Mass customization design_choice_menu


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Mass customization design_choice_menu

  1. 1. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006 Designing choice menus for mass customization Flávio Sanson Fogliatto (DEPROT/UFRGS) Giovani J. C. da Silveira (University of Calgary/Canadá) giovani.dasilveira@haskayne.ucalgary.caAbstractThis paper proposes a method for designing choice menus for mass customization. Themethod is based on the analysis of stated preferences on product or service attributesobtained through panel studies. The method is presented, followed by a real world caseapplication in a natural gas distribution company. The application indicated the method wasable to elicit stated preferences on a broad range of attributes enabling the design of choicemenus for alternative customer segments, balancing the trade-off between flexibility andvalue that is in the core of choice menu design.Keywords: Mass customization, Choice models, Conjoint analysis, Stated preference.1. IntroductionMass customization (MC) has been defined as the ability to produce individually designedproducts and services at near mass production cost (DAVIS, 1987). It is enabled by a series ofadvanced technologies and practices including flexible manufacturing systems, computeraided design, and lean manufacturing (Da SILVEIRA et al., 2001). Over the last decade, MChas evolved from being a visionary idea to become a widespread strategy in manufacturingand service industries. Research on MC has also progressed from an initial focus on themanufacturing capabilities to produce variety at low cost to a broader emphasis on supplychain coordination and customer involvement in the conception of MC products and services.In this expanded view of MC, facilitating customer involvement in the process ofspecification and design of a personalized product has become one major determinant of asuccessful customization strategy (DURAY, 2002). Due to the limitation of traditionaltechniques such as surveys and interviews to elicit individual customer preference in anefficient and reliable manner, firms have been increasingly using choice menus (LIECHTY etal., 2001). Choice menus consist of producer-user interfaces that enable customers to selectproduct attributes and features in a consistent and economical way (OLIVA, 2002).Notwithstanding the increasing presence of choice menus in business and consumer industriesranging from personal computers to financial services (SLYWOTZKY, 2000), few studieshave focused on problems associated to their design and configuration. In particular, thedesign of choice menus must balance a trade-off between flexibility and value to customers,as complexity aversion implies that the value of a menu often decreases with cardinality.Despite the documented cases of companies that have been challenged by this particulartrade-off (WIND & RANGASWAMY, 2001), there has been limited research on methods tospecify the set of options to use in choice menus.This paper proposes a method for choice menu design in a MC context. The method, based onthe analysis of customers’ stated preferences regarding product or service attributes, has someimportant features. First, it is based on the use of well-known market research techniques,such as focus groups, questionnaires and SP modeling, not demanding any special trainingfrom analysts. Second, it uses cluster analysis and experimental design techniques to guideICIEOM 2006 ABEPRO 1
  2. 2. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006data collection from customers, leading to databases that are both cost-efficient andrepresentative. Third, it proposes the use of SP modeling using logistic regression, which isboth easy to interpret and available from common statistical packages.2. Research backgroundThis literature review is divided in two parts. Initially, we present the current relevantresearch on choice menus. Then, we introduce basic aspects of stated preference modeling.The ability of customers to co-design products or services based on individual preferences isone of the most distinctive features of MC. Over recent years, firms have been increasinglyoffering choice menus to allow customers to design solutions by selecting items that can bestfulfill their needs (LIECHTY et al., 2001). Choice menus, also called choiceboards or designpalettes, involve a broad range of customer-supplier interfaces, from simple menus offeringproduct options and features to intelligent aid, mostly web-based systems assisting in theprocess of designing, comparing and cost-estimating mass customized orders.From a research perspective, the major challenge with choice menus is the specification of a setof options to balance flexibility and value to customers. On one hand, larger menus provide moreflexibility as they offer more options. On the other hand, complexity-aversion implies that thevalue of a menu decreases with cardinality (SONSINO & MANDELBAUM, 2002). Problemswith flexibility-value trade-offs are compounded by the fact that both flexibility demands andcomplexity-aversion levels will often vary across customer groups. Demand