Bio -based and A ppl ied Economics BAE Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 Copyright: © 2021 L. Lourenço-Gomes, T. Gonçalves, J. Rebelo. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: L. Lourenço-Gomes, T. Gon- çalves, J. Rebelo (2021). The distributors’ view on US wine consumer prefer- ences. A discrete choice experiment. Bio-based and Applied Economics 10(4): 325-333. doi: 10.36253/bae-10801 Received: April 17, 2021 Accepted: November 8, 2021 Published: March 31, 2022 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. Editor: Davide Menozzi. ORCID LL-G: 0000-0001-6509-1394 TG: 0000-0001-7492-1257 JR: 0000-0003-3564-7771 The distributors’ view on US wine consumer preferences. A discrete choice experiment Lina Lourenço-Gomes, Tânia Gonçalves*, João Rebelo Department of Economics, Sociology and Management (DESG), Centre for Transdisci- plinary Development Studies (CETRAD), University of Trás-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal *Corresponding author. E-mail: tgoncalves@utad.pt Abstract. This study explored the view that distributors have towards the most valued wine attributes by consumers in the US market, applying the discrete choice experi- ments technique. Furthermore, to explore the extent to which the distributors’ perspec- tive may reflect consumers’ preferences, the results are analyzed considering previous evidence with consumers in the same market. The results from a scaled multinomial logit, mixed logit and generalized logit models reveal similarities with consumer stud- ies’ findings, especially for the influence of medals/awards, the origin of the wine, grape variety, and price, and it also identifies possible trends in the market. This evi- dence suggests that data collected using the knowledge and experience of wine dis- tributors generates valuable information through a smaller sample at a lower cost than through applying consumer surveys, which is relevant in large markets with a higher number of consumers. Keywords: consumer choice, stated choice method, distributors’ perspective, wine choice. JEL codes: C25, D12, D20. 1. INTRODUCTION Consumer behavior has evolved over the years, and understanding the motivations, thought processes, and experiences of individuals as they make a choice is essential to improve marketing strategies and consumer welfare (Malter et al., 2020). This statement becomes particularly relevant in wine as it is considered a complex “experience good” (Ali & Nauges, 2007; Mueller et al., 2010) described by several intrinsic (e.g., wine-related, variety, alco- hol content, flavor, or style) and/or extrinsic (e.g., price-related, packaging, awards, ratings, and brand) attributes. On the demand side, wine consumption trends are undergoing sig- nificant changes (Castellini & Samoggia, 2018) related to consumer spend- ing habits, purchase power, new choice criteria or expectations (such as health-oriented, environmental-oriented, or based on cultural issues, iden- tity/authenticity), and to the existence of substitute products, like beer and http://creativecommons.org/licenses/by/4.0/legalcode 326 Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 Lina Lourenço-Gomes, Tânia Gonçalves, João Rebelo spirits. This became more relevant in the pandemic crisis (due to Covid-19) where consumer patterns will focus more on sustainability issues, which demands the strength of the greening process of the CAP (Vergami- ni et al., 2021). Accordingly, the wineries behavior, in terms of management decisions regarding technology, products, marketing, and other factors, is framed in a global market characterized by a monopolistic compe- tition structure, where there exists a large number of firms with different characteristics and sizes; restricted control over price-output; with product heterogeneity; asymmetric information; and freedom to enter or exit the market (Parenti et al., 2017). Despite competition in domestic market, in this industry, firms’ competitiveness is increasingly dependent on the ability to trade at an international level (Macedo et al., 2019). Both changes in market supply and demand have been appealing for a vertical and horizontal wine differ- entiation based on unique factors such as grape varieties, terroir, quality, and brand or, at the marketing level and