Introduction

The tourism sector is a key driver of the global economy, creating employment, promoting local culture, and boosting economic activities (World Travel and Tourism Council, 2024). It contributed 10% to the global GDP in 2024 (Reuters, 2024). While international tourism is gradually recovering post-pandemic (Statista, 2023), domestic tourism remains the key driver of tourism growth (McKinsey & Company, 2024). India has emerged as a major tourist hotspot, offering diverse experiences (Dixit, 2020). Reflecting global trends, domestic tourism contributes 88.4% to India’s tourism GDP (Munjal, 2023; Statista, 2024a), fueled by the rising spending power of the Indian middle class (Statista, 2024b). In 2024, domestic visitor spending increased to about 185.6 billion USD and is expected to grow to 16.4 trillion USD by the end of 2025 (Dwivedi, 2025).

Although scholarly work in tourism is growing rapidly, researchers have predominantly concentrated on international travel, often overlooking domestic tourism, particularly in emerging markets (Meenakshy et al., 2024; Sahoo et al., 2022). Previous literature has argued that market segmentation of travelers based on generational cohorts is useful due to their unique consumption patterns formed by diverse socio-cultural factors (Gardiner et al., 2015; Rita et al., 2019). However, recent studies recommend preference-based segmentation over generational cohorts in providing actionable market segments (Sahoo et al., 2022).

Very few studies have integrated generational analysis with preference-based segmentation to effectively cater to the diverse travel preferences of Indian tourists (Thangavel et al., 2021). Despite the prominence of the multi-generational trips, Anubha and Shome (2021) argue for an urgent need to examine multigenerational perspectives. Driven by recent surge in domestic tourism (Dwivedi, 2025), India has emerged as a potent market that warrants renewed academic exploration. Therefore, to address these gaps in the literature, this study aims to examine and compare the travel preferences of Baby boomers, Gen X, Gen Y and Gen Z, and classify them into latent profiles, thereby promoting preference-based traveler segmentation.

Given the growth of domestic tourism and limited existing literature, this study provides valuable insights for marketers, destination marketing organizations (DMOs), and policymakers to enhance their service delivery. The identified traveler profiles will enable DMOs and marketers in tailoring services to diverse traveler groups. The study offers insights to local government administrations on key priorities of Indian tourists that enhance seamless travel experiences at the destination.

Literature review

Theoretical foundations

According to the theory of tourism consumption systems (TTCS) (Woodside and Dubelaar, 2002), the tourism consumption system is defined as “the set of related travel thoughts, decisions, and behaviors by a discretionary traveler before, during, and following a trip” (p. 120). TTCS is built upon the foundational study by Becken and Gnoth (2004), which asserts that tourists engage in a chain of decisions both before and after their trip. According to Woodside and Dubelaar (2002), a leisure tour comprises of key behavioral indicators such as “holiday duration”, “accommodation type”, “leisure destination activities”, and “travel companions” of the travelers.

In tourism research, the TTCS is often used to examine travel behavior in different contexts. Recently, Nautiyal et al. (2022) applied the dimensions of TTCS, such as travel frequency, accommodation type, and destination activities, to conceptualize the typology of yoga travelers. The study demonstrated the application of TTCS in a niche tourism context and highlighted the importance of consumption-based segmentation.

Jog et al. (2024) applied the theoretical lens of TTCS to determine whether demographic variables such as age and gender moderate the effect of souvenir attributes on post-purchase dissonance and tourists’ souvenir purchase satisfaction. Their findings identified age as an important moderator. Yang et al. (2019) demonstrated how the TTCS framework can explain tourists’ accommodation choice. These studies reinforce the integrative power of TTCS in capturing the complex interactions between tourists’ preferences, decision-making, and behavioral outcomes across diverse market segments.

Past researchers (Crompton and Petrick, 2024; George, 2021) posit that it is essential to examine the interaction of personal, psychological, social, and cultural factors to understand consumer preferences and behavior in tourism. Therefore, in this study, the TTCS framework was used to understand consumer preferences towards travel, identify segments based on preferences, and explain the characteristics of these segments.

Domestic tourism

Recent tourism literature demonstrates interest in domestic tourism destinations (Matiza and Slabbert, 2024). The COVID-19 pandemic has shifted focus toward domestic tourism (Sotiriadis, 2021). Domestic tourism is defined as “tourism involving residents of one country traveling nationally” (Choo, 2023, p. 1). Its growth over the past decade is well-documented in countries, such as North America (Jiménez-Barreto et al., 2019; Pan et al., 2021), Europe (Žlender and Gemin, 2020), and Australia (Gardiner et al., 2014; 2015). While countries like China, Singapore, and India are actively promoting domestic tourism (Meenakshy et al., 2024), a notable gap remains in the literature on Indian domestic travel (Ahmad et al., 2020; Sahoo et al., 2022).

Past studies (Yousefi and Marzuki, 2015; Giddy and Webb, 2018) examined the preferences of international tourists. Yousefi and Marzuki (2015) found that international tourists are often driven by a desire for novelty, adventure, and cultural exploration, and are also likely to travel for ego enhancement. Giddy and Webb (2018) found that they seek immersive experiences that allow them to engage with different cultures and landscapes. However, the authors suggest that higher costs, visa restrictions, and geopolitical concerns can be significant barriers.

