AI Technology, Social Media Engagement, and Reuse Intention in Online Travel Platforms: The Mediating Roles of Enthused Participation and Customer Satisfaction Experience
Abstract
123Bina Nusantara University, Jakarta, Indonesia Correspondence: [email protected] This study examines how AI technology shapes reuse intention in online travel platforms (OTPs) through a Stimulus–Organism–Response (SOR) framework, in which AI is the stimulus, social media engagement, enthused participation, and customer satisfaction experience are organism states, and reuse intention is the behavioural response. Prior research has predominantly assumed a direct relationship between AI and behavioural outcomes, overlooking the mediating roles of customer experience and emotional engagement. Using a cross-sectional survey of 101 valid respondents (screened from 134) in the Greater Jakarta (Jabodetabek) area who had used OTPs such as Traveloka, Tiket.com, or Agoda within the past year, data were analysed via Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4. The results show that AI technology significantly predicts social media engagement (H1: β = 0.774, p < 0.001) and enthused participation (H2: β = 0.512, p < 0.001); social media engagement predicts enthused participation (H3: β = 0.274, p < 0.001); enthused participation predicts customer satisfaction experience (H4: β = 0.703, p < 0.001); and customer satisfaction experience predicts reuse intention (H5: β = 0.711, p < 0.001). Neither AI technology (H6: β = 0.058, p = 0.641) nor social media engagement (H7: β = 0.100, p = 0.324) exerts a significant direct association with reuse intention. These findings contribute to the SOR literature by demonstrating that organism-level states are necessary mediators between digital stimuli and behavioural responses
Keywords: AI technology; online travel platforms; SOR framework; enthused participation; customer satisfaction experience; social media engagement; reuse intention.
Introduction
The rapid integration of artificial intelligence (AI) into online travel platforms (OTPs) has transformed how consumers search for, evaluate, and book travel services. AIpowered features — including personalised recommendation engines, AI chatbots, and predictive pricing algorithms — are now embedded across leading platforms such as Traveloka, Tiket.com, and Agoda, reshaping customer interactions at every touchpoint.
However, while AI has demonstrably improved operational efficiency and user experience, it remains unclear whether these technological capabilities translate into sustained customer loyalty, operationalised here as reuse intention.
The Indonesian OTP market provides a compelling context for this investigation.
Statista (2025) projects the online travel market in Indonesia to grow substantially through 2029, and Traveloka alone reported nearly 140 million app downloads and Management https://www.ilomata.org/index.php/ijjm
approximately 50 million monthly active users in Southeast Asia as of January 2025, with 85% of Indonesian survey respondents reporting its use. This scale of adoption creates a large population of OTP users whose reuse behaviour is consequential for platform competitiveness. Yet high price sensitivity and cross-platform switching — characteristics of Indonesian OTP users (Nugraha & Susilo, 2021)— mean that AI-driven engagement does not automatically translate into loyalty. Understanding which mechanisms connect AI to reuse intention is therefore both theoretically important and practically urgent.
Prior research on AI in digital marketing has predominantly assumed direct relationships between AI capability and behavioural outcomes such as purchase intent or loyalty (Huang & Rust, 2021; L. Zhang et al., 2023). This direct-effect assumption overlooks the potential mediating roles of customer experience and emotional engagement. The specific unresolved contradiction is this: studies confirm that AI improves customer satisfaction and engagement (Bag et al., 2021; Perez-Vega et al., 2021), yet other evidence shows that AI effectiveness is perceived as purely functional, with users switching platforms based on price rather than past AIenhanced experience (Nugraha & Susilo, 2021; L. Zhang et al., 2023). This contradiction points to an underexplored indirect pathway through which AI may operate on reuse intention — through organism-level states rather than directly.
This study addresses that gap by grounding the analysis in the Stimulus–Organism–Response (SOR) framework (Mehrabian & Russell, 1974). In this model, AI technology is conceptualised as the stimulus (S) — an external technological trigger embedded in OTP interfaces. Social media engagement, enthused participation, and customer satisfaction experience are conceptualised as organism states (O) — the internal psychological processes through which AI stimuli are experienced and evaluated. Reuse intention constitutes the behavioural response (R). This SORbased framing allows the study to test not only whether AI influences reuse intention, but through which internal pathways such influence operates, addressing the gap in prior studies that treated all variables as parallel predictors rather than a coherent sequential chain.
