How flow drives engagement and loyalty Open Access

Online transactions are central to the contemporary travel and tourism industry, shaping consumer behavior and influencing the broader economic landscape (Cai et al., 2024). Shamim et al. (2025) argue that the sector’s digital growth is driven by the convenience and variety offered by online platforms, coupled with evolving consumer preferences toward digital retail experiences. Accordingly, a 2023 Statista report highlighted a sharp post-pandemic rebound in the online travel market, with revenues rising from US$225bn in 2020 to nearly US$600bn in 2023. This upward trajectory is projected to reach US$800bn by 2028, with much of this growth linked to an increased reliance on digital reservation platforms and changing post-COVID consumer behaviors, which predominantly favor online purchasing (Gupta and Mukherjee, 2022). In this context, social shopping, a hybrid form of e-commerce incorporating user-generated content, social interaction(s) and real-time engagement, has emerged as a powerful mechanism capable of influencing tourism purchase decisions. Tourists are increasingly drawn to online platforms that offer product diversity, personalized recommendations and electronic word-of-mouth (eWoM), and research suggests that digital-savvy consumers are often more informed, make better use of online tourism services (Farmaki et al., 2021; Wang et al., 2024), and typically compare travel products across platforms and providers, enhancing choice and encouraging impulsive buying behaviors (Yang et al., 2024; Xu et al., 2024). However, despite the increasing relevance of social shopping in tourism, research rarely investigates how psychological and technological factors combine to shape satisfaction and continued usage therein.

Specifically, this study contends that the relationship(s) between flow state, perceived usefulness, engagement, satisfaction and continuance intentions within the online tourism shopping context remain underexplored. While all have previously been studied in isolation, little empirical research has integrated each within the tourism domain to explain how consumers engage with social shopping platforms. For example, while Wu et al. (2020) demonstrated that flow (conceptualized as a psychological state characterized by immersive and enjoyable experiences) plays a pivotal role in shaping digital behavior, mediating the relationship between website quality and customer satisfaction, its influence within tourism-specific social shopping platforms, particularly in relation to post-purchase behaviors, remains overlooked. Thus, with online channels increasingly dominating the consumer journey, understanding the impact flow can have on tourism consumers’ social shopping experiences is vital for platform designers and marketers alike. Accordingly, this study addresses the following research questions:

In investigating the above, this study extends hospitality and tourism literature by integrating flow theory and expectation-confirmation model (ECM) (alongside the moderating impact of informational influence) to develop a comprehensive framework for understanding online consumer behavior. Flow theory explains the immersive, enjoyable experiences that promote deep engagement with digital platforms (Buhalis et al., 2022), while ECM elucidates how satisfaction and continuance intentions emerge from confirmed expectations. Prior hospitality studies show that perceived flow enhances digital service satisfaction and loyalty (Law et al., 2022), and that expectation-confirmation processes are central to post-adoption behaviors in online booking and service applications. By linking flow to perceived usefulness and engagement (and introducing informational influence as a moderating factor), this study offers a holistic model that captures the psychological and communal dimensions of social shopping in online tourism shopping contexts.

From a practical perspective, findings herein can assist platform designers, hoteliers and industry marketers to optimize digital customer experiences. Enhancing user interface design, promoting tailored engagement strategies and leveraging social testimonies through peer reviews can elevate flow experiences and perceived usefulness (Bilgihan et al., 2014; Taheri et al., 2017). These efforts can improve satisfaction and foster long-term consumer loyalty across booking platforms, hotel brand apps and integrated travel services. Ultimately, this study therefore addresses a critical gap in the hospitality e-commerce literature by exploring how digital interaction, emotional immersion and social influence intersect to shape social shopping outcomes (Dedeoğlu et al., 2020). It thus contributes both theoretical insights and practical strategies for designing next-generation tourism and hospitality shopping experiences that are engaging, trustworthy and satisfaction-driven.

