How Social Media Content Shapes Destination Image and eWOM: The Moderating Role of Personality in Lesser-Known Tourism Destinations

Abstract

This study investigates how user-generated content (UGC) and perceived experience of destination-generated social media content (DGC) shape satisfaction, destination image, and electronic word-of-mouth (eWOM) intention in lesser-explored tourism destinations. A dual-content model grounded in the stimulus-organism-response (SOR) framework is tested using partial least squares structural equation modeling (PLS-SEM) with data from 300 tourists who interact with destinations’ social media. Results reveal that UGC exerts limited influence on satisfaction, destination image, and eWOM intention, which diverges from much prior literature but is consistent with the scarcity and lower trustworthiness of UGC in small destinations. In contrast, perceived experience of DGC strongly enhances destination image and eWOM intention, highlighting the relevance of pre-visit digital experiences. In addition, moderation analysis shows that openness to experience significantly influences selected relationships, with stronger effects observed among tourists who are lower in openness. The findings underscore the importance of integrating pre-visit digital interactions and individual differences into destination marketing models and provide practical insights for destination management organizations (DMOs) in lesser-known destinations, emphasizing the strategic value of high-quality official content to compensate for limited UGC. This research advances destination marketing literature by jointly examining UGC and DGC and by introducing perceived experience of DGC and personality as key explanatory elements.

1. Introduction

The advent of digital technologies has led to major transformations across various sectors, including tourism and hospitality [1]. Specifically, regarding tourism destinations, online content generated on social media has emerged as a crucial tool for increasing destinations’ attractiveness and accessibility [2,3]. As a result, destination management organizations (DMOs) are increasingly leveraging their social media to enhance experiences and influence visitors’ perceptions [3]. Indeed, previous studies [4,5] acknowledge the importance of content produced by DMOs in terms of quality of information, ease of use and navigation, or credibility [6,7]. Likewise, traditionally, efforts of tourism organizations in building, promoting, and maintaining a destination’s image have long been considered crucial. However, other authors [8,9] also consider the content produced by users as relevant in influencing tourists’ behaviors due to the increasingly growing significance of electronic word-of-mouth (eWOM). In this sense, although the impact of social media content in the field of marketing destinations has focused on differentiating destination-generated social media content (DGC) and user-generated content (UGC) [10], it is a niche theme that has the potential to grow [11].

Furthermore, online content on social networks is increasingly regarded as an intangible asset that influences tangible assets and impacts business value [12]. Consequently, they are associated with the user experience (UX), which is related to a wide variety of meanings ranging from traditional usability to beauty, hedonic, affective, or experiential aspects of technology use [13]. Likewise, Verhulsdonck and Shalamova [14] suggest that, depending on how the contents are created and presented, the user will have a different perception and behavior towards the destination.

Content on social media also influences tourists’ evaluation or satisfaction, in some cases triggering a perceived destination image and word-of-mouth communications about the destination. This suggests that understanding visitor satisfaction, especially concerning different activities or attributes such as engagement with content sharing [15], is important for destination management [16].

Personality traits have increasingly been recognized as meaningful antecedents of tourist behavior [17,18], as they allow for a more nuanced understanding of individual differences beyond traditional sociodemographic segmentation variables such as age or gender [19]. In particular, openness to new experiences offers a valuable lens for examining tourist behavior in lesser-known destinations, where novelty seeking and tolerance for unfamiliarity may shape how tourists interpret information and respond to destination-related stimuli. Given the relevance of this trait to decision-making in tourism contexts [20,21], together with the mixed and sometimes inconclusive findings reported in previous personality-based tourism studies [22,23,24], the present paper also investigates the moderating role of personality traits within the proposed research framework.

In sum, this study contributes to broadening the application of the SOR framework [25] by conceptualizing UGC and DGC as two parallel stimuli, an empirical comparison that remains limited in destination marketing research, and by showing that their effects may differ in lesser-known destinations. Thus, the present research delimits the explanatory power of existing SOR-based models, which have largely been developed and tested in consolidated destinations characterized by high digital visibility and abundant UGC. Second, existing research on DGC has predominantly focused on content characteristics, destination image, or satisfaction, paying less attention to tourists’ perceived experience with official destination content, particularly at the pre-visit stage of the trip. Third, although personality traits have been identified as relevant predictors of tourist behavior, evidence of their moderating role in digital content evaluation remains fragmented, especially in contexts marked by lower visibility and greater uncertainty. Importantly, prior findings highlighting the persuasive strength of UGC have mostly been observed in highly visible destinations with abundant reviews, dense digital interaction, and stronger reputational cues [5,8]. By contrast, lesser-known destinations represent a structurally different informational environment, characterized by lower volumes of UGC, reduced content diversity, and weaker online reputational signals, all of which may diminish the credibility and diagnostic value of user-generated communication [7,26,27]. Accordingly, lesser-known destinations are not only an underexplored empirical setting, but also a theoretically relevant context for examining whether prevailing assumptions in digital tourism research remain valid under conditions of low visibility and greater uncertainty. From this perspective, the study is positioned as a boundary-condition test of established frameworks, explaining how online communication shapes tourists’ satisfaction, destination image, and eWOM. Grounded in the SOR framework, this study pursues two main objectives: (1) to analyze and compare the effects of UGC and tourists’ perceived experience with destination-generated content on satisfaction, destination image, and eWOM intention; and (2) to examine whether personality traits moderate these relationships in the context of lesser-known destinations. By doing so, the aim of the research is to advance theoretical understanding of digital destination marketing while offering practical insights for DMOs seeking to optimize promotional strategies under conditions of limited visibility and resources.

2. Literature Review and Development of Hypotheses

The stimulus-organism-response (SOR) framework, originally proposed by Mehrabian and Russell (1974) [25] within the field of environmental psychology, has been extensively utilized to investigate how environmental and media-related stimuli impact individual behavior (Baber & Baber, 2023) [8]. In tourism contexts, and specifically in this study, stimuli such as UGC and perceived experience of DGC operate as external cues that can shape tourists’ satisfaction with communications and/or perceived image of the destination (organism), and consequently influence their behavioral responses, such as sharing experiences via social media (eWOM intention).

Within this broader logic, expectation-confirmation theory (ECT) and, to a more limited extent, the technology acceptance model (TAM) are incorporated as complementary explanatory lenses to provide a more nuanced understanding of some of the mechanisms operating within the model. ECT contributes to explaining the role of satisfaction within the proposed model. According to this perspective, satisfaction emerges from the comparison between prior expectations and perceived performance or actual experience [28,29]. ECT thus supports the interpretation of satisfaction as an organism state through which online communication contributes to destination image and eWOM intention. TAM, in turn, posits that technology-related evaluations influence users’ attitudes toward digital environments and their subsequent behavioral responses [30]. In the present study, it serves to justify why tourists’ perceived experience of DGC may influence their internal evaluations (such as satisfaction or perceived image). Importantly, this construct is not intended as a direct operationalization of TAM dimensions, but rather as a narrower, context-specific perceived experience with official destination social media communication. This encompasses hedonic, interactive, informational, and participatory characteristics, as well as elements associated with perceived usefulness and perceived ease of use.

Thus, the SOR model provides the general causal structure of the model, whereas ECT and, to a lesser extent, TAM clarify specific mechanisms operating within that broader sequence.

Building on this theoretical integration, we will first formulate hypotheses related to UGC, followed by those addressing DGC, and finally, the hypothesis positing personality as a moderator.

2.1. User-Generated Content

UGC has become a central element of contemporary digital communication, particularly within tourism contexts [31]. Prior research conceptualizes UGC as content that is publicly accessible online, created with a degree of creativity, and produced by individuals outside professional or commercial settings [32]. It includes tourists’ narratives, opinions, evaluations, and recommendations shared across digital platforms such as social media, blogs, and online forums [33,34]. Through these channels, consumers actively participate in shaping the informational environment surrounding destinations.

Although closely related, UGC and eWOM represent distinct yet overlapping constructs. eWOM is generally defined more narrowly as consumption-related communication, such as online reviews, ratings, or recommendations, whereas UGC encompasses a broader range of user-created digital expressions [35,36]. While UGC emphasizes content creation, eWOM may also involve the dissemination or sharing of information originally produced by others. Despite this conceptual distinction, previous studies have frequently used the two terms interchangeably, particularly in tourism and marketing research [37,38]. Accordingly, the present study acknowledges their conceptual proximity, while treating UGC as the source of content and eWOM as a behavioral outcome reflecting information-sharing intentions [39].

