In recent years, the rapid advancement of technology and the proliferation of internet access have revolutionized the travel and tourism industry (Pencarelli, 2020). Among the most significant transformations in this domain has been the rise of online travel portals, which provide a one-stop solution for travellers to book various services, including transportation, accommodation, tours, and more. As consumer preferences shift towards convenience and efficiency, online travel portals have become a vital medium for booking accommodations, offering users the ability to compare prices, read reviews, and make reservations from the comfort of their homes (Datta, 2021). Given the importance of accommodation services in the overall travel experience, understanding the factors that affect consumer satisfaction and post-purchase behaviour in this segment is crucial for service providers to maintain competitiveness and retain customers.

Accommodation services, including hotels, guest houses, and vacation rentals, form a significant component of the offerings on online travel portals (Kowalska et al., 2023). In today’s competitive landscape, the quality of these services directly influences consumer satisfaction, which in turn impacts their decision to make repeat purchases or recommend the services to others. This has prompted travel portals to prioritize the enhancement of accommodation services by focusing on user-friendly booking experiences, competitive pricing, and ensuring the reliability of the service providers they feature. However, while online travel portals have succeeded in providing ease and accessibility, challenges remain in understanding the full spectrum of factors influencing consumer satisfaction, particularly in a market where consumer preferences are constantly evolving (Talwar et al., 2020).

The rapid growth in the use of online platforms for travel-related services has not only changed the way consumers book their travel accommodations but also how they engage with these platforms post-purchase. Post-purchase behaviour, which encompasses actions such as feedback, reviews, loyalty, and word-of-mouth recommendations, plays a pivotal role in the success of online travel portals. Therefore, identifying the key determinants of consumer satisfaction and understanding how these influence post-purchase behaviour are important for improving the long-term sustainability and profitability of these platforms.

The Evolution of Online Travel Portals

The online travel industry emerged as an essential component of the e-commerce ecosystem, driven by technological innovations and changes in consumer behaviour (Rita & Ramos, 2021). Early forms of online travel services focused primarily on flight bookings, but over time, the market expanded to include accommodations, transportation, tours, and activities. Today, online travel portals like Booking.com, Airbnb, Expedia, and MakeMyTrip offer consumers a comprehensive range of services at competitive prices, consolidating the fragmented travel market into easily accessible digital platforms (Perelygina et al., 2022 ).

Accommodation services offered by these platforms are often the primary driver of consumer engagement, as choosing the right accommodation is one of the most critical aspects of planning a trip. Online travel portals provide various tools and features, such as price comparisons, user-generated reviews, and visual content (photos and videos), to help consumers make informed decisions. These platforms have redefined the travel experience by eliminating the need for intermediaries like travel agents and empowering consumers to customize their travel experiences based on individual preferences.

The shift towards online booking for accommodations has also led to the rise of personalized services. Consumers can filter accommodations by preferences such as location, price range, amenities, and reviews, further enhancing their decision-making process.

Consumer Satisfaction

Consumer satisfaction is a fundamental determinant of success for businesses in the travel and tourism industry. For online travel portals, the ability to satisfy consumers with their accommodation services is critical, as it directly impacts their reputation, customer retention, and revenue (Sharma et al., 2022). The concept of satisfaction in this context refers to the degree to which a consumer's expectations are met or exceeded by the accommodation service they booked through the portal.

In the realm of accommodation services, several factors influence consumer satisfaction. These include the quality of the accommodation, the ease of the booking process, the accuracy of the information provided on the portal, and the customer support provided in case of issues. Additionally, factors such as the cleanliness, location, and amenities of the accommodation, as well as the behaviour of the staff, also play a significant role in shaping consumer perceptions (Lockwood & Pyun, 2020).

One of the key features that online travel portals offer to enhance consumer satisfaction is the ability to read and contribute reviews. User-generated content, such as ratings and reviews, provides potential customers with first-hand insights into the experiences of previous guests. This feedback loop is a critical component of the decision-making process for consumers, as they rely on the experiences of others to assess the quality and reliability of the accommodation options available.

