Uncorking Rural Potential: Wine Tourism and Local Development in Nemea, Greece

Abstract

This study investigates the economic role of wine tourism in Nemea, Greece, a prominent Protected Designation of Origin (PDO) wine-producing region. Employing a mixed-methods approach, the research combines interviews with local stakeholders and a structured post-wine-tasting visitor survey to assess wine tourism’s contribution to local development. A two-step multivariate analysis, incorporating Multiple Correspondence Analysis and Hierarchical Cluster Analysis, reveals five distinct visitor profiles differing in spending behaviour, familiarity with the destination, and engagement patterns. While high-spending visitors support winery revenues, their limited local integration reduces their broader developmental impact. Conversely, younger and repeat domestic visitors offer more dispersed economic benefits through overnight stays, gastronomy, and cultural participation. In addition, local stakeholders highlight the region’s viticultural identity and growing tourism interest as strengths but also note persistent weaknesses such as inadequate infrastructure, limited coordination, and underdeveloped visitor services. The study concludes that visitor segmentation offers actionable insights for enhancing wine tourism’s developmental role. Targeted strategies tailored to specific visitor types are essential for improving integration with the local economy. These findings contribute to ongoing discussions on how wine tourism can act as a lever for inclusive, sustainable rural development in traditional wine regions.

1. Introduction

Wine tourism, also referred to as oenotourism, can be defined as visitation to vineyards, wineries, wine festivals and wine shows for which wine tasting and/or experiencing the attributes of a wine region are the primary motivating factors for visitors (Hall et al., 2000, p. 3). In line with this, Byrd et al. (2016) highlight the focus on unique and immersive experiences, such as cellar-door visits, tastings, vineyard walks, and participation in wine-related events. Building on these perspectives, Alebaki and Ioannides (2017) emphasise the visitor-centred understanding of wine tourism as a multifaceted experience shaped not only by the wine product itself but also by the destination’s natural, cultural, and social attributes.

Moreover, wine tourism can foster synergies across sectors by linking wine production with gastronomy, retail, and cultural heritage, while promoting the conservation of natural and intangible resources, as emphasised in the Georgia Declaration on Wine Tourism (UNWTO, 2016). In this sense, beyond being an experiential form of rural and cultural tourism, it can act as a pathway toward sustainable development by integrating environmental stewardship, heritage preservation, and inclusive local economic benefits (UNWTO, 2016; Montella, 2017; Sharpley, 2020). It can thus serve as a strategic tool for rural development, contributing to economic diversification, place branding, and sustainability (Martínez-Falcó et al., 2024). This role is especially relevant in traditional wine-producing regions, where viticulture is embedded in cultural heritage and regional identity. As such, wine tourism reinforces the link between production and place-based identity while generating spillover benefits for local economies, making it a central component of rural development strategies.

Research from Mediterranean Europe highlights that wine tourism initiatives often generate measurable economic impacts across related sectors, particularly gastronomy and hospitality (Croce & Perri, 2017; López-Guzmán et al., 2011; Alebaki & Ioannides, 2017; Alebaki et al., 2020; Vazquez Vicente et al., 2021; Martínez-Falcó et al., 2024). This is also revealed in Asociación Española de Ciudades del Vino (ACEVIN) report (ACEVIN, 2024), according to which 2023 visits to wineries and museums generated over €102 million, whereas non-direct expenditures may triple this amount. Moreover, national assessments in Australia show that the combined sector of wine, including wine tourism, delivered A$40.2 billion to GDP and supported about 170,000 jobs, with wine-tourism alone accounting for A$9.2 billion, thus demonstrating powerful rural multiplier outcomes (Gillespie & Clarke, 2019).

Despite its potential, the implementation of wine tourism faces significant barriers. Correia and Brito (2016) highlight that many wine producers fail to recognise tourism as a value-adding enterprise because they lack understanding of core tourism principles, hampering the integration of wine and tourism networks. Research by López-Guzmán et al. (2014) and Getz and Carlsen (2005) highlights that limited knowledge of tourism management and inadequate training often hinder the ability of wineries to deliver high-quality, visitor-oriented experiences. Finally, governance and infrastructure deficits, especially in emerging regions, exacerbate these challenges by creating poorly signposted routes, limited accommodations, and regulatory hurdles (Baggio, 2008; Vos, 2019). Together, these structural, institutional, and educational deficiencies underline the need for comprehensive planning, stakeholder collaboration, and capacity-building strategies to realise wine tourism’s full potential.

In the case of Greece, initiatives such as wine routes have been introduced to enhance the visibility of regional wines and offer visitors experiential opportunities, including winery tours, tastings and participation in cultural activities (Tzimitra-Kalogianni et al., 1999). These efforts aim to foster stakeholder collaboration, promote local products and diversify rural economies (Gatti & Incerti, 1997; Millán-Tudela et al., 2024). In addition, several regions have established wine tourism networks and branding strategies through coordinated efforts involving wineries, local authorities and tourism stakeholders (Alebaki et al., 2020).

The role of wine tourism in rural development in Greece has gained attention, with recent studies focusing on visitor motivation, supply side organisation, and territorial branding (Alebaki & Ioannides, 2017; Anastasiadis & Alebaki, 2021). However, there is still limited empirical research on how different visitor profiles contribute economically at the local level and how this relates to the development goals in wine-producing areas. This study addresses that gap by exploring the interlinkages between wine tourism and wider local economic activities, aiming to assess its contribution to the local development of Nemea, one of the Greece’s most prominent PDO red wine region. Wine tourism in Nemea, while established, remains at an early stage of structured development and integration with the broader tourism economy, especially when compared to other major European wine regions (e.g., Tuscany, Bordeaux, or Rioja).

The present study draws on open-ended interviews with key stakeholders, a structured post-visit questionnaire and a purposive scan of TripAdvisor reviews as supplementary evidence to evaluate the impacts of wine tourism on local businesses and community dynamics. In doing so, the study provides insights into how place-based tourism strategies can foster sustainable rural development and support the long-term viability of winemaking communities.

2. Methodology

This study employed a mixed-methods design to examine the contribution of wine tourism to local development in Nemea (see Figure 1). The approach integrated qualitative and quantitative components to enhance robustness and capture different dimensions of the research scope. Specifically, three sources of evidence were combined: (i) open-ended interviews with key local stakeholders to explore perceptions of wine tourism and development challenges; (ii) a structured visitor survey analysed with multivariate statistical techniques (MCA and cluster analysis) to identify behavioural patterns and visitor profiles; and (iii) a purposive scan of TripAdvisor reviews, used solely as supplementary evidence for triangulation, to check whether visitor perceptions echoed the themes emerging from interviews and surveys.