Moreover, the increasing reliance on digital media and social networking platforms has substantially transformed how destination images are formed and communicated. Online interactions and user opinions shared on social media exert a significant influence on the perceived image of organizations and destinations [40]. In the tourism domain, perceived destination image has been widely recognized as a strategic asset that enhances destination competitiveness and fosters tourist loyalty [41]. Destination image is generally understood as a holistic mental representation formed through tourists’ beliefs, impressions, and associations derived from multiple information sources [3]. As such, it reflects a subjective and dynamic evaluation of a place rather than an objective reality [42].

From a branding perspective, destination image can be interpreted as a set of associations stored in tourists’ memory, encompassing functional, emotional, and symbolic attributes [42,43]. In digital environments, these associations are increasingly shaped by online content, contributing to what has been termed electronic reputation [44]. Consequently, destination image has become a focal topic in tourism research due to its influence on tourists’ perceptions, evaluations, and decision-making processes [45].

Recent studies emphasize that the quality and credibility of digital marketing initiatives, including DGC and social media marketing activities, play a critical role in shaping a favorable destination image [26]. However, beyond official communications, UGC has been shown to exert a strong influence on image formation [5,8]. In this regard, destination image is widely acknowledged as a multidimensional construct that integrates cognitive evaluations and affective responses shaped by online interactions [46].

In addition to influencing destination image, UGC also plays a crucial role in stimulating tourists’ intentions to engage in eWOM. Tourists are more inclined to share their experiences online when they perceive destination-related content as credible, useful, and influential [47,48]. Furthermore, motivational factors such as altruism, social interaction, and expectations of reciprocal benefits have been identified as key drivers of eWOM behavior [49]. As tourists consume and interact with UGC, their perceptions of the destination are reinforced, increasing their likelihood of disseminating information and recommendations through digital platforms. Based on this, we state the following hypotheses:

H1. 

User-generated content positively influences tourists’ perceived destination image.

H2. 

User-generated content positively influences tourists’ intention to engage in eWOM about the destination.

In line with the objectives of the present research, tourist satisfaction represents another key construct. Consumer satisfaction has long been regarded as a cornerstone of marketing theory due to its strategic relevance for organizational performance and long-term success [50]. Early conceptualizations describe satisfaction as an evaluative judgment formed after a purchase or as the cumulative result of repeated interactions between consumers and products or services [51]. This perspective emphasizes satisfaction as an attitudinal response emerging from consumption experiences.

More recent research has expanded this view by framing satisfaction as the outcome of a comparative process between consumers’ pre-consumption expectations and their post-consumption evaluations [52]. This approach is grounded in expectation-confirmation theory [28], which posits that satisfaction arises when perceived performance meets or exceeds prior expectations, whereas dissatisfaction occurs when performance falls short.

Applied to tourism destinations, this theoretical framework suggests that tourists experience higher levels of satisfaction when destination attributes, services, or experiences surpass what they initially anticipated [16]. Within digital tourism environments, UGC plays an important role in shaping satisfaction judgments. Online reviews and social media content provide experience-based information that helps tourists evaluate destination performance and identify specific sources of satisfaction or dissatisfaction [53]. As tourists increasingly rely on UGC to interpret and assess their travel experiences, such content becomes a salient antecedent of satisfaction formation. Therefore, the following hypothesis is posited:

H3. 

User-generated content positively influences tourists’ satisfaction with UGC about the destination.

Prior research has consistently highlighted the interconnected roles of tourist satisfaction and destination image in shaping tourists’ behavioral intentions. Empirical evidence suggests that both constructs operate jointly to influence post-consumption responses, including recommendations and loyalty-related behaviors [54,55]. For instance, Marques et al. [56] demonstrate that the affective dimension of destination image is significantly shaped by tourists’ satisfaction levels and subsequently influences post-visit behavioral outcomes. Similarly, Xu et al. [39] report that UGC exerts an indirect effect on tourist loyalty by simultaneously influencing destination image and satisfaction, highlighting the sequential and complementary nature of these constructs.

Moreover, tourism is widely recognized as an information-intensive industry in which travelers rely heavily on information exchanges throughout the decision-making and experience-evaluation processes. With the rapid expansion of social media and digital platforms, online content has become a powerful stimulus capable of shaping tourists’ intentions and enhancing their satisfaction with destinations [57,58].

Thus, considering all previous discussions, the following hypotheses are proposed:

H4. 

Tourists’ satisfaction with UGC positively influences their perceived destination image.

H5. 

Tourists’ satisfaction with UGC positively influences their eWOM intention.

2.2. Perceived Experience of Destination-Generated Social Media Content

UX is a multidimensional concept that refers to the overall experience a person has when interacting with a product, system, or service. Rather than focusing solely on usability or interface efficiency, UX encompasses a broader range of factors, including the user’s emotions, perceptions, interpretations, and responses before, during, and after interaction [59,60]. This holistic view includes not only the functional aspects—such as usefulness and ease of use—but also how the interaction aligns with the user’s expectations and internal state [61].

UX is recognized as a professional role (e.g., UX designer), an academic discipline (UX design), and a heuristic framework in usability studies. Across these domains, the emphasis lies on designing experiences that are intuitive, seamless, and emotionally resonant [14]. By centering design efforts on the full spectrum of user interaction, UX extends the traditional scope of usability, aiming to enhance overall satisfaction and engagement [62].

Moreover, most UX models distinguish between hedonic and pragmatic dimensions of technology, the former being related to the emotional and engagement appeal of the interaction, while the latter are related to functional aspects [63]. In a similar vein, scholars such as Mani [64] conceptualize some of these UX elements through the lens of virtual hospitality, describing digital environments ─such as online communities─ as spaces capable of hosting social interactions that reflect the authentic hospitality of a tourist destination. From a more technical perspective, Piccoli et al. [65] frame hospitality in terms of information convenience (i.e., easy access to relevant product details), site navigation (the ability to locate information efficiently), and consumer confidence. Furthermore, Manna et al. [66] identify some key factors (e.g., trust, service experience, customer care, service quality, or ease of use) which influence UX.

Building on this understanding of UX, it is important to consider how it applies specifically to digital interactions within the tourism context, particularly in relation to DGC. DGC refers to content created and disseminated by official tourism organizations or companies through online channels, especially social media, as part of their promotional strategies [5]. These communications are not only a key component of the digital marketing mix but also play a critical role in shaping tourist perceptions and encouraging destination loyalty. In contrast to traditional advertising, DGC leverages the interactive and participatory nature of digital platforms to engage users more directly. In this sense, prior literature e.g., [67,68,69] suggests that official social media communication can generate experiential responses through its hedonic characteristics, entertainment value, interactivity, informativeness, and capacity to encourage participation and sharing. Based on this logic, the present study defines perceived experience of DGC as tourists’ overall perception of the experience derived from interacting with official destination content on social media. Importantly, this construct is not approached as a broad measure of UX in the general technological or interface-related sense. Instead, it is conceptualized as a narrower, context-specific form of UX linked to the social media content itself and to users’ interaction with it.

As a result, the perceived experience of DGC—how intuitive, informative, and engaging users perceive this content—can significantly influence tourists’ attitudes and behaviors, including their intention to engage in eWOM [70,71]. Thus, in the present study, the perceived experience with online DGC is examined as a key construct to understand how destination communications affect tourists in lesser-known destinations.

In the context of digital tourism marketing, perceived experience with DGC ─with websites and social media managed by DMOs─ has become a central element influencing tourist behavior and perception. This construct refers not only to usability but also to how intuitive, accessible, and engaging users find the interaction with digital platforms [8]. As Lam et al. [68] emphasize, well-designed destination platforms, featuring high usability and user-centered design, enhance the overall effectiveness of destination marketing and play a crucial role in shaping visitors’ perceptions. This aligns with findings from Montero et al. [72], who argue that ensuring a positive experience across digital platforms, including inclusivity and accessibility, is fundamental for influencing tourists’ cognitive and emotional responses to destinations.

Prior studies suggest that high-quality digital content—characterized by clear navigation, relevant information, and user-friendly interfaces—can foster trust, credibility, and ultimately behavioral responses such as eWOM [73,74]. In the same vein, Saleem et al. [75] further confirm that users who perceive content as valuable and trustworthy are more likely to communicate online their experiences with others. Moreover, tourism marketing efforts that incorporate entertainment, personalization, and interactive features have been found to positively influence destination image [76,77]. Other prior studies suggest that users’ motivations and experiences on social media play an important role in shaping their willingness to share information online. For instance, Lee and Ma [78] found that social interaction on social media positively stimulates intentions to disseminate content. In the tourism context, Lai et al. [79] similarly show that tourists who enjoy a positive and satisfying cuisine experience are more likely to develop favorable eWOM intentions. Within the tourism domain, it has been emphasized that tourists’ satisfaction and favorable experiences are key antecedents of eWOM behavior [80,81].