Post Purchase Behaviour

Post-purchase behaviour plays a critical role in shaping the reputtion and future success of online travel portals. After consumers have completed a booking or stayed at an accommodation, their actions—including leaving reviews, engaging with customer service, and demonstrating loyalty—affect both the portal and the accommodation provider (Halvorsen et al., 2024). Online travel portals strongly encourage consumers to leave reviews and feedback on their accommodation experiences, as these reviews serve as a form of social proof for future customers (Wolters, 2022). Positive reviews can significantly boost bookings, not only for the accommodation but also for the portal itself, as consumers tend to rely heavily on the experiences of others when making decisions. On the other hand, negative reviews can damage the reputation of both the accommodation and the platform, making it vital for providers to ensure that they deliver a satisfactory experience.

Customer Satisfaction in Online Travel Portals for Accommodation Services

Customer satisfaction is one of the most critical factors contributing to the success of online travel portals. In the context of accommodation services, satisfaction is influenced by several interrelated factors, including service quality, pricing, user experience on the website, and trust in the platform. Understanding these factors is vital for online travel portals to create positive customer experiences and ensure long-term business sustainability.

Service Quality and Accommodation Experience

Service quality is central to customer satisfaction, particularly in the hospitality industry. SERVQUAL model has been applied extensively to measure service quality in both physical and digital environments (Zeithaml et al., 1996). In online travel portals, service quality extends to the accuracy of information provided about accommodations, the convenience of booking, and the post-purchase services offered (Datta et al., 2018). Consumers expect online descriptions of rooms, facilities, and amenities to match the actual service provided by the accommodation provider. Discrepancies between the advertised quality and the real experience often lead to dissatisfaction (Cai & Chi, 2021).

Pricing and Perceived Value for Money

Pricing is another significant determinant of customer satisfaction in online travel portals. Research by (Uzir et al., 2020) highlights the relationship between perceived value and satisfaction, showing that consumers’ assessment of whether they receive value for the money spent significantly affects their overall satisfaction. In the accommodation sector, customers often compare pricing across different platforms before making a booking decision. Fair pricing and transparent cost structures positively impact consumer satisfaction, while hidden fees or misleading pricing practices can lead to negative experiences (Rama, 2020).

Moreover, dynamic pricing strategies, which are commonly used by accommodation providers, have been examined in relation to their effects on consumer satisfaction. Consumers who perceive that prices are fluctuating unfairly or arbitrarily may feel exploited, leading to dissatisfaction (Delvasto & Acevedo, 2024). As a result, transparent pricing models and fare discounting strategies can enhance the trust and satisfaction that customers have in online travel portals.

Website Usability and User Experience

In online travel portals, the quality of the user experience on the website or mobile application is also essential for customer satisfaction. A study by (Wong et al., 2020) found that website usability factors, such as ease of navigation, mobile responsiveness, and the simplicity of the booking process, significantly influence satisfaction levels. Consumers appreciate platforms that allow for seamless searches, provide easy-to-use filters, and facilitate quick bookings. (Yang et al., 2024) further argued that the personalization of user interfaces, such as offering tailored accommodation recommendations based on previous bookings or consumer preferences, could enhance satisfaction by reducing the effort required to make decisions.

The role of secure and convenient payment processes also plays an integral part in shaping customer satisfaction. Research by (Ramanathan et al., 2020) suggests that consumers are more likely to be satisfied with an online travel portal if they feel their transactions are secure and transparent. A study by (Ruiz-Alba, 2022) also found that clear and immediate booking confirmations contribute to consumer confidence, increasing overall satisfaction with the platform.

Consumer Trust and Information Accuracy

Trust in online travel portals is another critical factor influencing customer satisfaction. Consumers rely heavily on the information provided by these portals to make informed decisions about their accommodation choices. According to (Huang & Liang, 2021), accurate and detailed information, including photos, room descriptions, and verified customer reviews, can enhance consumer trust. Inaccurate or exaggerated claims by accommodation providers, on the other hand, often result in customer dissatisfaction and can damage the reputation of both the travel portal and the provider.