distribution channels. Understanding the drivers of wine consumers’ purchasing decisions has been the object of a lively debate. As a highly differentiated product, wine preferences are distinctive and country-specific. In this sense, companies need to know consumer’s preferences for the attributes of wine to establish marketing strate- gies, which requires data collection and analysis. Typi- cally, companies use consumer panels through the appli- cation of surveys, which can be expensive (Windle & Rolfe, 2011), and whose validity depends on the sample size and randomness (Mitchell & Jolley, 2010). Over the last few years, there have been a large number of consumer-oriented studies, including in wine research, particularly those using the technique of discrete choice experiments (DCE), a stated preference method, to estimate which attributes are crucial to deci- sion-making by decomposing the good into its attributes or characteristics in light of Lancaster’s theory (Lancas- ter, 1966). The use of the DCE technique has attracted researchers’ interest as an alternative to more conven- tional techniques, as it improves the feasibility of valua- tion studies, and is relevant for research and policy. This method facilitates obtaining information about the most valued wine attributes in the decision-making process, providing information about how consumers value wine based on their intrinsic and extrinsic characteristics, and assessing a price premium or willingness to pay (WTP) for each wine characteristic. Empirical evidence provides the WTP measures for different wine cues, such as labe- ling (e.g., Combris et al., 2009; Mueller et al., 2010), wine origin (e.g., D’Alessandro & Pecotich, 2013; Kallas et al. 2013), grape variety (e.g., Corsi et al. 2012; Kallas et al. 2013), awards or medals (e.g., Combris et al. 2009; Corsi et al. 2012), brand and price (e.g., Xu et al. 2014). For marketing purposes, the results of these studies allow wineries to adjust the definition of their wines to the consumer’ profile, gathering the needs of each mar- ket and segment. However, to obtain robust consumer knowledge representative samples are required and con- sider sample selection issues to avoid biased and incon- sistent estimators (Heckman, 1979). Solving these issues requires surveying a large number of consumers with high costs. Alternatively, similar information may be collected easily and reliably, by inquiring intermediar- ies who continuously contact with wine consumers and have knowledge about their preferences and habits. The distributors make an appropriate linkage between the producer and the final consumer based on consumer insights, playing a pivotal role in choosing the product to sell in each specific market. The distributors decide which products to carry, the market segments to reach, and the prices to charge consumers for each product. Moreover, as Sashi & Stern (1995) attested, in some industries (such as producer goods industries), the intermediaries in the distribution channel are agents of product differentiation. After analyzing the sales of Aus- tralian wines on the British retail market, Steiner (2004) found that consumers associate a distribution channel with a specific product quality. In the same sense, Pu, Sun, and Han (2019) state that an increasing number of manufacturers are considering selling differentiated products through different channels as their distribution strategy through quality differentiation. Regardless of the question of the distribution chan- nel and its relationship with product differentiation, which has been gaining attention [reviewed by Pu et al. (2019)], wine distributors are agents with a deep knowledge of consumer’s preferences and behaviors when purchasing wine. Thus, they may act as key play- ers in collecting information for wineries to meet con- sumer needs, an increasingly complex and challenging demand. This alternative source of information has the advantage of obtaining data through smaller samples of the target markets. Therefore, supported by the DCE theoretical background, the goal of this paper is to test whether the distributors’ data may be an alternative source of information to convey consumers’ preferences and trends in the target market. Specifically, this article explores wine distributors’ perceptions