Csikszentmihalyi’s (1990) work on flow theory proposes that experiential enjoyment is optimized when one’s ability aligns with the challenge posed by an activity (Taheri et al., 2017). Therefore, dimensions such as task skill (one’s perceived competence with the task) and task challenge (the task’s perceived difficulty level) are central determinants of flow, with both critical for measuring users’ flow experience(s) (Taheri et al., 2017). These dimensions interact, forming an equilibrium point that determines whether flow will occur. Flow theory thus offers a suitable framework for analyzing interactive digital social shopping environments, such as online tourism platforms, where achieving flow state is affected by both design and psychological elements, including interactivity, authenticity and user experience. Accordingly, the characteristics of digital platforms greatly determine flow experiences. For example, Chen et al. (2025) demonstrate that aspects of interactivity and authenticity in livestreaming e-commerce can stimulate consumer purchase intentions via flow experience and trust. This highlights that, to arouse flow, online tourism platforms must incorporate aspects of real-world interaction that appeal to consumers’ expectations and hence reduce offline-online gaps (Arghashi and Yuksel, 2022).

Real-world social presence, easily lost in digital contexts, can be optimized through adopting measures which facilitate user interactions and hence improve the overall shopping experience (Wang et al., 2024). Studies contend that consumers experiencing flow state increase purchasing actions (Ruangkanjanases et al., 2024). Thus, flow is important for those seeking tourism products via online platforms. Extending literature, this study evaluates whether flow stimulates engagement during the online tourism shopping process and if it can increase consumer perceptions of the usefulness of social shopping. Doing so, we examine whether this ultimately stimulates consumer satisfaction and continuance behaviors. Thus, echoing the theoretical framework explained prior, this study examines the impact of flow on consumer behavior in the tourism context via two central mechanisms: engagement and perceived usefulness. As such, it deploys flow theory with specific emphasis on cognitive engagement and perceived usefulness.

Online tourism bookings are often shaped by goal-oriented information-seeking behaviors and perceptions of usefulness, which together facilitate decision-making (Carlson et al., 2017; Sinha et al., 2025). Consumers experiencing flow benefit from greater cognitive immersion in a digital platform, with this absorption strengthening their sense of control and competence. This psychological state shapes consumers’ perceptions of platform utility (Arghashi and Yuksel, 2022). Elements such as risk perception(s) and entertainment are commonly considered in literature as necessary prerequisites for flow, meaning they overlap with the structural components of flow (Farmaki et al., 2021; Lee et al., 2019). The social interaction dimension is also conceptually represented through the moderating role of informational influence in the model operationalized in this study. Therefore, deploying two variables (engagement and perceived usefulness) as primary mechanisms capable of affecting behavioral outcomes was considered theoretically appropriate and fully aligned with this study’s aim (Figure 1).

Despite the inherent digital nature of online tourism shopping, explaining consumer behavior therein solely through technology acceptance processes is insufficient (McLean and Wilson, 2019). Instead, the ECM can clarify the relationship between user repurchase intentions, satisfaction, perceived performance and expectations (Wu et al., 2020) and is thus notable in its ability to explain how consumers interact with online tourism products (Yu et al., 2024). Bhattacherjee (2001a) provided a framework to examine the relationships between the concepts of expectation and confirmation, with the resultant ECM used to explain the process of updating consumers’ expectations after obtaining new information about their own and others’ behaviors. This indicates that users’ expectations of services are iteratively updated according to new information and experiences (Tran-Thi-My et al., 2025). Yet, ECM is also deployed to understand how evaluation processes can shape repurchase decisions. Before any purchase, consumers form initial expectations about the product/service based on their current knowledge and previous experiences (Shamim et al., 2025). However, after using the product/service, consumers evaluate its performance by comparing perceived performance with initial expectations (Lee and Kim, 2020). Finally, the extent to which performance therein meets consumers’ initial expectations is examined to determine its likelihood of stimulating satisfaction and repurchase intentions.

Thus, while flow is crucial in stimulating interactions during the online shopping process, how this positive experience impacts upon behavioral outcomes is shaped by users’ post-experience evaluations (Bilgihan et al., 2015). As such, ECM offers a powerful theoretical framework for explaining consumers’ post-experience satisfaction levels and continued usage intentions (Oh et al., 2022) as, while the technology acceptance model (TAM) primarily focuses on the initial stage of usage intention, ECM offers a functional approach to explaining more advanced continuation behaviors.

