*2.4. Winescape*

The winescape is described as the synergic interaction of "vineyards, wineries and other physical structures, wines, natural landscape and setting, people and heritage, towns and their architecture and artefacts within them" [46] (p. 277). Alebaki and Lakovidou [47] (p. 123) describe winescape as "the whole region and its attributes". Thomas, Quintal, and Phau [14] also conceptualised seven key attributes of the winescape: (1) The winescape cluster, (2) the atmosphere, (3) the wine product, (4) complementary products, (5) the signage, (6) the layout, and (7) service sta ff attributes. Dimensions of the winescape include: (1) Nature-related; (2) wineries and vineyards; (3) wine and other products; (4) ambient factors; (5) signage and layout; (6) service sta ff and locals; (7) heritage-related towns; and (8) fun-based activities [48]. The winescape is also the primary driver of motivations for the wine tourists' hedonic experience [23] where much importance is placed on the winescape during the visit [49]. Bruwer and Gross [50] advocate that a winescape framework for wine tourism is conceptualised by five major dimensions: Infrastructure, natural setting, atmosphere, layout, and people. The winescape attributes shown above are considered in a multi-layered macro-context of a wine region.

#### *2.5. Wine Storytelling*

Moscardo [51] states that central themes and stories impact on tourists and their behaviour. Winery visits by tourists provide wine producers with a communication platform for their brand's stories, while also showcasing their product portfolio [52]. Winemakers may tell many stories about the wine production: Their families, their heritage, and their winemaking approach. The wine tourist may also evaluate the stories when deciding which wine to buy [53]. Wine-related stories become

part of the wine experience and may be relived by repeating the story [54]. As storytelling allows consumers to integrate the story of a wine brand or property [55] and enhance their wine experience, this element should also be measured, as storytelling value adds to the wine tourism experience.

#### *2.6. Wine Involvement*

According to O'Neill and Charters [52] winery visits increase the direct involvement with the tourist. The relationship between consumers' travel and their involvement with wine proves their strong dependence [11,56]. Wine tourism and involvement with wine are described as a consumer experience with a high hedonic charge [11]. Brown, Havitz, and Getz [57] found that the particular interest in a product (wine) has the e ffect of creating the desire to travel to the place where the product is made. Wine consumers' product involvement is also equated with their own personal involvement with wine [58]. Yuan et al. [59] maintain that wine consumers' feelings of importance and relevance towards a product, as well as their genuine level of interest in wine, are determined through a high level of product involvement. Bruwer and Alant [23] o ffer the view that the wine tourist is drawn to be involved with the wine and region where the wine is produced. Engagement by individuals in wine tourism is related to a desire to become better acquainted with the wine product and to enjoy an indulgent experience [23]. Sthapit et al. [60] attest that involvement is one of seven experiential tourism factors, significantly influencing the memorability of the tourists' experience.

Wine and wine tourism provide and drive a set of authentic and genuine experiences for wine tourists, which are increasingly di fferentiated and personalised [61]. Thus, the wine tourism experience is an amalgam of components and features related to wine, with dimensions such as wine excitement, wine sensory appeal, winescape, and wine involvement, which play a crucial role in the wine tourists' experience.

#### **3. Research Method**

#### *3.1. Scale Development Process*

Scale validity refers to the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure, while reliability refers to the degree to which a test is consistent and stable in measuring what it is intended to measure [62]. Consequently, to ensure the reliability and validity of the methods used to construct and validate the scale in this study, four aspects were taken into account: (1) Domain of construct, (2) item generation, (3) purifying the measurement, and (4) finalising the measurement [62,63], comprising the scale development process through the major methodological stages which focus on the scale development process.

#### *3.2. Item Generation*

Derived from several studies (Table 1), an initial pool of 20 items was constructed and generated. The initial items were then refined and edited for content validity by six experts in related academic or practical fields. With the intention of classifying the items into construct groups, a sorting procedure was used by the experts to refine items that were considered redundant or ambiguous. The items were not grouped or sequenced, and only one conceptual change resulted from the process where the experts found it di fficult to distinguish between 'wine sensory appeal' and 'wine excitement', this being replaced by 'wine tasting excitement'. The process resulted in 18 modified measurement items, classified into four categories: Wine tasting excitement, winescape, wine storytelling, and wine involvement (Table 1).

