From Clicks to Trips: Examining Online Destination Brand Experience in Ecotourism Decision Making

Abstract

Destination Management Organizations (DMO) increasingly harness social media to foster favorable online destination brand experiences (ODBEs) during travelers’ pre-trip planning. However, empirical knowledge about such experiences in ecotourism contexts remains limited. This study addresses the gap by proposing and validating an ODBE measurement scale adapted to ecotourism destinations. An online questionnaire was administered to Facebook users following seven certified Romanian ecotourism destinations, yielding 281 valid responses. Through exploratory factor analysis and confirmatory composite analysis, the scale was refined into three components—hedonic, utilitarian, and spatio-temporal—capturing emotional immersion, rational evaluation, and destination-specific spatial perceptions. Structural equation modeling further demonstrated that ODBEs exert a strong, positive effect on two key behavioral intentions: visiting the destination (β = 0.913) and sharing destination information online (β = 0.875). This study advances theories on tech-mediated pre-travel experiences by emphasizing nature and local culture. The findings provide DMOs with practical guidance for creating effective social media content to enhance destination branding and support sustainable tourism.

1. Introduction

In recent years, sustainable travel has garnered increasing attention, driven by rising environmental awareness, consumer demand for responsible tourism, and the global push for sustainability across various industries. Within this context, ecotourism has emerged as a key area of focus. Defined by Ecotourism Australia (2025) as “ecologically sustainable tourism with a primary focus on experiencing natural areas that fosters environmental and cultural understanding, appreciation, and conservation,” ecotourism has gained significant traction worldwide. Beyond offering breathtaking landscapes and diverse flora and fauna (Thong, 2024), ecotourism emphasizes authentic engagement with local communities, small-scale infrastructure, and respectful stewardship of natural resources. Such principles resonate with travelers seeking to balance enjoyment with active conservation efforts. The existence of a wide range of stakeholders with multiple and varied concerns gives rise to the need to find solutions that combine all environmental, social, and governance aspects (Anton et al., 2024).

As competition in the global tourism market intensifies, DMOs have increasingly turned to social media as a strategic tool to engage potential visitors. Social media provides a unique platform for creating ongoing dialogues and building relationships with tourists (Jabreel et al., 2017). Over the past few decades, social media has revolutionized how tourist destinations communicate with potential travelers (Xiang & Gretzel, 2010).

The relationship between travel and pre-travel experiences has evolved significantly with the rise of social media and smartphones. As a result, DMOs are increasingly using these platforms to create positive ODBEs (Jiménez-Barreto et al., 2019). Although limited studies have examined multidimensional ODBEs (Jiménez-Barreto et al., 2019, 2020; Khan & Fatma, 2021), most have adapted experience modules from product branding (Brakus et al., 2009; Schmitt, 1999). These studies identified sensory, affective, intellectual, and behavioral experiences that contribute to the overall ODBE. A qualitative study by Jiménez-Barreto et al. (2019) emphasized that social experiences—specifically the communicative elements of interacting with destination websites—are also key to shaping ODBEs. Furthermore, the unique focus of ecotourism on conservation, community engagement, and sustainability calls for a reconsideration of existing ODBE measurement tools (The International Ecotourism Society [TIES], 2015). To address this gap, the present study adapts and expands existing ODBE dimensions—incorporating social and spatio-temporal factors (Köchling, 2020)—to explore how Facebook-based interactions shape users’ perceptions and behaviors regarding Romanian ecotourism. This study focuses on the pre-travel phase, during which tourists assess whether the potential experiences align with their travel expectations and preferences.

In the digital era, ODBE plays a critical role in shaping tourist perceptions and influencing their decisions to visit and recommend destinations. Recent studies (Jiménez-Barreto et al., 2019; Lin et al., 2024) emphasize the importance of online interactions in cultivating a positive image of destinations and driving visitors’ intention to travel.

Despite the growing attention on social media marketing in tourism research (Cheng et al., 2016; Abbasi et al., 2023), empirical studies investigating how ODBE influences visitors’ behavioral intentions in the context of ecotourism destinations remain limited. This study aims to fill this gap by exploring the impact of ODBE on the decision-making process regarding Romanian ecotourism destinations, focusing on key components that drive engagement, potential visits, and the sharing of destination information. In line with this aim, the general objectives of the study are to (1) explore the dimensions of the ODBE for ecotourism destinations and (2) study the influence of ODBE on Facebook users’ behavioral intentions. In order to address these two goals, the study is guided by the following specific objectives: (O1) to identify specific dimensions of the ODBE for ecotourism destinations; (O2) to explore how the dimensions of ODBE are grouped in specific components; (O3) to highlight the influence of Facebook users’ intention to visit the destination; and (O4) to emphasize the influence of Facebook users’ intention to share information about the destination. To guide this exploration, the following research questions are addressed:

RQ1: 

What are the dimensions of the ODBE for ecotourism destinations?

RQ2: 

How are the dimensions of ODBE correlated with specific components (e.g., hedonic and utilitarian experiences)?

RQ3: 

Does ODBE influence Facebook users’ intention to visit the destination?

RQ4: 

Does ODBE influence Facebook users’ intention to share information about the destination?

By addressing these questions, the study aims to deepen the understanding of how ecotourism destinations can leverage social media engagement to influence potential tourists’ decision-making processes. It contributes to both theory and practice: theoretically, by extending the concept of ODBE to the ecotourism domain through an experience economy lens; and practically, by offering actionable insights for destination marketing organizations (DMOs) seeking to enhance their digital communication strategies. The findings are expected to reveal how different engagement dimensions—such as emotional resonance, perceived authenticity, and informational value—relate to users’ intentions to visit and promote ecotourism destinations.

The remainder of the paper is structured as follows: the next section reviews the relevant literature on destination brand engagement and ecotourism marketing; this is followed by the theoretical framework and methodology; results are then presented and discussed in relation to the research questions; and finally, implications, limitations, and future research directions are outlined.

2. Literature Review

2.1. The Specificity of Ecotourism Destinations

The International Ecotourism Society (TIES) defined ecotourism as “responsible travel to natural areas that conserves the environment, sustains the well-being of the local people, and involves interpretation and education” (The International Ecotourism Society [TIES], 2015). Despite the abundance of academic focus on ecotourism, there is no universally accepted definition of ecotourism destinations, so this concept term can be interpreted in many different ways. For example, Krider et al. (2010) outlined that ecotourism destinations should (1) feature the natural environment as a central attraction, (2) offer the prospect of learning or education, and (3) at least intend to be sustainable environmentally, culturally, and economically. Considering the present study’s focus on the Romanian ecotourism context, the authors embraced the definition of ecotourism destinations provided by Romanian tourism authorities, which is in line with TIES’ vision.

The Romanian Ministry of Economy, Entrepreneurship and Tourism (2025) requires ecotourism destinations to promote responsible marketing, support sustainable businesses and local communities, raise environmental awareness, and enforce nature conservation. Based on existing international standards (Nature and Ecotourism Accreditation Program and Nature’s Best), the Association of Ecotourism in Romania (2025) proposes a set of principles for ecotourism development and planning: (1) focus on natural areas; (2) interpretation of the ecotourism product; (3) environmental sustainability; (4) ecotourism assists in the preservation of nature; (5) ecotourism adds constructive input in the development of local communities; (6) ecotourism raises the tourists’ degree of satisfaction; and (7) adequate marketing.

