Wine Economics and Policy 10(2): 75-86, 2021 Firenze University Press www.fupress.com/wep ISSN 2212-9774 (online) | ISSN 2213-3968 (print) | DOI: 10.36253/wep-10382 Wine Economics and Policy Citation: Adeline Alonso Ugaglia, Britta Niklas, Wolfram Rinke, Dan Mosco- vici, Jeff Gow, Lionel Valenzuela, Radu Mihailescu (2021) Consumer preferences for certified wines in France: A com- parison of sustainable labels. Wine Economics and Policy 10(2): 75-86. doi: 10.36253/wep-10382 Copyright: © 2021 Adeline Alonso Uga- glia, Britta Niklas, Wolfram Rinke, Dan Moscovici, Jeff Gow, Lionel Valenzue- la, Radu Mihailescu. This is an open access, peer-reviewed article published by Firenze University Press (http:// www.fupress.com/wep) and distributed under the terms of the Creative Com- mons Attribution License, which per- mits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Consumer preferences for certified wines in France: A comparison of sustainable labels Adeline Alonso Ugaglia1,*, Britta Niklas2, Wolfram Rinke3, Dan Mos- covici4, Jeff Gow5, Lionel Valenzuela6, Radu Mihailescu7 1Bordeaux Sciences Agro, French National Institute for Agriculture, Food and Environ- ment, Umr Save ISVV, Gradignan, France. Email: adeline.ugaglia@agro-bordeaux.fr 2Ruhr-Universität Bochum, Institute of Development Research and Development Policy, Bochum, Germany. Email: britta.niklas@ruhr-uni-bochum.de 3Fachhochschule Burgenland GmbH, Eisenstadt, Austria. Email: Wolfram.Rinke@fh-bur- genland.at 4School of Natural Sciences & Mathematics, Stockton University, USA. Email: Daniel. Moscovici@stockton.edu 5University of Southern Queensland, Toowoomba, Australia and Stellenbosch University, Stellenbosch, South Africa. Email: Jeffrey.Gow@usq.edu.au 6Universidad Técnica Federico, Santa Maria, Chile. Email: lionel.valenzuela@usm.cl 7Stenden Hotel Management School, Leeuwarden, The Netherlands. Email: radu. mihailescu@nhlstenden.com *Corresponding author. Abstract. The wine industry has faced various environmental and social challenges. On the demand side, consumer demand for sustainable wines has been increasing but, to date, it is unknown whether consumers perceive wine companies’ efforts to obtain sus- tainable development (SD) certifications and labels as being valuable or how they dif- ferentiate them. On the supply side, sustainable wine production is increasing but pro- ducers report a lack of information to engage and select their SD strategy. This article uses a logistic regression and an artificial neural network model to show how French consumers differentiate and value different SD labels (Organic, Biodynamic, Sustain- able, Fairtrade, Natural). Results show that consumers’ willingness to buy and willing- ness to pay are influenced by the importance each consumer gives to the certification. For all other drivers, consumers differentiate between labels, highlighting the impor- tance of comparison between and knowledge about each of them, thereby aiding pro- ducers in choosing an appropriate marketing strategy. Keywords: consumer preferences, stated preferences, wine, certified wines. 1. INTRODUCTION The French wine industry dates back to ancient times and holds an important place in the French economy, representing the 2nd largest net trade surplus and creating numerous jobs in rural regions (Alonso Ugaglia et al., 2019; Cardebat, 2017; Porter and Takeuchi, 2013). French wines have an 76 Adeline Alonso Ugaglia et al. excellent reputation, based mainly on appellation regu- lations (Protected Designation of Origin [PDOs] and Protected Geographical Indications [PGIs]), but since the 1970’s the production and consumption of wine have been experiencing a long-term decline in France. At the same time, the wine industry has been facing a number of environmental1 and social challenges (Delmas et al., 2008) in the form of the growth of societal demands for more environmentally-friendly and ethical practices in the vineyards and the cellars. Sustainable development (SD) certification has, thus, became a critical success fac- tor (Sampedro, 2010), turning the wine industry into a ‘green business’ (Silverman et al., 2005). On the demand side, consumers are increasingly demanding to know what inputs are used in food pro- duction and processing, to know producers’ labor stand- ards, and to understand the environmental impacts of production (Paloviita, 2010; Pullman et al., 2009; Trienekens et al., 2012). Additionally, the wine industry is under considerable pressure from regulators to evalu- ate, reduce, and report its environmental and social impacts (Christ and Burritt, 2013), and to incorporate sustainability into its management practices. The attrib- utes of a wine, however, whether ethical, social or envi- ronmental, are not verifiable by consumers before pur- chase, or even after purchase and consumption in the case of sustainability attributes. Producers, therefore, must adopt a symbol on the bottle to solve this asym- metric information attribute. This symbol, SD certifi- cation or label, attests the compliance of the wine with a certain norm or a standard (Hoberika et al., 2013). It informs consumers and differentiates a wine from other wines (Giraud-Héraud and Hoffman, 2010). Until now, however, it has not been known whether consumers perceive wine companies’ efforts to obtain SD certifications and labels as being valuable (Barber et al., 2010) or how they differentiate between the various SD labels. In practice, the diffusion of such labels is still limited (Delmas and Gergaud, 2021). The way the con- sumers perceive the labels is therefore still an issue of discussion (Ashenfelter et al., 2018). On the supply side, the wine sector has seen the emergence of specific eco-certification schemes and labelling programs, including SD specifications, in response to this demand (Sogari et al., 2016), leading to a proliferation of voluntary and institutional social and environmental certification systems (McEwan and Bek, 2009). In this sense, we observe the development of mul- tiple SD labels such as Biodynamic, Fairtrade, Natural, 1 Water quality and use of chemicals, air pollution, soil erosion, waste, and land use, among others (Chris and Burritt, 2013). and Sustainable (Moscovici and Reed, 2018; Moscovici et al., 2020), corresponding to different definitions of what a sustainable wine can be. Sustainable wine production has also been increasing, not only for marketing purpos- es, but also because of wine producers’ personal convic- tions (Alonso Ugaglia et al., 2017). However, producers in many countries