Research has also highlighted that sharing through social media can itself be perceived as an enjoyable and entertaining activity, which encourages more active platform use [82]. Beyond entertainment, other experiential aspects of digital platforms (such as ease of use, effective search functionality, and appealing design) can significantly strengthen users’ tendency to engage in positive eWOM [83].

In line with this literature, it is proposed that a favorable perceived experience with online DGC will positively influence tourists’ perception of the destination image, as well as their intention to engage in eWOM behavior:

H6. 

Perceived experience of destination-generated social media content positively influences tourists’ perceived destination image.

H7. 

Perceived experience of destination-generated social media content positively influences tourists’ intention to generate eWOM.

Tourism organizations are increasingly using digital platforms to understand visitor preferences and deliver personalized, engaging content that enhances satisfaction [84,85]. Studies show that personalization, usability, and content quality positively influence trust and user attitudes toward online platforms [86,87,88]. Destinations providing user-friendly and visually appealing digital experiences are more likely to meet visitors’ expectations, improve perceptions of service quality, and generate satisfaction with DGC [89,90].

Tourists frequently consult social media communications during the pre-visit stage of the travel process [91]. Through this exposure, they obtain information and knowledge about destinations, products, and services, which contributes to the formation of expectations and, subsequently, influences satisfaction [92]. In this context, organizations that design effective social media marketing experiences are better positioned to establish positive relationships with their audiences, thereby fostering higher levels of satisfaction and loyalty [93]. More specifically, social media communication that combines informative value with features such as entertainment, interaction, and customization can enhance customer satisfaction [16]. Likewise, Verhagen et al. [94] showed that, in online environments, more enjoyable digital experiences lead users to report greater satisfaction with the website. In a similar vein, McAlexander et al. [95] emphasized that user interaction and communication within the platform are central to strengthening relationships, increasing satisfaction, and supporting long-term loyalty.

Therefore, a positive perceived experience with online DGC is expected to lead to higher satisfaction with that content:

H8. 

Perceived experience of destination-generated social media content positively influences tourists’ satisfaction with DGC.

Digital technologies and social media have become central channels through which tourists and destination management organizations (DMOs) construct and communicate destination images [96]. When users engage with DGC, they are exposed to curated information that shapes how they interpret and internalize the characteristics of a destination. This process of assimilating new stimuli contributes to the development of cognitive and affective evaluations that ultimately form the overall destination image [8,10]. Satisfaction with this official content is therefore essential, as trustworthy and credible sources tend to exert stronger influence on tourists’ perceptions [4]. According to ECT, tourists compare their expectations—often shaped by official content—with their actual experiences, influencing how they perceive the destination [68]. Therefore, greater satisfaction with DGC is likely to enhance destination image.

Beyond shaping image, satisfaction with DGC may also stimulate behavioral responses, including eWOM. Content originating from authoritative and reliable sources, such as DMOs, tends to elicit more favorable reactions and can motivate users to share information with others [97]. High-quality and engaging DGC strengthens digital marketing effectiveness and has been shown to influence tourists’ behavioral intentions, particularly their likelihood to recommend or comment on the destination online [98]. Moreover, official digital channels and eWOM collectively contribute to tourists’ satisfaction with a place and play a key role in triggering subsequent communicative behaviors [58,68,99]. Previous research has emphasized that destination image is not static, but evolves throughout and beyond the travel experience, thereby influencing tourists’ satisfaction and their subsequent behavioral responses, such as recommending the destination or revisiting it [4]. In online environments, satisfaction tends to increase when the actual experience with a brand or community meets or exceeds prior expectations, and this positive evaluation can strengthen both consumers’ purchase decisions and their willingness to disseminate information about the product or service [100]. In the tourism field, several empirical studies have likewise confirmed that satisfaction and positive experiences are closely associated with eWOM behavior [80,81].

More specifically, Lai et al. [79], in the context of cuisine tourism, found that tourists who enjoy satisfying consumption experiences are more likely to develop positive intentions to generate eWOM. In a similar vein, users who report satisfying experiences with a brand community tend to share their consumption experiences with others, both to assist other consumers and to express support for the brand [101].

Based on this, we state the following hypotheses:

H9. 

Tourists’ satisfaction with online destination-generated content (social media) positively influences their perceived destination image.

H10. 

Tourists’ satisfaction with online destination-generated content (social media) positively influences their eWOM intention.

Destination image has been extensively acknowledged in tourism literature as a fundamental antecedent of tourist behavior, exerting a significant influence on both travel-related decisions and post-visit outcomes [102,103]. A favorable destination image helps tourists form expectations and develop behavioral intentions toward the destination. Nevertheless, some scholars argue that visual or image-related cues alone may not always be sufficient to stimulate behavioral intentions, particularly when tourists exhibit low levels of involvement with the product or the experience [104].

Despite this limitation, when destination image is formed through a combination of cognitive evaluations (e.g., perceived quality, attractions, and infrastructure) and affective responses (e.g., emotions and feelings toward the destination), it becomes a powerful determinant of favorable behavioral outcomes. Prior studies indicate that a positive and well-established destination image enhances tourists’ propensity to engage in supportive behaviors, such as recommending the destination to others [105]. Consistent with this view, Baloglu and McCleary [106] identify destination image as a reliable predictor of word-of-mouth communication, including its electronic form.

Although eWOM has received considerable scholarly attention, recent research highlights the need for further investigation into its antecedents, particularly those related to perceptual and experiential constructs [107]. Empirical evidence suggests that destination image not only influences visit intentions and destination trust but also plays a critical role in encouraging tourists to share their experiences through online platforms [81]. A positive destination image may therefore increase tourists’ motivation to generate eWOM as a means of expressing satisfaction, reinforcing self-image, or assisting other potential travelers.

Based on these arguments, the following hypothesis is proposed:

H11. 

A positive destination image increases tourists’ intention to generate eWOM.

2.3. Personality Traits

Personality traits have long been examined as influential factors in decision-making and are recognized for their value as practical and observable criteria for segmenting tourism markets. These traits enable DMOs to better tailor their offerings to the preferences of specific tourist profiles. Moreover, personality-based segmentation provides deeper insight into the complexity and variability of tourist behavior, going beyond the limitations of traditional approaches that rely primarily on demographic or typological characteristics [19]. In parallel, moderation analysis has gained prominence in empirical research, particularly within the fields of information systems and broader business and management domains [108]. As such, incorporating personality traits as moderating variables offers a valuable opportunity to expand our understanding of tourist behavior across different contexts.

In line with this idea and the variables examined in the present study, previous research has shown that tourists’ personality traits significantly influence their perception of destination image [109]. As noted by Beerli and Martin [110], both psychological and demographic characteristics shape how individuals cognitively process and organize their perceptions, which in turn affects how they perceive a destination. Specifically, traits such as agreeableness and openness to experience have been linked to a higher perceived value of a destination [111]. Conversely, extraversion has been associated with a lower perception of value, while neuroticism has shown no significant impact [23].

In relation to satisfaction, certain personality traits have been found to positively influence behavioral intentions [112]. Traits such as extraversion, agreeableness, conscientiousness, and openness to experience have been identified as significant predictors of satisfaction, whereas neuroticism appears to have no notable effect [24]. Notably, openness to experience has demonstrated the strongest association with memorable tourism experiences, suggesting its potential impact on overall tourist satisfaction [21].

Tourist personality traits have been identified as influential factors in both destination choice and the intention to share experiences related to that destination [17]. Supporting this, d’Ament et al. [113] found that neuroticism specifically plays a role in shaping tourists’ intentions to communicate their experiences. Similarly, Lin et al. [114] observed that extraversion positively correlates with information-sharing behavior on social media, whereas agreeableness, conscientiousness, and neuroticism were negatively associated with such behavior. In contrast, according to Kordzadeh and Bozan [115], openness to new experience, conscientiousness, and extraversion are the ones influencing online review use and writing.

In accordance with mainstream literature, we anticipate personality as a moderating variable in the hypothesized relationships in our proposed model. Therefore, we propose the following hypothesis:

H12a–H12k. 

Personality traits significantly moderate the structural relationships between user-generated content, perceived experience of destination-generated social media content, online content satisfaction, perceived destination image, and the intention to communicate online (eWOM intention).

