Stimulus-Organism-Response Mechanism in Predicting Continuous Usage Intentions toward Digital Travel Platforms

Stimulus-Organism-Response Mechanism in Predicting Continuous Usage Intentions toward Digital Travel Platforms

Azlinda Hakim Lokman Hakim¹, *Hairunnisa Mohamad Ibrahim², Yeo Siok Mui³

¹School of Hospitality, Tourism & Events, Taylor’s University

²School of Pre-University Studies, Taylor’s College

³Taylor’s Culinary Institute, Taylor’s University

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000397

Received: 27 December 2024; Revised: 15 July 2025; Accepted: 18 July 2025; Published: 19 August 2025

ABSTRACT

The Digital Travel Platforms are considered an essential component of the traveler’s journey from planning to booking a room or airlines tickets on the platform. With the Digital Travel Platforms industry experiencing significant growth, this study will examine how key determinants and perceived value influence customers’ decisions to continue using these platforms. This research employs a structured questionnaire of age: 20-50+ regarding the selection of Digital Travel Platforms for hotel bookings, focusing on Agoda and Booking. Undefined Closed questions help to get accurate and non-biased data. The study found no significant link between Information Quality and Perceived Value, contradicting prior research. Key factors influencing continuous usage of Digital Travel Platforms include perceived interactivity, security, privacy, price, promotion, and e-WOM, with nuanced effects on behavioral intentions and purchase decisions. The methodological approach taken in this paper enables researchers and practitioners to examine key factors like information quality, website interactivity, safety, pricing, and online reviews. The research aims to uncover how these elements influence customers’ ongoing use of Digital Travel Platforms. It also introduces the idea that perceived value plays a crucial role in whether customers decide to keep using the platform.

Keywords: Digital Travel Platforms, continuous usage intention, stimulus-organism-response, digital platform

INTRODUCTION

The internet has significantly changed the travel and tourism industry, impacting every stage of a traveler’s experience, from planning to booking and exploring new destinations. Digital platforms have become essential for both travelers and industry professionals to navigate this dynamic market (Sadie, 2023). Among the various digital platforms, Digital Travel Platforms have gained prominence. In 2023, the Digital Travel Platforms industry saw a remarkable recovery, with several companies exceeding their pre-pandemic revenue levels. Booking Holdings led the way with approximately $21.4 billion in revenue, followed by Expedia Group with around $12.8 billion, and Airbnb with $9.9 billion. According to Statista Mobility Market Insights, the online travel and tourism market generated over $850 billion globally in 2023, covering sectors such as hotels, package holidays, vacation rentals, and cruises (Statista, 2023).

Since travel is considered a complex social phenomenon (Yang & Nair, 2014), travelers need to gather essential information to make informed decisions about their journeys.   Customers have the option of booking their travel arrangements directly from the service provider’s website, e.g. hotel website or directly through Digital Travel Platforms website that offers users to book travel services, such as flights, hotel rooms, car rentals, and vacation packages (Sousa, 2024). The hospitality industry, especially the hotel industry, is growing rapidly in the tourism industry, providing accommodation to travelers (Hospitality Global Market Report, 2023). The increasing popularity of Digital Travel Platforms and the intensifying competition have spurred academic research. Much of the academic research is concentrated on the pre-purchase effect (Pee et al., 2018) such as consumer perception associated with Digital Travel Platforms (Shalini et al., 2020). Some other examples of prior research investigated on various aspects of consumer behavior within the Digital Travel Platforms context, including satisfaction (Jedin & Ranjini, 2017), customer loyalty (Dwikesumasari & Ervianty, 2017), and service quality determinants (Dutta et al., 2017). Furthermore, Zailin et al. (January 2020) conducted research on Malaysian consumers’ attitudes toward online travel websites. However, according to Pee et al. (2018), while research has extensively studied how signals (price, brand, safety, information on websites to name a few) influence consumers’ initial purchase decisions, there is limited understanding of how these signals affect their intention to repurchase a product. Specifically, the impact of pre-purchase signals on future buying behavior and the interaction between these signals and post-purchase experiences remain underexplored.

Existing research on repurchase intentions includes studies by Fang et al. (2016) on how customer relationships influence online consumers repurchase decisions, Prodomos et al. (2022) studied on repurchase intentions in retail shopping where he emphasized key factors such as brand image, price sensitivity, perceived value, and customer satisfaction, which are crucial in improving customer retention and driving repeat business. Sajjad et al. (2023) concluded in their research that a company should actively engages with its customers by using artificial intelligence technology (e.g. social media) to enhance the overall customer ‘s experience which in turn can create a positive cycle that encourages repeat business. Ng et al. (2023) also investigated there’s a significant relationship between brand image, perceived value, and trust and intention to repurchase on Digital Travel Platforms. Ozturk et al. (2017) noted that despite technological advancements, significant research gaps remain regarding website interactivity and safety and privacy concerns in the hotel industry and according to Beránek et al., 2015 concluded that online tourism services must learn the factors that can influence the repeat purchase behavior of customers.  Therefore, investigating this area could uncover the specific factors that drive repeat purchase behavior, addressing a gap in the current literature. To address this gap, this research aims to explore online travel websites that affect customers’ ongoing intention to use them.”