about the most valued wine attributes by consumers using the DCE technique. This information is obtained by administer- ing a survey on wine distributors in the American mar- ket (USA), positioned as the world’s largest consumer in 2018 (OIV, 2019), but whose background and related 327The distributors’ view on US wine consumer preferences. A discrete choice experiment Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 studies about wine consumers’ preferences are few. As far as we know, this approach has not been conducted before, constituting an innovative research topic capable of promoting helpful knowledge to wineries and wine distributors. The paper is organized as follows. Section 2 pre- sents the methods comprising the study design, sample, and the methodology employed. Section 3 includes the results and discusses previous evidence on consumer preferences/choices in the US market. The conclusions of this study are presented in Section 4. 2. METHODS 2.1. Data An online survey comprised of four sections (gen- eral characterization of the distributor; ranking of wine characteristics importance; wine valuation scenarios (10 choice sets); business characterization of the distribu- tor) was distributed by a specialized external firm, the Nielsen Consulting company, through distributors that operate in the US market to collect information about the attributes and values in the consumers choice. From the 1109 distributors for US market (bestwineimporters. com in October 2019), a total of 92 valid questionnaires multiplied by the 10 choice sets provides a DCE sample size of 920 observations. As to the characterization of the data sample (Table 1), the distributors have been on the wine market for 18 years, on average. Red wine is the most important cat- egory in terms of market share of wine sales (on aver- age, 53%). For 50% of the distributors, white wine rep- resents up to 25% of wine sales, rosé represents up to 6%, and sparkling wine represents up to 5%. The spe- cialist retailer is the most relevant distribution channel in terms of share of wine sales, followed by the on-trade channel, hypermarkets/supermarkets, and small gro- cers. Moreover, 62% sell to hypermarkets/supermarkets, and 59.8% to small grocery stores. The wine sales rep- resent the most crucial portion of the distributors’ total sales (84%, on average). On average, online sales repre- sent near 7% of the total distributors’ business. Never- theless, for 66% of the distributors, the average share of online sales is zero. When asking distributors to identify the three most important attributes in the market they serve, the price attribute leads the ranking, followed by other relevant attributes, such as the expert ratings, grape variety, and country of origin (Figure 1). 2.2. Choice experiment The choice experiment used in this research includes six attributes (see Table 2), representing highly influen- tial cues for wine choice. Medals/awards: consumers perceive this attribute as an important sign of quality when choosing a wine (Corsi et al., 2012; Lockshin, Jarvis, D´Hauteville, & Per- routy, 2006). A gold medal with a “gold medal winner” description written in the middle was included. Alcohol level: the growing concern about the effects of overconsumption of alcohol explains the inclusion of this attribute, characterized by three different levels: low (12% vol), medium (13.5% vol), and high (15% vol) alco- hol wines. Origin: wine origin is well documented as one of the most important cues for wine choice (e.g., Kallas et al., 2013). Six levels describe this attribute at the country level: countries with a long history and tradition in wine production – Italy (54.8 mhl), France (48.6 mhl), and Portugal (6.1 mhl) – being in the top 5 in European pro- duction (OIV, 2019) and wines from the new producing countries – USA (23.9 mhl), Australia (12.9 mhl), and Table 1. Distributor’s business characterization. Mean Median Years in the market 18 15 Market share White 0.25 0.25 Red 0.53 0.50 Rosé 0.93 0.65 Sparkling 0.73 0.50 Others 0.30 0.00 Presence in market channels Hyper and supermarkets 0.62 Small grocers 0.60 Specialist retailers 0.92 On-trade 0.94 Online 0.34 Share of sales in each channel Hyper and supermarkets 0.30 Small grocers 0.17 Specialist retailers 0.44 On-trade 0.39 