The integration of these three components ensures that results are not solely dependent on one source of data. Instead, qualitative interviews provide depth and local context, the survey offers structured evidence for statistical segmentation, and TripAdvisor reviews extend the scope by reflecting spontaneous visitor experiences. This triangulation strategy is particularly valuable given the relatively small survey sample. In addition, this triangulated design is particularly appropriate for the Nemea context, where wine tourism still remains at an early stage of development. Only a limited number of wineries are fully open to visitors and are those most oriented toward wine tourism, having invested in tasting facilities, guided tours, or limited lodging services. On the other hand, many other wine producers in the region remain primarily focused on production and are not regularly open to visitors.

Given the above fact, the survey was conducted in wineries that are accessible to the public without prior arrangement. Although exact visitation shares across all wineries in Nemea are not systematically reported, local stakeholders consistently highlighted that five wineries are those that attract the vast majority of wine tourists. For this reason, they effectively represent the main gateway to wine tourism experiences in the region.

2.1. Qualitative Analysis

Participants included two wine-tour guides working in wineries that are actively engaged in wine tourism, the director of the Public Institute of Vocational Training (ELGO-DIMITRA, Agricultural School of Nemea), the president of the Nemea PDO Winemakers Association, the owner of a wine-focused restaurants in the municipality, and two managers of local hotels. These actors were purposively selected to represent diverse positions across the wine-tourism value chain and to reflect institutional, entrepreneurial, and service-sector viewpoints.

Interviews followed a concise guide (see Appendix B) covering five themes: (i) regional assets and constraints; (ii) winery readiness and service capacity; (iii) visitor profiles and behaviours; (iv) linkages with gastronomy, accommodation, and cultural/heritage offerings; and (v) policy, training, and governance needs. Additional stakeholder-specific prompts were used to ensure relevance (e.g., winery readiness, hotel capacity, vocational training), while all interviews covered the same core themes. Questions were open-ended to allow participants to elaborate freely, while the guide ensured coverage of core topics and comparability across interviews. Each interview lasted approximately 30–45 min and was conducted in person during spring–early summer 2025. The material was analysed thematically, combining deductive coding (see interview guide) with inductive coding to capture emergent issues.

2.2. Quantitative Analysis: Multivariate Framework

The Multivariate Analysis utilised in this study targets to identify distinct groups of visitors based on the questionnaires collected after their visitation to the wineries. Firstly, a Multiple Correspondence Analysis (MCA) is conducted to reduce dimensionality and identify latent behavioural patterns across responses to the survey questionnaires. Secondly, a Cluster analysis (combination of Hierarchical Cluster Analysis (HCA) and K-means clustering) is applied to the dimensions produced by the MCA aiming to classify wine-tourists into distinct clusters. The use of this approach is well-suited to small but high-dimensional categorical datasets, allowed for the robust identification of visitor profiles and behavioural patterns.

The analysis is based on the outcomes of 50 structured questionnaires that were filled by wine tourists in the five wineries that have, according to local stakeholders, among the biggest visitations shares and are inside, or very close to the Nemea municipality. The respondents completed the survey after their wine-tourism experience during the spring and early summer of year 2025, while experienced interviewers were present to reply to potential question and/or comments.

The questionnaire included items related to respondents’ socio-demographic characteristics, their spending behaviour, their satisfaction levels, their previous wine tourism experience, and their perception regarding their contribution to the local economy. More specifically, the survey includes indicators such as daily expenditures, satisfaction rates, and perceived local contributions are used to evaluate wine tourism’s role in rural development (Millán-Tudela et al., 2024; Martínez-Falcó et al., 2024). Followed also Gillespie and Clarke (2019), there are also questions to capture the main economic sectors affected by the development of wine tourism. A frequency distribution of survey responses is shown in Figure 2. The following chapters presents the two-step multivariate analytical framework that is followed in this analysis.

2.2.1. Multiple Correspondence Analysis (MCA)

MCA is a statistical technique used to explore and visualise relationships among several categorical variables (Greenacre, 2017). MCA reduces the complexity of large datasets by converting categorical data into numerical coordinates in a multidimensional space. These coordinates define new dimensions (or axes) that capture the greatest variation in the dataset, allowing for easier interpretation of the underlying structure and patterns among variable categories. Each dimension produced by MCA explains a portion of the overall inertia (i.e., variability), and the dimensions are ordered based on the amount of variance they account for, which simplifies both the interpretation and visualisation of the results (Husson et al., 2017). Given that the variables used in this analysis are mainly measured on Likert scales, MCA is preferred over Principal Component Analysis (PCA), as it is more appropriate for categorical data and avoids the metric assumptions required by PCA (Abdi & Valentin, 2007).

Mathematically, MCA begins by converting the categorical dataset into a binary indicator matrix, where each category of each variable is represented as a separate column. Formally, consider a dataset comprising $n$ observations and $p$ categorical variables. Each categorical variable $\mathrm{j} \mathrm{h}\mathrm{a}\mathrm{s} k_j$ categories. The dataset is transformed into an $n \times K$ indicator matrix $\mathit{X}$, where $K={\sum }_{j=1}^p{k}_j$. Each row in $\mathit{X}$ corresponds to an observation, and each column corresponds to a specific category of a variable, with elements $x_{ik}=1$ if observation $i$ belongs to category $k$, and 0 otherwise. From this matrix, MCA constructs a multidimensional space and extracts underlying dimensions that summarise the associations and patterns among the variable categories.

The MCA consists of the following steps (Greenacre, 2017):

• Standardisation of the Indicator Matrix: The indicator matrix is centred and scaled to produce matrix $\mathbf{Z}$ which is defined as:

  $$\mathbf{Z}={\displaystyle \frac{1}{\sqrt{np}}}\left(\mathit{X}-1_n{\mathit{r}}^{\top }\right){\mathit{D}}_c^{-1/2}$$

  (1)

  where $1_n$ is an $n$-dimensional vector of ones, $\mathit{r} \mathrm{a}\mathrm{n}\mathrm{d} \mathit{c}$ are the row and column marginals (proportions), respectively, and ${\mathit{D}}_c$ is the diagonal matrix of column sums.
• Singular Value Decomposition (SVD): Perform the SVD on matrix $\mathbf{Z}$:

  $$\mathbf{Z}=\mathbf{U}\mathsf{\Sigma }{\mathit{V}}^{\mathit{\top }}$$

  (2)

  where $\mathit{U} \mathrm{a}\mathrm{n}\mathrm{d} \mathit{V}$ containing left and right singular vectors, respectively, and $\mathsf{\Sigma }$ is a diagonal matrix of singular values.
• Determination of Dimensions: The principal dimensions are chosen based on the largest singular values. The inertia (analogous to variance in PCA) explained by each dimension is given by:

  $$\mathrm{Inertia}=\sum {\sigma }_{\mathrm{l}}^2\mathrm{ }$$

  (3)

  where ${\sigma }_{\mathrm{l}}$ are the l-th singular value of the matrix Z, represents the contribution of the l-th dimension to the total inertia (i.e., the total variance explained).
• Coordinates Calculation: The coordinates of the observations (rows) and categories (columns) in the reduced space are computed as:

  $$\mathrm{F}=\mathrm{U}\mathsf{\Sigma }\mathrm{ }\left(\mathrm{row} \mathrm{coordinates}\right),\mathrm{ }\mathrm{G}=\mathrm{V}\mathsf{\Sigma }\mathrm{ }\left(\mathrm{column} \mathrm{coordinates}\right)$$

  (4)
• Interpretation and Visualisation: Observations and categories can then be plotted in the reduced-dimensional space. The proximity of points in the scatterplot reflects associations or similarities among observations and categories.

