II. Theoretical background

The WTP for wine has been the subject of extensive research in several countries over the last 30 years. The literature has revealed a series of determinants that influence WTP, using econometric models based on Lancaster's (Reference Lancaster1966) theory of consumer behavior, which emphasizes the importance of product characteristics. Generally, WTP is assumed to be influenced by intrinsic and extrinsic wine cues, which consumers use according to experiential and psychological factors, such as wine knowledge (Dodd et al., Reference Dodd, Laverie, Wilcox and Duhan2005) and involvement (Cox, Reference Cox2009), as well as socioeconomic and demographic variables, including income, age, education, and socioeconomic status (Elliot and Barth, Reference Elliot and Barth2012; Lange et al., Reference Lange, Martin, Chabanet, Combris and Issanchou2002; Lerro et al., Reference Lerro, Vecchio, Nazzaro and Pomarici2020; Skuras and Vakrou, Reference Skuras and Vakrou2002). Outreville and Le Fur (Reference Outreville and Le Fur2020) present a descriptive review of empirical studies on the determinants of wine price during the 1993–2018 period. In the same line, Le Fur et al. (Reference Le Fur, Thelisson and Guyottot2023) include a recent and detailed literature review and a bibliometric analysis of academic research on wine prices in economics, covering 180 articles published in journals between 1992 and 2022. Both literature reviews provide useful knowledge on price predictors and the methodologies that have been used, highlighting the role of the theory-driven paradigm and the associated econometric estimation of hedonic price functions.

Early studies usually employed hedonic pricing models to analyze the formation of wine prices (Combris et al., Reference Combris, Lecocq and Visser1997; e.g., Oczkowski, Reference Oczkowski1994), evaluating the price premium associated with specific characteristics. More recent econometric efforts have expanded their framework to include experimental auctions (Vecchio, Reference Vecchio2013) and discrete choice experiments (D'Alessandro and Pecotich, Reference D'Alessandro and Pecotich2013; Gonçalves et al., Reference Gonçalves, Lourenço-Gomes and Pinto2020; Palma et al., Reference Palma, Ortúzar, Rizzi, Guevara, Casaubon and Ma2016), offering a deeper understanding of consumer preferences and their impact on WTP.

The results of previous studies obtained for sparkling wines suggest distinct preferences and WTP for appellations, Prosecco in this case (Onofri et al., Reference Onofri, Boatto and Bianco2015; Rossetto and Gastaldello, Reference Rossetto and Gastaldello2018; e.g., Thiene et al., Reference Thiene, Scarpa, Galletto and Boatto2013), brands (Vecchio et al., Reference Vecchio, Lisanti, Caracciolo, Cembalo, Gambuti, Moio, Siani, Marotta, Nazzaro and Piombino2019), and loyalty (Bassi et al., Reference Bassi, Pennoni and Rossetto2021). Culbert et al. (Reference Culbert, Ristic, Ovington, Saliba and Wilkinson2017) reveal that the production method influences the sensory profile of Australian sparkling white wine styles, and the Charmat is the preferred method. In turn, Lange et al. (Reference Lange, Martin, Chabanet, Combris and Issanchou2002) compare two mechanisms, the hedonic test and the Vickrey auction, to reveal consumers’ WTP for Champagne, concluding that, in general, participants are willing to pay more for wines from big brands, and older consumers are willing to pay higher prices than younger people for reserve wines. Pickering et al. (Reference Pickering, Duben and Kemp2022) focused on the effect of label information, compare the evaluated WTP and quality perception in different information scenarios for a set of two simulated sparkling wine labels (Champagne and Prosecco). They conclude that those who consider themselves more knowledgeable about sparkling wines are willing to pay more for the Prosecco wine style and that WTP increases with the amount typically paid for both wine styles. Researchers have also found that when sparkling wine is purchased for a special occasion (e.g., a celebration), consumers are often willing to spend more (Morton et al., Reference Morton, Rivers and Healy2004; Velikova et al., Reference Velikova, Charters, Fountain, Ritchie, Fish and Dodd2016). Verdonk et al. (Reference Verdonk, Wilkinson, Culbert, Ristic, Pearce and Wilkinson2017) point out that when consumers buy wine as a gift, they are more willing to purchase expensive, prestigious brands of sparkling wine, including Champagne.