Further, social information sharing can cause consumer expectations to shift dynamically in digital environments (Cruz-Cárdenas et al., 2025), and ECM’s emphasis on expectation verification processes becomes a theoretical necessity in explaining the transformation of flow experience(s) into behavior(s) within social shopping contexts. Therefore, by combining flow theory and ECM, this study is capable of explaining the relationship between consumer satisfaction and the adoption of technology, with implications for expanding extant understanding of online tourism shopping motivations cognizant of the ongoing shift in travel and tourism purchasing behaviors, alongside the established importance of digital technologies in supporting consumers’ pre-trip information seeking and general decision-making. As such, to better understand the nuanced nature of consumers’ online tourism-related social shopping behaviors, buttressed by the ECM, it is important to explore how flow shapes consumer experience(s), alongside its influence on purchasing processes enacted therein.

Recognizing both the dynamics that predicate the concept of flow, alongside its underlying dimensions, is crucial for understanding consumer engagement more generally (da Silva de Matos et al., 2021), with the extent to which flow affects online tourism receiving significant attention over the last decade (Taheri et al., 2017). For the hospitality and tourism industries, Bilgihan et al. (2014) propose that flow theory can explain customer engagement, ultimately contending that flow facilitates experiences embodied by intense participation in an activity (Jozani et al., 2020). Similarly, Kim et al. (2020) found that flow affects online engagement for restaurant customers, while Xu et al. (2024) investigated the effects of short videos used in the tourism industry, concluding that interacting with video content encourages consumers to enter a flow state during participation.

The relationship between flow state and perceived usefulness in online tourism shopping has also been established. For example, Wu et al. (2024) demonstrated that flow influences perceived usefulness for metaverse tourism intentions, while Hyun et al. (2022) reported similar effects for online retailers. As such, we propose that flow state positively affects perceived usefulness in varied e-commerce environments (Dang et al., 2014; Ruangkanjanases et al., 2024), including online tourism shopping. The dynamics between flow state, engagement and perceived usefulness in social shopping environments is thus a research area ripe for exploration as maximizing flow experiences can increase consumer engagement and perceived usefulness, leading to heightened satisfaction and purchase intentions. Thus, as digital platforms continue to develop, further research is required to refine extant understanding of online social shopping experiences. Accordingly, we propose:

With the contemporaneous ubiquity of online shopping evident within the hospitality and tourism industry, the extent to which perceived usefulness may be effective in increasing consumers’ online tourism purchasing actions and whether this effect will increase their engagement in turn, is examined herein. Arghashi and Yuksel (2022) determined that perceived usefulness has a regulatory effect on engagement when investigating augmented reality users. Similarly, McLean and Wilson (2019) state that perceived usefulness influences engagement in retailers’ interactions with customers through mobile applications. Additionally, Hussein and Hassan (2017) found that perceived usefulness affects customer engagement through attitude in their study on social media users. Thus, while there is considerable research on perceived usefulness in online shopping from a general perspective, knowledge gaps remain for some distinctive consumer groups, such as those engaging in online tourism shopping. Therefore, we postulate:

While engagement refers to the cognitive, emotional and behavioral interaction(s) that customers establish with a social shopping platform, continuance intentions capture whether they intend to continue using that platform in the future (Zhou et al., 2022). Therefore, while engagement reflects a more present-oriented level of interaction, continuance intention represents future-oriented behaviors and typically emerges due to positive engagement. Accordingly, Vo et al. (2020) determined that there is a relationship between customer engagement and customer satisfaction when investigating how consumers use hotel websites. Further, Bouchriha et al. (2024) examined the relationship between customer engagement and satisfaction to explain customer−employee interactions on online tourism platforms, demonstrating how value creation behaviors shape the relationship between customer engagement and satisfaction (Liang et al., 2011). Similarly, Seyfi et al. (2024) found that customer engagement in the heritage tourism context impacts behavioral intentions. Comparable findings have been observed when tourists assess destination brands (Chen and Li, 2020). Research also shows that higher levels of engagement stimulate stronger continuance intentions among tourists (Zhou et al., 2022). Additionally, Tzeng et al. (2021) highlights that shopping atmosphere and service orientation directly influence tourists’ expectations and experiences. Translating these findings into the social shopping context, we contend that the presence of an interesting social context can nurture a positive atmosphere for online tourism shopping, leading to increased satisfaction. This supports Lee and Choi (2020), who determined that shopping and destination attributes have asymmetrical impacts on satisfaction in the tourism context. Therefore, the incorporation of social shopping features could alter destination perceptions, enhance satisfaction and stimulate continuance intentions. Thus, we propose:

