Generation Z and Travel Motivations: The Impact of Age, Gender, and Residence

Abstract

This study investigates the relationship between demographic factors and travel motivations among Generation Z leisure tourists through the lens of the Travel Career Pattern (TCP) theory. More specifically, the research focuses on how gender, age, and area of residence influence the travel motivations of Generation Z. Using a quantitative approach, data were collected from 303 respondents aged 18 to 28 through an online survey. The questionnaire assessed 14 motivational factors and analyzed them in relation to the participants’ demographic characteristics using linear regression models. Results indicate that gender and age significantly influence travel motivations, with women showing higher interest in personal development and social relationships, while men prioritize nature and adventure. Furthermore, rural residents exhibit greater motivation for autonomy, self-development and self-realization, while urban residents lean towards novelty and social interactions. The findings offer valuable insights for tourism marketers, emphasizing the importance of creating segmented marketing campaigns based on demographic factors. It also contributed to overcoming the lack of studies that specifically cover this interrelation between the motivational factors of Generation Z and the demographic factors of age, gender and area of residence. Nevertheless, this study also has limitations, such as the use of a non-representative sample and the focus on quantitative methods, suggesting that future research should adopt qualitative approaches and examine additional demographic variables to gain deeper insights into youth travel motivations.

1. Introduction

Understanding travel motivations is crucial in predicting travel behavior, developing tourism strategies, and enhancing the overall tourist experience. As an intrinsic part of human behavior, motivations are complex, evolving, and influenced by multiple factors, including psychological, social, and demographic elements (Crompton, 1979; Dann, 1981). Several theoretical models, including Maslow’s hierarchy of needs (Maslow, 1970) and Pearce and Lee’s Travel Career Pattern (TCP) theory (Pearce & Lee, 2005), provide frameworks for analyzing these motivations. However, the research that has been carried out in this field is not sufficient and it is important to develop more studies that also cover more specific target audiences (Lam & Hsu, 2006; I. Egger et al., 2020).

Travel motivation is commonly acknowledged as a crucial concept to most tourism professionals and theorists (Lam & Hsu, 2006) and has been known as a driving force behind understanding traveler’s behavior (Venkatesh, 2006). However, the concept of travel motivation is not new (Pearce & Caltabiano, 1983). Researchers around the globe have applied travel motivation to determine individuals’ satisfaction levels (Snepenger et al., 2006; Lemmetyinen et al., 2016; Çelik & Dedeoğlu, 2019; Preko et al., 2019), predict leisure participation levels (Yan & Halpenny, 2019), identify travel patterns (Cavagnaro & Staffieri, 2015), understand tourists’ travel decisions and consumption behavior (Chang et al., 2015) as well as to develop more effective strategies and policies to increase demand for tourism (Heung et al., 2001; Papatheodorou, 2006). The complex nature of this concept has pushed many researchers to come up with different travel motives, and the central themes behind it revolved around push and pull factors, that have been extensively employed to assess tourists’ travel motivations (Kanagaraj & Bindu, 2013; Michael et al., 2017; Wijaya et al., 2018). Another context that has been frequently associated with tourist behavior and travel motivations is sociodemographic characteristics (Kara & Mkwizu, 2020). The role of demographic factors on tourists’ destination choice has been proven of significant importance and research shows that there is a link between demographic factors and visitors’ participation in tourism activities (Woodside & Lysonski, 1989; Um & Crompton, 1990; Moscardo et al., 1996; Begashe et al., 2024). Factors such as age, gender, family structure, social background, among others, have an impact on the decision of an individual to participate in leisure activities (Mieczkowski, 1990; Collins & Tisdell, 2002; Foot, 2004).

