“Influencers as Brand Architects: Co-Creation and Customer Experience in Tourism”

3.1 The Three Stages of Travel

In consumer behaviour, the pre-travel stage corresponds with the pre-purchase phase. Consumers research and evaluate options at this stage (Solomon et al. 2014). This includes destination research, budgeting, itinerary planning, and booking, similar to pre-purchase information collecting and evaluation. When choosing travel alternatives, consumers weigh cost, convenience, and personal preferences (Kotler and Keller 2016). The purchasing phase of consumer behaviour occurs during travel since product or service consumption occurs at this stage. This purchase phase involves exploring the location, participating in activities, and interacting with the local culture (Urry 2002; Cohen 1979). This step is crucial because it fulfils pre-travel expectations and can significantly impact the experience (Batra and Ahtola 1991). Post-travel resembles post-purchase in customer behaviour. Reflection, learning, and sharing occur in this stage (Pearce 2005). The post-purchase phase of consumer behaviour involves product evaluation and behavioural reactions such contentment, displeasure, and word-of-mouth communication (Kotler and Keller 2016). Travelers share their experiences and reviews, which might impact future travelers (Uriely 2005; White and White 2007).

3.2 Pre-Travel and Influencers’ Role

Influencers, mainly social media and travel vloggers, shape pre-travel intents and decisions (Stoldt et al. 2019). According to Silaban et al. (2022), client involvement and enjoyment of YouTube travel vlogs increase travel intention. Vlogs can influence consumer engagement and travel intention by presenting travel locations (Cheng et al. 2020). Klaus and Maklan (2013) studied residents’ travel decision-making to specific places and concluded that travel blogs influenced travel decision-making, particularly in the context of visitors’ impressions of destinations.

According to Chen et al. (2022), psychological variables, including travel patterns and environmental awareness, affect travel behaviour. This is consistent with the idea that psychological factors motivate travelers, influence their travel decisions, and affect their destination pleasure (Chon 1990). The impact of social media on tourism consumers’ decision-making and online buying trends has also been studied, highlighting potential relationships between participant demographics and variables like social media information, tourism service use, and travel experience sharing (Orhan et al. 2018). Influencers and travel vloggers shape consumer behaviour, travel intentions, and travel decisions in the pre-trip period. Their effect on potential travelers is a significant component of modern travel. Hence,

3.3 During-Travel and Influencers’ Role

In the stage “during travel”, influencers shape travelers’ experiences and behaviours. Influencers play a key role in sharing information about the destination, suggestions and experiences with travelers by interacting frequently with the them. This has a significant impact on the happiness of travelers, the purchase intentions and the future travel behaviours. In line with Aluri et al. (2015), integrated social media channels affect positively travelers happiness and purchase intentions throught their perceived enjoyment and social engagement. Therefore, this clearly highlights how this influencer’s content and involvement contributes to travelers’ experiences and intentions throughout their time spent at various locations during travel.

According to Wang and Li (2019), travel information, personal content, and firm support are the antecedents of post-travel evaluations. This implies that content of influencers during travel may shape the experience of travelers and their recollections of the trip. Additionally, Abdullah et al. (2022) revealed that travel influencers alsoimpact a traveler’s decision-making during travel. This implies that influencers are important in shaping a traveler’s behaviour and choice in real time. In addition, Wibowo et al. (2020) concluded that Instagram posts significantly affect the purchasing decision of a visitor. Hence

3.4 Post-Travel and Influencers’ Role

Influencers continue to shape travelers’ behaviours and experiences in the “post-travel” stage, including post-travel assessments, experience sharing, and future travel aspirations. Many studies have shown how travel influencers affect behaviours (Boukis et al. 2025) and decisions following their visits (Arsenis and Chatzopoulou 2020; Pan et al. 2007; Mainolfi et al. 2021; Yuan et al. 2022). Pan et al. (2007) examined visitor reviews on top travel blogs to understand the location experience and the lasting impact of influencers’ material on travelers’ post-travel assessments and reflections. Mainolfi et al. (2021) identified that hedonic and utilitarian motivations mediate travelers’ post-travel behavioural intentions, emphasising the sustained impact of influencers on travelers’ decisions and future travel plans.