The significance of this study lies in its exploration of the key factors influencing customer decisions to rebook through Digital Travel Platforms, In the rapidly evolving digital landscape, understanding these factors is crucial for businesses seeking to gain a competitive advantage in e-commerce (Ficky et al, 2023). Furthermore, the online customer experience is the focus of attention focus for marketers (Jaiswal and Singh, 2020).   Hence, the study is conducted with the following objectives:

• To examine the key determinants of the Digital Travel Platforms that influence the customer’s continuous usage intention.
• To explore the impact of perceived values on customer’s continuous usage intention on Digital Travel Platforms.

Both objectives contribute to a thorough understanding of the factors influencing customer usage intention on online travel websites.

Research questions:

• In what ways do the key determinants of Digital Travel Platforms influence customers’ continuous usage intention in the context of online travel agencies?
• How does perceived value impact customers’ continuous usage intentions on Digital Travel Platforms?

LITERATURE REVIEW

The research framework of this study is based on the S-O-R model by Mehrabian and Russell (1974), which explains that individuals’ behaviors arise from internal organismic processes activated by external stimuli. This model, often applied in environmental psychology, has been adapted in marketing and digital behavior studies to understand consumer responses to website features and digital interactions.

External stimuli can include physical surroundings, social cues, or digital interfaces such as website layout or app functionality. In this study’s context, the visual appeal, navigability, and presentation of an online travel agent (OTA) act as stimuli that influence user emotion and cognition. The “organism” represents internal affective and cognitive states, such as enjoyment, excitement, or control (Hewei & Youngsook, 2022). These emotional and psychological states ultimately shape the “response,” which manifests in behaviors like completing a booking or returning to the site.

By applying the S-O-R model, this study identifies key psychological triggers that OTAs can leverage to enhance user experience and loyalty. However, it is acknowledged that the current model observes behavior at a single point in time. Future studies could enhance this framework by incorporating longitudinal data to understand how emotional and behavioral responses evolve with repeated platform use.

Digital Travel Platforms: Digital travel platforms facilitate the booking of travel-related services. They are widely used due to their comprehensive listings, user-generated reviews, and ease of access (Talwar et al., 2020). Expedia and Priceline were early entrants, establishing the foundation for the global growth of OTAs (Barthel & Perret, 2015).

Numerous factors influence user trust and loyalty on these platforms, including accessibility, pricing, and service quality (Jedin & Ranjini, 2017). A consistent finding across studies is that trust—built through reliable reviews, user experience, and perceived website security—is a primary driver of repeat use (Setiawan & Widanta, 2021). Website serviceability has also been shown to impact customer satisfaction, brand attachment, and customer loyalty (Lee et al., 2017). Brand image, perceived value, and trust have a strong influence on repurchase intention (Pham & Nguyen, 2019; Razak et al., 2014), with user reviews and past experience playing critical roles in consumer satisfaction and ongoing platform use.

Tourist Behavior in Online Information Search: Social media is integral to travel planning, with platforms like Instagram and TikTok offering peer-generated content that influences destination choices (Islam, 2021; Armutcu, 2023). Tourists actively search, compare, and share travel experiences online, making digital word of mouth a dominant force in travel decision-making. The integration of GPS features makes it easier for tourists to share real-time experiences and location check-ins, helping future travelers in their planning process.

Online reviews and recommendations remain a critical source of information, with approximately 68% of consumers reading reviews before booking (Roller, 2023). Experienced travelers tend to rely on prior knowledge, while first-time travelers are more dependent on comprehensive and credible online content.

Research Model and Hypotheses

This research constructs an S-O-R model of the influence of continuous purchase intention on online travel agents. By understanding how environmental stimuli (S) influence internal states (O), and how these states drive behavior (R), online travel agents can better design their platforms to enhance user experience and continue to use the platform.

Information Quality: Information quality plays a pivotal role in influencing user behavior. It includes accuracy, relevance, completeness, and the presentation of content. Yeap et al. (2014) defined it as the usefulness of the provided information to the consumer. In online tourism platforms, quality content supports decision-making, increases satisfaction, and builds trust (Wang & Yan, 2022). Jeong and Shin (2020) emphasized that in intangible service environments like tourism, accurate and reliable content significantly affects users’ experiences. High-quality information—such as on destinations, accommodations, and user reviews—encourages users to revisit and reuse the platform. Lata and Rana (2021) also confirmed that information quality predicts online hotel booking intentions.