In contrast, domestic tourism often emphasizes convenience, cost-effectiveness, and emotional attachment to familiar environments (Menon et al., 2021; Özel and Kozak, 2012; Shinde, 2015). Özel and Kozak (2012) found that domestic travelers tend to prioritize relaxation, family visits, and regional heritage exploration that reflect a desire for emotional connectivity and reduced travel stress. Menon et al. (2021) highlighted that the domestic visitors are influenced by diverse regional, financial, and cultural aspects. Shinde (2015) found that Indian tourists are influenced by religious beliefs, highlighting pilgrimage tourism to destinations like Varanasi, Tirupati, and Amritsar. Furthermore, experiential tourism, centered on immersive activities such as wildlife safaris, adventure sports, and rural experiences, has emerged (Sneha and Nagarjuna, 2023). Nevertheless, traveling with family members from multiple generations is common in Indian domestic tourism.

Travel preferences and generational cohort theory

Travel preferences are a critical aspect of the travel decision-making process (Karl et al., 2020), influencing choices made before, during, and after a trip (Dixit et al., 2019). In the post-pandemic era, travel preferences have notably shifted, showing changing consumer priorities (Ivanova et al., 2021). Traditionally, tourism marketers have segmented the market using demographic and psychographic variables, such as age, gender, family structure, social class, income, race, ethnicity, and lifestyle (Dixit et al., 2019).

According to generational cohort theory (GCT), individuals belonging to the same generation display similar behavior and share similar beliefs, values, and attitudes (Bravo et al., 2020), given their exposure to similar life experiences (Pennington-Gray et al., 2002). The Center for Generational Kinetics (2024) categorize them as: the silent generation (born in 1945 and before), Baby boomers (born between 1946 and 1964), Gen X (1965–1976), Gen Y (1977–1995), and Gen Z (1996–2015) and Gen alpha (2015–Present).

Baby boomers, having witnessed the rise of the internet and several societal changes, tend to have a revolutionary outlook towards life (Groth et al., 2017). They exhibit active social interactions (Jain and Maheshwari, 2020) and use the internet to seek information before making purchase decisions (Groth et al., 2017). They consider vacation as an opportunity to escape their daily routine and to maintain a healthy lifestyle (Tomić et al., 2019). Whereas, the Gen X are adaptive and flexible (Jain and Maheshwari, 2020). They follow a conventional approach to balance between Baby boomers and Gen Y (Jain and Maheshwari, 2020). Their choice of destination is often influenced by their family members (Li et al., 2013) and they enjoy adventure tours and outspend Baby boomers on their vacations (Tomić et al., 2019).

Gen Y are tech-savvy (Jain and Maheshwari, 2020) and they use technology to gather information before planning vacations (Tomić et al., 2019). They travel frequently and prefer shorter vacations (Lewis et al., 2021). Therefore, they are considered as “next generation” in the travel industry (Tomić et al., 2019). Currently, Gen Z has emerged as a significant consumer base in the tourism sector (Robinson and Schänzel, 2019). They are considered to be intensely anxious and distrustful (Hertz, 2017). This may be due to their early life experience of chaos, uncertainty, volatility, and complexity in the socio-economic environment (Robinson and Schänzel, 2019). They crave safety and financial security as they have never seen a world without war and terrorism (Pinho and Gomes, 2024; Robinson and Schänzel, 2019). Butnaru et al. (2022) posit that Gen Z’s inclination toward responsible travel practices is their defining characteristic. They consider traveling to seek adventure, novelty (Robinson and Schänzel, 2019), and extraordinary experiences (Haddouche and Salomone, 2018).

Past studies on intergenerational differences focused on destination preferences (Huang and Lu, 2017; Li et al., 2013), lifestyles of mountain tourists (Špindler et al., 2025); gastronomic destination preferences (Lebrun and Bouchet 2024; Torres-Casado, López-Mosquera, 2025)); local food consumption (Chen et al., 2024); domestic destination attractiveness perception of Gen Y and Gen Z (Pompurová et al., 2023); information search (Beldona, 2005; Huang and Lu, 2017; Hysa et al., 2021; Kim et al., 2015; Li et al., 2013; Ruiz-Equihua et al., 2022); and online review usage for trip planning (Amaro et al., 2019). Numerous studies have investigated the use of eWOM, specifically among Gen Y (Anubha and Shome, 2021; Bevan-Dye, 2020; Kim and Hwang, 2022; Zhang et al., 2017), as they are the major target segment for hospitality (Heyes and Aluri, 2018) and tourism (Anubha and Shome, 2021; Giachino et al., 2020; Rita et al., 2019).