Silaban et al. (2022) also concluded that YouTube travel vlogs influence consumer behaviour via the use and gratification perspective, suggesting that influencers’ content continues influencing consumer choices and tourism information after the travel. Sedera et al. (2017) also suggest that social influence affects travelers’ post-travel experiences by influencing their social media comments and feedback. Yuan et al. (2022) also examined how social media affects Chinese tourists’ travel planning, suggesting that influencers’ strategies for specific user segments should be carefully observed and that influencers’ strategies continue influencing travelers’ future travel intentions after their trip. Hence,

3.5 Pre-Travel and Customer Experience

The customers prior beliefs, attitudes and motives about particular destinations through various travel influencers and Instagram celebrities create the clear image of the traveler before arrival at the destination. In addition, the impact of the influencers and celebrities on the consumers would lead to the pre-travel experience that can adjust the traveler’s expectation that would affect the revisits. According to McDougall and Levesque (2000) the associations between customer satisfaction and future behaviours, state that pre-travel pleasure and perceived value will determine travel choice. Yunduk et al. (2019) noted in their study that precognitive and post-travel mood and sentiments can influence hotel choice and loyalty. According to Choi et al. (2011) consumers assess, try and then decide to repurchase based on their prior purchases and consumption, demonstrating the persistent impact of pre-travel events on post-travel behaviour.

Kandampully and Suhartanto (2000), as well as Klaus and Maklan (2013). concluded that customer enjoyment and brand image affect customer experience. The pre-travel satisfaction can influence the afterwards comments and overall experience of the customer. According to Ye et al. (2022), information system quality positively enhances customer happiness and trust, which means that pre-travel experience will probably affect post-travel satisfaction, trust and travel intensions. Furthermore, Jang and Lee (2019) indicated customer pleasure can affect customer behaviour since their happiness before a trip may alter their behaviour. Finally, customer pleasure, perceived value and previous travel experiences have substantial influence on post-experience evaluation, sharing and travel intension. Hence,

3.6 During Travel and Customer Experience

Influencers can modify the travelers’ views, enjoyment and trip arrangements during traveling. The traveler’s experiences could be greatly affected by the travel influencer contents and recommendations (He et al. 2022). For example, Pan et al. (2007) conducted analysis on the visitor opinions in leading travel blogs in order to determine the destination experiences and they concluded that the content and stories derived by the influencers might considerably change the traveler’s experience as well as their perceptions about a destination. This demonstrates that influencers convey reflections and knowledge that shape the traveler’s anticipation towards places and suggested activities.

To improve travel intention with consumer engagement behaviour, Cheng et al. (2020) investigated what influencer’s material may affect consumer engagement and travel experience during travel. The results indicated that the influencer has a relationship with the traveler during the travel and thus the traveler will experience higher enjoyment through higher engagement and interaction with the influencer. Pai et al. (2020) explored the consumer happiness in various travel group composition, and found that travel experience and interaction can have a great influence on the consumer satisfaction and travel experience. This implies that travel influencers’ content and suggestions might affect consumer happiness and experience quality. Hence,

3.7 Post-Travel and Customer Experience

Tourists’ post-travel behaviour greatly affects their trip. Liu et al. (2021) observed that prior outward travel behaviour strongly influences post-pandemic overseas travel intentions, suggesting that past travel experiences influence future travel preferences. Akhoondnejad (2015) noted that post-travel attitudes affect trip value and visitor satisfaction, emphasising their lasting impact on travel. Post-travel behaviour includes sharing travel experiences on travel blogs and vlogs (Cheng et al. 2020). Travelers’ behaviour and inclination to spread good word-of-mouth regarding travel bloggers and their experiences depend on their trustworthiness (Mainolfi et al. 2021). Travel bloggers and readers are more connected and engaged thanks to social media (Maggiore et al. 2022). Travel blogs can allow self-presentation and performances, primarily via digital nomadism (Willment 2020).