Figure 1 presents the proposed model of this study.

3. Methodology

To address the research objective, a quantitative methodology was employed using a structured, ad hoc questionnaire comprising exclusively closed-ended items.

The measurement items used to operationalize the study variables were adapted from established scales found in the marketing literature, ensuring alignment with the specific context and aims of this research. The scales developed by Yadav et al. [116] and Schivinski and Dabrowski [5] were adapted to assess online UGC. The constructs of UGC satisfaction, DGC satisfaction, and destination image were measured using the scale by Schivinski and Dabrowski [5]. The construct “perceived experience of destination-generated social media content” was operationalized as tourists’ perceived experiential quality of interacting with official destination social media content. The scale was adapted from [8], whose items capture hedonic, interactive, informational, and participatory aspects of social media interaction, as well as items related to perceived usefulness and perceived ease of use. The construct was theoretically reframed in line with tourism UX and platform-use experience literature, emphasizing ease of use, interactivity, and perceived experiential quality in digital destination environments [67,68,69,117]. The eWOM intention was evaluated using items from Cheung et al. [118] and Afshardoost and Eshaghi [119]. Lastly, regarding the tourists’ personality traits, the study employed the scale proposed by Picazo-Vela et al. [120], which is grounded in the Big Five personality framework [18,120]. All constructs were measured using a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), and all items were treated as reflective indicators (see

Table 1. Operationalization of the constructs and measurement items.

Construct	Operational Definition in This Study	Source	Scale
User-generated content (UGC)	Tourists’ perceptions of the informativeness, credibility, usefulness, and attractiveness of online content generated by other users about the destination.	Yadav et al. [116] and Schivinski and Dabrowski [5]	7-point Likert scale (1 = strongly disagree; 7 = strongly agree)
Perceived experience of destination-generated social media content	Tourists’ context-specific perceived experience derived from interacting with official destination social media content, particularly its hedonic, informational, interactive, and participatory characteristics.	Guerreiro et al. [8], reinterpreted as a bounded, context-specific experiential construct, in line with Martínez-Sala et al. [67] and Lam et al. [68].	7-point Likert scale (1 = strongly disagree; 7 = strongly agree)
UGC satisfaction	Tourists’ satisfaction with the UGC available about the destination.	Schivinski and Dabrowski [5]	7-point Likert scale
DGC satisfaction	Tourists’ satisfaction with the communication generated by the destination itself on social media.	Schivinski and Dabrowski [5]	7-point Likert scale
Perceived destination image	Tourists’ overall positive cognitive evaluation and general impression of the destination.	Schivinski and Dabrowski [5]	7-point Likert scale
eWOM intention	Tourists’ intention to communicate their opinions and recommendations about the destination online.	Cheung et al. [118], Afshardoost and Eshaghi [119]	7-point Likert scale
Personality traits	Individual personality characteristics used as grouping variables in the multigroup moderation analysis.	Picazo-Vela et al. [120], Hu and Kim [18]	7-point Likert scale

Source: Author’s proposal.

with the operationalization of the constructs and measurement items).

Given the breadth of the constructs included in the model, additional clarification is required regarding the operationalization of UGC and perceived experience of DGC. In this study, UGC is not conceptualized as a multidimensional second-order construct, but as tourists’ overall evaluation of user-generated destination content. The items adapted from Yadav et al. [116] and Schivinski and Dabrowski [5] capture different facets of this overall perception, including informativeness, reliability, relevance, persuasiveness, usefulness, comprehensiveness, and attractiveness. These elements are treated as reflective indicators because they are understood as manifestations of a common underlying evaluation of UGC quality and influence, rather than as independent dimensions that form the construct. Similarly, although UX is widely recognized as a multidimensional concept in the broader technology and design literature, the present study does not aim to measure UX in its full interface-design sense. Instead, it focuses on tourists’ perceived experience of destination-generated social media content as a bounded, context-specific construct. This construct captures tourists’ overall experiential evaluation of official destination content, including hedonic, informational, interactive, participatory, usefulness-related, and ease-of-use aspects. These aspects are therefore treated as reflective manifestations of a general perceived experience with DGC, rather than as separate first-order dimensions. This operationalization is consistent with the objective of the study, which is to compare the overall influence of UGC and DGC-related experience within the SOR framework, rather than to decompose each content source into multiple subdimensions.

The survey was distributed via QR code to tourists as they exited the tourist information offices they had visited. A non-probability convenience sampling approach was adopted, which is the most common method in service-related studies, including hospitality and tourism [121]. Accordingly, six tourist offices located in less-visited destinations within the Valencian Region of Spain agreed to participate in the study.

Before completing the survey, participants were presented with an introductory statement outlining the purpose of the study, the confidentiality of their responses, and their right to decline participation or withdraw at any time without consequence, which contributed to reducing evaluation apprehension and social desirability bias. In addition, as data were collected through a single questionnaire and from a single source, the potential presence of common method bias was considered. Following the procedure reported by Ahmed and Abdul Manab [122], Harman’s single-factor test was conducted by performing an unrotated exploratory factor analysis on all measurement items. The results indicated that a single factor explained 35.605% of the total variance, which is below the commonly accepted 50% threshold for serious common method variance concerns [123,124], as cited in Ahmed & Abdul Manab [122].

To ensure consistency between the sampling procedure and the research objective, the questionnaire included a filter question asking respondents whether they had previously visited or interacted with the destination’s social media. Only respondents who met this criterion were included in the analysis. Moreover, data collection formed part of a broader research project that also examined the influence of interactions with tourist information staff and brochure characteristics, which required respondents to be approached face to face at tourist information offices. Therefore, although the present study focuses on digital and social media behavior, the sampling procedure captures tourists who combine prior digital engagement with the use of traditional, in-person destination information services. This approach is appropriate for examining tourists with different levels of exposure to destination-related online content; however, it may underrepresent tourists who rely exclusively or primarily on digital and social media sources.

Data collection was conducted during two separate periods: May to June 2024, and again from December 2024 to January 2025, resulting in a total of 300 valid responses. This final sample size can be considered adequate for the estimation of the proposed PLS-SEM model according to the criterion proposed by Hair et al. [125]. According to these authors, the minimum required sample size should be determined by multiplying by ten either the largest number of formative indicators used to measure a construct or the highest number of structural paths directed at any endogenous construct in the model. In the present study, the maximum number of structural paths pointing to a single construct is five, which yields a minimum threshold of 50 observations, which is widely exceeded in the case of the current sample. Moreover, a sensitivity power analysis was conducted using G*Power 3.1.9.7 [126]. Following the logic of the most complex regression equation in the PLS-SEM model, the analysis was based on five predictors, α = 0.05, statistical power = 0.80, and a total sample size of 300 respondents. The results indicated that the sample was able to detect effects of f² = 0.044 or larger. Since this value exceeds Cohen’s [127] conventional threshold for a small effect, f² = 0.02, the sample size can be considered adequate for estimating the overall structural model.

Table 2. General sample data.

Gender	%	Age	%	Occupation	%	Studies	%	Motivation of the Journey	%	Frequency of Travel per Year	%
Male	32%	18–23	15.00%	Student	17.6%	Primary	2.7%	Holidays	88%	≤1 times	32.4%
Female	68%	24–38	23.33%	Employee	52.3%	Secondary	12.3%	Business	1%	2–4 times	53.3%
		39–58	36.00%	Self-employed	12.6%	Higher vocational training	17.3%	Others	11%	>4 times	14.3%
		59–72	24.33%	Unemployed	3.6%	University	67.7%
		>72	1.33%	Retired	9%
				Other	3.6%

Source: Author’s proposal.

shows the demographic composition of the sample.

The dataset was used to evaluate the proposed model through a two-step analytical process, using the PLS-SEM approach, implemented via the SmartPLS 4.1.1.2 software. This method has been increasingly adopted in academic research due to its flexibility and numerous methodological advantages [128]. Notably, PLS-SEM is a nonparametric technique that does not require data to follow a normal distribution, making it suitable for a wide range of empirical applications [125,129]. This technique also enables the assessment of the significance of factor loadings and structural paths through the bootstrapping procedure, a resampling method introduced by Efron and Tibshirani [130].