Table 1. Variables incorporated in the MCA.

Categories	Variables
Demographics	Gender (1: Man; 0: Woman)
	Age class (1: 18–24; 2: 25–39; 3: 40–64; 4: >64)
	Education (1: Primary & secondary; 2: University; 3: Master)
	Nationality (1: Greek; 0: foreigner)
	Income class (1: <1000, 2: 1000–1500; 3: 1500–2500; 4: >2500)
Tourism behaviour	First Visit in Nemea (1: Yes, 0: No)
	Number of wineries visited (1: one, 2: two to three; 3: >three)
	Days spent in Nemea
Spending patterns:	Spending money for buying in wineries (1: <5 €, 2: 5–49 €; 3: 50–99 €; 4: 100–149 €; 5: 150+ €)
	Spending money for restaurants (1: <5 €, 2: 5–49 €; 3: 50–99 €; 4: 100–149 €; 5: 150+ €)
	Spending money for hotels (1: <5 €, 2: 5–49 €; 3: 50–99 €; 4: 100–149 €; 5: 150+ €)
	Spending money for café (1: <5 €, 2: 5–49 €; 3: 50–99 €; 4: 100–149 €; 5: 150+ €)
	Total spending score, i.e., the summation of spending scores in all categories including spending scores (i) for gas, (ii) super-market and (iii) other retail markets
Perceptions & preferences:	Perceived contribution to local economy (1: not at all; 2: slight; 3: moderate, 4: High)
	Rate of experience (from 1 to 5)
	Plan to revisit (2: Yes, 1: maybe; 0: No)

presents the specific variables incorporated in MCA.

2.2.2. Cluster Analysis: Hierarchical Cluster Analysis (HCA)/K-Means Clustering

HCA is a multivariate technique that groups observations according to their similarity across multiple dimensions, aiming to maximise intra-cluster homogeneity and inter-cluster heterogeneity. In this study, the HCA is applied to classify wine tourists based on the factor scores obtained from the Multiple Correspondence Analysis (MCA).

The analysis employed Euclidean distance as the dissimilarity metric, and Ward’s linkage method for agglomeration. Ward’s method minimises the total within-cluster variance by selecting at each step the pair of clusters whose merger results in the smallest possible increase in the total within-cluster sum of squares. Formally, for two clusters A and B, the change in within-cluster inertia ΔW after merging is defined as:

$$\Delta \mathrm{W}=({\mathrm{n}}_{\mathrm{a}}\times {\mathrm{n}}_{\mathsf{\beta }})/({\mathrm{n}}_{\mathrm{a}}+{\mathrm{n}}_{\mathsf{\beta }})\times {\Vert \overline{{\mathrm{x}}_{\mathrm{a}}}‒\overline{{\mathrm{x}}_{\mathsf{\beta }}}\Vert }^2$$

(5)

where ΔW is the increase in within-cluster inertia when merging two clusters; n_a, n_β: the number of observations in clusters A and B, respectively; $\overline{{\mathrm{x}}_{\mathrm{a}}}$, $\overline{{\mathrm{x}}_{\mathsf{\beta }}}$ are the centroids (mean vectors) of clusters A and B, respectively, and ${\Vert \overline{{\mathrm{x}}_{\mathrm{a}}}‒\overline{{\mathrm{x}}_{\mathsf{\beta }}}\Vert }^2$ is the Squared Euclidean distance between the two centroids. To determine the optimal number of clusters, the dendrogram structure and the applied internal validation indices, including the Calinski–Harabasz and Duda–Hart criteria are considered (Caliński & Harabasz, 1974; Duda et al., 2001; StataCorp, 2013). More specifically, the Calinski-Harabasz pseudo-F index and Duda/Hart Je(2)/Je(1) index criteria, are calculated using STATA 13.0. For both criteria, larger values indicate more distinct clustering. Presented with the Duda–Hart Je(2)/Je(1) values are pseudo-T-squared values. Smaller pseudo-T-squared values indicate more distinct clustering (StataCorp, 2013).

Following the identification of the optimal number of clusters, K-means clustering is performed using the predefined number of groups suggested by HCA. K-means is a widely used partitioning technique that aims to minimise within-cluster variance by iteratively optimising cluster assignments based on distance to cluster centroids (MacQueen, 1967). This approach combines the hierarchical structure detection of HCA with the refinement and stability of K-means partitioning (Murtagh & Legendre, 2011; Singh & Kaur, 2013).

The algorithm operates initialising K centroids, here based on the results of Ward’s hierarchical clustering, and then proceeds through by two consecutive steps; the assignment step and the update step. During the former step, each observation is assigned to the cluster whose centroid is closest, typically based on Euclidean distance. In the latter step, the centroid of each cluster is recalculated as the mean of all observations currently assigned to that cluster. These steps are repeated until convergence, typically defined as no further changes in cluster membership or centroid positions. The objective function minimised by K-means is the total within-cluster sum of squares (WCSS):

min ∑_k=1^K ∑_i∈C_k ‖x_i − μ_k‖²

(6)

where ∑_k=1^K: is the sum over clusters (from k = 1 to K); ∑_i∈C_k is the sum over all observations i in cluster C_k; x_i is observation I; μ_k is the centroid of cluster k and ‖x_i − μ_k‖² is the squared Euclidean distance between observation and its cluster centroid.

After the implementation of the cluster analysis, cross-tabulations, descriptive statistics, and graphical techniques are used to characterise each cluster according to demographic, behavioural, and operational variables. This facilitated a clearer interpretation of each group’s defining features and provided actionable insights into the heterogeneity of wine tourist profiles.

2.3. Triangulation with Online Visitor Reviews (TripAdvisor)

A purposive scan of TripAdvisor reviews related to winery visits in Nemea was undertaken as supplementary evidence for triangulation. Reviews posted within the two years before fieldwork were screened. Only substantive written comments were included and ratings-only entries were excluded. A simple thematic validation frame was used to code comments against service quality, infrastructure and accessibility, accommodation and gastronomy, authenticity and atmosphere, and overall satisfaction and loyalty. The reviews were used only to test convergence with interview and survey findings and were not used in the multivariate modelling.

This use of a third source follows well established guidance on triangulation, which seeks convergence across independent strands to enhance credibility rather than to give equal analytical weight to every source, as discussed by Decrop (1999) in tourism methods and recently by Pagliara et al. (2025). Treating online reviews as qualitative material is consistent with netnography (see e.g., Thanh & Kirova, 2018; Papadopoulou & Alebaki, 2022; Kozinets & Gretzel, 2024) and with applications that analyse TripAdvisor content as contextual evidence rather than model inputs, for example Mkono and Tribe (2017).

