The Profile of Wine Tourists and the Factors Affecting Their Wine-Related Attitudes: The Case of Türkiye

Abstract

The purpose of this study is to reveal the characteristics of wine tourists by determining their demographic and psychographic profiles, the effects of their travel motivations and lifestyles on their attitudes towards wine tourism, and the relationships between their demographic characteristics and attitudes. Based on a quantitative approach, study data were collected using a questionnaire. The sample consists of tourists who had visited a winery or participated in the vintage in a wine destination. Wine tourists’ principal motivations were pull factors and wine-related motivations. Their attitudes towards wine consumption had higher mean values than attitudes towards info-seeking, meaning that wine tourists preferred to taste wine and shop from wineries more than learn about wine. Regarding lifestyles, they were mostly innovators and experiencers. For destinations like Türkiye, wine tourism is a significant economic and sociocultural development tool. It is the first study to identify the wine tourists’ profile based on VALS-2, thereby providing a different perspective for the literature.

1. Introduction

For understanding wine tourism, it is very important to determine tourist motivations and to understand how they can be segmented effectively (Charters & Ali-Knight, 2002). Although wine tourism is seen as a niche within special interest tourism (SIT), there are certain differences within the market. Two methods are mostly used to segment and analyze wine tourists as a potential market. The first is to categorize wine tourists according to their demographic characteristics; the second is to profile them according to their psychographic characteristics, including values, attitudes, and lifestyles (Mitchell & Hall, 2006; Charters & Ali-Knight, 2002). The general profiles of wine tourists can also be better understood in terms of factors like their motivations to participate in wine tourism, lifestyles, general interests, personal resources and personality traits (Galloway et al., 2008).

Various demographic profiles have been used in different countries, such as Australia (Macionis, 1997), New Zealand (Treloar et al., 2004), Canada (Carmichael, 2005), South Africa (Bruwer, 2003), Spain (Lopez-Guzmán et al., 2008), and the USA (Dodd, 1995). This diversity challenges making a specific definition of wine tourist and demonstrates a need to identify factors that can be used for segmentation (Thompson & Prideaux, 2009). For this, market segmentation is very important for wine tourism operators for product development and new marketing strategies (Mitchell et al., 2000). Although market segmentation is generally based on socio-demographic variables, tourists with similar demographic characteristics may show differences in terms of their attitudes, lifestyles, and personalities (Bruwer et al., 2002). Thus, psychographic factors like motivations, lifestyles, interests, values, and personality traits are more helpful in understanding who wine tourists are and enabling a more detailed segmentation of the market (Galloway et al., 2008). Hence, marketers are now using more psychographic research in segmentation. In the present study, the segmentation of wine tourists consisted of two phases:

(1)

Demographic characteristics (age, sex, marital status, education, income, etc.);

(2)

Psychographic features (lifestyles and motivations).

The primary purpose of the current study is to create a wine tourist profile based on these two categories and to determine the distribution of wine tourists according to their demographic and psychographic characteristics. This is necessary because most studies on wine tourists focus mostly on the supply side (Alebaki & Iakovidou, 2011; Mitchell et al., 2000).

Türkiye is one of the world’s largest grape-producing countries. According to FAO, as of 2019, grape production is carried out in an area of 7.7 million hectares in the world, and 22.7% of this production area is in Spain. Spain is followed by France, China, Italy and Türkiye (FAOSTAT, 2021). Although it is located in a particularly fertile geographical land known as “Anatolia” that is highly favorable for grape growing, the country has not been considered a large worldwide wine producer (Koksal & Seyedimany, 2023) or a popular wine tourism destination due to poor marketing and the decline of large agricultural producers and factories, etc., just like in former socialist countries (Jovanović et al., 2025). Studies on wine tourism in Türkiye are generally categorized under gastronomic tourism or SIT studies. To the best of our knowledge, no previous study has determined the profile of wine tourists based on their lifestyles and attitudes (in terms of the VALS-2 scale) in Türkiye, where alcoholic beverages are excessively taxed and advertising is banned (Güzel et al., 2021; Koksal & Seyedimany, 2023). The reason for conducting this research is to contribute to the development of wine tourism in Türkiye, which has not been promoted sufficiently in the world. Based on the above-mentioned reasons, the study has two main aims: (1) to determine the demographic and psychographic profiles of wine tourism participants and determine their distribution; and (2) to determine the effects of the travel motivations and lifestyles of wine tourism participants on their attitudes towards wine tourism and, in parallel, to determine whether these attitudes differ according to their demographic characteristics.

This research article consists of the literature review, materials and methods, results, discussion, conclusion, limitations and implications. The literature review part includes demographic and psychographic characteristics and lifestyle typologies of wine tourists. In the materials and methods section, the study sample, data collection and analysis are explained in detail. The findings are presented in the results part. Then, these results are discussed, and lastly, fundamental conclusions are highlighted; study limitations and managerial and theoretical implications are presented in the final section.

2. Literature Review

2.1. Demographic Characteristics of Wine Tourists

Wine tourism refers to visits to vineyards and wineries, attending wine festivals, and wine shows where visitors’ primary motivation is wine tasting and/or experiencing the specialties of a wine region (Hall & Macionis, 1998). For those interested in wine-related travel, wine tourism is more of a lifestyle and personal growth experience than a principal leisure activity (Beames, 2003). Wine tourists expect to develop themselves in wine-related culture and have recreational experiences. According to O’Neill and Palmer (2004), a wine tourist can be anyone who evaluates all the elements associated with wine while visiting a production area. However, Alebaki and Iakovidou (2011) claim that people who participate in wine tourism are not only interested in wine tasting but also seek a general tourism experience that provides many benefits locally (Charters & Ali-Knight, 2002). Wine tourists’ demographic characteristics vary with destination (

Table 1. Exemplary studies on the demographic characteristics of wine tourists.

Authors	Destination	Demographics of Wine Tourists
Dodd (1995)	Texas, USA	High education and income
Treloar et al. (2004)	New Zealand	Mostly female, 30–50 years old, well-educated, professional, high-income, mostly domestic tourists
O’Neill and Palmer (2004)	Australia	Female, young, in a managerial or professional occupation, well-educated, and domestic tourists
Ignatov and Smith (2006)	Canada	Equal numbers of male and female tourists, average age, average education level, and high income
Lopez-Guzmán et al. (2008)	South of Spain	Aged 50–59, middle or high income, often visiting with family
Bekar and Şahin (2017)	Datca, Türkiye	Aged 50 and over, middle- and upper-income level, high education level

). In addition, wine tourists with similar demographic characteristics may have considerably different lifestyles and attitudes (Bruwer et al., 2002).