Furthermore, past studies often focused on Gen Y’ travel behavior (Alemi et al., 2018; Anubha and Shome, 2021; Choudhary and Gangotia, 2017; Giachino et al., 2020; Kaihatu et al., 2021; Lewis et al., 2021; Rita et al., 2019; Ruiz-Equihua et al., 2022). For instance, Giachino et al. (2020) examined the motivation for Gen Y to choose mountain tourism and identified factors like nature and wildlife, sport, relaxation and quiet, trendy location, and food and beverages as their motivators. Rita et al. (2019) found that relaxation and escaping the ordinary are the common motivational factors for Gen Y from the USA and UK. As Gen Y are considered to be the mainstream consumption group (Sun et al., 2020), there has been extensive attention on their travel preferences. Very few studies have been conducted to understand the travel preferences of older consumers like Baby boomers (Otoo and Kim, 2020; Otoo et al., 2020; Silva et al., 2021) and Gen X (Kim et al., 2015). This evidence also suggests that in South America, Africa, and Asian regions, the older tourism segment is under-researched (Hung and Lu, 2015). In Indian travel literature, generational cohort studies have been predominantly focused on Gen Y (Anubha and Shome, 2021; Choudhary and Gangotia, 2017).

Recent studies on Gen Z’s travel behavior examined the impact of social networking sites (Etrata et al., 2025) and user-generated content (Correia et al., 2025) on their behavioral intentions. Furthermore, these studies have focused primarily on sustainable travel behavior (Lima-Vargas et al., 2024; Liu-Lastres et al., 2025) and the gastronomic tourism behavior of Gen Z (Made Purnami et al., 2025; Purwanegara et al., 2025). A bibliometric study of Gen Z and tourism research (Ivasciuc et al., 2024) reveals that though there is increased research on the aforementioned research themes, there is still a void in unearthing new insights into Gen Z’s preferences.

Research gaps

The literature review reveals three key research gaps. First, prior studies often consider generational cohorts as homogeneous groups with uniform behavioral patterns, despite growing evidence of intra-generational differences in travel preferences (Glover and Prideaux, 2009; Otoo et al., 2020; Prayag et al., 2022). This challenges traditional segmentation approaches and underscores the need to reassess traveler preferences for more accurate market segmentation. Second, although preference-based segmentation is gaining practical relevance, it has rarely been combined with generational analysis to produce actionable traveler profiles (Sahoo et al., 2022), limiting a deeper understanding of evolving tourist decision-making. Profiling consumer segments using the TTCS framework, combined with key demographic indicators like generational cohorts, can offer a more nuanced understanding of the tourism market. Finally, despite the rapid growth of India’s tourism sector, existing literature has largely focused on international travel, overlooking domestic travel preferences. This is a significant gap, as domestic and international tourists often display distinct behavioral and consumption patterns (Chatterjee et al., 2017; Rishi and Chatterjee, 2023). Therefore, the study aims to address the following research question: “Are there any distinct profiles based on key travel preferences across Baby boomers, Gen X, Gen Y, and Gen Z?” This study is among the few to collect extensive data across four generations, aiming to classify tourists into latent profiles and extend preference-based traveler segmentation in India, grounded in the TTCS framework.

Methodology

This study employed a survey method to collect data from 980 Indian travelers across four generations (Baby boomers, Gen X, Gen Y, and Gen Z), aged 18 to 78 years. This age group represents the Indian travelers who have decision-making power. Although the target population is finite, no comprehensive sampling frame exists for generational traveler groups. Therefore, the sample was drawn from Facebook travel group members, leveraging the platform’s extensive reach in India. According to NapoleonCat (2024), India has over 566 million Facebook users, amounting to 39% of the national population and making it one of the largest user bases globally (Statista, 2024c). The selection criterion required respondents to be members of active Indian Facebook travel groups, administered by Indians and consisting of at least 5000 members.

Quota sampling was employed, as it is effective when probability sampling is not feasible due to the lack of a sampling frame (Seow et al., 2017). It also helps in obtaining a sample that closely represents the target population (Sharma, 2017). Quotas were set based on generational cohorts (age groups) to ensure balanced representation across Baby boomers, Gen X, Gen Y, and Gen Z. A minimum of 200 respondents from each generation was considered sufficient for the intended analysis.

To reduce selection bias inherent in quota sampling, the questionnaire link was distributed across diverse Facebook travel groups with members from varied age groups. The number of responses across different cohorts was monitored to ensure inclusivity, minimizing the overrepresentation of specific demographics. A total of 980 completed responses were received, and the demographic profile of the respondents is presented in Table 1. The differences between group sizes were relatively small, with the largest group (Gen Y with 262 respondents) and the smallest group (Baby boomers with 233 respondents) differing by only 29 individuals. The variation across the groups was approximately 11%, demonstrating reasonable group equivalence.

Table 1 Demographic profile.
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Survey instrument and variables

Taking cues from Woodside and Dubelaar (2002), several behavioral indicators of travel preferences were measured, such as “holiday duration,” “accommodation type,” “leisure destination activities,” and “travel companions.” Holiday duration assessed whether travelers preferred long holidays, short holidays, weekend getaways, and day trips. Accommodation types were classified as accommodation at a hotel, accommodation with family or friends, accommodation at bed and breakfast, accommodation at homestays, and camping. Leisure destination activities included sampling local food at destinations, sightseeing, shopping, attending events like music festivals or sporting competitions, visiting museums, outdoor activities like cycling or hiking, and partying. Travel companions included traveling alone, traveling with family, traveling with friends, and traveling with group travel packages. All the questions were measured using a five-point Likert scale.

