Developing a Wine Tourism Destination Image Measurement Scale

Abstract

Wine tourism plays an important role in the positioning strategies of wine destinations. As the competitiveness is high among wine destinations, it is important to identify the main factors that affect wine tourists’ decision making. One of the most important factors is the wine tourism destination’s image. To measure the image of a wine region, there is a need for a scale that is adapted to the characteristics of wine tourism destinations. Our purpose is to develop a scale that can measure any wine region’s image. We used a literature review and focus group interviews to collect scale attributes. We gathered responses using an online survey. Our respondents were mostly non-visitors. We used FA to analyze the data. We also tested the reliability of this scale using Cronbach’s Alpha. As a result, we developed a reliable scale with six factors. This can be further tested and used by any wine tourism destination to measure the people’s perceptions about them. The developed scale can have various purposes. It can be used by destination management organizations or marketing agencies. Measured images of wine region can be used in the planning of future positioning strategies and promotions. The scale can be used to compare the images of competitor wine regions. Images can also be studied during a period and any changes can be observed.

1. Introduction

Wine tourism significantly affects regional development [1]. Wine tourism can contribute to job creation, regional revitalization, preservation of the culture and customs, and wildlife protection [1]. To ensure the sustainability of wine tourism destinations (WTDs), it is important to manage and monitor its tourism development. Unmanaged tourism can be destructive for WTDs.

Marketing and promotion of a destination are part of its management, and assists in the positioning of wine regions in a way that prevents mass tourism and undesired touristic development. To plan and execute positioning of a wine region, the first step is to understand the perceptions that target markets have about the WTD. Researching the wine tourism destination image (WTDI) contributes to the sustainable development of a wine region.

In addition, today’s wine tourists are more aware of sustainability issues, their own responsibilities, and the environmental effects of their choice [2]. Sustainability is becoming a differentiating factor for WTDs [2]. For this reason, studying the image that target markets hold about WTDs is crucial.

Tourists’ decision making is strongly related to the destination image [3]. Images that tourists hold about destinations even affect travel satisfaction [3]. People only consider traveling to a destination that they are aware of or have some information and impressions about [4]. In addition, people are more likely to decide to visit a destination about which they have a strong image [4,5]. This is why, in order to be more competitive, destination developers and marketers need to create an attractive destination image [6]. A decision about visitation depends on the match between people’s perception of the destination’s offering and the people’s preferences [7]. It is also important to monitor tourists’ perceptions as they might not perceive destinations the same way as destination marketers project them. Even though destination branding contributes to the images that tourists hold, other information sources participate in the image-formation process [8]. All information sources, such as general and social media, word-of-mouth, and own experience of visiting a destination, are involved in image formation [9].

Echtner and Ritchie [9,10] conceptualized destination image. Based on this model, imagery can be attribute-based or holistic. Attribute-based imagery is measured with a structured methodology such as image scales, where respondents are asked about measurable attributes of the image, for example, price levels, safety, and friendliness of locals. Holistic imagery is a holistic impression of a destination, and it cannot be measured with attribute-based scales. Holistic imagery is studied using an unstructured methodology, such as a free-form description of one’s perceptions of a destination. Destination image characteristics can be functional and psychological. Functional characteristics are observable, for example, tourist sites or climate, whereas psychological characteristics are intangible, for example, friendliness, fame, or quality of service. Both characteristics can be measured by structured attribute-based scales and unstructured methods as a part of holistic imagery. The image characteristics can also be common and unique. Common attributes can be either functional or psychological and are opposed to unique attributes and features of a destination. For example, common attributes (both functional and psychological) can be measured by attribute-based scales as part of the structured measurement methodology. These attributes can be rated by respondents and can be compared with other destinations’ characteristics. On the contrary, unique characteristics cannot be compared or rated using the structured methodology. Unique features of the destination image can be measured by the unstructured methodology.

Echtner and Ritchie’s [9,10] destination image concept has different dimensions, but they should be understood as a whole and measured altogether. As an example, climate and price levels are functional and common dimensions that can be measured by attribute-based scales. However, they can also be part of a destination’s holistic image that can be measured by the unstructured methodology. The fame and quality of service are psychological and common attributes of a destination. They can be measured by attribute-based scales. However, they can also be captured as a part of the holistic imagery of a destination measured with unstructured methods. Finally, holistic imagery can only be captured by unstructured methods. Unique characteristics of the destination image can also only be captured by unstructured methods.