In the last decade, online marketing has become one of the main challenges of ecotourism development worldwide. The Internet and related technologies have transformed the way DMOs interact with their target audience and promote tourist destination brands (Khan & Fatma, 2021). The use of official destinations’ social media accounts provides an interactive real-time experience between tourists and destination brands and may become a key element in the decision-making and planning phases. As ecotourism destinations hold several particularities compared to other types of tourism sites, their DMOs need to adapt the social media approach according to the characteristics and expectations of target visitors. Subsequently, there has been an increasing interest in researching how tourists use the internet to search for information related to ecotourism (Sadiq & Adil, 2021; Nogueira & Carvalho, 2024).

2.2. Conceptualizing Pre-Travel ODBE

The experience economy, coined by Pine and Gilmore (1998), emphasizes the idea of consumers valuing brands for their ability to create unique experiences. Lindstrom (2005) extended this idea to branding, advocating for a multisensory approach beyond traditional two-sense strategies, as brand experience design plays a crucial role in brand management. S. Lee et al. (2018) argued that sensory marketing shapes customer expectations by helping them visualize future experiences, making them more tangible. With the rise of online brands due to ICT advancements, traditional brands also expanded their digital presence. In this context, Morgan-Thomas and Veloutsou (2013) defined online brand experience as a holistic consumer response to website stimuli, highlighting the evolving nature of brand engagement in the digital landscape. The tourist experience unfolds in three stages: before, during, and after the trip. During the inspiration and planning stage, prospective travelers engage in “distant” experiences, often mediated by digital technology (Sundbo & Dixit, 2020; Sundbo & Hagedorn-Rasmussen, 2008). Official destination platforms (official websites, social media networks) managed by DMOs play a key role in this process (S. Lee et al., 2018). These platforms aim to create engaging ODBEs that encourage future visits and enhance the destination’s competitiveness.

Points of contact between potential visitors and the destination during the pre-travel phase are very important for destination managers, as travelers’ mental and emotional perceptions of their expected on-site experience strongly influence their decision making (Larsen, 2007; Volo, 2010; Tung & Ritchie, 2011). The internet serves as an effective tool for fulfilling not only functional travel needs but also hedonic, aesthetic, innovative, and social desires during the information-seeking process (Gretzel & Fesenmaier, 2009; Vogt & Fesenmaier, 1998). By facilitating positive pre-trip experiences, digital platforms enhance travel planning and anticipation (Buhalis & Law, 2008; Navío-Marco et al., 2018). In this phase, travelers are not only gathering information but also seeking inspiration and envisioning potential experiences (Fesenmaier & Pearce, 2019; Larsen, 2007; Tung & Ritchie, 2011; Volo, 2010).

Social media, as a space for sharing and interaction, has become a significant source of travel inspiration (Fotis, 2015). As the online space is often the first point of contact between potential customers and tourism brands, studies on online tourism experiences have emerged (Vlahovic-Mlakar & Ozretic-Dosen, 2022). Studies have highlighted the significant influence social media has on tourists’ destination choices (Jiménez-Barreto et al., 2019; Boley et al., 2018; Molinillo et al., 2018). In particular, platforms like Facebook play a pivotal role in the pre-trip stage, offering critical information for travel planning and decision making (Jadhav et al., 2018). Although ecotourism destinations have exceptional biodiversity and cultural heritage, little empirical research has examined how these destinations utilize Facebook to create online brand experiences that align with ecotourism values (Dwivedi et al., 2021). This study addresses that gap by examining the role of social media, particularly Facebook, in creating favorable online destination brand experiences for Romanian ecotourism destinations.

While previous research has focused primarily on evaluating website features (Sun et al., 2017; Tang et al., 2012), fewer studies have examined the experiential aspects of destination online presence. Choi et al. (2016) explored how telepresence—defined as the sensation of “being there” in a virtual environment (Steuer, 1992)—impacts users’ experiences on destination websites. Their findings suggest that engaging website features foster telepresence, which in turn influences both hedonic and utilitarian website performance (Choi et al., 2016). Building on such insights, this paper centres on the virtual experience of ecotourism destinations on Facebook. Specifically, it examines how these online encounters influence prospective travellers in the anticipation phase of their journey. The necessity for DMOs to provide positive ODBE is growing along with the use of internet platforms for informational searches on tourist destinations. According to Li et al. (2017), consumers’ online interaction with official destination platforms (e.g., official websites, social media network: Facebook, Instagram, Twitter) allows them to experience destinations without physically visiting them. Online platforms play a key role in destination branding by creating an immersive environment that helps users form memorable impressions (Khan & Fatma, 2021).

The construct of brand experience was first introduced by Brakus et al. (2009), who argue that it ‘‘include specific sensations, feelings, cognitions, and behavioral responses triggered by specific brand stimuli. For example, experiences may include specific feelings, not just an overall liking’’ (Brakus et al., 2009, p. 53). Initially, Brakus et al. (2009) proposed a comprehensive model that captures four essential dimensions of destination brand experience: sensory, affective, intellectual and behavioral. Barnes et al. (2014) first applied this theoretical concept to destination brands, focused on the concept of destination brand experience.

Previous research suggests that customer experience also encompasses a social dimension (Verhoef et al., 2009). The way tourists interpret the social aspect of the ODBE reinforces recent findings emphasizing the importance of social presence in shaping meaningful online brand experiences (Bleier et al., 2019). Subsequently, Jiménez-Barreto et al. (2019) extended the four-dimensional brand experience scale and proposed two other dimensions that deepen the understanding of an ODBE: interactive and social. Their results indicate that the ODBE involves a more complex set of dimensions, as it reflects the way tourists process brand-related stimuli and engage in simulated experiences within online environments, particularly during the pre-visit phase. However, in a subsequent study, Jiménez-Barreto et al. (2020) employed the four-dimensional brand experience scale created by Brakus et al. (2009) when they studied the effects of ODBE on users’ behavioral intentions toward the destination when users navigate destination platforms. Jiménez-Barreto et al. (2019) demonstrated that sensory and cognitive experiences resulting from engagement with official destination websites positively influence tourists’ attitudes and their intention to visit and recommend the destination. Similarly, Lin et al. (2024) showed that the sensory and affective dimensions of brand experience significantly impact tourists’ visit intentions. In the context of ecotourism, where authenticity and sustainability are core values, ODBE plays an even more important role in shaping decisions and promoting the sharing of destination information, thereby enhancing organic promotion through user-generated content. Köchling (2020) went beyond the brand focus and analyzed pre-travel ODBEs among German millennials in a qualitative multi-method approach. Consistent with other scholars (Beritelli & Laesser, 2018; Kladou et al., 2017; Tasci, 2011), Köchling (2020) argued that the complexity of tourist destinations impedes destination branding and challenges ODBEs. The findings of her explorative study showed that the ODBE dimensions considered so far need to be complemented by a spatio-temporal dimension (perceived accessibility of expected experiences and experience density in the destination) and a social dimension. The social dimension of ODBE involves interactions with locals, other tourists, and travel companions, influencing on-site experiences and perceptions of prestige. Given the increasing complexity of these interactions, previous research recommends further exploring this evolving aspect of brand experience (e.g., Andreini et al., 2019; Jiménez-Barreto et al., 2019; Köchling, 2020).