In parallel with advances in econometric models, the development of data science and ML, well described in the literature, has opened new avenues for the analysis of wine WTP. ML techniques, known for their ability to deal with extensive data sets and discover nonlinear relationships, have demonstrated high potential in the wine industry. For example, some studies have applied various ML algorithms to predict wine quality (Jain et al., Reference Jain, Kaushik, Gupta, Mahajan and Kadry2023), taste preferences (Cortez et al., Reference Cortez, Cerdeira, Almeida, Matos and Reis2009), wine price (Niklas and Rinke, Reference Niklas and Rinke2020), and indirectly shedding light on WTP.

In summary, the literature review allowed us to conclude that the main predictors of WTP can be organized into four groups of variables: (i) socioeconomic characteristics (gender, age, education, marital status, place of residence, income); (ii) personal and behavioral aspects (consumer knowledge, who buys, motives of purchase, knowledge of production method, consumption frequency, consumption of other beverages); (iii) collective (region of origin, collective brand) and individual (brands and awards) reputations; and (iv) attributes of the sparkling wine (e.g., category, sweetness, production method, and organic production).

III. Data and methods

IV Methods

a Ordered probit model

Taking into account the nature of the dependent variable the price range or the WTP for sparkling wine ordered by classes, an econometric ordered probit model is estimated. Under this model, there is a latent continuous metric underlying the ordinal responses, being the latent continuous dependent variable ${y_i}^*$ determined by a vector of explanatory variables (x_i) and a disturbance term (${\varepsilon _i}$). Therefore, the ordered probit regression method allows modelling of the ordered outcome based on $J$ categories (in our case four categories) of WTP, as a linear function of the observed vector of x_i. The latent regression is specified as follows:

(1)\begin{equation}{y_i}^* = \beta {^{\prime}}{x_i} + {\varepsilon _i},{ }{\varepsilon _i}\sim N \left( {0,1} \right),\forall i = 1, \ldots ,N.\end{equation}

where i is the $N{\text{th}}$ observation, ${y^*}$ is the unobserved $N \times 1$ dependent variable, ${\beta}^{'}$ is the vector of $K \times 1$ estimated parameters, $x$ ($N \times K$) are the covariates (predictors) assumed to be independent of $\varepsilon $, and $\varepsilon { }\left( {N \times 1} \right)$ is the error term including unobservable factors. The probabilities underlying this model are given by

\begin{equation*}Prob\left[\,{y = 0} \right] = {{\Phi }}\left( { - \beta 'x} \right)\end{equation*}

\begin{equation*}Prob\left[\,{y = 1} \right] = {{\Phi }}\left( {{\mu _1} - \beta 'x} \right) - {{\Phi }}\left( { - \beta 'x} \right)\end{equation*}

\begin{equation*}Prob\left[\,{y = 2} \right] = {{\Phi }}\left( {{\mu _2} - \beta 'x} \right) - {{\Phi }}\left( {{\mu _1} - \beta 'x} \right)\end{equation*}

\begin{equation*} \ldots \end{equation*}

\begin{equation*}Prob\left[\,{y = J} \right] = 1 - {{\Phi }}\left( {{{\mu} _{J-1}} - \beta 'x} \right)\end{equation*}

where Φ(●) stands for the cumulative distribution function, and ${\mu _{\text{J}}},{ }j = 1, \ldots ,K,$ are the unknown threshold parameters, between which the categorical responses are estimated. The model estimation through the likelihood function is based on the implied probabilities.