Building on this framework, the study makes three contributions. First, it extends the SOR framework by mapping AI technology and social media engagement onto the stimulus role, positioning enthused participation and customer satisfaction experience as mediating organism states.
Second, it empirically challenges the assumption of a direct AI–reuse intention link by demonstrating that this effect operates entirely through experiential mediators. Third, it offers context-specific insights from Indonesian OTPs, where price sensitivity and platform-switching behaviour shape the boundary conditions under which AI converts into loyalty.
Literature Review
Artificial Intelligence Technologies in Online Travel Platforms
Online travel platforms serve as comprehensive digital ecosystems enabling users to search, compare, and book travel services. AI enriches these platforms through machine learning algorithms trained on large behavioural datasets, enabling personalised recommendations, predictive pricing, AI chatbots for real-time customer support, and automated postbooking communications (Buhalis & Cheng, 2020; Jabeen et al., 2021). These AI touchpoints are the primary stimuli that activate internal customer states, consistent with the SOR framework. Research confirms that AI in OTPs enhances operational efficiency and improves the quality of customer interactions (Qin et al., 2022; Samara et al., 2020), but the pathway from AI to sustained loyalty — rather than short-term engagement — remains contested.
Enthused Participation
Enthused participation (EP) is defined in this study as a psychological participation construct reflecting the degree of fervour, curiosity, and proactive immersion that users exhibit during OTP interactions — including exploratory browsing, feature engagement, peer endorsements, and reservation activities (Bag et al., 2021). EP is explicitly conceptualised as a psychological state, not a performance metric such as conversion rate or bounce rate, which would require objective platform data rather than self-reported perceptions. Within the SOR framework, EP is an organism state: an internal affective response to AI stimuli and social media engagement that
mediates between those external inputs and downstream satisfaction and behavioural outcomes. Prior research identifies emotional participation and proactive engagement as important precursors to satisfaction and loyalty in digital service contexts (Eigenraam et al., 2018; Pansari & Kumar, 2017).
Customer Satisfaction Experience
Customer satisfaction experience is a holistic evaluative and affective response to a service encounter, integrating cognitive appraisals of performance with emotional reactions such as delight, trust, or disappointment (De Oliveira Santini et al., 2020; Lee & Lee, 2020). In the OTP context, satisfaction crystallises across multiple touchpoints: the ease of the search interface, the relevance of AI recommendations, the quality of automated support, and the seamlessness of the booking process. The hospitality and tourism literature consistently identifies satisfaction as one of the strongest antecedents of loyalty and continued usage (Y. J. Kim & Kim, 2022; Oliver, 1999). Within the SOR framework, satisfaction is an organism state shaped by upstream stimuli and mediating states (enthused participation), which in turn strongly influences the behavioural response of reuse intention.
Reuse Intention
Reuse intention refers to a customer’s disposition to return to and transact through the same OTP in the future (Y.
J. Kim & Kim, 2022; Majeed et al., 2022). It is operationalised in this study as the specific behavioural response (R) in the SOR model, serving as the primary indicator of customer buying behaviour in the OTP context. Reuse intention is a critical strategic outcome because platform-switching costs in travel are relatively low, making it a meaningful proxy for platform loyalty (Nugraha & Susilo, 2021; Shi et al., 2018).
Hypothesis Development
As the stimulus in the SOR model, AI decodes user behaviours, forecasts actions, and personalises social content delivery, thereby activating deeper social media engagement as an organism state (Perez-Vega et al., 2021).
AI-powered platforms amplify organisational agility in information handling, increasing the relevance and resonance of social content for OTP users (Figure 1).
H1: AI technology is positively associated with social media
engagement.
AI features such as personalised recommendations, predictive search, and interactive chatbots are designed to heighten user immersion within the OTP (Bag et al., 2021; Herhausen et al., 2020). By surfacing relevant options and reducing decision friction, AI stimulates exploratory behaviour and participatory excitement as organism-level responses to the AI stimulus.
H2: AI technology is positively associated with enthused
participation.