Therefore, the present study first identified the demographic profile of wine tourists before investigating whether their attitudes towards wine tourism differ according to their demographic characteristics, as in the following hypotheses:

H₁: 

Participants’ attitudes towards wine tourism differ according to their demographic characteristics.

H_1a: 

Participants’ attitudes towards wine tourism differ according to their sex.

H_1b: 

Participants’ attitudes towards wine tourism differ according to their marital status.

H_1c: 

Participants’ attitudes towards wine tourism differ according to their age.

H_1d: 

Participants’ attitudes towards wine tourism differ according to their education level.

H_1e: 

Participants’ attitudes towards wine tourism differ according to their occupation.

H_1f: 

Participants’ attitudes towards wine tourism differ according to their income level.

2.2. Psychographic Characteristics of Wine Tourists

There is a general preference for psychographic segmentation, as demographic segmentation alone is considered a poor predictor of tourist behavior (Prentice et al., 1998). Psychographic dimensions include motivation, participation, attitudes, personality, and lifestyles. Lifestyle affects a wide range of daily consumer behavior, including choosing travel destinations and leisure activities (Matzler et al., 2005). Therefore, in this study, lifestyle was chosen as a segment criterion and examined together with motivations and attitudes to provide a better understanding of tourists visiting wine destinations in Türkiye, as segmentation based on lifestyle provides detailed information about market segments and a better understanding of customers (Berry, 1983).

Tourists have become more diverse as lifestyles change rapidly due to liberalization, privatization, and economic globalization, while tourism research shows that tourists now seek an integrated, multicultural, and unique hospitality experience (Bharwani & Jauhari, 2013). Therefore, psychographic variables reveal more meaningful information about tourists’ behavior and lifestyles (Cha et al., 1995). In order to develop a psychographic profile of wine tourists, which is a primary purpose of the present study, it is first necessary to examine the motivations and lifestyles of the participants.

According to Dann (1977), there are two motivational stages in a travel decision determined by push and pull factors. In the case of wine tourism, push factors are internal motivations that motivate a person to visit a winery (e.g., socializing, learning about wine, relaxation, meeting the winemaker); pull factors are extrinsic factors that attract the visitor to the winery, including the winery’s general characteristics or activities (e.g., wine tasting and purchasing wine, dining in a rural setting at the winery) (Mitchell et al., 2000).

Wine tourists are mainly motivated by wine tasting and purchasing (Charters & Ali-Knight, 2002), while their secondary motivations are socializing, learning new information about wine, entertainment, and the rural environment (Bruwer & Alant, 2009; Carmichael, 2005). Getz (2000) asserts that a pastoral, picturesque environment, which Page and Getz (1997) call the “village experience”, is a very significant pull factor to attract wine tourists. That is also a subject of wine research, as terroir is an important concept in wine-related tourism (Capitello et al., 2021; Marlowe & Bauman, 2019; Marlowe & Lee, 2018; Peršurić et al., 2019). Ravenscroft and van Westering (2001) highlight the importance of the educational dimension of the wine experience, with winemaking, wine tasting, and learning about wines being important motivations. Hall and Macionis (1998) find that the main motivations of wine tourists are wine tasting, rural scenery, meeting winemakers, and learning about wine types.

However, the relationship between these motivations and wine tourists’ attitudes towards wine tourism has not been adequately investigated. Therefore, the present study first categorized wine tourists according to their motivations before investigating the relationship, as in the following hypotheses:

H₂: 

Participants’ travel motivations affect their attitudes towards wine tourism.

H_2a: 

Participants’ wine-related motivations influence their attitudes towards wine consumption.

H_2b: 

Participants’ wine-related motivations influence their attitudes towards info-seeking.

H_2c: 

Push–pull motivations influence participants’ attitudes towards wine consumption.

H_2d: 

Push–pull motivations influence participants’ attitudes towards info-seeking.

2.3. Lifestyle Typologies of Wine Tourists

Wedel and Kamakura (2000) argue that although psychography and lifestyle are used in the same sense in the literature, there is a difference between them. That is, the main purpose of psychography is to evaluate the consumer as an individual according to more than one psychological dimension, including their ideas as a consumer on different subjects and their lifestyle. Lifestyle is closely related to psychological research, especially personality and attitude measurement (Schiffman et al., 2001). However, lifestyle research is more recent than personality research and more comprehensive than studies on personal values. The concept of lifestyle incorporates consumers’ demographic characteristics and their activities, interests, and ideas, and the patterns of spending their income and time (Blackwell et al., 2001). As wine is a lifestyle beverage that is preferred by a wide range of consumers, there is a growing need to understand consumer values and consumption patterns as reflected in lifestyle-based profiles (Bruwer et al., 2002).

The consumer behaviors of wine tourists have been studied using lifestyle typologies, some of which are based on motivations, while some are based on demographics. Taking travel motivation as the main criterion, Johnson (1998) divided wine tourists into two groups: “specialist winery tourists”, who visit vineyards, wineries, wine festivals, and wine shows for recreational purposes; and “generalists”, who visit wine destinations for other reasons. Bruwer et al. (2002) developed a five-dimensional lifestyle model of wine consumers based on wine consumption status, shopping method, quality, drinking rituals, and results of wine consumption, and they divided the Australian wine market into five groups: “purposeful inconspicuous wine drinkers”, “ritual-oriented conspicuous wine enthusiasts”, “enjoyment-seeking social wine drinkers”, “fashion/image-oriented wine drinkers”, and “basic wine drinkers”. Alebaki and Iakovidou (2004) used cluster analysis to divide winery visitors in the Macedonian region into four groups based on demographic characteristics and motivations: “wine lovers”, who have a high education level and income, and whose main motivation is to visit the winery, meet the winemaker, and learn how to make wine, “neophytes”, usually comprising students with low budgets and mainly motivated by visit the winery; “occasional wine tourists”, who are not primarily motivated to learn about wine but are influenced by local gastronomy; and the “hangers-on”, who are not interested in wine or winemaking, are not wine consumers in general, and see a visit to a winery as merely a tourist attraction.