associate ‘sustainability’ mainly with the environmental dimension and sometimes confuse the different terms and SD labels (Szolnoki, 2013). They complain about the lack of information about SD wine labels and the associated potential added value. One option for producers to choose the best certification for their wines considering the many choices could be to know more about consumers’ preferences for SD certi- fied wines and how these drive preference-based pur- chasing decisions (Poelmans and Rousseau, 2017; Tozer et al., 2015), as consumer perception is indeed an impor- tant issue to take into consideration when making busi- ness decisions (Lockshin and Corsi, 2012; Mariani and Vastola, 2015). This paper contributes to the growing literature on consumers’ valuation of SD certified wines. In compari- son with studies that deal with the general interest of consumers for SD wines (Casini et al., 2009; Schimmenti et al., 2016; Vecchio, 2013), sometimes without defining what they call ‘sustainable’ (Lanfranchi et al., 2019), this paper analyses whether consumers differ in their prefer- ences for various SD labels and certifications and why and how they value them. The paper is organized into six sections: Section 2 provides a literature review; Section 3 presents the sur- vey and the data; Section 4 explains the methodology; Section 5 includes the descriptive results from the logis- tic regression and the artificial neural networks model; and Section 6 discusses the results and draws conclu- sions. 2. LITERATURE REVIEW 2.1 SD labels and certifications Eco-labels signal to the consumer that the wine is an eco-friendly product (Delmas and Grant, 2010), with organic wines being the most discussed SD wines at present. The production and sale of organic wines has experienced a boom in recent years due to the pressure of consumer demand for environmentally-friendly agri- cultural products, the expectations of producers (health considerations), conversion subsidies, and the attrac- tiveness of the market (OIV, 2017). Organic viticulture represented 12% of French vineyards in 2018, with pros- pects for further growth in 2019. France is ranked third 77Consumer preferences for certified wines in France: A comparison of sustainable labels in worldwide production of organic wines and is set to become the world’s leading consumer of organic wine by 2021, overtaking Germany and having doubled its con- sumption since 2013 (IWSR, 2019) while conventional wine is experiencing a downturn. But there are around 300 definitions of sustainability (Manderson, 2006) and what is considered to be a ‘sustainable’ wine can be interpreted in different ways. This paper focuses on five main SD wine labels and on the associated certifications and specifications (Figure 1). For ‘Natural’ wine, there is no official bottle label, but production in France is grow- ing, despite there being no official rules corresponding to this designation. 2.2 Stated preference approach The literature on consumers’ perceived value of SD certified wines mainly addresses the issue through wine pricing and willingness to pay (WTP). Wine is an expe- rience good, meaning that the consumer cannot gain any utility from the product until it is consumed (Nel- son, 1970 in Ashton, 2014). Consumers generally have limited knowledge of wine production and it may, there- fore, be difficult for them to decide upon a sustainable wine. Signals can help them to make purchasing deci- sions based on their preferences, to form quality expec- tations, and influence whether they will purchase the product again (Gabrielyan et al., 2014; Tozer et al., 2015). Representative signals are usually available on packag- ing, hence labels in the case of wine. Consumers inter- ested in buying certified wine actively seek appropriate options and are willing to pay a price premium for such preferences (Poelmans and Rousseau, 2017; Sellers-Rubio and Nicolau-Gonzalbez, 2016). From a theoretical standpoint, the estimation of the WTP is a stated preference approach, while ‘revealed preference’ approaches obtain data from obser ved behavior. Both approaches link the derived utility to the observed (revealed) or stated choice (Carson and Lou- viere, 2011). The choices in stated preference approaches are made by choosing between different options offered in the context of hypothetical situations, mostly asked within the framework of surveys or interviews that also facilitates the offering of attributes that are not currently on offer or not on offer at a certain (desired) level. The stated preference methods are the contingent valuation method (CVM), the conjoint analysis method, conjoint behavior, and the stated choice method (Freeman et al., 2014; Louviere, 1988; 2001; Louviere et al., 2000). The CVM is the most commonly used method, asking con- sumers whether or not they are willing to pay a specific price premium for certain attributes of a product, which enhance the utility of consuming the product (Baker Figure 1. SD wine labels (examples and specifications) (Source: authors). 78 Adeline Alonso Ugaglia et al. and Ruting, 2014; Mitchell and Carson, 1989; Mogas et al., 2002). The answer to the question is interpreted as the expression of each consumer’s value for the respec- tive attribute (Freeman et al., 2014). This is the chosen approach for exploring our research question in line with other papers for different products (Amato et al., 2017; Sellers-Rubio and Nicolau-Gonzalbez, 2016; Skuras and Vakrou, 2002; Vecchio, 2013; Vecchio and Annun- ziata, 2015). 2.3 SD wine consumption The extant literature focuses mostly on eco-labels dealing with environmental specifications (Cholette et al., 2005; Loureiro, 2003; Mueller and Remaud, 2013) and focuses especially on organic labels (Burgarolas et al., 2005; Schmit et al., 2013). Remaud et al. (2008) sug- gest that there is a tendency to pay a price premium for organic wine. Mihailescu (2015) corroborates these findings for tourists in South Africa who show a WTP a premium for organic wine. Additionally, Corsi et al. (2013) mention that the premium alters the impact of other variables on the wine price. Bazoche et al. (2008), analyzing consumers’ WTP for French wines with envi- ronmental specifications on the label (including organic wine), show that consumers are only willing to pay a (quite low) premium for organic wine. Gow et al. (2020) show that Australian consumers are willing to pay more for biodynamic wines, while in Italy there is a WTP a price premium for Natural wine (Galati et al., 2019). Vecchio (2013) shows that customers are willing to pay between 23% and 57% more than the average price for the attribute ‘sustainability’. In New Zealand, Forbes et al. (2009) find that consumers believe that the quality of sustainable wines is superior to that of conventional wines and are prepared to pay higher prices for them. For South African Fairtrade wine sold in the US, Niklas et al. (2017) find that the price premiums are negative. Some studies underline that consumers have a higher WTP when social attributes are combined with environ- mental ones (Mueller Loose and Remaud, 2013). Some studies find no premiums for SD wines (Barber et al., 2009; Gabzdylova et al., 2009; Vecchio, 2013) or even decreasing demand when SD wines are associated with lower quality (Sogari et al., 2006). Some studies also provide insights into characteris- tics of wine consumers who are willing to buy or even to pay more for SD labels. Results are controversial and it is difficult to identify global trends. The main consen- sus is that women and younger consumers in general are willing to pay more for sustainable wines (Gow et al., 2020; Lanfranchi et al., 2019; Moscovici et al., 2020; Vec- chio, 2013). McDonal et al. (2013) and Tach and Olsen (2006) underline that young consumers are interested in both environmental and social concerns related to wine. Having knowledge and information about SD labeling is also of importance to determine the WTP (Barber et al., 2009; Bazoche et al., 2008; Galati et al., 2019; Vec- chio, 2013). Some other determinants are marital status, with unmarried people willing to pay more, education level, income level, the likelihood of buying eco-certified goods, the price consumers usually pay for wine, the occasion related to the purchase, lifestyle, and the link to wine tourism (Barber et al., 2009; Bazoche et al., 2008; Burgarolas et al., 2005; Gow et al., 2020; Lanfranchi et al., 2019; Moscovici et al., 2020; Vecchio, 2013). Yet, the numerous SD wine labels have led to confu- sion for consumers and exacerbate the imperfect percep- tion of products (OIV, 2017). Marette (2004) shows that this is particularly true for eco-labels that complement brands in signaling green, lead-free, fair-trade, organic, no child labour, and/or low-cholesterol attributes. There are few articles that compare SD labels with convention- al wines. Moscovici et al. (2020) compare five SD cer- tifications for the North eastern United States and find no specific differences between them. It remains unclear how consumers respond to the different eco-labels and how they value different SD certifications and labels. It is this gap in the literature that the current study aims to close. 3. DATA: WINE CONSUMER SURVEY This research project gathered data through sur- veys established on the Qualtrics survey platform, which has been used to access wine consumers in the United States, Australia, Chile, France, Italy, the Netherlands, and South Africa (Moscovici and Reed, 2018; Valenzuela et al., 2019). The research sample was obtained through convenience sampling. Eligibility criteria for the selec- tion of respondents were that they were adults (18 years of age or older) who were habitual consumers of wine. Exclusion criteria included those who worked in the wine or hospitality industries. Within these interna- tional data, this paper analyzes the French data sample based on 239 completed questionnaires. The survey was divided into four sections. In the first section, we asked consumers about their back- grounds and habits with respect to wine knowledge and consumption. Questions included motivations for drinking, favorite varieties, purchasing behaviors, and self-evaluated wine knowledge. The second set of ques- tions collected perspectives and opinions on the various 79Consumer preferences for certified wines in France: A comparison of sustainable labels environmental wine certifications. Questions included which certifications they have heard of, whether they had purchased any type of certified wine, future willing- ness to purchase certified wine, ranking of certifications, interest in further information about certifications, and labelling. In the third section, we asked consumers whether they would be willing to buy a certified wine. If the answer was ‘yes’, we asked the (maximum) price they would be willing to pay by offering a large number of predetermined price brackets from which to choose (‘take it or leave it’ approach) which finally results in the estimation of the value consumers connect to attributes of a product (in our case the respective wine certifica- tions) (Kealy and Turner, 1993; Mihailescu and Hecht, 2015; Sellers-Rubio and Nicolau-Gonzalbez, 2016). Final- ly, the fourth set of questions collected demographic information such as country of residence, gender, age, income, education, and marital status. The surveys were disseminated in each country through wine newsletters and social networks, especially LinkedIn and WhatsApp. 4. METHODOLOGY: LOGIT MODELS & MACHINE LEARNING First, we provide a generic description of the sample and then analyze the data. We explain our dependent variable, ‘Willingness to buy a certified wine’ (WTB), for different types of labels (Organic, Biodynamic, Nature, Sustainable, Fairtrade) from a set of quantitative and qualitative explanatory variables, and the probabili- ties for the two alternatives of the WTB question (yes, no – coded as 1/0) are estimated applying a binary logit model as suggested in the literature (Hanemann, 1984; Mogas et al., 2002). Second, we explain the variable ‘Willingness to Pay’ (WTP). For the WTP question, there are no bina- ry responses, but respondents could decide between six WTP categories. Models with categorical dependent var- iables in the economic literature are predominantly esti- mated by applying multinominal logit models (Mogas et al., 2002), which belong to the parametric models. Dis- ciplines such as engineering or stock exchange trading have been applying machine learning, especially artifi- cial neural networks (ANN), as the core technology for these kind of models over the past two decades (Shav- lik and Diettrich, 1990; Stone et al., 2016). ANN belong to the group of non-parametric models and are able to also capture non-linear relationships between independ- ent variables and dependent variables. Studies applying this approach suggest that ANN outperform multinomi- nal logit models in their predictive potential (Hensher et al., 2000; Lee et al., 2018; Mohammadian and Miller, 2002; Tran et al., 