Consumer trust is further reinforced by reliable user-generated content, such as ratings and reviews. (Miao et al., 2022) emphasizes that trust in e-commerce platforms, including online travel portals, not only increases customer satisfaction but also encourages repeat business. Platforms that implement strict guidelines for authentic reviews and transparent ratings are more likely to maintain consumer trust and satisfaction.

Post-Purchase Behaviour in Online Travel Portals for Accommodation Services

Post-purchase behaviour refers to the actions and attitudes consumers exhibit after completing their purchase, such as leaving reviews, participating in loyalty programs, and recommending the service to others. In the context of online travel portals, post-purchase behaviour is closely linked to the satisfaction consumers derive from their experience with both the platform and the accommodation.

Leaving Reviews and Feedback

One of the most visible forms of post-purchase behaviour in the accommodation sector is the act of leaving reviews or feedback. According to (Li et al., 2013), online reviews serve as a critical source of information for other potential consumers. Positive reviews act as a form of social proof, encouraging others to book accommodations through the same portal. Conversely, negative reviews can deter future customers and affect both the accommodation provider and the travel portal itself.

(Wu et al., 2016) highlight that consumers are more likely to leave reviews if their experiences are either extremely positive or negative. The act of leaving feedback not only influences the choices of other users but also gives consumers a sense of participation in the broader online community. In this way, post-purchase reviews serve as an outlet for customers to express their satisfaction or dissatisfaction, making them a powerful tool for both marketing and service improvement.

Loyalty and Repeat Bookings

Loyalty is another critical component of post-purchase behaviour in the online travel portal industry. Satisfied customers are more likely to return to the same platform for future bookings, leading to repeat business and increased customer lifetime value. A study by (Gogoi, 2020) found that satisfaction is a strong predictor of customer loyalty, especially in service-oriented industries like travel and hospitality.

Many online travel portals have implemented loyalty programs to encourage repeat bookings. According to (Hajdukiewicz, 2016), offering personalized discounts, special promotions, or rewards points can incentivize customers to remain loyal to a particular portal. In addition to direct financial benefits, personalized offerings based on a customer’s preferences and booking history can increase loyalty by providing a more tailored experience (Tomczyk et al., 2022). Loyalty, in turn, is closely linked to positive word-of-mouth, which can significantly influence the platform's reputation and attractiveness to new customers.

Word-of-Mouth and Recommendations

Satisfied customers are more likely to engage in word-of-mouth marketing, recommending the platform and its services to their social circles. According to (Zeithaml et al., 1996), positive word-of-mouth plays a crucial role in acquiring new customers, as it builds trust and credibility for the platform. In today’s digital age, recommendations often extend beyond personal networks to social media platforms, where consumers share their experiences with a wider audience.

A study by (Minazzi & Minazzi, 2015) demonstrated the importance of social media in amplifying word-of-mouth effects in the travel and accommodation sectors. Satisfied customers are more likely to post about their experiences on platforms like Facebook, Instagram, or Twitter, further enhancing the visibility and reputation of both the travel portal and the accommodation provider. These posts not only act as testimonials but also encourage others to use the same platform when planning their travels.

Handling Complaints and Negative Feedback

Handling complaints and negative feedback is an essential aspect of post-purchase behaviour that can influence future customer satisfaction. According to (Liu et al., 2021), the way online travel portals respond to complaints, process refunds, and handle issues such as cancellations or booking errors can significantly impact consumer perceptions. Platforms that offer prompt and fair resolutions are more likely to retain customers, even after a negative experience.

Furthermore, (Mazhar et al., 2022) found that customer support is a crucial element of post-purchase behaviour in online travel portals. When consumers feel their issues are addressed promptly and professionally, they are more likely to continue using the platform for future bookings, regardless of initial dissatisfaction (Chen et al., 2022). This indicates the importance of post-purchase customer service in maintaining long-term relationships with consumers.