Currently, there are four identifiable generations whose age permits them to engage in travel activities (Hysa et al., 2021). Those are Baby Boomers (born between 1940 and 1959), Generation X (born between 1960 and 1979), Generation Y (born between 1980 and 1994), and Generation Z (born after 1995) (Francis & Hoefel, 2018; Pricope Vancia et al., 2023). Members of those generations exhibit specific behaviors and consumption patterns due to the context in which they were born and lived most of their lives. In this context, it becomes important to address these demographic issues and the influence they may have on leisure travel choice patterns, especially among younger generations, i.e., Generation Z. This generation is unique in that it is the first truly digital generation, heavily influenced by global connectivity, rapid technological advancements, and socio-economic challenges, such as the 2008 financial crisis and the COVID-19 pandemic (Pinho & Gomes, 2023). These experiences have shaped their consumption habits, social behaviors, and travel preferences. Unlike their predecessors, Generation Z values personalized, immersive experiences, and their travel decisions are significantly shaped by online reviews, social media influencers, and digital platforms (Richards, 2015). Therefore, understanding the travel behavior of Generation Z is increasingly relevant as this cohort emerges as a key driver of global tourism. Representing over 30% of the global population, around 27% of the workforce and 40% of global consumers (OECD, 2021), Gen Z is not only shaping current travel trends but is also poised to become the core consumer group in the tourism sector. Their preferences for authenticity, digital experiences, and social engagement influence local economies and destination strategies, especially in urban and culturally dynamic areas. Moreover, their travel spending contributes significantly to accommodation, food services, and creative tourism industries, particularly in youth-centric destinations (Pricope Vancia et al., 2023). As such, exploring their motivations and, more specifically, the influence of specific demographic factors, is essential to designing tourism products aligned with their values and maximizing their socioeconomic impact.

Thus, this study seeks to fill this gap by examining how gender, age, and area of residence influence the travel motivations of Generation Z travelers. Using the TCP theory as a conceptual framework, the study explores how different individual characteristics relate to travel preferences among young tourists. Specifically, the research aims to determine how living in urban versus rural areas affects motivation to travel. By providing a detailed analysis of these relationships, this study offers practical insights into tourism marketing and destination management.

2. Literature Review

2.1. Travel Motivation and the Travel Career Pattern (TCP) Theory

Travel motivations have long been a central theme in tourism research, given their role in explaining why people travel and what they seek from their travel experiences. Motivation can be understood as an internal psychological drive that compels individuals to act in specific ways (Iso-Ahola, 1982). In tourism, motivations are typically categorized into push factors, which are the internal desires that drive a person to travel, and pull factors, which are the external attributes of a destination that attract tourists (Crompton, 1979; Dann, 1981). Push factors might include the need for relaxation, adventure, or socialization, while pull factors could include a destination’s cultural richness, scenic beauty, or entertainment options (S. Kim & Lehto, 2013).

Over the years, this topic has been studied by several authors. Hills (1965) addressed tourists’ motives as a response to psychosomatic exhaustion, while Crompton (1979) defined motivations as just one of many contributing factors that inspire individuals to travel to different places and experience new things (Um & Crompton, 1990). Crompton (1979) and Todorović and Jovičić (2016), argued that the main motivation for going on vacation is the search for a break from the usual schedule and settings that allow them to relax and reduce mental fatigue. Dann (1981) confirmed that tourists’ state of mind influences them to travel and visit new places. Li and Cai (2013) and Simková and Holzner (2014) explored individuals’ travel patterns that are closely related to psychological patterns, and which can be analyzed to predict the factors that motivate people to travel. More recently, Yousaf et al. (2018) carried out a comprehensive literature review related to the motivations of tourists, with a focus on young people, analyzing existing motivational theories. Thus, the constant development and changes in travel and tourism continue to drive the need to identify tourists’ motivations for traveling. Understanding the motivational forces behind tourists’ behaviors can help destination managers adjust their offerings to become more attractive and attract their target audience. In this context, Yousaf et al. (2018) identified the main motivational theories widely accepted in tourism. Maslow’s hierarchy of needs served as the basis and study for other theories, such as Dann’s Theory of push and pull motivations, the Travel Career Ladder (TLC) and the theory of Travel Career Patterns (TCP). The TCP model emphasizes that travel motivations evolve as individuals move through different stages of their lives, reflecting changing goals and desires. For instance, younger travelers may be motivated by novelty and excitement, while older individuals may prioritize self-actualization and personal growth (Pearce & Lee, 2005). This theory is particularly useful in studying youth tourism, as it recognizes that motivations are not static but change with the individual’s personal development (Oktadiana & Agarwal, 2022). The TCP is centered on 14 motivational factors: novelty; escape/relax; relationship (strengthen); autonomy; nature; self-development (host-site involvement); stimulation; self-development (personal development); relationship (security); self-actualize; isolation; nostalgia; romance; and recognition. These factors play an extremely important role in determining the motivations that lead individuals to travel to distant places and influence travelers’ decision-making processes (Pearce & Lee, 2005).

