The role of context definition in choice experiments: A methodological proposal based on customized scenarios

Boncinelli, Fabio et al. Article The role of context definition in choice experiments: A methodological proposal based on customized scenarios Wine Economics and Policy Provided in Cooperation with: UniCeSV - Centro Universitario di Ricerca per lo Sviluppo Competitivo del Settore Vitivinicolo, University of Florence Suggested Citation: Boncinelli, Fabio et al. (2020) : The role of context definition in choice experiments: A methodological proposal based on customized scenarios, Wine Economics and Policy, ISSN 2212-9774, Firenze University Press, Florence, Vol. 9, Iss. 2, pp. 49-62, https://doi.org/10.36253/web-7978 This Version is available at: https://hdl.handle.net/10419/284499 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Wine Economics and Policy 9(2): 49-62, 2020 Firenze University Press www.fupress.com/wep ISSN 2212-9774 (online) | ISSN 2213-3968 (print) | DOI: 10.36253/web-7978 Wine Economics and Policy Citation: Fabio Boncinelli, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini (2020) The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios. Wine Economics and Policy 9(2): 49-62. doi: 10.36253/web-7978 Copyright: © 2020 Fabio Boncinelli, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini. 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 Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios Fabio Boncinelli*, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini University of Florence, Department of Agriculture, Food, Environment and Forestry - DAGRI, P.le delle Cascine 18, 50144 Florence, Italy *Corresponding author. E-mail: fabio.boncine[email protected] Abstract. One of the most critical points for the validity of Discrete Choice Experiments lies in their capability to render the experiment as close to actual market conditions as possible. In particular, when dealing with products characterized by a large number of attributes, the construction of the experiment poses the issue of how to express the choice question providing sufficient information. Our study verifies the role of scenario definition in choice experiments and proposes a methodology to build customized scenarios by eliciting responses from interviewees on the main choice criteria, which makes it possible to render the conditions of the experiment more realistic. This methodology is applied to the case study of wine and is introduced by a systematic review of the Discrete Choice Experiments conducted on wine. The findings show that customized scenarios result in different preference estimates compared to the conventional approach. In particular, we found a significant decline in the importance of the price attribute, which could be attributed to a better definition of the product being evaluated. Moreover, the methodology is capable of gathering information on the decision-making process that would otherwise remain unobserved and that can be used for a better segmentation analysis. JEL: D12, Q13. Keywords: choice-based conjoint, choice modeling, experimental design. 1. INTRODUCTION The market potentials of new product attributes were assessed by means of various methodological approaches including discrete choice experiments (DCEs), which are the most widely used stated preference method in the literature of applied economics and marketing (Hensher, 2010; Lancsar and Louviere, 2008; Louviere et al., 2000). This methodology consists of an attribute-based measure of benefit and is built on the hypothesis that any product can be described by its attributes and be assessed via the levels of the attributes themselves (Ryan, 2004). 50 Fabio Boncinelli, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini The DCEs are conducted by means of interviews that seek to reproduce a choice situation as close as possible to that of a real purchasing decision (Ben-Akiva et al., 2019). The interviewee is presented with several product alternatives that differ by the different levels of the attributes considered. The choice of these attributes and levels is a crucial point in carrying out the DCE. This issue becomes particularly important when dealing with complex products (such as wine, beer, motor vehicles, and property), the valuation of which is subject to a large number of stimuli. In fact, while considering many elements of value to describe the products can, on one hand, render the experiment more realistic, on the other hand, a large number of attributes and levels makes the experimental design difficult to manage (Hoyos, 2010), increases the variance of the error term, and entails a cognitive effort for the respondent