2019) and possess higher capabilities to identify the (non-linear) relationships between depend- ent and independent variables. To explain WTP we are interested to find the dominating variables for each type of label and their average semi-elasticities2 related to WTP. Farsi (2007) shows that a non-linear WTP esti- mation has a higher accuracy (higher R square) than a linear estimation model. Therefore, we decided to apply a non-linear modelling technique for our WTP analy- sis and chose a machine learning model based on ANN, which allows to make use of the above-mentioned non- linear estimation advantages (Rinke, 2015)3. For each sustainable wine label, we use a separate fully interconnected feed forward ANN model, which consists of 18 nodes for the input layer representing all selected independent variables, five nodes for the hidden layer, and one node for the output layer which represents the dependent variable WTP. These ANN models are used to calculate the dependency factors (Rinke, 2015) and the average semi-elasticities for each label4. Depend- ency factors are sometimes called ‘average linear impor- tance factors’ and can be compared to significance lev- els of a normal OLS regression (Yeh and Cheng, 2010). The average semi-elasticities show the percentage change of the dependent variable (WTP in this case) according to a unit change in the respective independent variable (Owen, 2012). 5. RESULTS 5.1 Descriptive results Table 1 shows the descriptive statistics of the vari- ables which are used in the models below. The survey was answered by 54% women (128) and 46% men (111) and the age of the respondents was grouped into 7 cat- egories with an average age of 37.7 years for women and 43.2 years for men. The annual household income was grouped into 11 categories with the majority of respond- ents being in the income group 35,000 to <50,000  € annually and an average annual income of 44,906  €. The majority of respondents (42.26%) had a Master’s or equivalent degree, followed by those with a Bachelor’s 2 Average semi-elasticities are derived from the sensitivity analysis of the ANN model according to Hashem (1992), Yeh and Cheng (2010) and Owen (2012). 3 For a more detailed explanation of the approach, please refer to Hornik (1990), Rumelhart (1986) or Witten (2017). 4 For a more detailed description of the approach, please see Niklas and Rinke (2020). 80 Adeline Alonso Ugaglia et al. degree (16.32%) and those with a High school certificate (13.81%). On average, the respondents usually pay 8.99  € per bottle of wine, spend about 724  € per year on wine, and visit a winery 3.64 times per year. With regard to preferences, respondents articulate a high importance of the Place of Origin (5.78 on a 1-7 scale), an above aver- age importance of SD certifications in general (3.99) and, interestingly, lower importance of expert ratings (2.87). 94% of the respondents had heard of and 82% had bought Organic wine previously, while the numbers are lower for all other certifications: Biodynamic (67%/49%), Fairtrade (55%/12%), Natural (58%/33%) and Sustainable (34%/15%). The importance of the certification rang- es from 3.27 for Organic, 3.09 for Sustainable, 2.99 for Fairtrade, 2.95 for Biodynamic, to 2.84 for Natural. 78% of the respondents say that they are willing to buy an Organic wine, 70% a Fairtrade wine, 65% a Biodynamic wine, and 63% a Natural or Sustainable wine. The WTP categories are quite similar for all certifi- cations, representing an average WTP of 2.56 € for Sus- tainable, 2.71 € for Natural, 2.92 € for Fairtrade, 3.03 € for Organic and 3.07 € for Biodynamic wine (see fig- Table 1. Descriptive statistics for important model variables (Source: authors). Variable name Mean SD Variable coding Age group 3.04 1.70 Age ranging from 18-24 years =1 to >75 years = 7 Gender 0.54 0.50 0 = male, female = 1 Education Group 4.65 1.89 Education ranging from High School = 1 to Doctorate = 7 Yearly income group 3.24 2.00 Yearly income ranging from <20,000 = 1 to >165,000 = 11 Average price paid per bottle 8.99 4.96 Average price in € Annual expenditure on wine in € 725 2,060 Annual expenditure in € How often do you buy certified food 3.84 1.13 1-6 (1 = never, 6 = always) Knowledge of Wine 2.85 1.57 1-6 (1 = very little knowledge, 6 = wine expert) Days of Winery visits per year 3.64 8.79 Visits in days Importance of Eco-Certification 3.99 1.60 1-7 (1 = no importance, 7 = very high importance) Importance of Expert Rating 2.87 1.32 1-7 (1 = no importance, 7 = very high importance) Importance of Place of Origin 5.78 1.28 1-7 (1 = no importance, 7 = very high importance) Heard of Biodynamic before 0.67 0.47 0 = No, 1 = Yes Heard of Fairtrade before 0.55 0.50 0 = No, 1 = Yes Heard of Natural before 0.58 0.50 0 = No, 1 = Yes Heard of Organic before 0.94 0.24 0 = No, 1 = Yes Heard of Sustainable before 0.34 0.47 0 = No, 1 = Yes Bought Biodynamic before 0.49 0.50 0 = No, 1 = Yes Bought Fairtrade before 0.12 0.33 0 = No, 1 = Yes Bought Natural before 0.33 0.47 0 = No, 1 = Yes Bought Organic before 0.82 0.39 0 = No, 1 = Yes Bought Sustainable before 0.15 0.35 0 = No, 1 = Yes Importance of certification Biodynamic 2.95 1.26 1-5 (1 = not at all important, 5 = extremely important) Importance of certification Fairtrade 2.99 1.12 1-5 (1 = not at all important, 5 = extremely important) Importance of certification Natural 2.84 1.24 1-5 (1 = not at all important, 5 = extremely important) Importance of certification Organic 3.27 1.24 1-5 (1 = not at all important, 5 = extremely important) Importance of certification Sustainable 3.09 1.17 1-5 (1 = not at all important, 5 = extremely important) WTB Biodynamic 0.65 0.48 0 = No, 1 = Yes WTB Fairtrade 0.70 0.46 0 = No, 1 = Yes WTB Natural 0.63 0.48 0 = No, 1 = Yes WTB Organic 0.78 0.41 0 = No, 1 = Yes WTB Sustainable 0.63 0.48 0 = No, 1 = Yes WTP Biodynamic 2.80 0.97 Categories ranging from no price premium = 1 to > 13 € = 6 WTP Fairtrade 2.77 0.94 Categories ranging from no price premium = 1 to > 13 € = 6 WTP Natural 2.64 1.03 Categories ranging from no price premium = 1 to > 13 € = 6 WTP Organic 2.82 0.92 Categories ranging from no price premium = 1 to > 13 € = 6 WTP Sustainable 2.61 0.99 Categories ranging from no price premium = 1 to > 13 € = 6 81Consumer preferences for certified wines in France: A comparison of sustainable labels ure 2), but the differences in means are still significant according to the Kruskall Wallis H-test5, so that it is interesting to analyze the determinants of the WTB and WTP for the respective certifications. 