2.2. Youth Travel and Generation Z

People’s expectations, requirements, or preferences regarding the purpose, place, and time of their trips vary depending on their individual characteristics and, consequently, their age (Hysa et al., 2021). Therefore, the tourism industry has evolved with each generation, adding new dynamics (D. Y. Kim & Park, 2019). Understanding how generational changes affect tourist behavior can help predict and adapt to future tourism trends more effectively (Gardiner et al., 2014).

The youngest generation, Generation Z (born between 1996 and 2010), present different behaviors and expectations in tourism, influenced by globalization, technological advances and the increasing digitalization of society (Urry & Larsen, 2012). This generation actively uses Information and Communication Technologies (ICT), promotes the personalization of tourist experiences and contributes to the growth of the collaborative economy (Hamari et al., 2016). Often referred to as “digital natives” (Prensky, 2001), this generation grew up in a highly connected environment and relied almost exclusively on the internet to plan and share tourism experiences (Monaco, 2018; R. Egger et al., 2016). Thus, in this context, the travel industry acknowledges the importance of understanding travel motivations of young travelers (Pricope Vancia et al., 2023).

According to UNWTO (2008), travel for young people, or youth travel, includes all independent travel for periods of less than one year by people aged between 16 and 29 motivated, in part or entirely, by the desire to get to know other cultures, build life experience and/or benefit from formal and informal learning opportunities outside the usual environment. Youth travel has become one of the fastest growing segments representing 23% of global international arrivals in 2019 (WYSE Travel Confederation, 2023). The advances in technology have facilitated physical mobility and created more widespread social relationships, which has further strengthened the trend towards mobility (Richards, 2015).

Within this segment of youth tourism, Generation Z stands out for its distinct travel behaviors and preferences. As digital natives, Generation Z is more likely to plan and organize trips online, using social media and review platforms like TripAdvisor or Booking.com to gather information and make decisions (Monaco, 2018). Their travel motivations are influenced by a strong desire for personalized and unique experiences, cultural immersion, and sustainability (Buffa, 2015; Richards, 2015). Unlike previous generations, Generation Z places a high value on authenticity and social responsibility in their travel choices. They are more likely to engage in sustainable tourism practices and seek out destinations that align with their ethical values, such as supporting local communities or protecting the environment (Han, 2021). Furthermore, the advent of the sharing economy, with platforms like Airbnb, has facilitated Generation Z’s preference for more personalized and budget-friendly travel options (Hamari et al., 2016).

Recent studies have deepened our understanding of Generation Z’s distinct behaviors and expectations as tourists. Lee and Lee (2024) examined urban nightlife and found that immersive, socially engaging experiences appeal strongly to Gen Z travelers, reinforcing the importance of designing nightlife experiences that balance novelty and social connectedness. Similarly, Weerasekera and Assella (2025) demonstrated that immersive travel experiences can increase travel intention by enhancing feelings of happiness, suggesting that emotional resonance is a critical motivator for this cohort. In parallel, research by Zhang et al. (2022) explored how Gen Z’s familiarity with digital tools and expectations for innovation—shaped by the COVID-19 pandemic—affect their behavior toward technology use in hospitality contexts. This reinforces the notion that Gen Z seeks efficiency, personalization, and interactivity in both the planning and consumption stages of travel.