that can become an error of evaluation (Arentze et al., 2003; Caussade et al., 2005). Moreover, it is also fundamental to not omit the attributes that are important for the majority of consumers, so as to avoid overestimating the importance of the attributes included in the choice task (Boncinelli et al., 2017; Casini et al., 2009; Corduas et al., 2013), and to avoid respondents making inferences about omitted attributes without the researcher being able to have information about them (Lancsar and Louviere, 2008). In this regard, Ben-Akiva et al. (2019) point out that the presentation of incomplete product profiles in the DCEs is a widespread issue among scholars. The same authors claim that the resulting fill-in problem puts the interviewees in the condition of making unrealistic and heterogeneous assumptions about missing attributes. Many studies have tackled this issue defining in greater detail the context of reference where the actual choice is made. In this manner, the attributes considered important, but that are not included in the experiment, are described in context by the researcher, and therefore represent a scenario shared by all choices and all respondents. This solution presents some difficulties, however. In fact, when dealing with complex products, an excessively detailed description of the scenario can lead to high rates of no-choice, as excessively specific products are proposed that may not prove interesting to many consumers. Furthermore, scenarios with too many details would lead to creating an experiment that would be valid only for specific cases, and therefore, incapable of assuming a general value. In order to make the experiment as realistic as possible, Ben-Akiva et al. (2019) recommend building it so as to maintain the same complexity of the real market in defining the products, possibly also incorporating the filtering heuristics in the choice of the product. Indeed, as pointed out by Swait and Adamowicz (2001), in a real market where goods comprise many attributes, consumers often adopt filtering heuristics that consists of screening out products that fail to pass thresholds on selected attributes. In view of making a contribution to these issues, our study proposes a methodology to build the choice experiment in which defining the scenario is based on what each interviewee states about the attributes and levels considered for the choice of the product being analyzed, according to a procedure analogous to that of filtering heuristics. It is thereby possible to obtain a choice scenario tailored to respondents’ behavior. In literature, the studies that have attempted to adapt the experiment to the respondents have modified the attributes of the choice sets, applying the Adaptive Choice Experiments or Menu Choice methodologies (Contini et al., 2019; Liechty et al., 2001; Toubia et al., 2004; Yu et al., 2011). In the ambit of environmental economics, the personalization of the experiment concerned the statusquo option (see, as example, Ahtiainen et al., 2015). To our knowledge, however, there are no studies that have worked on personalizing the choice scenario, which makes our proposal the first contribution in this sense. The article illustrates this proposal of methodology applied to the case study of wine. The choice of wine derives from the consideration that it is a complex product whose preferences depend on an abundance of extrinsic and intrinsic attributes (Charters and Pettigrew, 2007; Contini et al., 2015; Oczkowski and Doucouliagos 2015; Schmit et al., 2013). The literature review presented in the following section illustrates the way these attributes were used in building the choice experiments on wine. In our DCE, besides the attributes used in the choice sets, the scenario was described leaving the interviewees free to choose the attributes they felt were most important from among the principal attributes of literature. Using a mixed logit model, the results of this approach are compared with those obtained by applying the conventional methodology in which the researcher chooses a priori the elements to define the scenario. Moreover, the information collected on the choice criteria of the interviewees can be utilized for further analyses on consumer behavior. In our case, for example, this information was used to obtain a more meaningful segmentation by a latent class analysis. In the discussions section, a critical analysis is performed on the methodology and several suggestions are made for a further development of studies. 