The formation of a destination image relies on various information sources, which can be categorized as either primary or secondary. Primary sources are derived from individuals’ personal visitation experiences at the destination, while secondary sources encompass information gathered from external sources [11]. Secondary sources encompass a wide range of channels, including travel guides, advertising, recommendations from friends and family, online platforms, destination management organizations, and various media outlets [12]. In other words, visitors hold images based on their own experiences, while non-visitors hold images based on secondary sources. The primary and secondary images can be different from each other, and secondary images might change after the visitation [13]. The perception of information from different sources varies among individual travelers, leading to subjective expectations [14]. As a result, the actual decision to visit a destination is influenced by the alignment between tourists’ preferences and their perceived offerings of the destination [7].

When talking about a specific type of destination, such as WTD, an image can be formed through the same general information sources, as well as some more specific ones. For example, wine tastings, wine-related media, movies, and books will contribute to the image. The main object of WTDI research is a wine region referred to as a winescape [15]. “WTDI studies beliefs, perceptions, thoughts, feelings, ideas, expectations, and knowledge about a wine tourism destination, in other words, wine region or winescape, that is projected by destination management bodies and perceived by wine tourists.” [15] (p. 758). Currently, no uniform scale is available to measure WTDI [16] (p. 500). Sekhniashvili’s [15] review of the WTDI literature published between 2001 and 2020 shows that authors have researched the WTDI topic but a valid scale suitable for WTDI measurement has not been created. Bruwer et al. [17] suggest that there is a gap in the WTDI research, and, due to the nature of wine tourism, a WTDI measurement approach, different from the mainstream tourism destination image studies, should be developed [17].

2. Materials and Methods

Based on a WTDI literature review by Sekhniashvili [15], it was found that there is no unified measurement technique for WTDI. The goal of this research is to develop a WTDI scale using the example of Georgia.

As we found during the literature review [8,9,15], the most reliable way of studying a destination’s image is by using a combined methodology. This means using qualitative and quantitative techniques. These two methods supplement each other. The qualitative or holistic method is used to define holistic and unique aspects of the image, while the quantitative method measures attribute-based and common image features as well as functional and psychological dimensions [10]. This methodology is still widely used by destination image researchers [18,19]. As our objective was not to measure a WTDI of a specific wine region, but to develop an attribute-based scale for measuring any wine regions’ image, we followed the scale development steps below and disregarded studying the holistic and unique imagery of Georgia.

Echtner and Ritchie [9,10], Ritchie and Crouch [20], and Jenkins [3] recommend using a two-phase methodology to develop a scale for the quantitative part of the study. This starts with qualitative research of a relevant market to determine the attributes that can be used in the second stage of the quantitative data collection. The initial research is important as it helps in designing a reliable scale suitable for the target audience.

As this kind of research requires large amounts of funding and time, WTDI research often simply incorporates a literature review as a tool to collect attributes for quantitative research. As the image of a WTD can be properly researched only with the combined methodology, including quantitative and qualitative methods, we decided to contribute to the quantitative methodology. To do so, it is crucial to develop a scale incorporating all the winescape attributes.

The methodology used in our research has two phases. In the first stage, we try to develop attribute scale with the inputs from the literature review and the wine tourists’ perceptions of different WTDs. At this point we design an attribute-based scale which covers common psychological and functional dimensions [10].

In the second step, we use this scale and collect the quantitative data about the WTDI of Georgia. We chose FA as a data analysis method and we also tested Cronbach’s Alpha to confirm the reliability of the scale.

The scale was developed by following a methodology similar to that used by Echtner and Ritchie [10]. Below we describe the process step by step:

• Literature review to identify attributes.
• Qualitative data collection using focus groups to gather more attributes.
• Content analysis to determine the list of the attributes collected.
• Merge the attributes into a new scale.
• Quantitative data collection and analysis.

3. Results

Following the methodology described in the previous section, we collected attributes and tested the scale. The scale was tested by collecting data on a WTD in Georgia. This study resulted in a WTDI scale that can be used in future to measure the WTDI of any wine region. Below we describe the research in detail as well as its results.