As part of the analysis, Henseler’s multi-group analysis (MGA) procedure was employed to examine moderating effects [108,131]. Before conducting the MGA, respondents were segmented into two groups based on their personality trait scores, using the midpoint of the seven-point Likert scale, grouping scores from 1 to 4 and from 5 to 7. This midpoint-split method is a widely accepted approach in PLS-SEM multi-group analysis and is particularly common in personality-based segmentation studies [132,133,134,135]. This approach was adopted because the aim of the study was to examine group-based heterogeneity in the structural relationships rather than to estimate a continuous latent interaction effect. In PLS-SEM, moderation can be assessed through either interaction terms or group comparisons, depending on the research objective [136]. Since this study adopts a segmentation-oriented perspective and compares lower- and higher-trait respondent profiles across multiple paths, multigroup analysis was considered more appropriate, whereas latent interaction approaches are better suited to estimating continuous moderation on specific relationships [136,137].

4. Analysis of Results

The validity and reliability of the measurement model were assessed by examining factor loadings, Cronbach’s alpha, composite reliability (CR), and the average variance extracted (AVE) for each latent construct within the proposed model. The Cronbach’s alpha coefficients surpassed the commonly accepted threshold of 0.7, indicating satisfactory internal consistency [138,139]. Similarly, the composite reliability values were well above the recommended minimum of 0.7, reinforcing the consistency of the constructs [140]. Furthermore, all AVE values exceeded the 0.5 benchmark, indicating that each construct accounts for more than 50% of the variance in its respective indicators [141](see

Table 3. Measurement instrument of the structural model: reliability and convergent validity.

Constructs/Indicators	Loading	T	Cronbach’s α	CR	AVE
User-generated content
UGC1 Online reviews are informative	0.834 **	34.856	0.942	0.950	0.637
UGC2 Online reviews are reliable	0.813 **	36.037
UGC3 Online reviews are up to date	0.774 **	29.784
UGC4 Online reviews are relevant	0.882 **	51.457
UGC5 Online reviews are convincing	0.828 **	34.839
UGC6 Online reviews are persuasive	0.660 **	16.511
UGC7 Online reviews are useful	0.899 **	76.374
UGC8 Online reviews are comprehensive	0.788 **	25.837
UGC9 The more contrasting online reviews are, the more likely I believe in these reviews	0.731 **	18.094
UGC10 I believe in online reviews if majority of reviewers recommend them	0.743 **	20.713
UGC11 The content generated by other users about the destination is very attractive	0.800 **	33.864
Perceived experience of destination-generated social media content			0.880	0.905	0.546
PEdgc1 Using tourism destinations’ social media is fun	0.789 **	33.135
PEdgc2 The content of tourism destinations’ social media seems interesting	0.788 **	32.950
PEdgc3 Tourism destinations’ social media enables information sharing with others	0.785 **	26.601
PEdgc4 Conversation or opinion exchange with others is possible through tourism destinations’ social media	0.787 **	27.576
PEdgc5 It is easy to give my opinion through tourism destinations’ social media	0.696 **	17.904
PEdgc6 The content of tourism destinations’ social media is the newest information	0.717 **	20.638
PEdgc7 Using tourism destinations’ social media is very trendy	0.631 **	14.133
PEdgc8 I would like to pass information from tourism destinations’ social media to my friends	0.702 **	20.994
User-generated content (UGC) satisfaction			0.893	0.949	0.903
UGCsat1 I am satisfied with the content generated on social media sites by other users about the destination	0.950 **	104.037
UGCsat2 The level of the content generated on social media sites by other users about the destination meets my expectations	0.951 **	95.762
Destination-generated content online (DGC) satisfaction
DGCsat1 I am satisfied with the communication of the destination on social networks	0.880 **	59.034	0.888	0.922	0.748
DGCsat2 The level of communication of the destination on social media meets my expectations	0.874 **	39.732
DGCsat3 The destination’s communications on social networks are very attractive	0.855 **	42.132
DGCsat4 The content generated on social networks by the destination itself performs well compared to other destinations	0.850 **	41.333
Perceived destination image
IMG1 I have a pleasant idea of the destination	0.931 **	82.399
IMG2 The destination has a good reputation	0.933 **	88.838
IMG3 I associate positive characteristics with the destination	0.943 **	90.333
Electronic word of mouth (eWOM)
eWOM1 I will express my personal needs on this destination’s online platforms	0.889 **	64.705	0.865	0.909	0.714
eWOM2 I will look for solutions to my problems on this destination’s online platforms	0.771 **	26.323
eWOM3 I will recommend the place I have visited on this destination’s online platforms	0.878 **	44.562
eWOM4 I will spread this destination to my friends through online media	0.838 **	34.527

Statistically significant at ** p < 0.01. Source: Author’s proposal.

). These results collectively support the reliability and convergent validity of the measurement model. These empirical findings further support the treatment of UGC and perceived experience of DGC as reflective unidimensional constructs. For UGC, all indicators were statistically significant and the construct showed high internal consistency and convergent validity (Cronbach’s α = 0.942; CR = 0.950; AVE = 0.637) (see Table 3). Similarly, perceived experience of DGC presented satisfactory reliability and convergent validity (Cronbach’s α = 0.880; CR = 0.905; AVE = 0.546), with all indicators loading significantly on the construct. These results suggest that the indicators share sufficient common variance to represent global evaluative constructs in the context of the present model.

Next, discriminant validity was evaluated using the heterotrait-monotrait ratio of correlations (HTMT), as suggested by Henseler et al. [142]. The results indicated that all HTMT values were below the recommended threshold of 0.90, thereby providing evidence of satisfactory discriminant validity [133,142] (see

Table 4. Discriminant validity (HTMT criterion).

	1	2	3	4	5	6
1. User-generated content (UGC)	—
2. Perceived experience of destination-generated social media content	0.324	—
3. UGC satisfaction	0.891	0.388	—
4. DGC satisfaction	0.318	0.881	0.411	—
5. Perceived destination image	0.334	0.694	0.331	0.683	—
6. eWOM intention	0.227	0.728	0.313	0.690	0.493	—

Note: HTMT criterion. Source: Author’s proposal.

).

Following the confirmation of the psychometric properties of the measurement instrument, the structural model was estimated to evaluate the hypothesized causal relationships. This analysis employed a bootstrapping procedure with 5000 resamples [143]. Additionally, the model’s explanatory and predictive capabilities were assessed through the coefficient of determination (R²) and the Q² predictive relevance test [140], which presented acceptable values (UGC satisfaction: R² = 0.676, Q² = 0.673; DGC satisfaction: R² = 0.613, Q² = 0.608; Image: R² = 0.456, Q² = 0.404; eWOM intention: R² = 0.443, Q² = 0.400) (see Table 4).

After confirming the reliability and validity of the measurement model, the structural equation model was assessed.

Table 5. Structural equation model results.

	Causal Relationship	Hypothesis	Standardized Beta	t	f2
H1	UGC → Image	Supported	0.201 *	2.201	0.024
H2	UGC → eWOM	Not supported	−0.072 ns	0.896	0.003
H3	UGC → UGC satisfaction	Supported	0.822 **	31.766	2.087
H4	UGC satisfaction → Image	Not supported	−0.105 ns	1.111	0.006
H5	UGC satisfaction → eWOM	Not supported	0.089 ns	1.079	0.004
H6	PEdgc → Image	Supported	0.350 **	4.357	0.086
H7	PEdgc → eWOM	Supported	0.421 **	5.255	0.112
H8	PEdgc → DGC satisfaction	Supported	0.783 **	28.707	1.582
H9	DGC satisfaction → Image	Supported	0.327 **	4.030	0.074
H10	DGC satisfaction → eWOM	Supported	0.261 **	2.981	0.043
H11	Image → eWOM	Not supported	0.010 ns	0.160	0.000

UGC satisfaction: R² = 0.676, Q² = 0.673; DGC satisfaction: R² = 0.613, Q² = 0.608; Image: R² = 0.456, Q² = 0.404; eWOM intention: R² = 0.443, Q² = 0.400. Statistically significant at * p < 0.05; ** p < 0.01; ns = statistically non-significant. Source: Author’s proposal.

presents the results of the hypothesized relationships.

The results of the structural paths indicate that, among the hypotheses related to the UGC side of the model, both H1 (between UGC and destination image) and H3 (between UGC and UGC satisfaction) exhibit a positive and statistically significant effect, respectively (β₁ = 0.201, p < 0.05; β₃ = 0.822, p < 0.01). However, the remaining hypotheses on this side (H2, H4, H5) did not reach statistical significance, nor did H11, which examines the relationship between destination image and eWOM intention. In contrast, all hypotheses on the DGC side of the model were supported. Specifically, the perceived experience of DGC shows a significant and positive relationship with destination image (H6: β₆ = 0.350, p < 0.01); eWOM intention (H7: β₇ = 0.421, p < 0.01), and DGC satisfaction (H8: β₈ = 0.783, p < 0.01). Furthermore, DGC satisfaction is positively and significantly associated with both destination image (H9: β₉ = 0.327, p < 0.01) and eWOM intention (H10: β₁₀ = 0.261, p < 0.01).