The face validity of the survey instrument was assessed by four experts in the area of consumer behavior and marketing research. Based on the qualitative feedback and recommendations obtained from the experts, the survey instrument was revised. Furthermore, 11 individuals (non-experts) from the target population were also considered to assess whether the survey instrument effectively captured all the important travel preferences of domestic travelers. This step further refined the questions included in the survey instrument to suit the context of Indian domestic tourism. After implementing revisions based on the feedback, a final survey instrument was shared in Facebook travel groups.

Results

Following Mekoth et al. (2024), an exploratory factor analysis (EFA) was conducted to reduce the dimensions and identify factors. It was followed by latent profile analysis (LPA) to identify distinct profiles of travelers based on the factors. Finally, a multinomial logistic regression (MLR) was used to examine the association between the profile membership and demographic variables and examine for the confounding effects. The descriptive statistics for the variables across four generations are presented in Table 2.

Table 2 Descriptive statistics for travel preferences across generations.
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Factor analysis

The underlying dimensions of the respondents’ travel preferences were identified using EFA. The Bartlett’s test of sphericity (p < 0.05) and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (0.739) scores indicated the data is suitable for principal component analysis. Varimax rotation was used to examine travel preferences. Factor loadings of at least 0.50 were deemed acceptable for item inclusion, and an eigenvalue of 1.0 was employed for the factor extraction criterion (Hair et al., 2017). After removing six items with low factor loadings (<0.5), varimax rotation was used to re-analyze the remaining items (See Table 3). Five components explained 62.089% of the total variance, each with high reliability coefficients and eigenvalues exceeding 1.00. The identified factors were: planned holiday, impromptu holiday, holiday with acquaintances, basic travel priorities, and exciting destination experience. Internal consistency for each factor was confirmed using Cronbach’s Alpha, all exceeding the 0.7 threshold. For factors with fewer items, inter-item correlations were examined in line with Pallant’s (2020) recommendations, and scores were within the acceptable range of 0.2–0.4. Following this, LPA was conducted to identify traveler profiles based on these factors.

Table 3 Rotated component matrix.
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Latent profile analysis

LPA is a person-centered (Howard and Hoffman, 2017; Spurk et al., 2020), mixture-model (Magidson and Vermunt, 2002; Song and Shim, 2021) technique that addresses the identification of unobserved subpopulations (latent profiles) within a heterogeneous population by utilizing a specific set of variables. In this study, LPA was employed to identify unobserved subgroups of travelers based on the travel preference factors determined and established from the EFA. Past tourism research on travel motivation (Song and Bae, 2018), and tourist perceptions of an urban park (Song and Shim, 2021) employed LPA to identify latent subgroups within the heterogeneous population. Unlike traditional clustering techniques, such as k-means or hierarchical clustering, which rely on researchers’ subjective interpretations (Bergman and Trost, 2006), the LPA identifies subgroups using rigorous statistical parameters, including entropy and fit indices (Song and Shim, 2021). Therefore, the current study employs LPA, considering its superiority and robustness, to categorize travelers from four generations based on their travel preferences.

This study uses the snowRMM (Seol, 2025) module built in Jamovi version 2.6 (2024) to conduct the LPA. Jamovi is an easy-to-use statistical tool that can conduct many single and multivariate analyses (Şahi̇ and Aybek, 2019). While examining the LPA results, the model fit parameters such as Bayesian Information Criterion (BIC), Akaike Information Criteria (AIC) and entropy were evaluated based on the recommendations of the existing studies (Dong et al., 2023; Kwarikunda et al., 2022; Nwosu et al., 2023; Wang and Ngok, 2024). These studies suggest that the lower values for BIC and AIC, reflect a better model fit. Dong et al. (2023) suggest the entropy level which determines the quality of classification in LPA must be above 0.6. The entropy below 0.6 is unacceptable as it indicates an individual classification error rate as high as 20% (Dong et al., 2023). Furthermore, bootstrap likelihood ratio test (BLRT) was also examined. The BLRT p-values below 0.05 indicate that a model with k profiles is a better fit than a model with k-1 profiles (Gonzálvez et al., 2021; Nwosu et al., 2023). Gonzálvez et al. (2021) also recommends to have at least 25 members per profiles for meaningful interpretation.

While the model with six profiles had lowest AIC value, the model with four profiles was found to have a better classification accuracy and significant BLRT-p value (See Table 4). Posterior‐probability diagnostics also showed a satisfactory separation of individuals into four profiles (Prob_max = 0.88; Prob_min = 0.70). Furthermore, the six-profile model produced the smallest class comprising only 4% of the sample, that raised concerns about interpretability. Therefore, the model with four profiles was evaluated as the best-fitting model. Figure 1 shows the visualization of the travel preference factors (estimated means) across four profiles (classes).

Table 4 Fit indicators for latent profile models.
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Fig. 1
figure 1

Travel preference factors (estimated mean) of four travel profiles (class) from latent profile analysis.

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Profile 1 comprised 16.3% of the total sample. Members exhibited moderate preferences across most travel preference factors, with the lowest scores for basic travel priorities. This suggests a modest and balanced approach to travel planning, with less emphasis on essentials and more flexibility in their overall travel behavior. Therefore, they were named “Holistic travelers.”