Moreover, Robinson and Schänzel (2019) introduced the idea of a “tourism influx”, describing how Gen Z travel experiences are increasingly shaped by digital mobility and flexible social structures. These tourists construct highly individualized and socially curated travel narratives, often prioritizing authenticity and ethical consumption. Complementing this perspective, Dale and Ritchie (2020) investigated the motivations and constraints influencing Gen Z in structured travel settings, such as school excursions, revealing that even institutional travel contexts must now account for this generation’s desire for autonomy, self-expression, and experiential richness. Together, these findings underscore that Gen Z’s travel motivations are multidimensional, shaped by both psychological and technological factors, and that their expectations are redefining the nature of youth tourism globally. Therefore, it can be argued that Generation Z currently plays a crucial role in shaping the future of the tourism industry and seems very important to understand better which more specific demographic factors can influence different travel behaviors among this segment.

2.3. Demographic Factors and Travel Motivations

Demographic factors are essential in understanding travel motivations, offering insights into how different populations prioritize and engage with travel experiences. Analyzing demographics such as age, gender, and area of residence is crucial because these factors can shape travelers’ motivations, preferences, and behaviors in unique ways. Studies have consistently shown that demographic factors influence travel motivations, as illustrated by variations in travel motives across diverse nationalities (You et al., 2000; S. Kim & Prideaux, 2005), age groups (Luo & Deng, 2008; Irimias et al., 2016), and even specific tourism forms (Gu et al., 2015). However, although most existing studies on travel motivations emphasize demographic elements such as age, gender, and income (Yung-Kun et al., 2015; Marques et al., 2018), little attention has been addressed to the area of residence. When residence area is mentioned, it is often grouped alongside other sociodemographic factors without isolating its distinct influence.

Studies like those by Kozak (2002) and Fan et al. (2015) show that motivations can differ across international travelers and various destination types. However, they lack emphasis on how motivations might vary based on a traveler’s local area of residence within these regions. Even when motivation differences are noted across large geographical contexts, the localized impacts of living in urban vs. rural settings are largely unexplored. Some researchers, such as Wade et al. (2001) and Mkwizu (2018), focus on regional tourism patterns, often examining motivations specific to nature or history-based tourism in certain countries. Still, these studies frequently treat area of residence as secondary to other sociodemographic factors, overlooking the potential distinctions between motivations of travelers from different local contexts within the same region.

Therefore, this research seeks to study the influence of age, gender, and more specifically the area of residence as particularly influential yet underexplored dimension, contributing to a more nuanced understanding of travel motivations within youth tourism and offering a valuable perspective to tourism practitioners and policymakers.

The importance of age as a demographic factor is reflected in contrasting motivations between younger and older travelers. Previous research reveals that younger tourists often seek novelty and adventure, while older travelers might prioritize relaxation or cultural learning (Ma et al., 2018; Kara & Mkwizu, 2020). However, there are inconsistencies, such as seen in the findings of Luo and Deng (2008) where age was seen to negatively impact travel motivation, suggesting younger tourists are more motivated by novelty. These differing findings underscore the need to further explore age’s role in shaping travel motivation.

Gender, too, affects travel preferences, with studies identifying differing motivations between male and female tourists across various settings (Yung-Kun et al., 2015). Gender-specific motives may involve distinct preferences for adventure or relaxation, and understanding these nuances allows for more tailored marketing and service approaches within the tourism sector. As women are often more motivated by social and cultural experiences, men may be more drawn to adventure and outdoor activities (Collins & Tisdell, 2002).

The area of residence adds another layer, as travel motivation and travel behavior may differ between urban and rural residents due to variations in lifestyle, access to travel information, and exposure to travel opportunities (Djeri et al., 2014). Urban residents might exhibit higher motivations for nature-based escapes or adventure tourism, whereas rural residents may seek cultural or urban experiences when traveling. While area of residence remains a less-studied aspect of travel motivations, its consideration can highlight broader behavioral patterns that are critical for targeted marketing and product development (Djeri et al., 2014). The area of residence, particularly regarding the distinction between urban and rural living environments, has received scarce attention in tourism literature. Urban residents may have greater access to diverse cultural experiences and are often more motivated by novelty and social interactions, while rural residents might prioritize nature, autonomy, and escape from daily routines.