Research Gap

The review of existing literature highlights significant advancements in understanding consumer satisfaction and post-purchase behaviour in online travel services, particularly with respect to booking platforms. However, a research gap remains concerning the accommodation services provided by these online travel portals (OTPs). While much focus has been placed on overall customer experiences with booking and travel arrangements, limited studies address the specific factors that influence consumer satisfaction with accommodations booked via OTPs, especially in the context of post-purchase behaviours such as reviews, loyalty, and recommendations. Moreover, there is a lack of research specifically focused on the Indian market and how consumer satisfaction and post-purchase behaviours differ based on cultural, demographic, and service expectations in the accommodation sector. This study aims to fill this gap by exploring these determinants in the Indian context, providing actionable insights for improving service quality and consumer engagement.

The present study adopted an exploratory research design to gain insights into consumer satisfaction and post-purchase behaviour, specifically among tourists who booked accommodation services through Online Travel Portals (OTPs). The aim was to explore key factors influencing consumer experiences and the behavioural outcomes following their use of such services.

Research Objectives:

• To explore factors affecting consumer satisfaction with the online services provided by Online Travel Portals.
• To analyse the post-purchase behaviour of consumers after availing online services.

To gather data, a self-administered structured and non-disguised questionnaire was designed, focusing on measuring two primary constructs: consumer satisfaction and post-purchase behaviour regarding the accommodation services offered by online travel agencies. The questionnaire was developed based on existing literature and validated scales, ensuring its relevance to the research objectives. Prior to the full-scale study, a pilot test was conducted to evaluate the clarity, reliability, and validity of the instrument. A total of 18 questionnaires were completed by participants with the direct assistance of the researcher. This pilot testing process helped to identify minor issues with the wording and interpretation of some questions. Based on the feedback, minor revisions were made to improve the clarity and avoid any ambiguity in the final version of the questionnaire.

The study targeted travellers in the Delhi NCR region who had used online travel portals for booking accommodation during the period of April to September 2024. These travellers were selected regardless of the purpose of their trips, which included travel, excursions, business trips, and leisure vacations. A convenience sampling method was employed to reach participants who were willing to respond to the survey. Out of 240 distributed questionnaires, 229 responses were collected. However, 16 questionnaires were found incomplete and were excluded from further analysis. This left a total of 213 usable responses for statistical analysis. The collected data was analysed to identify patterns and relationships between the identified variables, using methods such as exploratory factor analysis (EFA) to determine the factors influencing satisfaction and post-purchase behaviour, as well as regression analysis to understand the strength of these relationships.

When the above data was collected and cleaned, it was used to perform analysis on SPSS and SmartPLS. The results and discussions have been elaborated in the upcoming sub-sections.

Objective 1: To explore factors affecting consumer satisfaction with the online services provided by Online Travel Portals.

After collecting and cleaning the data, it was analysed using SPSS and SmartPLS. The results and discussions are detailed in the following sub-sections.

The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity provide important insights into the suitability of data for factor analysis in the study of consumer satisfaction and post-purchase behaviour in online travel portals. The KMO value of 0.966 is significantly higher than the recommended threshold of 0.6, indicating that the sample size is more than adequate for conducting factor analysis. A KMO value closer to 1 suggests that the correlations among variables are compact, which allows for reliable factor extraction.

Bartlett's Test of Sphericity yields a Chi-Square value of 3413.642, with a significance level (p-value) of 0.000. This result is highly significant, confirming that the correlation matrix is not an identity matrix and that factor analysis is appropriate. The test suggests that there are meaningful relationships among the variables under study, making them suitable for exploring the underlying factors affecting consumer satisfaction on online travel portals.

The communalities table from the Principal Component Analysis (PCA) highlights how much variance in each item (CS1 to CS20) is explained by the extracted factors, which are critical to understanding consumer satisfaction in online travel portals.

Initially, all items have a variance of 1.000, as expected. The extraction column shows how much of that variance is retained after factor extraction. For example, CS14 has the highest extraction value of 0.737, indicating that 73.7% of the variance in CS14 is explained by the factors, suggesting it is highly relevant to consumer satisfaction. Conversely, CS18 has the lowest extraction value of 0.595, but still above the acceptable threshold (0.5), which suggests it contributes less to the factors but is still relevant.