Profile 2 accounted for 19.1% of the sample. Members of this profile showed the strongest preference for basic travel priorities and the least interest in impromptu holidays, indicating a highly structured and cautious travel style. Their moderate interest in holiday with acquaintances further reflects a grounded and socially rooted approach to travel. Accordingly, this group was labeled “Rooted travelers.”

Profile 3 represented 56.1% of the sample, the largest segment. Members of this profile showed the strongest preference for exciting destination experiences and ranked second in impromptu holiday preferences, reflecting their desire to travel spontaneously and travel to exciting destinations. Given their enthusiasm for dynamic and adventurous travel, this group was labeled “Spirited travelers.”

Profile 4 comprised of only 8.5% of the sample and showed the highest preference for impromptu holidays and holiday with acquaintances, but the lowest interest in exciting destination experiences. This suggests that their travel behavior is driven more by social connection than by novelty or adventure. Reflecting their emphasis on companionship and spontaneity, this group was labeled “Seasoned social wanderers.” Demographic characteristics for all four profiles are summarized in Table 5.

Table 5 Demographic characteristics of the profile members.
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Multinomial logistic regression

After identifying the latent profiles, MLR was conducted to investigate how demographic variables predict the probability of membership in each profile relative to a reference group. It examined whether generation, gender, education, income, employment status, and marital status predicted membership in the four travel preferences profiles (reference class = Profile 1). The results revealed the overall model had better fit and goodness of fit than the intercept-only model, χ² (42) = 243.35, p < 0.001, and accounted for a modest but meaningful share of the variance (McFadden R² = 0.11; Nagelkerke R² = 0.14). Fit indices were also acceptable (AIC = 2091.33; BIC = 2311.27) (See Table 6).

Table 6 Model fit and goodness-of-fit statistics.
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Omnibus likelihood-ratio tests (See Table 7) showed that generation, education, income, and employment status each contributed uniquely to the model (p < 0.05), whereas gender and marital status did not.

Table 7 Omnibus likelihood-ratio tests.
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Key predictors

The results revealed that compared to Baby boomers (reference category), members of Gen Z were only 0.13 times likely to occupy Profile 2 (SE = 0.62, p < 0.01) and 0.08 times likely to occupy Profile 4 (SE = 1.05, p < 0.05). Similarly, Gen Y were only 0.31 times likely to occupy Profile 4 (SE = 0.47, p < 0.05), whereas their likelihood of being members of Profile 2 and Profile 3 did not significantly differ from Baby boomers (p2 = 0.23 and p3 = 0.36). Generation X did not display a significant difference from Baby boomers across any profiles (all p > 0.05). No generations had significant differences for Profile 3. These results indicate that younger generations, specifically Gen Z, are less likely to be part of Profile 2 or Profile 4 compared to Baby boomers, whereas in the case of Profile 3, there was a negligible generational difference.

Regarding education, the individuals with a bachelor’s degree had a higher likelihood of being in Profile 2 (Rooted travelers) with an odds ratio (OR) of 2.44 (SE = 0.32, p < 0.05), Profile 3 (Spirited travelers) with an OR of 2.63 (SE = 0.26, p < 0.001), and Profile 4 (Seasoned social wanderers) with an OR of 3.31 (SE = 0.42, p < 0.01) than the individuals with below bachelor’s degree education (the reference category). A stronger pattern was observed among master’s degree holders, who were 2.64 times more likely to belong to Profile 2 (SE = 0.37, p < 0.05), 3.23 times more likely to belong to Profile 3 (SE = 0.30, p < 0.001), and 2.88 times more likely to belong to Profile 4 (SE = 0.47, p < 0.05). However, respondents with a PhD or higher level of education showed no statistically significant likelihood of belonging to Profile 2 (OR = 2.99, SE = 0.76, p > 0.05) or Profile 3 (OR = 2.83, SE = 0.68, p > 0.05). For Profile 4, no reliable estimates could be generated due to the absence of PhD participants in that group, resulting in an odds ratio and standard error of zero.

Income emerged as the strongest demographic variable, which played a significant role in profile membership. Compared to respondents with annual income less than 1 million INR, respondents with 1–2 million INR income were 6.82 times more likely to belong to Profile 2 (SE = 0.44, p < 0.001), 3.31 times more likely to belong to Profile 3 (SE = 0.42, p < 0.01), and 4.59 times more likely to belong to Profile 4 (SE = 0.50, p < 0.01). The effect was notably stronger for the highest income members (above 2 million INR). These respondents were 10.91 times more likely to be in Profile 2 (SE = 0.76, p < 0.01), 5.72 times more likely to be in Profile 3 (SE = 0.74, p < 0.05), and 7.66 times more likely to be in Profile 4 (SE = 0.82, p < 0.05) compared to the reference category.

Finally, for employment status, compared to unemployed (reference category), employed individuals were 0.32 times likely (SE = 0.45, p < 0.05) and self-employed individuals were 0.33 times likely (SE = 0.48, p < 0.05) to be in Profile 2. Whereas for Profile 3 and Profile 4, there was a negligible difference for employment status.

The overall findings highlight income, education, generation, and employment status as strongest predictors of profile membership. However, gender and marital status did not have any significant impact (p > 0.05). Table 8 summarizes the odds ratios and significance values for the MLR.