Since the present study focuses primarily on psychological motivations as outlined in the TCP framework, it is important to recognize that external factors—such as destination characteristics (e.g., natural vs. built environments, urban vs. rural zones) and socioeconomic conditions (e.g., income, employment, education level)—also play a crucial role in shaping travel behavior. For instance, travelers’ choices may be influenced by accessibility, local infrastructure, or perceived safety (Fan et al., 2015). Moreover, income disparities can impact not only the frequency of travel but also the type of experience sought (Djeri et al., 2014). Although these destination-level variables fall outside the scope of this study, they remain relevant to understanding the full complexity of travel motivations and warrant future inclusion in multi-layered analyses.

Thus, this study intends to answer the following research questions:

Research Question 1 (RQ1):

How does gender influence travel motivation among Generation Z travelers?

Research Question 2 (RQ2):

How do age differences within Generation Z influence travel motivation?

Research Question 3 (RQ3):

How does the area of residence impact travel motivation among Generation Z travelers?

3. Methods

3.1. Sample and Data Collection

The sample for this study was collected through a questionnaire, based on the TCP proposed by Pearce and Lee (2005) and adapted to investigate the main motivational factors that can influence the leisure travel options of Generation Z. It was made available online through social networks (LinkedIn and Facebook) and the authors’ contact networks between 1 July and 31 December 2024. In this way, it is a non-probabilistic sample collected for convenience. The target audience is young Portuguese people over the age of 18 and under 28 years old. Participation in the study was anonymous and voluntary, and informed consent was obtained from all participants. A questionnaire pre-test was carried out with 10 participants with different sociodemographic characteristics to assess their understanding of the questions and the average response time. The pre-test results revealed that participants had no difficulty understanding the questions, and the average response time was 5 min. The final sample has 303 participants.

3.2. Measures

The questionnaire, which was adapted from Pearce and Lee (2005), consisted of the following two sections: (i) questions relating to the sociodemographic characterization of the participants and (ii) motivational factors for travel consisting of 48 items divided into 14 factors (novelty, with four items; escape/relax, with four items; relationship (strengthen), with three items; autonomy, with three items; nature, with two items; self-development (host-site involvement), with five items; stimulation, with four items; self-development (personal development), with four items; relationship (security), with four items; self-actualize, with four items; isolation, with three items; nostalgia, with two items; romance, with two items, and recognition, with four items). All items were measured on a 5-point Likert scale, with 1 being not at all important and 5 being very important.

3.3. Data Analysis

Firstly, a statistical analysis of the items measuring each construct was conducted using SPSS software (v.25) to ensure data quality and descriptive consistency. Subsequently, linear regressions were performed using the Ordinary Least Squares (OLS) estimation method to explore the relationships between participants’ sociodemographic characteristics and their motivational factors for taking a trip. The use of OLS regression offers several advantages: it provides unbiased and efficient parameter estimates under standard assumptions, is straightforward to interpret, and allows for quantifying the individual contribution of each predictor variable. This approach is particularly useful for identifying significant demographic predictors of travel motivation in a clear and statistically robust manner.

The estimated simple regression was specified by the following equation:

Y_i = β₀ + β₁X_i + ε_i

where

• Y_i = dependent variable (gender, age and residence);
• X_i = independent variable (motivational factors to travel);
• Β = intercept;
• ε_i = error term (the difference between the observed and predicted value for observation).

4. Results

4.1. Sociodemographic Description of Participants

The study sample consists of 303 participants. Regarding sociodemographic characterization (

Table 1. Profile of sample participants.

	N	Percentage (%)
Gender
Female	176	58.2%
Male	115	38.2%
Other	11	3.6%
Age
18–20 years	147	48.5%
21–23 years	86	28.4%
24–26 years	40	13.2%
27–28 years	30	9.9%
Occupation
Student	205	67.7%
Employee	69	22.8%
Self-Employed	15	4.6%
Unemployed	15	5%
Residence
Urban area	203	67%
Rural area	60	19.8%
Beach area	28	9.2%
Mountain area	3	1%
Island	9	3%

), 58.2% are female, 38% male and 3.6% other. Regarding age, 48.5% of participants are between 18 and 20 years old, 28.4% are between 21 and 23 years old, 13.2% are between 24 and 26 years old, and 9.9% are between 27 and 28 years old. 67.7% are students, 22.8% are employees, 4.6% are self-employed and 5% are unemployed. Regarding residence, 67% live in an urban area, 19.8% in a rural area, 9.2% in a beach area, 1% in a mountain area and 3% on an island.