As the above review indicates, previous wine tourist typologies have been formed separately in the literature. In the present study, VALS-2 was used because the purpose was profiling based on segmentation, not creating a typology. The Values and Lifestyles Attitudes Scales (VALS and VALS2) are based on Maslow’s concept of hierarchy of needs and social character theory (Matzler et al., 2005). There are eight types of VALS: innovators, thinkers, believers, achievers, strivers, experiencers, makers and survivors.

According to SRI consulting’s Values and Lifestyles (VALS)1 (Shih, 1986), innovators are always open to new information, new technologies and new ideas. They are experimental but skeptical about advertisements. They are future-oriented and trust in science and research and development. Thinkers always plan, research and think before they act. They are not interested in trendy things. They choose traditional intellectual ways and prefer to buy known products. Believers have strict beliefs for maintaining a good life. They trust in faith and spirituality. They want to be around friendly communities. They do not like uncertainty. They tend to be loyal customers. Achievers think that money is the power. They are committed to their families and their jobs. Strivers are the center of street culture. They want to make their lives better in terms of wealth and status but have difficulties in doing that. They love having fun. Experiencers want to experience everything. They follow the latest trends and want to be trendy. They seek thrill. They describe themselves as very social. They are mostly spontaneous in their decisions. Makers are mostly interested in outdoor activities with their families and close friends. They are conservative in their lives but try to seem intellectual to others. Survivors are cautious and avoid taking risks. They love their routine, being in familiar places and with familiar people. They are loyal to brands and products.

Shih (1986) used the VALS segmentation model to develop a tourism marketing strategy. The VALS-2 model is recognized as a universal lifestyle scale (Adnan et al., 2022; Ahmadimanesh & Helaliyan, 2022; Rodríguez et al., 2022). However, no previous studies have examined whether the lifestyle segments included in the VALS-2 lifestyle segmentation affect attitudes towards wine tourism. To fill this gap, the present study therefore tests the following hypotheses:

H₃: 

The participants’ lifestyles affect their attitudes towards wine tourism.

H_3a: 

Being a believer affects participants’ attitudes towards wine tourism.

H_3b: 

Being a striver affects participants’ attitudes towards wine tourism.

H_3c: 

Being an experiencer affects participants’ attitudes towards wine tourism.

H_3d: 

Being an innovator affects participants’ attitudes towards wine tourism.

H_3e: 

Being a maker affects participants’ attitudes towards wine tourism.

H_3f: 

Being an achiever affects participants’ attitudes towards wine tourism.

H_3g: 

Being a thinker affects participants’ attitudes towards wine tourism.

H_3h: 

Being a survivor affects participants’ attitudes towards wine tourism.

3. Materials and Methods

3.1. Study Sample and Data Collection

Wine tourism in Türkiye continues to develop in wine production centers and wine-producing tourist destinations. Türkiye’s first wine route is the Thrace Vineyard Route in the Marmara Region, while other important routes include the Bozcaada Vineyard Route in the same region and the Urla Vineyard Route (Yıldız & Güner, 2021). Other important wine routes and destinations include Urla, Denizli, and Şirince village in the Aegean Region and Cappadocia in the Central Anatolia Region, which host many festivals and vintage events, with many businesses and workshops offering wine tasting. Datca is another destination for the development of wine tourism in the Aegean Region, where various activities are held related to grape and wine production and promotion (Bekar & Şahin, 2017). There are also important wine-growing areas in Turkey that do not yet offer wine tourism activities. These include Ankara in Central Anatolia, Tokat in the Black Sea region, Elazig in Eastern Anatolia, and Diyarbakir in Southeastern Anatolia (Yıldız & Güner, 2021).

The population of the present study consisted of tourists visiting the Thrace Vineyard Route, including Tekirdag, Sarkoy, Gelibolu, and Kirklareli; Bozcaada, Cappadocia, Şirince, Urla, and Datca, which are defined as wine tourism destinations in Turkey (Figure 1). The sample comprised 518 domestic and foreign tourists who voluntarily agreed to participate in the study and visited wineries or wine producers and/or participated in the vintage in these destinations. For this study, the definition of wine tourist by Alebaki and Iakovidou (2011) was adopted, namely tourists traveling with the main attraction of the wine and wine region, seeking service, recreation and leisure in varying proportions. The data were collected by the researchers face-to-face.

A quantitative approach was adopted to profile wine tourists according to their demographic characteristics (sex, age, education level, marital status, place of residence, and income) and psychographic characteristics (motivations and lifestyles) and to determine the effects of their demographic characteristics, motivations, and lifestyles on their attitudes towards wine tourism. As this was a segmentation study, we took the participants’ sex into account during the analysis. A three-part survey was used for data collection. The first part included two attitude and motivation scales regarding wine tourism, adapted from the scales used in Hall (1996), respectively. The second part classified the participants’ lifestyles based on the VALS-2 scale (Bruwer et al., 2002).

The 14-item motivation scale and the 11-item attitudes scale were first independently translated from English to Turkish by two linguists who are fluent in English and Turkish. The original items and the Turkish version of the scales were then evaluated by a different foreign language expert before the necessary corrections were made. In addition, a field expert and a Turkish language expert evaluated the scales from a cultural and theoretical perspective. The VALS-2 scale was previously translated into Turkish by Yıldırım et al. (2018). Since the data were collected from both domestic and foreign tourists, the adapted Turkish and retranslated English forms of the questionnaire were both used. The participants responded to all scales using a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree).

Before starting data collection, a pilot study was conducted with 60 tourists (42 men, 18 women) in Şirince to examine the item-total correlation values of the scales and Cronbach’s α values for internal validity. The findings showed that the item-total correlation value of each item was over 0.30, while the internal validity coefficients were over 0.822 (wine tourism = 0.822, attitudes = 0.914 and VALS-2 = 0.837). Data were collected from the most visited wine destinations of Türkiye, mostly during vintage or wine festivals. All participants gave a verbal statement in order to participate in this study.