51 The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios 2. LITERATURE REVIEW We conducted a systematic review of the articles published on the study of wine preferences from 1998 to 2019 by applying DCEs. Relevant articles were identified and gathered from two scientific article databases (Scopus, Web of Science) and a web search engine (Google Scholar) by means of using the following keywords: “choice experiment” AND “wine”, “choice modeling” AND “wine”, “discrete choice” AND “wine”. We selected only articles published in journals indexed in WOS and Scopus, excluding conference proceedings. We found a total of 35 studies. The various attributes that appeared in the selected articles were reclassified in the following 15 categories: “alcohol content”; “awards” includes awards and mentions in guidebooks; “brand” includes the indication of the producer, bottler, and brand notoriety; “format” includes characteristics like bottle capacity and shape; “functional properties” concerns the presence of information on health benefits; “price”; “production methods” conveys information on the production process, including various certifications of an environmental nature, such as organic; “promotion” states whether a discount is offered; “protected geographical indication” includes the geographic indications of different countries and regions like, for example, the DOCGs in Italy or the AOCs in France; “region of production”; “sulfites” i.e. the absence of added sulfites; “taste”, such as, for example, fruity, sweet, tannic, and full-flavored; “typology” includes the typologies red/ white, still/sparkling, the grape variety, and the name that identifies the wines, such as, for example, Chianti or Champagne; “winery distinctiveness” includes information about the producer, such as company history, label graphics, and company web site; “consumption advice” includes advice to enhance the consumption experience by means of pairings with particular dishes, and indications on the best modalities for enjoying the wine, such as, for example, the serving temperature. In addition to these elements, we also examined the “occasion”, which is to say the special or usual situation of consumption, at home or with friends, insomuch as the preference for the attributes evaluated in the DCEs also depends on the situational variables connected with the social and physical environment in which the wine is consumed (Boncinelli et al., 2019). The experiments reviewed utilized the aforesaid categories either to describe the choice context, which is to say the scenario defined by the researcher and shared by all of the choice sets, or as attributes that characterize the alternatives in the choice set. The different use in the choice experiment is synthetically illustrated in Table 1, where “C” means that the element is used in describing the context, and “A” indicates that the attribute describes the choice option. In addition to price, the review shows that the category most utilized in the literature is wine “typology”, which is found in experiments both as a choice attribute (17 articles) and as a context (13 articles). To be more exact, the information on color and style (still or sparkling) is used in defining the context, while the information on grape variety or wine name are among the choice set attributes. Next in line for frequency of use is the “region of production” (21 articles), which was always used in the DCEs as a choice attribute. Conversely, the “format” was almost always considered as a context variable (18 times out of 19). “Brand”, “designation of origin”, “production methods”, “alcohol content”, “taste”, “winery distinctiveness”, “acknowledgements”, and “consumption advice” are less studied in the literature and are mostly treated as choice attributes. In particular, to date, no studies have used awards and the evaluation in specialized guidebooks as a context, which is to say that none have formulated a DCE in which the preference for awardwinning wines is evaluated. Finally, only a limited number of studies have used choice attributes like absence of added sulfites (2 articles), nutraceutical characteristics (2 articles), and offer of discounts (2 articles). Defining the “occasion” is used as a context variable and is found in 22 articles out of 35. This description shows that almost all of the 15 categories of attributes considered are found in a consistent number of studies, thus confirming that the choice process of wine takes numerous attributes into account. The difficulty of implementing DCEs with all of the important attributes, however, has led researchers to select only a few attributes in making the experiments, inevitably reducing the realistic nature of the choice. In particular, in building the choice sets, an average of 4 categories are employed (each of which almost always represented by a single attribute), while the definition of the scenario involves, on the average, 1-2 categories more. Our study proposes to surpass these limits by defining a methodology to create the DCE that makes it possible to take account of most of the attributes of the complex product that are considered important, guaranteeing sufficient effectiveness in developing the experiment. 