Arghashi and Yuksel (2022) highlight the significant role of perceived usefulness in enhancing customer satisfaction within the online tourism context. Recent literature has explored the link between perceived usefulness and customer satisfaction through frameworks such as the technology acceptance model and ECM, with Liu et al. (2021) exploring this relationship via the latter. Similarly, Filieri et al. (2021) found that perceived usefulness affects consumers’ intention(s) to continue using online tourism platforms like TripAdvisor, with Choi et al. (2019), who studied continuance intentions for mobile travel applications, reaching similar conclusions. Further, Zhou et al. (2022), confirmed a connection between perceived usefulness and consumers’ continuance intentions. Additionally, technology acceptance research posits that perceived ease of use augments perceived usefulness in creating a unified model of online shopping behavior (Tran-Thi-My et al., 2025). A contrary argument, however, is raised by Asghar et al. (2024), who contend that perceived usefulness may not have any significant influence on fast-food restaurant consumer behavior. Nevertheless, we hypothesize:

Flow state, conceptualized by Csikszentmihalyi in 1990 but examined across various contexts since, can explain online shopping behaviors specific to the tourism and hospitality industries (Chen et al., 2025). For example, Karasakal and Albayrak (2022) examined flow within the context of adventure tourism, emphasizing that flow has an important role in stimulating satisfaction. Similarly, others conclude that flow has a direct positive effect on tourists’ satisfaction (da Silva de Matos et al., 2021), with Lee and Kim (2020) extending this into the domain of health tourism. We expect a similar relationship to emerge specific to online tourism shopping.

Flow state can thus enable consumers to repeat behaviors during the purchasing process (Wu et al., 2020), with the hospitality and tourism industries particularly adept at encouraging consumers who are hooked by flow to exhibit continuation intentions. Accordingly, Wu et al. (2021) suggested that flow state affects customer intentions for those engaging in museum visits using VR. Moreover, literature has stressed the direct relationship between flow and consumer behavior consequences, yet the function of social interactions in facilitating flow in the context of online shopping is under-investigated. With the emergence of social shopping sites, it is necessary to conduct research into how social interactions shape the flow experience and, in turn, facilitate satisfaction and continuance intentions. As such, we propose:

When consumers engage with an event or action, this increases the extent to which flow state shapes their satisfaction with that event/action post-situ. Capturing this, Filep (2008) demonstrates that flow state plays an important role in stimulating tourist satisfaction for students studying abroad and that engagement strengthens this relationship. Similarly, exploring augmented reality platforms, Arghashi and Yuksel (2022) found that engagement mediates the relationship between flow state and consumers’ behavioral intentions and attitudes. As such, with satisfaction an established precursor of consumer attitudes, it may be similarly affected (Mansoor et al., 2025). Engagement is thus likely to mediate the relationship between flow state and satisfaction. Therefore, we hypothesize:

Online shopping has become ubiquitous due to continuous technological advancements (Fu et al., 2020), and the tourism and hospitality industry has experienced a corresponding increase in its adoption (Chen and Li, 2020). An underlying stimulant of online shopping stems from consumers becoming immersed in flow therein, with this enhanced by the perceived usefulness of, and engagement with, online shopping experiences. Arghashi and Yuksel (2022) support this notion, demonstrating that perceived usefulness mediates the relationship between flow state and behavioral intentions for augmented reality users. Accordingly, the role of perceived usefulness in explaining the behaviors of consumers engaged in online tourism shopping is examined herein. Customer satisfaction with online shopping is often linked to perceived service adequacy (Tzeng et al., 2021) as, when customers experience flow during the online purchasing process, they maintain positive perceptions of both the experience and the brand (Carlson et al., 2017), stimulating greater satisfaction (Wu et al., 2020).