In general, the communalities show that the majority of the items retain a substantial proportion of their variance, validating their inclusion in the analysis. This suggests that the identified factors effectively explain the main drivers of customer satisfaction in online travel portals.

The "Total Variance Explained" table from the Principal Component Analysis (PCA) provides key insights into the factors affecting consumer satisfaction in online travel portals. The table shows how much of the total variance in the data is explained by the extracted components (factors). In this case, two components were extracted, as indicated by the Extraction Sums of Squared Loadings and Rotation Sums of Squared Loadings.

The first column under Initial Eigenvalues highlights how much variance each component explains before rotation. The first component explains 61.943% of the total variance, which is a significant portion, suggesting that a large part of consumer satisfaction can be captured by this component. The second component explains an additional 3.731%, bringing the cumulative variance explained to 65.675%. The remaining components explain much smaller portions of the variance, and their eigenvalues are below 1, making them less relevant for interpretation.

The extraction values indicate that the first component continues to explain 61.943% of the variance even after factoring in the extraction process. This confirms the dominance of the first component in explaining consumer satisfaction. Together, both components explain a cumulative 65.675% of the variance, which is considered a strong explanatory power in social science research, implying that these two components effectively capture the majority of consumer satisfaction determinants.

After rotation, the variance explained by the two components is more evenly distributed. The first component now explains 33.506% of the variance, and the second explains 32.169%, providing a more balanced interpretation. Rotation helps to achieve simpler and more interpretable factors, spreading the explanatory power more evenly and making it easier to understand how each component contributes to customer satisfaction.

Overall, this analysis shows that two key components account for approximately 65.675% of the variance, providing a robust framework for understanding the primary factors affecting consumer satisfaction in online travel portals. This highlights the complexity of consumer satisfaction, which is influenced by multiple intertwined factors, but primarily driven by the two extracted components.

Figure 1. Scree Plot

The scree plot visually supports the results of the "Total Variance Explained" table by showing a clear elbow at the second component. This indicates a sharp drop in the eigenvalues after the second component, which suggests that the first two components capture the most significant variance in the dataset. After component 2, the eigenvalues drop substantially, with the remaining components contributing only marginally to the total variance. The scree plot thus validates the decision to retain two components for further analysis, as these components explain the majority of the variance (65.675%). The plot effectively illustrates the diminishing importance of the subsequent components, reinforcing that the first two factors are the most critical in explaining consumer satisfaction in online travel portals.

The rotated component matrix using Varimax rotation with Kaiser normalization shows the loading of each variable onto two distinct components. This table helps in identifying two key factors that represent distinct dimensions of consumer satisfaction when using online travel portals for booking accommodations.

Component 1 has high loadings on CS2, CS3, CS4, CS6, CS7, CS9, CS10, CS12, CS13, CS15, and CS20. The variables associated with this component primarily focus on "Pricing and Perceived Value" and "Accommodation Experience". These factors reflect the consumer's perception of value, pricing, and the quality of the accommodation experience, indicating that consumer satisfaction is influenced by the perceived value for money and the accuracy of information provided during the booking process. Customers place high importance on the actual services meeting their expectations and the pricing being transparent and competitive. Factor 1: Pricing and Accommodation Experience – This factor encompasses variables related to the consumer's satisfaction with the pricing, value for money, and the overall accommodation experience. It covers aspects such as the reasonableness of the pricing, perceived value, and alignment of expectations with the actual service provided by the accommodation.

Component 2 has high loadings on CS1, CS5, CS8, CS11, CS14, CS16, CS17, CS18, and CS19. This component is more associated with "Website Usability and Trust". The variables reflect aspects related to the online travel portal's user experience, including ease of navigation, clarity of information, accuracy of the descriptions, and trust in the platform. The trust factor is crucial as it encompasses both the security of payment systems and the reliability of the information provided. This suggests that consumers expect a seamless, reliable, and trustworthy user interface when booking accommodations online. Factor 2: Website Usability and Consumer Trust – This factor captures consumer satisfaction with the usability of the online portal and their trust in the platform. It includes ease of use, visual appeal, accuracy of the information presented, and the security of the payment system.