Online 0.07 Share of wine sales in the total sales 0.84 0.98 Less than 50% 0.14 50 - 75% 0.123 76% or more, less than 100% 0.25 100% 0.49 328 Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 Lina Lourenço-Gomes, Tânia Gonçalves, João Rebelo Chile (12.9 mhl) – also being in the top 5 in the New World production (OIV, 2019) accounting for the chang- es in the international wine market. Grape variety: this factor is a choice driver, espe- cially for the New World wines (Corsi et al., 2012; Kallas et al., 2013). Regarding consumers’ preferences for wine varieties, in 2018, the best-selling wine varietals in the US market based on volume included Chardonnay, Cab- ernet Sauvignon, and Red Blends (Nielsen, 2019). There- fore, two well-known red varieties were selected (Caber- net Sauvignon and Syrah) and a Red Blend. Closure: this packaging trait may function as a sig- nal of expected quality (Bekkerman & Brester, 2019). Two bottle closure types, screw cap, and cork closure are the most common closures in the wine market. The screw cap closure and the cork closure covered with a capsule were realistically presented in the survey. Price: it is one of the primary drivers of choice and is commonly used as an indicator of quality (e.g., Lock- shin et al., 2006; Corsi et al., 2012). Four price levels were included between the range of $8.99 and $24.99. The choice of price levels was based on the actual price range of red wine in the off-channel in the US market. A D-efficient design with no priors was obtained using the Ngene software. The attributes’ levels were combined into alternative wines and arranged in 10 sequential choice sets1. Each choice set was formed by three alternative wines plus a none-option, as displayed in Figure 2. Distributors were asked to select their pre- ferred option or bottle of wine that fits better the mar- ket they serve in terms of the consumers’ preferences, 1 The number of choice sets S was selected based on the equation: S≥K/ (J-1), where K= #parameters including constant; J=#alternatives (Ngene v1.2.1 software, ChoiceMetrics, 2018). 0 10 20 30 40 50 60 70 80 Coun try of o rig in Reg ion of o r ig in Alco hol c onten t Grap e Med als Exp ert Appella tio n Wine c ate go ry Pub lic recog nitio n Vintag e Vines age Cert i fie d lan dsc ap e Produ cer siz e Produ cer ty pe Lab el s tyl e Lab el c olor Bottle sh ap e Color Scr ew ca p Cork clo usur e Orga nic Biody na mic Pric e Responses 1st 2nd 3rd Figure 1. Three most attractive wine attributes in the market in which the distributor operates. Table 2. Attributes and levels used in the choice experiment. Attributes Medals/ Awards Alcohol level Origin Grape variety Closure Price Levels Yes No+ 12% vol. 13.5% vol. 15% vol. France Italy Portugal USA Australia+ Chile Cabernet Sauvignon Syrah Red blend No information+ Cork Screw Cap+ $8.99 $12.99 $17.99 $24.99 + reference level on dummy coding. 329The distributors’ view on US wine consumer preferences. A discrete choice experiment Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 according to the question: “Imagine you have three dif- ferent types of wine. Which of the following wines do you find as the most successful in serving wine consum- ers in your market?”. 2.3. Discrete choice model The method of discrete choice experiments has its roots in the Lancaster (1966) model of consumer behav- ior, which defines a good in terms of its characteristics, and on the random utility theory (McFadden, 1974), where an individual is a rational decision-maker aiming to maximize her or his utility. Respondent n (n=1, …, N) chooses among different J alternatives in T choice situa- tions. A random utility expression represents each alter- native j, according to the following equation: Unjt = β’xnjt + εnjt (1) xnjt is the vector of explanatory variables and includes product attributes and respondents’ characteristics, εnjt is the random component. The alternative that gives the highest utility is chosen, such that Pnj = prob(β’xnj + εnj > β’xnk + εnk) ∀ j≠k∈C, where C is the choice set of J alter- natives, j=1, …, J. In the present application, the utility associated with a particular set of alternatives J can be derived as follows: UJn = βmedals * MedalsJ + βalcohol * AlcoholJ + βFrance * FranceJ + βItaly * ItalyJ + βPortugal * PortugalJ + βUSA * USAJ + βChile * ChileJ + βcabernet * CabernetJ + βsyrah * SyrahJ + βblend * BlendJ + βclosure * ClosureJ + βprice * PriceJ + εn (2) In the mixed logit (MIXL) model (Train, 2009), also known as the random parameters logit model, the parameters are assumed to vary from one individual to another, such that: βn = β + ∆zn + Γun (3) in which β, ∆, Γ are parameters to be estimated, Γ is the lower triangular Cholesky matrix, zn a set of character- istics of individual n, un is a vector of random compo- nents, capturing non-observable effects, and β + ∆zn stands for heterogeneity in the mean of the distribution of the random parameters. The choice probabilities from the model are: (4) Omitting the observed heterogeneity captured in ∆zn, by convenience, the generalized mixed logit model (GMXL) includes scale heterogeneity across respond- ents through random alternative-specific constants (Fie- big, Keane, Louviere, & Wasi, 2010; Greene & Hensher, 2010). Consequently: βn = σn β + [γ + σn (1-γ)]Γun (5) where σn = exp( + τwn) is the individual specific standard deviation of the idiosyncratic error term, τ cap- tures the unobserved scale heterogeneity, and wn cap- tures unobserved heterogeneity. The mean parameter in the variance, , is not identified independently from τ, such that σn is normalized to 1 by setting = -τ2⁄2. γ is a weighting parameter, bounded between 0 and 1, controlling how the variance in residual preference het- erogeneity varies with scale. If γ = 0, the GMXL model reverts to the scaled mixed logit model (Greene & Hen- sher, 2010), βn = σn[β + Γun]; when σn (τ = 0), the GMXL reverts to MIXL; and when var (un) = 0 it reverts to the scaled multinomial logit model (SMNL). 3. RESULTS AND DISCUSSION Table 3 presents the SMNL, MIXL and GMXL mod- el results, using maximum simulated likelihood methods with 500 Halton draws in NLOGIT 6. Following Greene, Hensher, and Rose (2006) and Kragt (2013), a con- strained triangular distribution was used for the random Figure 2. Example of a choice set. 330 Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 Lina Lourenço-Gomes, Tânia Gonçalves, João Rebelo price parameter, and a normal distribution was defined for the other attributes (Kragt, 2013). The scale heterogeneity parameter (τ) was equal to 0.821 and highly significant, indicating the presence of substantial scale heterogeneity, such that respondents varied in terms of certainty/consistency in their choic- es. Results show that accounting for taste heterogene- ity by introducing random parameters provides a better fit than SMNL, with GMXL achieving best performance indicators. The majority of standard deviations for the random parameters are significant, showing taste differ- ences across wine consumers in the perspective of wine distributors, which suggests individual preference het- erogeneity. However, while the results from MIXL show preference homogeneity for a red blend wine and Ameri- can origin, GMXL reveals that preferences are homoge- neous for US, Chilean, and awarded wines and contra- dicts MIXL revealing heterogeneity in preferences for red blend wines. The coefficients on Cabernet Sauvignon and Chilean origin become insignificant when introducing random coefficients in the MIXL. Nevertheless, GMXL suggests that these attributes affect wine choice. Both MIXL and GMXL suggest the relevance of cork closure. The results show the importance of medals/awards, wine origin, grape variety, closure, and price. In par- ticular, the present study shows that French origin and blended wines are significant and positive drivers for distributors’ choice, while Australian origin has the opposite effect. These findings support a DCE’s out- comes on wine consumers’ preferences (Gonçalves et al., 2020) which also found a positive impact of an awarded wine and the negative influence of price and Australian wines on consumers’ choice. Moreover, the coefficient on closure is statistically significant, suggesting that this attribute (cork closure compared to screw cap) positively affects the utility of choosing a wine when introducing Table 3. Results from SMNL, MIXL and GMXL models. Attributes SMNL MIXL GMXL Mean Mean SD Mean SD Medals 0.906*** (0.196) 0.997*** 0.691*** 1.200*** 0.462 Alcohol -0.014 (0.039) -0.014 0.147*** 0.031 0.151*** Country of origin