The user-friendliness of the online shopping environment/platform also contributes to satisfaction (Wu et al., 2021). Easily navigable websites boost flow state, ultimately increasing consumer satisfaction with the service. Continuance intention, or repeated usage of online shopping websites, is greatly affected by flow state and perceived satisfaction, which in turn are related to perceived usefulness. Huang et al. (2023) demonstrate how social presence can enhance immersive experiences for online shopping sessions, where the greater the extent to which users perceive a rich, interactive atmosphere, the greater level of satisfaction they experience, therefore stimulating intentions to continue to shop. The mediating role of perceived usefulness becomes essential in such scenarios; as both an antecedent and outcome, it raises the satisfaction levels of users and increases the possibility of continued use of the platform. Thus, we propose:

For many contemporary businesses, social media has become the dominant communication channel, allowing for frequent and near-instant updates to marketing and promotional materials (Yu et al., 2024). It is thus increasingly favored over traditional media campaigns by both sides of the firm-consumer dyad, as it is considered more capable of fostering engagement, with this further supported by recent upticks in AI integration (Castillo et al., 2021). For e-commerce, those caught up in the flow of shopping experience higher engagement, with the presentation of information on social media enhancing this effect (Kim et al., 2020). This aligns with Informational Social Influence theory, which suggests that people observe others’ actions and modify their behavior(s) accordingly (Fu et al., 2020). Thus, informational influence may moderate the relationship between flow and engagement during online shopping. Customer engagement and perceived usefulness are influenced by the information and experiences customers encounter while shopping online (Arghashi and Yuksel, 2022). Therefore, information and experience can combine to increase both engagement (Hyun et al., 2022) and perceived usefulness (Kucukusta et al., 2015). Accordingly, when information is limited, consumers are less likely to engage with or find online shopping useful compared to those with more information. Several studies highlight the significance of perceived enjoyment and perceived ease of use in influencing online shopping behavior, but the role of informational influence as a dual mediator influencing both engagement and perceived usefulness remains overlooked. Yet, some studies (Mohammadi and Dickson, 2024; Wang et al., 2024) argue the changing nature of consumer experience and satisfaction depends on perceived risks and enjoyment, with this encouraging deeper investigation into how informational cues can shift perceived enjoyment over-and-above conventional engagement measures. Thus:

Figure 1 visually demonstrates the conceptual model.

This study used a self-reported questionnaire distributed over a 48-h period via Prolific, an online platform renowned for its user-friendly interface, fast response times and rigorous recruitment standards (Torres et al., 2024). Prolific ensures high-quality recruitment by implementing background checks using filter questions, validating prior participation records and applying timeout mechanisms to address delayed responses. The survey targeted online service customers in the UK who had previously purchased tourism and hospitality products through platforms such as booking.com and Expedia. To maintain data quality, Prolific used safeguards to prevent duplicate participation, including IP monitoring, unique questionnaire links and access codes. The instrument incorporated attention-check questions, requiring participants to follow specific instructions or provide predetermined responses to filter out inattentive or automated answers. Overall, 830 responses were collected. Response times were monitored to identify excessively rapid or slow completions, which were flagged as potential disengagement indicators. Following quality control procedures, e.g. removing cases that failed attention checks or those with inconsistent/poor-quality answers, 800 responses were retained for analysis.

Common method bias (CMB) was assessed following Podsakoff et al. (2003) using established constructs, anonymized responses, and separating dependent/independent variables. Harman’s single-factor test showed a single factor accounted for 30.17% of variance (Eigenvalue: 0.801), with a Kaiser–Meyer–Olkin value of 0.890 and significant Bartlett’s test (p < 0.000). Echoing Hair et al. (2017), common method factor analysis revealed average independent variance of 0.61 versus method-based variance of 0.015 (40:1). Nonsignificant factor loadings confirmed CMB was not a concern.

This research was conducted in strict adherence to institutional guidelines governing studies involving human participants, with approval obtained from the institution of one author prior to data collection. All respondents received detailed information sheets outlining the study’s objectives, procedures, data usage policies and their rights as participants. Informed consent was obtained electronically, with participants explicitly confirming their understanding and agreement regarding: (a) the purpose and scope of the study, (b) the confidentiality and anonymity of their responses, (c) the secure handling and storage of their data solely for academic purposes and (d) their right to withdraw from the study at any point without penalty. Although this study did not involve high-risk interventions, safeguards were implemented to ensure participant well-being. For example, any interactive or immersive components involving digital media and websites were carefully selected to suit the general population, with participants encouraged to take breaks to minimize cognitive fatigue. Data collection via Prolific included built-in checks for attention, consent and data integrity, further ensuring ethical compliance and participant safety throughout the process.