Social media platforms facilitate communal exchanges, peer influence, and information sharing that immerse users in experiential OTP content (Voorveld et al., 2018; Wang et al., 2019). As users encounter positive peer narratives, travel imagery, and promotional content, their participatory enthusiasm within the OTP is expected to increase. Both are organism states, with social media engagement as an antecedent that amplifies internal participatory excitement.
H3: Social media engagement is positively associated with
enthused participation.
When users are emotionally activated and proactively engaged with OTP features, they are more likely to perceive their interaction as valuable, contributing to a positive satisfaction evaluation (Eigenraam et al., 2018; Pansari & Kumar, 2017). Enthused participation is thus an organism state that, once activated, elevates the affective component of the satisfaction experience.
H4: Enthused participation is positively associated with
customer satisfaction experience.
Satisfaction is consistently identified as one of the most robust antecedents of loyalty and repeat purchase behaviour in service contexts (J. Kim et al., 2022; Oliver, 1999). In OTPs, a satisfying booking experience encompassing platform usability, recommendation relevance, and support quality is expected to translate into a stronger intention to return to the same platform.
H5: Customer satisfaction experience is positively associated
with reuse intention.
Some prior studies propose that AI features directly influence customer loyalty by providing consistently superior experiences that reduce platform-switching motivation (Garg et al., 2020; L. Zhang et al., 2023). This study tests whether such a direct stimulus–response path exists, independently of the organism states.
H6: AI technology is positively associated with reuse intention.
Social media interactions including peer endorsements, user-generated content, and brand responses have been proposed as direct drivers of repurchase intent by building affective bonds and platform credibility (Brodie et al., 2013; Y.
J. Kim & Kim, 2022). This study tests whether this direct path holds in the OTP context, where platform-switching is common and price comparisons are pervasive.
H7: Social media engagement is positively associated with
reuse intention.
Methods
This study employed a cross-sectional survey design.
Survey items used a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Measurement scales for all constructs were adapted from validated instruments in the prior literature.
AI technology items (5 items; AI1 removed due to low loading) were adapted from Alter (2021) and Bag et al. (2021). Social media engagement items (5 items, SME1–SME5) were adapted from Brodie et al. (2013) and (De Oliveira Santini et al., 2020). Enthused participation items (3 retained items from 5; EP2 and EP4 removed due to low factor loading) were adapted from Bag et al. (2021) and Eigenraam et al. (2018).
Customer satisfaction experience items (5 items, CSE1–CSE5) were adapted from De Oliveira Santini et al. (2020) and Vo et al. (2020). Reuse intention items (5 items, RUI1–RUI5) were adapted from Y. J. Kim & Kim (2022) and Majeed et al. (2022).
The target population comprised adults aged 18 and above who had booked travel services through OTPs such as Traveloka, Tiket.com, Agoda, or Klook within the past year.
Primary data were collected through online surveys distributed via convenience sampling to participants in the Jabodetabek region (Jakarta, Bogor, Depok, Tangerang, Bekasi). A total of 134 responses were initially collected. Data screening included standard deviation checks to identify straight-liners, outlier removal using SmartPLS diagnostics, and verification of eligibility criteria (OTP usage within the past year), resulting in 101 valid responses retained for analysis. All respondents provided informed consent prior to participation. To mitigate common method bias, questionnaire items were presented in randomised order and respondent anonymity was ensured throughout.
The final sample size of 101 valid responses satisfies the minimum requirement for PLS-SEM analysis. The maximum number of structural paths pointing to any single endogenous construct in this model is three (pointing to Enthused Participation: from AI, from SME, and onwards to CSE), requiring a minimum of 30 observations under the 10-times rule (Hair et al., 2014). The sample of 101 comfortably exceeds https://www ilomata org/index php/ijjm this threshold. PLS-SEM is additionally appropriate for exploratory models with smaller samples and non-normal data (Hair et al., 2019). However, the convenience sampling approach and concentration of respondents in Jabodetabek limit generalisation to other Indonesian regions or demographic groups.
Analysis proceeded in SmartPLS 4 through sequential steps: measurement model evaluation via factor loadings (threshold ≥ 0.70), Cronbach’s α (≥ 0.70), composite reliability (CR ≥ 0.70), and average variance extracted (AVE ≥ 0.50); discriminant validity via Fornell–Larcker criterion; and structural model assessment using path coefficients (β), bootstrapped t-statistics, and p-values (5,000 subsamples).