Furthermore, Köchling and Lohmann (2022) suggest that pre-travel ODBEs have particularities such as a spatio-temporal component and higher interrelation of singular experience aspects, and a more context-specific measurement instrument is needed. They proposed two interrelated experience dimensions—hedonic and utilitarian—which can be explained by the dual-process theory (Holbrook & Hirschman, 1982) and have also been used to explain general website performances (Huang, 2005). Items expressing sensory and affective aspects as well as space-time aspects are included in the hedonic dimension, which is especially reliant on the online experiential design (e.g., video, pictures) (Köchling & Lohmann, 2022). The utilitarian experience value includes an assessment of the anticipated destination experience’s benefits, encompassing intellectual, behavioral, and affective components (Köchling & Lohmann, 2022).

We can conclude that ODBE is a multidimensional construct that requires specific measurement tools and its testing in various contexts and destinations (Barnes et al., 2014; Jiménez-Barreto et al., 2019). Therefore, the present paper proposes a scale for the measurement of OBDEs, adapted to the specificities of ecotourism destinations, aiming to enhance the understanding of the dimensionality of this construct in the context of Romania.

2.3. Model Development and Hypotheses

This research seeks to reassess the dimensionality of the ODBE construct and further the development and validation of a measurement tool. The ODBE was conceptualized as a construct consisting of 32 items based on insights from two preliminary studies (Jiménez-Barreto et al., 2020; Köchling & Lohmann, 2022), covering various aspects of the experience (sensory, affective, intellectual, spatio-temporal, social, and behavioral). Specific items that explore the unique online experience of ecotourism destinations have been added by the authors to each dimension, which represents a novel approach to this research (see

Table 1. Items used for the conceptualization on ecotourism ODBE scale.

Construct	Dimension	Items	Codification
ODBE	Sensitive	The destination impressed me visually with its natural landscapes.	S1
		The destination impressed me visually with its cultural values.	S2
		The presentation of the destination did not particularly attract me (control item).	S3
		The destination looks great.	S4
		The presentation of the destination stimulated my senses.	S5
	Affective	I imagined how good I would feel during a visit to the destination.	A1
		I had positive feelings toward the destination while browsing its Facebook page.	A2
		The destination left me indifferent (control item).	A3
		I felt that nature is well-preserved and protected in this destination.	A4
		I felt that the cultural values specific to the area are preserved in this destination.	A5
	Intellectual	The destination seemed boring to me (control item).	I1
		The Facebook page sparked my curiosity to learn more about the destination.	I2
		The destination seems interesting to me.	I3
		I found the necessary information on the destination’s Facebook page for a better understanding of nature.	I4
		I found the necessary information on the destination’s Facebook page for a better understanding of the local culture.	I5
	Spatio-temporal	I realized where the destination is located while browsing its Facebook page.	ST1
		The destination seemed difficult to reach (control item).	ST2
		While browsing the Facebook page, I figured out where the main tourist attractions in the destination are located.	ST3
		I learned about the most suitable time to travel to the destination.	ST4
		While browsing the Facebook page, I realized which protected natural areas are located within the destination.	ST5
	Social	I believe it is easy to interact with locals in the destination.	SOC1
		I believe the destination is suitable for a vacation with my family or friends.	SOC2
		I think the destination is crowded (there are too many tourists in the destination) (control item).	SOC3
		I could impress my friends and family if I visit the destination.	SOC4
		I believe that in this destination, one can have memorable encounters with other people.	SOC5
	Behavioral	The Facebook page triggered my desire to visit the destination.	C1
		I could easily imagine myself in the destination while browsing the Facebook page.	C2
		I believe a vacation in this destination is affordable for me.	C3
		I think I could have diverse experiences in the destination.	C4
		I believe the destination’s Facebook page creates unrealistic expectations about the experiences tourists can have there (control item).	C5
		I believe the activities promoted on the Facebook page have minimal negative impact on the natural and socio-cultural environment.	C6
		The destination’s Facebook page contains information about adopting environmentally friendly behavior.	C7
	Based on the information found on the Facebook page, I intend to visit the destination within the next two years.		INT_visit
	There is a possibility that I will share positive information about the destination.		INT_share

The 32 items of the ODBE construct included in the survey are listed, adapted from Jiménez-Barreto et al. (2020) and Köchling and Lohmann (2022). Italicized text indicates that the item was added by the authors, and it is specific to ecotourism destinations.

). These specific items for ecotourism destinations have been proposed by the authors, based on the principles of ecotourism formulated by the Association of Ecotourism in Romania (2025).

Previous research confirmed that ODBE is a significant determinant of visitor outcomes, specifically intention to revisit and intention to recommend (Barnes et al., 2014). According to Jiménez-Barreto et al. (2020), users who had not yet visited a destination placed greater emphasis on their ODBE when shaping their behavioral intentions compared to those who had already been there. Research adopting an experiential perspective on users’ navigation of destination platforms has shown that positive online experiences—whether through websites (Jiménez-Barreto et al., 2019; S. Lee et al., 2018; W. Lee & Gretzel, 2012) or social media (Boley et al., 2018)—play a crucial role in influencing users’ intentions to visit. Moreover, recent studies highlight that engaging social media interactions with a destination significantly predict users’ likelihood to visit and recommend it (Boley et al., 2018).

Jiménez-Barreto et al. (2020) emphasized that visual stimuli, such as videos and images, are key drivers of sensory ODBEs but require engaging content to effectively motivate travel. Additionally, digital simulations on social media complement physical experiences, reinforcing visit intentions (Jiménez-Barreto et al., 2019). Social media further amplifies ODBE’s impact by shaping destination image and influencing travel decisions (Molinillo et al., 2018). Destination marketers can enhance tourist involvement and trust by providing user-friendly, engaging content and encouraging participation, particularly in cognitive aspects such as infrastructure, safety, and sustainability. Aligning social media communication and product development with shareable destination characteristics strengthens a destination’s online appeal.

Henceforth, the present study proposes the following:

H1: 

ODBE positively influences users’ intention to visit the destination within the next two years.

A positive online user experience not only drives visit motivation but also plays a key role in destination recommendation (Zhang et al., 2018). Khan and Fatma (2021) highlighted a positive association between ODBE and word-of-mouth regarding the destination. Mariani et al. (2016) found that visual content strongly influences engagement, as tourists are drawn to images of destinations they have visited or plan to visit. Additionally, visual content and the average length of posts positively impact user interaction on social media. The intention to share information about the destination must be analyzed, as it significantly contributes to the pre-travel experience of other Facebook users. This study, therefore, proposes the following:

H2: 

ODBE positively influences intention to share information about the destination.

The comprehensive conceptual model is presented in Figure 1 and will be discussed in the following section.

