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Article

Identifying Patterns among Tourism-Oriented Online Communities on Facebook

by
Eva Zabudská
and
Kristína Pompurová
*
Department of Tourism, Faculty of Economics, Matej Bel University in Banská Bystrica, Tajovského 10, 975 90 Banská Bystrica, Slovakia
*
Author to whom correspondence should be addressed.
Tour. Hosp. 2024, 5(3), 830-847; https://doi.org/10.3390/tourhosp5030048
Submission received: 4 July 2024 / Revised: 12 August 2024 / Accepted: 6 September 2024 / Published: 8 September 2024
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Abstract

:
The proliferation of social media has transformed how people engage in communication and community building, with platforms like Facebook becoming central to connecting individuals with shared interests. Despite the extensive formation of tourism-oriented online communities on these platforms, there is a notable lack of comprehensive studies examining their structural and managerial dynamics. This study addresses this gap by systematically analyzing fifty international tourism-focused Facebook communities to develop a novel typology based on the nature and type of information shared. The research identifies significant variations in community sizes, engagement levels, and management structures, highlighting that only 6% of these communities qualify as large, with membership exceeding one million. Contrary to common assumptions, a direct link between community size and engagement was not found, with qualitative factors like community purpose and content type being more influential. A notable correlation was observed between the number of administrators and moderators and the member count, emphasizing the importance of effective community governance. The study’s findings contribute to a deeper theoretical understanding of online community dynamics and offer practical implications for tourism marketers and community managers aiming to optimize engagement strategies on social media platforms. The research sets a foundation for future exploration of the interplay between virtual community management and tourism-related discourse.

1. Introduction

In recent years, the advent of social media has fundamentally transformed the landscape of communication and community building. Platforms such as Facebook have become pivotal in connecting individuals with shared interests, thereby fostering the creation of diverse online communities. Within this context, tourism has emerged as a prominent theme around which numerous online communities have been established, serving as hubs for sharing experiences, exchanging information, and providing support among members with a common interest in travel and tourism.
Despite the rapid proliferation and influence of these communities, there exists a notable gap in the systematic study of their structures, behaviors, and impacts, particularly on a platform as ubiquitous as Facebook. While much research has been dedicated to understanding user interactions and content dissemination within online communities, there has been limited focus on developing a comprehensive typology that accounts for the managerial and structural dynamics that underpin these tourism-focused groups. Existing studies tend to prioritize content analysis or user behavior, overlooking the crucial role of administrative practices and community management in shaping engagement and participation.
This study endeavors to bridge this gap by employing analytical techniques to identify and characterize tourism-focused online communities on Facebook. By doing so, the research seeks to develop a comprehensive typology of tourism-oriented online communities on Facebook by systematically analyzing their structural and managerial dynamics. This investigation not only aims to enrich the theoretical understanding of online community dynamics but also to provide practical implications for tourism marketers and community managers seeking to leverage these platforms effectively.
The study is structured to comprehensively explore the dynamics of tourism-oriented online communities on Facebook. The theoretical background reviews relevant literature, discussing various typologies of online communities. The Materials and Methods Section details the identification and analysis of the fifty largest tourism-oriented Facebook communities, including data collection and statistical techniques. The results and discussion present key findings, including community characteristics and a new typology. The conclusions summarize the study’s contributions, practical implications, and limitations, and suggest directions for future research.

2. Theoretical Background

2.1. Online Communities: Their Specificities and Classification

In the extant scholarly literature, online communities have been examined from various perspectives, including tourism, e.g., refs. [1,2,3,4,5], information systems, e.g., ref. [6], management, e.g., ref. [7], sociology and communication, e.g., ref. [8], psychology, e.g., ref. [9], pedagogy, e.g., ref. [10], and to some extent healthcare, e.g., ref. [11].
Researchers employ diverse approaches to studying online communities, reflecting a heterogeneity of perspectives regarding the definition and conceptualization of these communities. This variability in understanding is often attributed to the multidisciplinary nature of the research in this area. Generally, multiple authors [12,13,14,15,16,17] view online communities as groups of individuals who interact and communicate through electronic means. Peng et al. [5] similarly define online communities as groups of stakeholders with common interests, purposes, and goals, interacting exclusively via the internet. They also highlight that each online community possesses a certain level of interaction and activity among its members.
Agostinih and Mechant [18] describe online communities as social groupings in virtual environments, composed of individuals sharing common opinions, feelings, interests, or goals, thus fostering human relationships in cyberspace. In their view, cyberspace equates to the internet. Dennis and Halbert [19] perceive online communities as aggregations of interacting individuals sharing a specific identity. This identity is reterritorialized, meaning the virtual unit can manifest in various specific moments and places without being tied to a precise location and time [20]. Muryanto et al. [21] support this view, asserting that online communities are characterized by their independence from geographical distances, unlike traditional communities that rely on physical proximity and specific times for interaction and communication. This understanding is rooted in Aoki’s typology [22], which classifies online communities based on their relationship to physical communities, emphasizing the intensity of physical dependence among members.
Online communities based on physical communities typically provide essential information (geographic location, exact time, and date) for social events, facilitating physical interaction and communication. Conversely, communities partly based on physical communities exhibit both physical and online interactions, meaning members meet regularly in person while continuously communicating via computer-mediated channels. Communities unrelated to physical communities generally do not involve personal meetings, with members often geographically dispersed, making physical contact impossible, or preferring anonymity. Thus, these communities engage exclusively in online interactions and communications [23].
In the available scientific literature, no single, universally accepted typology of virtual communities exists. Armstrong and Hagel [24] propose a typology based on users’ social needs, focusing on interaction levels, subject breadth, and the desire to escape reality. They identify four types: Communities of Relationship, engaging in regular, broad-topic interactions forming strong bonds; Communities of Fantasy, engaging in fictional settings like multi-user dungeons; Communities of Transaction, focused on economic exchanges; and Communities of Interest, intensely discussing specific topics with significant interpersonal communication.
Lazar and Preece [23] identify four key characteristics for classifying online communities: their attributes, supporting software, relationship to physical communities, and the sociological concept of boundedness. However, they offer a characterization of online communities rather than a strict classification.
Burnett [25] provides a typology of information exchange within virtual communities, categorizing activities into non-interactive behaviors like “lurking” and interactive behaviors, further divided into hostile and collaborative interactions. Bagozzi and Dholakia [26] adopt a sociological perspective, delineating a typology based on the degree of cooperative group action: fully cooperative group action, partially cooperative group action, and minimally cooperative group action.
Stanoevska-Slabeva [27] distinguishes discussion communities, task- and goal-oriented communities, virtual worlds, and hybrid communities. Porter [28] differentiates online communities based on their creators, identifying member-initiated and organization-sponsored communities. Member-initiated communities are established and managed by members, while organization-sponsored communities are backed by commercial or non-commercial organizations with inherent stakeholders or beneficiaries.
Virnoche and Marx [29] present a typology based on the intersection of geographic and virtual spaces, identifying six “ideal types”: ongoing, intermittent, dispersed, virtual, virtual extensions, and community networks. Masson and Parmentier [30] differentiate typologies based on activity (community of practice, epistemic community, action community, crisis community, innovation community) and participants (brand community, user community).
It is evident that such typologies of online communities are applicable to all online communities regardless of the content of shared information (e.g., tourism, pedagogy, healthcare).