The 18-item instrument was pretested with a convenience sample of 65 participants who had a wine experience at Porto and Madeira wine cellars, as wine tourism destinations, during July 2019. The goal of this pre-test was to identify possible weaknesses, ambiguities, missing and redundant questions, and poor reliability [62]. As Netemeyer et al. [64] argue, the construct validity can be supported by this process, as the exclusion of items that may be conceptually inconsistent is allowed.

To determine the scale dimensions, exploratory factor analysis (EFA) was performed, which is a preliminary technique in the scale development process and construct validation [65]. An inspection of the strength of the relationship between the items is necessary to assess whether a particular data set is suitable for factor analysis [66]. It was found that no items had factor loadings lower than 0.4 or cross-loaded on more than one factor. A Cronbach's alpha reliability score higher than 0.7 indicated that the variables exhibited moderate correlation with their factor groupings and were regarded as internally consistent and stable [66]. As a result, no items had factor loadings lower than 0.4 or cross-loaded on more than one factor, and therefore no item was eliminated [66]. A total of 18 items with four constructs remained: Wine tasting excitement, winescape, wine storytelling, and wine involvement. A confirmatory factor analysis (CFA) analysis was then performed to confirm the structure of the scale. Moreover, CFA also evaluates the relationships between observed measures or indicators and latent variables or factors in detail [65]. CFA was applied, allowing free correlations for the whole sample and for a randomly split subsample. Convergent and discriminant analysis were used to test the scale as well as model fit. The last steps were to test a second-order factor analysis and then the multigroup analysis was applied.


**Table 1.** Initial scale items of wine experience.

#### *3.3. Purifying the Measurement*

The list of resulting measurement items was verified with 379 wine tourists who had visited Madeira and Porto wine cellars, and these items were measured using a seven-point Likert scale, varying from 1 (strongly disagree) to 7 (strongly agree). The final survey (multilingual: English, Spanish, French, and Portuguese) was administered by the researcher to a convenience sample of wine tourists visiting Porto and Madeira wine cellars between July and September 2019. The data analysis was carried out in two stages: An (1) EFA, followed by a (2) confirmatory factor analysis (CFA), using SPSS (version 26) and AMOS (version 26). An exploratory factor analysis (EFA) using the generalised least squares as extraction method with a varimax rotation and Kaiser normalisation was undertaken

on the data collected to determine the dimensions of the scale. The criteria used to extract factors was an eigenvalue > 1. The EFA was run separately for each factor.

The EFA identified four dimensions, explaining 58.94% of overall variance, labelled: (1) Wine tasting excitement, (2) winescape, (3) wine storytelling, and (4) wine involvement. Both Bartlett's test of sphericity (a statistical test for the presence of correlations among the variables) and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy were measured to assess data factorability. A KMO value of 0.942 exceeds the acceptable minimum value, which is 0.6 [66]. Bartlett's test of sphericity was found to be significant (*p* < 0.000), within the recommended boundaries (Table 2). The findings presented Cronbach reliability scores ranging from 0.86 to 0.92. In addition, during the factor extraction process, no items were removed. Factor loadings were not revealed to be cross-loaded on different factors, and therefore no item was eliminated.


**Table 2.** Exploratory factor analysis results for the initial measurement scale (wine tourists *n* = 647).

> KMO: 0.942, Bartlett's test of sphericity: 7860.099, Sig.: 0.000

#### **4. Results and Discussion**

## *4.1. Sample Profile*

The sample (Table 3) was balanced in terms of gender, with most visitors from the United Kingdom, France, Portugal, or Germany, and the majority being adults between 25 and 54. The sample had high education levels and a medium- to high-level job standard, and represented the main market in Portugal.


**Table 3.** Socio-demographic profile of the sample—whole data (*n* = 647).

#### *4.2. Finalising the Measurement*

Further robust and consistent data collection was carried out to assess the reliability and validity of the measurement scale. Likewise, the data gathered from the sample of wine tourists recruited in Madeira and Porto wine cellars (*n* = 647) was used to accomplish the CFA, because the development sample must be sufficiently large [62,64]. In total, 323 responses were collected in Madeira wine cellars and 324 responses were collected in Porto wine cellars between late July and September 2019 (the high season). Therefore, a total of 647 self-administrated questionnaires were considered valid and usable for data analysis.

The confirmatory factor analysis (CFA) was conducted using the generalised least squares method [72,73]. to assess the validity and reliability of the constructs. As result, 18 indicators were retained for inclusion in the final scale (Table 4). The adjustment results improved significantly, yielding the values in Table 4 and the adjustment values expressed. As concerns validity and reliability, for the average variance extracted (AVE), the value obtained also exceeds the reference cut-off value (≥0.50) according to the literature [66,70] (Table 5).