To test hypotheses H12a-k, a multigroup analysis (MGA) was conducted. Among the personality traits examined, the “openness to new experiences or complexity” emerged as the only one with a statistically significant moderating effect on the model’s relationships in this specific context.

Prior to executing the MGA, measurement invariance across groups was assessed to ensure the comparability of constructs. The three-step procedure proposed by Henseler et al. [128] was employed (see

Table 6. Results of invariance measurement testing.

		Step 2				Step 3
Factors	Step I Conf. Inv.	C	5% Quantile of Cu	p-Value	Comp. Inv.	Mean Diff.	95% Confidence Interval	p-Value	Equal Mean	Variance Diff.	95% Confidence Interval	p-Value	Equal Variance
User-generated content (UGC)	Yes	0.999	0.999	0.112	Yes	−0.973	[−0.229; 0.244]	0.000	No	1.149	[−0.408; 0.335]	0.000	No
Perceived experience of destination-generated content	Yes	0.999	0.998	0.463	Yes	−0.152	[−0.263; 0.244]	0.231	Yes	0.098	[−0.349; 0.324]	0.589	Yes
UGC satisfaction	Yes	1.000	1.000	0.667	Yes	−0.800	[−0.245; 0.240]	0.000	No	0.784	[−0.341; 0.277]	0.000	No
DGC satisfaction	Yes	1.000	0.999	0.202	Yes	−0.127	[−0.242; 0.239]	0.302	Yes	−0.029	[−0.340; 0.310]	0.854	Yes
destination image	Yes	1.000	1.000	0.937	Yes	−0.177	[−0.243; 0.241]	0.157	Yes	0.297	[−0.419; 0.349]	0.111	Yes
eWOM intention	Yes	0.998	0.997	0.104	Yes	0.274	[−0.243; 0.246]	0.025	No	−0.110	[−0.267; 0.254]	0.419	Yes

Note: Conf. inv. = configural invariance; C = correlation; Cu = composite score correlation; Comp. inv. = compositional invariance; Diff. = difference. Source: Author’s proposal.

). Specifically, configural invariance was first confirmed, compositional invariance was subsequently assessed using permutation tests, and finally, the equality of composite means and variances across the two personality-based groups was examined. The results indicated that only three constructs—DGC satisfaction, perceived experience with DGC, and destination image—exhibited equality in both mean and variance, suggesting partial measurement invariance. This level of invariance is sufficient to proceed with MGA [128].

The MGA results indicate that the personality trait “Open to new experiences, complex” significantly moderates two structural relationships. Following Henseler’s MGA criterion (p < 0.05 or p > 0.95; [144]), significant differences were found for the relationships UGC–UGC satisfaction (H12c) and DGC satisfaction–eWOM intention (H12j). The group-specific path coefficients show that both effects are stronger among tourists with lower openness to new experiences. For the UGC–UGC satisfaction relationship, low-openness tourists show a stronger effect (β12c = 0.858; p < 0.05) compared with high-openness tourists (β12c′ = 0.719; p < 0.05). Likewise, the effect of DGC satisfaction on eWOM intention is substantially stronger for the low-openness group (β12j = 0.548; p < 0.05) than for the high-openness group (β12j′ = 0.139; p < 0.05). No significant differences were observed for the remaining personality-moderated paths (H12a-H12b, H12d-H12i, H12k), as their MGA p-values did not meet the required thresholds (see

Table 7. Henseler’s MGA of personality.

Hypothesis		Relationships	β Low Openness	β High Openness	β Difference	PLS-MGA p-Value
H12a	Not supported	UGC → Image	0.074 ns	0.277 **	−0.203	0.395 ns
H12b	Not supported	UGC → eWOM intention	−0.187 ns	−0.028 ns	−0.159	0.343 ns
H12c	Supported	UGC → UGC satisfaction	0.858 **	0.719 **	0.139	0.019 *
H12d	Not supported	UGC satisfaction → Image	−0.045 ns	0.097 ns	−0.142	0.831 ns
H12e	Not supported	UGC satisfaction → eWOM intention	0.080 ns	0.056 ns	0.024	0.924 ns
H12f	Not supported	PEdgc → Image	0.370 *	0.321 **	0.049	0.799 ns
H12g	Not supported	PEdgc → eWOM intention	0.223 ns	0.504 **	−0.281	0.090 ns
H12h	Not supported	PEdgc → DGC satisfaction	0.792 **	0.781 **	0.011	0.829 ns
H12i	Not supported	DGC satisfaction → Image	0.279 ns	0.324 **	−0.045	0.825 ns
H12j	Supported	DGC satisfaction → eWOM intention	0.548 **	0.139 ns	0.409	0.018 *
H12k	Not supported	Image → eWOM intention	−0.069 ns	0.053 ns	−0.122	0.369 ns

Note: β = standardized beta. Low openness: UGC satisfaction: R² = 0.736, Q² = 0.724; DGC satisfaction: R² = 0.627, Q² = 0.611; Image: R² = 0.386, Q² = 0.328; eWOM intention: R² = 0.480, Q² = 0.332. High openness: UGC satisfaction: R² = 0.517, Q² = 0.508; DGC satisfaction: R² = 0.610, Q² = 0.603; Image: R² = 0.513, Q² = 0.458; eWOM intention: R² = 0.451, Q² = 0.424. * p < 0.05; ** p < 0.01; ns = statistically non-significant. Source: Author’s proposal.

). Thus, H12 is only partially supported, with moderation detected exclusively for H12a and H12j. Furthermore, to ensure the groups’ sample-size justification, additional sensitivity checks were conducted for each openness-based subgroup [126]. The lower-openness group comprised 96 respondents and was able to detect effects of approximately f² = 0.142 or larger, whereas the higher-openness group comprised 204 respondents and was able to detect effects of approximately f² = 0.065 or larger. Following Cohen’s [127] thresholds, these results indicate that the larger subgroup was sufficiently powered to detect small-to-medium effects (f² = 0.02; 0.15), while the smaller subgroup was mainly powered to detect effects close to medium size (f² = 0.15). Therefore, the MGA findings are interpreted cautiously, particularly regarding small between-group differences, and are presented as exploratory evidence of group-based heterogeneity rather than as definitive moderation effects.

5. Discussion and Implications

Grounded in the stimulus-organism-response (SOR) framework and incorporating notions from ECT and the technology acceptance model, this study advances understanding of how different types of online content influence tourists’ cognitive, affective, and behavioral responses in the context of lesser-known tourism destinations. By jointly analyzing UGC and DGC within a dual-content model, the findings reveal an asymmetric influence of these content sources on satisfaction, destination image, and eWOM intention.

Contrary to the dominant stream of tourism literature that tends to treat UGC as a broadly powerful and transferable driver of tourist responses [8,9,10], several hypothesized relationships on the UGC side of the model were not supported. Specifically, UGC did not significantly influence eWOM intention, and satisfaction with UGC did not significantly influence destination image or eWOM intention. These findings should not be interpreted as evidence that UGC is irrelevant in lesser-known destinations, since UGC still showed a positive effect on perceived destination image and on satisfaction with UGC. Rather, they suggest that the influence of UGC is selective and depends on its capacity to function as a sufficiently diagnostic information cue.

This interpretation provides a clearer mechanism for understanding the limited role of UGC in the present study. Prior research on online reviews and eWOM indicates that consumers are more likely to adopt online information when it is perceived as relevant, credible, current, factual, and useful for decision-making [145,146]. In highly visible and digitally mature destinations, UGC is usually abundant, diverse, recent, and socially validated by multiple users, which increases its perceived representativeness and credibility. Under these conditions, UGC can operate as a salient stimulus that reduces uncertainty, supports expectation formation, and stimulates subsequent evaluations and behavioral intentions. However, in lesser-known destinations, the informational environment is structurally weaker, as UGC is often scarcer, less diverse, and less socially reinforced [7,26,27]. Consequently, UGC may provide insufficient diagnostic value for tourists, limiting its ability to shape satisfaction-based evaluations and directly stimulate eWOM intention.

