-fragmentation. The resulting modularity score was 0.634, indicating a moderately high level of community structure and suggesting a clear segmentation of the network into functionally cohesive clusters.

Following the algorithm’s execution, communities with fewer than 5 members were excluded from the final structure to maintain analytical focus on meaningful and stable clusters. As a result, the network revealed eight distinct communities, comprising a total of 95 nodes and 306 edges. These communities reflect key areas of tourism services including transportation, accommodation, food, and location-based offerings. The structure and segmentation of these communities are visually depicted in Fig. 8.

Fig. 8

Community formation within the ecosystem using SNA, identifying eight cohesive clusters of tourism-related websites.

To facilitate a more detailed analysis of the network, each of the communities within the network has been identified and labeled (Table 3). The subsequent analysis will delve into each of the eight identified communities.

• 1. Ticket and Tour Booking: This community accounts for 31.96% of the entire network and is primarily responsible for providing services related to purchasing train, airplane, and bus tickets, as well as hotel reservations and travel tours. Notable sites within this community include “alibaba.ir”, “ respina24.ir”, “ eligasht.com”, “ mrbilit.com” and “flightio.com” The revenue model for these sites is predominantly based on ticket sales, hotel reservations, and travel tour bookings.

• 2. Accommodation (Hotels): The second largest community in the network is comprised of websites offering accommodation services, particularly hotels and apart-hotels. This community contributes 11.34% to the network. Revenue for these sites is generated through intermediary commissions for hotel reservations. Key sites within this community include “snapptrip.com” “hotelyar.com” and “iranhotelonline.com”.

• 3. Online Taxi Services: The presence of this community in the network may be attributed to the significant demand for online taxi services during travels. These websites also support intercity travel, contributing to 8% of the Iranian tourism website ecosystem. Notable sites include “snapp.ir” and “ tapsi.ir” Revenue for these sites is generated through intermediary commissions.

• 4. Accommodation (Suites and Cottage): This smaller community, constituting approximately 6% of the total network, is distinct for its focus on providing accommodation services in suites, cabins, and small lodgings. Notable sites within this community are “jajiga.com,” “otaghak.com,” and “jabama.com.” Revenue for these sites is earned through rental fees.

• 5. Food and Cooking: This community is likely formed due to tourists’ interest in local cuisine. These websites primarily focus on providing culinary education and introducing local dishes. Contributing around 7% to the network, key sites include “ashmazi.com,” “irancook.com,” and “parsiday.com” The revenue model for these sites mainly revolves around advertising.

• 6. Location Services: The second largest community in the Iranian tourism website ecosystem involves location-based service providers. Comprising 13% of the total network, sites like “Nashan.org,” “balad.ir,” “ cafeyab.com,” and “fidilio.com” excel in showcasing locations such as cafes, restaurants, shopping centers, museums, cinemas, and offering map-based services like routing.

• 7. International Tours and Migration: In contrast to the “Ticket and Tour Booking” community, this community focuses more on selling international tours and providing migration services, including visa acquisition and consultation. Representing 11% of the total network, significant sites are “lastsecond.ir” “lahzeakhar.com” and “hamimohajer.com”.

• 8. Bus Ticketing: The final community in the Iranian tourism website ecosystem pertains to bus ticketing. This community holds an 8% share in the network. Key sites within this community are “payaneha.com,” “payaneh.ir,” “bazargah.com,” and “safar724.com” These sites generate revenue through ticket sales commissions.

Table 3 Community Names within the Ecosystem of Tourism-Related Websites and Their Contribution to the Total Community.