2.2. Studies Focused on Online Communities in the Tourism Sector

Among the numerous researchers focusing on the exploration of online communities from various perspectives, a significant proportion, e.g., refs. [1,2,3,4,5,16,31,32,33], primarily investigate online communities that influence consumer behavior exclusively among tourism visitors. Currently, such online communities provide essential information related to travel, tourist destinations, tourism services, and activities undertaken during participation in tourism both domestically and internationally, which significantly impacts the purchasing behavior and decision-making of their members. These members can also be perceived as potential tourism visitors [34].
In their contributions and studies, these authors predominantly focused on analyzing the shared information within the communities [16,32,35,36,37,38], comparing them [39] and delineating the needs of their members [13,34,40].
Online communities providing information about tourism offerings have also been studied from other angles, such as consumer behavior of their members [33,41,42,43,44,45,46,47,48,49,50,51], participation and interaction among their members [1,5,52], membership intentions [2,12,14], commercial use of online communities by businesses and tourism organizations [53,54], relationships among their members [55,56], member satisfaction [31], content and quality of posts [57,58], integration of new members [3,59], and reviews of previous studies [4].
A considerable number of studies have focused on the examination of online communities situated within the broader internet environment (Table 1). For the analysis of online communities in this context, data directly obtained from specific websites have been frequently utilized. In addition to data from online community websites, data from specific travel agency websites, which function as online communities where interactions occur between members or between community members and the travel agency, have also been utilized in studies (Table 1).
However, only a few authors [13,40,58] have explored online communities in the tourism sector that are hosted on social networks. Among these, Dencheva [58] and Stepaniuk [13] uniquely based their research on data from online communities on the social network Facebook.
Dencheva [58] provides an analysis of how online communities function, particularly in the context of the hospitality sector, using Hotel Dobrudzha-Dobrich as a case study. Stepaniuk [13] offers a model for effectively managing virtual tourist communities, emphasizing the importance of the administrator’s role in content curation and community engagement. This model is useful for building and managing virtual communities that enhance the brand image of tourist destinations and improve user experience and interaction.

3. Materials and Methods

The study was structured to scrutinize the dynamics within the fifty most expansive international online communities on the digital networking platform, Facebook. These communities were pinpointed via a strategic application of pertinent search terms, including “travel communities” and “tourism communities”.
The selection of analyzed online communities was based on several established criteria. One of the fundamental criteria was the nature of their shared content and the preferred language of communication between online communities’ members and administrators. Consequently, the sample consists of Facebook online communities where the content is presented in a global language and directly relates to tourism. This includes posts concerning activities undertaken during tourism participation, natural and cultural–historical attractions at specific destinations, tourism services, information about organized events, and general tourism infrastructure (e.g., insurance, car rentals, vaccination requirements, parking options, etc.).
The identification of communities that met the established criteria was achieved through content observation. For online communities that fulfilled the criterion of offering tourism-related content in English, it was subsequently verified if these communities also conformed to the characteristics of a Facebook or social group. This criterion was chosen because “Facebook groups”, unlike “Facebook business pages”, foster a community centered around a shared interest, thereby exhibiting a communal character. The determination of whether the selected online communities satisfied the conditions of a Facebook group was based on information found directly on their official accounts, indicating their classification as “social groups”.
Executed in January 2024, the analytical process was meticulously designed to dissect various quantitative metrics reflective of each community’s digital footprint. These parameters encompassed the community’s membership size (articulated in units of thousands), the cadre of administrators and moderators presiding over the group, the historical longevity of the community since its inception, the average number of comments per post, and the daily rate of content proliferation within the group, quantified as percentage growth of posts. Furthermore, the research extended to evaluate the thematic focus of the content, particularly its geographical specificity and the delineation of its intended audience, categorizing the information dissemination as either public or private in nature.
In this case, a comprehensive and time-consuming data collection process was undertaken, requiring manual extraction of data directly from the accounts of the studied online communities. The data collection process was conducted without the use of any platforms or software specialized in web scraping that would allow for the mass extraction of data from social networks. Consequently, only a limited amount of data was available for analysis, with which certain results could be achieved. Data that would allow for a detailed analysis of consumer behavior among members of the selected online communities were not accessible to the average Facebook user at the time of collection, which precluded the examination of this aspect of the issue.
To explore the potential existence of dependencies between various variables, a correlation analysis was conducted, supplemented by multiple statistical tests.
In relation to establishing an acceptable typology of online communities providing information on tourism offerings, a non-hierarchical cluster analysis was performed using statistical clustering methods. The aim was to classify the examined online communities into clusters based on their mutual similarities regarding shared content. This analysis was based on relevant data (category of published information on tourism offerings, nature of published information on tourism offerings, community management, and community intent) obtained through data collection from the studied international online communities with accounts on the social networking site Facebook.