From this perspective, the non-significant effects of UGC can be explained through an information-diagnosticity and uncertainty-reduction mechanism. Tourism is an information-intensive sector in which tourists search for information to reduce uncertainty and perceived risk, especially when destinations are less familiar or less established [147,148]. If UGC is fragmented, limited, or weakly validated, it may remain a peripheral cue rather than a central stimulus capable of activating stronger organismic states or behavioral responses. This mechanism is aligned with the SOR framework, according to which external stimuli influence internal evaluations and behavioral responses only when they are sufficiently salient, meaningful, and capable of being processed by individuals [25] and Bigné et al. [149]. It is also consistent with expectation-confirmation logic, since less diagnostic UGC may provide insufficiently stable cues for forming or confirming expectations, thereby weakening its effect on satisfaction [16,28], a point also reflected in Meneses et al. [53].

In contrast, all hypothesized relationships related to DGC were supported. Perceived experience of destination-generated social media content emerged as a particularly strong stimulus, significantly influencing satisfaction, destination image, and eWOM intention. These results reinforce previous studies emphasizing the strategic role of official destination communication in shaping tourists’ perceptions, especially when content is perceived as credible, informative, and easy to use [e.g., 5, 91]. Prior research has shown that secondary and official information sources encountered before the visit can significantly influence the image formation process and account for the gap between projected and perceived destination image, particularly when tourists rely on them in the absence of richer alternative cues [4]. Thus, the stronger role of DGC further supports the proposed mechanism: when UGC lacks sufficient diagnosticity, official communication may become a more central and reliable information source. In low-visibility destinations, DGC should therefore not be viewed merely as a complementary source that reinforces UGC. Rather, it may assume a compensatory or even substitutive role, offsetting the scarcity and weakness of UGC and becoming a key digital mechanism through which tourists form evaluations, develop satisfaction with online communication, and generate eWOM intentions. In this sense, the present findings extend prior literature by showing that DGC may operate not only alongside UGC, but in certain contexts in place of it.

Nevertheless, these conclusions should be interpreted in light of the specific empirical context of the study, as the data were collected in lesser-known destinations within a single Spanish region, the Valencian Region of Spain. Therefore, the findings should not be generalized directly to all tourism destinations, particularly highly consolidated, internationally recognized, or digitally mature destinations. Rather, the results are most applicable to regional and lesser-known destinations characterized by limited digital visibility, lower volumes of UGC, and stronger dependence on official destination communication.

Importantly, by incorporating the perceived experience of destination-generated social media content as a pre-visit construct, the study captures tourists’ evaluations formed during the early stages of the travel journey, responding to recent calls to integrate pre-visit, on-site, and post-visit experiences within unified destination marketing models [150,151].

Another noteworthy finding concerns the non-significant relationship between perceived destination image and eWOM intention. While traditional tourism models often assume a direct link between image and communicative behavior [106], recent studies suggest that cognitive evaluations alone may be insufficient to trigger eWOM [152,153], particularly in low-involvement or emerging destinations. In such contexts, affective and experiential constructs appear to play a more decisive mediating role in transforming perceptions into behavioral responses. This interpretation is aligned with the current findings, which show that satisfaction with DGC and the tourist experience have a stronger influence on eWOM intention than destination image itself.

Finally, the moderation analysis reveals that, among all the personality traits examined, only openness to new experiences significantly moderates two relationships in the model: UGC with UGC satisfaction and DGC satisfaction with eWOM intention. Moreover, both effects are stronger for tourists who are lower in openness. Openness is the personality dimension most closely associated with novelty seeking, uncertainty tolerance, and information processing [154]. In the context of lesser-known destinations—characterized by limited visibility, scarce UGC, and higher uncertainty—individuals low in openness rely more strongly on available informational cues when forming satisfaction and behavioral intentions. Consequently, UGC evaluations and satisfaction with official content become more influential for this group. Other personality traits, more closely related to social interaction or emotional regulation, appear less relevant in shaping digital information processing at the pre-visit stage. This selective moderation is aligned with previous tourism research highlighting the dominant role of openness in digital content evaluation and eWOM behavior under conditions of uncertainty [18,20].

5.1. Theoretical Implications

From a theoretical perspective, this study primarily contributes to destination marketing literature by refining the application of the SOR framework in digital tourism contexts. More specifically, it shows how a model with two sides (UGC and DGC) of communications influences satisfaction with these contents and perceived image (organisms), and consequently, the tourists’ response behavior (eWOM intention). Within this overarching logic of the SOR model, ECT [28] offers a complementary explanation for the specific role of satisfaction, as satisfaction can be interpreted as a post-evaluative state that emerges when destination-related communication meets or confirms tourists’ prior expectations, even when confirmation is not modeled as a separate construct. This interpretation is consistent with recent tourism research [29] applying ECT to explain how expectation-related factors shape satisfaction and, subsequently, behavioral outcomes. By contrast, TAM [30] plays a more limited supporting role, contributing to the explanation of why tourists’ perceived experience of destination-generated social media content may be influenced by technology-related evaluations linked to ease, usefulness, and quality of digital interaction. Accordingly, while the SOR model provides the main causal architecture of the model, ECT and, more marginally, TAM serve as complementary lenses that clarify specific evaluative mechanisms within that broader sequence.

Moreover, the study refines the SOR framework in digital tourism by demonstrating that the effectiveness of online stimuli is contingent on the destination’s digital visibility and informational density environment, rather than being universally stable across contexts. In this sense, the findings suggest that UGC cannot be treated as a uniformly strong stimulus across contexts, but rather as a source whose effectiveness depends on the availability, diversity, and credibility of user-generated information. It also contributes to debates on content credibility by suggesting that, in low-visibility destinations, official destination-generated communication substitutes the scarce and less persuasive UGC. Finally, by incorporating perceived experience of destination-generated social media content as a pre-visit antecedent, the study highlights the theoretical importance of early-stage digital evaluations in shaping later image and eWOM responses.

The results also contribute to the literature by identifying a possible mechanism explaining why UGC may lose explanatory strength in lesser-known destinations. Specifically, the findings suggest that the influence of UGC depends not only on its existence, but also on its informational diagnosticity. Previous research has shown that online consumer information is more likely to be adopted when it is perceived as relevant, credible, current, factual, and useful for decision-making [145,146]. In tourism contexts, where information search plays a central role in reducing uncertainty and perceived risk [147,148], UGC may only operate as a strong stimulus when it provides sufficient social validation and decision-making value. Therefore, when UGC is scarce, fragmented, or weakly validated, as may occur in lesser-known destinations, it may be insufficient to activate stronger organismic evaluations or behavioral responses. This refines the application of the SOR framework by showing that online stimuli differ in their capacity to activate satisfaction, image, and eWOM depending on their salience, credibility, and perceived diagnostic value [25] and Bigné et al. [149]. Accordingly, the study advances destination marketing literature by moving beyond a general context-dependent explanation and proposing informational diagnosticity and uncertainty reduction as mechanisms through which the effectiveness of UGC is conditioned in low-visibility destination environments.

In addition, it introduces the experience with DGC as a key pre-visit antecedent, addressing a notable gap in prior research that has predominantly focused on content characteristics rather than experiential evaluations of official digital communication [5,10]. Importantly, the results indicate that in low-visibility destinations, official destination communication may assume a compensatory or even substitutive role, rather than acting merely as a complement to UGC, when user-generated information is too scarce or weak to effectively shape tourists’ evaluations. Third, the findings nuance existing assumptions regarding the role of destination image by showing that image alone may not be sufficient to stimulate eWOM intention, thereby reinforcing the importance of affective and experiential mediators e.g., [155]. Finally, by examining personality traits as moderators, the study advances understanding of individual differences in digital content processing, highlighting openness to experience as the most contextually relevant trait in uncertain destination environments.

5.2. Practical Implications

Before deriving practical implications, it is important to note that these findings should be interpreted within the specific profile of the sample and the regional context of the study. The data were collected from tourists in lesser-known destinations within the Valencian Region of Spain who had previously interacted with destination social media but were recruited at tourist information offices. Therefore, the implications are especially relevant for DMOs managing regional or lesser-known destinations that seek to integrate digital communication with traditional or in-person visitor information services, rather than for all tourism destinations in general. Thus, from a practical standpoint, the findings suggest that DMOs in these types of less consolidated destinations should treat official digital communication not as a secondary promotional channel, but as a core pre-visit information infrastructure. In contexts where UGC is scarce, fragmented, or weakly persuasive, DGC becomes the main digital source through which potential visitors reduce uncertainty and evaluate the destination. This implication is relevant not only for DMOs, but also for small tourism enterprises, which often face coordination problems, limited digital marketing capabilities, low innovation awareness, and restricted financial resources due to their size and weaker access to public support [156,157]. In such environments, official communication can function as a shared strategic asset for the destination.