for flexibilityincreases with increased uncertainty about future tastes; complexity aversion increases with thedesire to minimize the risk of making wrong decisions (STODDER, 1997). Thus, as pointed outby DeShazo and Fermo (2002), designers must build choice sets that minimize the detrimentaleffects of choice complexity to a customer category. In practice, information about customerpreferences must be used to tailor the design of the choice menu itself, customizing the set ofoptions presented to buyers and promoting sales (SLYWOTZKY, 2000).The stated preference (SP) method is an applied conjoint paradigm that quantifiesrespondents’ choices regarding hypothetical market situations (UNTERSCHULTZ et al.,1997). Preferences are given to commodity alternatives decomposed into separable attributes,each of which can be examined for their individual influence on choice. This approach isderived from Lancaster’s (1966) theory of characteristics stating that utility is derived notfrom goods themselves, but from the attributes or characteristics of goods.The SP method is based on random utility theory (RUT) choice models. The theory is derivedfrom the observation that an individual can make different judgments from one occasion tothe next. Therefore, utility is expressed as a sum of observable and non-observable (random)components (HENSHER et al., 1999): U in = Vin + ε in , (1)where U in is respondent n’s utility of choosing alternative or scenario i, Vin is the systematic,observable component of utility and ε in is the random component. The utility Vin ofalternative i is a function of its attributes, which is often assumed to have linear form, Vin = β 0 + β1 xin1 + β 2 xin 2 + ... + β k xink , (2)where Vin is respondent n’s systematic utility of alternative i, xink are the attributes ofalternative i for respondent n and β 0 to β k are the coefficients to be estimated.ICIEOM 2006 ABEPRO 2
  3. 3. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 20063. MethodThe method proposed in this paper is illustrated in Figure 1. There are three phases, each witha number of operational steps and decision points detailed next. Although the method may beapplied in the design of product and service choice menus, we hereafter refer to products only. Figure 1 – Phases and operational steps of the method proposedThe main objective in Phase 1 is to obtain a comprehensive list of product attributes relevantto customers. Such attributes will be treated as candidate choice menu variables. We start thisphase by analyzing the product market in terms of customer diversity. When customers havetheoretically distinct customization demands it may be advisable to qualitatively cluster them,using expert opinion, prior to data collection; otherwise, a representative random sample ofunclusterized product customers may be used in the focus group sessions.Data collection using focus groups is typically accomplished in six steps: (i) focus groupplanning, (ii) participants selection, (iii) definition of questions and session moderationguidelines, (iv) definition of sessions logistics, (v) choice of moderator and (vi) data collectionand analysis. For details on each step, see Greenbaum (2000).Information in Phase 1 may be organized in a table, with columns headings corresponding to adescription of the ad hoc clusters (in case they arise in the analysis), and row elements given by theattributes elicited by individuals in focus groups. Ad hoc clusters are identified by Ci′ , i = 1 I ′ ,  and attributes by Aij , j = 1 J . It is expected that attributes in different clusters coincide. ¡Objectives in Phase 2 are (i) to obtain importance weights wij to attributes Aij listed in Phase1 and (ii) group customers using formal clustering techniques. To accomplish objective (i) wepropose a quantitative research using a questionnaire for data collection; to accomplishobjective (ii) a cluster analysis may be performed.Using the proper sampling technique is fundamental in the research design. If the sample isintended to be a smaller scale representation of a population of interest, the researcher shouldknow beforehand whether customers differ substantially regarding customization demands. Incase there is evidence to the existence of clusters of customers, their proportion in thepopulation should be estimated, to enable a probabilistic sampling of that population.Otherwise, a non-probabilistic sampling strategy may be appropriate (for details on samplingstrategies and samples size determination see LEVY & LEMESHOW, 1999).Questionnaires must be elaborated such that the objective of data collection is clear toICIEOM 2006 ABEPRO 3