4.2. Descriptive Statistics of Items

Table 2. Descriptive statistics (mean and standard deviation).

Items	Mean (M)	Std. Deviation
Novelty	4.27	0.995
NOV1	4.45	0.998
NOV2	4.23	0.982
NOV3	4.03	1.042
NOV4	4.37	0.957
Escape/relax	4.19	1.030
ES1	4.17	1.039
ES2	4.15	1.063
ES3	4.09	1.053
ES4	4.35	0.964
Relationship (strengthen)	3.94	1.126
REL1	3.89	1.088
REL2	3.87	1.222
REL3	4.06	1.067
Autonomy	3.81	1.111
AUT1	3.81	1.107
AUT2	3.88	1.120
AUT3	3.75	1.105
Nature	4.06	1.040
NAT1	4.22	0.989
NAT2	3.89	1.091
Self-development (host-site involvement)	4.00	1.035
SD1	4.08	1.028
SD2	4.19	0.999
SD3	3.96	1.074
SD4	4.11	0.988
SD5	3.67	1.088
Stimulation	4.06	1.066
ST1	3.98	1.059
ST2	4.22	1.009
ST3	4.04	1.084
ST4	3.99	1.113
Self-development (personal development)	4.11	1.042
PD1	4.18	0.988
PD2	4.13	1.062
PD3	4.08	1.040
PD4	4.05	1.079
Relationship (security)	3.96	1.098
RELS1	4.20	1.036
RELS2	4.10	1.095
RELS3	3.82	1.128
RELS4	3.70	1.131
Self-actualize	3.90	1.138
SA1	3.98	1.087
SA2	4.00	1.119
SA3	3.80	1.138
SA4	3.80	1.207
Isolation	3.85	1.103
ISOL1	4.09	1.051
ISOL2	3.91	1.043
ISOL3	3.54	1.214
Nostalgia	3.33	1.222
NOS1	3.18	1.236
NOS2	3.48	1.207
Romance	3.04	1.364
ROM1	3.16	1.362
ROM2	2.91	1.365
Recognition	2.92	1.306
REC1	3.58	1.201
REC2	2.81	1.362
REC3	2.69	1.331
REC4	2.60	1.328

shows the mean and standard deviation of the items that measure the 14 motivational factors.

On average, the motivational factor most valued by participants was novelty (M = 4.27), followed by escape/relaxation (M = 4.19), self-development (personal development) (M = 4.11), nature (M = 4.06) and stimulation (M = 4.06). The motivational factor least valued by participants was recognition (M = 2.92). Figure 1 shows the average response values to the items by motivational factors.

4.3. Relationship Between Motivational Factors and Sociodemographic Characteristics of Participants

The results of the estimations of the simple linear regressions between gender, age and area of residence of the participants are found in

Table 3. Relationship between motivational factors and sociodemographic characteristics of participants.

	Gender		Age		Residence
	Coefficient	p-Value	Coefficient	p-Value	Coefficient	p-Value
Novelty	0.201	0.000	−0.125	0.037	−0.098	0.008
Escape/relax	0.163	0.008	−0.158	0.009	−0.186	0.009
Relationship (strengthen)	0.180	0.002	−0.114	0.002	−0.205	0.007
Autonomy	0.118	0.291	−0.096	0.001	0.041	0.007
Nature	−0.116	0.002	−0.150	0.006	−0.065	0.007
Self-development (host-site involvement)	0.114	0.381	−0.125	0.003	0.100	0.003
Stimulation	0.216	0.001	−0.123	0.140	−0.155	0.009
Self-development (personal development)	0.236	0.000	−0.121	0.003	−0.131	0.008
Relationship (security)	0.121	0.003	−0.138	0.350	−0.092	0.006
Self-actualize	0.122	0.008	−0.106	0.076	0.052	0.008
Isolation	0.131	0.034	−0.096	0.243	−0.055	0.007
Nostalgia	0.154	0.008	−0.124	0.180	−0.109	0.009
Romance	0.153	0.013	−0.193	0.001	−0.106	0.003
Recognition	0.185	0.001	−0.120	0.114	−0.088	0.005

. Given the objective of the study, the dependent variables were the sociodemographic characteristics (gender, age and residence) and the independent variables were the motivational factors.