3.1. Literature Review to Identify Attributes

In the beginning, literature about the WTDI was reviewed and the attributes were collected. The list of attributes is displayed in Table 1. We used the same 21 articles about WTDIs published during 2001–2020 as reviewed by Sekhniashvili [15]. To see the exact list of the literature, consult Table 2 on page 759–760 of Sekhniashvili [15]. To make sure we were not missing any attributes, we also consulted the tourism destination image research by Echtner and Ritchie [10]; Table 1 also present the attributes from the study by Echtner and Ritchie [10]. The authors of these articles mainly used a literature review and free elicitation when collecting the attributes. The attributes are both functional and psychological. For example, “purchasing good wine” or “accommodation” is functional or tangible, while “exciting” and “tranquil” are more psychological or intangible. Some of the attributes resemble the same concepts; hence, later, during the merging step we discard some of them.

Table 1. List of the attributes used in WTDI studies.

#	Attributes	#	Attributes	#	Attributes
1	Variety of nature	34	Appealing interior design of the buildings	67	Good availability of wineries
2	Beautiful scenery and landscape	35	Proximity of the region to a main city	68	Winery staff knowledgeable about wine
3	Good settings of the wineries	36	Reputation	69	Wineries are visitor friendly
4	Great vineyard landscapes	37	Local transportation	70	Purchasing good wine
5	History and culture	38	Peaceful	71	Opportunity to taste lots of wine
6	Customs	39	Slightly crowded.	72	Wines from this region are of high quality.
7	Cultural activities	40	Relaxing	73	Positive references to wine quality, value, price, etc.
8	Towns/villages	41	Quality of life	74	There is sufficient signage to the winery
9	Rich wine culture	42	Safety	75	The signage is large enough to be seen
10	Availability of tourist information	43	Cleanliness	76	The signage makes it easy to find your way
11	Shopping	44	Climate	77	The signage is easy to be understood
12	Lack of urbanization	45	Unpolluted environment	78	The layout makes it easy to get to the winery
13	Good value for money	46	The odors/scents are pleasant	79	Signage to get to and move through the region
14	Gastronomy	47	Friendly people	80	Employees give prompt service
15	Other local products/cottage industries	48	Prices	81	Employees are always willing to help
16	Nightlife	49	Exciting	82	Employees are neat in appearance
17	Entertainment	50	Pleasant	83	Employees have knowledge to answer queries
18	Quality of the restaurants/pubs	51	Interest arousing	84	Employees are consistently courteous
19	Leisure and recreation	52	Fun	85	Employees give individual attention to me
20	Infrastructures	53	Tranquil	86	Service staff and local residents/People and hospitality great
21	Accommodation	54	A sense of escapism	87	Accessibility
22	Appealing architecture of the buildings	55	A sense of discovery	88	Personal safety
23	Tourist sites/activities	56	Cities	89	Ease of communication
24	National parks/wilderness activities	57	Accommodation/restaurants	90	Customs/culture
25	Historic sites/museums	58	Architecture/buildings	91	Different cuisine/food and drink
26	Beaches	59	Costs/price levels	92	Hospitality/friendliness/receptiveness
27	Fairs, exhibits, festivals	60	Climate	93	Restful/relaxing
28	Scenery/natural attractions	61	Crowdedness	94	Atmosphere (familiar versus exotic)
29	Nightlife and entertainment	62	Cleanliness	95	Opportunity for adventure
30	Shopping facilities	63	Degree of urbanization	96	Opportunity for increase knowledge
31	Facilities for information and tours	64	Economic development/affluence	97	Family or adult oriented
32	Sports facilities/activities	65	Extent of commercialization	98	Quality of service
33	Local infrastructure/transportation	66	Political stability	99	Fame/reputation

3.2. Qualitative Data Collection to Gather More Attributes

The next step in the scale design was to collect the data with qualitative research. The research instrument was focus group interviews with 47 respondents. At this stage, it was decided that the study subject would be wine tourists who had traveled to wine regions and/or participated in wine tourism activities at least once in the past three years. Interviews were held in April and May of 2022. The respondents were found online through social media, and interviews were held via Zoom. We posted in Facebook groups related to wine tourism and travel, as well as general travel- and tourism-related groups. The post asked people to volunteer as participants in focus group interviews if they had traveled to wine regions and/or participated in wine tourism activities at least once in the past three years. This question was once again asked before starting the interviews; anyone who had not participated in wine tourism activities in the past 3 years was not recruited for the interviews. In each focus group, there was an average of 3–4 participants. We recorded the interviews to use the script later during data analysis. We asked the respondents two questions. Overall, 567 words and short phrases were collected after we manually scripted the interviews. The nationalities of the sample varied (from all the continents).