  4. 4. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006respondents upfront; for that matter, a title and introduction text must always be used. Ifpossible, the analysis should group similar customization attributes and provide a subtitle toeach group. As the number of attributes increases, grouping of items becomes crucial.To gather importance weights wij from respondents there are two possible approaches. Incase j > 15 and attribute descriptions are complex, a good strategy is to ask respondents torank attributes within each group. An item ranked in the k-th position will be given animportance weight 1 k (similar to situations where a ratio scale is used, importance of itemsbecomes directly comparable within a group). If the number of attributes is small, weightsmay be elicited using an importance scale, such as the Likert 5-point scale. Weights from allrespondents are then added up to obtain the final importance weight for a given attribute.Once importance weights are available to each attribute, cluster analysis is performed using wijas classificatory variables. The objective is to formally identify clusters of customers with similardemands in terms of product customization attributes and optimize data collection in Phase 3. Ifad-hoc clusters of customers were previously identified in Phase 1, formal cluster analysis willallow validation of such clustering. We denote the new clusters by Ci , i = 1 I , and point out  that I ′ may differ from I , and that the final clustering of customers should arise from carefulanalysis of the groupings in Ci′ and Ci , since they were generated using different information.The main objective in Phase 3 is to obtain preference models relating stated preference (SP) and productattributes. Design of experiments (DOE) in conjunction with SP modeling is used for that purpose. DOEguarantees a data collection both efficient and economically feasible. The SP method allows datamodeling where the influence of individual attributes as well as their interactions on customer preferencemay be assessed. Once SP models are at hand choice menus may be defined for the product, both interms of attributes to be customized as in terms of choice levels within each attribute.We stress the importance of building models where the significance of interactions betweenattributes is assessed. SP data are usually summarized using main effect models, which demand alow cost data collection. However, customization attributes of a product may not affectindependently customer preference and therefore the significance of interaction terms should beverified. Clustering of customers in Phase 2 allows us to use more complex, yet still economicallyfeasible data collection designs enabling SP modeling of both main effects and interactions.The SP method is used here to compare a control (or reference) scenario against severalalternative scenarios. The control scenario expresses the standardized version of the productunder analysis. In the alternative scenarios, product attributes are made flexible to createdifferent customized products. To make an attribute flexible implies in setting it to a givenlevel. Therefore, an SP customization model will indicate not only the relative importance ofattributes but also the market benefits from offering each attribute at different levels.As previously mentioned, data collection for SP modeling must be planned separately withinclusters of customers. Recall that customers in a given cluster value similarly the samecustomization attributes. Therefore, creating SP scenarios specifically for each cluster enablesto include only attributes that are highly valued by them, reducing the number of attributesand consequently the number of alternative scenarios in the SP study.We recommend using factorial designs to organize the SP data collection process. In a factorialdesign applied to collect SP data from a given cluster of customers, attributes are varied withinpre-defined levels to generate alternative scenarios such that the total number of scenariosICIEOM 2006 ABEPRO 4
  5. 5. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006analyzed will be given by N = ∏ j =1 k j , where k j denotes the number of levels of attribute j. JClearly, a large number of attributes and levels will lead to a large data collection, which is usuallyundesirable. Therefore, we should restrict the SP study to include attributes with large values of wij(excluding attributes not included in the study from the choice menu). Similarly, the number oflevels of an attribute should be greater than two only if there is strong evidence of a non-linearrelationship between attribute levels and the response (i.e. the stated preference). Fractioning and/orblocking the experimental data collection matrix are usually necessary in SP studies and are likely tobe needed when applying the method we propose. If clustering of customers in Phase 2 leads toclearly defined clusters, blocking the data collection within clusters is the recommended course ofaction; otherwise, fractioning should be preferred. Once the experimental matrix is defined, datacollection may take place following the guidelines of SP studies such as Bateman et al. (2002).Data modeling should lead to preference models for each cluster, following the guidelines insection 2. Eq. (2) gives the respondent’s preference to a given scenario, which is the usualoutcome of an SP study. Here, respondents are customers grouped in clusters and it isassumed that they share the same model. Using such models, it is possible to arrive to choicemenus for the product under study. This is done upon inspection of their regressioncoefficients and preference values, as