Regarding gender (1—male; 2—female), the results reveal that men are more willing to travel motivated by Nature. All other factors, except autonomy and self-development (personal development), which are not statistically significant, are motivational factors for women to travel. Within the motivational factors, being a woman has a greater impact on the motivational factors of self-development (personal development) (β = 0.236), stimulation (β = 0.216) and novelty (β = 0.201).

In terms of age (1—18 to 20 years; 2—21 to 23 years; 3—24 to 26 years; and 4—27 and 28 years), the youngest participants within the sample’s target audience have the greatest impact on motivational factors. Age was not statistically significant in affecting motivations regarding stimulation, relationship (security), isolation, nostalgia and recognition. The fact that the participants are younger has a more positive impact on the motivational factors of romance (β = −0.193), escape/relax (β = −0.158) and nature (β = −0.150).

In terms of residence (1—urban area; 2—rural area, beach, mountain or islands), the fact that participants live in a rural area, beach, mountain or islands positively affects the motivational factors of autonomy (β = 0.041), self-development (host-site involvement) (β = 0.100), and self-actualize (β = 0.052). The participants’ residence in an urban area positively affects the remaining motivational factors.

5. Discussion of Findings

This study underscores the diversity of travel motivations within Generation Z, influenced by demographic factors such as gender, age, and, with particular attention, area of residence. Each of these factors shapes travel preferences in unique ways, providing important insights into tourism marketing strategies.

Gender has a strong influence on several motivational factors. while women place higher importance on self-development (personal development), stimulation and novelty, men, on the other hand, were more motivated by nature. Therefore, considering the research question “(RQ1): How does gender influence travel motivations among Generation Z travelers?”, the findings indicate that gender influences travel motivations (Cheng et al., 2024), with women more likely to seek social interactions and personal development opportunities, while men are drawn to nature and outdoor activities (Maiurro & Brandão, 2024). These findings are consistent with previous studies, suggesting that women tend to seek travel experiences that provide personal enrichment and social connections (Collins & Tisdell, 2002). Women’s interest in novelty may reflect a broader desire for exploration and self-discovery through travel. Tourism marketers can use these insights to tailor campaigns more specifically according to gender of Generation Z travelers (Meng & Uysal, 2008). Promoting experiences like wellness retreats, cultural immersion programs, and social travel experiences may resonate more for women. For men, focusing on nature-based adventure travel, such as hiking trips, eco-tours, and outdoor activities, could prove more effective. Understanding these gender-based differences allows for more precise and effective marketing strategies that cater to the unique motivations of each group.

Considering age, younger participants are more likely to prioritize romance, escape/relax and nature as found by Mitsuhashi and Wada (2025). As a tourist group, these individuals are seeking a break from their daily routines, often driven by the desire to escape the pressures of early adulthood, school, or family responsibilities. Therefore, considering the research question “(RQ2) How do age differences within Generation Z influence travel motivation?”, the results indicate that Generation Z reflects a natural progression from the desire for relaxation and escape among younger travelers to a focus on personal growth and self-actualization for older members of the generation (Mavrin et al., 2024). The findings also align with the Travel Career Pattern theory, which suggests that travel motivations evolve with age and reflect changing life goals and personal development stages (Pearce & Lee, 2005). Younger individuals are more likely to be motivated by short-term desires for fun, relaxation, and escape, while older individuals may view travel as an opportunity for personal and professional growth. For tourism providers, this suggests that marketing communication should vary according to age within Generation Z (Yamagishi et al., 2024). For younger travelers, promotions that emphasize excitement, adventure, and the opportunity to escape from the stress of daily life will likely resonate. For older Generation Z travelers, messaging should focus on the personal and professional development opportunities that travel can offer, such as educational tours, volunteering programs, or transformative travel experiences.