4. Results and Discussion

4.1. Characteristics of Tourism-Oriented Online Communities

In the realm of social media, an analysis conducted on the Facebook platform revealed the identification of 50 online communities focused on tourism within an international context, as delineated in Table 2.
The most substantial online communities, providing essential information pertaining to tourism and boasting memberships exceeding one million individuals, include “DIY Travel Philippines”, “Girls LOVE Travel®”, and “Best Destination to Travel”. The online community with the longest history was identified as “Backpacking Europe”, which has been in existence for 17 years. The highest number of administrators and moderators, totaling 24, was reported in the “Travel Philippines” community.
In the examined tourism-oriented online communities, the average membership was found to be approximately 165,000, with a substantial standard deviation of 334,000, indicating a wide variation in community sizes. The average age of these communities is 7 years, with the average number of administrators and moderators being around 4, albeit with a standard deviation of 4, suggesting variability in community management structures.
However, the high variability indicated by the standard deviation and the coefficient of variation points to the mean’s vulnerability to outliers, which undermines its reliability as a descriptive measure, as demonstrated in Table 2. To circumvent this issue and obtain a more accurate representation of the data, the median was employed as a more robust measure of central tendency. The median membership size was found to be 48,000, implying that about half of the communities have a membership size around this figure, which provides a more stable indication of the typical community size, unaffected by extreme values.
Regarding the age of the communities, the median closely mirrors the arithmetic mean of 7 years, suggesting that outliers do not significantly distort the age distribution. Similarly, the median number of administrators and moderators closely aligns with the arithmetic mean, indicating a consistent management structure across the majority of the communities.
Among all the identified online communities, it was observed that those comprising the smallest number of members, specifically ranging from one thousand to one hundred thousand members, were the most prominently represented, constituting 66% of the total. Conversely, a mere 6% of the communities were classified as large online communities, based on a membership count exceeding one million.
From the analysis of survey data, it has been inferred that online communities, recognized for their substantial membership, do not invariably exhibit the highest average daily growth rates in post frequency. Specifically, “DIY Travel Philippines”, notable for its membership exceeding 1.5 million, was found to occupy the twenty-fifth position in terms of daily post growth rate velocity, registering a modest average daily increment of merely 4.35% in post volume. Similarly, “Girls LOVE Travel”, despite being classified among the larger communities, was discerned as one of the markedly slowest in daily post growth rate acceleration, as documented in Table 2.
Contrastingly, the online community known as “Best Destination to Travel”, distinguished by the swiftest daily post growth rate, was also identified among the communities with a considerable number of members. This observation alludes to a potential disconnection between the size of an online community, as measured by member count, and the velocity of its daily post growth rate. In pursuit of validating this presumed independence between the aforementioned variables, a correlation analysis was undertaken, supplemented by various statistical tests. The purpose was to investigate the possibility that the community size, reflected by the number of members, might be influenced by other variables under examination, such as the community’s age and the count of administrators and moderators. The correlation analysis demonstrated that the age of a community does not influence its size, with a 95% confidence level supporting their independence (Sig. = 0.953). This finding indicates that the size of a community, measured by its member count, is independent of the duration of the community’s existence. A similar conclusion was drawn regarding the relationship between the size of online communities, based on their member count, and their daily post growth rate within the studied online communities (Sig. = 0.399).
Conversely, as suggested by Chet et al. [1], engagement is more likely to be influenced by the quality of interactions and the management structure rather than the sheer number of members.
Therefore, in addition to analyzing the average growth of posts, the average number of reactions in the form of comments per post was also examined (Table 2) to verify the significance of this indicator in the given context. In this case, the correlation analysis confirmed a dependency between the size of the communities and the engagement of their members, measured by the average number of comments per post (Sig. = 0.023). The results of the correlation analysis indicate that the intensity of member reactions, through commenting on posts, is partially dependent on the size of the communities.
Statistical significance was also confirmed regarding the variable indicating the number of administrators and moderators, suggesting a correlation with the member count in the examined online communities. In this instance, with 95% confidence, it can be affirmed that an increase in the number of administrators and moderators is associated with an increase in the member count of online communities (Sig. = 0.005). This relationship could be bidirectional, as an increase in member count might also necessitate an increase in the number of administrators and moderators. This correlation can be rationalized by the fact that the growth in new members within an online community concurrently escalates the managerial demands of the community, subsequently necessitating a larger number of administrators or moderators. The resultant increase in new administrators and moderators can, in turn, ensure smoother management and governance of the online community. Facilitating smoother community management and governance may attract the interest of potential members to join the community, thereby ensuring further growth in its member count. As Stepaniuk [13] emphasized, the administrator’s role in content curation and community engagement is crucial within Facebook-hosted online tourism communities.
An overwhelming majority (86%) of the online communities reviewed are accessible to all individuals possessing accounts on the social network Facebook. These are specifically public online communities, whose posts, shared content, and comments are available to anyone with an account on Facebook, irrespective of their membership status in the community. The remaining 14% of online communities in tourism are private, indicating that these are communities not open to the public, requiring membership for access.
Within the scope of the study, it was observed that nearly half (42%) of the online communities related to tourism on the social network Facebook provide information or publish posts focusing on various destinations without specificity towards any particular continent. Approximately 26% (13) of the examined communities are dedicated to information pertaining to destinations or tourism services in South Asia (e.g., the Philippines, India, Thailand, etc.), while 20% (10) of the communities are oriented towards disseminating information about tourism in European countries (e.g., Albania, Greece, Malta, etc.). The least representation among the studied online communities was noted for those providing essential information related to tourism in countries located in Central and North Asia (6%), Africa (4%), and the Americas (2%). Among all the online communities examined, none were identified that focused on information regarding destinations and tourism services in Australia and Oceania.

4.2. Factors Influencing Engagement among Members of Online Communities

In relation to the confirmed dependencies between variables, attention was directed toward analyzing factors that could potentially influence the level of member engagement in communities. The analysis abstracted from the variable indicating the frequency of post publication, due to the results of the correlation analysis, which indicated that the growth rate of posts is independent of any of the examined variables. Therefore, member engagement in online communities is analyzed solely in terms of the average number of comments per post, as this data shows a mutual dependency with several variables.
Member engagement in online communities is significantly influenced by the size of the communities. The Pearson correlation coefficient (r = 0.321) suggests a direct dependency; however, it also indicates that this factor only weakly stimulates the growth of the average number of reactions per post. The correlation analysis also reveals that as the number of administrators and moderators in communities increases, the number of members also grows. However, when examining these relationships, it was found that there is no direct or indirect influence of the number of administrators and moderators on the level of member engagement in the form of posting comments under published posts (Sig. = 0.157).
Based on this result, it is concluded that although the number of administrators and moderators affects the growth in the number of community members, this relationship between variables does not result in any increase in the average number of comments per post. It follows that member engagement is independently influenced by the size of the communities, regardless of other factors.
The survey investigated whether other factors might influence the level of engagement in the studied communities. In this analysis, nominal data were used, indicating the necessity of employing the test of independence (Pearson’s Chi-square) and Cramér’s V correlation coefficient.
The p-values from Pearson’s Chi-square test for the independent variables indicating the type of information, community purpose, and geographic focus of the information on a specific continent were found to be below the established alpha level of 0.05, confirming their statistical significance. In light of this confirmed dependence, it is also meaningful to interpret the strength of these dependencies using Cramér’s V correlation coefficient. The correlation coefficients (Cv) for all three variables were greater than 0.7, indicating a strong intensity of influence. Based on these results, it can be confirmed that member engagement, measured by the average number of comments per post, depends on the dominant type of information in the posts (such as tourism services, attractions at destinations, insurance, car rental options, etc.), the community’s purpose (providing tips/seeking advice), and the geographic focus of the information on a specific continent (information about destinations located in Africa, America, Australia and Oceania, Central and Northern Asia, South Asia, Europe, or destinations without specified geographic boundaries) (Figure 1).
The highest levels of member engagement were observed in communities focusing on destinations in Africa (averaging 85 comments per post) and Europe (averaging 20 comments per post). Significantly lower engagement was recorded for information focusing on various destinations without a specific continental focus (averaging eight comments per post), destinations in America (averaging seven comments per post), and Asia (averaging one comment per post).
Regarding the community’s purpose, those used primarily for seeking advice were the most successful (averaging 21 comments per post). Successful engagement was also noted in online communities where both seeking advice and providing tips occurred (averaging 15 comments per post), as opposed to communities solely focused on providing tips (averaging five comments per post).
The level of member engagement also depends on the type of information. In this regard, information about general infrastructure, such as insurance, parking availability, and car rental options, was the most significant (averaging 24 comments per post). Conversely, the least comments were associated with posts related to tourist attractions (averaging nine comments per post) and tourism services at destinations (averaging three comments per post).