This model illustrates how ODBE for ecotourism destinations—operationalized through six dimensions (sensory, affective, intellectual, social, behavioral, and spatio-temporal experience values)—serves as the focal “treatment” driving followers’ behavioral intentions on Facebook. Specifically, enhanced ODBE increases users’ intention to visit the destination and their intention to share destination-related content with their networks. The six experience values jointly shape the overall ODBE construct, which in turn exerts direct, positive effects on both intention-to-visit and intention-to-share.

3. Materials and Methods

Romania features a network of ecotourism destinations that reflect a balanced interaction between biodiversity conservation and cultural landscape preservation. Ținutul Zimbrului serves as a critical area for the rewilding of European bison within extensive forest ecosystems. Băile Tușnad and its surrounding area are distinguished by their volcanic geomorphology, therapeutic mineral waters, and the unique glacial Saint Anne Lake. Eco Maramureș offers a model of sustainable tourism rooted in traditional wooden architecture and well-preserved rural customs amid a rich natural setting. Țara Dornelor combines mountainous ecosystems, spa resources, and ecotourism infrastructure to support nature-based recreation. Țara Hațegului-Retezat integrates geological heritage, including notable paleontological sites, with the ecological value of Retezat National Park. Pădurea Craiului is recognized for its karst landscape, hosting numerous caves and a high level of biodiversity, supporting both scientific study and ecotourism. Colinele Transilvaniei exemplifies the synergy between cultural heritage, including fortified Saxon churches, and the conservation of species-rich grasslands within a mosaic of traditional agricultural practices.

In order to respond to the research questions, an online survey was conducted among visitors to the Facebook pages owned by the seven certified Romanian ecotourism destinations: Ținutul Zimbrului, Băile Tuşnad şi împrejurimile, Eco Maramureş, Țara Dornelor, Țara Hategului-Retezat, Pădurea Craiului, and Colinele Transilvaniei. Thus, a complex online questionnaire was posted on these Facebook pages with the support of their administrators. Since Facebook pages are important sources of information and inspiration for users about ecotourism destinations, they were considered strong drivers in developing the ODBE.

3.1. Questionnaire Design

Following Churchill’s (1979) approach to scale development, the questionnaire contains 32 items, presented in Table 1 (5 items for each dimension of the ODBE, except the behavioral dimension, which was measured using 7 items). A seven-level Likert scale with equal distances, from 1 to 7, was used (where 1 means “strongly disagree” and 7 means “strongly agree”). In literature, such a seven-level scale is considered to perform well in this context (Peng & Finn, 2016). Each scale point was numerically designated, while only the endpoints were labeled. This approach created the perception of equal spacing between response options, allowing participants to interpret the scales as interval measures (Alreck & Settle, 2004; Fowler, 2014). The questionnaire ends with some characterization questions related to the respondents’ age, education, and residence.

To eliminate any ambiguity in the wording of the questions (Podsakoff et al., 2012), the questionnaire was pre-tested on 15 individuals. They were asked to evaluate their ODBE using the proposed items and provide feedback on any items that seemed unclear or irrelevant to the task. An expert panel of five ecotourism scholars and practitioners reviewed all 32 items for relevance and clarity. Two independent coders then evaluated the panel’s feedback and resolved any discrepancies (Cohen’s κ = 0.81). Item-level CVI (I-CVI ≥ 0.78; S-CVI/Ave = 0.92), AVE ≥ 0.50, and CR ≥ 0.70 thresholds were used to retain items.

3.2. Data Collection and Sample Members

The data collection process was conducted online by using a web-based questionnaire. With the help of page administrators, the questionnaire was posted on selected ecotourism Facebook pages to collect responses. Users of these pages were invited to participate voluntarily by accessing and completing the questionnaire online.

At the start of the questionnaire, participants were informed about the confidentiality of their responses and that the results of the research would be used only for statistical and scientific purposes, without using any personal data. They were then given the opportunity to opt out of the survey. Those who consented to participate in the research continued to complete the answers to the questions.

Respondents were instructed to imagine they were deciding whether their next trip should be to an ecotourism destination and were asked to explore the assigned Facebook page for 5 min to gather information about the destination and form an impression of their potential vacation. For the ODBE assessment, participants reviewed the Facebook page and evaluated the destination and the associated vacation experience using the 32-item scale.

After cleaning the data, 281 fully completed questionnaires were retained for analysis. Since the Facebook pages can serve as forming ODBE without visiting the destination, the individuals who have previously visited the destination were not excluded from the study.

3.3. Operationalizing the ODBE

The ODBE was designed as a construct of 32 latent variables, evaluated by the respondents based on their attitudes after viewing the ecotourism destination’s Facebook pages. Thus, to avoid strong multicollinearity, the dimensionality of the construct was assessed by using the Exploratory Factor Analysis. Before analysis, the negatively worded control items were reverse-coded. A principal component analysis (PCA) with Equamax rotation was computed. Exploratory factor analysis via PCA was conducted in International Business Machines Statistical Package for the Social Sciences (IBM SPSS) Statistics 20 vs. Equamax rotation was chosen because it balances factor- and variable-level simplicity. This suited our ecotourism items that showed no single dominant factor—thereby enhancing interpretability of all reflective constructs (Costello & Osborne, 2005). The results returned three components with an eigenvalue higher than 1. Therefore, according to the research results, the number of initially proposed dimensions was reduced to three components. These components were labeled according to their content as follows: hedonic (immersive experience), utilitarian (rational experience), and spatio-temporal (space- and time-related experience).

The ODBE was conceptualized as a higher-order reflective-reflective construct for several reasons. First of all, the hedonic, utilitarian, and spatio-temporal constructs were modeled as reflective first-order constructs, where the latent variable influences its observed indicators. This implies that higher levels of a given experience result in consistently higher ratings across its corresponding indicators, confirming the reflective nature of the constructs.

Preliminary studies (Jiménez-Barreto et al., 2020; Köchling & Lohmann, 2022) indicated that individual experience elements are strongly correlated in the context of online destinations, which is expected to hold true for the three proposed ODBE components. This approach ensures conceptual consistency, measurement reliability, and comparability with prior research. At the first-order level, each ODBE dimension is treated as a composite latent construct, with its indicators influenced or caused by the underlying latent variable (J. F. Hair et al., 2020). At the second-order level, the ODBE itself is considered a latent construct that manifests through the identified components, rather than being formed by them.

Furthermore, modeling the ODBE as a reflective-reflective construct offers advantages over formative models, particularly in terms of interpreting the reliability and validity of the scale (J. F. Hair et al., 2020). Empirical findings further support this conceptualization. Preliminary studies indicate a strong correlation among the first-order dimensions—Hedonic, Utilitarian, and Spatio-Temporal—confirming that they are manifestations of a broader ODBE construct rather than distinct formative elements (Jiménez-Barreto et al., 2020; Köchling & Lohmann, 2022).

To evaluate the dimensionality and validity of the developed ODBE scale, a Confirmatory Composite Analysis (CCA) was conducted with the reflective measurement models (J. F. Hair et al., 2020), using SmartPLS 4.0 software for Partial Least Squares Structural Equation Modeling. The model was developed using the repeated indicators approach proposed by Sarstedt et al. (2019). We incorporated the three components and their corresponding underlying items identified through PCA as first-order constructs, while the overall ODBE was treated as a second-order construct.