Result and Discussion
Respondent Profile
Table 1 presents the demographic profile of the 101 valid respondents. Most respondents were aged 31–40 years (54.60%) and employed (71.54%). Traveloka was the most frequently used platform (38.75%), followed by Tiket.com (35.42%) and Agoda (17.50%). Most respondents were based in Jakarta (46.92%) and Tangerang (32.31%). Domestic travel was the predominant destination (52.53%), with a travel frequency of 2–3 times per year (51.54%). Most respondents reported a travel booking budget of Rp 1,000,000–Rp 4,999,999 (59.23%), and 73.85% indicated awareness of AI features in online travel booking
Measurement Model: Reliability and Validity
Table 2 presents the reliability and validity results. All retained items achieved factor loadings above 0.70. Three items were removed due to low factor loadings: AI1, EP2, and EP4. Cronbach’s alpha values ranged from 0.760 to 0.841, all exceeding the recommended threshold of 0.70 (Hair et al., 2014). Composite reliability (CR) and average variance extracted (AVE) values confirmed convergent validity, with AVE values ranging from 0.557 to 0.708, all above the 0.50 threshold. VIF values for all constructs were below 3.3, indicating no collinearity concerns. R² values for endogenous constructs were: Social Media Engagement = 0.599; Enthused Participation = 0.554; Customer Satisfaction Experience = 0.494; Reuse Intention = 0.685.
Discriminant Validity
Discriminant validity was assessed using the Fornell– Larcker criterion (Table 3). For each construct, the square root of the AVE (diagonal values) exceeds all inter-construct correlations, confirming that each construct is more strongly associated with its own indicators than with other latent constructs. Although some HTMT values exceeded the 0.90 threshold, the Fornell–Larcker results support acceptable discriminant validity, and the theoretical distinction between constructs provides additional conceptual justification.
Structural Model and Hypothesis Testing
Table 4 presents the path coefficients, bootstrapped t-
statistics, p-values, and hypothesis decisions. Hypotheses H1 through H5 are all supported at p < 0.05 or better. AI technology is positively associated with social media engagement (β = 0.774, t = 18.177, p < 0.001) and enthused participation (β = 0.512, t = 4.640, p < 0.001). Social media engagement is positively associated with enthused participation (β = 0.274, t = 2.505, p < 0.001). Enthused participation is positively associated with customer satisfaction experience (β = 0.703, t = 13.821, p < 0.001).
Customer satisfaction experience is positively associated with reuse intention (β = 0.711, t = 9.666, p < 0.001). Neither AI technology (β = 0.058, t = 0.466, p = 0.641) nor social media engagement (β = 0.100, t = 0.986, p = 0.324) exerts a significant direct association with reuse intention; H6 and H7 are not supported.
This study examined how AI technology shapes reuse intention in online travel platforms through a SOR-based model incorporating social media engagement, enthused participation and customer satisfaction experience The findings reveal a nuanced pattern: while AI technology is a powerful activator of organism-level states, it does not directly produce the behavioural response of reuse intention. Instead, the effect of AI on reuse intention operates entirely through a sequential mediation chain — from enthused participation to customer satisfaction experience.
The non-significant direct paths from AI (H6, β = 0.058, p = 0.641) and social media engagement (H7, β = 0.100, p = 0.324) to reuse intention carry important theoretical implications for the SOR framework (Figure 2). A foundational premise of SOR is that stimuli can produce responses directly, with organism states serving as amplifiers. The present findings challenge this direct stimulus–response assumption: in the OTP context, the organism layer is not optional — it is necessary.
AI and social media engagement are insufficient stimuli to generate reuse behaviour unless they activate the internal states of enthused participation and satisfaction. This extends the SOR framework by establishing that the organism layer functions as a full mediator rather than merely a moderator in
digitally mediated, high-switching service environments. This finding diverges from L. Zhang et al. (2023), who reported a direct positive relationship between AI personalisation and reuse behaviour, but aligns with Huang & Rust (2021), who argued that AI contributes primarily to functional efficiency rather than emotional loyalty.