Chen et al. (2022) also suggested that sharing mobile social media travel experiences might affect tourists’ post-travel assessments, emphasising the lasting impact of post-travel behaviour on the travel experience. Additionally, post-trip novelty has been connected to travel satisfaction, demonstrating its lasting influence (Bello and Etzel 1985). User-generated content and customer-to-customer interactions also affect travelers’ post-travel behaviours, suggesting that these interactions and content continue to shape the travel experience and travel decisions (Izogo et al. 2021). Destination image also affects tourist behaviour after the trip, demonstrating the long-term impact of post-trip impressions on future travel (Azeez 2021). Hence,

3.8 Moderating Role of Co-Creation

Co-creation is firmly rooted in Service-Dominant Logic, which posits that value is co-created collaboratively by firms and customers rather than delivered unilaterally (Vargo and Lusch 2004, 2008). This perspective suggests that customer involvement can amplify the effects of antecedent variables on key outcomes, particularly in experiential settings. Campos et al. (2018) in their research revealed that co-creation moderates the relationship between experience value and satisfaction in tourism, strengthening the impact when customers actively engage. Similarly, Prahalad and Ramaswamy (2004) conceptualize co-creation as an interactional process that reshapes customer experiences and thus can alter the strength of established relationships.

Co-creation indeed plays a crucial moderating role in shaping the overall customer experience across the three stages of travel: pre-, during, and post-travel. The concept of co-creation, as defined by Prahalad and Ramaswamy (2004), involves the active involvement of customers in the creation of value, allowing them to actively participate in shaping their travel experiences, from the initial planning stages to the actual travel experience and even after the trip has concluded. Co-creation is quite evident during the pre-travel stage. According to Zhang et al. (2017), a scenario experiment approach was used to examine experience value co-creation process on destination online platforms and showed that, even before travel, customers co-create their experiences. This demonstrates that customer’s are actively participating in their own travel experience before the travel event has even been occurred. Buhalis (2019) supports this by emphasizing that technology empowered tourism experiences support travelers to co-create through-out all stages of travel including pre-travel.

During the second stage co-creation is still pivotal in shaping the consumers choices. Co-creation experiences have a direct impact on the outcome variables (Mathis et al. 2016) meaning that the customers’ active participation during the travel experience affects their overall behaviour (Zhang and Prebensen 2025). In addition, co-creation persists beyond the pre-planning and experiencing stages affecting the overall experience (Lei et al. 2021) which means that co-creation affects the overall experience after the trip has been completed. Co-creation is also vital in shaping the customers’ reflections and feedbacks in the post-travel stage. Grissemann and Stokburger-Sauer (2012) argued that company support and customer satisfaction within the co-creation activities would influence the conformation of post-travel experiences. The level of support and satisfaction given by the firm throughout the co-creation activities during the travel, influences the general post-travel perception and reflections of the customers. Hence:

4.1 Sample and Data Collection

An online self-administered questionnaire survey was utilised to gather samples of influencers’ perceived impact on consumers’ purchasing decisions and customer experience. The data were collected via the Google Forms platform, and the survey link was distributed through social media channels via public posts and direct invitations on Instagram and TikTok users, along with an email sent to an extended network of consumers. A pilot test was carried out, and modifications were made before distributing the questionnaire’s link. The final data set conducted a final usable sample of 538 responses.

The sample for this study was selected using a purposive sampling strategy, a sampling technique guided by specific criteria aligned with the research aims (Bell et al. 2022). Because it was necessary to include participants who follow influencers on social media, a non-probability sampling technique of purposeful sampling (Taherdoost 2016) was employed. As defined by Bougie and Sekaran (2019), purposeful involves selecting information-rich participants who may have been chosen based on factors such as age, gender, status, or experience with the phenomenon. Therefore, employing a purposeful sampling technique was crucial for recruiting participants 1) who followed travel influencers on social media and 2) who had engaged with travel influencer content in the past 6 months, in order to gather the information required to design an accurate survey. To ensure the selection of qualified respondents, two screening questions were asked regarding whether the participants follow an influencer and their level of interaction with the influencer’s activity. In addition to this prior empirical studies in this emerging space (Lou and Yuan 2019) have also relied on non-probability samples of social media users.