We also estimated the impact of ODBE on behavioral intentions (“intention to visit” and “intention to share”) using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0. For each endogenous construct, we report R² and adjusted R², assess overall fit via SRMR (<0.08), and examine path coefficients (β) to evaluate the strength and significance of the hypothesized relationships.

4. Results

To provide context for the study’s findings, it is important to first describe the demographic and experiential characteristics of the survey participants.

Table 2. Sample structure.

Category	Percent
Age
18–24 years	61.9%
25–35 years	7.8%
26–45 years	15.7%
46–55 years	7.1%
56–65 years	6.0%
Above 65	1.4%
Education
High school	28.5%
Bachelor	39.1%
Master	25.6%
Doctorate	6.8%
Residence
Urban	73.3%
Rural	26.7%
Have visited the destination before
Yes	56.6%
No	43.4%

outlines key attributes of the respondents, including their age, education level, place of residence, and prior visitation experience.

The age distribution indicates a predominant representation of young adults aged 18–24 (61.9%). Educational attainment reveals a balanced spread, with 28.5% of respondents holding a high school diploma, 39.1% possessing a bachelor’s degree, 25.6% completing a master’s degree, and 6.8% having a doctorate. In terms of residency, a majority of respondents (73.3%) reside in urban areas, with the remaining 26.7% in rural locations. Additionally, prior visitation patterns show that 56.6% of respondents have visited the destination before, while 43.4% are first-time visitors.

4.1. Descriptive Statistics

Overall, most variables exhibit high mean values (above 5 on a 1–7 scale), suggesting a generally positive perception of the online experience of the analyzed destinations (Appendix A). For instance, S1 (“The destination impressed me visually with its natural landscapes”) has a mean of 6.36, indicating a strong agreement with this statement. Conversely, variables such as C6 (“I believe the activities promoted on the Facebook page have minimal negative impact on the natural and socio-cultural environment”) show lower mean values (3.99), suggesting a less favorable perception of the sustainability of promoted activities. Standard deviations range from approximately 1 to 2.3, indicating varying levels of response dispersion. A higher standard deviation, such as for C6 (2.381) or C5 (2.268), suggests greater diversity in participants’ opinions, while lower standard deviation values, such as for S1 (0.973) or I3 (1.042), indicate stronger consensus among respondents.

4.2. Scale Development

To enhance the clarity of the ODBE assessment measure and examine the dimensionality of the construct, the full dataset (n = 281) was utilized. A principal component analysis (PCA) with equamax rotation was performed on all 32 items (refer to Table 1). Equamax rotation was applied to maximize variance distribution across components while balancing factor independence and interpretability (

Table 3. Rotated component matrix.

	Component
	1	2	3	4
S4	0.742
I3	0.721
S5	0.708
S1	0.707
A2	0.665
I2	0.638
A1	0.631
A5		0.707
C7		0.685
A4		0.669
SOC1		0.663
SOC4		0.608
I5
SOC5
ST3			0.768
ST4			0.755
ST1			0.738
ST5			0.697
A3				0.793
C5				0.697
I1				0.686
ST2				0.650
S3				0.647

Extraction method: principal component analysis. Rotation method: Equamax with Kaiser Normalization (rotation converged in 8 iterations).

). This approach, which combines elements of both Varimax (orthogonal) and Oblimin (oblique) rotations, was chosen to enhance the differentiation of components while allowing for some flexibility in factor correlations. The rotation converged in eight iterations, producing a clear structure where the extracted components align with the theoretical dimensions of ODBE.

The suitability of the data for analysis was confirmed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, which yielded a value of 0.940 (Kaiser & Rice, 1974). Additionally, Bartlett’s test of sphericity was significant (χ²(496) = 0.000, p < 0.001). Following Kaiser’s criterion (Kaiser & Rice, 1974), three components (hedonic, utilitarian, and spatio-temporal) with eigenvalues exceeding one were identified, accounting for 55.12% of the total variance.

The scree plot (Appendix B) and parallel analysis (Horn, 1965) suggested that three components should be retained. The initial PCA revealed four components with eigenvalues greater than 1. However, the fourth component included only negatively worded control items (A3—The destination left me indifferent; C5—I believe the destination’s Facebook page creates unrealistic expectations about the experiences tourists can have there; I1—The destination seemed boring to me; ST2—The destination seemed difficult to reach; S3—The presentation of the destination did not particularly attract me). These reverse-phrased items often cluster due to their wording, not because they reflect a true construct. This is known as a method factor (Van Dam et al., 2012; Suárez-Álvarez et al., 2018). It reflects response style or cognitive processing differences rather than substantive meaning. Such effects can distort the true factor structure. Therefore, we retained only three components that represent meaningful constructs. The fourth component is reported for transparency but was not included in the final interpretation.

Two items (SOC3 and C6) were removed due to low communalities (≤0.7), while nine additional items (S2, I4, I5, SOC2, SOC5, C1, C2, C3, C4) were excluded because of high cross-loadings (i.e., loadings > 0.3 on multiple components with a difference of ≤0.3 between them).

In the revised analysis, five items (S3, A3, I1, ST2, C5) were excluded from the scale after the second PCA run, as they did not load onto any of the components explaining most of the covariance. Additionally, the items primarily functioned as a control variable, further supporting its removal to enhance the scale’s conceptual clarity and measurement validity.

The resulting three-component solution explained 55.8% of the variance (see

Table 4. Extraction sums of squared loadings.

Component	Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %
1	9.858	39.431	39.431
2	2.717	10.868	50.300
3	1.379	5.516	55.816

). Although the three-factor solution accounted for 55.0% of cumulative variance—below the 60–65% guideline—such levels (50–60%) are considered acceptable in behavioral-science research (J. F. Hair et al., 2020; Tabachnick & Fidell, 2013; Comrey & Lee, 2013; Worthington & Whittaker, 2006). The first component represents the predominantly affective immersive part (hedonic value). The second component represents its rational evaluative part (utilitarian value) of the ODBE, while the third component represents the spatial-temporal part (spatio-temporal value).

4.3. Confirming the Measurement’s Validity and Reliability

To evaluate the dimensionality and validity of the developed ODBE scale, the full dataset (n = 281) was utilized to perform a Confirmatory Composite Analysis (CCA) (J. F. Hair et al., 2020) using Smart PLS 4 (Ringle et al., 2014). CCA, a variance-based structural equation modelling (SEM) approach, was selected over Confirmatory Factor Analysis (CFA), a covariance-based SEM method, due to the early stage of theory development for the ODBE construct. Given this phase, the analysis prioritized content validity, which is better supported by CCA, as it tends to yield higher item loadings and retains a greater number of indicators compared to CFA (J. F. Hair et al., 2020).

The research model evaluation based on PLS analysis was carried out in two phases: (1) testing the reliability and validity of the measurement model; and (2) evaluating the structural model.