Table 8 Summary of multinomial logistic regression analysis.
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Traveler profiles

Profile 1: Holistic travelers

This profile had a mix of all four generations, and members had moderate preferences across all travel factors (refer to Fig. 1). It represents a value-oriented segment, characterized by balanced travel behavior rather than strong inclinations toward any specific dimension. The findings are consistent with existing tourism literature (Amaro et al., 2023; Fung and Jim, 2015; Carrascosa-López et al., 2021), which highlights that tourists often exhibit multi-faceted preferences when selecting destinations. This segment demonstrates a broad interest in various travel aspects, reflecting a flexible and well-rounded travel orientation.

Income and education levels provided contextual explanation for this profile. With over 94.38% earning less than 1 million INR annually, members tend to prefer balanced, budget-friendly travel. Existing travel research suggests economic factors significantly influence travel choices in developing countries (Struwig, du Preez, 2024). Additionally, 26.9% had the lowest education level, the highest among all profiles, suggesting a strong desire to gain knowledge through new experiences. This aligns with the “want-it-all” traveler segment identified in existing literature (Fung and Jim, 2015).

Profile 2: Rooted travelers

This profile had a higher representation of Baby boomers and Gen X respondents, indicating a shared preference pattern that reflects a combination of generational values and economic status. Members of rooted travelers exhibited a higher preference for basic travel priorities such as comfortable accommodation, sightseeing, and trying local food. They also preferred traveling with acquaintances, which reflects a shared orientation towards reliable and comfortable travel experiences.

The existing literature postulates that a mature generation like the Baby boomers is much more resistant to any change in their lives (Laukkanen et al., 2007), suggesting their adherence to a fixed schedule-based lifestyle (Tsadok-Cohen et al., 2023) and the emphasis on basic structured travel routines that would reduce both physical and cognitive strain (Patterson, 2006). One of the plausible explanations for this behavior can be derived from Sheth’s (1981) work, where the author states that several individuals might opt for consistency in life rather than striving for new activities. Furthermore, their preference of traveling with acquaintances might stem from their desire to develop new social connections. Existing research also provides evidence to this notion by suggesting that strong social engagement among older tourists positively contributes to their overall well-being by enhancing self-esteem (Qiao et al., 2022). The Gen X, often referred to as the ‘sandwich generation’ (Miller, 1981) are commonly tasked with the added responsibility of taking care of their children and parents (Sudarji et al., 2022). This dual caregiving role might explain their inclination for travel preferences that prioritizes basic needs and socially rewarding conversations. Given the likelihood of traveling with children or older parents, they focus on comfort, convenience, and basic needs without seeking novel experiences. Furthermore, establishing social relationships and interacting with supportive companions provides a vital outlet to cope with this challenging situation and foster a sense of emotional validation (Pashazade et al., 2024).

Profile 3: Spirited travelers

This profile predominantly consists of Gen Z and Gen Y travelers who demonstrate a dual focus on meeting basic travel needs while also seeking exciting destination experiences. Despite nearly 78.9% of the members earning less than INR 1 million annually, they report the highest travel frequency, that is, 37.27% travel once a year and 24% at least twice a year. This suggests that for spirited travelers, travel is clearly a priority, and they tend to favor value-for-money plans that balance essential comforts with engaging experiences.

These travelers also prefer an “exciting destination experience” (attending events; adventure sporting etc.) For instance, in India the number of music festivals is expected to grow exponentially from 8000 in 2018 to 16700 in 2025 (Baruah, 2023), driven predominantly by the Electronic Dance Music concert culture (Chakravarty and Bennett, 2024; Chaturvedi, 2025). Several studies (D’Andrea, 2007) have highlighted that tourism enables youth to break away from their monotonous daily routines, often through engagement in subcultures such as rave parties (D’Andrea, 2007; Boirot and Thurnell-Read, 2023; Mach et al., 2022).

Shopping has also emerged as a critical factor influencing destination choice among Indian youth, offering both psychological gratification and social value (Khan, 2019; Choi et al., 2018). Notably, while the overall tourism sector in India is growing at a compound annual growth rate (CAGR) of 10%, sports tourism has surged ahead with a 17.5% growth rate, making it a key driver for city branding and youth engagement (Chanda, 2024; Szmigin et al., 2017). Additionally, Indian youth increasingly gravitate toward adventure-based outdoor activities such as rafting, trekking, and camping (Kar and Roy, 2020; Times of India, 2024) as a means to seek thrill, escape routine (Brymer and Feletti, 2020; Lekies et al., 2015), and improve overall well-being (Próchniak and Próchniak, 2023).

Young consumers across several countries have demonstrated a strong preference for novel and adventurous tourism experiences (Lepp and Gibson, 2008; Williams and Soutar, 2009). For instance, youth in China and Singapore have shown a particular affinity for adventure tourism, reinforcing the relevance of our findings within Asian contexts that share similar domestic tourism dynamics (Gardiner et al., 2023). This inclination toward adventure-seeking behavior is closely linked to the concept of ‘self-identity,’ which highlights the significance of the roles that individuals occupy within the social structure and how they express them through role-consistent behavior (Gardiner et al., 2023, p.2).