The supplementary use of TripAdvisor reviews is relevant in the case of Nemea, as they provide triangulation and reflect the experiences of a wider set of visitors, including international tourists who may not have been represented in the survey. A coding matrix with representative categories and excerpts from reviews is provided in Appendix C.

2.4. The Area of Nemea

One of the most prominent wine-producing areas in Greece is Nemea. It stretches across approximately 2100 hectares within 17 villages in the northeast Peloponnese, spanning the southern part of Corinthia and a small segment of Argolida at elevations between 200 and 850 m (see Figure 3 and Figure 4). The region is closely associated with the Agiorgitiko grape variety, primarily cultivated within the Nemea Protected Designation of Origin (PDO), which holds both economic and oenological significance for the Greek red wine sector (Miliordos et al., 2024; Kazou et al., 2023).

Within this physically diverse landscape, approximately forty wineries operate, comprising large modern estates, alongside small family-run cellars. Besides the predominance of Agiorgitiko, wineries conduct altitude-informed sub-zone production, ranging from pale, fresh rosés to robust age-worthy reds. Nemea is also the base of a Public Institute of Vocational Training for viticulture. Located at the heart of the region’s viticultural zone, the institute offers specialised education in viticulture and oenology, playing a strategic role in building local capacity and supporting the modernization of the wine sector.

3. Results

3.1. Open Interviews with Local Stakeholders

Open-ended interviews with local stakeholders revealed a multidimensional perspective on wine tourism in Nemea. While participants acknowledged numerous constraints, they also highlighted important assets underpinning the region’s potential as a wine tourism destination. One of the wine-tour guides emphasised that: “tourism is still seen by many producers as secondary to winemaking.” Similarly, according to the hotel manager that participated in the survey: “…until recently, the accommodation options in town were limited, something that limited their willingness to extend their stay.” At the same time, several encouraging developments were highlighted. A wine-tour guide also stressed that “events like the Great Days of Nemea really help us showcase our wines and attract new visitors who might not have considered the region otherwise.” The PDO Association president observed that “Nemea’s PDO status gives us a strong identity, Agiorgitiko is a brand in itself, and this recognition is a real advantage for wine tourism.” Finally, one of the hotel managers that was interviewed added a forward-looking note: “we notice more visitors are staying overnight compared to a few years ago, a promising sign that the region’s potential is gradually being realized.”

Stakeholders consistently underscored endogenous strengths that position Nemea well for wine tourism growth. For example, one of the wine-tour guides points out that “younger staff trained locally are more open to welcoming visitors”. Proximity to Athens was also highlighted as a major advantage. One of the hotel managers states: “less than two hours from the capital makes it ideal for weekend escapes.”

Interviewees also identified structural constraints that limit performance and visitor integration. Infrastructure was the most persistent issue, e.g. “the appearance of the town itself does not reflect the quality of the vineyards,” and “Nemea feels underdeveloped compared to the landscapes around it.” The restaurant owner also echoed the concerns about the urban environment: “The town looks uninviting; even if the wines are excellent, visitors leave with the impression that Nemea itself does not match the quality of its vineyards.”

Several participants also emphasised fragmentation across HORECA and wine actors: “everyone invests individually, but without a shared strategy we lose opportunities.” Beyond infrastructure, stakeholders pointed to an insufficient tourism mindset. As one of them explained: “many producers still see tourism as a distraction, not as a core part of their business model,” and linked this to “a lack of trained staff for storytelling and hospitality.” The vocational training director emphasised the role of education in shifting this mindset: “Without structured training in hospitality and tourism, we cannot expect consistent quality of wine tourism experience; training is essential for professionalising services.” This perspective aligns with the view that “younger generations of winemakers need to embrace wine tourism as an important part of wineries’ activities,” highlighting that professional training and a cultural shift among new entrants to the sector are both necessary for wine tourism sector to be sustainably developed.

Market-level issues were also highlighted, especially seasonality: “international tourists arrive but find few wineries open, which discourages them from staying longer.” The limited integration of archaeological and cultural heritage was viewed as another missed opportunity. One interviewee said, “we have the ancient stadium and the sanctuary of Zeus just next door, yet most tours ignore them, this is a wasted synergy.” However, as a hotel manager observed: “seasonality in tourist arrivals has recently shown diminishing signs”, and encouraging change, indicative of untapped potential for further growth and diversification of wine tourism throughout the year.

Taken together, the interviews highlighted both assets and constraints. Stakeholders pointed to Nemea’s PDO identity, its proximity to Athens, seasonal anchor events and the presence of a vocational training institute as important strengths, while also underlining enduring weaknesses such as inadequate infrastructure, fragmented investment, a limited tourism culture, weak links with cultural heritage and modest international visibility. Overall, these findings depict a sector at a crossroads: strong assets coexist with persistent barriers, leaving wine tourism underdeveloped and fragmented. As a stakeholder summarised, “the potential is here, but unless we organise ourselves better, Nemea will never be much more than a day-trip stop.”

3.2. Multivariate Analysis

3.2.1. MCA

The results of the MCA are provided in Figure 5, Figure 6, Figure 7 and Figure 8 and in

Table 2. Summary table of the dimensions.

Dimension	% Inertia	Suggested Name	Key Contrasts	Interpretive Insight
Dim 1	21.11%	Overall Economic Engagement	High-spending, multiday tourists vs. low-spending, short-stay/day-trippers	Distinguishes high-value visitors with greater local economic contribution
Dim 2	17.55%	Visitor Type & Familiarity	Repeat, engaged visitors vs. first-time, low-engagement tourists	Captures depth of visitor experience and future return potential
Dim 3	9.28%	Demographic & Educational Profile	Older, affluent, highly educated vs. younger, less affluent and less educated	Reveals socioeconomic diversity and orientation toward premium experiences
Dim 4	7.81%	Spending Orientation (Gastronomy vs. Winery)	Food- and café-oriented visitors vs. winery-focused spenders	Indicates variation in visitor priorities and preferred types of experiences
Dim 5	4.40%	Tour Structure (Overnight vs. Local)	Hotel-using, multiday tourists vs. day-trippers or local participants	Reflects logistical and infrastructural engagement with the destination

Note: Although the MCA extracted up to 15 dimensions, only the first five were retained for analysis, as they explain 60.15% of the total inertia. Dimensions beyond the fifth contributed only marginally and were excluded to ensure clarity and parsimony.