3. METHODOLOGY This section opens with a presentation of the procedure applied in our experiment; it then presents the 52 Fabio Boncinelli, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini Table 1. Factors used in the choice experiments on wine classified as context elements (C) or as choice attributes (A). Author(s)/ Date Occasion Wine Attributes Alcohol content Awards Brand Consumption advice Format Functional properties Price Production methods Promotion Protected geographical indication Region of production Sulfites Taste Typologya Winery distinctiveness Boncinelli et al. (2019) C A C A A A C  Escobar et al. (2018) C A C A A A Palma et al. (2018) A A C A A C A A Scozzafava et al. (2018) C C A A Delmas and Lessem (2017) C A A A A C Ghvanidze et al. (2017) C A A A A C Huang et al. (2017) C A A C Williamson et al. (2017) A A A C A Palma et al. (2016) C A A A Scozzafava et al. (2016) C C A A Troiano et al. (2016) C A A A A A Williamson et al. (2016) C C A C C A A A A Gassler (2015) C  A     A A A  A  A C A Costanigro et al. (2014) A A A A C Lontsi et al. (2014) A A A Stasi et al. (2014) A C A A A Xu and Zeng (2014) C A A A A C Cicia et al. (2013) A A C Mueller and Remaud (2013) C A A A A A A A A A Kallas et al. (2013) C A A A A Sáenz-Navajas et al. (2013) A A A C A A A A A Thiene et al. (2013) A C A A A Corsi et al. (2012) C A A C A A A A Kallas et al. (2012) C A A A A Zhllima et al. (2012) C A A A A Jarvis et al. (2010) C A A C A A C Mueller et al. (2010a) C A A A A C A Mueller et al. (2010b) C C C A C A A C A A A Mueller et al. (2010c) C A A A C A A A A C Barreiro-Hurlé et al. (2008) A C A A A A C Hertzberg and Malorgio (2008) C A C A A A Alimova et al. (2007) A A A A Lockshin et al. (2006) C A C A A A C Mtimet and Albisu (2006) C A C A A A Perrouty et al. (2006) A A A A Notes: a Typology includes the type of wine (red/white, still/sparkling), the grape variety, and the names of the wines used (e.g. Chianti, Brunello di Montalcino). 53 The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios econometric model employed, and ends with a description of the sample. 3.1. Experimental procedure Our experiment was conducted in January 2018 by administering an on-line questionnaire to a sample of 600 Italian wine consumers. A company specialized in market research (Toluna Inc.) handled recruiting participants and collecting data. In particular, the experiment consisted of a DCE divided into two treatments. Following a betweensubject approach, each respondent was randomly assigned to only one of the treatments. In this manner, two subsamples of 300 respondents each were formed. We called the first treatment “limited information”. It is tantamount to a conventional unlabeled DCE in which the description of the scenario conveys the information that the experiment concerned a 0.75-liter bottle of red wine for an occasion of everyday home consumption. In the second treatment, which we called “full information”, every single respondent received the same information as the first treatment, plus a description of the scenario that was more detailed and consistent with his purchasing habits. The description of the scenario was based on questions asked prior to the choice experiment. The procedure of the second treatment can be divided into 3 steps. In the first step, respondents were asked to select, from a list we drew up based on the literature review, the criteria that they normally use in choosing wine. The criteria they could select from were: the wine’s region of origin, the grape variety, the brand, alcohol content, and mention in guidebooks. In the second step, for each criterion selected, the participant was asked to select their preferred option from a dropdown menu containing the principal possible alternatives (Table 2). For example, if the interviewee indicated grape variety as a choice criterion, then he was asked to select the one he habitually preferred from a list of 20 grape varieties. In the third step, the respondents participated in a DCE where the choice scenario was defined on the basis of the information collected in phases 1 and 2. In other words, the respondents received a choice scenario “personalized” to their purchasing habits. In this manner, we were able to work around the problem that each respondent could make