HI: Information quality has a positive influence on perceived value

Perceived Interactivity: Perceived interactivity refers to the extent to which users feel they can meaningfully engage with a website. Buhalis & Martinez (2020) defined it as users’ ability to interact with site features. High interactivity—such as personalization and user-friendly interfaces—enhances user engagement and satisfaction. When information is perceived as complete, accurate, and relevant, users process it more deeply, forming positive attitudes toward the destination (Wang & Yan, 2022). A positive experience with these elements drives continued usage and loyalty.

H2: Perceived Interactivity has a positive influence on perceived value

Safety and Privacy: Ensuring the safety and privacy of users is essential in online environments. Users are more likely to engage with platforms they perceive as secure (Liu et al., 2005; Hermawan, 2022). Elements like data protection, clear privacy policies, and visible security features promote user confidence. Trust in the platform influences behavioral intentions (Lisdayanti & Hapsari, 2024; Sidanta et al., 2022). Therefore, implementing robust safety measures enhances user loyalty and fosters continuous engagement.

H3: Safety and Privacy has a positive influence on perceived value

Price and Promotion: Effective pricing and promotional strategies are key in attracting and retaining OTA users. Consumers are motivated by competitive pricing and the availability of deals (Yoon et al., 2014; Sharma et al., 2022). OTAs serve as intermediaries between providers and consumers, and price transparency significantly impacts decision-making. Tools like price comparison, dynamic pricing, and personalized offers encourage repeat use. These strategies influence perceived value by making travel services feel more affordable and accessible.

H4: Price and promotion have a positive influence on perceived value

Electronic word of mouth (e-WOM): Electronic word of mouth (e-WOM) significantly influences consumer behavior, especially for experiential services like travel (Sharma et al., 2022). Reviews, ratings, testimonials, and user-generated content shape perceptions of service quality and reliability. Positive e-WOM reinforces trust and creates a cycle of continuous use and recommendation (Ryu & Park, 2020). Customers who encounter favorable content are more likely to engage in repeat purchases and recommend the platform to others (Kaya et al., 2019).

H5: Electronic word of mouth has a positive influence on perceived value

Perceived Value: Perceived value is a key determinant of satisfaction and loyalty in OTA use. It involves a user’s evaluation of benefits versus costs (Kim et al., 2021; Gao et al., 2024). Factors such as user-friendly navigation, excellent customer service, and booking convenience shape this perception. Tourists consider extrinsic influences such as previous experiences and perceived risks when assessing value (San-Martín et al., 2020). Numerous studies affirm that perceived value positively affects repurchase intentions (De Morais Watanabe et al., 2020; Kuo et al., 2009).

While this study captures perceived value at a single time point, future research should consider how value perceptions shift over time. Longitudinal approaches would help track how users develop trust and loyalty through repeated interactions.

H6: Perceived value has a positive influence on continuous usage intention

Online booking continuous usage intention: Continuous usage intention refers to users’ propensity to revisit the same OTA platform for future bookings. Clement et al. (2021) argue that continuous use arises from immersive and interactive experiences. In travel contexts, features like virtual tours, real-time updates, and customized recommendations sustain user interest. Satisfaction with the platform encourages not just repeat purchases but also advocacy behavior (Buhalis & Martinez, 2020).

While this study takes a cross-sectional view, understanding long-term usage patterns would benefit from longitudinal methods that track evolving behaviors, preferences, and satisfaction over time.

Framework

MATERIAL AND METHODS

Data collection

Quantitative techniques involve scientific and structured procedures for data collection and analysis, aimed at producing precise, categorizable, and numerical findings. This study investigates the factors that influence customers in selecting one Digital Travel Platform over another when booking tourist destinations online. By utilizing quantitative methods, the study seeks to improve the accuracy of results and minimize the ambiguity often associated with qualitative approaches, thereby reducing mono-method bias and aligning with theoretical priorities in understanding consumer choice in digital travel services.

The survey targeted users of popular platforms such as Agoda and Booking.com. Data were collected using Google Forms, with a total of 150 participants selected through random sampling across various age groups. The questionnaire was distributed via social media platforms like Facebook and WhatsApp, ensuring easy access and minimal respondent inconvenience. It was structured into two main sections: one focused on demographic information, and the other on need-based and perception-based factors influencing customer preferences toward Digital Travel Platforms. However, it is important to note that while the sample size is adequate for exploratory analysis, the findings may have limited generalizability due to the relatively small and regionally concentrated participant pool. This constraint should be considered when interpreting the broader applicability of the results.