V. Results and discussion

The results of an ordered probit model with ordered price intervals (in euro) as the dependent variable, which is a proxy for WTP for a bottle of a sparkling wine, include the ordered list of the 34 covariates or predictors, their respective regression coefficients and standard errors (Table 1 and Table A2, Appendix A). It is important to highlight that the null hypothesis of errors normally distributed is observed, the value of the likelihood ratio test also confirms that the parameters are globally significant at a 1% level and the ratio of correctly predicted cases (AR) is 45% (Table 1). Based on the decreasing level of significance (1%, 5%, and 10%), the 15 predictors statistically significant of the ordered probit WTP (Table 1) are the Champagne brand, income, importance of awards (ImpDCE2), being the one who buys or purchases the product, the importance given by the respondent to a set of variables related to organic production (ImpDCE6), production method (ImpDCE5), and sweetness (ImpDCE4), to be a consumer of red wine, to buy sparkling wine as a gift, the importance of the brand, gender, local of residence, region of production and not being protected designation of origin (PDO).

Table 1. Top 15 features selected using the algorithm mRMR, ${\chi ^2}$, ReliefF, ANOVA, Kruskal–Wallis (KW) Test and InforGain

AR = accuracy ratio defined as the ratio of cases correctly predicted

For the sake of simplicity, only the results of five FRAs that present the highest AR among the ML algorithms available in CL and Orange (Table 1) will be presented, although it is important to note that the results of different FRAs are very similar. For example, in CL, the results obtained with ${\chi ^2}$, ANOVA and KW are very alike. The features ranked by ANOVA and KW in the first eleventh positions are the same and sorted in the same order. Three of the last four features selected by these two algorithms are also the same, although they are ordered differently. Features selected with ${\chi ^2}$ are also similar and only differ in the order between some pairs of successive features. The other algorithms (ReliefF, mRMR, InforGain) selected only some of the same features, not always in the same order. However, approximately the same set of features tend to be selected for the top seven positions, namely Income, Champagne, NotPDO, ClassTrad, ImpBrand, BuySparkling, and Tavora. The features selected with the other FRAs of Orange are relatively similar. For example, the set of features in the top seven positions is very similar, except for BuySprakling and Tavora, which are only selected once. On the other hand, BuyGift, ImpDCE1, and ImpDCE3 are selected twice by the Orange FRA. The IngorGain and Gini select the same variables but in slightly different order. The other three used FRAs present some similarities in the common features selected.

Regarding the performance of classification models, the AR is slightly higher for the ordered probit model (45%) than for the ML methods, which ranges between 41% and 43%. AR is only slightly higher for models available in Orange than in CL. In the case of CL FRA, the classifiers with the highest performance are the linear discriminant analysis model for ${\chi ^2}$ and KW, weighted KNN (nearest neighbor classifier) for ReliefF, Ensemble Subspace Discriminant (ensemble classifier) for mRMR, Kernel Naïve Bayes for ANOVA. For Orange, the SVM provides the highest AR for ${\chi ^2}$, Neural Networks for InofrGain e Gini, and Naïve Bayes for gain ratio and ReliefF.

Since, for ${\chi ^2}$, ANOVA and KW, the scores correspond to—log(p), it is important to mention that for these FRA the number of features with p-values below 5% is 11 for ANOVA, 12 for ${\chi ^2}$ and 14 for KW. However, classification models were calibrated with several predictors ranging between 12 and 15 with very similar AR. These results suggest that the inclusion of additional, but less important predictors does not lead to better-performing models. Results obtained with ML methods suggest a tendency toward similarity in the main predictors/features selected by the different methods. This finding is likely a consequence of the fact that the initial features (predictors) in the ML approach are the same as the covariates in the probit model, the inclusion of which is supported by previous studies.

In general, the findings highlight the relevance of the same variables, namely income, the Champagne brand, not being PDO, the traditional/classic production method and the importance of the brand. Additionally, the ordered probit stresses the significance of the six variables that express the personal importance of the respondents given to a set of variables related to production region, awards, categories, sweetness, production method, and organic. Although all ML models confirm the importance of income, Champagne and ClasTrad, the evidence reported for the other variables is different, which draws attention to the need to choose the best method based on a given performance measure.