inferences about the attributes important for them but not included in the choice experiment and that the researcher could therefore not survey. By way of example, the respondent who selected Tuscan wines produced from the Sangiovese grape variety and with an alcohol content of 13° performed the choice experiment reported in Fig. 1. The attributes included in the choice tasks, identical for the two treatments, number 4 (Table 3). The first attribute concerns the organic production method with two levels: conventional (the product does not have an organic certification) and organic (the product carries the European logo concerning organic certification). The second attribute concerns sulfites with two levels: contains sulfites, no sulfites added. The third attribute considered concerns the geographical indications (GI). The levels of GI are those regulated by the Italian classification system of GI wine (Italian Law 238/2016). The levels utilized for the GIs are: DOCG (Designation of Table 2. Information to form the choice scenario. Criteria Available Options Origin Abruzzo, Basilicata, Calabria, Campania, Emilia Romagna, Friuli-Venezia Giulia, Lazio, Liguria, Lombardy, Marche, Piedmont, Apulia, Sardinia, Sicily, Tuscany, Trentino Alto Adige, Umbria, Valle d’Aosta, Veneto, International wine. Grape variety Aglianico, Barbera, Bardolino, Bonarda, Cabernet, Cabernet Sauvignon, Cannonau, Corvina, Dolcetto, Gutturnio, Lambrusco, Merlot, Montepulciano, Morellino, Negroamaro, Nero D’Avola, Primitivo, Sangiovese, Syrah, Teroldego, Other Brand Well-known, Unknown Alcohol content Less than 12%, 12%, 13%, 14%, 15%, more than 15% Mention in guidebooks Mentioned, Not mentioned Figure 1. Example of a choice experiment. Imagine you need to purchase a 0.75-litre bottle of red wine from Tuscany, made from the Sangiovese grape variety and with an alcohol content of 13% for everyday consumption (which is to say not tied to special occasions). In each choice set, from among the alternatives proposed, choose the one you would purchase. In the event that none of the alternatives is to your liking, you can select the no-choice option 54 Fabio Boncinelli, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini Controlled and Guaranteed Origin), DOC (Designation of Controlled Origin), and IGT (Typical Geographical Indication). The DOCG wines are subjected to stricter regulations than the DOC wines. The DOC wines instead respect stricter regulations than the IGT wines. Finally, the fourth attribute is price with 4 levels: € 2, € 6, € 10, € 14. Each respondent was required to answer 8 choice questions, indicating in each choice task their preferred wine between two product alternatives that differed by attribute levels. Each choice task also included a no-buy option. The experimental design was done by means of the Ngene software version 1.1.2, applying an orthogonal fractional design. 3.2. Econometric model DCEs have their theoretical foundations in Lancaster’s consumer theory (1966), which postulates that the utility deriving from the consumption of a certain good is a function of the same good’s characteristics. We can therefore model the product’s utility in function of the attributes included in the choice tasks and handle the information collected with the DCE by means of a mixed logit model (Train, 2009) that takes account of the unobserved heterogeneity across the sample. The utility function of the individual i obtained from the choice alternative j in the choice task t is as follows: Uijt = ASC + αPRICEijt + β’ixijt + εijt (1) where ASC is an alternative-specific constant that represents the no-buy option; α is the marginal utility of the price; PRICE represents the price levels offered to the respondent to purchase a bottle of wine; βi is the vector of utility parameters for participant i; xijt is the vector of the wine’s attributes and their levels with respect to alternative j, individual i and choice task t. Finally, εijt is an unobserved random term. In the specification of our model, PRICE and ASC have been estimated as fixed coefficients, while the coefficients of the other attributes (organic certification, sulfites, and GI) have been assumed as independently distributed following a normal distribution. Therefore, in addition to the median effect, for each attribute, a standard deviation was estimated for each of the random components. The model has been estimated by STATA 15.1. We used the mixed logit model to compare the results of our approach with those obtained by applying the conventional methodology in which the researcher chooses a priori the elements to define the scenario. We then created a latent class model (LCM) in order