France 1.497*** (0.409) 1.310*** 0.633*** 2.069*** 0.391* Italy 1.232*** (0.412) 0.672** 0.742*** 1.192** 0.763** Portugal -0.940*** (0.334) 0.784*** 0.794*** 1.165** 1.347*** USA 0.958** (0.389) 0.699** 0.069 1.315** 0.408 Chile 0.982** (0.434) 0.499 0.753** 1.350** 0.225 Grape variety Cabernet Sauvignon 0.284* (0.162) 0.137 0.987*** 0.482* 1.137*** Syrah -0.062 (0.211) 0.013 0.785*** 0.263 1.182*** Red blend 0.598* (0.313) 0.592** 0.010 1.012** 0.947** Closure 0.176 (0.114) 0.420*** 0.801*** 0.580*** 0.879*** Price -0.057*** (0.013) -0.079*** 0.079*** -0.095*** 0.029** ASC1 -0.434 (0.509) -1.186** -0.136 Variance parameter in scale (τ) 0.821*** 0.730*** Weighting parameter (γ) 0.064 Sigma: Sample mean 0.985 0.933 Sample standard deviation 0.895 0.670 Log-likelihood -1110.9 -1008.2 -997.0 AIC 2249.9 2064.3 2048.0 BIC 2317.1 2179.6 2173.0 McFadden pseudo-R2 0.11 0.20 0.20 Observations 920 920 920 Standard errors in parenthesis; SD = standard deviation; ***, **, * significance at 1%, 5%, 10% level, respectively. 1 Alternative specific constant – Included for the none-option and it represents the respondent n’s preference towards the opt-out choice compared to the three alternatives included in our experiment. 331The distributors’ view on US wine consumer preferences. A discrete choice experiment Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 random coefficients. This finding is in line with Kelley et al. (2015) results, which found that consumers are more willing to increase purchases if bottles have cork clo- sures using the conjoint analysis technique. Additionally, wine distributors perceive red blend varieties as a rele- vant attribute for consumers’ choice. Regarding willingness to pay measures, presented in Table 4, the results from distributors’ perspective sug- gest the highest price premium for French origin (from $20.82 to $26.04 among models), followed by medals (from $15.76 to $17.33). There is also a positive price pre- mium for the other origins compared to the Australian one. The results also reveal the importance of red blend variety, with a premium ranging between $8.25 and $10.78, and cork closure compared to screw cap (from $6.10 to $7.57 among models). Summing up, despite being a data source from dis- tributors, the results are in line with those obtained from consumers in the same market, using either the same/ similar methodology (Gonçalves et al., 2020; Kelley et al., 2015) or with different methodologies (Chrysochou et al., 2012; Lockshin et al., 2015; Pomarici et al., 2017; Thach et al., 2020). Among these, Chrysochou et al. (2012) show the importance of grape variety using the Best-Worst Scaling (BWS) approach. This result was later confirmed by Lockshin et al. (2015) and Pomarici et al. (2017) using the same method. These scholars also reveal the importance of the origin of the wine (Lockshin et al., 2015; Pomarici et al., 2017), price (Pomarici et al., 2017), and medals/awards (Lockshin et al., 2015). Additionally, in line with the present study, Thach et al. (2020) also reported the relevance of blended wines, which might reflect a recent trend among American wine drinkers towards red blends instead of monovarietal. 4. CONCLUSION This study employs a DCE in the US market, to assess the perspective of wine distributors regarding consumers’ preferences. It explores whether the percep- tion of a market distributor, who knows the market well, may reflect the evidence suggested by consumers’ prefer- ences studies for wine. This study supports the impor- tance of attributes such as price, medals, country of ori- gin, and grape variety. As first highlighted in the previ- ous questions of scoring an extensive list of attributes and identifying the three most attractive attributes, the alcohol content is not a significant attribute in choos- ing one bottle of wine over another. When faced with trade-offs between only six attributes, the closure attrib- ute is relevant, suggesting a market trend favoring cork stoppers over screwcaps. We believe that bottle closures may influence the consumers’ perception of the quality of a wine and consequently how much they are willing to pay for the product. A recent study (Bekkerman and Brester, 2019) found that, on average, US consumers are willing to pay more for wines with cork closures rather