4.2 Measures and Measurements

A quantitative survey was conducted using the questionnaire tool to obtain data from social media users, as described at Table 1. Items in the closed-type questionnaire were designed and constructed by adapting from previous studies. The questionnaire consisted of five parts. The first part was related to the influencers’ role, specifically their impact on consumers’ decisions (modified from Ki and Kim 2019; Lee and Eastin 2021). The second part dealt with pre-travel behaviour, which included the procedure of dream and awareness, information seeking and evaluation of alternatives in travel planning and booking procedures (modified from Jimenez-Castillo and Sanchez-Fernandez, 2019; Ki and Kim 2019). The third part covered during-travel behaviour, such as action and recreational activities (adapted from Wang et al. 2012) and post-travel behaviour, which included preferences for revisiting, writing reviews on social media, and travel memorability (adapted from Wang et al. 2012; Zatori et al. 2018). The fourth part dealt with customer experience (adapted from Srivastava and Kaul 2016) and co-creation (modified from Bu et al. 2022) of the respondents modified from the influencers’ perspective. The last part portrayed the respondents’ profile through demographic characteristics (gender, age, educational level, marital status).

All items, except for the demographics, were measured using a seven-point Likert scale, where one indicated “Strongly disagree” and seven indicated “Strongly agree”. The validity and reliability of the research are ensured through measurement scales previously examined. Confirmatory factor analysis with the principal components’ method was performed using SPSS 28.0 to validate the factors of the measurement model. CFA connects factor analysis with structural equation modelling (SEM), resulting in the extraction of the variable Pre-travel behaviour into three factors (K-M-O 0.622; Barlett’s Test of Sphericity 1,129.426, sig 0.000), the variable During travel behaviour into two factors (K-M-O 0.918; Barlett’s Test of Sphericity 1,552,138, sig 0.000), and finally, the Post-travel behaviour into two factors (K-M-O 0.855; Bartlett’s Test of Sphericity 1,441,960, sig 0.000). The Kaiser-Meyer-Olkin (K-M-O) was used to assess the appropriateness of the factor model and sampling adequacy, with values above 0.5 indicating satisfactory factor analysis. Barlett’s Test of Sphericity evaluates the variance equality among the questions and their significance level. Furthermore, in order to test the internal cohesion of the questionnaire’s elements and ensure that the group questions measure the same variable (Howitt and Cramer 2005), the method of reliability alpha was applied. The tool’s reliability was also tested using Cronbach’s Alpha (a) reliability coefficient. The coefficient for the whole questionnaire was found equal to 0.808, which indicates very high reliability at statistical level 0.000 (Hotelling’s T-squared: 2,379.204; F: 61.759; p < 0.000). Regarding the individual construct, high reliability was observed, as detailed in Table 1.

4.3 Methods of Analysis

This study employed Partial Least Squares (PLS) Structural Equation Modeling (SEM) using SmartPLS four software (Ringle et al. 2022). PLS-SEM is considered a robust alternative to covariance-based SEM, shifting the focus from theory testing to predictive modeling with an emphasis on component-based analysis (Chin and Newsted 1999; Reinartz et al. 2009). Unlike traditional methods, PLS maximizes the explained variance of endogenous constructs through iterative least squares regressions, making it suitable for complex models, non-normal data, and smaller sample sizes (Hair et al. 2018). Moreover, PLS can concurrently handle both metric and categorical indicators, allowing for models that include reflective and formative latent variables (Sarstedt et al. 2022).

In this research, PLS-SEM was deemed appropriate due to the inclusion of formative constructs, the complexity of the model as per Hair et al. (2019), and its prevalent use in recent studies across various fields, such as co-creation (e.g. Cambra-Fierro et al. 2017; Cao et al. 2023). Jarvis et al. (2003) established the criteria distinguishing between formative and constructs. This study based on these criteria, characterised the influencers’ role, memorability, customer experience and co-creation constructs as formative based on Soderlund’s (2006) recommendations, and all the others were characterised as reflective.