The questions that the respondents were asked to gather the characteristics of the regions were adapted from Echtner and Ritchie [10], as follows:

• What images or characteristics come to mind when you think of XXX as a travel destination?
• Please describe the atmosphere or mood that you would expect to experience while visiting XXX?

By asking these questions, we were able to collect data about functional and psychological holistic elements of the destination image perceived by wine tourists. This information helped us to collect a list of attributes for the WTDI scale that we aimed to develop. This step was necessary as using only a literature review does not ensure a full list of the attributes.

In this section, wine tourists were asked to provide their images of five wine regions as travel destinations. A total of 62% of the respondents answered “no” when we asked whether they had visited the wine region in question. Our objective was to develop a scale that can measure perceptions of the people who have visited the WTD before and perceptions of the people who have not. This is why we included responses from both groups of wine tourists. We chose diverse wine regions to ensure that the final scale would be relevant to different kinds of wine tourism destinations globally. The wine regions were Mendoza (in Argentina), Napa Valley (in USA), Barossa Valley (in Australia), Marlborough (in New Zealand), Kakheti (in Georgia), Colchagua Valley (in Chile), Tokaj (Hungary), Peloponnese (in Greece), Chianti (in Italy), and Stellenbosch (in South Africa). A different group of five regions from the ten was used in the interviews.

3.3. Content Analysis to Determine the List of the Attributes Collected

We had many analysis options for the data received from the focus group interviews. Mainly, we had to choose whether to undertake this manually or with software. We decided to manually code the words and phrases collected during the interviews. Firstly, we transcribed the interviews in Excel. After that, two different experts coded and labeled the words and phrases. The experts were instructed to read the words and phrases and correct any obvious spelling mistakes, then to group the same or similar words together and name the groups with a representative label. There were no restrictions regarding the label names or placement of each word or phrase in a specific group. Each word and phrase was assigned to one of the labels by experts. Each expert worked independently. After labeling the first 100 words and phrases, the experts met and discussed differences in label names, as well as allocation of each word or phrase in the specific group. They reached consensus during the meeting and agreed on label names; they also corrected the discrepancies. After coding each 100 of the words and phrases, the experts organized a meeting to further clarify the result and reach an agreement. As a result, we obtained 41 attributes/labels.

3.4. Merge the Attributes into a New Scale

The final step was to merge the list of attributes generated by the literature review with those derived from the wine tourists’ replies and content analysis. Some of the labels overlapped, so we discarded the duplicate attributes. We also discarded the items that were duplicated or resembled the same concepts. We obtained 70 attributes after merging and discarding overlapping attributes. The results are displayed in Table 2.

Table 2. List of the attributes after merging.

#	Attributes	#	Attributes	#	Attributes
1	Nice Scenery/natural attractions	25	Good opportunity for increase knowledge	49	Fun environment
2	Good settings of the wineries	26	Interesting fairs, exhibits, festivals	50	A sense of escapism
3	Great vineyard landscapes	27	Interesting sports facilities/activities	51	A sense of discovery
4	Nice beaches	28	Interesting tourist sites/activities	52	A sense of nostalgia
5	Interesting history/customs/culture	29	Interesting national parks/wilderness/outdoor activities	53	A sense of freedom
6	Interesting cultural activities	30	Interesting historic sites/museums	54	A sense of happiness
7	Interesting cities/Towns/villages	31	Variety of offers/discounts/sales	55	A sense of calmness/peaceful
8	Rich wine culture	32	Good level of safety	56	Restful/relaxing environment
9	Good availability of tourist information	33	Good level of cleanliness	57	Good quality of life
10	Good shopping facilities	34	Nice climate	58	Familiar/Friendly atmosphere
11	Good value for money	35	Unpolluted environment	59	Good availability of wineries
12	Rich gastronomy	36	Pleasant odors/scents	60	Wineries that are visitor friendly
13	Interesting local products/cottage industries	37	Good Price levels	61	Availability of purchasing good wine
14	Attractive nightlife and entertainment	38	Good level of economic development/affluence	62	Opportunity to taste lots of wine
15	Good quality of accommodation/restaurants	39	Acceptable extent of commercialization	63	High quality wines
16	Suitable atmosphere/facilities for leisure and recreation	40	It’s easy to communicate with locals	64	Interesting wineries
17	Comfortable local infrastructure/transportation	41	Politically stable	65	Interesting wine tasting experiences
18	Nice architecture/buildings	42	Easily accessible	66	Interesting wine Styles
19	An acceptable proximity of the region to a main city	43	Hospitable/friendly/receptive	67	Great wine tourism destination
20	Good fame/reputation	44	Crowded	68	Winery staff is knowledgeable about wine
21	Family oriented environment	45	Urbanized	69	Wine quality is good
22	Adult oriented environment	46	Exciting environment	70	Wines are good value for money
23	Good quality of service	47	Pleasant environment
24	Good opportunity for adventure	48	Interest arousing environment