The survey required 10 min to complete, yielding 800 valid responses. The sample consisted of 53.2% male respondents, with age distribution showing: 18–25 years (21%), 26–35 years (30.2%), 36–45 years (34.8%) and 46+ years (20%). Regarding education, 8% of respondents had less than a high school diploma, 31.7% held a high school diploma/GCSE and 60.3% attained higher education degrees. For the education-level item, we recorded qualifications using the UK (England) measure “GCSE”. However, as this term may be unfamiliar to international readers, we reported it alongside the equivalent term “high school diploma” to ensure clarity while preserving accuracy.

All measures stemmed from extant studies and were rated on a five-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). We assessed perceived usefulness of social shopping using three items adapted from Dang et al. (2014). Satisfaction with social shopping was measured using a three-item scale from Liang et al. (2011). The higher-order construct flow state included two dimensions: task skill and task challenge, each measured via a four-item scale from Wu et al. (2020). Continuance intention of social shopping was evaluated using a three-item scale from Bhattacherjee (2001b), while engagement was measured with four items from Jozani et al. (2020). Informational influence was assessed via Mangleburg et al.’s (2004) four-item scale.

To evaluate the conceptual model, we used partial least squares structural equation modeling (PLS-SEM). Per Hair et al. (2017), PLS modeling is particularly effective for analyzing complex models as it estimates subsets of parameters iteratively rather than simultaneously, unlike covariance-based SEM. We incorporated a combination of reflective and higher-order constructs herein, bolstered by PLS-SEM’s ability to handle reflective, formative and higher-order constructs (Hair et al., 2017). This technique is well-suited for data sets of normal and non-normal distribution. The complexity of our model and the presence of non-normal data (skewness and/or kurtosis exceeding ±3) necessitated SmartPLS 4.0 for analysis (Hair et al., 2010). Nonparametric bootstrapping was conducted with 800 cases and 5,000 randomly generated subsamples to ensure robust results.

The measurement model was evaluated by examining construct reliability, convergent validity and discriminant validity for first-order variables (Hair et al., 2017). Construct reliability for first-order reflective constructs was assessed via composite reliability (CR), Cronbach’s alpha (α) and Dijkstra-Henseler’s rho (ρA). All CR, α and ρA values exceeded the threshold (>0.70), confirming construct reliability (Table 1). The square roots of the average variance extracted (AVE) for first-order constructs surpassed corresponding cross-correlations (Table 2). Additionally, AVE values were >0.50 (Table 1). All factor loadings exceeded 0.60 and were statistically significant with substantial t-values (Table 1). Discriminant validity was assessed via heterotrait-monotrait ratio of correlation (HTMT). HTMT values for first-order reflective constructs were below the cut-off (0.85), confirming discriminant validity.

For higher-order constructs, evaluation was conducted by analyzing weights of first-order constructs, their significance and multicollinearity indicators (Hair et al., 2017). Weights of all sub-dimensions of the higher-order construct were statistically significant (p < 0.05), with t-values >1.96, indicating significance (at 0.05). Weights of first-order constructs were >0.1 (i.e. Task skill = 0.315; Task challenge = 0.389) (Hair et al., 2017). Multicollinearity was assessed using variance inflation factors (VIF), which were <5 for each dimension (i.e. Task skill = 2.135; Task challenge = 2.349) (Hair et al., 2017). Thus, there was no evidence of multicollinearity.

The standardized root mean square residual (SRMR) was 0.065, falling below the recommended value (0.08) (Hair et al., 2017). Normed fit index (NFI) demonstrated a value of 0.92, exceeding the acceptable criterion (0.9). Tables 3 and 4 indicate the results of hypothesis testing for direct and indirect effects. Following Hair et al. (2010), Cohen’s effect sizes (f²) signify 0.01 (small), 0.06 (medium) and 0.14 (large) effects within SEM. All paths exceeded the medium effect size. Mediation analysis was guided via bootstrapping (Hair et al., 2017) (Table 4). Further, 95% confidence interval (CI) of parameter estimates (5,000 resamples) was applied.