5.2 Logistic regression First, the correlation matrix of the most important independent variables was checked and with only two values being higher than 0.3 but still below 0.5, corre- lations and multicollinearity were not considered to be a problem. With the dependent variable being a binary 5 A Kruskal-Wallis H test was conducted to determine if the average price premium was different for the five certificates: Biodynamic (n = 205), Fairtrade (n = 211), Organic (n = 229), Natural (n=221) and Sus- tainable (n=204). The Kruskal-Wallis H test showed that there was a statistically significant difference in the average price respondents are willing to pay, χ2(2) = 10.850, p = 0.0283. response variable, both probit and logistic models are optional approaches to analyze the impact of various independent variables on the WTB a certified wine6. To evaluate the goodness of fit of both probit and logistic models, the pseudo Mc Fadden’s R2 revealed that they have very similar degrees of efficiency in explaining the WTB, but we favor the logistic approach as it can be readily transformed to the odds ratio. The general model can be described as follows: WTBij = f (socio economic characteristics of consumers, wine knowledge, wine purchasing and consumption pat- terns, knowledge and importance of certifications, purchas- ing behavior with regard to certified wine and food)7 For each label j=1,…,5 and i respondents The answer to the WTB question is coded as 1 if the response indicates a ‘yes’ and 0 if it indicates a ‘no’. Table 2 only shows significant variables of the logistic regression for WTB. These results suggest that the probability to buy a certified wine is significantly enhanced by the ‘impor- tance’ the consumer attaches to a label and ranges from a higher probability of 2.31 for Organic up to 3.24 times for Sustainable wines which is in line with general household theory, as wine consumers seem to buy wine according to their respective preferences (Varian, 2010). The variable ‘Heard of certification’ is also signifi- cant for Biodynamic and Sustainable labels. The prob- ability to buy Organic, Natural, and Fairtrade wines is higher for consumers who often buy certified food in general. This result supports suggestions that households have a similar behavior with regard especially to organi- cally certified food and wine (Di Vita et al., 2019). For Fairtrade, the variable ‘Bought this certification before’ changes the probability significantly. Here, being an experience good is important for wine, because those who have experienced a Fairtrade wine before seem to like it and purchase it again. 5.3 ANN model The ‘WTP’ answers in the survey are coded from 1 to 6 into categories, from ‘no WTP’ to a very high WTP. For each label, dependency factors and semi-elasticities are calculated as described in the methodology section and, in Table 3, those results with a high importance for 6 The approaches differ only with regard to the distribution of the errors. While the logistic model assumes a cumulative logistic distribution function, the probit model assumes a cumulative normal distribution function. Both are estimated by maximum likelihood, the results hardly differ (Horowitz and Savin, 2001). 7 For a more detailed overview of the variables, please see table 1. Attribute Distribution Mean Std. Dev. Min. Max. Average Price Premium Biodynamic 3.07 0.87 0 13.5 Average Price Premium Organic 3.03 0.87 0 13.5 Average Price Premium Fairtrade 2.92 0.91 0 13.5 Average Price Premium Natural 2.71 1.00 0 13.5 Average Price Premium Sustainable 2.56 1.04 0 13.5 Figure 2. Mean, standard deviation, minimum and maximum of bids per wine certification (Source: authors). 82 Adeline Alonso Ugaglia et al. the WTP and the respective semi-elasticities are high- lighted in bold numbers. For Biodynamic wine, the average expenditure per year on wine in general is the most important variable for the WTP, showing the more they spend on wine, the high- er the WTP for Biodynamic wine, and those who judge the certification as being important as well have a posi- tive WTP. Knowledge about the certification and the vari- able ‘Bought certification before’ perform a negative influ- ence which suggests some bad experiences and that the Biodynamic wines did not meet consumer expectations. Biodynamic is the only certification where the results do not show an important difference for age groups, none- theless, the younger the respondents the higher the WTP. The WTP is hardly influenced by the average price that respondents usually pay for a wine bottle. For Organic wine, again the average expenditure per year on wine in general and the importance of the certi- fication (and additionally of eco-certifications in general) are important variables for the WTP. This is in line with the literature which shows that Organic is the strongest label and the most visible one for consumers. Another important variable with a positive impact on the WTP is the average price that the respondents usually pay for a wine bottle and, to a lesser degree, the ‘Place of ori- gin’. As for all other wines (except for Biodynamic), the younger the respondents the higher the WTP. The only variable that has a negative influence is ‘Days of winery Table 2. Results of the Logistic Regression (Source: authors). WTB Biodynamic WTB Organic WTB Fairtrade WTB Sustainable WTB Natural Bought certification before 1.951981 0.98 1.142199 0.16 4.828255* 2.12 1.195554 0.23 2.139022 1.18 How often do you buy certified food 1.540636 1.5 2.115084** 2.88 1.754584** 2.61 1.306585 1.33 1.664622* 2.4 Expenditure/Year in € 0.999916 -0.44 0.9998272 -0.95 1.00008 0.43 0.9996783 -1 1.000337* 2.02 Heard of certification before 7.778052** 3.24 1.607506 0.49 1.621343 1.23 2.379079* 2.06 2.154973 1.73 Importance certification 2.854495*** 3.84 2.305237*** 4.67 2.359608 4.97 3.235772*** 7.01 2.940351*** 6.32 N 225 225 225 225 225 pR2 51.6 38.11 28.39 35.21 37.93 Table 3. Results of the ANN (Source: authors). WTP Biodynamic WTP Organic WTP Fairtrade WTP Sustainable WTP Natural dependency avg. dy/dx dependency avg. dy/dx dependency avg. dy/dx dependency avg. dy/dx dependency avg. dy/dx Average price per bottle in € 0,281 0,172 0,889 0,366 0,709 0,554 0,801 0,113 0,752 0,124 Bought certification before 0,720 -0,127 0,410 -0,160 0,385 0,112 0,963 -0,469 0,554 -0,030 Customer education 0,410 0,080 0,361 0,068 0,724 -0,211 0,510 -0,352 0,321 -0,225 Customer age group 0,604 -0,179 0,805 -0,170 0,706 -0,325 0,831 -0,285 0,718 -0,198 Days of winery visits 0,367 -0,013 0,841 -0,579 0,513 