Cette recherche a pour objectif d’étudier le consentement à payer (CAP) accordé au label ducommerce équitable. Elle se structure en deux volets : le premier vise à identifier le prix maximum quele consommateur est prêt à consentir pour l'achat de produits labellisés « équitable » et à situerl'importance du label « équitable » par rapport à d'autres attributs tels que la qualité intrinsèque et leprix du produit. Le deuxième volet s'attache à déterminer l'influence de quatre catégories de variables(sociodémographiques, de personnalité, de valeurs personnelles et attitudinales) sur le consentement àpayer les produits équitables.Pour répondre à ces questions, nous avons mené deux enquêtes sur le chocolat dans le cadre de deuxapproches méthodologiques différentes : l’expérimentation économique et l’analyse conjointe. Lesrésultats montrent que le label « équitable » accroît significativement le CAP moyen desconsommateurs. De même, la qualité intrinsèque du chocolat influe sur le CAP équitable.L’importance du label « équitable » dans la préférence du consommateur moyen est comparable àcelle de la qualité intrinsèque du produit et est inférieure à celle du prix. Enfin, les déterminants duCAP équitable diffèrent selon la qualité intrinsèque du produit. Il s’agit, dans le cas d’un chocolat debonne qualité, de variables sociodémographiques (sexe, CSP et niveau d’études), de personnalité(matérialisme, générosité, convictions de contrôle interne, libéralisme et évaluation morale del'argent), de valeurs personnelles (d'ordre individuel et social) et des variables attitudinales(connaissances des produits équitables, attitudes envers les produits labellisés « commerce équitable »,confiance dans la labellisation « équitable » et implication vis-à-vis des produits labellisés« équitable»). / The objective of this research is to examine the willingness-to-pay (WTP) granted to the fair tradelabel. The study consists of the two parts: the focus of the first one is to identify the maximum pricethe consumer is willing to accept to buy products labeled "fair" and to define the importance of thelabel "fair" compared with other attributes such as the intrinsic quality and price. The second part aimsto determine the influence of the four categories of variables (socio -demographic, personality,personal values and attitudinal) on the consumer willingness-to-pay for fair trade products.To answer these questions, we have conducted two surveys on chocolate products using two differentmethodological approaches: economic experimental study and conjoint analysis. The results show thatthe label "fair" significantly increases the average WTP of consumers. Similarly, the intrinsic qualityof the chocolate affects the equitable consent of the WTP. The importance of "fair" label of theaverage consumer' preference is comparable to the intrinsic quality of the product and is lowercompared to the price. Finally, the determinants of the equitable consent of WTP vary depending onthe intrinsic quality of the product. It relates, in the case of a good quality chocolate, to the sociodemographicvariables (gender, educational level and WTP), variables related to personality(materialism, generosity, internal control beliefs, liberalism and moral evaluation of money), personalvalues (individual and social) and attitudinal variables (knowledge of fair trade products, attitudestoward products labeled "fair trade", confidence in the fair labeling and involvement with respect toproducts labeled "fair").

Tolerances play an important role in product fabrication. Tolerances impact the needs of the designer and the manufacturer. Engineering designers are concerned with the impact of tolerances on the variation of the output, while manufacturers are more concerned with the cost of fitting the parts. Traditional tolerance control methods do not take into account both these needs. In this thesis, the author proposes a framework that overcomes the drawbacks of the traditional tolerance control methods, and reduces subjectivity via fuzzy set theory and decision support systems (DSS). Those factors that affect the manufacturing cost (geometry, material etc) of a part are fuzzy (i.e. subjective) in nature with no numerical measure. Fuzzy comprehensive evaluation (FCE) is utilized in this thesis as a method of quantifying the fuzzy (i.e. subjective) factors. In the FCE process, the weighted importance of each factor affects the manufacturing cost of the part. There is no systematic method of calculating the importance weights. This brings about a need for decision support in the evaluation of the weighted importance of each factor. The combination of FCE and DSS, in the form of Conjoint Analysis (CA), is used to reduce subjectivity in calculation of machining cost. Taguchi's quality loss function is considered in this framework to reduce the variation in the output. The application of the framework is demonstrated with three practical engineering applications. Tolerances are allocated for three assemblies; a friction clutch, an accumulator O-ring seal and a Power Generating Shock Absorber (PGSA) using the proposed framework. The output performances of the PGSA and the clutch are affected by the allocated tolerances. On using the proposed framework, there is seen to be a reduction in variation of output performance for the clutch and the PGSA. The use of CA is also validated by checking efficiency of final tolerance calculation with and without use of CA.