The overall goodness-of-fit index (Table 5) displayed a suitable level of fit: χ2 = 406.302; df = 129; *p* = 0.000; χ<sup>2</sup>/df = 3.15; GFI = 0.93; AGFI = 0.907; RMSEA = 0.058, with the result in keeping with what is suggested in the literature [66], confirming the scale's goodness of fit. These results sugges<sup>t</sup> that the proposed model fits well with the empirical data. This study represents one of the first major efforts to propose wine experience factors at wine tourism destinations and, following the accepted scale development procedure [62,64]. developed a measurement scale for wine experience. The final analysis to validate the scale comprises wine storytelling (5 items), wine involvement (5 items), winescape (4 items), and wine tasting excitement (4 items).


**Table 4.** Confirmatory factor analysis results for final measurement scale (wine tourists *n* = 647).

> Notes: \*\*\* *p*-value < 0.01.



Notes: \*\*\* *p*-value < 0.01.

The structural equation model and values of standardised structural coefficients are shown in Figure 1. It was proven by the statistical analysis that all dimensions contribute to the definition of the wine experience construct. The evaluation of the significance of a regression coefficient was performed by an analysis of the *t*-test [74]. The existence of a significant regression coefficient (the value of t exceeds 1.645) assumed that the relationship between the two latent variables was demonstrated empirically [66]. In addition, the case of a positive or satisfactory evaluation of adjustment measures confirmed the predictive validity of the model [74]. In this study, it was assumed that in unilateral cases (direct and positive influence), significant relations would present a t-value of greater than 1.645. Overall, the data supported that wine experience was explained by the four latent factors: Wine storytelling, wine involvement, winescape, and wine tasting excitement.

**Figure 1.** Structural equation model of final measurement scale. \*\*\* *p*-value < 0.01.

Following the SEM analysis, variable correlations were tested for invariance among two different groups of wine tourists. A multigroup analysis (Table 6) highlighted how the Porto and Madeira wine cellars differ from each other from the wine tourists' perspective within these two wine tourism destinations, based on the proposed scale. Overall, the findings supported all the hypothesised relationships in both tourism destinations, which reinforces the consistency of the wine experience scale. The two main differences were wine storytelling and winescape. Wine storytelling by the wine tourists was more evident in Madeira (0.718, *p* < 0.05) than in Porto (0.574, *p* < 0.05). It is expected that this discrepancy was related to greater personalisation of the guided wine tours in Madeira wine cellars as compared to Porto wine cellars. The winescape was more evident in Porto (0.696, *p* < 0.05) than in Madeira (0.655, *p* < 0.05), probably due to the cellar landscape, scenery, ancient architecture, and panoramic views around the cellars.


**Table 6.** Multi group analysis.

Notes: \*\*\* *p*-value < 0.01; \*\* *p*-value < 0.05; \* *p*-value < 0.10.

Advancing these results, meaningful conclusions were drawn and explained, and confirm that the dimensions focus on experiential wine tourism in a holistic way, directly demonstrated by the nature of their corresponding items. Thereby, the wine experience is shaped by four dimensions (wine storytelling, wine tasting excitement, wine involvement, and winescape), directly correlated between them in a composite way, justifying their inclusion on the same scale. Moreover, the results identified dimensions with stronger relevance and impact; foremost was wine storytelling, followed by wine involvement and wine tasting excitement (both very close), and finally winescape. These statements underline the premise value of holistic and hedonic wine experience and yield valuable insights through the increased participation of the wine tourists in the visits. Asero and Patti [75] regarded wine as a decoy that attracted visitors, considering it the soul of the wine tourism, and that it is an experience derived from the hedonic nature of wine tasting [76]. The wine experience dimensions (wine storytelling, wine tasting excitement, wine involvement, and winescape) fulfil a congruen<sup>t</sup> logic that is undoubtedly justified by the relationship between them as the results suggest. The research results highlight the relevance of these dimensions to provide and guarantee an immersive experience to offer a "best holistic wine experience" to wine tourists and potential visitors. It is noteworthy that the wine tourists appreciate a holistic tourism experience due to interactions with other wine visitors and winery staff [76]. Moreover, these findings align with several studies [3,7,19,22,39,77,78].