4.3. Typology of Online Communities in Tourism

In an endeavor to devise a typology suitable for international online communities in tourism on the social network Facebook, several clusters were formed using statistical clustering methods, from which distinct categories of online communities in tourism were subsequently derived. The foundation was laid on the outcomes obtained from a non-hierarchical cluster analysis, aimed at classifying online tourism communities based on the category (e.g., destinations, tourism services) and nature (e.g., discussion posts, promotional materials, photos, videos) of the information they publish.
Through the application of cluster analysis, 50 international online communities in tourism were reclassified into seven categories based on their mutual similarities, identified via the mode value. The mode was employed in this context due to the inclusion of nominal variables in the cluster analysis (Table 3).
The typology of online communities in tourism was created through further categorization of the previously established seven clusters. These clusters were subsequently consolidated into three main categories based on a variable indicating the category of information published within individual online communities. This categorization was informed by the terminology of tourism offerings (Gúčik, 2020), distinguishing between primary offerings that typically motivate individuals to travel to a destination (natural potential of the destination, cultural–historical prerequisites, and organized events) and secondary offerings that provide services essential for travel and stay at the destination (e.g., accommodation, catering services, tour operator services, local infrastructure, transportation infrastructure, medical infrastructure, police, and security components).
Consequently, online communities were categorized into those focusing on providing information about the primary tourism offering (information about the destination’s natural attractions, cultural–historical attractions, organized events), the secondary tourism offering (information about tourism services and general infrastructure), and both primary and secondary offerings together (Figure 2).
Online communities focused on primary tourism offerings exclusively provide essential information about attractions located at destination sites. These communities were further divided into audiovisual and promotional categories. In audiovisual communities, mutual interaction among members occurs through the sharing of various photos and videos (audiovisual material) about natural and cultural–historical attractions at specific destinations. Conversely, promotional communities provide necessary information (geographic location, precise time, and date) regarding various social events (organized events, events organized by cultural enlightenment institutions) through the sharing of promotional material, such as posters created for specific events.
Online communities focused on providing information about the secondary tourism offering were divided based on whether the content of their posts aligns with the infrastructure of tourism facilities (information about services in accommodation and dining establishments) or with the general infrastructure of tourism (information about insurance, currency exchange, healthcare). Necessary information about the infrastructure of tourism facilities is contained within reference and discussion communities. Reference communities are characterized by the publication of hypertext links or connections to specific pages related to accommodation, dining, or transportation, where individuals can reserve or purchase services, often linking to sites such as Booking.com or directly to the reservation systems of specific tourism facilities. However, discussion communities are characterized by the fact that interaction among their members exclusively occurs through discussion posts concerning services in tourism. Information about necessary insurance at a specific destination, taxi services, or healthcare, i.e., about the general infrastructure of tourism, is also the subject of a discussion community, with the content of their discussion posts exclusively containing such information.
Mixed online communities can be perceived as a combination of both defined main categories of online communities in tourism. These communities are categorized based on their content into versatile and promotional communities. Discussion posts, online audiovisual material, and hypertext links are part of versatile communities, while promotional communities are characterized by providing necessary information (geographic location, precise time, and date) concerning not only events but also created products of tour operators (packaged tours).
Specific characteristics are typical for particular subcategories of online communities. Audiovisual communities, which belong to those focused on the primary tourism offering, are managed exclusively through the interaction of the members themselves. In practice, this means that the audiovisual material is shared solely by the members of these communities. Promotional communities, also focused on the primary tourism offering, are distinguished by content shared exclusively by external enterprises intending to promote various social events (organized events, events by cultural enlightenment institutions). The purpose of these subcategories of communities is to provide their members with tips about the primary tourism offering (Table 4).
Most communities providing information about the secondary tourism offering are managed by the members themselves. These are discussion communities where members interact through discussion posts concerning either the infrastructure of tourism facilities or general infrastructure (Table 4). The difference in the characteristics of discussion communities focused on the secondary tourism offering lies in their differing objectives. While discussion communities focused on the infrastructure of tourism facilities aim to provide tips among their members, members of discussion communities focused on general infrastructure seek specific advice through discussions on topics such as insurance for a particular destination, visa requirements, and similar. Reference communities, also aimed at the secondary tourism offering, are only partially managed by their members. Hypertext links or connections to specific pages related to accommodation, dining, or transportation are published within the community not only by its members but also by moderators to provide useful tips.
The pursuit of advice and provision of tips are integral to the objectives of versatile communities. In these subcategories, content such as discussion posts, online audiovisual material, and hypertext links is published by the members, demonstrating that community management occurs through mutual member interaction.
Promotional communities, distinct for their provision of necessary information (geographic location, precise time, and date) related not only to events (organized events, events organized by cultural enlightenment institutions) but also to products created by tour operators (packaged tours), are managed by external enterprises aiming to offer tips.
Additional characteristics that define the various subcategories of communities were also examined, including the average number of members, daily growth rate of posts, average age, and number of administrators and moderators (Table 5).
Audiovisual communities focused on the primary tourism offering have a higher average number of members compared to promotional communities within the same category, with averages of nearly 190,000 members and approximately 43,000 members respectively. Both community categories exhibit nearly identical daily post growth rates, while the average age and number of administrators and moderators in audiovisual communities are slightly higher than in promotional communities.
As for communities focused on the secondary tourism offering, discussion communities providing information about the infrastructure of facilities boast the highest average number of members, with nearly 440,000 members, significantly more than other categories of secondary offering communities (Table 5). These communities also feature the fastest average daily post growth rate at 6.01%, while reference and general infrastructure-focused discussion communities exhibit a notably slower rate of growth.
The average age and number of administrators and moderators among discussion communities focused on both the infrastructure of facilities and general infrastructure are nearly identical, with both subcategories averaging 7 to 8 years in age and six to seven administrators and moderators. Reference communities, however, have fewer administrators and moderators and are slightly younger compared to other communities focused on secondary tourism offerings.
Versatile communities are classified among the large online communities in tourism due to their high average number of members (374.68 thousand). These communities are also characterized by their average age (9 years) and the average number of administrators and moderators (11), while their average posting frequency intensity (3.93%) is lower compared to other online communities providing information about tourism offerings on the social network Facebook.
Promotional communities focused on providing information about social events (organized events, events organized by cultural enlightenment institutions) and products created by travel agencies (tours) have the lowest number of members (26.17 thousand). These communities are also described by a relatively rapid average daily growth rate of posts (6.39%) and a specific average age and number of administrators and moderators.