follows. Coefficients that are significant at (1 – p)% areranked in importance according to their p-values (typical choices are p < 0.05 or p < 0.1); theyindicate the attributes to be included in the choice menu. Attributes that appear significantexclusively in interaction terms should also be considered for inclusion in the menu. Thenumber of levels of an attribute in a menu should be defined considering the attribute’s rankposition and the practical relevance of offering a large number of levels to customers.Menus may also have their attributes and levels defined based on preference threshold valueschosen by the analyst, performing simulations with the preference models. For example, itmay be possible that only certain levels of an attribute yield predicted preference values abovethe threshold; these are thus the levels to be included in the choice menu. It may also happenthat even excluding an attribute from the model one is able to determine combinations oflevels for the remaining attributes that yield predicted preference values above the threshold.If parsimony is sought, such attributes could be also excluded from the choice menu.4. Case applicationThe method proposed in this paper was applied in a case study where the main objective wasto determine product, service and technological attributes valued by potential natural gas(NG) customers of a Brazilian distribution company, as well as their proper customizationlevels. Although available in several regions in central Brazil, only recently the NGdistribution network reached the south of the country, where the present study took place. Awide variety of industrial, commercial, automotive and residential NG applications arepossible, and customer needs regarding the commodity tend to vary according to end use.Following the steps proposed in section 3 we were able to identify clusters of NG customersin the geographical region of interest and create choice menus for them, as detailed next.We start describing the steps in Phase 1. To obtain the list of NG attributes from focus groupsin the case study, ad-hoc clustering of customers was mandatory for two main reasons. First,there are a large number of uses for NG reported in the literature (for example, as a vehicularfuel in cars and buses as well as in the air-conditioning of hospitals). Although the product isessentially the same, some of its attributes are more or less valued according to the final use.Therefore, treating all potential NG customers as members of the same population would leadICIEOM 2006 ABEPRO 5
  6. 6. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006to a list of attributes excessively broad and non-representative. Second, data collection tendsto be time consuming if applied to a large number of focus groups. Therefore, we mustdeviate from the ideal case where a list of customization attributes is obtained from each typeof NG customer, and collect data from groups of customers that use the commodity similarly.The first clustering of NG customers was obtained following a three-step approach. We firstidentified economic sectors with a reported history of NG use in the literature. We then verifiedboth the presence and the economical representation of such sectors in the geographical regionof interest. Finally, ad-hoc clustering of sectors was obtained based on expert opinion frommembers of the NG distributor technical staff. A total of 39 relevant economic sectors, allpotential users of NG, were identified in the region. Their typical NG applications were listedand served as basis to clustering. As partially shown in Table 2 (first and second columns),eleven clusters of customers (identified as economic sectors) were formed. Focus groups werelimited to ten participants. In a given focus group, the number of cluster sector representativeswas determined based on their economical relevance and NG usage potential. Economic sectors Ad-hoc clustering ( Ci′ - Phase 1) Formal clustering ( Ci - Phase 2) Supermarkets 1 5 Meat markets 1 1 Open malls 2 1 Hotels 2 5 Hospitals 2 5 Food manufacturers 3 2 Rubber processors ¢ 3 ¢ 5 ¢ Tanneries 11 5 Table 1 – Sample of ad-hoc and formal clusters in the case studyInformation gathering from the 11 focus groups was completed in approximately 4 months.Sessions varied in length from 60 to 90 minutes and were moderated by one individual and twoassistants. Invited members from sectors in each cluster voluntarily participated in the meetings.They were requested to list important attributes related to the product, the distribution network (andservices provided by the distributor) and the technological aspects of using NG (in particular thoserelated to equipment conversion and its maintenance). At the end of each section, participants wererequested to rank attributes in importance within each category. Although respondents were awarethat attributes listed should be preferably customizable, there were exceptions (for example, supplylongevity). Table 2 displays a list of the highest ranked attributes in each category over all groups. Product Network EquipmentPrice Time to supply in large scale SafetyOperative performance Network capillarity Compliance to legislationEmission of pollutants Contractual conditions Specific technical solutionsStorage space Supply regularity MaintainabilityReliable measurement system Supply diversity Technical literature availabilityNG adoption projects provided by Standardized valves and Technical training prior todistributor connectors equipment useSupply longevity Network maintenance Convertibility Cost to install NG tubulation Multi-fuel capacity (NG / LPG) Network pressure Operational cost Table 2 – Some NG attributes as elicited by consumers in focus groups ′Ranking of attributes varied substantially among clusters. For example, customers in C1ICIEOM 2006 ABEPRO 6