This study hypothesized informational influence would moderate the relationship between flow state and engagement (H14) and between flow state and perceived usefulness of social shopping (H15). Following Chin et al. (2003), the PLS-SEM product-indicator approach was used. This is appropriate for complex models involving latent variables, effectively handling moderation analysis (Figure 1). For the interaction effect of informational influence on the relationship between flow state and engagement, the path coefficient was β = 0.322, with t-value (7.298) and p-value (<0.001). This indicates a significant moderating effect, suggesting that the impact of flow state on engagement depends on the level of informational influence. Specifically, greater informational influence enhances the positive relationship between flow state and engagement. Similarly, for the relationship between flow state and perceived usefulness of social shopping, the interaction effect yielded a path coefficient of β = 0.341, a t-value of 11.022 and a p-value < 0.001. This also demonstrates a significant moderating effect, revealing that higher informational influence amplifies the positive impact of flow state on perceived usefulness in social shopping contexts.

As outlined in Table 3, our findings confirm that flow positively impacts consumer engagement, supporting H1. Experiencing a flow state stimulates deep engagement, where consumers become fully immersed in the task at hand. For tourism shopping platforms, this deep engagement during browsing, planning and exploring travel options makes shopping more enjoyable and enhances the overall experience. This aligns with Carlson et al. (2017), who argue flow increases emotional investment, making shopping feel more personalized and enjoyable. For tourism, this emotional investment is often tied to a desire to travel and explore new destinations. Thus, when consumers experience flow, they intuitively interact with a platform, and this boosts perceptions of how well-designed and useful that platform is, supporting H2. Flow also enhances user focus, allowing consumers to efficiently process information, compare destinations, book accommodation or customize itineraries. This perceived efficiency strengthens their belief that the platform is useful in achieving their goals, consistent with Hyun et al. (2022).

Table 3 also highlights that perceived usefulness exerts the strongest positive influence on consumer engagement, supporting H3. When platforms are considered useful, this creates a positive feedback loop, where consumers feel satisfied, confident and motivated to engage more deeply with that platform, enhancing their shopping experience (Arghashi and Yuksel, 2022). This, in turn, increases satisfaction with social shopping and strengthens their intention to continue using the platform, supporting H4 and H5. This suggests that active consumer engagement fosters an emotional connection with the platform, further enhancing the overall shopping experience as enjoyment and fulfillment derived from interacting with content, reviews and recommendations therein increase user satisfaction. This emotional connection encourages repeat visits, making social shopping a popular choice for future tourism-related purchases, as confirmed elsewhere (Busalim and Ghabban, 2021; Yu et al., 2024). Additionally, the perceived usefulness of social shopping is positively linked to satisfaction and continued platform use, supporting H6 and H7. This emphasizes that consumers value platforms they perceive as useful, whether through better prices, personal recommendations or access to a vibrant community, all of which enhance satisfaction. High perceived value motivates consumers to continue using the platform, believing it offers a clear return on their time and effort, echoing Busalim et al. (2024). Table 3 again demonstrates that flow state has the strongest positive impact on satisfaction and continued use of social shopping, supporting H8 and H9. The intuitive navigation and seamless interactions that stimulate flow make shopping more enjoyable and efficient, increasing overall satisfaction. Accordingly, customers may return to platforms that offer frustration-free experiences, strengthening their intention to continue social shopping, echoing Mohammadi and Dickson (2024) and Wu et al. (2020).

Following Hosen et al. (2024), we also conducted a mediating effect test. Per Table 4, results confirm that engagement acts as a mediator, enhancing the positive effect of flow state on satisfaction and continued intention with social shopping, supporting H10 and H11. When consumers experience flow, they are more likely to engage with social features such as reviews, recommendations or discussions. Active participation fosters a sense of community and social bonding, which in turn boosts satisfaction by making the shopping experience more engaging, echoing Mohammadi and Dickson (2024). Similarly, our findings show that perceived usefulness serves as a mediator between engagement, flow state and continuance intentions. This supports H12 and H13, indicating that when consumers perceive a platform as useful, they feel more confident in their buying decisions, as the platform provides relevant information. This confidence strengthens their attachment to the platform and increases their intention to continue using it, consistent with Busalim and Ghabban (2021) and Carlson et al. (2017). Finally, supporting H14 and H15, our findings show that informational influence strengthens the relationship between flow, engagement and perceived usefulness. Extending Mangleburg et al. (2004), Shamim et al. (2025) and Wang et al. (2024), we find that high-quality, credible information amplifies the positive effects of flow and engagement, ensuring that consumers perceive the platform as both enjoyable and valuable for online tourism shopping.