These two components together explain a significant portion of the variance in consumer satisfaction. Factor 1 suggests that consumers value an accommodation experience that aligns with their expectations and pricing transparency, while Factor 2 highlights the importance of a user-friendly and trustworthy website interface. Together, they provide a comprehensive view of the key drivers of consumer satisfaction in online travel portals.

Figure 2. SEM Model (CFA)

The SEM model confirms the factor structure of consumer satisfaction derived from EFA, validating two key latent constructs. The model visually represents 20 observed variables (CS1 to CS20), which load onto two latent constructs. Factor loadings, ranging from 0.782 to 0.904, suggest that each observed variable strongly contributes to its respective factor, confirming internal consistency and construct validity.

The strong correlation (0.950) between the two latent constructs indicates a significant relationship, implying that consumers' satisfaction with the online travel portal is highly linked to their satisfaction with the

accommodation. This suggests that the ease of use, trust in information, and reliability of the portal have a major influence on the perceived quality and experience of the accommodation.

Overall, the SEM model demonstrates that both Pricing and Accommodation Experience and Website Usability and Consumer Trust are critical drivers of overall consumer satisfaction, and improvements in one area are likely to enhance the other. This validation offers a comprehensive view of how digital platforms and service quality work in tandem to shape consumer experiences in the online travel sector.

The table provides standardized factor loadings from the SEM model, showing how each observed variable (CS1 to CS20) relates to one of two latent factors. Factor 1 is predominantly linked to variables concerning accommodation experience (e.g., CS1, CS11, CS14), with loadings ranging from 0.757 to 0.819, indicating strong contributions. Factor 2 represents online travel portal usability and trust (e.g., CS10, CS12, CS15), with loadings ranging from 0.741 to 0.804. High factor loadings (above 0.70) suggest that all variables significantly contribute to their respective constructs, confirming that both accommodation and portal satisfaction are critical dimensions of overall consumer satisfaction.

Table 6 provides key metrics for assessing the reliability and validity of the two latent constructs—Factor 1 (Pricing and Accommodation Experience) and Factor 2 (Website Usability and Consumer Trust). Cronbach's alpha for both Factor 1 (0.937) and Factor 2 (0.944) exceeds the acceptable threshold of 0.7, indicating high internal consistency and reliability of the items within each factor. Composite reliability (rho_c) values are also very high (Factor 1: 0.938, Factor 2: 0.944), confirming that the observed variables consistently measure their respective constructs. Average Variance Extracted (AVE) values for Factor 1 (0.625) and Factor 2 (0.607) are above the recommended threshold of 0.50, demonstrating good convergent validity—i.e., the constructs explain more than 50% of the variance in the observed variables. The table confirms that both factors exhibit strong reliability and validity, meaning the model is robust in measuring consumer satisfaction for accommodation services on online travel portals.

Table 7 presents the Model Fit Results, comparing the estimated model with the null model. Chi-square (χ²) for the estimated model (341.999) is significantly lower than that of the null model (3555.529), indicating better fit. The ChiSqr/df ratio (2.024) is within the acceptable range (≤3), suggesting a good model fit. RMSEA (Root Mean Square Error of Approximation) for the estimated model is 0.069, below the threshold of 0.08, indicating a reasonable fit. The confidence interval (90% CI: 0.059–0.080) further supports this. GFI (0.868) and AGFI (0.837) values, while slightly below the ideal threshold of 0.9, still indicate an acceptable fit. PGFI (0.699) shows a balanced fit relative to model complexity. SRMR (0.034), a measure of residuals, is well below 0.08, showing an excellent fit. High values for CFI (0.949), TLI (0.942), and NFI (0.904) further confirm good comparative model fit. Overall, these indices indicate that the estimated SEM model fits the data well and significantly outperforms the null model, supporting its validity.

Objective 2:         To analyse the post-purchase behaviour of consumers after availing online services

In order to achieve the above mentioned objective, we conducted regression analysis using Smart PLS, and the results are summarized below.