5.1 Sample Profile

As depicted in Table 2, the sample is balanced concerning gender (50–50 %); almost 50 % of participants are aged between 21 and 40 years old and hold at least a high school degree. Participants worked as employees (42.6 % private and 15.7 % public sector workers), gaining 701–1,200 (40.7 %). The sample is divided into two marital status categories: single (32.2 %) and married (46.3 %). Most of them are the main ones responsible for the family’s buying decisions, who can safely assume they are the influencers’ audience.

5.2 Measurement Model Assessment

This study conducted a comprehensive validity and reliability assessment of all reflective measurement scales. Indicator reliability was evaluated through construct loadings, which were confirmed to be statistically significant via bootstrapping with 5,000 iterations and Student’s t-test (all p-values<0.01). Items with outer loadings below 0.5 were excluded, aligning with Sarstedt et al. (2022), resulting in all loadings exceeding the recommended threshold of 0.7. Reliability and convergent validity were assessed using Cronbach’s Alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE), all surpassing the standard cut-offs – CA and CR above 0.7 (Bagozzi and Yi 1988; Hulland 1999) and AVE above 0.5 (Hair et al. 2018). When first-order scales constitute a second-order construct, indicator evaluation criteria are adjusted; specifically, AVE values may be acceptable if above 0.4 (but below 0.5), provided CR exceeds 0.8 (Rossiter 2002; Ping 2004). The convergent validity of the measurement model was supported by AVE results, as presented in Table 3, confirming the scales’ validity.

The Fornell–Larcker criteria and the Heterotrait-Monotrait Ratio (HTMT) were employed to assess discriminant validity. Table 4 displays the comparison between the square root of AVE for each construct and the correlations among constructs. The results strongly support discriminant validity, as the square roots of AVE along the table’s diagonal exceed the recommended 0.5 threshold (Hair et al. 2018). Additionally, all HTMT values are below 0.85, further confirming discriminant validity in the measurement model (Henseler et al. 2015; Hair et al. 2018).

5.3 Structural Model and Hypotheses Testing

The final model showed a good fit, with several fit indices assessed: χ²/df is 4.928 with p < 0.1; the Normed Fit Index is 0.98, the Standardised Root Mean Square Residual is 0.06, and the Comparative Fit Index is 0.92 (Lohmoller and Lohnoller 1989; Henseler et al. 2015). Additionally, the standardized chi-square falls between one and 5 (Bagozzi and Yi 1988); NFI exceeds 0.90 (Hu and Bentler 1999); SRMS is below 0.08 (Dash and Paul 2021); and the CFI -recommended for samples over 100 observations is above 0.9 (Bentler 1990; Levy and Varela 2003). All meet or exceed the recommended cutoff criteria (Schuberth et al. 2023).

The predictive power of the model was evaluated by analysing the variance explained in the endogenous constructs. When examining the main model through the standardised path coefficients, the influencers’ role (path coefficient 0.569, p < 0.000) explains 32.3 % of pre-travel behaviour, and it has a positive effect supporting the H1. All facets of pre-travel contribute equally to developing the customer journey, except for the “Plan” variable, which has a stronger effect (path coefficient 0.664, p < 0.000), while all paths are significant. The Influencers’ role (path coefficient 0.362, p < 0.000) explains 12.9 % of variables related to travel behaviour. The construct During-Travel Behaviour, a second-order variable combining customers’ action experience and recreational activities, explaining 30.2 % and 30.7 % of the construct, respectively. The significant path from influencers’ role to during travel behaviour ssupports H2. The influencers’ role also directly influences post-travel behaviour (path coefficient 0.095, p = 0.039), confirming H3. The variables of memorability and post-travel behaviour intention explain 57.5 % and 77 % of post-travel behaviour, but the interpretation rate is weak (0.7 %), indicating limited influence at this stage. Customer experience is affected by pre-travel behaviour (path coefficient 0.164, p = 0.041), during travel behaviour (path coefficient 0.345, p < 0.000) and post-travel behaviour (path coefficient 0.089, p = 0.038), supporting H4, H5 and H6. The indirect effects of Influencers’ Role on Customer Experience revealed medium significance through paths Influencers’ Role ->During Travel Behaviour ->Customer Experience and Influencers’ Role ->Pre-Travel Behaviour ->Customer Experience. In contrast, the path Influencers’ Role ->Post Travel Behaviour ->Customer Experience showed a weaker effect. Figure 2 illustrated that the proposed model explains 20.9 % of the variance in the customer experience construct, indicating a medium level of explanatory power.