3.5. Quantitative Data Collection and Analysis

We then used the quantitative method with a survey as an instrument. The goal of this questionnaire was to validate the scale. The online survey had closed-ended questions. The survey was designed in Google Forms. It was posted in different social media groups to collect responses between December 2022 and January 2023. Similar to the qualitative data collection, we posted on Facebook groups related to wine tourism and travel, as well as general travel- and tourism-related groups. In the post, we asked people to fill in our survey, which would take approximately 7–8 min and would be about WTDI. We also mentioned that Georgian nationals could not participate. As a result, the nationalities of the sample were varied but excluded Georgians. The questions of the survey were grouped into different sections. The questionnaire was tested on 20 respondents to eliminate any bias. We slightly corrected the survey after the test.

The questions of the interview were grouped into different sections. The first section asked whether respondents had ever visited Georgia.

The second section gathered demographic information such as nationality, age, gender, education, marital status, and occupation. In this section, we also asked an extra question to determine the ratio of wine tourists to the total number of respondents:

• Have the respondents visited any region in the past 2 years with the purpose of visiting vineyards, wineries, wine tasting, consuming and/or purchasing wine or attending wine events?

The third section asked respondents about characteristics of Georgia as a WTD. We used a 7-point Likert answer format, from the answer “strongly disagree” to the answer “strongly agree”. As we expected that most of our sample had not visited Georgia, we decided to add an additional answer, i.e., “no opinion”. This answer allowed non-visitors to skip a question if they had no information from secondary sources regarding a specific attribute we were asking about. Later, during the analysis, we only included responses on the 7-point Likert scale and disregarded “no opinion” entries. The statements that respondents rated were worded in the following format:

• I think that as a wine tourism destination, Georgia has interesting history/customs/culture.
• I think that as a wine tourism destination, Georgia is easily accessible.
• I think that in a wine tourism destination Georgia it’s easy to communicate with locals.

We collected 298 responses to our questionnaire. Most of our results are based on non-visitor perceptions, as 85% of the respondents answered that they had never visited Georgia before. In terms of age, most of our respondents were between 18 and 24 years old (41%), 38% of them were between 25 and 34 years old, and 13% were between 35 and 44 years old. We received the fewest answers from other age groups, with 4% being from people between 45 and 54 years old, 3% of people were between 55 and 64 years old, and only 1% of the respondents were 65 years or older. A total of 60% of our respondents were female and 38% were male; 2% did not wish to answer. Most of the respondents had Bachelor’s (39%) or Master’s degrees (30%). Most of our respondents were students (51%) or employees (39%). In terms of marital status, most of the respondents were either in a relationship (35%), single (43%), or married (17%). A total of 9% of the respondents were never involved in wine tourism, while the rest had been involved. The demographics of our sample is shown in Table 3.

Table 3. Demographic data of the sample.

Demographic characteristics of the sample	Share in total responses
Visitors and non-visitors
No	85%
Yes	15%
Involvement in wine tourism	Share in total responses
No	9%
Yes	91%
Age	Share in total responses
18–24 years	41%
25–34 years	38%
35–44 years	13%
45–54 years	4%
55–64 years	3%
Age 65 or older	1%
Gender	Share in total responses
Female	60%
I don’t wish to answer	2%
Male	38%
Highest degree or level of education completed	Share in total responses
Bachelor’s degree	39%
High school graduate	13%
I don’t wish to answer	1%
Less than high school	3%
Master’s degree	30%
Ph.D.	4%
Some college, no degree	10%
Occupation	Share in total responses
Employee	39%
I don’t wish to answer	1%
Intern	1%
Self-employed	7%
Student	51%
Unemployed	1%
Marital status	Share in total responses
Divorced	1%
I don’t wish to answer	1%
In a relationship	35%
Married	17%
Single	43%
Widowed	1%
Nationalities	Share in total responses
Hungarian	18%
British	17%
American	12%
German	4%
Indian	4%
Dutch	3%
Italian	3%
Other	38%

We used SPSS to analyze the data that we collected using the questionnaire. In the beginning of the process, we checked the convenience of factor analysis. We wanted to examine how suitable our data was for factor analysis. We found the value of the Kaiser–Meyer–Olkin (KMO) test for sampling adequacy was 0.967. As this value was close to 1, it means our data were suitable for factor analysis.