, while the statistics for each dimension are provided in detail in Appendix A. According to Figure 5, the first five dimensions (above the red line) account for a cumulative 60.15% of the total inertia which exceeds commonly accepted thresholds in social science studies (e.g., Sulewski et al., 2018; Guédé & Koffi, 2019), and strikes an appropriate balance between analytical depth and parsimony for subsequent clustering and interpretation (e.g., Husson et al., 2017; Greenacre, 2017). Especially in case of MCA studies in social sciences, it is common to retain dimensions until approximately 60% of variance is explained, because variance is typically more evenly spread across dimensions compared to PCA (Hjellbrekke, 2018). The 6th dimension, while still interpretable, only contributes 4.12%, and its marginal increase in explained variance does not outweigh the loss in clarity and complexity it would introduce to subsequent cluster analysis or interpretation. Furthermore, there is a clear drop in the contribution to explained inertia after the fifth dimension. Using 5 dimensions instead of 6 also improves the performance and stability of the subsequent cluster analysis, especially with the relatively small sample n = 50. In general, the more dimensions used, the more the risk of overfitting or artificial segmentation.

Ultimately, based on the cumulative explained variance, interpretability, and diminishing marginal inertia contributions beyond the fifth dimension, five MCA dimensions were retained. Below there is a presentation of the five dimensions (see also Figure 6 and Table 2).

• Dimension 1: Overall Economic Engagement (21.11% of inertia explained). This is the most impactful dimension in terms of inertia explained. It captures the contrast between economically impactful visitors and more modest or passive tourists. High scorers on this axis are characterised by significant expenditures across multiple categories, particularly wineries, restaurants, and hotels, and longer stays, often exceeding two days. These individuals likely contribute the most to the local economy, not only through direct purchases in wineries but also through their use of local services. At the other end of the spectrum, low scorers tend to be day-trippers or budget-conscious tourists, making minimal purchases and engaging with fewer tourism touchpoints. Their economic footprint is relatively limited, even if their presence is valued from a volume or awareness-building perspective. This dimension is crucial in distinguishing high-value segments from low-intensity visitors, both of which play different but complementary roles in the regional tourism ecosystem.
• Dimension 2: Visitor Type and Familiarity (17.55% of inertia explained). The second dimension, explaining 17.55% of the inertia, reflects visitor experience and relational familiarity with Nemea as a wine tourism destination. Tourists with high scores on this axis tend to be repeat visitors, often visiting multiple wineries and expressing clear intentions to return. Their behaviour indicates both personal investment in the destination and sustained interest in its wine-related offerings. In contrast, low scorers are primarily first-time visitors, many of whom limit their exploration to a single winery and do not plan a return trip in the near future. These visitors may have arrived through broader tourism flows rather than a dedicated interest in wine. This dimension therefore articulates a spectrum from loyal, targeted wine tourists to casual or accidental participants, offering insight into how engagement evolves across different types of visitors.
• Dimension 3: Demographic and Educational Profile (9.28% of inertia explained). This dimension explained a much lower but still significant portion of the inertia (9.28%). It reveals clear demographical patterns in terms of age, income, and education level. High scores are associated with older, highly educated, and affluent individuals, typically those aged 40–64, holding postgraduate degrees, and reporting household incomes above €2500. These visitors may exhibit preferences for more structured, refined, or educational wine tourism experiences. On the lower end of the axis, tourists tend to be younger (18–24), less formally educated, and within lower income brackets. While still important to the tourism base, they may be less likely to engage with premium offerings or complex narratives around terroir and wine production. This axis thus captures the socioeconomic and cultural capital of visitors, which influences both their motivations and the types of experiences they value.
• Dimension 4: Spending Orientation—Gastronomy vs. Winery (7.81% of inertia explained). The fourth dimension differentiates visitors based on how they allocate their spending during their visit. High scorers prioritise gastronomic experiences, spending significantly in restaurants and cafes, often emphasising food and social interaction as key components of their trip. By contrast, low scorers tend to focus their spending on direct winery purchases, suggesting a more wine-driven orientation. These may be individuals who are interested in building their personal wine collection or learning about wine as a commodity, rather than as part of a wider cultural experience. This dimension offers useful insight into visitor priorities, highlighting opportunities for targeted promotion, e.g., food pairing events for high scorers, or cellar-door incentives for low scorers.
• Dimension 5: Tour Structure—Overnight vs. Local (4.40% of inertia explained). This dimension contrasts the structure of a visitor’s trip, particularly whether they are overnight guests or local/same-day visitors. High scorers report hotel spending and longer stay, reflecting a more immersive travel model, often with time allocated to additional cultural or leisure activities. Low scorers, on the other hand, are typically day-trippers or residents of nearby areas, who may participate in winery tours without engaging with the broader tourism infrastructure. Their presence is valuable for volume, local brand awareness, and word-of-mouth, even if their per-visit impact is limited. This dimension is essential for tourism planning, as it relates directly to infrastructure usage, accommodation demand, and strategic investment needs.

The above findings are further justified by the MCA coordination Plot and the MCA dimension projection plot (Figure 7 and Figure 8, respectively). The coordination plot (Figure 7) displays the relationships among the categorical variables across dimensions 1 and 2, the two primary dimensions, which together explain approximately 38.6% of the total inertia (21.1% and 17.5%, respectively). The projection of categories in this space reveals meaningful patterns in how visitors differ based on their demographics, behaviours, and perceived impact on the local economy.

The MCA coordination plot articulates a spectrum: from committed, high-spending, repeat wine tourists, to casual or first-time visitors with lower engagement. As is also presented in the dimension description, the first dimension appears to capture variations in economic engagement. Categories positioned on the positive end, such as higher income classes, longer stays in Nemea, and increased spending in wineries and restaurants, suggest a profile of visitors who are not only more wealthy but also more likely to make a substantial contribution to the local economy. In contrast, the negative side of this axis is associated with lower-income groups, minimal spending, and shorter visits, pointing to a segment of tourists with limited economic impact.

The second dimension is more closely aligned with familiarity and purpose of visit. Visitors who have previously travelled to Nemea or other Greek wine regions, and who indicate a stronger intention to revisit, cluster on the upper end of this axis. This contrasts with first-time visitors or those who view the experience more casually, who appear on the lower end. This axis helps differentiate experienced, wine-motivated tourists from accidental or leisure-driven participants with less direct engagement with wine as a travel motivator.

The MCA dimension projection plot (Figure 8) offers a view of how each categorical variable contributes across all five retained dimensions. Dimension 1 is strongly shaped by income level, number of days spent in Nemea, and spending behaviours, reinforcing its interpretation as a measure of economic involvement. Dimension 2 is defined largely by variables such as first-time visitation and familiarity with other wine regions, further validating its link to experiential orientation and touristic intentionality. Additional dimensions, though accounting for smaller portions of inertia, capture more subtle differences. For example, Dimension 3 appears to reflect demographic traits such as nationality and gender, while Dimension 5 includes perceptions of local economic impact, suggesting a latent awareness of tourism’s broader contribution.