4.3.1. Testing the Reliability and Validity of the Measurement Model

The inter-component correlations ranged from 0.486 to 0.703, indicating a moderate to strong relationship among the extracted components. This supports the assumption that the components are interrelated, justifying the use of oblique rotation (e.g., Direct Oblimin). Furthermore, the results confirm that the hedonic, spatio-temporal, and utilitarian components function as reflective subdimensions of a higher-order construct.

Additionally, all three components demonstrated high reliability, with Cronbach’s alpha (CA) and composite reliability (CR) exceeding 0.8, ensuring internal consistency. The average variance extracted (AVE) was greater than 0.5, confirming convergent validity (

Table 5. Reliability and Convergent Validity Metrics for the Hedonic, Utilitarian, and Spatio-temporal components.

Dimension	Hedonic	Utilitarian	Spatio-Temporal
Cronbach’s alpha (CA)	0.915	0.832	0.865
Composite reliability (CR)	0.921	0.880	0.908
Average variance extracted (AVE)	0.663	0.596	0.711

).

The measurement model exhibited favorable reliability and validity, as all values exceeded the commonly accepted thresholds of 0.8 for composite reliability (CR) (Nunnally, 1978), 0.7 for Cronbach’s alpha (CA) (J. Hair et al., 1998), and 0.5 for average variance extracted (AVE) (Fornell & Larcker, 1981).

To ensure that multicollinearity was not a concern, collinearity was assessed using the Variance Inflation Factor (VIF). All VIF values remained below 3.0, confirming the absence of multicollinearity issues and ensuring the stability of the structural model relationships (J. F. Hair et al., 2020). Since all VIF values are below 3.0, collinearity is not a concern, supporting the robustness of the measurement model.

Discriminant validity was assessed using the Heterotrait–Monotrait ratio (HTMT). All HTMT values remained below the commonly accepted threshold of 0.85 (Henseler et al., 2015), confirming that the constructs are sufficiently distinct.

The hedonic and utilitarian constructs exhibited strong correlations with ODBE. Additionally, the spatio-temporal and utilitarian components demonstrated moderate correlations, indicating some interconnectedness while maintaining their distinctiveness. Since none of the HTMT values exceed 0.85, there are no substantial concerns regarding construct redundancy in the model.

To further confirm discriminant validity, bootstrapped 95% confidence intervals (CIs) for the HTMT ratios were examined (Henseler et al., 2015). The results indicated that for all construct pairs, the upper bound of the HTMT confidence intervals remained below 1.00 (0.000). This provides additional support that the constructs are sufficiently distinct.

4.3.2. Evaluating the Structural Model

We expected positive ODBE values to be a predictor of the visit to the destination within the next two years (H1) and share positive information about the destination (H2).

The structural model was assessed by examining the R-square (R²) values, which indicate the proportion of variance explained by the ODBE construct in its three components: hedonic, spatio-temporal, and utilitarian experiences. The adjusted R-square accounts for potential model complexity, ensuring a more precise estimate of explanatory power. The results reveal that the ODBE construct explains 45.8% of the variance in the hedonic component (R² = 0.458, adjusted R² = 0.456), making it the most strongly predicted component. In contrast, the spatio-temporal component exhibits a moderate level of explained variance (R² = 0.275, adjusted R² = 0.272). The utilitarian component has the lowest explained variance (R² = 0.236, adjusted R² = 0.233). Despite differences in explained variance across the three components, the small difference between R² and adjusted R² in all cases suggests that the model is well-structured and stable. This implies that additional predictor variables would not substantially improve the explanatory power of the model, reinforcing the robustness of the current construct structure. Additionally, overall model fit was assessed via the Standardized Root Mean Square Residual (SRMR = 0.045), which is well below the recommended 0.08 threshold and thus supports the adequacy of the model.

Additionally, the path coefficients indicate strong relationships between ODBE and its components: hedonic (β = 0.677), utilitarian (β = 0.486), and spatio-temporal (β = 0.524). These values suggest that the hedonic component is the most strongly associated with ODBE, followed by spatio-temporal and utilitarian components.

The final model (Figure 2) also examined the impact of ODBE on behavioral intentions, specifically intention to visit (β = 0.913) and intention to share (β = 0.875). Both path coefficients are strong and positive, suggesting that users’ experience with an online destination significantly drives both their likelihood to visit and share information about the destination. Therefore, both hypotheses are confirmed.

Moreover, the individual indicator loadings for each component exceeded the commonly accepted threshold of 0.7, confirming strong construct validity.

Table 6. Factor loadings.

	Hedonic	ODBE	Spatio-Temporal	Utilitarian
A1	0.792	0.538	0.529	0.564
A2	0.854	0.545	0.568	0.659
A4	0.566	0.298	0.460	0.758
A5	0.597	0.343	0.476	0.775
C7	0.516	0.423	0.557	0.801
I2	0.822	0.670	0.550	0.585
I3	0.799	0.524	0.489	0.533
INT_share	0.578	0.875	0.412	0.372
INT_visit	0.630	0.913	0.518	0.489
S1	0.781	0.504	0.510	0.520
S4	0.798	0.466	0.412	0.538
S5	0.849	0.568	0.570	0.598
SOC1	0.486	0.309	0.485	0.736
SOC4	0.559	0.456	0.495	0.788
ST1	0.512	0.398	0.814	0.501
ST3	0.547	0.476	0.857	0.542
ST4	0.582	0.487	0.873	0.536
ST5	0.514	0.394	0.826	0.598

presents the factor loadings.

Factor loadings above 0.70 indicate strong associations between indicators and their respective constructs, while cross-loadings above 0.40 suggest potential construct overlap. The hedonic construct exhibited strong loadings for affective, sensory, and intellectual components, supporting its role as the most experience-driven aspect of ODBE. Items A1 (0.792), A2 (0.854), I2 (0.822), I3 (0.799), S1 (0.781), S4 (0.798), and S5 (0.849) loaded significantly on this component, confirming its emotional and immersive nature.

The ODBE construct showed high loadings for behavioral intention indicators: INT_share (0.875) and INT_visit (0.913), suggesting that users’ engagement with online destination content significantly influences their intent to visit and share information about the destination. Moderate loadings across multiple dimensions suggest that ODBE integrates hedonic, spatio-temporal, and utilitarian components while maintaining its distinctiveness.

The spatio-temporal component demonstrated strong loadings, particularly for indicators related to users’ spatial and temporal awareness of the destination. Items ST1 (0.814), ST3 (0.857), ST4 (0.873), and ST5 (0.826) exhibited high loadings, reinforcing the importance of spatial perception in shaping the pre-travel experience. The acceptable cross-loadings support construct validity, confirming that spatial awareness is distinct from purely hedonic or functional evaluations.

The utilitarian component showed high loadings for items A4 (0.758), A5 (0.775), C7 (0.801), SOC1 (0.736), and SOC4 (0.788), supporting its role in rational and functional evaluations of the destination. However, A4 and A5 also exhibited moderate cross-loadings with the hedonic component, suggesting conceptual overlap between affective engagement and practical evaluations. This indicates that users’ rational perceptions of a destination may still be influenced by emotional and sensory elements, rather than purely functional considerations.