3.2. Data Analysis

The collected data were first input into the SPSS 22.0 program and subjected to analysis of extreme values, multivariate normality, validity, and reliability. Exploratory factor analysis (EFA) was then applied to the three scales, followed by structural equation modelling (PLS-SEM) to test the research model. There are two different models for SEM: variance-based (PLS-SEM) and covariance-based (CB-SEM). The first factor analysis of the motivations for the wine tourism scale indicated that three items were overlapping. These three items were therefore removed, and the factor analysis was repeated. The two factors obtained were named “wine-related motivations” (hereafter WRM) and “push-pull motivations” (hereafter PPM). The Kaiser–Meyer–Olkin (KMO) value was 0.809; Bartlett’s test of sphericity was p < 0.01; X² = 2412.253; DF: 55; the explanation rate of the total variance was 58.638%. The contributions of WRM and PPM to the total variance were 33.460% and 25.178%, respectively. Factor loadings for all 11 items ranged from 0.836 to 0.657. The Cronbach’s α coefficients for WRM and PPM were 0.871 and 0.826, respectively.

In the first factor analysis for the attitudes towards wine tourism scale, one item had a variance below 0.30, so it was removed before repeating the analysis. The second factor analysis indicated that the items loaded onto two factors: “attitudes towards wine consumption” (hereafter AWC) and “attitudes towards info-seeking (about wine)” (hereafter AIS). The KMO value was 0.890; Bartlett’s test of sphericity was p < 0.01; X² = 3016.792; DF: 45; and the proportion of the total variance explained was 67.467% (AWC = 37.116%; AIS = 30.351%). The factor loadings for the 10 attitude items varied between 0.882 and 0.598. The Cronbach’s α coefficients were 0.891 and 0.837 for AWC and AIS, respectively.

In the first factor analysis for VALS-2, six items had a variance of less than 0.30. Five items were removed because the difference between the factor load values of the two dimensions was less than 0.10 in the second factor analysis. In the final analysis, 24 items were grouped under six factors from the VALS-2 model (believers, strivers, experiencers, innovators, makers, and achievers). Two factors in the original scale, thinkers and survivors, were excluded from the present study because they were overlapping and/or had low factor loadings. In the final factor analysis, the KMO value was 0.785; Bartlett’s test of sphericity was p < 0.01; X² = 3965.269; DF: 276; and the explanation rate of the total variance was 58.989%. The contributions of the six factors (believers, strivers, experiencers, innovators, makers, and achievers) to the total variance were 11.695%, 11.180%, 10.150%, 9.974%, 8.851%, and 7.140%, respectively.

A measurement model was created to test the validity and reliability of the scale structures determined by the EFA. Internal validity, convergent validity, and discriminant validity were evaluated with the measurement model. To test interval validity, Cronbach’s α and composite reliability (CR) values were examined. For convergent validity, average variance extracted (AVE) by factor loads was examined. For discriminant validity, cross-loads, the Fornell and Larcker (1981) criterion, and the HTMT criterion suggested by Henseler et al. (2015) were examined.

Table 2. Measurement model results.

	Item Code	Factor Load	Cronbach’s α	CR	AVE
WRM			0.871	0.903	0609
My motivation for this travel is to visit wineries.	WRM1	0.841
…is to meet the winemaker.	WRM2	0.796
…is to learn winemaking.	WRM3	0.795
…is to buy wine.	WRM4	0.802
…is to taste wine.	WRM5	0.780
…is to attend the vintage.	WRM6	0.653
PPM			0.770	0.894	0.809
…is to taste local food and drinks.	PPM3	0.934
…is the cultural attractions in the region.	PPM5	0.863
AWC			0.892	0.920	0.699
I like to taste types of wine.	AWC1	0.888
I like to drink wine.	AWC2	0.872
Drinking wine is good for the health as long as it is not in excess.	AWC3	0.793
I buy wine from wineries.	AWC4	0.836
I visit wineries.	AWC5	0.787
AIS			0.838	0.884	0.605
I read magazines about wine.	AIS1	0.775
I follow TV programs about wine.	AIS2	0.764
I meet many people through wine.	AIS3	0.789
I know most types of wine.	AIS4	0.797
I attend wine or vintage festivals.	AIS5	0.764
Believers			0.790	0.855	0.544
Religion is the most important way to know what’s morally correct.	VALS27	0.830
The government should encourage prayers in public schools.	VALS13	0.806
No matter how much evil I see in the world, my faith in God is strong.	VALS29	0.705
Just as the (Holy Book) says, the world literally was created in six days.	VALS6	0.675
There is too much sex on television today.	VALS20	0.655
Strivers			0.811	0.860	0.674
I like to dress in the latest fashions.	VALS19	0.727
I follow the latest trends and fashions.	VALS5	0.781
I dress more fashionably than most people.	VALS12	0.940
Experiencers			0.696	0.814	0.525
I like trying new things.	VALS17	0.811
I would like to understand more about how the universe works.	VALS34	0.654
I like doing things that are new and different.	VALS32	0.779
I like to learn about art, culture, and history.	VALS8	0.638
Innovators			0.854	0.901	0.753
A good negotiator doesn’t just get the food in the bowl, but the bowl itself.	VALS31	0.957
I like a lot of excitement in my life.	VALS23	0.823
I often crave excitement.	VALS9	0.815
Makers			0.623	0.764	0.529
I like making things of wood, metal, or other such material.	VALS25	0.914
I love to make things I can use every day.	VALS4	0.642
I like to make things with my hands.	VALS30	0.582
Achievers			0.621	0.762	0.522
I like to lead others.	VALS21	0.783
I like being in charge of a group.	VALS7	0.554
I like the challenge of doing something I have never done before.	VALS28	0.804

shows that the Cronbach’s α values were over 0.621, the CR values were over 0.70, the AVE values were over 0.50, and the factor loadings varied between 0.621 and 0.892. Although their loadings were below 0.70, WRM6, VALS6, VALS20, VALS34, VALS8, VALS4, VALS30, and VALS7 were kept in the model because their AVE and CR values were above the threshold value as suggested by Hair et al. (2014). However, PPM1, PPM2, and PPM4 were removed because they had factor loadings of 0.40, while VALS35, VALS11, VALS26, and VALS30 were removed because their AVE values were less than 0.5. Removing these items from the model caused the AVE and CR values to exceed the threshold value. Internal validity and convergent validity were provided according to Cronbach’s α, CR, AVE, and factor loads.

Table 3. Convergent validity results (Fornell and Larcker criterion).