The area of residence also significantly impacted travel motivation (Qiao et al., 2024). While rural residents were more motivated by autonomy, self-development and self-realization, urban residents were more motivated by all other motivational factors. These findings suggest that the environment in which individuals live shapes their travel preferences, with urban residents seeking new and diverse experiences, while rural residents are drawn to the tranquility and solitude of nature. Thus, considering the research question “(RQ3): How does the area of residence impact travel motivation among Generation Z travelers?”, results indicate that the environment in which Generation Z travelers live shapes their travel preferences. Urban residents, surrounded by social and cultural diversity in their daily lives, may seek even greater novelty and social engagement when they travel. Meanwhile, rural residents, accustomed to more tranquil surroundings, are motivated by a desire for autonomy and a deep connection to nature, reflecting a preference for experiences that offer relaxation and solitude. These findings also are consistent with research suggesting that the area of residence influences leisure preferences and travel motivations (Djeri et al., 2014). Urban residents, accustomed to the fast-paced, multicultural life of cities, may seek novelty and opportunities to meet new people, as their everyday lives are already rich in social stimuli. Conversely, rural residents, who may experience less daily social interaction and cultural diversity, are drawn to nature and autonomy, seeking travel experiences that offer a break from routine and greater personal freedom (Zhuang & Wang, 2024).

However, it is also important to distinguish between generational characteristics and those associated with life stage. While many travel behaviors observed among Gen Z—such as a desire for novelty, escape, or personal development—are typical of young adults more broadly, Gen Z’s motivations are also shaped by their specific historical and cultural context. As digital natives who have come of age during global crises such as the 2008 financial downturn and the COVID-19 pandemic, Gen Z exhibits heightened sensitivity to sustainability, ethics, and social connectedness (Han, 2021; Hysa et al., 2021). Therefore, their travel behavior reflects both generational identity and the transitional life stage they occupy. A hybrid perspective that acknowledges both factors provides a more accurate lens for interpreting their motivations (Gardiner et al., 2014).

For tourism businesses and marketers, this analysis highlights the importance of tailoring travel experiences to different living environments. Urban residents may respond well to offerings that emphasize unique cultural or social experiences, such as festivals, city tours, or international travel. Conversely, rural residents may prefer adventure tourism, eco-tourism, or destinations that offer opportunities for solitude and self-guided exploration.

6. Conclusions

This study brings some important implications for tourism marketers and destination managers. The differences in travel motivations based on gender, age, and area of residence highlight the need for more targeted and segmented marketing strategies and show the complexity of travel motivations within this Generation Z. Each of these demographic factors contributes to a distinct set of travel motivations, underscoring the need for tailored marketing strategies in the tourism sector. In terms of gender, the data reinforces the importance of personal growth and social interaction for women, while men are more inclined toward adventure and nature. The age within Generation Z shows a clear progression from a desire for relaxation and escape among younger individuals to a focus on self-actualization and personal development as they grow older. Finally, the area of residence results reveal that urban and rural residents have different travel needs, with urbanites seeking novelty and rural residents valuing autonomy and natural environments. The interest in this study proved to be particularly relevant in the analysis of the demographic factor related to the area of residence, as the literature currently available on this point is clearly insufficient.

Thus, this study contributes to the growing body of literature on youth tourism and contributes to more personalized and effective marketing campaigns among Generation Z travelers. Tailoring travel experiences and messages to the specific motivations of different demographic groups within Generation Z can enhance engagement, increase customer satisfaction, and ultimately drive tourism demand.

7. Limitations and Suggestions for Further Studies

During this study it was possible to identify limitations. The non-probabilistic nature of the sample limits the generalizability of the findings, and the use of a purely quantitative approach may have overlooked more nuanced motivational factors. Future research should consider employing qualitative methods, such as interviews or focus groups, to gain deeper insight into the motivations of Generation Z. Additionally, further studies should explore other demographic factors, such as income and level and area of the education and examine how these variables interact with travel motivations.

By continuing to investigate the complexities of youth tourism, researchers can contribute to a more holistic understanding of this emerging market segment, helping to shape the future of tourism and better meet the needs of young travelers. It is also important to conduct longitudinal studies to examine how the travel motivations of Generation Z evolve over time.