The non-significant effect of social media engagement on reuse intention further suggests that engagement-driven awareness and engagement-driven loyalty are distinct phenomena. Users may actively interact with OTP social content without forming the relational bonds needed to motivate return visits — particularly when competing platforms offer similar content and lower prices (Nugraha & Susilo, 2021). This finding refines Brodie et al. (2013), who proposed that social media interactions should drive behavioural outcomes, by revealing that the OTP context introduces boundary conditions — high price sensitivity and low switching costs — under which engagement alone does not translate into loyalty.
Conversely, the strong path from customer satisfaction experience to reuse intention (β = 0.711, p < 0.001) confirms Oliver (1999) relational theory of satisfaction as a primary loyalty driver, and underscores that AI and social media engagement must ultimately deliver satisfying experiences to convert into behavioural outcomes. Enthused participation’s significant role as a mediator (β = 0.703 on CSE) further indicates that emotional activation and proactive engagement are necessary precursors to satisfaction in the OTP context.
All associations are cross-sectional and do not establish causal relationships.
Conclusion
Theoretical Implications
This study contributes to the SOR literature by demonstrating that digital stimuli — AI technology and social media engagement — do not produce direct behavioural responses (reuse intention) in the OTP context, but operate through a sequential mediation chain involving enthused participation and customer satisfaction experience. This challenges the simplistic S→R assumption and establishes the organism layer as a necessary mediator, not merely a moderator.
Second, the study repositions AI as an enabler of organism states rather than a determinant of loyalty. This distinction, consistent with Huang & Rust (2021), implies that AI’s value in OTPs is conditional on its capacity to generate meaningful experiential and affective responses, not merely functional efficiency.
Third, by demonstrating that social media engagement does not directly drive reuse intention, the study advances the customer engagement literature (Brodie et al., 2013) by distinguishing engagement-driven awareness from engagement-driven loyalty. Future theoretical frameworks should incorporate this distinction, particularly in highswitching digital service markets.
Fourth, the significant satisfaction–reuse intention path reaffirms Oliver (1999) relational theory and supports relationship marketing theory’s emphasis on experiential value as the foundation of loyalty. These theoretical contributions are framed as exploratory contextual evidence from a cross-sectional convenience sample and should be replicated with stronger designs before broader claims are made.
Practical and Managerial Implications
The following recommendations are grounded directly in the significant empirical results and should not be interpreted as confirmed interventions given the cross-sectional design.
The finding that AI does not directly drive reuse intention (H6 not supported) implies that OTP providers should not rely on AI investment alone to build platform loyalty. AI tools — chatbots, recommendation engines, automated notifications — should be evaluated for their capacity to generate enthused participation and satisfaction, not merely functional transactional efficiency.
The strong satisfaction–reuse intention path (H5, β = 0.711) and the enthused participation–satisfaction path (H4, β = 0.703) together indicate that user experience design should prioritise features that stimulate participatory excitement: interactive exploration tools, transparent AI recommendation explanations, and responsive customer support. Platforms should invest in consistently satisfying end-to-end booking experiences, including smooth cancellation and refund processes, as these are more reliable drivers of reuse than social media campaigns alone.
The non-significant social media engagement–reuse intention path (H7 not supported) cautions against treating social media metrics as proxies for loyalty. Engagement activities should be complemented by strong value propositions — exclusive member benefits, personalised follow-up communications, and trust-building transparency — to bridge the gap between online interaction and repeat booking behaviour.
Recommendations regarding sustainability features, loyalty programmes, and virtual previews are beyond the scope of the tested model and are ff d f t h di ti th th fi di g based conclusions.
Limitations and Future Research
This study has several limitations that should inform the interpretation of findings and guide future research. First, the cross-sectional design precludes causal inference; all results are associational. Longitudinal designs would be needed to establish temporal precedence between AI stimuli, organism states, and reuse behaviour. Second, all data are self-reported and subject to social desirability and recall biases. Third, the convenience sampling approach and geographic concentration of respondents in Jabodetabek (predominantly Jakarta) limit generalisation to other Indonesian regions and Southeast Asian markets with different platform ecosystems and consumer norms. Fourth, the valid sample size of 101 respondents, while adequate for the PLS-SEM model tested, constrains statistical power for detecting smaller effect sizes. Fifth, common method bias cannot be fully ruled out in a single-source survey design, despite item randomisation and anonymity assurance. Sixth, reuse intention is used as a proxy for customer buying behaviour; actual behavioural data from platform transaction logs would provide more objective outcome measures. Future research should address these limitations by incorporating longitudinal data, objective behavioural outcomes, and larger geographically diverse samples, and by extending the model to include trust, perceived risk, and sustainability awareness as additional constructs.