Profile 4: Seasoned social wanderers

In the final profile, Baby boomers and Gen X exhibited the highest preference for holidays with acquaintances and impromptu travel. While mature generations are typically associated with structured and planned travel (Patterson, 2006; Jang and Wu, 2006), this profile deviates from the norm, revealing a segment driven by social connection and spontaneous getaways. A comparable trend was observed in Germany, where older travelers also showed a growing inclination toward making spontaneous travel decisions (Tourism Review News Desk, 2019).

A key factor explaining this behavior is that approximately 44.8% of the members are either self-employed or retired, indicating greater temporal flexibility and control over their schedules. Huber (2019) highlights that personal and professional commitments often constrain travel preferences, but once these responsibilities lessen, older individuals are more inclined to adopt new hobbies like travel. Life stage transitions, such as reduced career pressure and post-retirement freedom, enable greater spontaneity in travel planning (Hysa et al., 2021; Pascual-Fraile et al., 2024). Additionally, the awareness of limited time remaining in life influences senior tourism behavior, with Fleischer and Pizam (2002) noting that this stage is often guided by a desire for self-fulfilling experiences.

However, despite their inclination towards spontaneous travel plans, they primarily seek socially enriching connections. These members showcase a desire to travel and spend time with their close companions, like family and friends (Pascual-Fraile et al., 2024), since social engagement enhances their overall well-being and self-esteem (Qiao et al., 2022). Thus, rather than planning for exciting experiences, this profile predominantly focuses on spontaneous travel plans with a close-knit group.

Theoretical contributions

The study enhances the understanding of domestic travelers’ preferences by integrating the TTCS with GCT. This integration provides a dynamic framework for analyzing and predicting travel behavior in culturally diverse and socio-economically evolving countries like India. By identifying distinct traveler profiles, the study demonstrates how generational attributes influence travel preferences and uncovers key intra-generational variations, offering deeper insight into travel preferences across age groups. The study also confirms that generational differences serve as an important subdivision within the TTCS. The latent profile approach combines the two traditional theories and reveals the coexistence of multiple consumption system subgroups within the generational cohort.

The study empirically identifies four profiles: holistic travelers, rooted travelers, spirited travelers and seasoned social wanderers based on their travel preferences. By offering multi-layered insights into travel preference patterns across four generations, it challenges the commonly held assumption of homogeneity within generational cohorts in tourism literature. This re-evaluation contributes meaningfully to the advancement of GCT in the tourism domain. Also, by illustrating how generational shifts in lifestyle and values influence travel-related consumption systems, the study reinforces the validity and relevance of integrating GCT with the TTCS framework.

TTCS is often contextualized in western or international tourism contexts (Li et al., 2013). By contextualizing GCT within the domestic tourism setting of an emerging market, this study offers valuable insights into India’s rapidly growing tourism landscape. The study demonstrates that the holistic travelers profile included mix of the four mainstream generation who displayed a similar pattern. While rooted travelers and seasoned social wanderers had majority of older travelers, the former emphasized basic travel priorities and family-oriented vacation whereas the latter profile members emphasized spontaneity with strong need for social connections and traveling with acquaintances. The spirited travelers, with majority younger profile members were leaning more towards excitement in their travel experiences along with fulfilling basic travel needs.

The study findings also highlight the intricacies of domestic tourist behavior thereby contributing to the growing literature on Indian domestic tourism. A report by McKinsey forecasts that India will be the biggest source of leisure tourists due to rising economic prosperity (Aggarwal et al., 2023). Therefore, it is imperative to examine the different travel preferences of Indian consumers to effectively manage the tourism market. The present study highlights the interplay between family orientations, societal bonds, and spontaneity in shaping the travel preferences of Indian domestic travelers. These findings on the consumption pattern of the younger and mature generations contributes to the evolving discourse on tourism literature.

Practical and policy implications

The travel preferences of Indian tourist profiles offer practical implications for DMOs and travel service providers to tailor their offerings and marketing strategies. The findings indicate that younger generations are drawn to exciting destinations and prefer traveling with acquaintances. DMOs and service providers can capitalize on this by designing social group travel packages and offering group discounts to encourage friend circles and families to travel together. Curated itineraries that combine daytime adventure activities with evening social experiences, such as café outings or nightlife options, can effectively meet both thrill-seeking and social bonding needs. Likewise, as younger travelers also value comfort, providers should ensure packages include basic amenities and conveniences. DMOs partnering with popular online travel platforms can further enhance their outreach by deploying targeted digital campaigns specifically aimed at the youth segment.

Another interesting finding of the study is the presence of older adults across three distinct travel segments. While their inclusion in the holistic and rooted traveler groups aligns with existing expectations, their presence in the “seasoned social wanderers” segment is particularly novel. These older travelers show a strong preference for impromptu holiday plans and traveling with acquaintances, indicating a desire for spontaneous, socially engaging travel experiences. DMOs targeting this segment can promote easily accessible tourist destinations ideal for short trips, especially on online travel platforms. Also, bundling tour packages with long weekend holidays can further appeal to this demographic, offering them flexible and convenient travel options that suit their evolving lifestyles.