Regression Analysis Using SmartPLS

Figure 3. Structural Model Using SmartPLS

The provided structural equation model (SEM) visually depicts the relationship between customer satisfaction (CS) and post-purchase behaviour (PPB). The model shows a direct path from customer satisfaction (in yellow) to post-purchase behaviour (in blue) with a coefficient of 0.718, indicating a strong positive influence. The associated p-value is 0.000, suggesting that the relationship is statistically significant at the 1% level. This implies that customer satisfaction significantly improves post-purchase behaviour.

The Intercept is 0.000 (p=0.000), showing no notable additional influence on post-purchase behaviour beyond the effect of customer satisfaction. The R-squared value for post-purchase behaviour is 0.847, which means that customer satisfaction explains 57.6% of the variance in post-purchase behaviour. Overall, the model suggests that customer satisfaction plays a critical role in enhancing post-purchase behaviour, and the results are highly reliable with a p-value of 0.

Table 8. R Square Results (SmartPLS)

The R-square value of 0.718 indicates that the model explains 71.8% of the variance in post-purchase behaviour (PPB), suggesting a strong fit. The adjusted R-square (0.717) is close to the R-square value, implying minimal overfitting and further reinforcing the model’s reliability. The Durbin-Watson test value of 2.178 is within the acceptable range (1.5 to 2.5), indicating that there is no significant autocorrelation in the residuals. This suggests that the regression model is both statistically sound and robust in predicting post-purchase behaviour based on the input variables.

Table 9. Summary Coefficients (SmartPLS)

The unstandardized coefficient of 0.784 for customer satisfaction (CS) suggests that for every one-unit increase in CS, post-purchase behaviour (dependent variable) increases by 0.784 units. The standardized coefficient of 0.847 indicates a strong positive relationship between CS and post-purchase behaviour. The T-value of 23.183 (greater than 2) and a P-value of 0.000 (less than 0.05) show that the relationship is highly statistically significant. The standard error (SE) of 0.034 indicates low variability in the coefficient estimate. The 95% confidence interval ranges from 0.717 to 0.850, further confirming the precision of the estimate. The intercept value of 0.622 is also statistically significant with a P-value of 0.000.

Table 10. Summary ANOVA (SmartPLS)

The ANOVA table indicates that the regression sum of squares (128.885) reflects the variation explained by the model, while the error sum of squares (50.598) represents the unexplained variation. The mean square for regression (128.885) is significantly higher than the mean square for error (0.240), resulting in a large F-value of 537.468, indicating that the regression model is statistically significant. The P-value of 0.000 confirms this, as it is well below the standard significance threshold of 0.05, implying a strong and significant relationship between the independent variable(s) and post-purchase behaviour. The model effectively explains a substantial portion of the variance in the dependent variable.

Table 11. Coefficients (SmartPLS)

The coefficient table shows that customer satisfaction (CS) has a standardized coefficient of 0.847, indicating a strong positive relationship between CS and post-purchase behaviour (PPB). This suggests that an increase in customer satisfaction significantly enhances post-purchase behaviour. The intercept value of 0.000 implies that when CS is zero, the model predicts no baseline level of post-purchase behaviour. Overall, CS is a key determinant in influencing PPB.

Figure 4. SmartPLS QQ Plot and Histogram

The QQ plot shows deviations from the straight line, indicating that the residuals are not perfectly normally distributed, especially in the tails. The curvature in the plot suggests the presence of some skewness or kurtosis. In the residual histogram, the distribution appears approximately normal but has noticeable deviations, with some bars peaking higher than the normal distribution curve. This combination suggests that while the residuals are close to normal, some deviations from the assumption of normality are present.

The study aimed to analyse consumer satisfaction and post-purchase behaviour in the context of online travel portals, using a comprehensive factor analysis and structural equation modelling (SEM). The results provide valuable insights into the critical factors driving consumer satisfaction and how this satisfaction influences subsequent behaviors.