Exploration of the Network Structure in the Ecosystem of Iranian Tourism Websites

As previously mentioned, the graphical representation is rooted in user transitions from one website to another. Consequently, the graph depicted in Fig. 8 is directed in nature. A directed network permits the examination of user movement trajectories between various communities. Thus, comparing the inflows and outflows of a given node, along with pre-and post-connection dynamics within the network, holds substantial significance and enables an understanding of the ecosystem’s intricate dynamics based on user behavior. To accomplish this, each community is initially treated as a unified node. This approach implies that instead of analyzing individual components within each community, all members are collectively regarded as a singular entity, and the interactions among them are subject to analysis. The weight of the connection between any two nodes (referred to as edge weight) indicates the average weight of interactions from members of the source community to members of the target community. This weight is calculated using the subsequent formula:

$${W}_{{ij}}=\frac{{\sum }_{i=1}^{n}{\sum }_{j=1}^{m}{W}_{{ij}}}{n* m}$$

Table 4 illustrates the weighted interaction matrix among the eight identified communities within the Iranian tourism website ecosystem. Each value represents the average intensity of user transitions from a source community (row) to a target community (column). These weights serve as proxies for behavioral affinity—that is, how often users navigate from one type of service to another during their digital tourism journey.

Table 4 Distribution of Weighted Inter-Community Connections within the Ecosystem of Tourism Websites.

Given the directed nature of the network, values are asymmetric. For instance, users frequently transition from “Ticket and Tour Booking” to “Accommodation (Hotels)” (25.6), but not necessarily in reverse. Blank cells denote negligible or non-existent transitions.

The color-coded gradient in Table 4 enhances interpretability: green indicates strong user flow, yellow/orange represents moderate interactions, and red denotes weak behavioral linkage between communities.

To clarify the interpretation of the values presented in Table 4, it is important to understand the nature of the weighted interaction matrix. Each numerical value in the table represents the average intensity of user transitions from one community (row) to another (column), based on aggregate user flows. For instance, a value of “45.1” from “Ticket and Tour Booking” to “International Tours and Migration” signifies that, on average, there is a strong behavioral affinity where users who start their journey in ticket or tour platforms frequently continue to engage with international travel-related services. This value does not indicate a percentage but rather a relative weight, normalized across all user transitions between the respective communities. The values are derived from the mean of all individual edge weights between member websites of the two communities and serve as indicators of transition strength—not absolute traffic volume. This method allows for a comparative assessment of directional behavioral linkages within the tourism service ecosystem.

As evidenced in Table 4, several patterns emerge that are significant from both a behavioral and ecosystemic perspective. The “Ticket and Tour Booking” community functions as the central orchestrator within the tourism network, evidenced by consistently high outbound weights to related service domains—particularly “International Tours and Migration” (45.1), “Accommodation (Hotels)” (25.6), and “Bus Ticketing” (15.2). This highlights a user behavior model in which transportation decisions form the entry point of the planning journey, followed by downstream engagement with complementary services.

In contrast, communities such as “Online Taxi Services” and “Food and Cooking” exhibit weak or absent connections with others, underscoring their role as on-demand or contextual services rather than core planning components. This divergence is also reflected in low transition weights such as from “Online Taxi Services” to “International Tours and Migration” (1.2) and from “Food and Cooking” to “Bus Ticketing” (2.4), suggesting limited cross-service integration.

Such directional patterns support the theoretical framing provided by Service Ecosystem Theory, particularly in its emphasis on value-in-use unfolding through sequential service engagement. Users do not interact with all communities equally, but instead traverse the ecosystem in structured behavioral pathways that mirror logistical, experiential, and temporal priorities.

Figure 9 depicts the directed network of eight tourism service communities, with edge thickness proportional to the average user transition weight and node size reflecting total degree centrality. Several salient patterns emerge:

• 1. Dominance of Ticket & Tour Booking: Positioned centrally and exhibiting the largest node size, this community serves as the primary gateway for users entering the ecosystem. Its thick outbound links to International Tours & Migration (45.1), Accommodation (Hotels) (25.6), and Bus Ticketing (15.2) highlight its role in coordinating foundational travel decisions.