Author contributions
Stella Maryanthi was responsible for conceptualisation, data collection, and writing the first draft. Asnan Furinto contributed to methodology and reviewing. Dewi Tamara was involved in reviewing and editing.
Acknowledgements
The authors acknowledge all parties who provided supportive roles in the research or publication process, including technical guidance, data collection assistance, and mentorship
References
Alter, S. (2021). Understanding Artificial Intelligence in the Context of Usage: Contributions and Smartness of Algorithmic Capabilities in Work Systems. International Journal of Information Management, 102392. https://doi.org/10.1016/j.ijinfomgt.2021.102392
Bag, S., Srivastava, G., Al Bashir, M. M., Kumari, S., Giannakis, M., & Chowdhury, A. H. (2021). Journey of Customers in the Digital Era: Understanding the Role of Artificial Intelligence Technologies in User Engagement and Enthused Participation. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-07-2021-0415
Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2013). Customer Engagement in a Virtual Brand Community: An Exploratory Analysis. Journal of Business Research, 66(1), 105–114. https://doi.org/10.1016/j.jbusres.2011.07.029
Buhalis, D., & Cheng, E. S. Y. (2020). Exploring the Use of Chatbots in Hotels: Technology Providers’ Perspective. In Information and Communication Technologies in Tourism 2020 (pp. 231–242). Springer. https://doi.org/10.1007/978-3-030-36737-4_19
De Oliveira Santini, F., Ladeira, W. J., Pinto, D. C., Herter, M. M., Sampaio, C. H., & Babin, B. J. (2020). Customer Engagement in Social Media: A Framework and Meta-Analysis. Journal of the Academy of Marketing Science, 48(6), 1211–1228. https://doi.org/10.1007/s11747-020- 00731-5
Eigenraam, A. W., Eelen, J., Van Lin, A., & Verlegh, P. W. (2018). A Customer- Based Taxonomy of Digital Customer Engagement Practices. Journal of Interactive Marketing, 44, 102–121. https://doi.org/10.1016/j.intmar.2018.07.002
Garg, P., Gupta, B., Dzever, S., Sivarajah, U., & Kumar, V. (2020). Examining the Relationship Between Social Media Analytics Practices and Business Performance. International Journal of Information Management, 52, 102069. https://doi.org/10.1016/j.ijinfomgt.2020.102069
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2014). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report Results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Herhausen, D., Mikolon, S., Wieseke, J., & Schmitz, C. (2020). When Are Monetary Incentives Counterproductive? Journal of the Academy of Marketing Science, 45(2), 193–210.
Hu, L., & Olivieri, M. (2021). Social Media Management in the Traveller’s Customer Journey. Current Issues in Tourism, 24(12), 1768–1779. https://doi.org/10.1080/13683500.2020.1819969
Huang, M. H., & Rust, R. T. (2021). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
Jabeen, F., Al Zaidi, S., & Al Dhaheri, M. H. (2021). Automation and Artificial Intelligence in Hospitality and Tourism. Tourism Review. https://doi.org/10.1108/TR-09-2019-0360
Kim, J., Kim, M., & Holland, S. (2022). Factors Affecting Customer Satisfaction and Loyalty in OTAs. Journal of Hospitality and Tourism Technology, 13(2), 241–257. https://doi.org/10.1108/JHTT-05-2021-0135
Kim, Y. J., & Kim, H. S. (2022). The Impact of Hotel Customer Experience on Satisfaction Through Online Reviews. Sustainability, 14(2), 848. https://doi.org/10.3390/su14020848
Lee, S. M., & Lee, D. (2020). “Untact”: A New Customer Service Strategy. Service Business, 14(1), 1–22. https://doi.org/10.1007/s11628-019-00408-2
Majeed, M., Asare, C., Fatawu, A., & Abubakari, A. (2022). An Analysis of the Effects of Customer Satisfaction and Engagement on Social Media on Repurchase Intention. Cogent Business & Management, 9(1), 2028331. https://doi.org/10.1080/23311975.2022.2028331
Mehrabian, A., & Russell, J. A. (1974). An Approach to Environmental Psychology. MIT Press.