5. Conclusions

This study presents several theoretical implications that contribute to the originality and depth of research in the field of online tourism communities. By introducing a novel typology of tourism-oriented online communities on Facebook, the study provides a fresh perspective on the categorization and dynamics of these virtual environments. Unlike previous research, which primarily focused on content analysis or user behavior, this study integrates structural and managerial dimensions, offering a comprehensive framework for understanding the complexities of online community interactions.
The research demonstrates that effective community management, particularly the roles of administrators and moderators, plays a crucial role in influencing community size and thus engagement levels. This insight challenges traditional assumptions that equate larger community size with higher engagement, suggesting instead that qualitative factors such as the purpose of the online communities, their geographical focus, and the type of exchanged information are more critical. This shift in focus from quantitative to qualitative dimensions offers a new lens through which online community dynamics can be explored.
Furthermore, the study’s methodological approach, utilizing statistical clustering to develop a detailed typology, sets a precedent for future research in the domain of social media communities. The categorization of communities based on both content type and community intent provides a robust theoretical foundation for analyzing diverse community structures, facilitating a deeper understanding of the interplay between community dynamics and tourism-related discourse.
By expanding the theoretical framework to encompass the managerial and organizational aspects of online communities, this research paves the way for future studies to investigate the role of governance and leadership in virtual environments. This contribution not only enhances the theoretical landscape but also offers practical insights for community managers and tourism marketers aiming to optimize engagement and interaction strategies on social media platforms.
The practical implications of this study are manifold. For tourism marketers and community managers, understanding the typology and characteristics of different online communities can inform the development of targeted strategies to enhance user engagement and information dissemination. The findings highlight the necessity for robust administrative structures, particularly in larger communities, to maintain high levels of engagement and manage growth effectively. Additionally, the study’s insights into the relationship between community size and engagement rates can help practitioners design more effective content and interaction strategies tailored to the specific needs of different community types. This knowledge is crucial for leveraging social media platforms to promote tourism destinations and services more effectively, thereby driving higher engagement and influencing travel-related decision-making.
Several limitations of the study must be acknowledged. First, the study’s reliance on publicly available data from Facebook communities may not capture the complete scope of interactions and content shared within private or less accessible groups. Second, the cross-sectional nature of the data collection provides a snapshot of the communities at a single point in time, potentially overlooking dynamic changes in community composition and behavior over time. Third, the study focuses exclusively on Facebook, which, while significant, is only one of many social media platforms where tourism-related communities exist. This focus may limit the generalizability of the findings to other platforms with different user behaviors and community structures.
Future research should address the limitations identified by expanding the scope of analysis to include longitudinal studies that track changes in community dynamics over time. Such studies could provide deeper insights into the factors driving long-term engagement and growth in online communities. Additionally, comparative studies across multiple social media platforms would help generalize the findings and identify platform-specific characteristics influencing community behavior. Further investigation into the impact of various management practices on community engagement and member retention could also yield valuable practical recommendations for community managers. Finally, exploring the interactions between online and offline activities within these communities could offer a more holistic understanding of their role in shaping tourism trends and behaviors.