The results are in line with the findings of previous studies. The brand, Champagne appellation and income are strong determinants of Portuguese WTP for sparkling wine (e.g., Pickering et al., Reference Pickering, Duben and Kemp2022). The greatest importance of these three characteristics for modeling WTP was evidenced with the ordered probit model, four of the five MATLAB classifiers and one Orange model, although two other Orange classifiers select champagne and income in the first three positions. Individuals are willing to pay more for big brands, confirming that Champagne has a strong collective reputation as an indicator of status (Combris et al., Reference Combris, Lange and Issanchou2006; Dal Bianco et al., Reference Dal Bianco, Boatto, Trestini and Caracciolo2018; e.g., Lange et al., Reference Lange, Martin, Chabanet, Combris and Issanchou2002; Pickering et al., Reference Pickering, Duben and Kemp2022; Verdonk et al., Reference Verdonk, Wilkinson, Culbert, Ristic, Pearce and Wilkinson2017). Pickering et al. (Reference Pickering, Duben and Kemp2022) compared Champagne and Prosecco wine style labels and found that respondents with higher incomes are willing to pay more for both sparkling wine styles than their counterparts. In the same line, as expected, the WTP for sparkling wine is influenced by the absence of a PDO, suggesting that the terroir collective reputation that comes from the designation of origin, affects the WTP for sparkling wine.

Additionally, there seems to exist some differentiation between male and female consumers, in line with findings from previous studies. Female consumers are found to consume significantly more sparkling wine than men. Women are slightly more willing to pay for sparkling wine than men, which could reflect the perception that sparkling wine is “feminine” or a “women's drink,” possibly due to its connotations of glamor and romanticism (Bruwer and McCutcheon, Reference Bruwer and McCutcheon2017; Stephen Charters, Reference Charters2005). Furthermore, women tend to be the main shoppers in their households, which may also explain differences in the WTP (e.g., Marshall and Anderson, Reference Marshall and Anderson2000). Our results show that being responsible for purchasing wine increases the WTP of sparkling wine because it enhances the involvement with wine, which is related to the amount typically spent on a bottle of wine, as spending consumers are the most involved with wine in general (Thach and Olsen, Reference Thach and Olsen2015).

A relationship is also observed between the consumption of sparkling wine and the consumption habits of other drinks, in the sense that red wine consumers seem to be positively correlated to the WTP of sparkling wine. This relationship may be associated with the strong presence of a traditional consumption model, in which families buy more wine, especially still red wine, to consume with meals (Dal Bianco et al., Reference Dal Bianco, Boatto, Trestini and Caracciolo2018). In this sense, there is a complementary effect between the consumption of red wine with meals and the WTP for sparkling wine for consumption outside meals.

In line with other studies (e.g., Stephen Charters, Reference Charters2005; Steve Charters et al., Reference Charters, Velikova, Ritchie, Fountain, Thach, Dodd, Fish, Herbst and Terblanche2011, for still wine), our results suggest that sparkling wine is perceived as a separate product type from other beverages (e.g. still white wine, beer, soft drinks, spirits, and sangria). One reason for this result may be that households that consume significant quantities of wine tend to purchase cheaper products, and sensitivity to branded prices is not a significant determinant of their WTP for sparkling wines (Dal Bianco et al., Reference Dal Bianco, Boatto, Trestini and Caracciolo2018). As demonstrated previously, the present study also confirms that consumers are usually willing to spend more on sparkling wines purchased for special occasions, such as festive events/seasons and offers/gifts, which attest to the importance of the purchasing context (e.g., Morton et al., Reference Morton, Rivers and Healy2004; Velikova et al., Reference Velikova, Charters, Fountain, Ritchie, Fish and Dodd2016; Verdonk et al., Reference Verdonk, Wilkinson, Culbert, Ristic, Pearce and Wilkinson2017).