	AIS	AWC	A	B	E	I	M	PPM	S	WRM
AIS	0.778 *
AWC	0.663	0.836 *
Achievers (A)	0.103	0.177	0.723 *
Believers (B)	−0.273	−0.489	0.057	0.738 *
Experiencers (E)	−0.065	0.117	0.315	0.028	0.725 *
Innovators (I)	0.070	0.091	0.365	0.139	0.332	0.867 *
Makers (M)	0.066	0.047	0.191	0.123	0.281	0.193	0.727 *
PPM	0.114	0.128	0.164	0.080	0.285	0.154	0.098	0.899 *
Strivers (S)	0.134	0.072	0.204	0.152	0.209	0.198	0.072	0.069	0.821 *
WRM	0.603	0.692	0.184	−0.285	0.082	0.179	0.080	0.196	0.077	0.780 *

* square root values of AVE.

shows that the square root of the AVE value of each construct is higher than the correlation between the other constructs.

The HTMT values should theoretically be 0.90 and below for constructs that are close to each other and 0.85 and below for constructs that are far from each other (Hair et al., 2014).

Table 4. Discriminant validity results (HTMT criterion).

	AIS	AWC	A	B	E	I	M	PPM	S	WRM
AIS
AWC	0.759
Achievers (A)	0.116	0.186
Believers (B)	0.320	0.568	0.150
Experiencers (E)	0.103	0.145	0.497	0.125
Innovators (I)	0.104	0.085	0.523	0.199	0.430
Makers (M)	0.097	0.078	0.404	0.210	0.477	0.277
PPM	0.136	0.148	0.215	0.123	0.389	0.175	0.122
Strivers (S)	0.121	0.067	0.290	0.221	0.286	0.303	0.206	0.096
WRM	0.698	0.766	0.213	0.315	0.124	0.190	0.127	0.232	0.089

shows that all values in the model were below 0.85. Finally, the cross-loads of each item had the highest factor loading in its own construct. Thus, the model’s discriminant validity was demonstrated based on the Fornell and Larcker (1981) criterion, the HTMT criterion, and the cross-load condition.

PLS-SEM was then applied to the structural model to test the research hypotheses. Cluster analysis was used to determine which clusters the participants belonged to, based on their lifestyles. Demographic information and descriptive information about the participants are presented in tables and figures using percentage values, arithmetic mean and standard deviations. Student’s t-test and analysis of variance (ANOVA) were used to determine whether the participants’ attitudes towards wine tourism differed according to their demographic characteristics. The Scheffe test was used to determine the specific groups that any differences originated from.

4. Results

4.1. Results on the Demographic Profile of Wine Tourists

The demographic characteristics of the sample are summarized in

Table 5. Demographic characteristics of the participants.

Sex	Percentage	Marital Status	Percentage
Female	52.7	Married	33.4
Male	47.3	Single	66.6
Age (years)		Monthly average income (per person) *
18–24	32.4	EUR 435 or below	29.0
25–34	33.2	EUR 436–740	18.9
35–44	17.8	EUR 741–1050	18.5
45–54	7.1	EUR 1051–1355	11.0
55 and older	9.5	EUR 1356 or above	22.6
Education level		Having children
Primary and/or secondary education	11.4	Yes	24.7
Associate/bachelor’s degree	66.2	No	75.3
Postgraduate degree	22.4
Place of Residence		Occupation
Türkiye	91.3	Student	31.5
USA	2.5	Private Sector Employee	28.6
Spain	1.7	Public Sector Employee	15.8
France	1.5	Self-employed	9.1
Germany	1.5	Retired	7.5
Japan	1.4	Unemployed	7.5
Travel companion
Alone	12.7
With friends	47.5
With partner	23.6
With family (partner and children)	9.1
With parents	7.1

* personal income was asked in Turkish lira. The findings are presented in euros based on the exchange rate during the data collection period (EUR 1 = TRY 6.5).

. About half of the participants who visited wine tourism destinations and participated in the study were female (52.7%), while the majority of participants were young or middle-aged. Most participants had associate or undergraduate degrees (66.2%), followed by those with postgraduate degrees (22.4%) or primary or secondary diplomas (11.4%). About a third of participants were married (33.4%), while 24.7% had children. The majority of participants were in the low- or middle-income group, while 22.6% belonged to the upper-income group.

Almost all participants (91.3%, n = 473) visiting wine-related destinations were domestic visitors, while the rest (8.7%, n = 45) were foreigners from the USA, Spain, France, Germany and Japan, respectively. Almost 30% of the participants were private sector employees, 15.8% were public employees, 9.1% were self-employed, and 7.5% were retired. Over a third of participants were unemployed (39%), the majority of whom were students. Approximately half of participants (47.5%) were traveling with their friends.

4.2. Results on the Psychographic Profile of Wine Tourists

For almost 60% of participants, their main motivations were seeing historical places, sightseeing and participating in recreational activities. Just over one-third (37%) of participants gave wine or wine-related factors and participating in wine-related activities as their main motivation. The participants’ travel motivations were examined in terms of the two dimensions identified by the EFA, as explained above: WRM and PPM. These are presented as $\overline{\mathrm{x}}$ ± SD in Figure 2. The participants’ primary travel motivations were push-pull factors (4.12 ± 0.86). Factors such as the region’s cultural attractions and tasting local food and beverages were the primary motivations, whereas wine-related factors, such as tasting wine, participating in the vintage, buying wine, and learning about winemaking, were secondary motivations.

According to the cluster analysis carried out to determine which lifestyle groups the wine tourists belong to, 25.9% of the participants could be categorized as experiencers and 17.8% were innovators. Believers represented 17.2% of the participants. Achievers represented 16% of the participants. Finally, 15.4% of the participants were categorized as strivers.

4.3. Results on Relationship Between Demographics and Psychographics and Attitudes Towards Wine Tourism

Regarding attitudes towards wine tourism (hereafter AWT), the participants’ responses to the items towards wine tourism grouped under two factors (Figure 3): AWC and AIS. The participants were more positive for AWC than AIS, given that the overall means were 3.58 ± 1.07 and 2.91 ± 0.93, respectively. For AWC, certain items received particularly strong agreement: “I like to taste wine types” (3.79 ± 1.28) and “Drinking wine is good for the health as long as it is not in excess” (3.78 ± 1.22), followed by “I buy wine from wineries” (3.36 ± 1.36), “I like to drink wine” (3.73 ± 1.26), and “I visit wineries” (3.26 ± 1.28).