We also preformed Bartlett’s sphericity test to determine whether there was a strong enough correlation in our data to use factor analysis and principal components analysis (PCA) to reduce dimensionality. Bartlett’s sphericity test determines whether the correlation matrix of the variables is an identity matrix. The correlation matrix was not an identity matrix in our analysis, as shown by the Bartlett’s test result, which also reveals an approximate chi-square value of 24,721.075 with 2415 degrees of freedom and a p-value of 0.000; this indicates that the data can be used for factor analysis. The results of KMO and Bartlett’s test are shown in Table 4.

Table 4. KMO and Bartlett’s tests.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.967
Bartlett’s Test of Sphericity	Approx. chi-square	24,721.075
	df	2415
	Sig.	0.000

To analyze the data and reduce dimensionality, we used factor analysis (FA) including PCA. Promax with Kaiser normalization was used to standardize the data before factor analysis. This rotation technique gave us the cleanest results.

In our analysis, six components had eigenvalues greater than 1 and they were retained. Together, these six elements accounted for 71.66% of the variance, which indicates that they represented the most important variables in the dataset. Our minimum factor loading was set at 0.3.

We measured the internal consistency reliability of a collection of items or variables using the Cronbach’s Alpha coefficient. Examining the Cronbach’s Alpha values for each component is crucial, in addition to looking at the overall Cronbach’s Alpha value. We found that all the results of Cronbach’s Alpha coefficients were close to 1. As the reliability was high, we did not discard any item. We display the results of the factor analysis and reliability analysis in Table 5.

Table 5. Results of factor analysis and reliability test (Cronbach’s Alpha).

Factor	Number of Items	Cronbach’s Alpha
Wine and wine tourism	12	0.975
Atmosphere/environment	16	0.971
Security/cleanliness/economic situation/prices	9	0.944
Tourism facilities	12	0.942
Natural and cultural attractions	12	0.952
Comfort and infrastructure	9	0.932

The first component is linked to wine and the wine tourism experience. It includes factors such as wine quality, availability of wineries, opportunity to taste lots of wine, interesting wine styles, and tasting experiences. It is not surprising that a wine tourism destination image is strongly defined by wine and wine-related characteristics. The items included in the first component are mostly functional.

The second component explains the atmosphere and environment of the WTD. It includes factors such as sense of freedom, discovery, escapism, and happiness, as well as a pleasant, hospitable, and easily accessible environment. It seems like the affective characteristics of the destination are an important part of its image. The second component items can be interpreted as the psychological dimension of the WTDI.

The third component includes factors related to cleanliness, nice climate, price levels, and level of safety. As for any other type of destination, safety, cleanliness, and other social factors are crucial. The third component items are mostly functional.

The fourth components are all about tourism facilities, i.e., shopping facilities, nice beaches, availability of tourist information, crowdedness, urbanization levels, and quality of service. We can assign some of the items to the psychological dimension of the WTDI, for example, good quality of service. The others can be considered as functional dimensions, such as interesting fairs, exhibits, and festivals.

The fifth component is linked to cultural and natural attractions, such as rich wine culture, nice scenery, vineyard landscapes, winery settings, opportunity for adventure, and increasing knowledge. This factor is mostly connected to the functional destination image component.

The sixth factor explains the comfort and infrastructure in the WTD. For example, variables such as quality of accommodation and restaurants, interesting local products, gastronomy, nightlife, and entertainment seem to be an important part of the WTDI. We included all the attributes that are part of the six components in Table 6 so that they are easily accessible for future research purposes. Some of the items can be categorized as the functional WTDI dimension, while others are psychological.

Table 6. Retained attributes that are part of six factors as a result of factor analysis.