0,161 0,889 0,321 0,921 -0,043 Expenditure/year in € 1,000 0,840 0,889 0,466 0,295 0,129 0,595 -0,065 1,000 -0,165 Importance certification 0,799 0,316 1,000 0,253 1,000 0,637 1,000 0,674 0,843 0,405 Importance eco-certification in general 0,636 0,187 0,771 0,059 0,404 0,090 0,511 0,382 0,655 0,223 Importance expert opinion 0,245 0,049 0,696 0,029 0,736 0,151 0,688 0,319 0,596 0,000 Importance PoO 0,687 0,095 0,740 0,033 0,431 -0,121 0,732 0,467 0,384 0,108 Knowledge about certification 0,772 -0,018 0,236 0,097 0,244 -0,105 0,472 -0,124 0,471 0,165 N 149 179 154 140 140 R2 0,894 0,920 0,922 0,956 0,865 83Consumer preferences for certified wines in France: A comparison of sustainable labels visits’, which is a little surprising but is supported by the results for Natural wine and, with lesser importance, Biodynamic wine. If we assume that visiting wineries means to learn more (Bazoche et al., 2008), this might be due to some negative experiences during the visit or – that by accident as we do not have a random sample – respondents visited more wineries offering ‘non-certi- fied’ or Sustainable or Fairtrade wines. For Fairtrade wine, the importance of the certifica- tion is again the most important variable for the WTP. As in the case of Organic, there is a positive impact of the average price that the respondents usually pay for a bottle of wine on the WTP. Fairtrade is the only cer- tification where expert opinion is important for the WTP. As wine is an experience good, consumers tend to obtain information on quality and study expert opin- ions. Again, the younger the respondents, the higher the WTP. This impact is stronger for Fairtrade wines com- pared to all other labels. Fairtrade is also the only label for which education performs an important and nega- tive impact on the WTP. One explanation might be that those being higher educated know more about fair trade and are aware of the fact that it focuses more on social than environmental aspects, with the latter being more favored by this consumer group. For Sustainable wine, the importance of the certifi- cation is again the most important variable for the WTP. As in the case of Organic and Fairtrade, there is a posi- tive impact of the average price that the respondents usu- ally pay for a bottle of wine on the WTP. This holds also for the variables ‘Place of Origin’ and ‘Days of winery visits’. This means that they had good experiences when visiting and they link ‘sustainable’ to this experience. When consumers only ‘Bought the certification before’, the WTP is lower, which means that if they just buy a bottle their WTP is lower and are disappointed when not linking the label with the experience of the winery visit. The younger the respondents the higher the WTP. For Natural wine, the importance of the certifica- tion and the average price that the respondents usually pay for a bottle of wine have a positive impact on the WTP and the younger the respondents, the higher the WTP. Those respondents with high annual expenditures on wine – even if they have a WTB – don’t want to pay higher premiums for Natural wine. The same holds for those who have many annual winery visits. 6. DISCUSSION AND CONCLUSIONS The wine industry is facing major environmen- tal challenges and a growing concern among consum- ers about environmental and SD issues can be observed (Casini et al., 2009; Schimmenti et al., 2016; Vecchio, 2013). On the other hand, we know little about consum- ers’ preferences relating to a growing number of differ- ent sustainable certifications that serve as producers’ sig- nals of sustainable attributes. Extant studies focus either on Organic wine, show contradictory results, or fail to compare different sustainable labels. Our paper seeks to fill this gap by comparing five different labels and to show commonalities and differences between labels with regard to determinants for WTB and WTP. The WTB analysis shows that the respondents who judge the certification important have a higher prob- ability of buying a wine possessing this certification, irrespective of the certification. Further, this holds true for those who regularly buy certified food (for Organic, Fairtrade and Natural wines) and have heard of the cer- tification before (for Biodynamic and Sustainable wines). Those who have bought Fairtrade wine before will tend to buy it again. Additionally, our WTP analysis shows which deter- minants impact respondents’ WTP for a certified wine. The results of the ANN model suggest that the impor- tance consumers attach to a label positively influences their WTP. The higher the importance they give to a label, the higher the WTP, irrespective of which label it is. The average price usually paid for a bottle of wine also positively influencing WTP (expect for Biodynam- ic wine). In general, the younger the customers are, the higher their WTP. In addition, those with higher expen- ditures per year on wine have a higher WTP, but only for Biodynamic and Organic certified wines. These f indings highlight two main and robust results. First, the importance that consumers give to the certifications influences their WTB and WTP, irre- spective of the SD label, showing the importance of their personal values and knowledge about the certi- fications. Second, the drivers differ for each SD label, meaning that consumers behave differently with regard to their purchasing decisions for each label. This con- firms the interest in having different SD labels or cer- tifications representing different kinds of social and environmental practices, giving producers a reason to engage in sustainable practices and the need to signal these on the label. Thus, we open a new direction for further investigations with regard to marketing and policy implications to better promote SD wines as, with more information, producers will be able to bet- ter choose an appropriate strategy (Mariani and Vas- tola, 2015). These results could also have further impli- cations with the development of online purchasing (and online communication from the supply side) and 84 Adeline Alonso Ugaglia et al. crowdfunding campaigns as a new form of early pur- chase (Bargain et al., 2018). There are some methodological limitations to our study. There is still a chance that people taking part in such surveys seek to satisfy social norms more than their true preferences (Fischer and Katz, 2000). The method directly eliciting WTP for attributes without forcing respondents to make trade-offs between product attrib- utes (e.g., product price vs. organic) can result