Regarding the relationship between the demographic variables and attitudes (

Table 6. Participants’ attitudes towards wine tourism according to demographic characteristics.

Demographics	Dimensions of AWT
	AWC					AIS
	n	$\overline{\mathrm{x}}$ ± SD	t	F	p	n	$\overline{\mathrm{x}}$ ± SD	t	F	p
Female	273	3.53 ± 1.10	−1.144	-	0.253	273	2.85 ± 0.95	−1.503	-	0.134
Male	245	3.64 ± 1.03				245	2.97 ± 0.91
Married	173	3.57 ± 1.10	−0.266	-	0.790	173	2.89 ± 1.00	−0.295	-	0.776
Single	345	3.59 ± 1.05				345	2.92 ± 0.09
18–24	168	3.35 ± 1.11	-	3.349	0.010 *	168	2.74 ± 0.91	-	2.018	0.091
25–34	172	3.75 ± 1.10				172	3.01 ± 0.86
35–44	92	3.61 ± 1.16				92	2.93 ± 1.06
45–54	37	3.67 ± 0.09				37	3.02 ± 0.96
55 and above	49	3.71 ± 0.95				49	2.98 ± 0.92
Primary and/or secondary education	59	2.55 ± 1.34	-	35.212	0.000 *	59	2.24 ± 1.06	-	18.545	0.000 *
Associate/bachelor’s degree	343	3.71 ± 0.91				343	2.97 ± 0.84
Postgraduate degree	116	3.73 ± 1.07				116	3.07 ± 1.00
Unemployed	39	2.98 ± 1.19	-	5.941	0.000 *	39	2.35 ± 0.81	-	4.700	0.000 *
Private sector employee	148	3.80 ± 0.95				148	3.05 ± 0.93
Public sector employee	82	3.69 ± 1.01				82	2.93 ± 0.91
Retired	39	3.66 ± 1.00				39	2.97 ± 0.94
Self-employed	47	3.88 ± 0.93				47	3.16 ± 0.96
Student	163	3.38 ± 1.13				163	2.81 ± 0.92
EUR 435 and below	150	3.07 ± 1.20	-	13.717	0.000 *	150	2.53 ± 0.89	-	9.000	0.000 *
EUR 436–740	98	3.73 ± 0.99				98	3.09 ± 0.95
EUR 741–1050	96	3.73 ± 1.00				96	3.05 ± 0.95
EUR 1051–1355	57	3.82 ± 0.94				57	3.03 ± 0.83
EUR 1356 and above	117	3.89 ± 0.83				117	3.06 ± 0.88

* p < 0.05.

), AWT did not differ statistically significantly according to sex and marital status, although men and unmarried participants had higher mean scores than women and married participants for both AWC and AIS.

Regarding age, participants aged 25–34 had the highest mean AWC scores, while participants aged 18–24 had the lowest mean scores. However, these values were significantly above the mean levels in all age groups (p < 0.05). The Scheffe test results indicated that the significant difference lay between the 18–24 and 25–34 age groups. For AIS, both the 18–24 and 55-and-above age groups had significantly lower mean scores (p > 0.05). There was a significant positive correlation between education level and both AWC and AIS scores (p < 0.05). The Scheffe test results indicated that, for both dimensions, the significant difference lay between secondary education graduates and associate/undergraduate and postgraduate participants. There were also significant correlations between occupation and both AWC and AIS scores. More specifically, unemployed participants (AWC = 2.98 ± 1.19, AIS = 2.35 ± 0.81) and students (AWC = 3.38 ± 1.13, AIS = 2.81 ± 0.92) had lower scores, whereas private sector workers (AWC = 3.80 ± 0.95, AIS = 3.05 ± 0.93) and self-employed (AWC = 3.88 ± 0.93, AIS = 3.16 ± 0.96) participants had higher scores. For AWC, the Scheffe test results indicated a significant difference between unemployed participants and privately, publicly, or self-employed participants. For AIS, unemployed participants had significantly different scores from private sector and self-employed participants (p < 0.05). Income level was also positively correlated with AWT. Although the overall AIS mean was lower than the AWC mean, AIS differed significantly with income status (p < 0.05). For both dimensions, the significant difference lay between participants in the EUR 435/month income group and the other income groups.

Based on these findings, H₁ was partially supported (

Table 7. Hypothesis results for the relationship between AWT and demographic characteristics.

Hypothesis	Sub-Hypothesis	Paths	Result
H1	H1a AWT—Sex	AWC—Sex	unsupported
		AIS—Sex	unsupported
	H1b AWT—Marital status	AWC—Marital status	unsupported
		AIS—Marital status	unsupported
	H1c AWT—Age	AWC—Age	supported
		AIS—Age	unsupported
	H1d AWT—Education	AWC—Education	supported
		AIS—Education	supported
	H1e AWT—Occupation	AWC—Occupation	supported
		AIS—Occupation	supported
	H1f AWT—Income	AWC—Income	supported
		AIS—Income	supported

). More specifically, H_1d, H_1e, and H_1f, which hypothesized that AWT differs in educational status, occupation, and income level, were supported, whereas H_1a and H_1b, which hypothesized a relationship with sex and marital status, were not supported. Finally, while AWC differed by age group, AIS did not (H_1c).

Table 8. Model coefficients.

Paths	R2	Q2	VIF	f2
WRM → AWC PPM → AWC Believer → AWC Striver → AWC Experiencer → AWC Innovator → AWC Maker → AWC Achiever → AWC	0.585	0.399	1.235 1.150 1.204 1.108 1.343 1.295 1.126 1.259	0.641 0.001 0.231 0.008 0.003 0.001 0.001 0.008
WRM → AIS PPM → AIS Believer → AIS Striver → AIS Experiencer → AIS Innovator → AIS Maker → AIS Achiever → AIS	0.410	0.240	1.235 1.150 1.204 1.108 1.343 1.295 1.126 1.259	0.409 0.003 0.030 0.030 0.038 0.000 0.008 0.000

shows the results of the path analysis for the study hypotheses. The structural model was evaluated based on multicollinearity (inner VIF), explanatory power (R2), beta coefficients (β), t-values, predictive power (Q2), and effect size (f2) (Hair et al., 2014). To measure the inner VIF values, β coefficients, f2 and R2 values were calculated, and the PLS algorithm was executed. The Q2 values were measured using blindfolding analysis, while bootstrapping analysis was performed to determine the significance levels of the path coefficients created to test the hypotheses. Finally, t-values were calculated.