The demographic profile of the respondents has been presented based on gender, age, occupation and education level which has been presented in the cross tabulation below wherein a total of 150 respondents have participated in the study. Regarding gender distribution, the respondents were almost equal: 52% male (78) and 48% female (72). Distribution by age also reveals 60.7% (91) of the respondents fall in the 21-30 years age bracket. Other age respondents are Those with age 31-40 years account 14.7% with 22 respondents, 20 years old and below which accounts 10.7%, 41-50 years old which accounts 9.3% with 14 respondents and 51 years old and above, they account for 4.7% with 7 respondents. In terms of occupation, four categories emerged whereby the largest group comprised of students representing 52.7%: 79 in number. The largest group among the respondents was the employed individuals, out of which 40 respondents replied to be 26.7%. In the same vein, the self-employed /businessowners were 11.3% (17 respondents); while the retirees were 4.7% (7 respondents). Another 4.7% or seven respondents fell under the unemployed category.

Table 1 Demography respondents

RESULTS

Model assessment using PLS-SEM

Table 2 shows PLS-SEM measurement model analysis based on Hair, Hollingsworth, Randolph, and Chong’s (2017) guidelines starting with indicator reliability being greater than 0.70 and convergent validity being below 0.40. The result reveals that the observed scores of the construct and the predicted values for the pairs of items have the highest and lowest correlation coefficients due to construct validity of the test, where IP3 and GSI4 have the highest and the lowest scores, respectively. The best values for composite reliability (CR) should be equal to or above 0.7 and the AVE values equal to or above 0.5.

Table 2 Measurement Model

Discriminant validity

In the analysis, discriminant validity was determined using Fornell-Larcker’s test to reduce the probability of cross loading measurement as presented in the table 3 below. Third, the AVE for value added through triangulation was found to be larger than the correlations yet smaller than 0. 90, as established by Kline (2023) and Hair et al. (2019).

Table 3 Fornell-Larcker’s test

Structural Model Analysis

The estimates of the modelled relationships between the constructs are elaborated by the PLS algorithm. The coefficients of these paths were statistically analyzed using the bootstrap procedure in which t-values of each path estimate were obtained. The model fit of the analysis was assessed using R-Squared (R2) and Predictive relevance as the two criteria for evaluating the outcome variables. Hair et al (2019) Tables for R-Square include high: 0.67/0.33/0.19. The R-squared values of the endogenous latent variables Perceived Value (PV) & Continuous Usage Intention (CUI) are 96.1% & 62.6% respectively which supposes high level of model fitness. The effect size for each relationship is reported in Table 4 and the interpretation of the f² is as follows: 0.02 (small), 0.15 (medium), and 0.35 (large).

As recommended by Hair et al. (2019) the presence of multicollinearity was examined. Variance Inflation Factor (VIF) for all the two endogenous variables were below 4, recommended cut off value, thus, suggesting no collinearity bugbear. Subsequently, model fit was evaluated using standardized root mean square residual (SRMR). As the SRMR value of this research model was 0.07, it is less than the cut off 0.08, thus, it can be concluded that the model is well fitted. The PLS analysis was done on the structural model presented in table 5 shows the estimates path coefficients and t-values.

Out of them, only H1 is rejected in the present study while the other five research hypotheses are supported in table 4. T-statistics value is used to accept or reject any hypothesis, and the value should be greater than 1.96. H2 also hypothesizes that PI has a significant impact on PV, and we find that this hypothesis holds true in our study. From the result of H2 within this study as presented by T-statistic of 11.607, the Path coefficient (β) of 0.668, P value of .000 or <0.0001. Based on T-statistic significant value more than the table value of 1.96 and P values less than 0.05, thus, the Perceived Interactivity (PI) has an influence towards the Perceived Value (PV). This result provides empirical evidence for Hypothesis 2.

The results for Hypothesis 4 and 5 depict that the path coefficients of various constructs are as follows: Price and Promotion (PP) is 0.79 Electronic Word of Mouth (EWM) is 0.126 is less than 0.4. The hypothesis H2b suggests a significant and positive impact of Price and Promotion (PP), as well as Electronic Word of Mouth (EWM) on Perceived Value (PV). Out of these, Price and Promotion had the highest significance in determining the Perceived Value. However, Safety and Privacy (SP) is negative and significantly influential towards the Perceived Value (PV) with the Path coefficient (β) of -0.479 and the P values of 0.000. This is empirically supportive of Hypothesis 3.

Table 4 Hypotheses

Perceived Value as Mediator

Regarding the mediating role of Perceived Value on the relationship between Information Quality (IQ), towards Continuous Usage Intention (CUI), mediation relationship was found, since the indirect effect through Perceived Value was significant (β = 0.201, T = 2.107, p Value = 0.004). Perceived Interactivity (PI), Safety and Privacy (SP), Price and Promotion (PP) and Electronic Word of Mouth (EWM) are partially mediators toward Continual Usage Intention (CUI) through Perceived Value and the beta value is 0.092, 0.073, 0.108 and 0.123 respectively as depicted in Table 5.