Overall, the findings highlight the multidimensional nature of wine tourist profiles in Nemea. Visitors differ not only in terms of how much they spend or how long they stay, but also in their prior exposure to wine-related tourism and their intentions for future engagement. Understanding these patterns can help inform strategies for segmenting the wine tourism market and tailoring offers to different types of visitors, from loyal, high-spending oenophiles to first-time, experience-seeking travellers.

3.2.2. HCA and K-Means

The dendrogram and the stopping rules used for this analysis are presented in Figure 9 and Figure 10. As the cluster analysis results indicate the optimal cluster numbers appeared to be between 4 and 5 clusters, based on the Calinski/Harabasz pseudo-F index and Duda/Hart Je(2)/Je(1) index criteria, respectively. This is also visually presented in the cluster dendrogram (see Figure 10) where the two alternative cluster groups are presented. The difference between the two results is based on the splitting or not of the one bigger cluster in two groups. Indeed,

Table 3. Populations of Four- vs. Five-clusters solution.

	Five-Cluster
Four-cluster	1	2	3	4	5	Total
1	12					12
2		9				9
3			14	9		23
4					6	6
Total	12	9	14	9	6	50

presents the population per cluster in the four and five-cluster solutions. The difference appears in the case of clusters 3 and 4 which are grouped together in the 4-cluster solution. The optical appearance of the dendrogram, support the adoption of the 5-clusters solution that keep the dissimilarity index low and allow the formation of relatively more balanced (at least in terms of population) groups.

Table 4. Average dimension scores per cluster.

Cluster	Overall Economic Engagement (dim1)	Visitor Type & Familiarity (dim2)	Demographic & Educational Profile (dim3)	Spending Orientation (Gastronomy vs. Winery) (dim4)	Tour Structure (Overnight vs. Local) (dim5)	Profile Summary
1	−0.77	0.71	0.62	0.49	−0.46	Local Day-Trippers (24%): Repeat visitors; short stays, food-focused
2	−0.33	0.68	−0.29	−1.38	0.56	Repeat Mid-Spenders (18%): Repeat visitors focused on wine purchases; moderate income
3	0.79	0.16	−0.24	0.23	−0.43	High-Spend Short-Stay Tourists (28%): High-spending one-timers; low overnight stay
4	−0.14	−0.78	−1.17	0.68	0.65	Curious, Educated Explorers (18%): First-timers; younger; food & experience-driven
5	0.40	−1.63	1.51	−0.48	0.10	International Premium Tourists (12%) Affluent, new visitors; detached but valuable

present a summary of the clusters based on the average score per dimension and a corresponding profile summary, while

Table 5. Results from Kruskal–Wallis tests for equality of dimensions’ scores across clusters.

Dimensions	Kruskal–Wallis Average Rank Sum per Cluster					Kruskal–Wallis, χ2 Test and Associated Probability
	Clus_1	Clus_2	Clus_3	Clus_4	Clus_5	Chi-Squared (4 d.f.)	Probability
Dimension 1	13.50	36.25	35.92	32.67	18.25	20.85	0.000
Dimension 2	21.22	35.33	22.56	7.22	33.67	28.29	0.000
Dimension 3	38.93	26.43	23.86	27.64	19.93	33.79	0.000
Dimension 4	24.44	13.11	5.00	36.33	36.22	24.21	0.000
Dimension 5	26.17	5.67	43.67	17.33	24.67	12.73	0.013

provides the results of a Kruskal- Wallis test for the for equality of dimensions’ scores across clusters. The results of Table 5 indicate clearly statistically significant differences across clusters. Finally,

Table 6. Summary table of Cluster characteristics and effect in local economy.

Cluster	Type/Naming	Demographic	Visit Duration	Spending Focus	Revisit Intention	Economic Impact
Cluster 1	Local Day-Trippers	Older, well educated, likely higher income	<1 day	Food-oriented, limited overnight stay	High	Low
Cluster 2	Repeat Mid-Spenders	Mid-age, loyal	~2 days	Winery purchases, product-focused	Moderate	Medium
Cluster 3	High-Spend Short-Stay Tourists	Mixed, affluent	<1 day	Food, wine	Low	High
Cluster 4	Curious, younger explorers	Younger, lower income/education	1–2 days	Wineries & gastronomy	High	High
Cluster 5	International Premium Tourists	Non-Greek, affluent	2–3 days	Winery-focused	Low	High

summarises the five identified clusters, based on their structural characteristics and their effect in local economy.

Cluster 1, Local Day-Trippers (24%), represents a group of repeat wine tourists who are relatively familiar with the Nemea region. They score highly on Visitor Familiarity (dim2 = 0.71) and show positive values on Demographic Profile (dim3 = 0.62), suggesting they are well-educated, and likely more affluent. Their spending orientation is skewed toward gastronomy (dim4 = 0.49), indicating interest in culinary experiences beyond just wine. However, they report low overall economic engagement (dim1 = −0.77) and less overnight stay behaviour (dim5 = −0.46), pointing to frequent short visits with moderate economic impact to the local economy.

Cluster 2, Repeat Mid-Spenders (18%), also includes repeat visitors (dim2 = 0.68), but unlike Cluster 1, they report higher engagement with accommodations (dim5 = 0.56). Their scores on Demographic Profile and Economic Engagement are slightly negative, suggesting middle-income or moderate-resource travellers. What sets this group apart is their strongly negative value on Spending Orientation (dim4 = −1.38), indicating that they are highly product-focused, i.e., they are primarily interested in wine purchases over food or broader cultural offerings.

Cluster 3, High-Spend Short-Stay Tourists (28%), contains tourists with the highest levels of Economic Engagement (dim1 = 0.79), suggesting significant spending across multiple categories. They score negatively on Tour Structure (dim5 = −0.43), suggesting more limited overnight stay behaviour, and their Demographic Profile (dim3 = −0.24) suggests they are less affluent or younger. These may be high-spending one-off visitors who travel for a special occasion or premium winery experience.

Cluster 4, Curious, Educated Explorers (18%), includes visitors who are clearly first-timers (dim2 = −0.78) and are the most socioeconomically distinct from the rest (dim3 = −1.17). Interestingly, they show relatively high values on Spending Orientation (dim4 = 0.68) and Tour Structure (dim5 = 0.65), indicating that although new to wine tourism and less affluent, they are willing to stay overnight and spend more on gastronomy and experience-based services. This group may represent a valuable emerging segment of younger, engaged explorers.

Finally, cluster 5, International Premium Tourists (12%), stands out for having the highest values on Demographic Profile (dim3 = 1.51), pointing to older, highly educated, and affluent individuals. However, they score very low on Visitor Familiarity (dim2 = −1.63), indicating a lack of previous contact with Nemea and weak intentions to return. Their moderate scores on Economic Engagement (dim1 = 0.40) and other dimensions suggest these may be more detached, event-driven tourists, i.e., high-value in one visit but not yet committed to long-term engagement.

3.3. Triangulation with Online Visitor Reviews (TripAdvisor)

To triangulate interview and survey findings, we screened recent TripAdvisor reviews of winery visits in Nemea. These reviews are supplementary evidence and were not used in the MCA or clustering; they serve only to validate whether visitor perceptions converge with stakeholder views and survey results.