than screw caps. The same study also found that this premium increases for lower-priced wines and decreases for more expensive wines, suggesting that the bottle’s closure has an enormous impact on the perceived qual- ity of the wine. Results from this study reinforce that both price and medals are well-known wine cues for choice in the ana- lyzed market (both in consumer and distributors’ views). The red blend is a positive and significant choice driver for wine in the view of distributors, which suggests red blends as an opportunity in the US wine market. There are important implications based on this study. First, it reflects the view of distributors, who are important players in the wine value chain, about the most valued attributes in the US market, which is rel- evant for wineries to adapt their supply. Second, this study suggests that distributors know consumer’s pref- erences in the respective market, potentially foreseeing emerging trends. Hence, the distributors can provide robust information on wine consumers’ preferences and behaviors, representing a potential alternative to directly obtaining this information from the consumers. As usual, this research is not free of drawbacks. First, to reach this specific target of respondents, an Table 4. Willingness to pay estimates1, in US$. SMNL MIXL GMXL Medals 15.76*** 17.33*** 15.92*** Alcohol -0.25 -0.33 0.18 Country of origin France 26.04*** 22.32*** 20.82*** Italy 21.42*** 11.94** 12.84*** Portugal 16.34*** 13.38*** 11.85*** USA 16.66** 12.69** 10.89*** Chile 17.07** 8.34 8.093** Grape variety Cabernet Sauvignon 4.93* 1.41 3.51*** Syrah -1.07 -0.08 -0.11 Red blend 10.39** 10.78** 8.25*** Closure 3.06 7.57*** 6.10*** ***, **, * significance at 1%, 5%, 10% level, respectively. 1 WTP values for SMNL were estimated as WTP = -(βk / βprice), while for the MIXL the WTP were calculated based on uncondi- tional estimates. In the case of GMXL, the model was re-parameter- ized in “WTP space” to directly produce the WTP estimates. 332 Bio-based and Applied Economics 10(4): 325-333, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10801 Lina Lourenço-Gomes, Tânia Gonçalves, João Rebelo external consulting company was contacted to distrib- ute the survey. This action has costs, and it was possible because this research was funded. Second, as it is com- mon in similar studies, there is no certainty that all rel- evant attributes are included in the survey, so the results may not fully capture the market preferences. Addition- ally, the comparison with results from other consumer studies only indicates preference matching since the survey design, technique, and analysis period are not synchronized. Thus, future research should compare data from these different sources (distributors and con- sumers) using the same technique and period to obtain more solid conclusions. Additionally, inquiring about this specific target (distributors) with market knowledge and experience may also benefit from a more qualitative study to investigate, for example, barriers and drivers of wine placement. 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Comparing Responses from Internet and Paper-Based Collection Methods in more Complex Stated Preference Environmental Valuation Surveys. Economic Analysis and Policy, 41(1), 83–97. https://doi.org/10.1016/S0313-5926(11)50006-2 Xu, P., Zeng, Y. C., Song, S., & Lone, T. (2014). Willing- ness to pay for red wines in China. Journal of Wine Research, 25(4), 265–280. Volume 10, Issue 4 - 2021 Firenze University Press The effect of attribute framing on consumers’ attitudes and intentions toward food: A Meta-analysis Irina Dolgopolova, Bingqing Li, Helena Pirhonen, Jutta Roosen* Rural areas between locality and global networks. Local development mechanisms and the role of policies empowering rural actors Francesco Mantino Understanding the bioeconomy: a new sustainability economy in British and European public discourse Irene Sotiropoulou1, Pauline Deutz2 Sustainable water resources management under population growth and agricultural development in the Kheirabad river basin, Iran Ghasem Layani1,*, Mohammad Bakhshoodeh2, Mansour Zibaei2, Davide Viaggi3 The distributors’ view on US wine consumer preferences. A discrete choice experiment Lina Lourenço-Gomes, Tânia Gonçalves*, João Rebelo