As Table 8 shows, the VIF values varied between 1.108 and 1.343, indicating no multicollinearity problem as suggested by Kock (2015) and Hair et al. (2014). Q2 values were then examined to calculate the model’s predictive power. These were 3.399 for AWC and 0.240 for AIS, which meets the threshold suggested by Hair et al. (2014). In the next step, the R2 value used to evaluate the explanatory power of the structural model was examined. In the present study, the R2 values for AWC and AIS were 0.585 and 0.410, respectively. Finally, the f2 values were measured to determine the effect size between the paths (Table 8), whereby 0.02, 0.15, and 0.35 indicate small, medium, and large effects, respectively (Cohen, 1988).

The analysis indicated that WRM and AWT had positive and significant effects on both AWC (β = 0.573, p < 0.01) and AIS (β = 0.546, p < 0.01). However, PPM had no significant effect on either AWC or AIS (β = 0.017 and β = −0.048, p > 0.05). Therefore, H₂ was only partially supported.

Regarding the effect of lifestyle group on AWT, being in the believers group was significantly associated with lower AWC scores (β = −0.340, p < 0.01). Being in the other groups (strivers, experiences, innovators, makers, or achievers) had no statistically significant effect on AWC scores. Regarding AIS, being in the believers group (β = −0.146, p < 0.01) or experiencers group (β = −0.174, p < 0.01) was significantly associated with lower AIS scores. None of the other lifestyle groups were significantly associated with AIS scores (p > 0.05). Accordingly, H₃ was partially supported (

Table 9. Hypothesis test results.

		Paths	Beta Co.	SD	t	p	Result
H2	H2a	WRM → AWC	0573	0.032	18.118	0.000 **	Supported
	H2b	WRM → AIS	0.546	0.039	13.871	0.000 **	Supported
	H2c	PPM → AWC	0.017	0.033	0.511	0.609	Unsupported
	H2d	PPM → AIS	0.048	0.038	1.260	0.208	Unsupported
H3	H3a	Believer → AWC	−0.340	0.032	10.774	0.000 **	Supported
	H3b	Striver → AWC	0.059	0.038	1.569	0.117	Unsupported
	H3c	Experience → AWC	0.043	0.041	1.050	0.294	Unsupported
	H3d	Innovators → AWC	−0.020	0.035	0.580	0.562	Unsupported
	H3e	Makers → AWC	0.016	0.036	0.458	0.647	Unsupported
	H3f	Achievers → AWC	0.066	0.035	1.895	0.058	Unsupported
	H3a	Believer → AIS	−0.146	0.040	3.667	0.000 **	Supported
	H3b	Striver → AIS	0.139	0.052	2.693	0.007 **	Supported
	H3c	Experiencer → AIS	−0.174	0.055	3.144	0.002 **	Supported
	H3d	Innovators → AIS	−0.009	0.045	0.192	0.848	Unsupported
	H3e	Makers → AIS	0.072	0.060	1.198	0.231	Unsupported
	H3f	Achievers → AIS	0.017	0.038	0.434	0.664	Unsupported

** p < 0.01.

).

Figure 4 shows the results of the structural model. Accordingly, the effect of WRM and PPM on AWC and AIS, and the effect of the lifestyle of the participants clustered as believers, strivers, experiencers, innovators, doers and achievers according to lifestyle (WALS-2 model) on AIS and AWC are displayed. The statistical results (beta co. and p-value) regarding these effects are also presented in Table 9.

5. Discussion

This study was carried out to contribute to the limited literature on the wine tourism market, which has started to develop in Türkiye in recent years, by examining tourists visiting wine destinations. The findings indicate that wine tourists can be segmented in terms of motivations, attitudes, lifestyles, and demographics, and that there are effects and differences based on their attitudes. Among Türkiye’s wine destinations, the present study collected data from the Urla Vineyard Route, the Thrace Vineyard Route, and Bozcaada, Cappadocia, Şirince, and Datca. Regarding their demographic characteristics, tourists visiting wine destinations in Türkiye are approximately equally distributed by sex, most of them are in the young or middle-aged group, have a high education level, and tend to be single with a medium-level income. These findings only partially agree with previous research. For example, Charters and Carlsen (2006) report that wine tourists are generally male, with high incomes, unlike our findings, but are middle-aged like our participants. Both O’Neill and Palmer (2004) and Dodd (1995) report that wine tourists are generally well-educated and have professional lives. According to Treloar et al. (2004), wine tourists are mostly women, aged between 30 and 50 years, well-educated, professional, with a good income, and generally domestic tourists living close to the region, which is a similar finding to our study in terms of the participants’ home residences. Lopez-Guzmán et al. (2008) claim that typical wine tourists are aged between 50 and 59 years, have a medium or high income, and are usually traveling with their family. These different findings may be partly due to differences in the destinations and time periods studied and the data collection tools used. In addition, the majority of the participants in the present study were domestic visitors. Our results are thus in line with O’Neill and Palmer (2004) and Treloar et al. (2004), who report that domestic tourists generally participate in wine tourism. The high number of students among the participants in the present study may be due to collecting data from Şirince, which is frequently visited by university students from nearby cities, especially on weekends.

The main motivation of more than half of the participants in choosing the region is pull factors, such as seeing historical places, sightseeing, and participating in recreational activities. For about 40%, their main motivation was to participate in wine-related activities. The primary motivations of the participants were regional cultural attractions, tasting local food and beverages, etc., whereas tasting wine, participating in the vintage, buying wine, learning how to make wine, etc., were their secondary motivations. This result supports the claim that the participants’ choices of destination were mainly motivated by non-wine-related factors, such as seeing historical places, sightseeing, and participating in various activities. In contrast, Charters and Ali-Knight (2002) suggest that wine tourists are primarily motivated by opportunities to taste and buy wine, while their secondary motivations are socializing, learning about winemaking and wine types, having fun, relaxing, etc. The reason for the different results in this study may be that not all the studied destinations are specifically wine-focused, offering thematic, boutique wine tourism experiences. That is, the only wine village was Şirince while there were only two wine routes (Urla Vineyard Route and Thrace Vineyard Route), which can be labeled as “terroir” of wine culture, as in the case of Marlowe and Bauman (2019) in Oregon and Peršurić et al. (2019) in Istria. In contrast, Cappadocia, Bozcaada, and Datça use wine as a secondary attraction besides their other tourism attractions. Charters and Ali-Knight (2002) also claim that wine tourists’ attitudes and behaviors may vary across different destinations. This can be considered a limitation of the present study.