Author Contributions

Conceptualization, K.P. and E.Z.; methodology, E.Z.; validation, K.P.; formal analysis, E.Z.; data curation, E.Z.; writing—original draft preparation, K.P.; writing—review and editing, K.P. and E.Z.; visualization, E.Z.; supervision, K.P.; project administration, K.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was elaborated within the framework of the project VEGA 1/0136/23 from resilience to sustainability. The impact of data on sustainable and competitive development of tourism.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, T.-L.; Lai, W.-C.; Yu, T.-K. Participating in online museum communities: An empirical study of Taiwan’s undergraduate students. Front. Psychol. 2021, 11, 565075. [Google Scholar] [CrossRef] [PubMed]
  2. Marx, S.; Flynn, S.; Kylänen, M. Digital transformation in tourism: Modes for continuing professional development in a virtual community of practice. Proj. Leadersh. Soc. 2021, 2, 100034. [Google Scholar] [CrossRef]
  3. Marx, S.; Freundlich, H.; Klotz, M.; Kylänen, M.; Niedoszytko, G.; Swacha, J.; Vollerthum, A. Towards an Online Learning Community on Digitalization in Tourism; Universitätsverlag Potsdam: Postupim, Germany, 2021. [Google Scholar] [CrossRef]
  4. Zhou, Y.; Kim, W.G.; Okumus, B.; Cobanoglu, C. Understanding online travel communities: A literature review and future research directions in hospitality and tourism. J. Travel Tour. Mark. 2021, 38, 194–212. [Google Scholar] [CrossRef]
  5. Peng, J.; Yang, X.; Poon, P.; Xie, L. Enhancing users’ well-being in virtual medical tourism communities: A configurational analysis of users’ interaction characteristics and social support. Technol. Soc. 2022, 71, 102084. [Google Scholar] [CrossRef]
  6. Zhu, H.; Kraut, R.; Kittur, A. Effectiveness of shared leadership in online communities. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, Seattle, DC, USA, 11–15 February 2012; Available online: https://dl.acm.org/doi/10.1145/2145204.2145269 (accessed on 13 February 2024).
  7. Wahyuni, A.S. Delivering online community service from community perspective: A critical review. J. Community Serv. Empower. 2023, 4, 486–491. [Google Scholar] [CrossRef]
  8. Zannettou, S.; Caulfield, T.; Blackburn, J.; De Cristofaro, E.; Sirivianos, M.; Stringhini, G.; Suarez-Tangil, G. On the origins of memes by means of fringe web communities. In Proceedings of the IMC ’18 ACM, Boston, MA, USA, 31 October–2 November 2018. 15p. [Google Scholar] [CrossRef]
  9. Skarzauskiene, A.; Mačiulienė, M. Assessment of users’ behavior in Lithuanian online communities. Front. Psychol. 2023, 14, 1265341. [Google Scholar] [CrossRef]
  10. Chou, M.; Frank, J. Designing of online communities of practice to facilitate collaborative learning. In Proceedings of the 2018 5th International Symposium on Emerging Trends and Technologies in Libraries and Information Services (ETTLIS), Noida, India, 21–23 February 2018; pp. 100–104. [Google Scholar]
  11. Rolls, K.; Hansen, M.; Jackson, D.; Elliott, D. How health care professionals use social media to create virtual communities: An integrative review. J. Med. Internet Res. 2016, 18, e166. [Google Scholar] [CrossRef]
  12. Lee, K.-H.; Hyun, S.S. A model of behavioral intentions to follow online travel advice based on social and emotional loneliness scales in the context of online travel communities: The moderating role of emotional expressivity. Tour. Manag. 2015, 48, 426–438. [Google Scholar] [CrossRef]
  13. Stepaniuk, K. The model of tourist virtual community members engagement management. Bus. Theory Pr. 2015, 17, 74–79. [Google Scholar] [CrossRef]
  14. Agag, G.; El-Masry, A.A. Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Comput. Hum. Behav. 2016, 60, 97–111. [Google Scholar] [CrossRef]
  15. Zannettou, S.; Caulfield, T.; De Cristofaro, E.; Kourtellis, N.; Leontiadis, I.; Sirivianos, M.; Stringhini, G.; Blackburn, J. The web centipede: Understanding how web communities influence each other through the lens of mainstream and alternative news sources. In Proceedings of the 2017 Internet Measurement Conference, London, UK, 1–3 November 2017. [Google Scholar]
  16. Belanche, D.; Casaló, L.V.; Flavián, C.; Guinalíu, M. Reciprocity and commitment in online travel communities. Ind. Manag. Data Syst. 2019, 119, 397–411. [Google Scholar] [CrossRef]
  17. Ye, Z.; Huang, X. Adoption of a deep learning-based neural network model in the psychological behavior analysis of resident tourism consumption. Front. Public Heal. 2022, 10, 995828. [Google Scholar] [CrossRef]
  18. Agostini, S.; Mechant, P. Hacia una definición de la Comunidad Virtual. Signo Y Pensam. 2019, 38, 74. [Google Scholar] [CrossRef]
  19. Dennis, M.; Halbert, J. Preface. In Community Engagement in the Online Space; IGI Global: Hershey, PA, USA, 2023; pp. xiv–xxiii. [Google Scholar] [CrossRef]
  20. Beutler, D.; Teixeira, A. As complexidades da cibercultura em Pierre Lévy e seus desdobramentos sobre a educação. In XXI Workshop de Informática na Escola; Sociedade Brasileira de Computação: Porto Alegre, Brasil, 2015; pp. 514–523. [Google Scholar] [CrossRef]
  21. Muryanto, M.A.; Nuzula, I.F.; Venkatesan, T. Online communities, brand recovery, consumer relationships, and repurchase intention. J. Manag. Stud. Dev. 2023, 2, 50–73. [Google Scholar] [CrossRef]
  22. Aoki, K. Virtual communities in Japan. In Proceedings of the Pacific Telecommunications Council 1994 Conference, Honolulu, HI, USA, 23–25 November 1994. [Google Scholar]
  23. Lazar, J.; Preece, J. Classification schema for online communities. In AMCIS 1998 Proceedings (Paper 30); AIS eLibrary: Atlanta, GA, USA, 1998; Available online: http://aisel.aisnet.org/amcis1998/30 (accessed on 12 February 2024).
  24. Armstrong, A.; Hagel, J. Real profits from virtual communities. McKinsey Q. 1995, 3, 127–141. [Google Scholar]
  25. Burnett, G. Information exchange in virtual communities: A typology. J. Comput. Mediat. Commun. 2000, 5, 82. Available online: https://informationr.net/ir/5-4/paper82.html (accessed on 13 February 2024).
  26. Bagozzi, R.P.; Dholakia, U.M. Intentional social action in virtual communities. J. Interact. Mark. 2002, 16, 2–21. [Google Scholar] [CrossRef]
  27. Stanoevska-Slabeva, K. Toward a community-oriented design of internet platforms. Int. J. Electron. Commer. 2002, 6, 71–95. [Google Scholar]
  28. Porter, C.E. A typology of virtual communities: A multi-disciplinary foundation for future research. J. Comput. Commun. 2004, 10, JCMC1011. [Google Scholar] [CrossRef]
  29. Virnoche, M.E.; Marx, G.T. Only connect—E. M. Forster in an age of electronic communication: Computer-mediated association and community networks. Sociol. Inq. 1997, 67, 85–100. [Google Scholar] [CrossRef]
  30. Masson, Z.; Parmentier, G. Drivers and mechanisms for online communities performance: A systematic literature review. Eur. Manag. J. 2023, 41, 590–606. [Google Scholar] [CrossRef]
  31. Choi, S.; Kim, I.; Cha, K.; Suh, Y.-K.; Kim, K.-H. Travelers’ parasocial interactions in online travel communities. J. Travel Tour. Mark. 2019, 36, 888–904. [Google Scholar] [CrossRef]
  32. Li, L.; Lee, K.Y.; Yang, S.-B. Exploring the effect of heuristic factors on the popularity of user-curated ‘Best places to visit’ recommendations in an online travel community. Inf. Process. Manag. 2019, 56, 1391–1408. [Google Scholar] [CrossRef]
  33. El-Manstrly, D.; Ali, F.; Steedman, C. Virtual travel community members’ stickiness behaviour: How and when it develops. Int. J. Hosp. Manag. 2020, 88, 102535. [Google Scholar] [CrossRef]