to provide an example of how the information obtained with our proposed procedure can be used to obtain a more meaningful segmentation. The LCM represents the semi-parametric version of a mixed model inasmuch as heterogeneity has a discrete distribution with C mass points, where C represents the number of classes with which the model is estimated (Greene and Hensher, 2003; Hynes and Greene, 2016). The LCM considers that every single individual belongs to a specific latent class c, where c = 1, ..., C; where all of the individuals belonging to that class have homogeneous preferences but are heterogeneous with respect to the individuals belonging to other classes. We can therefore write that following Greene and Hensher (2003), the probability that individual i in the choice task t chooses the alternative j among the J alternatives is: (2) where βc is the vector of utility parameters of class c. The model estimates the parameters of the attributes for each class, as well as the probability of each individual πic to belong to a specific class c. This process too, can be modeled as a multinomial logit (Greene and Hensher, 2003; Ouma et al., 2007; Wu et al., 2019): (3) where zi is the vector of the respondent’s observed individual characteristics and γc is the parameter vector for consumers in class c. In our case, zi represents the criteria that respondents stated they normally use in choosing wine, which is to say the information collected in the first step of the experimental procedure with the full information group. Table 3. Attributes and levels in the choice experiment. Attributes Levels Organic claim Organic, none No sulfites added No sulfites added, contains sulfites Geographical indications DOCG, DOC, IGT, none Price €2, €6, €10, €14 55 The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios 3.3. The sample Six hundred Italian respondents filled in the questionnaire, 300 for each treatment. All participants were screened to ensure they were over 18 years of age and had consumed wine in the previous months. The overall sample consists of approximately 48% men and 52% women. The different age categories are well represented and most of the respondents have a secondary education. However, the consumers with a university degree are slightly over-represented. The two sub-samples have the same socio-demographic make-up as shown by the Chi-squared test (Table 4). 4. RESULTS This section presents the choice criteria selected in the first step of the experiment, the results of the mixed logit models and the latent class analysis. 4.1. Choice criteria Table 5 reports the frequencies with which respondents chose criteria in the course of the first step of the experiment. The information most used is origin, indicated by 77% of the respondents, followed by brand, selected by approximately 69% of the interviewees. Guidebooks are utilized by just over one-fifth of the sample and represent the criterion used less frequently. As interviewees were given the possibility to choose one or more criteria, an overall 30 combinations were chosen, the first 10 of which represent 73% of all of the respondents (Fig. 2). The combination of origin and brand is the most numerous, and is utilized by almost 14% of respondents. The successive combinations add to these two criteria, alcohol content and grape variety. The group of respondents that utilizes all 5 criteria (8.7%) is quite consistent, while the groups that use a single criterion are few. Among these, the most conspicuous is in fact the group that only considers origin, which represents only 4% of respondents. The results of this first explorative analysis confirm that the choice of wine is very complex, that there are large differences between consumers, and that defining the product in creating the choice experiment can therefore be critical. 4.2. Likelihood ratio tests for pooled models To test whether the coefficients between the two models are equal, we used the likelihood ratio (LR) test. The LR test is calculated as: Table 4. Sample composition (%). Limited information scenario Full information scenario Prob.>Chi2 Gender Male 48.67 49.00 Female 51.33 51.00 0.93 Age 18–34 years 24.00 23.00 35–54 years 35.67 36.33 55–80 years 40.33 40.67 0.95 Education Primary education 7.67 7.67 Secondary education 49.67 55.00 Tertiary education 42.67 37.33 0.06 Geographical area Northern Italy 46.67 47.00 0.99 Central Italy 18.33 18.00 Southern Italy and Islands 35.00 35.00 Table 5. Frequencies with which the respondents chose criteria in the course of the first step of the experiment. Attributes Relative frequency (%) Origin 77.00 Brand 69.33 Alcohol content 50.00 Grape variety 49.67 Mention in guidebooks 21.67 Figure 2. Frequencies concerning the first 10 combinations of the habitual choice criteria. 