The participants’ attitudes towards wine tourism were grouped under two factors, AWC and AIS, which is in parallel with Ravenscroft and van Westering (2001). They emphasize the importance of the educational dimension of the wine experience and suggest that it is also important to learn about wine tasting and wine. Hall and Macionis (1998) and Fucile Franceschini et al. (2025) suggest that wine tourists prioritize wine tasting and learning about wine types, similar to our study. We also found that the participants liked to taste wine types and considered drinking wine beneficial for the health as long as it was not excessive, which is in line with the findings of Koksal and Seyedimany (2023).

Regarding lifestyle groups, although no single group was prominent, the participants were mostly characterized as experiencers or innovators. Experiencers are independent, have a strong character, a good palate for new things, and have high incomes. They are energetic and active, willing to learn new things, have the confidence to experience them and have a wide range of interests. Innovators are individuals with high socioeconomic status, intellectual interests, and openness to innovation and change. Thus, tourists participating in wine tourism activities can be expected to come from these two lifestyle groups. According to Pratt (2014), experiencers and innovative tourists interested in wine are more interested in wine tourism and more motivated about visiting wine regions due to their positive attitudes.

The proportion of believers in the present study was close to the proportion of innovators. Individuals in this segment generally have a conservative, predictable and more traditional lifestyle and an average education level. They are people who are not very open to innovations, give importance to the concept of family, and are loyal to simple truths and wrongs for a good life. Adnan et al. (2022) found that the believer was the most significant one amongst VALS, and the believer factor of VALS was ranked highest. They claim that the reason for this could be the imperative importance of the family factor in the regular lifestyles of a consumer. Following the believers and achievers with high incomes who are self-confident and love to be special and interested in special products and brands. The achiever’s segment is followed by strivers, who characteristically do not have broad interests and have more limited access to economic, social, or psychological resources. It is perhaps surprising that there are relatively high proportions of believers and strivers among wine tourists. This may be because these destinations (Cappadocia, Şirince) are frequently visited by tourists for religious reasons or for sightseeing and because they are close to their places of residence. Finally, the smallest lifestyle group in the present study was makers, who prefer to spend their free time in outdoor activities with family and close acquaintances and have introverted lives.

Regarding the relationship between attitudes and demographic variables, middle-aged people (25–54) are more inclined to learn about wine and wine-related activities. People with higher education levels are more interested in wine consumption and learning about wine. Income level can be an important factor in wine-related behavior, suggesting that people with a higher level of income tend to buy wine from wine destinations. Finally, having a believer lifestyle has a significant negative effect on AWC. This could be because people in this lifestyle group are more conservative in terms of their religious faith. In Türkiye’s case, as with most Muslims, they may consider it wrong to drink alcoholic beverages because drinking alcohol is forbidden in Islam. Hence, being in the believers or experiencers lifestyle group was significantly and negatively associated with AIS scores.

6. Conclusions, Limitations and Implications

Despite the limitations of the study, a certain wine tourist profile based on demographics and psychographics has been constructed for Türkiye, which is the primary purpose of this research. With regard to the main aims, the demographic and psychographic profiles of wine tourism participants and their distribution have been determined. The findings indicate that wine tourists do not constitute a homogeneous group; rather, they differ according to the destination. Also, the travel motivations and lifestyles of wine tourism participants have been determined, and it has been found that wine tourists appear to be motivated by both push and pull factors. The effects of the travel motivations and lifestyles of wine tourism participants on their attitudes towards wine tourism and whether these attitudes differ according to their demographic characteristics have also been revealed. The hypothesis analyses’ results show that the participants’ sex and marital status do not have an impact on their attitudes towards wine tourism. On the other hand, their education level, occupation and income level have an impact on their attitudes. Also, age influences their attitudes towards wine consumption, but not their attitudes towards information-seeking about wine. In terms of motivations, it has been revealed that participants wine-related motivations affect their attitudes towards wine tourism, while push and pull motivations do not affect their attitudes towards wine tourism, which is an interesting result, as their primary travel motivations for visiting a wine region are found as push and pull factors. Concerning the lifestyles of the participants, it has been observed that having a believer lifestyle has an impact on participants’ attitudes towards wine tourism, while having an innovator, maker or achiever lifestyle does not.

Given these findings, this study has many significant outcomes underlining the characteristics of wine tourists, which are the main focus of tourism marketing. Also, since not much research has been done in the region, this study provides a better understanding of the wine tourists of Türkiye and other Islamic countries. This helps marketers design more accurate and better marketing strategies (Koksal & Seyedimany, 2023).

An important limitation of this study is that the sample consists of mostly, almost completely, Turkish wine tourists. Therefore, future studies could include more foreign tourists and also investigate one particular wine destination or route as a case study. That is, wine routes and wine destinations should be studied separately to compare their findings to those of the present study. In addition, future studies may include geographical location and time as variables in their analyses. Also, the study adopts VALS-2 for measuring lifestyles, but its usefulness may also increase with a more detailed analysis of its application in the cultural and religious context of Türkiye in future studies.

Wine tourism provides wine producers with new opportunities and offers sustainable development opportunities to a wine destination. For businesses, it is recommended to market wine products via international channels or to promote them by organizing wine tourism activities (Gómez-Carmona et al., 2023) or festivals and events. As wine sales increase during festivals, wine festivals enhance the wine tourism experience. Also, as suggested by Fucile Franceschini et al. (2025), tourists have become more conscious about ecological and sustainable practices and will prefer experiences focusing on environmental responsibility. Wineries and wine regions offering sustainable practices and eco-friendly experiences will be responding to the demand for responsible tourism. Wine destinations should either create a route with nearby regions or promote themselves as thematic wine towns or wine villages. Also, wine businesses can create unique brands that can be identified with the region and even can be a lovemark of a region.