Finally, broad consistency was observed for personal preferences underlying the choice in the decision-making process and WTP highlighting the importance of wine cues (e.g., ImpDCE1 to ImpDCE6) (e.g., Ferreira et al., Reference Ferreira, Costa Pinto and Lourenço-Gomes2021); production region (e.g., Verdonk et al., Reference Verdonk, Wilkinson, Culbert, Ristic, Pearce and Wilkinson2017); awards or reputation; categories; sweetness (e.g., Combris et al., Reference Combris, Lange and Issanchou2006; Verdonk et al., Reference Verdonk, Wilkinson, Culbert, Ristic, Pearce and Wilkinson2017); production method (e.g., Culbert et al., Reference Culbert, Ristic, Ovington, Saliba and Wilkinson2017); and being organic (e.g., Schäufele and Hamm, Reference Schäufele and Hamm2017). This outcome reinforces the importance of the characteristics revealed to the consumer (on the bottle and label), to explain differences in the consumers’ price (e.g., Combris et al., Reference Combris, Lange and Issanchou2006; Lecocq and Visser, Reference Lecocq and Visser2006).

VI. Conclusion

This study analyzes consumers’ WTP for sparkling wine in Portugal. Research on this topic has typically been backed by a theoretical model grounded in utility theory, employing econometric estimation methods tailored to the data structure and study objectives. This study deepens the analysis by comparing the results of a traditional model used to understand the determinants of sparkling wine WTP (ordered probit model) of Portuguese consumers, with the evidence produced by recently emerging alternative methods rooted in ML algorithms.

The results suggest that there is no absolute supremacy of any of the approaches in terms of global performance, although the ordered probit model presents a slightly better performance in the accuracy rate of correctly predicted cases. The two approaches tend to select the same main predictors, highlighting the relevance of the income variable, the Champagne brand, not being a PDO and being a red wine consumer as the main predictors of WTP for sparkling wine in Portugal. However, the advances suggested by ML are quite variable depending on the algorithm and platforms used, which draws attention to the need to choose the best method based on another specific performance measure. In this sense, it should be pointed out that ML classification models are characterized by being parameterizable algorithms. Thus, although a large number of methods were used in this study, this number could have been much higher if other options/parameterizations had been chosen. On the other hand, although the use of ML tools does not require prior knowledge of the relationship to be modelled, it benefits from knowledge of the characteristics of each algorithm and the relationships to be modelled.

This paper contributes to consolidating knowledge on the modelling of consumer behavior and provides useful information for wineries’ marketing strategies. Specifically, the results indicate that to increase sparkling wine sales at a higher price, wineries should segment the market according to income, focusing on higher-income niches that are also red wine consumers. At the same time, they should follow the Champagne strategy as a benchmark, create dynamics of collective and individual reputation, reinforce and benefit from the PDO, boost wine routes, carry out visits and tastings in the vineyard and in the cellar, participating in competitions and tastings, and developing cooperative actions with hotels and restaurants that value the sparkling wine. Moreover, since the ordering of WTP predictors changes with the used method, a detailed analysis and weighting of the different ordering, i.e., a quantified sensibility analysis, is recommended for the robustness of the winery’s marketing plan outlined to a target market.

The authors are aware that the used methods and, consequently, the obtained results of this study can be extended, for instance, by applying and integrating the two analytical paradigms in the choice modelling perspective, as highlighted by van Cranenburgh et al. (Reference van Cranenburgh, Wang, Vij, Pereira and Walker2022). Moreover, in this study, the departing features of the ML methods are the same as the covariates (predictors) of the ordered probit model, which is supported by previous studies, remaining the research question as to whether ML methods are not especially suited for unstructured big data with limited knowledge about the influence on WTP. In this way, it is suggested as a research trend to apply ML techniques to data from digital platforms such as Google Trends and Vivino.