in invalid and unrealistically high attribute importance (Louviere and Islam, 2007). Further, we cannot prove to which ref- erence price respondents relate their price mark-ups; it is possible that respondents refer their respective answers to other prices than those given in response to the question on their average wine purchase price (Islam et al., 2007). Additionally, as this was a convenience rather than a ran- dom sample, the results cannot be assumed to be repre- sentative of French wine consumers in general. We did not have the chance to develop experimental economics (no tasting) or a real market in our survey. However, this remains an interesting approach that could confirm the precision of our results under experimental conditions. In this case, it would be interesting to control for conven- tional (i.e. non-certified) wines. REFERENCES Alonso Ugaglia, A., Cardebat, J.M., Jiao, L. (2019). The French wine industry (Chap.2), in: A. Alonso Uga- glia, J.M. Cardebat and A. Corsi (Eds.), The Pal- grave Handbook of Wine Industry Economics, Pal- grave Macmillan, Cham, pp. 17-46. Alonso Ugaglia, A., Cardebat, J.M., Dupuy, L., Sloop, S. (2017). Sustainability certifications in the wine industry: what are the drivers for adoption? INFER conference, Wine sustainability 1-2 September, Pes- cara, Italy. Amato, M., Ballco, P., Lopez-Galan, B., De Magistris, T., Vernau, F. (2017). Exploring consumers’ perception and willingness to pay for “non-added sulphite” wines through experimental auctions: A case study in Italy and Spain, Wine Economics and Policy, 6(2), 146-154. doi: 10.1016/j.wep.2017.10.002. Ashenfelter, O., Gergaud, O., Storchmann, K., Zimb, W. (Eds) (2018). World Scientific Reference on Hand- book of the Economics of Wine, Volume 1. Prices, Finance and Expert Opinion, World Scientific Handbook in Financial Economics Series, vol. 6. Singapore and Hackensack, NJ: World Scientific. Ashton, R.H. (2014). Wine as an experience good: price versus enjoyment in blind tastings of expensive and inexpensive wines, Journal of Wine Economics, 9(2), 171-182. Baker, R., Ruting, B. (2014). Environmental Policy Analy- sis: A Guide to Non-Market Valuation, Productivity, Commission Staff Working Paper, Canberra. Bargain, O., Cardebat, J.M., Vignolles, A. (2018). Crowd- funding in the wine industry, Journal of Wine Eco- nomics,13(1), 57-82. Bazoche, P., Deola, C., Soler, L.G. (2008). An experimen- tal study of wine consumers’ willingness to pay for environmental characteristics, European Associa- tion of Agricultural Economists (EAAE) 2008 Inter- national Congress, August 26-29, 2008, Ghent, Bel- gium. doi: 10.22004/ag.econ.4365. Cardebat, J.M. (2017). Économie du vin, Ed. La Décou- verte, Paris, 128 p. Carson, R.T., Louviere, J.J. (2011). A Common Nomen- clature for Stated Preference Approaches, Environ- mental and Resource Economics, 49(4), 539-59. Christ, K., Burritt, R. (2013). Critical environmental con- cerns in wine production: an integrative review, Journal of Cleaner Production, 53, 232-242. Delmas, M.A., Gergaud, O. (2021). Sustainable practices and product quality: Is there value in eco-label cer- tification? The case of wine, Ecological Economics, 183, 106953. doi: 10.1016/j.ecolecon.2021.106953. Di Vita, G., Pappalardo, G., Chinnici, G., La Via, G., D’Amico, M. (2019). Not everything has been still explored: Further thoughts on additional price for organic wine. Journal of Cleaner Production, 231, 520-528. IWSR (2019). Organic Wine Report, 2019. Available at https://www.theiwsr.com/organic-wine-report/ (Accessed on 29/04/2021). Farsi, M. (2007). Risk-Aversion and Willingness to Pay in Choice Experiments, ETH-Zürich, CEPE Working Paper No. 55 / February 2007. Forbes, S.L., Cohen, D.A., Cullen, R., Wratten, S.D., Fountain, J. (2009). Consumer attitudes regarding environmentally sustainable wine: an exploratory study of the New Zealand marketplace, Journal of Cleaner Production 17, 1195-1199. Freeman I.I.I., A.M., Herriges, J.A., Kling, C.L. (2014). The Measurement of Environmental and Resource Values: Theory and Methods, RFF Press: Routledge. Gabrielyan, G., Marsh, T.L., McCluskey, J.J., Ross, C.F. (2018). Hoppiness is Happiness? Under-fertilized Hop Treatments and Consumers’ Willingness to Pay for Beer, Journal of Wine Economics, 13(2), 160-181. Galati, A.G., Schifani, M. Crescimanno, G., Migliore, G. (2019). “Natural Wine” consumers and interest in label information: An Analysis of Willingness to 85Consumer preferences for certified wines in France: A comparison of sustainable labels Pay in a New Italian Wine Market Segment, Jour- nal of Cleaner Production, 227(1), 405-413. Gow, J., Moscovici, D., Rezwanul, R., Mihailescu, R., Alonso Ugaglia, A., Valenzuela, L., Rinaldi, A., Coelli, R., 2020. Consumer Willingness to pay for environmental characteristics of Australian wine, AAWE Working paper. Hanemann, W.M., (1984). Welfare evaluations in contin- gent valuation experiments with discrete responses, American Journal of Agricultural Economics, 66, 332-341. Hashem, S., (1992). Sensitivity for feetforward artificial neural networks with differentiable activation func- tions, Proceedings of the international joined confer- ence on neural networks, Baltimore, USA, 419-424. Hornik, K., Stichcombe, M., White, H. (1990). Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural Networks, 3, 551-560. Horowitz, J.L., Savin, N.E. (2001). Binary Response Mod- els: Logits, Probits and Semiparametrics, Journal of Economic Perspectives, 15(4), 43-56. Kealy, M.J., Turner, R.W. (1993). A test of the equality of closed-ended and open-ended contingent valua- tions, American Journal of Agricultural Economics, 75, 321-331. Lee, D., Derrible, S., Pereira, F.C. (2018). Comparison of Four Types of Artificial Neural Network and a Mul- tinomial Logit Model for Travel Mode Choice Mod- eling, Transportation Research Record, 2672(49), 101-112. doi: 10.1177/0361198118796971. Louviere, J.J., Hensher, D.A., Swait, J.D. (2000). Stated Choice Methods. Analysis and Application, Cam- bridge: Cambridge University Press. Manderson, A.K. (2006). A systems-based framework to examine the multi- contextual application of the sustainability concept, Environment, Development and Sustainability, 8(1), 85-97. Marette, S. (2014). Economics benefits coming from the absence of labels proliferation, Journal of Agricul- tural and Food Industrial Organization, 12, 65–73. doi: 10.1515/jafio-2013-0014. Mariani, A., Vastola, A. (2015). Sustainable winegrowing: Current perspectives, International Journal of Wine Research, 