Table 5 Mediating Effect

DISCUSSION     

The first research question of this study was to establish the factors that affect customer cumulative usage intention on online travel websites. Consequently, the result of this study revealed that the finding of a cross-sectional correlation between the variable of Information Quality (IQ) and the variable of Perceived Value (PV) is insignificant, which fails to support H1. Yeap et al. (2014) captured information quality by equating it as “how the provided information is useful for the consumer,” contradicting this result in prior studies. The information that returns has to be correct, pertinent and as much as possible, most closely connected with the topic and context the user was searching for.  By only claiming that the first component in customer persuasion is informativeness (Wang & Yan, 2022).

The analysis of the studies presented in this paper indicates that perceived interactivity, safety and privacy, price, promotion and e-WOM can all affect continuous usage intention. As for Perceived WQ, interactivity together with security and privacy are antecedents of Behavioral Intention and e-WOM in Digital Travel Platforms users (Hermawan, 2022). Also, the relationship between price and e-WOM has been established to influence the purchase behavior on the Shopee application; therefore, competitive prices and positive e-WOM help to improve consumer visitation decisions (Aprianti & Avianti, 2023).

Paradoxes or informative findings may be observed, if we consider the diversity of the impact of such factors. For example, security and privacy are the antecedents of e-WOM, but customization and character impact e-WOM indirectly through security and privacy (Yürük-Kayapinar, 2021). Furthermore, Park & Lee (2023) posited that price perception, e-WOM and brand awareness are significant predictors of the purchase intentions, and the findings revealed that price perception has a high negative correlation with brand awareness and e-WOM on Instagram. Perceived trust and e-W OM review attitude strongly influence purchase intention, but perceived trust does not influence e-WOM review attitude (Augusto et al., 2019). Furthermore, it was found that experience and Advertised will influence the impact of e-WOM on brand attitude and purchase intentions (Jones et al., 2009).

CONCLUSIONS

Therefore, this study concludes by stressing the relevance of identifying antecedents that encourage customers to make repetitive purchase intentions towards Digital Travel Platforms. Features like, information credibility, Perceived interactivity, Safety and privacy, Price strategies and eWOM all contribute to the perceived value of the Digital Travel Platforms. This improved perceived value is crucial in convincing customers to continue to rely on the same platform in their travelling needs as the growth of the digital travel market continues. Digital Travel Platforms that aim at improving these determinants through providing higher quality, interactivity and security of the experience to the customers have more chances for achieving greater longevity of the consumers’ loyalty and a competitive advantage. Therefore, a profound comprehension of the perceived value based on these key factors will be highly beneficial when working with the Digital Travel Platforms industry and striving for clients to come back for more.