  34. Najafipour, A.A.; Heidari, M.; Foroozanfar, M.H. Describing the virtual reality and virtual community: Applications and implications for tourism industry. Kuwait Chapter Arab. J. Bus. Manag. Rev. 2014, 3, 12–23. [Google Scholar] [CrossRef]
  35. Lueg, C.H. Mediation, expansion and immediacy: How online communities revolutionize information access in the tourism sector. In Proceedings of the Fourteenth European Conference on Information Systems, ECIS 2006, Göteborg, Sweden, 12 July 2006. [Google Scholar]
  36. Lee, H.; Law, R.; Murphy, J. Helpful reviewers in TripAdvisor, an online travel community. J. Travel Tour. Mark. 2011, 28, 675–688. [Google Scholar] [CrossRef]
  37. Wang, J.; Song, Y.; Jin, M.; Ren, P. Empirical research on knowledge sharing factors in travel online community. Pak. J. Stat. 2014, 30, 815–826. [Google Scholar]
  38. Yuan, D.; Lin, Z.; Zhuo, R. What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors? Comput. Hum. Behav. 2016, 63, 68–74. [Google Scholar] [CrossRef]
  39. Dippelreiter, B.; Grün, C.; Pöttler, M.; Seidel, I.; Berger, H.; Dittenbach, M.; Pesenhofer, A. Online Tourism Communities on the Path to Web 2.0: An Evaluation. Inf. Technol. Tour. 2008, 10, 329–353. [Google Scholar] [CrossRef]
  40. Wang, Y.; Yu, Q.; Fesenmaier, D.R. Defining the virtual tourist community: Implications for tourism marketing. Tour. Manag. 2002, 23, 407–417. [Google Scholar] [CrossRef]
  41. Illum, S.F.; Ivanov, S.H.; Liang, Y. Using virtual communities in tourism research. Tour. Manag. 2009, 31, 335–340. Available online: https://ssrn.com/abstract=1413628 (accessed on 14 February 2024). [CrossRef]
  42. Wu, J.-J.; Chen, Y.-H.; Chung, Y.-S. Trust factors influencing virtual community members: A study of transaction communities. J. Bus. Res. 2010, 63, 1025–1032. [Google Scholar] [CrossRef]
  43. Casaló, L.V.; Flavián, C.; Guinalíu, M. Understanding the intention to follow the advice obtained in an online travel community. Comput. Hum. Behav. 2011, 27, 622–633. [Google Scholar] [CrossRef]
  44. Elliot, S.; Li, G.; Choi, C. Understanding service quality in a virtual travel community environment. J. Bus. Res. 2013, 66, 1153–1160. [Google Scholar] [CrossRef]
  45. Bui, M.-T.; Jeng, D.J.-F.; Lin, C. The Importance of Attribution: Connecting Online Travel Communities with Online Travel Agents. Cornell Hotel. Restaur. Adm. Q. 2014, 56, 285–297. [Google Scholar] [CrossRef]
  46. Gao, L.; Bai, X.; Park, A. Understanding sustained participation in virtual travel communities from the perspectives of is success model and flow theory. J. Hosp. Tour. Res. 2017, 41, 475–509. [Google Scholar] [CrossRef]
  47. Ku, E.C.S. Distributed fascinating knowledge over an online travel community. Int. J. Tour. Res. 2014, 16, 33–43. [Google Scholar] [CrossRef]
  48. Lee, K.-H.; Hyun, S.S. A model of value-creating practices, trusting beliefs, and online tourist community behaviors: Risk aversion as a moderating variable. Int. J. Contemp. Hosp. Manag. 2016, 28, 1868–1894. [Google Scholar] [CrossRef]
  49. Jeon, H.; Jang, J.; Barrett, E.B. Linking website interactivity to consumer behavioral intention in an online travel community: The mediating role of utilitarian value and online trust. J. Qual. Assur. Hosp. Tour. 2016, 18, 125–148. [Google Scholar] [CrossRef]
  50. Fang, J.; Chen, L.; Wang, X.; George, B. Not all posts are treated equal: An empirical investigation of post replying behavior in an online travel community. Inf. Manag. 2018, 55, 890–900. [Google Scholar] [CrossRef]
  51. Fang, J.; Li, J.; Prybutok, V.R. Posting-related attributes driving differential engagement behaviors in online travel communities. Telemat. Inform. 2018, 35, 1263–1276. [Google Scholar] [CrossRef]
  52. Kamboj, S.; Rahman, Z. Measuring customer social participation in online travel communities: Scale development and validation. J. Hosp. Tour. Technol. 2017, 8, 432–464. [Google Scholar] [CrossRef]
  53. Baglieri, D.; Consoli, R. Collaborative innovation in tourism: Managing virtual communities. TQM J. 2009, 21, 353–364. [Google Scholar] [CrossRef]
  54. Casaló, L.V.; Flavián, C.; Guinalíu, M. Relationship quality, community promotion and brand loyalty in virtual communities: Evidence from free software communities. Int. J. Inf. Manag. 2010, 30, 357–367. [Google Scholar] [CrossRef]
  55. Kunz, W.; Seshadri, S. From virtual travelers to real friends: Relationship-building insights from an online travel community. J. Bus. Res. 2015, 68, 1822–1828. [Google Scholar] [CrossRef]
  56. Luo, Q.; Zhang, H. Building interpersonal trust in a travel-related virtual community: A case study on a Guangzhou couchsurfing community. Tour. Manag. 2016, 54, 107–121. [Google Scholar] [CrossRef]
  57. Nimrod, G. The benefits of and constraints to participation in seniors’ online communities. Leis. Stud. 2012, 33, 247–266. [Google Scholar] [CrossRef]
  58. Dencheva, S. Analysis of virtual communities in tourism. EcoForum J. 2013, 2, 18–21. [Google Scholar]
  59. Casaló, L.V.; Flavián, C.; Guinalíu, M. New members’ integration: Key factor of success in online travel communities. J. Bus. Res. 2013, 66, 706–710. [Google Scholar] [CrossRef]
  60. Nimrod, G. Probing the audience of seniors’ online communities. J. Gerontol. Ser. B 2013, 68, 773–782. [Google Scholar] [CrossRef]
Tourismhosp 05 00048 g001
Figure 1. Factors influencing online community member engagement.
Figure 1. Factors influencing online community member engagement.
Tourismhosp 05 00048 g001
Tourismhosp 05 00048 g002
Figure 2. Categorizations of international online communities on Facebook.
Figure 2. Categorizations of international online communities on Facebook.
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Table 1. Overview of the surveyed online communities in the tourism sector.
Table 1. Overview of the surveyed online communities in the tourism sector.
Authors of Studies Sorted ChronologicallyNature of the Online CommunityExamined Online Communities
Wang et al. [40] undefined social network
  • undefined
Lueg [35]online community website
  • Australien
Dippelreiter et. al. [39]online community website
  • Lonely Planet Thorn Tree
  • Travel Pod
  • Virtual Tourism
  • Travelistic
  • TripAdvisor
  • Travel Blog
  • WikiTravel
  • Yahoo Trip Planner
  • Bergfex
  • Cool Austria
  • Couchsurfing
Baglieri & Consoli [53]travel agency website
  • CTS (Cento Turistico Studentesco)
Illum et al. [41]online community websites for tourism students
  • TRINET (Tourism Research Information Network)
  • ATLAS (Association for Tourism and Leisure Education)
  • EUROCHRIE (European Council on Hotel, Restaurant and Institutional Education)
  • ELMAR (Electronic Marketing Academic Community of the American Marketing Associaton)
Casaló et al. [43]online community website
  • Minube
  • Me Gusta El Turismo
  • LastMinute
Lee et al. [36]online community website
  • TripAdvisor
Casaló et al. [59]online community website
  • undefined
Dencheva [58]Facebook
  • Hotel Dobruhja
Nimrod [60]online community website
  • Age Concern
  • Age Net
  • Circels of Friends
  • Elderly Forum
  • Florida Retirement Forums
  • Fifty Plus Forum
  • My Senior Portal
  • Over 50 s
  • Seniors Discussion Forum
  • Pensioners Forum
  • Retimerent Forum
  • 50 Plus Club
  • 50 Plus Forum
Elliot et al. [44]Travel agency website
  • C-Trip
Bui et al. [45]online community website
  • Lonely Planet
  • TripAdvisor
  • Travellers Point
  • Virtual Tourism
  • Wayn
Ku [47]online community website
  • Backpackers
  • Eurotravel
  • TripAdvisor
Najafipour et al. [34]online community website
  • undefined
Wang et al. [37]online community website
  • undefined
Kunj & Seshadri [55]online community website
  • CouchSurfing