  7. 7. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006ranked price, supply regularity and operative performance as most important attributes, while ′customers in C11 elected time to supply in large scale, price and supply longevity. Thatexemplifies the importance of pre-clustering customers prior to focus group data collection.Phase 2 starts with quantitative research to obtain importance weights wij to attributes in Table 2. Aquestionnaire was prepared for that, with 18 attributes (six in each category) to be ranked inimportance by respondents. We used a questionnaire where items are ranked in importance.Questionnaires were customized for clusters of clients presenting the attributes most valued by themin Phase 1, but applied separately to customers in the 39 economic sectors previously identified.The average sample size per economic sector was 16 questionnaires, estimated using proper samplesize estimation techniques. This average value was adjusted to include the following informationregarding each sector: (i) relative percentage of electricity consumption, (ii) relative number ofparticipants in sector (in %) and (iii) NG adoption potential (given as a probability). The smallestand largest adjusted samples sizes were 5 and 50, respectively. A total of 450 questionnaires werepersonally applied by a team of administrators. As previously mentioned, questionnaire respondentswere asked to rank attributes in importance regarding their customization requirements. To convertranks into scores, we used the reciprocal of the attribute rank position as explained in section 3.A formal cluster analysis was carried out using the 18 attributes as clustering variables.Responses from a same economic sector were added and normalized to generate a (39×18)data matrix used in the analysis. Cluster analysis was carried out in two steps. First, the propernumber of clusters to be used was identified using a hierarchical approach. Once the idealnumber of clusters was identified, sectors to integrate each cluster were determined using a k-means partitioning algorithm. The final assignment of sectors to clusters is partially presentedin the last column of Table 2. As expected, clustering in Phase 2 yielded different results fromclustering in Phase 1. We entered Phase 3 of the method using the Ci clusters.We now describe the steps in Phase 3. Twelve experiments were planned to collect SP datafrom clusters. Each experiment was comprised of six attributes, corresponding to the twomost important in each category, explored at different levels. Price, an attribute included in allclusters, was explored at four levels; all remaining attributes were investigated in two levels.Thus, the total number of scenario alternatives investigated in each experiment was 128(25×41). Further, it was decided that 16 scenarios would be presented to each respondent, toavoid fatigue. Therefore, experiments were divided into 8 blocks with 16 scenarios each.Attributes were coded A to F and attribute levels were coded -1, -0.3, +0.3, +1.Each of the eight blocks was presented as a questionnaire to respondents and replicated tentimes. As discussed earlier, in a customization SP study alternatives may be compared to acontrol scenario where the product is presented in a standardized format; the alternativestherefore make the product flexible regarding attributes of interest. However, in our study,NG was not yet available to customers. Therefore, the control scenario was established to bethe alternative energy sources, e.g. PLG, oil, wood, etc. (and their correspondingcharacteristics) used at that moment by customers from each cluster. Alternative scenariospresented levels of NG attributes in each cluster in comparison to that control scenario.Due to the large number of clusters in this case study, we restrict ourselves to present resultsfrom cluster 5 that grouped a large number of industrial and commercial sectors. Thepreference loglinear model for cluster 5 is:ICIEOM 2006 ABEPRO 7