• 2. Absence of Links for Online Taxi Services: The Online Taxi Services node sits on the periphery, without any inbound or outbound edges. This indicates that users rarely navigate to or from taxi-booking platforms as part of their initial itinerary planning. Instead, taxi services are typically accessed in situ—once travelers have arrived at their destination—reflecting a post-planning, context-dependent usage pattern rather than a sequential planning step.

• 3. Isolation of Food & Cooking: Similarly, the Food & Cooking community shows no connections to other clusters. This suggests that culinary platforms function as specialized, on-demand experiences, accessed independently of the main travel planning flow. Their isolation may also signal a market opportunity for integrating gastronomic recommendations directly within central planning platforms to enhance user value co-creation.

• 4. Selective Peripheral Integration: Accommodation (Suites & Cottages) and Location Services maintain only weak ties to core planning communities (e.g., Suites → Ticketing: 4.8; Location → International Tours: 1.2). These modest connections imply niche user segments—such as travelers seeking boutique stays or local discovery tools—that fall outside the dominant logistics-first pathway.

Fig. 9

Network of inter-community connections, showing directional flows and degrees of centrality among tourism service communities.

By interpreting the structural gaps and strongholds in Fig. 9, we reveal a hierarchical user journey: primary logistics services emerge first, followed by accommodations, with experiential and auxiliary services accessed later or separately. The lack of integration for taxi and culinary websites not only reflects current user priorities but also uncovers untapped synergies where platform interoperability could deepen engagement and drive co-created value.

Discussion

This study employed Social Network Analysis to investigate the structural and behavioral dynamics of Iran’s digital tourism ecosystem, revealing eight distinct service communities and their interconnected value flows. By situating our findings within Service Ecosystem Theory and the Service-Dominant Logic paradigm, we provide insights into how digital platforms orchestrate value co-creation and how stakeholders can leverage network structures to enhance ecosystem performance.

Theoretical Contributions and Literature Integration

Sub-ecosystems and community formation

Our analysis identified eight cohesive communities within the Iranian tourism network, each representing functionally integrated service clusters. This finding directly supports Lusch and Nambisan (2015) conceptualization of digital service ecosystems as modular structures where actors combine operant and operand resources to deliver distinct value propositions. The high modularity score (0.634) confirms that these communities exhibit strong internal cohesion while maintaining clear boundaries from other clusters, consistent with prior network studies in tourism contexts (Baggio et al. 2010).

The emergence of “Ticket and Tour Booking” as the dominant community (31.9% of nodes) aligns with Adner (2017) innovation ecosystem framework, which emphasizes the role of orchestrating actors in coordinating interdependent services. This community functions as what Vargo and Lusch (2016) term an “institutional arrangement” - a governance mechanism that facilitates resource integration across multiple service domains. Unlike previous studies that examine tourism platforms in isolation (Christou et al. 2023; Neuhofer et al. 2015), our network perspective reveals how central platforms serve as ecosystem orchestrators, enabling value co-creation through coordinated resource flows.

The identification of parallel accommodation communities - “Accommodation (Hotels)” and “Accommodation (Suites and Cottages)” - provides empirical support for Campos et al. (2018) observation that co-creation occurs through differentiated service offerings tailored to distinct consumer segments. This segmentation reflects what Wieland et al. (2017) describe as context-dependent value emergence, where similar services create different value propositions based on target market configurations.

Value Flow Dynamics and Sequential Resource Integration

Our analysis of inter-community transitions reveals hierarchical patterns of resource integration that extend existing understanding of tourist behavior. The strong directional flows from “Ticket and Tour Booking” to “International Tours and Migration” (45.1) and “Accommodation (Hotels)” (25.6) demonstrate what Vargo and Lusch (2016) conceptualize as sequential value-in-use creation. This pattern challenges the traditional linear customer journey models prevalent in tourism literature (Gursoy et al. 2022) by revealing non-linear, ecosystem-wide navigation patterns.