Nugraha, R., & Susilo, H. (2021). Price Sensitivity and Platform Switching Behaviour in Indonesian Online Travel Customers. International Journal of E-Business Strategy, 9(3), 112–125.
Oliver, R. L. (1999). Whence Consumer Loyalty? Journal of Marketing, 63(4), 33– https://www.ilomata.org/index.php/ijjm
44. https://doi.org/10.1177/00222429990634s105
Pansari, A., & Kumar, V. (2017). Customer Engagement: The Construct, Antecedents, and Consequences. Journal of the Academy of Marketing Science, 45(3), 294–311. https://doi.org/10.1007/s11747-016- 0485-6
Perez-Vega, R., Kaartemo, V., Lages, C. R., Razavi, N. B., & Männistö, J. (2021). Reshaping the Contexts of Online Customer Engagement Behaviour via Artificial Intelligence. Journal of Business Research, 129, 902–910. https://doi.org/10.1016/j.jbusres.2020.11.002
Qin, M., Zhu, W., Zhao, S., & Zhao, Y. (2022). Is Artificial Intelligence Better Than Manpower? Sustainability, 14(7), 3974. https://doi.org/10.3390/su14073974
Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial Intelligence and Big Data in Tourism: A Systematic Literature Review. Journal of Hospitality and Tourism Technology. https://doi.org/10.1108/JHTT-12-2018- 0118
Shi, S., Mu, R., Lin, L., Chen, Y., Kou, G., & Chen, X. J. (2018). The Impact of Perceived Online Service Quality on Swift Guanxi. Internet Research. https://doi.org/10.1108/IntR-12-2016-0389
Tuten, T. L., & Solomon, M. R. (2020). Social Media Marketing (4th ed.). SAGE
Publications.
Vo, N. T., Chovancóvá, M., & Tri, H. T. (2020). The Impact of E-Service Quality on Customer Satisfaction and Customer Engagement Behaviours Toward Luxury Hotels. Journal of Quality Assurance in Hospitality & Tourism, 21(5), 499–523. https://doi.org/10.1080/1528008X.2019.1695701
Voorveld, H. A. M., van Noort, G., Muntinga, D. G., & Bronner, F. (2018). Engagement With Social Media and Social Media Advertising. Journal of Advertising, 47(1), 38–54. https://doi.org/10.1080/00913367.2017.1405754
Wang, X. W., Cao, Y. M., & Park, C. (2019). The Relationships Among Community Experience, Community Commitment, Brand Attitude, and Purchase Intention in Social Media. International Journal of Information Management, 49, 475–488. https://doi.org/10.1016/j.ijinfomgt.2019.07.018
Zhang, D., Pee, L., & Cui, L. (2021). Artificial Intelligence in E-Commerce Fulfillment. International Journal of Information Management, 57, 102304. https://doi.org/10.1016/j.ijinfomgt.2020.102304
Zhang, L., Wang, Y., & Chen, J. (2023). The Role of AI Personalization in Online Consumer Behaviour. Information & Management, 60(2), 103625. https://doi.org/10.1016/j.im.2022.103725 https://www.ilomata.org/index.php/ijjm
Social Media Engagement
Social media engagement encompasses the dynamic exchanges between users and digital content — likes, shares, comments, clicks, and session durations — that reflect psychological commitment to co-creating value through interactive brand encounters (Garg et al., 2020; Tuten & Solomon, 2020). In the OTP context, user-generated content such as travel reviews and peer endorsements functions as a credibility mechanism that shapes booking decisions (Hu & Olivieri, 2021; Wang et al., 2019). AI amplifies social media engagement by analysing sentiment patterns, personalising content delivery, and identifying high-engagement themes (Perez-Vega et al., 2021; D. Zhang et al., 2021). Within the SOR framework, social media engagement is an organism state activated by AI stimuli — the degree to which a customer is psychologically immersed in platform-mediated social content.