  8. 8. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006 ln ( y (1 − y )) = −1.488 + 0.241X 1 (.000) + 0.176 X 2 (.001) + 0.344 X 3 (.000) + 0.267 X 4 (.000) + (3) 0.214 X 5 (.000) + 0.770 X 6 (.000) + 0.170 X 1 X 2 (.002) + 0.300 X 3 X 6 (.000) + 0.232 X 4 X 6 (.002)where y denotes the response (i.e. preference of a given scenario for each cluster) and X i (i = 1,…, 6) are theattributes investigated (detailed in Table 3). P-values are given within parentheses after their correspondingmodel term. Transformation in response y led to a linear model, with coefficients determined using thestatistical package SPSS. The model fit given by its coefficient of determination was R 2 = 0.704 .Coefficients in eq. (3) were directly comparable in view of the attributes level coding. As expected,Price appeared as the most important attribute, followed by Emission of pollutants and Convertibility.Three interactions were significant: Supply regularity × Cost to install NG tubulation, Emission ofpollutants × Price and Convertibility × Price. Since all interaction coefficients were positive, theywere easy to interpret. Consider interaction X 1 X 2 , for example, and suppose a menu where allattributes are set at their most favorable levels, including X 1 and X 2 . Customer preference in that casewould be 77.3%; ignoring the interaction the preference value decreases to 74.2%. It is alsonoteworthy that some interactions had larger coefficients than individual attributes. Ignoring suchinteractions, as in classical SP data analysis, would lead to a less representative model. Attribute Description Levels X1 = Supply regularity Degree to which customers may be exposed to (-1) Irregularity possible interruptions in NG supply (+1) Irregularity not possible X2 = Cost to install NG Cost incurred by customers to extend tubulation (-1) High; (+1) Low tubulation from distributor’s network to point of use X3 = Emission of NG emission of pollutants into environment in (-1) Pollutes more pollutants comparison to energy sources to be replaced (+1) Pollutes less X4 = Convertibility Degree of equipment convertibility to NG (-1) Low; (+1) High X5 = Operational safety Level of equipment operational safety using NG in (-1) Safety decreases comparison to energy sources to be replaced (+1) Safety increases X6 = Price Indicates expenses with GN in comparison to the (-1) 10% larger; (-0.3) About energy sources it is intended to replace the same; (+0.3) 10% smaller (+1) 10% to 30% smaller5. ConclusionsThis paper proposed a method to design choice menus in a MC context. The methodincorporates stated preferences to define which attributes of a product or service should beoffered at different levels to be selected by individual customers. The method aims mainly atdesigning choice menus with an appropriate number of options to balance the flexibility-complexity trade-off indicated by Stodder (1997), among others. This appears to be one of thefirst methods to support the design of choice menus for MC.Due to its originality and stated objectives, this study has limitations, most of which can beaddressed by further studies. First, the method did not incorporate approaches to update choicemenus through combining revealed preferences with the stated preferences assessed in the panelstudy. Second, the idea of incorporating interactive terms to assess the moderated effect of oneattribute on the regression estimate of another attribute may have broad implications formodularity and bundled choice, but these implications were not sufficiently explored in this paper.ReferênciasBATEMAN, I.J.; CARSON, R.T. & DAY, M. Economic Valuation with Stated Preference Techniques: AManual. Cheltenham: Edward Elgar, 2000.Da SILVEIRA, G., BORENSTEIN, D. & FOGLIATTO, F.S. Mass customization: literature review andresearch directions. International J. of Production Economics, Vol. 72, 1-13, 2001.DAVIS, S. Future Perfect. Reading (MA): Addison-Wesley, 1987ICIEOM 2006 ABEPRO 8
  9. 9. XII ICIEOM - Fortaleza, CE, Brasil, October 9 - 11, 2006DeSHAZO, J.R. & FERMO, G. Designing choice sets for stated preference methods: the effects of complexityon choice consistency. J. of Environmental Economics and Management, Vol. 44, 123-143, 2002.DURAY, R. Mass customization origins: mass or custom manufacturing? Int. J. Oper. & Prod. Manag., Vol. 22, 314-328, 2002.GREENBAUM, T.L. Moderating Focus Groups: A Practical Guide. Thousand Oaks: Sage, 2000.HENSHER, D., LOUVIERE, J. & SWAIT, J. Combining sources of preference data. J. Econometrics, Vol. 89, 197-221, 1999.LANCASTER, K.J. A new approach to consumer theory. J. of Political Economy, Vol. 74, 132-157, 1966.LEVY, P.S. & LEMESHOW, S. Sampling of Population – Methods and Applications. New York: Wiley, 1999.LIECHTY, J.; RAMASWAMY, V. & COHEN, S.H. Choice menus for mass customization: an experimentalapproach for analyzing customer demand. J. of Marketing Research, Vol. 38, 183-196, 2001.OLIVA, R.A. Way beyond web sites. Marketing Management, Vol. 11, n. 6, 46-48, 2002.SLYWOTSKY, A.J. The age of the choiceboard. Harvard Business Review, Vol. 78, n.1, 40-41, 2000.SONSINO, D. & MANDELBAUM, M. On preference for flexibility and complexity aversion: experimentalevidence. Theory and Decision, Vol. 51, 197-216, 2001.STODDER, J. Complexity aversion: simplification in the Herrnstein and Allais behaviors. Eastern Economic J., Vol. 23, 1-15, 1997.UNTERSCHULTZ, J.; QUAGRAINIE, K.K. & VINCENT, M. Evaluating Quebecs preference for Albertabeef versus US beef. Agribusiness, Vol. 13, 457-468, 1997.WIND, J. & RAMASWAMY, A. Customerization: the next revolution in mass customization. J. InteractiveMarketing, Vol. 15, 13-32, 2001.ICIEOM 2006 ABEPRO 9