The weak connectivity of “Food and Cooking” and “Online Taxi Services” communities contradicts assumptions in the smart tourism literature about integrated service delivery (Gretzel 2022). While scholars like Bhuiyan et al. (2022) argue for comprehensive ecosystem integration, our findings suggest that certain services remain contextually accessed rather than systematically integrated into core travel planning processes. This disconnect represents what Frow et al. (2014) identify as unfulfilled value propositions - potential service integrations that could enhance ecosystem performance but remain unrealized due to structural barriers.

Addressing research questions through theoretical lens

Research Question 1 sought to identify distinct communities and their functional synergies. Our findings reveal that community formation follows functional rather than merely technical boundaries, supporting Lucas (2019) argument that ecosystem structures emerge from user behavior patterns rather than predetermined organizational relationships. The eight communities represent what Ritala and Almpanopoulou (2017) describe as “specialized sub-ecosystems,” each contributing unique resources while maintaining interdependencies with complementary services.

Research Question 2 examined how inter-community interactions reveal user preferences and decision hierarchies. The asymmetric flow patterns we observed align with Rihova et al. (2015) findings on customer-to-customer value co-creation, but extend beyond dyadic interactions to reveal system-wide orchestration mechanisms. The dominance of transportation-first navigation patterns supports Del Chiappa and Baggio (2015) observation that certain actors occupy privileged positions in tourism networks, enabling them to influence ecosystem-wide value flows.

Research Question 3 explored strategic interventions to enhance ecosystem cohesion. Our identification of structural holes between core and peripheral communities provides empirical support for Guarda et al. (2022) call for improved stakeholder integration in digital tourism environments. The weak integration of experiential services (food, taxi) with core planning platforms represents what Jaakkola et al. (2015) identify as unrealized co-creation potential, where enhanced connectivity could generate additional value for all ecosystem participants.

Managerial Implications Grounded in Empirical Findings

Service provider strategies

The network centrality patterns revealed in our analysis provide specific guidance for service providers seeking to enhance their ecosystem position. Platforms operating within the “Ticket and Tour Booking” community should leverage their orchestration role by developing API-level integrations with accommodation and experience providers. This recommendation builds directly on our finding that 45.1% of transitions from ticketing platforms flow to international tours, suggesting strong user demand for integrated booking experiences.

The isolation of “Food and Cooking” platforms presents a clear strategic opportunity. Our data shows minimal connectivity (less than 2.4 average transition weight) between culinary services and core travel planning communities. Service providers in this domain should develop partnership agreements with central booking platforms to capture users during the active planning phase rather than relying on post-arrival discovery. This strategy addresses what Trunfio and Della Lucia (2019) identify as the challenge of stakeholder engagement in digital tourism ecosystems.

Investment decision framework

Our network analysis provides investors with empirical evidence for identifying high-potential sectors within the tourism ecosystem. The “Accommodation (Suites and Cottages)” community’s moderate connectivity (4.8 average transition weight from ticketing) combined with growing consumer interest in boutique experiences suggests an underserved market segment. This finding aligns with Pencarelli (2020) observation about the digital revolution creating new market niches in tourism.

The structural hole between “Location Services” and core booking platforms represents a strategic investment opportunity. With only 1.2 average transition weight to international tours despite 13% ecosystem representation, location-based services remain underintegrated. Investors could capitalize on this gap by funding platforms that bridge local discovery with core travel planning, addressing what Kozak and Buhalis (2019) identify as the challenge of destination marketing integration.

Policy and regulatory interventions

Our finding that no international platforms emerged in the network despite analyzing both inbound and outbound transitions provides specific guidance for policymakers. The complete absence of global platforms like Booking.com and TripAdvisor reflects what Werthner et al. (2015) describe as institutional barriers to ecosystem development. Policymakers could enhance ecosystem competitiveness by developing regulatory frameworks that enable controlled integration with international platforms while protecting domestic service providers.