Viewed through a lens combining flow theory and ECM, our findings offer theoretical insight that enhances understanding of online tourism-related social shopping behaviors. Per flow theory, individuals achieve an optimal state of engagement and enjoyment when fully immersed in an activity. We reveal that this holds true for online tourism shopping, where the design and interactivity of such platforms are key to enhancing user experiences. Such improvements can stimulate greater engagement, increase perceived usefulness and, ultimately, intensify customer satisfaction and loyalty. Accordingly, our findings extend Carlson et al. (2017) and Mohammadi and Dickson (2024), while echoing Kucukusta et al. (2015), who identified perceived usefulness as a key determinant in online tourism booking behavior(s), suggesting consumers are more likely to engage with platforms they find “useful.”

Similarly, ECM provides a framework for understanding how initial expectations of online tourism services impact consumer satisfaction and future behavioral intentions. Per ECM, if customer expectations are met/exceeded, they are more likely to be satisfied, which increases the likelihood of continued platform use (Wu et al., 2020). Our findings thus align with Shamim et al. (2025), who emphasized the importance of engagement, perceived usefulness and trust in shaping customer expectations and satisfaction within the online tourism shopping context. Further interactions between flow state and expectation-confirmation are evident in how customer engagement creates a positive feedback loop. The more immersed customers become in well-designed tourism platforms (experiencing flow), the greater their satisfaction, which strengthens their intention to return for future purchases/bookings (Wu et al., 2020). This echoes Hyun et al. (2022) and Mohammadi and Dickson (2024), who found that tourists’ attention to online tourism product information significantly influences their purchase intentions, enhancing flow and stimulating satisfaction. We can thus summarize that our assimilation of flow theory and ECM proffers insight into how consumer involvement, perceived usefulness, satisfaction and continued intention interact within online tourism social shopping contexts. Finally, incorporating informational influence into our conceptual model strengthened our analysis by further demonstrating how the social components of consumer behavior can influence decision-making and involvement for online tourism marketplaces.

This study offers practical implications for online tourism shopping platform managers. By optimizing factors such as flow experience, perceived usefulness, informational influence and customer satisfaction, companies can foster an environment that promotes sustained engagement and customer loyalty. This strategic approach not only enhances individual experiences but also impacts the commercial success and long-term sustainability of such platforms. Achieving a state of flow (characterized by complete immersion in an activity) is critical to success for online shopping environments, as when customers experience flow, they are more likely to engage deeply with content, which can lead to increased satisfaction and stronger purchase intentions. To facilitate optimal flow state(s), platforms should focus on designing intuitive, user-friendly interfaces that minimize distractions while offering highly immersive experiences. This includes incorporating engaging visuals, seamless navigation and responsive interactions.

Moreover, to further enhance engagement through personalization, platforms should implement tailored recommendations based on user preferences, search history and behaviors, with increased AI integration capable of strengthening innovations therein. Importantly, incorporating interactive features such as gamification, chatbots and social sharing can foster deeper interaction and, thus, engagement. Accordingly, the quality of online shopping atmospheres, such as user interface design and interactivity, are key to stimulating user involvement. Our results indicate that well-structured interfaces that allow for easy navigation and interesting content can enhance user retention and lead to more frequent use of the platform. Additionally, perceived usefulness has a significant impact on a consumer’s intention to use tourism platforms, indicating technical features should be attuned to user expectations for greater overall satisfaction. Therefore, online tourism platforms should carefully curate their offerings in line with consumer expectations to successfully engage users and stimulate ongoing patronage.

While this study offers theoretical and practical insight, it is not without limitation. First, data collection was limited to the UK, curtailing cross-national generalizability. Future research should gather data from elsewhere to provide comprehensive, robust comparative insight, revealing whether-and-why certain nationalities engage more readily with SS experiences. Second, our study utilized cross-sectional data, capturing customer behavior related to online tourism shopping during a specific time frame. Future research could therefore adopt longitudinal designs or qualitative approaches to examine customer behavior across varying time periods and contexts, further revealing the behavioral, emotional and cognitive nuances that shape decision-making therein.

This study was conducted in accordance with institutional ethical guidelines for research involving human participants. Ethical approval was received from one of the authors.