The reviews largely echoed the same themes, showing a consistent pattern of positive evaluations alongside recurring challenges. On the positive side, visitors consistently praised wine quality and the distinctiveness of the PDO Agiorgitiko variety. One reviewer described this as an “authentic PDO Agiorgitiko identity,” highlighting the role of local grape varieties in shaping the experience. The vineyard landscapes and built environment were also highly valued, with travellers praising the “vineyard views and traditional stone cellars” that contributed to “a very authentic experience.” In addition, the professionalism and hospitality of staff were frequently mentioned, with comments underscoring the knowledge, friendliness and engaging style of guides. Reviewers also appreciated the integration of food and wine, noting that cheese accompaniments and meals provided alongside tastings enhanced the experience.

At the same time, several limitations appeared. Some visitors reported navigation challenges and limited road signage. Others noted that accommodation options close to Nemea can be thin. A minority of comments pointed to organisational issues at busy times, such as tastings that felt rushed during harvest. In sum, the reviews indicate that Nemea’s wine tourism offer is anchored in strong oenological and experiential resources, notably the quality of PDO Agiorgitiko wines, the authenticity of vineyard settings and the professionalism of staff, while future development is constrained by practical gaps in accessibility, logistics and nearby accommodation.

Taken as triangulation, these online reviews reinforce the findings from interviews and surveys: Nemea’s wine tourism is built on strong oenological and experiential assets but constrained by persistent infrastructural and organisational weaknesses.

4. Discussion

The qualitative findings highlight a consistent set of strengths and weaknesses shaping wine tourism development in Nemea. The core strengths are the Agiorgitiko PDO identity, authentic vineyard landscapes and built heritage. These assets drive satisfaction and explain why visitors speak about authenticity, scenery and the welcome they receive.

At the same time, the analysis pointed to persistent weaknesses in infrastructure and service provision. Many wineries are small-scale and lack dedicated hospitality infrastructure. These deficiencies restrict the capacity of the region to host and retain visitors. Education and training initiatives are therefore essential for building a skilled workforce and professionalising wine tourism services. The current variability in service quality suggests that positive visitor experiences often depend on individual initiative rather than a coordinated regional standard. Stakeholders also acknowledged that many wineries lack the training, readiness, or inclination to deliver structured visitor services, which constrains the potential for consistent destination branding. These gaps align with findings from strategic assessments elsewhere and underline the relevance of the resilience benchmarking framework proposed by Alebaki et al. (2020), which stresses flexibility, stakeholder coordination, and service innovation as prerequisites for long-term viability.

Institutional and organisational constraints further limit the sector’s development. Stakeholders underlined the lack of coordinated promotion, joint route development, and bundled offers that integrate wine with gastronomy and culture. Seasonal events such as the “Great Days of Nemea” generate short-term visitation peaks but fail to establish year-round flows, leaving the sector dependent on a narrow seasonal window. Both stakeholders and visitors highlighted the limited integration of wine tourism with Nemea’s archaeological and cultural heritage. Therefore, despite being home to Greece’s most prominent red wine and a well-established PDO, the region has not fully leveraged the coexistence of a strong viticultural identity with significant cultural landmarks, including the ancient site of Nemea with its sanctuary of Zeus and the Panhellenic games.

This underutilised synergy represents a strategic opportunity. Curated itineraries that combine winery visits with guided tours of archaeological sites, joint ticketing schemes, and co-branded events such as wine festivals in heritage venues could extend visitor stays and strengthen the perception of Nemea as both a cultural and viticultural landscape. Storytelling that links the symbolism of wine in ancient Greek rituals with modern PDO production could further enrich the visitor experience, deepening both emotional attachment and cultural appreciation. In practical terms, partnerships between wineries, cultural authorities, and heritage organisations could facilitate cross-promotion, resource sharing, and the design of multidimensional packages attractive to both domestic and international markets. Compared with established international wine destinations such as Tuscany or Bordeaux, Nemea lacks the governance structures, integrated branding, and international visibility needed to translate these opportunities into long-term development.

Parallel to this qualitative framework, the MCA and Cluster analyses provides further insights into how different visitor clusters contribute to the local economy. Cluster 3 (High-Spend Short-Stay Tourists) shows the highest overall economic engagement, with broad spending at the cellar door and ancillary services. Their trips are typically short and their revisit propensity appears weaker than the repeat-oriented segments, which limits wider spillovers beyond the winery. Their connection to the region appears transactional, as they engage mainly with the wine rather than developing a deeper cultural attachment. This highlights an important consideration: while Cluster 3 significantly supports winery income, their effect on year-round local development (employment, hospitality sector viability, off-season activity) remains constrained. Strategies aimed at deepening their connection to the destination, such as exclusive events, branded experiences, or return incentives, could increase their contribution beyond the cellar door.

In contrast, Cluster 1 (Local Day-Trippers) consists of repeat, well-educated visitors who are loyal to the destination. They tend to make short visits with modest overall spend and a food-leaning orientation, which limits their per-visit impact but supports awareness and events. They are also more likely to promote the destination informally through word-of-mouth and social circles. Although this group may not drive immediate revenue, their long-term value lies in maintaining a stable base of visitors who keep local businesses survive in off-peak periods and act as cultural ambassadors for the region. They may also participate in community-based development, such as volunteering or returning for educational events, making their role important for local engagement.

Cluster 4 (Curious, Educated Explorers) is composed largely of younger, first-time visitors with moderate spending power. Despite their lower incomes, they exhibit notably high engagement with local gastronomy and overnight stays, indicating a broader footprint across the local economy. Their openness to experiences and longer staying, position them as high-potential contributors to local development, particularly through the food service, accommodation, and experiential tourism sectors. Investing in the “experience economy” (e.g., wine-food pairings, local tours, workshops) tailored to this group could convert them into loyal return visitors and encourage greater integration into the local tourism ecosystem. Over time, they could become a foundation for sustainable growth, particularly if linked to affordable tourism products and youth-focused branding.

Cluster 5 (International Premium Tourists) includes the most socioeconomically advantaged visitors, marked by high levels of education and income, with a spending pattern that remains more winery-focused than gastronomy- or stay-led. Yet, they show limited attachment to the region and low familiarity with its wines, suggesting that their visits may be accidental, event-driven, or opportunistic. This group represents an untapped opportunity for high-value engagement. If converted through personalised storytelling, cultural interpretation, and premium offers, they could support boutique lodging, fine dining, and export-oriented wine purchasing. Their spending capacity has clear implications for economic diversification, supporting upscale services and job creation. However, without strategic targeting, their impact will remain sporadic and peripheral to local development. Leveraging this group requires more deliberate brand-building and integration with high-end tourism circuits, possibly through international partnerships or cross-marketing with heritage tourism.