REFERENCES

1. Al Faruq Abdullah, M., Endah Retno Wuryandari, N., Desty Febrian, W., Fathihani, F., H Pangaribuan, Y., & Hikmah Perkasa, D. (2023). The Effect of Brand Awareness, Price Perception and Electronic Word of Mouth (E-WOM) Toward Purchase Intention on Instagram. KnE Social Sciences. https://doi.org/10.18502/kss.v8i12.1371
2. Armutcu, B., Tan, A., Amponsah, M., Parida, S., & Ramkissoon, H. (2023). Tourist behaviour: The role of digital marketing and social media. Acta psychologica, 240, 104025. https://doi.org/10.1016/j.actpsy.2023.104025
3. Aprianti, D., & Avianti, W. (2023). How Does Electronic Word of Mouth (E-WoM) and Price Affect Purchasing Decisions? Innovation Business Management and Accounting Journal, 2(4), 227–232. https://doi.org/10.56070/ibmaj.v2i4.61
4. Augusto, M., Torres, P., & Torres, J. (2019). The impact of trust and electronic word-of-mouth reviews on purchasing intention. International Journal of Entrepreneurship and Small Business, 37(1), 136. https://doi.org/10.1504/ijesb.2019.10021496
5. Baber, R., & Baber, P. (2023). Influence of social media marketing efforts, e-reputation and destination image on intention to visit among tourists: application of SOR model. Journal of Hospitality and Tourism Insights, 6(5), 2298-2316. https://doi.org/10.1108/JHTI-06-2022-0270
6. Barthel, J., & Perret, S. (2015). OTAs–A Hotel’s Friend or Foe? How Reliant are Hotels on OTAs?. HVS. Retrieved January 01, 2020 from https://www.hospitalitynet.org/file/152005663.pdf
7. Bhayani, S., & Bavaria R.D. (2018). An Empirical study of consumer perception towards online travel agency. Inspira- Journal of Modern Management & Entrepreneurship
8. Buhalis, D., López, E. P., & Martinez-Gonzalez, J. A. (2020). Influence of young consumers’ external and internal variables on their e-loyalty to tourism sites. Journal of Destination Marketing & Management, 15, 100409. https://doi.org/10.1016/j.jdmm.2020.100409
9. Chatterjee, S. (2020). Factors Impacting Behavioral Intention of Users to Adopt IoT In India: From Security and Privacy Perspective. International Journal of Information Security and Privacy (IJISP), 14(4), 92-112. https://doi.org/10.4018/IJISP.2020100106
10. Chang, Y.W., Hsu, P.Y., Lan, Y.C. Lan. (2019). Cooperation and competition between online travel agencies and hotels. Tourism Manage., 71 (2019), pp. 187-196, https://doi.org/10.1016/j.tourman.2018.08.026
11. Clement Addo, P., Fang, J., Asare, A. O., & Kulbo, N. B. (2021). Customer engagement and purchase intention in live-streaming digital marketing platforms. The Service Industries Journal, 41(11–12), 767–786. https://doi.org/10.1080/02642069.2021.1905798
12. De Morais Watanabe, E. A., Alfinito, S., Curvelo, I. C. G., & Hamza, K. M. (2020). Perceived value, trust and purchase intention of organic food: A study with Brazilian consumers. British Food Journal, 122(4), 1070–1184. https://doi.org/10.1108/BFJ-05-2019-0363
13. Denizci Guillet, B., Mattila, A., Gao, L., 2019. The effects of choice set size and information filtering mechanisms on online hotel booking. Int. J. Hospitality Manage. 102379. https://doi.org/10.1016/j.ijhm.2019.102379
14. Dutta, S; Chauhan, R.K; & Chauhan, K.(2017). The impact of Electronic Service Quality ‘s Dimensions on Customer Trust for Online Travel Agencies in India. TURIZAM, Vol. 21, No. 3, 111-122. DOI:5937/turizam21-16103
15. Dwikesumasari, P. R., & Ervianty, R. M. (2017, July). Customer Loyalty Analysis of Online Travel Agency App with Customer Satisfaction as A Mediation Variable. In 2017 international conference on organizational innovation (ICOI 2017) (pp. 107-111). Atlantis Press. 10.2991/icoi-17.2017.18
16. Fazal-e-Hasan, S. M., Mortimer, G., Ahmadi, H., Adil, M., & Sadiq, M. (2024). Examining the impact of tourists’ hope, knowledge and perceived value on online hotel booking intentions. Asia Pacific Journal of Tourism Research, 29(6), 719–735. https://doi.org/10.1080/10941665.2024.2343058
17. Fang, J., Wen, C., George, B.T., & Prybutok, V.R. (2016). Consumer Heterogeneity, Perceived Value, and Repurchase Decision-Making in Online Shopping: The Role of Gender, Age, and Shopping Motives. Journal of Electronic Commerce Research, 17:116-131.
18. Ficky Tyoga Aditya, Mahrinasari Mahrinasari, Roslina (2023). Influence of E-Service Quality, E-Trust, E-Satisfaction, and E-WOM on Repurchase Intention at Travel Media Online (Traveloka) in Indonesia. Journal of Economics, Finance and Management Studies ISSN (print): 2644-0490, ISSN (online): 2644-0504 Volume 06 Issue 10 October 2023 Article. https://doi.org/10.47191/jefms/v6-i10-20
19. Gao, Z., Cheah, J. H., Lim, X. J., Ng, S. I., Cham, T. H., & Yee, C. L. (2024). Can travel apps improve tourists’ intentions? Investigating the drivers of Chinese gen Y users’ experience. Journal of Vacation Marketing, 30(3), 505-534. https://doi.org/10.1177/13567667231152938
20. Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial management & data systems, 117(3), 442-458.
21. Hair, Joseph F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
22. Hair, Joseph F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
23. Hermawan, D. (2022). The effects of web quality, perceived benefits, security and data privacy on behavioural intention and e-WOM of online travel agencies. International Journal of Data and Network Science, 6(3), 1005-1012. https://doi.org/10.5267/j.ijdns.2022.1.011
24. Hewei, T., & Youngsook, L. (2022). Factors affecting continuous purchase intention of fashion products on social E-commerce: SOR model and the mediating effect. Entertainment computing, 41, 100474. https://doi.org/10.1016/j.entcom.2021.100474
25. Islam, M. T. (2021). Application of social media in the tourism industry: A review. SEISENCE Journal of Management, 4(1), 59–68. ISSN: 2617-5770 (online) https://doi.org/10.3321 5/sjom.v4i1.556.
26. Jedin, M. H., & Ranjini, K. (2017). Exploring the key factors of hotel online booking through online travel agency. International Journal of Business Information Systems (IJBIS), Vol. 35, No 1, 2020. http://www.inderscience.com/link.php?id=109531
27. Jaswal, S., & Singh, A (2020). Influence of the Determinants of Online Customer Experience on Online Customer Satisfaction. Paradigm Research Journal.  Volume 24 Issue 1, June 2020. DOI:1177/0971890720914121
28. Jeong, M., & Shin, H. H. (2020). Tourists’ experiences with smart tourism technology at smart destinations and their behaviour intentions. Journal of Travel Research, 59(8), 1464-1477. https://doi.org/10.1177/0047287519883034
29. Jones, S. A., Aiken, K. D., & Boush, D. M. (2009). Integrating Experience, Advertising, and Electronic Word of Mouth. Journal of Internet Commerce, 8(3–4), 246–267. https://doi.org/10.1080/15332860903467664
30. Kim, S. E., Lee, K. Y., Shin, S. I., and Yang, S. B. (2017). Effects of tourism information quality in social media on destination image formation: The case of Sina Weibo. Inf. Manag. 54, 687–702. https://doi.org/10.1016/j.im.2017.02.009
31. Kim, J. J., Chua, B. L., & Han, H. (2021). Mobile hotel reservations and customer behaviour: Channel familiarity and channel type. Journal of Vacation Marketing, 27(1), 82-102. https://doi.org/10.1177/1356766720952122
32. Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
33. Kuo, Y. F., Wu, C. M., & Deng, W. J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887–896. https://doi.org/10.1016/j.chb.2009.03.003
34. Kaya, B., Behravesh, E., Abubakar, A. M., Kaya, O. S., & Orús, C. (2019). The moderating role of website familiarity in the relationships between e-service quality, e-satisfaction and e-loyalty. Journal of Internet Commerce, 18(4), 369-394. https://doi.org/10.1080/15332861.2019.1668658
35. K. W., Han, H., & Hwang, J. (2017). Developing a matrix for assessing serviceability of US online travel agency websites. Social Behavior and Personality: an international journal, 45(1), 115-126.
36. Liu, C., Marchewka, J. T., Lu, J., & Yu, C. S. (2005). Beyond concern—a privacy-trust-behavioural intention model of electronic commerce. Information & Management, 42(2), 289-304.
37. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. The MIT Press. Hospitality Global Market Report (Nov 1, 2023) Hospitality Industry statistics to have on your radar 2024. https://hospitalityinsights.ehl.edu/hospitality-industry
38. Ng, W. X., Cheng, C.Y., Quah, W.B (2023). Online Travel Agents (OTAs) and Malaysian’s Repurchase Intention based on “The Three Musketeers”–Brand Image, Perceived Value, and Trust. Team Journal of Hospitality & Tourism 20(1), 51-64. (PDF) Help! Help! The Invasion of Artificial Intelligence Technology Towards Travel and Tourism Industry: What Malaysian Say About It (researchgate.net)
39. Ozturk, A.B., Bilgihan, A., Nusair, K., Okumus, F(2017). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. Int. J. Inf. Manage., 36 (6) (2016), pp. 1350-1359. https://doi.org/10.1016/j.ijinfomgt.2016.04.005
40. Pee, L.G., James Jiang, Gary Klein (2018). Signaling effect of website usability on repurchase intention. International Journal of Information Management, Volume 39, 2018, Pages 228-241, ISSN 0268-4012,  https://doi.org/10.1016/j.ijinfomgt.2017.12.010
41. Pham, H., & Nguyen, T. (2019). The effect of website quality on repurchase intention with the mediation of perceived value: The case study of online travel agencies in Vietnam. Journal of Global Business Insights, 4(1), 78-91. https://doi.org/10.5038/2640-6489.4.1.1041
42. Pinto, I., & Castro, C. (2019). Online travel agencies: factors influencing tourists’ purchase decision. Tourism & Management Studies, 15(2), 2019, 7-20. DOI:18089/tms.2019.150201