The weak integration between “Bus Ticketing” and other transportation services (only 11.6 average transition weight) suggests regulatory opportunities to promote intermodal travel coordination. This finding supports Chen et al. (2021) argument that policy interventions can enhance ecosystem-wide value creation by reducing friction between complementary services.

Limitations and Future Research Directions

Several methodological and contextual limitations constrain the generalizability of our findings. The exclusive focus on Iranian platforms reflects unique geopolitical and economic constraints that may not characterize other digital tourism markets. Economic sanctions and regulatory restrictions have created what Esmaeili Mahyari et al. (2024) describe as an “insular digital ecosystem,” potentially limiting the transferability of our community structures and flow patterns to more integrated international markets.

Our reliance on Alexa traffic data introduces sampling biases that may overrepresent popular platforms while underestimating emerging or niche services. This limitation is particularly relevant given recent scholarship on the long tail of digital tourism services (Ozdemir et al. 2023a). Future research should incorporate multiple data sources, including mobile app analytics and direct user surveys, to achieve more comprehensive ecosystem mapping.

The behavioral scope of our analysis captures navigation flows but does not investigate the psychological or cultural factors that influence platform selection. This limitation prevents deeper understanding of why certain transitions occur and others do not. Future studies should integrate frameworks from technology acceptance and consumer behavior literature to explain the motivational drivers underlying the network structures we observed.

The rapid evolution of artificial intelligence and machine learning technologies in tourism platforms (Panda and Khatua 2025) may fundamentally alter the community structures and flow patterns we identified. Longitudinal research is needed to examine how AI-driven personalization affects ecosystem dynamics and whether current structural patterns persist as platforms become more intelligent and automated.

Future research should also expand beyond single-country analyses to conduct comparative studies across different digital maturity levels and regulatory environments. Such cross-national comparisons would test the robustness of our theoretical framework while identifying context-specific variations in ecosystem structure and behavior.

Conclusion

This study offers a comprehensive network-based investigation into the structural configuration and behavioral patterns of a digitally mediated tourism ecosystem in an emerging market context. By applying Social Network Analysis to a curated dataset of Iranian tourism websites, the research reveals how distinct service communities emerge through user navigation flows, reflecting both functional differentiation and sequential engagement logic. The identification of eight cohesive communities—ranging from core logistical services to peripheral experience-based platforms—demonstrates that value creation in tourism is contingent upon inter-platform interdependencies and the orchestration of complementary resources.

Theoretically, this study advances Service Ecosystem Theory by operationalizing its key constructs—such as institutional arrangements, resource integration logics, and multi-level dynamics—within a digital tourism framework. The integration of network centrality metrics and inter-community flow patterns provides a structural lens to interpret value co-creation as an emergent property of ecosystem design, rather than isolated platform functionality. Moreover, the empirical findings support and extend the conceptualization of tourism platforms as orchestrators of value-in-use, where user transitions embody pathways of sequential resource deployment.

From a practical standpoint, the analysis highlights specific structural gaps and integration opportunities across the ecosystem. Central actors—particularly those in the ticketing and accommodation clusters—are strategically positioned to drive cross-service collaboration, while isolated communities such as food services and taxi platforms reveal latent potential for enhanced interoperability. These insights carry actionable implications for service providers aiming to improve user retention, for investors seeking to identify innovation nodes, and for policymakers interested in fostering a more cohesive and competitive digital tourism landscape.

Looking forward, future research should expand the comparative scope across diverse markets to examine the transferability of observed structures under varying institutional and infrastructural conditions. In addition, the incorporation of real-time user behavior data and AI-driven personalization mechanisms would enable a deeper understanding of the evolving dynamics in digital service ecosystems. Ultimately, this research underscores the necessity of theory-informed, data-intensive, and system-level approaches to navigating the complexity of tourism in the digital age.