Also, the Bayesian Information Criterion (BIC) was used to evaluate the goodness-of-fit and complexity of the statistical model. The hedonic component exhibits the best model fit (BIC = −161.871), confirming that pre-travel online engagement is primarily emotional and immersive. The spatio-temporal component (BIC = −80.025) shows a moderate fit, suggesting that ODBE influences users’ spatial and temporal perceptions, but additional contextual factors may contribute. The utilitarian component (BIC = −65.436) presents the highest BIC, indicating that rational evaluations rely on broader decision-making factors beyond ODBE.

5. Discussion

The developed scale is intended to differentiate the overall experiential values along these three components, as generated by ecotourism destination Facebook pages, rather than describing specific individual experiences (e.g., particular emotions). This approach aligns with the brand experience scale (Brakus et al., 2009).

In this study, ecotourism ODBEs are represented through three underlying components that incorporate elements identified in previous research (Jiménez-Barreto et al., 2019; Köchling & Lohmann, 2022) while also reflecting the unique characteristics of ecotourism destinations. The first component pertains to the hedonic value of the destination experience, encompassing sensory, affective, and intellectual dimensions. This hedonic ODBE component is anticipated to be particularly prominent during the inspiration phase. Then, Facebook users engage in fantasizing about potential holidays without immediate travel plans or specific needs (Dai et al., 2022), and their involvement is relatively low (Petty & Cacioppo, 1986; Tang et al., 2012). The term “intellectual” in this context refers to the cognitive dimension of the destination experience. It encompasses the stimulation of visitors’ thinking, curiosity, and mental engagement through activities such as exploring local culture, learning about the history of the destination, or participating in experiences that provoke reflection and deeper understanding. Intellectual experiences contribute to the overall hedonic value by fostering opportunities for personal growth and the acquisition of knowledge, complementing the sensory and emotional aspects of the experience. This interpretation contrasts with the findings of Köchling and Lohmann (2022), who categorized intellectual items from the evaluation scale as part of the utilitarian component rather than the hedonic component.

The second ODBE component, which complements the hedonic component, aligns with utilitarian thinking as described by Holbrook and Hirschman (1982). The second component focuses on social and behavioral aspects. It aligns with utilitarian thinking during the information phase. However, the affective items indicate that emotions still play a role in shaping responses to the destination experience. These affective elements likely contribute to the way individuals connect emotionally with the information they process, reinforcing their engagement and decision making. This reflects a more nuanced understanding of the information phase, where higher involvement and cognitive processing coexist with emotional influences, as users evaluate content with both rational and affective considerations. This phase follows the inspiration stage, where the content of Facebook pages is processed in a more cognitively driven manner, with higher levels of involvement (Petty & Cacioppo, 1986; Tang et al., 2012).

The third ODBE component addresses spatio-temporal aspects, as ecotourism destinations are deeply tied to place because the main motivation of their visitors is to observe and enjoy nature and local customs regarding nature (Association of Ecotourism in Romania, 2025). This includes considerations such as the uniqueness of the destination’s landscapes and the interplay between human activity and natural ecosystems. These aspects shape the visitor’s experience by emphasizing a sense of place and fostering a connection with nature. Understanding this component is very important in ecotourism, as it influences both the experiential quality and the conservation efforts tied to these destinations.

The results of the CCA confirm the robustness and validity of the developed ODBE measurement model. The findings indicate that the three identified components—hedonic, utilitarian, and spatio-temporal—function as reflective subdimensions of a higher-order construct. This aligns with prior research on experiential consumption, which suggests that online experiences are multidimensional and interrelated (Jiménez-Barreto et al., 2020; Köchling & Lohmann, 2022).

A critical aspect of model evaluation is ensuring the absence of multicollinearity. The Variance Inflation Factor (VIF) values, all below 3.0, suggest that multicollinearity is not a concern, reinforcing the stability of the model. This further strengthens the argument that the developed ODE scale provides distinct and non-redundant measures of the online destination experience. However, the relatively strong correlation between the hedonic and utilitarian components (HTMT = 0.805) suggests some conceptual overlap. This is consistent with previous studies indicating that affective and cognitive evaluations of an experience are often interlinked, particularly in tourism and brand experience research (Brakus et al., 2009; Jiménez-Barreto et al., 2020).

Interestingly, the spatio-temporal component exhibited moderate correlations with both the hedonic and utilitarian components. This suggests that perceptions of space and time in an online tourism context are shaped by both affective and cognitive elements. Users may form an emotional attachment to a destination based on its perceived accessibility and attractiveness while simultaneously assessing its practical aspects, such as ease of access and recommended travel periods. These findings contribute to the growing body of literature on online destination experiences, supporting the notion that online environments can simulate pre-travel engagement in ways that influence future travel behavior (Gretzel et al., 2006; Dai et al., 2022).

The evaluation of the structural model provides strong empirical support for the role of ODBE in shaping both perceived experience dimensions and behavioral intentions. The results demonstrate that ODBE strongly predicts users’ intention to visit the destination and share information about it, reinforcing the critical role of online engagement in tourism decision making.

The R² values indicate that the hedonic component is the most strongly predicted component of ODE (R² = 0.458, adjusted R² = 0.456), confirming that affective and sensory engagement are the dominant aspects of online destination experiences. This finding aligns with previous research suggesting that emotional and immersive content plays a central role in shaping users’ perceptions of travel destinations (Jiménez-Barreto et al., 2020; Dai et al., 2022).

In contrast, the spatio-temporal component exhibited a moderate level of explained variance (R² = 0.275, adjusted R² = 0.272), suggesting that while online experiences contribute to users’ spatial awareness and temporal perceptions of the destination, additional factors (e.g., external sources such as travel guides and user-generated content) may also play a role in shaping these perceptions.

The utilitarian component had the lowest explained variance (R² = 0.236, adjusted R² = 0.233), indicating that while practical and rational evaluations of a destination are influenced by ODBE, they are likely supplemented by more fact-based and logistical information sources. This aligns with findings from digital tourism research, which suggests that social media content tends to be more effective for inspiration and emotional engagement rather than purely functional decision making (Tang et al., 2012).

The spatio-temporal component, while moderately associated with ODBE, remains an essential component, particularly in ecotourism contexts where the location of attractions and travel logistics play an important role.

While Jiménez-Barreto et al. (2020) proposed a tripartite model comprising cognitive, affective, and behavioral components, their framework was developed in the context of urban destination branding and emphasized brand-related self-congruence. In contrast, our findings reveal that in ecotourism settings, engagement is more strongly driven by values associated with authenticity, sustainability, and environmental education, which reconfigure how these components manifest and interact. Similarly, Köchling and Lohmann (2022) explored ODBE within mass tourism, focusing on digital touchpoints and customer journey stages. Thus, while structurally similar to existing models, this study contributes to novel conceptual distinctions that better reflect the motivations and behavioral patterns of ecotourism-oriented social media users.