56 Fabio Boncinelli, Caterina Contini, Francesca Gerini, Caterina Romano, Gabriele Scozzafava, Leonardo Casini LR = -2(LLpooled - (LLlim_info + LLfullinfo)) (4) where LLlim_info is the log-likelihood of the model applied to the sub-sample with limited information, LLfullinfo is that of the model for the group that received the treatment with full information, while LLpooled is the log-likelihood pertaining to the pooled model. The LR test has a Chi squared distribution with a number of degrees of freedom equal to the difference of the number of parameters. Table 6 reports the results of the LR test calculated both with a model specified in the utility space and with a model specified in the WTP space. The latter model serves to make sure that the results are the same in both of the specifications and to take into account the scale heterogeneity between the two subsamples. For both of the models, the LR statistics do not significantly exceed the critical values. Based on this outcome, we can affirm that the results between the two sub-samples are different. 4.3. Parameter estimates Table 7 reports the results of the mixed logit models for the limited information scenario, the full information scenario, and the pooled model. In both scenarios, the parameters of the attributes are 99% significant and bear the expected signs. With the exception of that of the IGT with limited information, the coefficients associated with the standard deviations are also all significant, which indicates a substantial heterogeneity in consumer preferences with respect to the attributes considered in the model. Specifically, the coefficient of the no-buy option is negative in both models, which indicates that the consumers receive a greater utility from choosing at least one of the options presented compared to the no-choice option. As expected, the coefficient of price is negative for both of the scenarios, indicating that the increase in price corresponds to a decrease in consumer utility. For this parameter, the magnitude is substantially different in the two scenarios, -0.10 for the limited information scenario compared to -0.05 for the full information scenario, indicating the lesser role of the price attribute in the utility function in the latter case. The parameters of the other attributes’ levels all prove to be positive in both of the scenarios, thus indicating that the consumers prefer wines without added sulfites, with geographical indication, and organic. In particular, the absence of added sulfites is the parameter with the greatest magnitude and thus constitutes the characteristic that on a par with other conditions confers greater utility to wine. From the analysis of the confidence intervals, we can also note that the two models substantially differ only by the parameter of price. Indeed, as we have already pointed out, the coefficient of price for the full information scenario is about half that of the limited information scenario, and the confidence intervals in the two models do not overlap. To further verify the determinants of the differences between the two sub-samples, a new model was performed on the pooled sample, inserting variables of Table 6. Results of the log-likelihood ratio tests. Preference Space Model WTP Space Model Log likelihood limited information scenario -2011.78 -1969.72 Log likelihood full information scenario -2040.38 -1969.69 Log likelihood pooled model -4065.40 -3951.07 LR test statistics 26.49 23.31 Degrees of freedom 12 13 p-value 0.009 0.039 Table 7. Results of the mixed logit models. Attributes Limited Information Scenario Full Information Scenario Coef. 95% C.I. Coef. 95% C.I. Random parameters in utility functions Organic 0.41 *** (0.11; 0.71) 0.37 *** (0.09; 0.65) No sulfites added 1.79 *** (2.02; 1.55) 1.73 *** (1.98; 1.48) IGT 0.77 *** (0.47; 1.06) 0.92 *** (0.62; 1.22) DOC 0.94 *** (0.6; 1.27) 0.93 *** (0.61; 1.25) DOCG 0.68 *** (0.43; 0.93) 0.73 *** (0.49; 0.98) Non-random parameters in utility functions Price -0.10 *** (-0.13; -0.08) -0.05 *** (-0.08; -0.03) No-buy -1.36 *** (-1.71; -1.01) -0.96 *** (-1.29; -0.62) Standard deviation Organic 0.94 *** (0.74; 1.13) 0.46 *** (0.19; 0.74) No sulfites added 1.20 *** (0.96; 1.43) 1.42 *** (1.17; 1.67) IGT -0.12 (-1.04; 0.79) 0.66 *** (0.29; 1.03) DOC 0.94 *** (0.64; 1.24) 0.53 *** (0.15; 0.91) DOCG -0.85 *** (0.53; 1.17) 0.91 *** (0.61; 1.21) Observations 7,200 7,200 BIC 4130.14 4187.35 AIC 4047.55 4104.77 Notes: Asterisks indicate the following significance levels: *= 10%; **= 5%; ***= 1%.; Coef. = Coefficient; C.I. = Confidence interval.