The study highlights a strong investment opportunity for adventure sport operators in India, driven by the younger generation’s preference for exciting travel experiences. Tour operators and DMOs in lesser-known destinations should promote unique offerings like rock climbing in Malshej Ghat (Maharashtra), scuba diving in Kapu (Karnataka), and trekking at Ginnorgarh (Madhya Pradesh). With most adventure tourism concentrated in Uttarakhand and Himachal Pradesh, there’s untapped potential in promoting alternative locations. DMOs can also expand offerings to include moderately challenging activities tailored for older travelers (Wilson et al., 2017).

Beyond adventure tourism, music and sports tourism are also rapidly gaining traction in India, especially among the youth who readily travel to attend specific events. This trend presents an opportunity for DMOs to create targeted tour packages combining major events, often held in cities like Goa, Kolkata, or Delhi, with short sightseeing itineraries. Integrating event merchandise (e.g., music bands or sports teams) into these packages can further enhance the appeal. A coordinated strategy involving event promoters, local transit authorities, and merchandising partners can significantly boost tourist engagement and increase average visitor spending.

The findings suggest that basic travel elements such as sightseeing, comfortable hotel stays, and local food appeal to all generations. These should be treated as essential by DMOs, as ensuring these basics can extend visitor stays and boost revenue for mid-range hotels, eateries, and transport services. Investing in such infrastructure, especially in lesser-known destinations, is crucial to revitalizing domestic tourism in India. Also, with many tourists preferring to travel with family, enhancing family-friendly tourism facilities can make destinations more attractive. These improvements not only support tourism growth but also create new revenue streams and employment opportunities for local communities.

With over 18 million foreign tourists visiting India in 2023 (Ministry of Tourism, 2024), the country has emerged as a leading global travel destination. To enhance foreign visitor satisfaction, strategic upgrades such as multilingual signage, multilingual tour guides, and universal QR code payment systems are essential. These measures can improve tourist experiences and generate tangible returns for both DMOs and private operators. Leveraging campaigns like ‘Chalo India’ and the ‘Incredible India Content Hub,’ DMOs have both the opportunity and responsibility to deliver high-quality services, as positive word-of-mouth from foreign tourists can significantly boost the sector’s growth.

From a societal perspective, policymakers can expand pro-tourism initiatives in lesser-traveled areas to generate employment and uplift local communities. Tourism can benefit a range of stakeholders, from hotel staff and tour guides to food vendors showcasing regional cuisine. It also presents opportunities for indigenous businesses, such as Kashmir’s Khatamband or Odisha’s Pipili Chandua artwork, to gain global visibility. This can support socio-economic development through increased exports and regional growth. The findings are also applicable to other developing countries with similar demographics, offering a framework to stimulate their domestic tourism sectors.

Limitations

While the study offers valuable insights, it has certain limitations. The use of cross-sectional quantitative data restricts the understanding of how travel preferences evolve over time. Self-reported responses may also be influenced by biases like social desirability, affecting the accuracy of the findings. Moreover, relying on Facebook travel groups for data collection may exclude less tech-savvy individuals or those from rural areas with limited internet access, thus limiting the study’s overall representativeness. Another limitation is that although the study examines the role of demographic variables, such as generation, income, education, gender, employment status and marital status, it does not consider potential confounding variables such as family responsibility. Although the study incorporated various travel preferences based on Woodside and Dubelaar’s (2002) framework, it did not account for travelers’ information search behavior or their use of technology in trip planning, which are increasingly relevant in shaping modern travel decisions.

Future research directions

The limitations open avenues for future research. In the future, longitudinal studies could track changes in travel preferences across generational cohorts over time, capturing the effects of societal and economic shifts on behavior. A mixed-methods approach, especially incorporating in-depth qualitative interviews, can yield deeper insights into domestic traveler preferences. Future research may also broaden the scope by focusing on rural or economically disadvantaged travelers, offering a more inclusive view of India’s domestic tourism landscape. Further, future studies could compare domestic travelers across emerging markets to enhance generalizability. Exploring variables like family responsibilities, life-cycle stages, and external factors such as climate change or economic downturns can guide adaptive tourism strategies.

Conclusion

This study provides a comprehensive understanding of the travel preferences of Indian domestic tourists across four generational cohorts by integrating TTCS with GCT. Using latent profile analysis, it identifies four distinct traveler segments, each reflecting unique patterns shaped by generational values, socio-economic factors, and lifestyle preferences. These include: holistic travelers, rooted travelers, spirited travelers, and seasoned social wanderers. Holistic travelers, comprising a mix of all generations, exhibit similar, balanced travel preferences. Rooted travelers, primarily older cohorts, focus on basic needs and family-oriented vacations. Spirited travelers, largely younger individuals, seek adventure and excitement while ensuring comfort. Seasoned social wanderers, mostly mature travelers, value spontaneity and prefer traveling with close acquaintances, highlighting the importance of social connection.

The findings have several theoretical, practical, and policy implications. The integration of TTCS and GCT provides a robust framework for understanding tourism behaviors in culturally diverse countries like India. Practically, the insights enable DMOs and marketers to design tour packages and develop targeted marketing strategies. These findings also underscore the universal appeal of core travel elements, such as comfortable accommodation and local cuisine, across generational cohorts.