Figure 2:

PLS results of the main model.

5.4 Moderation Effect

The Co-Creation variable was introduced as a moderator in the initial model. Following Hair et al. (2018), the suitable tests for moderation were conducted, confirming support for H7. Co-creation, functions as a pure moderator of pre-travel behaviour on customer experience, with medium effect size (path coefficient 0.159, p = 0.012) as stated in Η7a. This indicates that the engagement with the influencer prompts direct communication during preparations phases, leading to a stronger customer experience. Co-creation had a significant moderating effect (path coefficient 0.229, p < 0.000) on the relationship between during travel behaviour and customer experience, supporting H7b. Additionally, it moderates the relationship between post-trip behaviour and customer experience (path coefficient 0.166, p = 0.010), confirming H7c.

To evaluate co-creation’s influence further, the R² values for customer experience were compared. Figure 2 illustrates that the main model, incorporating influencers’ role and customer journey stages, explains 20.9 % of the variance in customer experience. Including the interaction effect of co-creation with the customer journey stages, as shown in Figure 3, raised the explained variance to approximately 34.3 %. The variance of customer experience increased R², by adding the interaction, from 0.209 to 0.343. The effect size of these interactions ranged from medium to strong, with an estimated f² of 0.203 (calculated as f² = 0.343−0.209]/[1−0.343] = 0.203), based on the formula and thresholds set by Chin et al. (2003) and Cohen (1988, pp. 407-414). Therefore, the relationship between customer journey stages and customer experience is positively moderated by co-creation, suggesting a positively moderate to strong explanatory power. All findings are summarized in Table 5.

Figure 3:

Interaction model – results for moderation effects.

6.1 Theoretical Discussion

This study confirms that influencers have a significant impact on travelers’ behaviour, especially during the pre-travel stage. Literature highlights their crucial role during the search, planning, and anticipation stages, shaping the overall customer experience (Fotis et al. 2012; Abdullah et al. 2022). Influencer stories influence destination choices and bookings, and Gretzel (2017) suggests they offer a more nuanced view of destinations than traditional marketing methods. Kotler and Keller (2016) associate this stage with the “pre-purchase,” where travelers assess destinations, budgets, and booking options. Psychological factors like environmental awareness also impact behavior at this stage (Chen et al. 2022; He and Filimonsu, 2020; Chon 1990).

During the travel, the influencers impact remains significant, indicating they shape travelers’ experiences and choices in real time. Researchers such as Aluri et al. (2015) and Abdullah et al. (2022) point out that influencers affect tourists’ entertainment, interaction with others and consumption habits. Their presence on platforms, such as Instagram, shapes choices for activities and locations (Wibowo et al. 2020). Their psychological impact affects how travelers interpret their experiences (Wang and Li 2019).

In the post-travel stage, influencers’ impact persists but diminishes. They still influence memory, evaluation, and the desire to share experiences (Arsenis and Chatzopoulou, 2020; Mainolfi et al. 2021). Influencer storytelling shapes the way travelers remember and share their travel experience (Pan et al. 2007; Yuan et al. 2022), reinforcing long-term behavioural effectsr. Even after the journey ends, influencer content continues to shape travelers’ perceptions and future intentions (Izogo et al. 2021; Azeez 2021).