Item-Total Statistics
	Scale Mean If Item Deleted	Scale Variance If Item Deleted	Corrected Item-Total Correlation	Squared Multiple Correlation	Cronbach’s Alpha If Item Deleted
(1) Winery staff is knowledgeable about wine	45.29	675.150	0.820	0.738	0.974
(1) Wine quality is good	44.84	676.957	0.837	0.802	0.974
(1) Wines are good value for money	44.99	670.933	0.860	0.786	0.973
(1) Great wine tourism destination	44.78	679.722	0.823	0.719	0.974
(1) Good availability of wineries	45.17	664.223	0.879	0.807	0.973
(1) Wineries that are visitor friendly	45.26	665.120	0.864	0.802	0.973
(1) Availability of purchasing good wine	45.05	664.240	0.869	0.800	0.973
(1) Opportunity to taste lots of wine	44.95	659.990	0.900	0.850	0.972
(1) High quality wines	44.98	665.323	0.882	0.823	0.973
(1) Interesting wineries	44.92	661.553	0.899	0.867	0.972
(1) Interesting wine tasting experiences	45.19	661.670	0.884	0.837	0.973
(1) Interesting wine Styles	45.21	666.239	0.852	0.798	0.973
(2) Nice architecture/buildings	62.50	921.254	0.741	0.563	0.970
(2) Easily accessible	63.14	938.290	0.656	0.561	0.971
(2) Hospitable/friendly/receptive	62.57	916.832	0.760	0.686	0.969
(2) Exciting environment	62.53	903.691	0.855	0.812	0.968
(2) Pleasant environment	62.36	904.681	0.859	0.822	0.968
(2) Interest arousing environment	62.53	900.607	0.863	0.834	0.968
(2) Fun environment	62.99	903.421	0.847	0.794	0.968
(2) A sense of escapism	62.53	901.637	0.815	0.750	0.969
(2) A sense of discovery	62.26	906.724	0.800	0.723	0.969
(2) A sense of nostalgy	63.24	916.743	0.725	0.559	0.970
(2) A sense of freedom	62.86	898.752	0.859	0.782	0.968
(2) A sense of happiness	62.74	899.113	0.872	0.815	0.968
(2) A sense of calmness/peace	62.53	904.586	0.837	0.746	0.968
(2) Restful/relaxing environment	62.55	899.702	0.869	0.807	0.968
(2) Good quality of life	63.22	910.764	0.789	0.705	0.969
(2) Familiar/Friendly atmosphere	63.02	908.023	0.780	0.725	0.969
(3) Variety of offers/discounts/sales	30.30	232.519	0.698	0.542	0.941
(3) Good level of safety	29.92	229.091	0.811	0.723	0.935
(3) Good level of cleanliness	29.84	226.838	0.855	0.781	0.932
(3) Nice climate	29.00	237.202	0.710	0.559	0.940
(3) Unpolluted environment	29.79	228.753	0.796	0.709	0.936
(3) Pleasant odors/scents	29.89	226.864	0.798	0.647	0.936
(3) Good Price levels	29.52	228.951	0.765	0.650	0.937
(3) Good level of economic development/affluence	30.10	231.778	0.802	0.708	0.935
(3) Acceptable extent of commercialization	29.87	228.455	0.790	0.685	0.936
(4) Nice beaches	37.33	440.221	0.553	0.346	0.943
(4) Good availability of tourist information	37.08	415.509	0.793	0.737	0.934
(4) Good shopping facilities	37.46	417.953	0.787	0.734	0.934
(4) Family oriented environment	37.15	424.957	0.729	0.626	0.936
(4) Adult oriented environment	36.69	423.411	0.710	0.582	0.937
(4) Good quality of service	36.83	413.750	0.835	0.735	0.932
(4) Interesting fairs, exhibits, festivals	36.79	418.536	0.746	0.664	0.936
(4) Interesting sports facilities/activities	37.32	418.589	0.801	0.722	0.934
(4) It’s easy to communicate with locals	37.27	430.609	0.720	0.565	0.937
(4) Politically stable	37.33	436.505	0.676	0.511	0.938
(4) Crowded	37.66	437.593	0.715	0.583	0.937
(4) Urbanized	37.28	434.053	0.734	0.645	0.936
(5) Nice Scenery/natural attractions	48.41	525.030	0.785	0.685	0.947
(5) Good settings of the wineries	49.33	520.188	0.754	0.772	0.948
(5) Great vineyard landscapes	49.06	517.320	0.787	0.808	0.947
(5) Interesting history/customs/culture	48.41	522.445	0.813	0.747	0.947
(5) Interesting cultural activities	48.80	522.190	0.784	0.679	0.947
(5) Interesting cities/Towns/villages	48.57	521.425	0.805	0.734	0.947
(5) Rich wine culture	49.10	522.811	0.721	0.607	0.950
(5) Good opportunity for adventure	48.66	528.489	0.776	0.684	0.948
(5) Good opportunity for increasing my knowledge	48.59	529.959	0.728	0.587	0.949
(5) Interesting tourist sites/activities	48.67	526.355	0.792	0.697	0.947
(5) Interesting national parks/wilderness/outdoor activities	48.91	531.227	0.708	0.642	0.950
(5) Interesting historic sites/museums	48.71	524.292	0.785	0.732	0.947
(6) Good value for money	31.53	229.085	0.685	0.557	0.929
(6) Rich gastronomy	31.26	227.807	0.731	0.578	0.926
(6) Interesting local products/cottage industries	31.16	224.654	0.761	0.597	0.924
(6) Attractive nightlife and entertainment	32.08	227.125	0.757	0.655	0.924
(6) Good quality of accommodation/restaurants	31.56	221.668	0.855	0.759	0.918
(6) Suitable atmosphere/facilities for leisure and recreation	31.38	226.668	0.774	0.640	0.923
(6) Comfortable local infrastructure/transportation	32.09	229.066	0.756	0.632	0.924
(6) An acceptable proximity of the wine regions to a main city	31.76	223.647	0.777	0.620	0.923
(6) Good fame/reputation	31.56	238.719	0.642	0.478	0.931