Factor analysis identified two primary components influencing consumer satisfaction: "Pricing and Accommodation Experience" and "Website Usability and Trust." These components, together explaining 65.675% of the variance, highlight the importance of both the quality of the accommodation and the ease of using the travel portal. Consumers prioritize value for money and transparent pricing while also expecting a seamless and trustworthy online booking experience.

The SEM model confirmed the robustness of these findings, revealing strong loadings for all observed variables on their respective latent factors. High reliability indices, including Cronbach’s alpha and composite reliability, further validated the internal consistency of the model. The strong correlation between the two latent constructs indicates that improvements in one area (e.g., website usability) can enhance the overall accommodation experience and vice versa.

Furthermore, regression analysis using SmartPLS demonstrated a significant relationship between consumer satisfaction and post-purchase behaviour, with a standardized coefficient of 0.847. This suggests that higher consumer satisfaction leads to more favourable post-purchase behaviour, reinforcing the need for online travel portals to focus on both service quality and user experience to foster customer loyalty.

In conclusion, the study confirms that consumer satisfaction is a multifaceted concept driven by both practical factors like pricing and subjective experiences such as trust and usability. Online travel portals must strategically improve these areas to enhance consumer satisfaction and post-purchase engagement.

IMPLICATIONS OF THE STUDY

Societal Implications

The growing reliance on online travel portals has transformed how people plan and book travel accommodations. This study highlights the importance of transparency, information accuracy, and service quality in shaping consumer trust and satisfaction. For society, this means heightened awareness about the critical factors that influence travel decisions, helping consumers make more informed choices. Additionally, by shedding light on post-purchase behaviors like reviews, recommendations, and handling complaints, the study reinforces the role of customer feedback in maintaining accountability and encouraging businesses to uphold ethical standards. Ultimately, the study underscores the growing importance of digital literacy in today's society, helping users to navigate and evaluate online services more effectively.

Managerial Implications

From a managerial perspective, the study offers valuable insights for businesses operating in the online travel sector. The findings suggest that customer satisfaction is largely driven by the quality of services, accuracy of information, and the usability of online platforms. To improve these aspects, managers should invest in enhancing the user experience (UX) of their websites and apps, ensuring smooth navigation, secure transactions, and personalized recommendations.

Additionally, the strong relationship between customer satisfaction and post-purchase behavior highlights the importance of fostering loyalty and encouraging positive word-of-mouth. Managers can leverage this by offering loyalty programs, prompt customer service, and efficient complaint resolution mechanisms. Encouraging satisfied customers to leave reviews and provide feedback can further enhance the reputation of the platform, attracting new users through trust and credibility.

Overall, the study provides a strategic framework for managers to improve consumer engagement, satisfaction, and retention, which are key to long-term business success in the highly competitive online travel industry.

FUTURE SCOPE OF STUDY

The current study provides valuable insights into the factors affecting consumer satisfaction and post-purchase behaviour in the context of online travel portals, specifically for accommodation services. However, several avenues for future research can expand upon these findings and explore additional dimensions of consumer behaviour.

Firstly, this study primarily focuses on online accommodation services. Future research could broaden its scope to include other services provided by online travel portals, such as transportation, tour packages, and dining options. This would provide a more holistic understanding of consumer satisfaction and post-purchase behaviour across the entire spectrum of travel services. Secondly, the study was conducted within a specific geographic region (Delhi NCR) and during a limited time frame (April-September 2024). Future research could extend the geographical scope to include other regions, both within India and internationally, to examine whether consumer behaviour varies across different cultural and regional contexts. Longitudinal studies could also help in understanding how consumer satisfaction and post-purchase behaviour evolve over time, especially in response to changing market dynamics, technological advancements, and consumer preferences. Lastly, given the rapid advancements in digital technologies and artificial intelligence, future studies could investigate the impact of emerging technologies, such as chatbots, virtual reality (VR) tours, and AI-based recommendation systems, on consumer satisfaction and post-purchase behaviour. This would allow businesses to stay ahead of trends and enhance their service offerings in the competitive online travel industry.

By exploring these areas, future research can build on the current findings and offer further practical implications for online travel portals in enhancing customer satisfaction and loyalty.

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