The study suggests that pre-travel behaviour is positively related to customer experience, confirming that research and anticipation influence final satisfaction. According to McDougall and Levesque (2000) and Tsao et al. (2015), perceived value before consumption impacts future travel intentions. Silaban et al. (2022) reinforce the view that influencers shape attitudes and intentions already during the planning phase. Kotler and Keller (2016) note that preparation involves critical choices of cost, convenience and personal preferences, which are influenced by digital content. Hypothesis H5 confirms that experiences during the travel have a significant impact on the final customer experience. Literature shows that influencer stories and images modify even more the perceptions of destinations (Pan et al. 2007; Pai et al. 2020). With the dynamic features of social media (Foroudi et al. 2018; Hays et al. 2013; Tussyadiah and Zach 2015), travelers make decisions in real-time, and influencers’ content enhances participation, engagement, and satisfaction (Cheng et al. 2020), deepening travel immersion. H6 further links post-travel behaviour with customer experience, showing that influencers affect social sharing, emotional responses, and future choices (Arsenis and Chatzopoulou, 2020; Mainolfi et al. 2021; Yuan et al. 2022). Additionally, user-generated content spreads the experience further (Izogo et al. 2021), while the destination’s image remains key for future actions (Azeez 2021).

Hypothesis H7 explores how co-creation influences the relationships between travel stage behaviours and customer experience. The findings reveal a significant strengthening of the relationships at all stages: pre-travel, during travel and post-travel with all results being statistically significant. Customer active participation in creating their experience enhances perceived value and satisfaction, aligning with Xu et al. (2025). As Buhalis (2019) and Mathis et al. (2016) indcate, technology facilitates collective value creation at all stages. Co-creation extends beyond planning, but continues after the experience, influencing reflections and recommendations (Lei et al. 2021). Furthermore, Grissemann and Stokburger-Sauer (2012) demonstrates that provider support and satisfaction enhance co-creation during the post-travel stage. Overall, the results confirm that influencers are more than communication and promotional agents, they are key shapers of the travel experience, with a strong contribution at all stages, especially when the experience involves co-creation with the traveler.

The above findings consolidate and expand the existing theoretical framework. First, the Theory of Planned Behavior (Ajzen 1991) is reaffirmed, which states that attitudes, subjective norms and perceived control influence intentions and ultimately behavours. Influencers, by showing selectively destinations, activities, and stories, indirectly but significantly shape users’ attitudes, social acceptance of their choices, and perceptions of how easily they can enjoy and live similar travels or experiences. Additionally, the Theory of Co-creation (Prahalad and Ramaswamy 2004) is viewed through new dynamics. The tourist experience is no longer a passive consumption but emerges from participatory planning, communication and personal contribution. Travelers do not simply accept a storytelling, they interact with it, comments on it, shares it, lives it, and ultimately transforms it into their own creating it with their footprints. This is especially relevant in social networks, where content consumption and creation blend. This perspective is further supported by the “Uses and Gratifications” theory (Katz et al. 1973), which explains that media users seek content fulfilling personal needs, informational, entertainment, social acceptance, or personal empowerment (Li et al. 2024). Influencers, through genuine and personal voices, meet these needs more directly and emotionally than traditional tourism sources. Travelers are not just inspired by content, but they choose it because it enables connection, aspiration, and participation.

In sum, this study extends co-creation theory by embedding it in a multi-stage customer journey framework, demonstrating how participatory behaviors moderate and enrich each stage of the experience. By situating influencers as active facilitators of co-created experiences, the findings contribute a more holistic behavioral model of influencer engagement in tourism, bridging individual and collective value creation processes.

6.2 Practical Implications

The study’s findings provide clear guidance for all tourism stakeholders. Influencers are encouraged to shift towards a more interactive, authentic and co-creative role, moving from the “digital guide” model to become “digital co-travelers”. By engaging Q&A sessions, participatory polls, call-to-action stories and challenges, they can strengthen the relationship of trust and create meaningful experiences for travelers. The content should be tailored to each stage of the customer journey in order to inspire the users. Thus, at the pre-travel stage, influencers should focus on creating content that is both inspirational and informative, while ensuring that it is easy for audiences to navigate. This may include the development of interactive stories, comparison reels, and curated itineraries. Collaborations with brands should emphasize planning tools and decision-making prompts that actively encourage audience engagement. During the Travel stage, real-time interaction is essential. Influencers could have the opportunity to host live question-and-answer sessions, share location, specific content that highlights “hidden gems,” and promote user-generated content (UGC). These approaches enhance co-creation and facilitates memory retention among the audience. In the post-travel stage, influencers may encourage reflective practices through storytelling challenges, recap videos, or hashtag campaign, to extend their message impact. Platforms such as TikTok and Instagram Reels are particularly effective for these initiatives due to their short-form, high-engagement formats.