7(1), 37-48. doi: 10.2147/IJWR.S68003. Mihailescu, R., Hecht, K. (2015). Is there a scope for Organic wine tourism development? A focus on South African wine industry, Revista Di Scienze Del Turismo, 6(1-2), 1-11. Mitchell, R.C., Carson, R.T. (1989). Using Surveys to Val- ue Public Goods: The Contingent Valuation Method, Washington, D.C.: Resources for the Future. Mc Donald, J.B., Saliba, A., Bruwer, J. (2013). Wine choice and drivers of consumption explored in relation to generational cohorts and methodology, Journal of Retailing and Consumer Services, 20(3), 349-357. Mogas, J., Riera, P., Bennett, J. (2002). A Comparison of Contingent Valuation and Choice Modelling: esti- mating the environmental values of Catalonian Forests. Occasional Paper No. 1, National Centre for Development Studies, Australian National Univer- sity, Canberra, Australia. Mohammadian A., Miller E.J., 2002. Nested logit mod- els and artificial neural networks for predicting household automobile choices: comparison of per- formance, Transportation Research Record: Journal of the Transportation Research Board, 1807(1). doi: 10.3141/1807-12. Moscovici, D., Rezwanul, R., Mihailescu, R., Gow, J., Alonso Ugaglia, A.,  Valenzuela, L., Rinaldi, A., 2020. Preferences for eco certified wines in  the United States, International Journal of Wine Busi- ness Research, 23. doi:10.1108/ijwbr-04-2020-0012. Moscovici, D., Reed, A. (2018). Comparing Wine Sustain- ability Certifications around the World: History, Status, and Opportunity, Journal of Wine Research, 29(1), 1-25. Nelson, P. (1970). Information and consumer behav- ior. Journal of Political Economy, 78(2), 311-329. Niklas, B., Storchmann, K., Vink, N. (2017). Fairtrade Wine Price Dispersion in the United Kingdom, Journal of Wine Economics, 12(4), 446-456. Niklas, B., Rinke, W. (2020). Pricing Models for German Wine: Hedonic Regression vs. Machine Learn- ing, Journal of Wine Economics, 15(3), 284-311. doi:10.1017/jwe.2020.16. OIV, 2017. Prospective filière française des vins biologiques, Les Études FranceAgriMer, FranceAgriMer Vin, April 2017, 199p. Owen, G.W. (2012). Applying point elasticity of demand principles to optimal pricing in management accounting, International Journal of Applied Eco- nomics and Finance, 6, 89–99. Paloviita, A. (2010). Consumers’ Sustainability Percep- tions of the Supply Chain of Locally Produced Food, Sustainability, 2, 1492-1509. Poelmans, E., Rousseau, S. (2017). Beer and Organic Labels: Do Belgian Consumers Care?, Sustainabil- ity, 9(9), 1509-1523. Porter, M., Takeuchi, H. (2013). The French Wine Clus- ter, Microeconomics of Competitiveness, Harvard Business School, Winter 2013, 33p. Pullman, M., Maloni, M., Carter, C. (2009). Food for thought: Social versus environmental sustainabil- 86 Adeline Alonso Ugaglia et al. ity practices and performance outcomes, Journal of Supply Chain Management, 45(4), 38-54. Remaud, H., Mueller, S., Chvyl, P., Lockshin, L. (2008). Do Australian Wine Consumers Value Organic Wine?, International Conference of the Academy of Wine Business Research. Refereed paper, Siena, Ita- ly. Rinke, W. (2015). Calculating the dependency of compo- nents of observable nonlinear systems using arti- ficial neural networks, MakeLearn & TIIM confer- ence proceedings, 367-374. Available from: https:// EconPapers.repec.org/RePEc:tkp:mklp15:367-374. Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986). Learning representations by backpropagating errors, Nature, 323, 533-536. Sellers-Rubio, R., Nicolau-Gonzalbez, J.L. (2016). Esti- mating the willingness to pay for a sustainable wine using a Heckit model, Wine Economics and Policy, 5(2), 96-104. Shavlik, J.W., Diettrich, T.G. (eds.) (1990). Reading in Machine Learning, San Mateo, CA: Morgan Kauf- mann. Skuras, D., Vakrou, A. (2002). Consumers’ willingness to pay for origin labelled wine - Greek case study, British Food Journal, 11, 898-912. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., Kraus, S., Leyton-Brown, K., Parkes, D., Press, W., Saxenian, A., Shah, J., Tambe, M., Tell- er, A. (2016). Artificial intelligence and life in 2030. One hundred years study on artificial intelligence: Report of the 2015-2016, Study Panel, 8. Available from: https://ai100.stanford.edu/sites/default/files/ ai100report10032016fnl_singles.pdf. Szolnoki, G. (2013). A cross-national comparison of sus- tainability in the wine industry, Journal of Cleaner Production, 53, 243-251. Tach, E.C., Olsen, J.E. (2006). Market segment analysis to target young adult wine drinkers, Agribusiness, 22(3), 307-322. Tozer, P.R., Galinato, S.P., Ross, C.F., Miles, C.A., McClus- key, J.J. (2015). Sensory Analysis and Willingness to Pay for Craft Cider, Journal of Wine Economics, 10(3), 314-328. Tran V.H., Takumi A., Mikiharu A. (2019). Determina- tion of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods, Journal of Trans- port Geography, 78(2019), 70-86. doi: 10.1016/j. jtrangeo.2019.05.015. Trienekens, J.H., Wognum, P.M., Beulens, A.J.M., van der Vorst, J.G.A.J. (2012). Transparency in complex dynamic food supply chains, Advanced Engineering Informatics, 26, 55-65. Valenzuela, L., Moscovici, D., Mihailescu R., Alonso Uga- glia, A., Gow, J., Rinaldi, A. (2019). Wine consum- ers market sustainability: an international study, 13th Annual AAWE Conference, 14-18 July, Vienna, Austria. Varian, H.R. (2010). Intermediate Microeconomics. A Modern Approach, 78th Edition. New York, USA, W.W. Norton & Company, 73-89. Vecchio, R. (2013). Determinants of Willingness-to-Pay for Sustainable Wine: Evidence from Experimental Auctions, Wine Economics and Policy, 2(2), 85-92. Vecchio, R., Annunziata, A. (2015). Willingness-to-pay for sustainability-labelled chocolate: an experimen- tal auction approach, Journal of Cleaner Production, 86, 335-342. Witten, I., Eibe F., Hall, M. (2017). Data mining – practi- cal machine learning tools and techniques, 4th Edi- tion. New York, USA, Morgan Kaufmann, 261-269. Yeh, I.C., Cheng, W.L. (2010). First and second order sen- sitivity analysis of ML, Neurocomputing, 73, 2225– 2233. Wine Economics and Policy Volume 10, Issue 2 - 2021 Firenze University Press Productive efficiency of wine grape producers in the North of Portugal Micael Santos1,*, Xosé Antón Rodríguez2, Ana Marta-Costa3 Organic and conventional grape growing in Italy: a technical efficiency comparison using a parametric approach Federica Cisilino1, Fabio A. 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Ricardo Sellers-Rubio Adaptive market hypothesis: An empirical analysis of the Wine Market Anoop S Kumar