Cluster 2 (Repeat Mid-Spenders) represents moderate economic engagement but demonstrates valuable behavioural patterns: repeat visits, consistent use of local accommodation and a product-focused spending pattern centred on cellar-door purchases while also presents high interest in winery-based experiences. These traits make them ideal candidates for loyalty-building programmes, wine club enrolment, and off-season travel incentives. Their steady presence supports multiple local sectors (e.g., lodging, gastronomy, retail) and may contribute to employment stability and income retention for small businesses. From a development perspective, Cluster 2 can serve as a core group sustaining year-round tourism activity, reducing the sector’s dependence on seasonal events.

Taken together, the segmentation confirms that wine tourism’s economic contribution in Nemea does not strictly correlate with visit frequency or familiarity. Clusters 3 and 5 are high spenders, but not necessarily loyal; Clusters 1 and 2 offer stability and brand reinforcement, but differ in financial impact; and Cluster 4 presents long-term potential through youth engagement and economic integration. These dynamics underline the importance of designing differentiated destination strategies that follows established guidance that destination development should be segment-driven and network-coordinated, leveraging wine routes to integrate wineries with gastronomy, hospitality, and cultural assets (Dolnicar, 2020; Tkaczynski et al., 2009; Croce & Perri, 2017; López-Guzmán et al., 2011).

To translate the cluster typology into implementable actions, the proposals are aligned with segmentation-led destination planning and wine-route literature. Prior work shows that segment-specific interventions and route coordination improve market fit and local spillovers (Dolnicar, 2020; Tkaczynski et al., 2009; Croce & Perri, 2017; López-Guzmán et al., 2011; OECD, 2014). In addition, evidence from Spain indicates that coordinated wine-route strategies strengthen regional growth and cross-sector linkages (Vazquez Vicente et al., 2021; ACEVIN, 2024), while sustainability frameworks emphasise designing offers that connect wine, gastronomy, culture, and place identity (UNWTO, 2016; Montella, 2017; Martínez-Falcó et al., 2024). Within this evidence base, our cluster-specific strategies are:

• Clusters 3 and 5 (high-spend, low-loyalty): premium, export-friendly programmes (private tastings, cellar allocations, limited releases), and curated cultural add-ons to deepen attachment beyond the cellar door (Croce & Perri, 2017; UNWTO, 2016; López-Guzmán et al., 2014).
• Cluster 2 (repeat mid-spenders): loyalty schemes and route-based passes that bundle accommodation, tastings, and gastronomy to drive off-season demand (Dolnicar, 2020; Vazquez Vicente et al., 2021; ACEVIN, 2024).
• Cluster 4 (younger, experience-oriented): affordable experiential products (food-wine pairings, workshops, short trails) that enhance learning and local engagement (UNWTO, 2016; Montella, 2017; Martínez-Falcó et al., 2024).
• Cluster 1 (local day-trippers): event-led programming and urban-amenity upgrades to sustain frequent short visits and strengthen place branding (OECD, 2014; Baggio, 2008; Alebaki et al., 2020).

These actions are consistent with Greek evidence on wine route networking and supply chain integration (Tzimitra-Kalogianni et al., 1999; Alebaki & Ioannides, 2017; Anastasiadis & Alebaki, 2021) as well as Spanish experience, where coordinated wine route management has generated measurable economic benefits (Vazquez Vicente et al., 2021; ACEVIN, 2024). Sustainability frameworks further emphasise designing experiences that link wine with heritage and local identity to enhance both visitor value and community benefits (UNWTO, 2016; Montella, 2017; Martínez-Falcó et al., 2024). By recognising these distinctions and aligning wine tourism development efforts accordingly, local actors in Nemea can amplify the sector’s contribution to inclusive, sustainable local development, spanning employment, entrepreneurship, infrastructure use, and cultural vitality.

Survey responses also revealed that average daily expenditure per visitor remained relatively modest, typically below €50, with spending distributed across wineries, restaurants, retail, and accommodation. While this supports a range of local enterprises, it falls short of the levels observed in other destinations. Prior Greek studies confirm that wine tourism has the potential to activate wider local value chains (Tzimitra-Kalogianni et al., 1999), and recent research on the Greek wine supply chain underscores the interdependence between wineries, hotels, gastronomy, and cultural services in co-delivering the tourism experience (Anastasiadis & Alebaki, 2021). The evidence suggests that wine tourism in Nemea is embedded within a broader regional economic system rather than operating as an isolated activity.

Despite high visitor satisfaction, which is consistent across both survey responses and online reviews, challenges in infrastructure, training, and coordination remain major barriers to competitiveness. Visitors valued the natural setting, hospitality, and perceived authenticity, all of which are known drivers of competitiveness and loyalty in rural wine tourism (Martínez-Falcó et al., 2024). Nevertheless, the sector remains fragmented, seasonal, and underdeveloped, with significant scope for professionalisation and capacity building.

Importantly, the patterns identified through the survey and statistical analysis were consistent with the qualitative insights from stakeholder interviews and online reviews. No contradictions emerged across methods; instead, the triangulation of evidence confirmed overlapping strengths such as the PDO identity, gastronomy potential, and hospitality, alongside weaknesses including infrastructure gaps, seasonal dependency, and fragmented coordination.

5. Conclusions

This study examined the contribution of wine tourism to local development in Nemea, Greece, using a mixed-methods design that combined stakeholder interviews and a visitor survey, with online reviews used for triangulation. The findings confirm that wine tourism already supports the local economy through visitor spending, spillovers to gastronomy, hospitality and retail, and reinforcement of regional branding. However, its potential remains underutilised due to fragmented promotion, infrastructural gaps and uneven service provision.

Five visitor profiles were identified with different economic footprints and levels of engagement. High-spending visitors primarily support wineries, while younger and repeat domestic visitors spread expenditure more widely across sectors. In practice, developmental impact depends not only on how much visitors spend but on how and where they engage with the destination. To maximise that impact, destination strategy should be differentiated by cluster, from cultivating loyalty among repeat domestic visitors to curating premium offers for high-spending but less engaged segments. Unlocking this potential also requires tackling structural constraints: improving infrastructure and wayfinding, raising skills through professional training and shared service standards, and strengthening coordination among wineries, local authorities and tourism operators.

Re-examining Nemea against international benchmarks such as Tuscany, Bordeaux and Rioja provides context. Like these mature destinations, Nemea shows clear segmentation in which high-spending tourists coexist with loyalty-driven repeat visitors, each offering distinct economic and cultural value. Unlike its international counterparts, Nemea still faces capacity gaps and limited coordination that hinder the translation of visitor diversity into sustained local development. Aligning segmentation-led policies with investment in infrastructure, professionalisation and cultural integration can move Nemea towards more resilient, year-round models.

Future research should monitor seasonal variation in visitor behaviour and test the effectiveness of targeted interventions. By aligning wine-tourism strategy with visitor segmentation, Nemea can enhance inclusivity, extend stays, and generate broader local benefits.