From the perspective of tourism brands and destination organizations, the study recommends a move from mere sponsorship to co-creation strategies. Collaborations should involve joint content creation, co-designed experiences, inclusion of guest voices and ongoing community engagement. These practices boost authentic communication and foster long-term loyalty and emotional bonds. The travel preparation phase, the pre-travel stage, is especially crucial because the influencer’s impact is at its peak, as expectations and travel intentions are being formed. The content should be “catchy”, inspiring and experience-oriented, using short videos, interactive tools, personal stories, challenges and direct booking options to influence consumer decisions effectively.

Furthermore, destination marketing organizations (DMOs) and tourism brands can operationalize co-creation strategies by incorporating interactive digital tools such as collaborative itinerary builders, gamified storytelling challenges, and dedicated community platforms for travelers to share and curate experiences collectively. For example, integrating features like real-time polls on Instagram Stories, hashtag-driven UGC campaigns, and “choose-your-own-adventure” content formats can empower travelers to actively shape narratives while simultaneously generating authentic promotional content. Such practices align with the study’s findings, emphasizing the strategic value of co-creation not only for influencers but also for broader tourism stakeholders.

Finally, the study underscores the importance of audience segmentation and personalized engagement strategies in influencer marketing. Since the influence of digital narratives varies across the pre-travel, during-travel, and post-travel stages, tourism stakeholders should tailor content and messaging to match the customer’s current stage of the journey. For instance, pre-travel audiences may respond better to inspirational and planning-oriented content, while during-travel audiences prioritize immediacy, authenticity, and practical tips. Post-travel segments, on the other hand, can be engaged through storytelling challenges or campaigns that prompt reflection and advocacy. Leveraging data-driven segmentation to align influencer content with these distinct needs can enhance effectiveness and foster stronger emotional connections with travelers.

6.3 Limitations and Future Research

The study also recognises several limitations, which could be converted into future research options. The operationalisation of co-creation focused on self-reported interactive behaviours, including actions such as commenting, reposting, and engaging with influencer-led prompts. These behaviours reflect subjective perceptions of participation and are consistent with prior literature on consumer–brand co-creation in social media contexts. However, they do not capture objective platform-level data such as click-through rates, view duration, or algorithmically tracked interactions. This distinction should be considered when interpreting the moderation effects, as the perceived intensity of co-creation may not fully align with behavioural trace data available to platforms or brands. Future research could incorporate digital trace analytics to validate or extend the current findings.

Our findings should be interpreted in light of several limitations, including potential bias related to the research design. Consistent with the approach of other studies (Baumgartner and Weijters 2021; Podsakoff et al. 2003), we used procedures such as anonymity, varied scale endpoints, and separating constructs in the questionnaire to limit CMB. Nevertheless, we acknowledge that residual CMB may persist, and recommend that future research use multi-source, temporal, or experimental designs to address this limitation more robustly.

Although the model yields important insights, its explanatory power for customer experience remains moderate (R² = 0.209 without co-creation). This finding suggests that customer experience is influenced by factors beyond digital interactions, including service quality, pricing, prior relationships with the destination, cultural background or the traveler’s personal traits. Furthermore, self-administered questionnaires, despite careful design, are limited by factors like memory, biases and the desire for social acceptance. The lack of experimental research and digital data (as mentioned above clicks, viewing time, engagement metrics) restricts causal analysis. Future research should combine digital platform data with qualitative insights and explore variations across cultural groups or generations (such as Gen Z vs Millennials).

Further research is needed to understand the characteristics of brand-influencer collaborations and co-creation within this shared action framework, such as examining if the same influencers on various platforms co-create value independently. Future research could incorporate digital trace analytics, such as click-through rates, view duration, and real-time engagement metrics, to validate or extend the current findings. Combining self-reported co-creation behaviors with objective behavioral data would enable researchers and practitioners to more accurately measure the intensity and quality of co-creation, offering actionable insights for campaign optimization. Such integration could also provide managers with measurable KPIs to monitor and refine influencer strategies in dynamic digital environments.