6. Conclusions

The findings of this study highlight that the proposed ODBE scale for ecotourism destinations comprises three components hedonic, utilitarian, and spatio-temporal. This directly addresses RQ1 by demonstrating how these components together capture the emotional, rational, and place/time-based elements of online experiences. Moreover, each component measures different aspects of ODBE (RQ2). For example, affective immersion relates to the hedonic component, while practical evaluations pertain to the utilitarian aspect. Additionally, spatial or seasonal awareness is associated with the spatio-temporal component. Despite these distinctions, the components show moderate to strong correlations. This suggests some overlap between emotional and cognitive evaluations of a destination. Also, it indicates that users approach ecotourism content in a holistic manner, where sensory appeal and logistical considerations both inform their impressions before travel.

Additionally, the findings offer clear evidence in response to RQ3 and RQ4 regarding the relationship between ODBE and key behavioral intentions. First, ODBE exerts a strong influence on users’ likelihood to visit, underscoring the critical role of an engaging, visually compelling, and informative online presence in motivating actual travel. Second, ODBE similarly shapes intentions to share destination information on social media, reflecting how positive interactions with official Facebook pages can transform travelers into advocates who disseminate destination highlights within their own networks.

6.1. Theoretical Contributions

This study contributes to the growing body of research on ODBE by validating a three-dimensional model that includes hedonic, spatio-temporal, and utilitarian components. The findings largely confirm existing theories on experiential consumption in digital tourism but also present key distinctions that refine current models. Moreover, the present paper extends the ODBE framework to conservation-driven ecotourism settings by demonstrating how online brand experiences can both engage potential visitors and reinforce preservation goals. It also identifies spatio-temporal experience value as a third dimension, alongside hedonic and utilitarian factors. It captures users’ sense of place and timing, which is vital for observing and conserving natural and cultural environments. This three-factor structure is important because it shows how experiential, functional, and place-bound elements uniquely influence intention-to-visit and intention-to-share, enhancing the understanding of ODBE in nature-based contexts. First, the study reinforces the dominance of hedonic experiences in shaping online engagement with destinations, aligning with prior research on digital tourism experiences (Jiménez-Barreto et al., 2020; Dai et al., 2022).

The introduction of the spatio-temporal dimension refines previous ODBE models, which have traditionally focused on sensory, affective, and cognitive aspects. The results indicate that spatial awareness and time perception play a distinct role in pre-travel engagement, particularly for ecotourism destinations where geographic and seasonal factors are important. While previous studies on destination branding have emphasized functional and emotional attributes (Brakus et al., 2009; Khan & Fatma, 2021), this research demonstrates that users also form impressions based on perceived accessibility and travel timing, which may impact their decision-making process.

Additionally, this study provides empirical evidence that ODBE strongly predicts behavioural intentions. The high path coefficients for intention to visit (β = 0.913) and intention to share (β = 0.875) confirm that online engagement translates into real-world actions, reinforcing Gretzel et al.’s (2006) argument that digital experiences drive tourism behaviour. Based on these outcomes, we consider that ecotourism destinations should enhance their online presence with immersive experiences like virtual tours and real visitor stories, as these significantly boost the intention to visit. To further increase engagement and visibility, destinations should promote user participation through hashtag campaigns and content sharing initiatives.

6.2. Practical Implications

From a managerial perspective, the results highlight the importance of creating immersive and informative online experiences for tourism destinations. Since the hedonic component exhibits the strongest reliability and validity, destination marketers should prioritize emotionally engaging content, such as high-quality visuals, storytelling, and interactive features, to enhance the pre-travel experience. Additionally, the correlation between spatio-temporal and utilitarian components suggests that practical information, such as travel logistics and seasonal highlights, should be seamlessly integrated into engaging narratives rather than presented as standalone factual content.

Furthermore, the strong relationship between the ODBE construct and behavioral intentions (e.g., intention to visit and share information) confirms that ODBE significantly influences user engagement and decision making. Tourism boards and digital marketers should leverage this insight by designing social media campaigns that encourage interaction and sharing, as this can enhance both destination awareness and visit intent.

Given that the hedonic component is the most influential component of ODBE, online content should prioritize visual storytelling, user-generated experiences, and immersive multimedia formats to evoke positive emotions and enhance user engagement.

The moderate role of the spatio-temporal component suggests that destination marketers should also include relevant geographic and seasonal information within engaging content formats. For example, incorporating interactive maps, live videos, and seasonal recommendations could enhance the impact of spatial and temporal perceptions without sacrificing emotional engagement.

Since the utilitarian component has the weakest association with ODBE, marketers should recognize that practical details such as pricing, accommodation, and transportation are better presented through direct website links, FAQs, or interactive chat support rather than relying solely on social media content.

6.3. Limitations and Future Research Directions

While the current study provides strong empirical support for the validity and reliability of the ODBE scale, some limitations should be acknowledged. First, the study focuses exclusively on Facebook pages as the online platform of analysis. Future research should explore whether similar dimensional structures apply to other social media platforms (e.g., Instagram, TikTok) or virtual reality experiences in tourism. The sample may over-represent engaged Facebook users, and missing demographic data limits external validity. Future studies should include this information.

Additionally, while the findings suggest a strong relationship between ODBE and behavioral intentions, future studies should incorporate longitudinal approaches to assess whether online experiences translate into actual visits over time. Future research should explore how to communicate safety effectively—especially via social media—in pre-travel planning and destination marketing.

The study was conducted within the context of Romanian ecotourism destinations, with limited survey respondents. Future research should examine whether the same model applies to different cultural and regional contexts to determine the generalizability of the findings. They could also analyze secondary data from various panels or years. Relying on self-reported data poses a bias risk, so incorporating clickstream data could validate observed destination-brand engagement (ODBE). Also, using a longitudinal or two-wave panel approach would better track behavioral changes over time. Comparative research in high-volume or Global South ecotourism markets would further test the model’s cultural robustness and scalability.

Bui et al.’s (2024) concept of “AI-thenticity” highlights how the perceived authenticity of AI-generated images affects trust and tourist patronage. Future research could explore how different types of AI-generated content, such as text, images, and virtual influencers, influence user engagement across various platforms.

Recruiting participants exclusively through official ecotourism Facebook pages may skew the sample toward younger, tech-savvy users. These individuals often exhibit stronger digital engagement and more positive attitudes toward online platforms, potentially limiting the study’s representation of travelers who rely on traditional information channels (e.g., travel agencies or print media). Future research could diversify participant recruitment—for instance, by distributing surveys at visitor centers or through local communities—and compare respondent demographics to national tourism data to gauge the extent of this bias and refine the study’s generalizability.

Despite these limitations, this study advances scholarship by providing a validated ODBE scale that integrates emotional, cognitive, and contextual dimensions of online ecotourism experiences. It deepens understanding of how digital engagement shapes traveler intentions, highlighting the pivotal role of online brand experience in influencing both visitation and information-sharing behaviors. The key findings of this study include the following: (1) an ODBE measurement scale with specific dimensions for ecotourism destinations; (2) an ODBE construct in ecotourism, including hedonic, utilitarian, and spatio-temporal components; (3) a strong prediction of ODBE on users’ intention to visit ecotourism destinations; and (4) the effect of ODBE on users’ online information-sharing intention. This framework offers a valuable tool for future research and practical strategies in sustainable tourism marketing.