43. Prodromos Chatzoglou, Dimitrios Chatzoudes, Athina Savvidou, Thomas Fotiadis, Pavlos Delias (2022). Factors affecting repurchase intentions in retail shopping: An empirical study. Heliyon, Volume 8, Issue 9, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2022.e10619.
44. Razak, N. S. A., Marimuthu, M., Omar, A., & Mamat, M. (2014). Trust and repurchase intention on online tourism services among Malaysian consumers. Procedia –Social and Behavioral Sciences, 130, 577-582. https://doi.org/10.1016/j.sbspro.2014.04.067
45. (2023). Gen Z travel trends & statistics in 2023. https://www.roller.software/blog/gen-z-travel-trends-and-statistics
46. Ryu, S., & Park, J. (2020). The effects of benefit-driven commitment on usage of social media for shopping and positive word-of-mouth. Journal of Retailing and Consumer Services, 55, 102094. https://doi.org/10.1016/j.jretconser.2020.102094
47. Sadie, W (Dec 28, 2023). How has the Internet affected the travel & Tourism Industry.  Tourist Secrets. Retrieved August 11, 2024, from  https://www.touristsecrets.com/travel-tips/how-has-the-internet-affected-the-travel-and-tourism-industry/
48. Sajjad Nazir, Sahar Khadim, Muhammad Ali Asadullah, Nausheen Syed (2023), Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach, Technology in Society, Volume 72, 2023, 102190, ISSN 0160-791X. https://doi.org/10.1016/j.techsoc.2022.102190.
49. Shalini, T., Amandeep, D., Puneet, K., Matti, M., (2020). Why do people purchase from online travel agencies (OTAs)? A consumption values perspective. International Journal of Hospitality Management Volume 88, July 2020, 102534. https://doi.org/10.1016/j.ijhm.2020.102534
50. Statista, 2023. Digital Travel Sales Worlwide from 2019 to 2023 (in Billion US Dollars) Retrieved – August 11, 2024 from https://statista.com/statistics/934995/revenue-of-leading-otas-worldwide/
51. Setiawan, P., & Widanta, A. (2021). The effect of trust on travel agent online use: Application of the technology acceptance model. International Journal of Data and Network Science, 5(3), 173-182. https://doi.org/10.5267/j.ijdns.2021.6.015
52. Sidanta, K., Christian, B., Hadi, H., Angelia, W., & Istijanto, I. (2022). Faktor-Faktor yang memengaruhi kepuasan konsumen milenial pada e-commerce Di Indonesia. Indonesian Business Review, 5(2), 156-173. https://doi.org/10.21632/ibr.5.2.156-173
53. Sharma, M., Antony, R., Sehrawat, R., Cruz, A. C., & Daim, T. U. (2022). Exploring post-adoption behaviors of e-service users: Evidence from the hospitality sector/online travel services. Technology in Society, 68, 101781. https://doi.org/10.1016/j.techsoc.2021.101781
54. San-Martín, S., Jimenez, N., & Liebana-Cabanillas, F. (2020). Tourism value VS barriers to booking trips online. Journal of Retailing and Consumer Services, 53, https://doi.org/10.1016/j.jretconser.2019.101957.
55. Souzsa, H (Retrieved 13 August 2024), Third party sites vs booking directly. https://www.touristsecrets.com/travel-tips/third-party-sites-vs-booking-directly/
56. Wang, Huimin. and Yan, Jinzhe (2022). Effects of social media tourism information quality on destination travel intention: Mediation effect of self-congruity and trust. Front Psychol. 2022; 13: 1049149. doi: 3389/fpsyg.2022.1049149
57. Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Why do people purchase from online travel agencies (OTAs)? A consumption values perspective. International Journal of Hospitality Management, 88, 102534. https://doi.org/10.1016/j.ijhm.2020.102534
58. Yoon, S., Oh, S., Song, S., Kim, K. K., & Kim, Y. (2014). Higher quality or lower price? How value-increasing promotions affect retailer reputation via perceived value. Journal of business research, 67(10), 2088-2096. https://doi.org/10.1016/j.jbusres.2014.04.017
59. Yang, C.L. and Nair, V. (2014), “Risk perception study in tourism: are we really measuring perceived risk”, Procedia-Social and Behavioral Sciences, Vol. 144 No. 1, pp. 322-327. DOI: Doi: 10.1016/j.sbspro.2014.07.302
60. Yürük-Kayapinar, P. (2021). The mediating role of security and privacy on the relationship between customer interface features and e-word of mouth marketing. TURKISH JOURNAL OF MARKETING, 6(2), 125–142. https://doi.org/10.30685/tujom.v6i2.118
61. Zailin, Z.A., Nur Khairina, A.R., Azizul, Y.Y., Siti, S.O., Baker, R., Nur Surrayya, M.S. (Jan 2020). Malaysian Consumers’ Attitudes Towards Online Travel Website. Solid State Technology 63(6):8374-8385. (PDF) Malaysian Consumers’ Attitudes Towards Online Travel Websites (researchgate.net)
62. Zeqiri, J., Ramadani, V., & Aloulou, W. J. (2023). The effect of perceived convenience and perceived value on intention to repurchase in online shopping: the mediating effect of e-WOM and trust. Economic research-Ekonomska istraživanja, 36(3). https://doi.org/10.1080/1331677X.2022.2153721