The results also show that the experiential quality of official social media communication matters more than mere online presence. Accordingly, DMOs and local tourism businesses should prioritize content that is clear, updated, interactive, and easy to engage with, combining practical value with visual appeal. Since smaller firms often require different digital strategies than larger organizations [158], lesser-known destinations may benefit more from simple, coordinated, and manageable communication efforts than from high-cost campaigns. This is consistent with recent tourism marketing research emphasizing context-sensitive and platform-specific digital strategies [159]. Additionally, DMOs in lesser-known tourism destinations that normally have limited resources can leverage UGC on social media with incentives to enhance interaction and entertainment, such as participation in quizzes, lotteries, and proper hashtags or emojis in social media messages [6,160].

A further implication concerns psychographic segmentation [19]. Given that tourists who are lower in openness rely more strongly on structured and reliable information, DMOs could develop segmented promotional campaigns that distinguish between more reassurance-oriented and more novelty-seeking audiences, for instance, by using more concrete and confidence-building formats such as FAQs, “what to expect” videos, local service explanations, maps, practical recommendations, and curated visitor pathways. By contrast, content directed at more novelty-oriented tourists can place greater emphasis on discovery, uniqueness, and experiential storytelling.

In addition, destination management organizations with limited budgets may benefit from selective creator partnerships rather than costly mass promotion. Recent research shows that influencers and creator-led storytelling can improve destination image and reduce uncertainty, especially in rural or less-established places [161]. In practice, collaborations with micro-influencers, local ambassadors, and niche creators may offer a more credible and cost-effective alternative than large-scale campaigns.

Finally, AI-generated content may enable smaller destinations to produce visuals, short videos, translations, and message variations more efficiently. Recent studies suggest that AI can support resource-constrained DMOs in scaling content production [162]. At the same time, tourism research also indicates that explicit AI disclosure may reduce perceived originality and travel intention [163]. For this reason, AI should be used primarily as a support tool to organize and publish content, monitor the performance of social media content, and automate user responses (using social media analytics tools). In contrast, and more specifically in the context of lesser-known tourism destinations, human curation and local authenticity remain essential for maintaining trust, reinforcing the idea that small firms are often perceived as more authentic and reliable with more customized services than larger firms [164].

6. Limitations and Future Research

The present study has several limitations that, in some cases, offer opportunities for future research. First, the empirical analysis focuses on lesser-known tourism destinations, which may limit the generalizability of the findings to more established destinations. Comparative studies across different levels of destination maturity would help validate the robustness of the proposed model. Furthermore, the empirical analysis focuses on lesser-known tourism destinations located within a single Spanish region, the Valencian Region of Spain. This regional context is relevant because destination visibility, tourists’ social media behavior, trust in official communication, and the availability of UGC may vary across territories and cultural settings. Therefore, the findings should be interpreted as context-bound and should not be generalized directly to highly consolidated destinations, international tourism hubs, or destinations with more mature digital ecosystems. Future research should replicate the model in other regions, countries, and destination types to assess whether the relative influence of UGC and DGC varies according to destination maturity, digital visibility, and cultural context.

Second, although the total sample of 300 respondents is adequate for estimating the overall PLS-SEM model, the complexity of the framework and the use of multigroup moderation analysis should be acknowledged as limitations. The midpoint split based on openness to experience produced unequal subgroups. While both groups are acceptable for exploratory PLS-SEM multigroup comparisons, the smaller subgroup has more limited statistical power, particularly for detecting small between-group differences. Therefore, the MGA findings should be interpreted cautiously as exploratory evidence of group-based heterogeneity. Future studies should replicate the model with larger and more balanced samples and test moderation using alternative approaches, such as continuous interaction terms or latent interaction models.

Third, the use of a cross-sectional design restricts the ability to capture changes in tourists’ perceptions across the pre-, during-, and post-visit stages; longitudinal approaches are therefore encouraged. Fourth, the perceived experience of destination-generated social media content was measured perceptually, and future research could complement survey data with behavioral or experimental methods. Fifth, another limitation concerns the operationalization of UGC and perceived experience of DGC as global reflective constructs. Although this approach is consistent with the objective of comparing the overall influence of user-generated and destination-generated content within the proposed SOR model, both constructs may also be conceptualized multidimensionally. For instance, future studies could distinguish between informational, credibility-related, persuasive, and attractiveness-related dimensions of UGC, or between hedonic, pragmatic, interactive, and participatory dimensions of perceived experience with DGC. Such multidimensional modeling would allow researchers to identify which specific facets of online content are most influential in shaping satisfaction, destination image, and eWOM intention.

Sixth, although a filter question ensured that only respondents who had previously interacted with the destination’s social media were included in the analysis, participants were recruited at tourist information offices. Consequently, the sample may overrepresent tourists who actively seek official or in-person destination information and may underrepresent tourists who rely exclusively, or primarily, on digital and social media sources. This may have influenced the relative importance of DGC and UGC in the model, as tourists who visit information offices may be more receptive to official destination communication. Therefore, the findings should be interpreted as applying to tourists who combine social media use with traditional destination information-seeking behavior, rather than to all digital tourists or social-media-only users. Future research should replicate the model using online or social-media-based sampling strategies, such as recruiting respondents directly through destination social media profiles, travel communities, or platform-based advertising, to better capture tourists with stronger digital engagement.

Finally, although multiple personality traits were examined, only openness to experience showed significant moderation effects, suggesting that future studies should explore additional individual-difference variables.

Future research could also extend the current model by incorporating affective variables (e.g., emotions, anticipated enjoyment, or trust) as mediators between online content and behavioral intentions, particularly given the non-significant direct effect of destination image on eWOM intention observed in this study. Such extensions would contribute to a more comprehensive understanding of how cognitive, affective, and experiential mechanisms jointly drive tourists’ online communication behaviors. Furthermore, in line with Marti et al. [165], examining AI-generated content alongside UGC and DGC would allow future studies to assess whether AI acts as a complement to or substitute for human-generated content in the context of lesser-known destinations and to explore its implications for satisfaction, destination image formation, and eWOM behavior.

7. Conclusions

This study examined how user-generated content (UGC) and perceived experience of destination-generated social media content influence satisfaction, destination image, and electronic word-of-mouth (eWOM) intention in lesser-known tourism destinations. Grounded primarily in the stimulus-organism-response framework [25], the findings show that the effects of online communication are not uniform across content sources. While prior tourism research has frequently emphasized the persuasive role of UGC in shaping tourist perceptions and behaviors [5,8], the present results indicate that, in this empirical context, UGC exerts only limited influence beyond its positive effect on destination image and satisfaction with UGC. This limited role may be explained by the lower informational diagnosticity of UGC in lesser-known destinations, where user-generated information may be scarcer, less diverse, and less socially validated. By contrast, destination-generated communication appears to be particularly relevant in this context. Perceived experience of destination-generated social media content positively influenced DGC satisfaction, destination image, and eWOM intention, while DGC satisfaction also strengthened image and eWOM. These results suggest that, in lesser-known destinations with weaker digital visibility, official communication may help compensate for the limited availability [27] and diagnostic value of UGC [145,146], thereby qualifying some assumptions in the existing literature.

The study also shows that destination image alone is not sufficient to stimulate eWOM intention, which supports more recent views that cognitive evaluations do not always translate directly into communicative behavior in the absence of stronger experiential or affective triggers [107]. In addition, the moderation analysis revealed that openness to experience was the only personality trait with significant effects, highlighting that individual differences matter when tourists process online destination information under conditions of uncertainty [18,20]. However, these moderation findings should be interpreted cautiously, given the exploratory nature of the subgroup analysis and the unequal size of the openness-based groups.

Overall, the article contributes to destination marketing research by comparing UGC and DGC within the same model, introducing perceived experience of destination-generated social media content as a pre-visit antecedent, and demonstrating that the relative effectiveness of digital content depends on the destination’s informational environment. Nevertheless, these conclusions should be interpreted within the specific empirical scope of the study. The data were collected from tourists in lesser-known destinations located within the Valencian Region of Spain, and respondents were recruited at tourist information offices after having previously interacted with destination social media. Therefore, the findings are most applicable to tourists who combine social media use with traditional destination information-seeking behavior in similar regional, low-visibility destination contexts. Future research should replicate the model across other territorial, cultural, and digital contexts, using larger and more balanced samples and alternative sampling strategies, to assess the robustness and transferability of these findings.