4. Discussion

Destination image plays an important role in tourism marketing research. The importance of the destination image is linked to its influence on an individual’s behavior regarding travel decision making [21,22,23,24,25,26]. The WTDI has been studied by a few authors who emphasize that WTDI research is limited [16,17,25], Williams [26] notes that it might be easier for wine tourism destinations to be noticed among other types of tourism destinations but it is a challenge to differentiate one wine region from another. To be successful and attract visitors, WTDs should make sure that the projected images match wine tourist’ preferences [26]. Bruwer et al. [17] suggests that the WTDI needs a research perspective that is differentiated from the common DI studies due to its unique nature. To fill the gap in the literature, we developed a scale that is adapted to WTD’s nature and can measure its image. We used Georgia as a WTD in this study and measured its image. As a result, we obtained a scale with six factors. These factors are the most significant part of the WTDI. The questionnaire that we developed includes 70 items that can measure the attribute-based WTDI. However, it should be used along with the three open-ended questions [8] to explore the holistic WTD imagery and learn about the unique characteristics of the wine region.

Our study contributes to the WTDI research by developing a WTDI measurement approach that is differentiated from generic tourism destination image (TDI) measurement scales. As wine tourism regions, products, and experiences have unique characteristics, studying the WTDI requires a research method that is different from the common TDI approaches. Our attempt to develop a WTDI measurement scale contributes to the structured/quantitative part of WTDI research. While it is possible to improve the scale we developed, in its present state it can serve as a tool to measure and compare images of different wine regions, compare images of a specific WTD during longitudinal studies, and compare images of a wine region before and after application of positioning/promotional strategies.

In terms of practical implications of our research results, the scale is useful for wine regions’ promotional and positioning strategies. By measuring an image, WTDs can learn about their image among their competitors and work on differentiation of their wine tourism product and strategy. In this way, wine regions can learn about their competitive advantages or create plans to promote these advantages using social media or other channels. WTDs can also learn about their weaknesses and the negative images people hold about them. In this way, they can implement actual improvements, as well as plan ways to change people’s negative impressions. For example, if people believe that winery staff are not knowledgeable about wine, this weakness can be solved by training of staff as well as suitable advertising strategies to change people’s beliefs or perceptions.

The main limitation of our scale is that it consists of 70 attributes, which might be too many. The length of the survey can affect the quality of the responses, as well as the response rates [27]. However, researchers should not forget the importance of each and every item. We suggest that the scale should be further tested to reduce the number of items, without neglecting any important characteristics of the WTDI.

We also recommend that this scale should be tested in different wine tourism destinations, and further statistical methodologies should be applied, such as CFA.

It would be necessary to collect new data and assess the reliability and the validity of the present scale.

Finally, whenever measuring an image of a specific WTD, we recommended applying both qualitative and quantitative methodologies to capture the full spectrum of WTDI characteristics.