Lee & Hyun [12]online community website
  • undefined
Stepaniuk [13]Facebook
  • 70 online communities
  • Keywords for search purposes: “Tourism”, “Journey”, “Holiday”
Agag & El-Masry [14]online community website
  • undefined
Lee & Hyun [48]online community website
  • undefined
Luo & Zhang [56]online community website
  • CouchSurfing
Gao et al. [46]online community website
  • QYER
Jeon et al. [49]online community website
  • undefined
Kamboj & Rahman [52]online community website
  • undefined
Fang et al. [50]online community website
  • MafengWo
Fang et al. [51]online community website
  • MafengWo
Belanche et al. [16]online community website
  • undefined
Choi et al. [31]online community website
  • undefined
Li et al. [32]online community website
  • QYER
El-Manstrly et al. [33]online community website
  • WAYN (Where are you now)
Chen et al. [1]online community website
  • undefined
Marx et al. [2]online community website
  • undefined
Marx et al. [3]online community website
  • undefined
Peng et al. [5]online community website
  • undefined
Table 2. Overview of international online communities in tourism on Facebook.
Table 2. Overview of international online communities in tourism on Facebook.
Online Community NameMembership Size
(in Thousands)		Age Number of Administrators and ModeratorsAverage Number of Comments per PostAverage Daily Growth Rate of Posts in %
DIY Travel Philippines1501.24810124.35
Girls LOVE Travel®1438.18826322.72
Best Destination to Travel1176.64575213.20
Discover Italy | Best Places and Travel Tips672.2133105.05
The Solo Female Traveler Network539.4678463.15
World Traveling Group236.4315506.89
Tourist Helpline222.281016122.97
Travel community211.593300.00
Nomads199.18106893.36
Travel community170.42616312.97
Backpackers of the Philippines149.71111245.56
Travel147.316201.69
Croatia Travel136.65114115.45
Dubai Travel—Plans & Bookings117.9912100.00
Travel Inspiration Community116.953912.17
I Tourismhosp 05 00048 i001 PUGLIA | Best Places and Travel Tips116.1323455.00
Travelling to Malta100.9777350.83
Visit Albania94.4314354.98
Bali Travel Community82.0091166.68
Backpacking Europe80.0617975.81
Travel Philippines70.39122408.21
Worldwide Travel Bloggers & Travellers69.896504.11
TravelFree group—cheap flights and more67.555701.28
Florence & Tuscany Travel Tips60.0145173.78
I Tourismhosp 05 00048 i001 TUSCANY | Best Places and Travel Tips50.5423206.61
Travel Egypt44.721112802.21
Travel in Sri Lanka39.538805.48
Travel & Tourism31.514200.00
Travel Agent in India28.726403.09
Travel Guide and Tourism27.644801.23
Malaysia Travel27.62131386.10
Travelling27.159605.10
Travel26.13302.63
WE Travel Agents24.729301.55
Backpacking South America22.316670.63
Sri Lanka Travel & Tourism Original Page20.035312.79
Best Travel Packages18.594205.46
World Travel Destinations13.9131308.47
Conscious Travel Community12.854510.00
Backpacking South-East Asia12.818225.84
Traveling in Georgia 11.2392134.64
Vietnam Travel Guide7.4313324.44
Tourist and Hotels6.4413200.00
Asia traveling, backpacking, hiking and tour activities4.767202.95
Travel Sri Lanka Group4.054804.67
Visit Greece Travel Tips3.05103711.11
Malaysia Hotels, Tourism, Culinary / Food & Beverages Dire2.0312500.00
Pune Tourism-Tours & Travels-Trekking-Car Hire-Hotels-Rest1.793203.23
Ultimate Travel Group1.3632011.76
Travel Agents—Travel Destinations Travel Deals Worldwide1.33305.62
Selected quantitative characteristics of examined online communities
Mean ( x – ) 164.997.344.12%11.716.56
Standard deviation (sx)±334.26±4.04±3.01±20.40±5.41
Coefficient of variation (vk)202.59%55.04%73.06%174.21%82.47%
Median ( x ~ ) in thousand47.627.003.951.005.00
Mode ( x ^ ) in thousand1.303.000.000.003.00
Table 3. Classification of international online communities in tourism according to the category and nature of published information.
Table 3. Classification of international online communities in tourism according to the category and nature of published information.
Cluster NameCategory of Published InformationNature of Published Information
General Tourism Infrastructure Communitiesinformation on general tourism infrastructure
(insurance, parking, car rentals, etc.)		discussion posts
Tourism Services
Communities		information on tourism services
(accommodation services, dining services, ancillary services, etc.)		hyperlinks
Comprehensive Tourism Services and Destinations Communitiesinformation on tourism services and destinationsdiscussion posts, hyperlinks, online audiovisual materials
Focused Tourism Services Communitiesinformation on tourism services
(accommodation services, dining services, ancillary services, etc.)		discussion posts
Destination Highlight Communitiesinformation on destinations
(natural attractions, cultural–historical attractions, organized events)		online audiovisual materials
Destination Promotion Communitiesinformation on destinations
(natural attractions, cultural–historical attractions, organized events)		promotional materials
Integrated Tourism Services and Destinations Promotion Communitiesinformation on tourism services and destinationspromotional materials
Table 4. Characteristics of categories of online communities in tourism based on qualitative attributes.
Table 4. Characteristics of categories of online communities in tourism based on qualitative attributes.
Category of Online CommunitiesSubcategory of Online CommunitiesCommunity ManagementCommunity Intent
Online communities centred on primary offeringsAudiovisual communitiescommunity membersproviding advice
Promotional communitiesexternal businessesproviding advice
Online communities centred on secondary offeringsLink communitiesmoderators and membersproviding advice
Discussion communities (tourism infrastructure)community membersproviding advice
Discussion communities (general infrastructure)community membersseeking advice
Mixed online communitiesVersatile communitiescommunity membersseeking and providing advice
Promotional communitiesexternal businessesproviding advice
Table 5. Profile of online communities.
Table 5. Profile of online communities.
Category of Online CommunitiesSubcategory of Online CommunitiesQuantitative Variables
Average Number of Members
(in Thousands)		Average Daily Growth Rate of Posts
(in %)		Average Age of the CommunityAverage Number of Administrators and Moderators
Online communities centered on primary offeringsAudiovisual communities186.675.518.48
Promotional communities43.255.926.825
Online communities centered on secondary offeringsLink communities29.792.034.334
Discussion communities (tourism infrastructure)438.656.018.06
Discussion communities (general infrastructure)102.103.737.07
Mixed online communitiesVersatile communities374.683.938.711
Promotional communities26.176.396.56
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Zabudská, E.; Pompurová, K. Identifying Patterns among Tourism-Oriented Online Communities on Facebook. Tour. Hosp. 2024, 5, 830-847. https://doi.org/10.3390/tourhosp5030048

AMA Style

Zabudská E, Pompurová K. Identifying Patterns among Tourism-Oriented Online Communities on Facebook. Tourism and Hospitality. 2024; 5(3):830-847. https://doi.org/10.3390/tourhosp5030048

Chicago/Turabian Style

Zabudská, Eva, and Kristína Pompurová. 2024. "Identifying Patterns among Tourism-Oriented Online Communities on Facebook" Tourism and Hospitality 5, no. 3: 830-847. https://doi.org/10.3390/tourhosp5030048

APA Style

Zabudská, E., & Pompurová, K. (2024). Identifying Patterns among Tourism-Oriented Online Communities on Facebook. Tourism and Hospitality, 5(3), 830-847. https://doi.org/10.3390/tourhosp5030048

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