This study uses conjoint analysis to examine the preferences of buyers for Bureau of Land Management (BLM) wild horses based on physical attributes of wild horses and individual characteristics of the buyers. Generalized ordered logit models and multinomial logit models are used to study the impact of the buyers’ demographic characteristics such as age, gender, knowledge about wild horse care, and number of wild horses previously adopted on physical attributes of the horses such as color, age, height, training status, temperament, conformation, and unique markings. Using a choice experiment, taken together, these attributes determine buyer’s preferences for a wild horse. This study reveals that characteristics of buyers have significant effects on their preferences for wild horses. Their gender, age, knowledge about wild horse care, and the number of horses previously adopted influence the importance that buyers place on physical attributes of a wild horse in their decision to purchase a wild horse.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Made available in DSpace on 2015-03-26T13:32:08Z (GMT). No. of bitstreams: 1 texto completo.pdf: 801719 bytes, checksum: 87934fb7e8c6635218cccfd6cf57b830 (MD5) Previous issue date: 2010-02-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Conjoint Analysis (CA) and the Constant Sum (CS) approach were both applied in a consumer preference study in order to estimate Relative Importance (RI%) of attributes. The main goal of this study was to verify if the CS approach can be used to evaluate estimates of RI% obtained from CA. A sample of 192 consumers, residents of Viçosa city, Minas Gerais State, evaluated 8 alternative packages of a milk based drink, formed by the combination of 3 factors with two levels each: Size (200mL and 1000mL), name (coffee and chocolate flavor and chocolate and coffee flavor) and information decaffeinated (with and without). We applied Descriptive (Box and Whisker plots, descriptive measures and dispersion plots) and inferential (paired and independent two sample t tests, general linear hypothesis tests) statistical analysis were applied using estimates of RI obtained from both methods and we concluded that CS does not provide a good evaluation for estimates obtained from CA, although this method can be used as a prior study in order to select factors for a CA study, specially when there exists many candidate factors. / A proposta deste trabalho foi comparar os métodos Soma Constante (SC) e Análise Conjunta de Fatores (ANCF) para estimar a importância relativa ((IR%)) dos fatores em estudos da preferência do consumidor. O objetivo principal foi verificar se o método SC pode ser utilizado para avaliar as estimativas de IR (%) obtidas pela ANCF. Foi realizado um estudo para avaliar a intenção de compra de uma bebida láctea sabor café com base na embalagem. Utilizou-se uma amostra de 192 julgadores residentes na cidade de Viçosa. Os fatores e respectivos níveis avaliados foram: tamanho da embalagem (200 mL e 1000 mL), nome do produto (café e chocolate e chocolate e café) e informação descafeinado (com e sem). Com as estimativas de IR (%) fornecidas pelos julgadores obtidas pelos dois métodos foram realizadas análises descritivas: gráficos Box e Whisker, histogramas e tabelas com medidas descritivas; e também análises inferenciais: teste t para dados pareados e para amostras independentes e testes de hipóteses lineares gerais. Optou-se por realizar as análises com e sem a exclusão de julgadores que não se adequaram ao modelo da ANCF, com base na não significância (valor p > 0,20) da Análise de Variância (ANOVA) realizada para os três fatores com os dados fornecidos por cada julgador. Após a realização das análises estatísticas concluiu-se que o método SC não se mostrou apropriado para avaliar os valores de importância relativa dos fatores estimados na ANCF. Devido à facilidade de aplicação, o método SC pode ser utilizado como um pré-estudo na seleção fatores para serem submetidos à ANCF, principalmente quando existem muitos fatores avaliados no estudo.

This research is the outcome of a preeminent interest in the topic of price perception. Pointedly, the perception of prices is part of the purchasing process, the same willingness to pay and the actual purchase behaviour, and is indubitably a perceptual construct. As such, perception is problematic to measure as it does not relate to an observable behaviour. On the other hand, pricing is regarded as an important variable in the marketing mix. This research contributes to theory by augmenting the current knowledge on the perception of prices including the methods used in the measurement of such perception. Moreover, this research addresses a gap in the understanding of how diverse ethnic groups perceive prices. The relationship set in this study between ethnicity and price perception is thought-provoking as it contributes to the current discussion around diversity in the marketplace. For example, the literature shows advances in areas such as multicultural and ethnic marketing and this research makes a significant contribution to these areas from price perception. Accordingly, this study involved a systematic review of the literature and presented a framework that suggested that the formation of price perception is affected by external factors such as culture and ethnicity. Furthermore, a qualitative study examined the formation of price perception around ethnic groups. Next, this research used a quantitative study that sought differences in price perception among ethnic groups. Thus, the quantitative study used a price perception scale (Lichtenstein et al., 1993) and a choice-based conjoint analysis. Also, the study adopted structural equation modelling (SEM) to measure differences among scales and the multinomial logit model to analyse the choice-based conjoint analysis. The findings of both the quantitative and the qualitative studies link to the systematic review and support the framework for the formation and measurement of price perception originally proposed.

(in English): In this thesis, I investigate preferences of humanities-oriented 3rd year students of grammar schools in their choice of university they would apply for. The theoretical part provides a summary and data for a content analysis of previous research, which I use to identify key features of universities, which may affect the choices made by students. In the methodological part, I introduce and define the conjoint analysis, which is a tool best suited for capturing the decision making process. The analysis shows that the most important factors are reputation and atmosphere. Other rather influential attributes include those proposed by other authors such as distance from home, location, peer influence and communication of the university with the prospective students. In contrast to findings from other countries though, the influence of the entry exams has been found only for students with excellent grammar school study results. This is directly contrary to the effect of this attribute in previous research. Another distinct feature of my research is the inclusion of the possibility to study abroad as an attribute, which has been completely ignored in previous studies of this type. In this case, it is the fourth most important attribute. There are tendencies which suggest that future research...

O mercado de Educação Superior privado no Brasil apresenta altos índices de concentração, caracterizando-se como um oligopólio, podendo, portanto, ser estudado sob a ótica da Teoria dos Jogos. Uma das técnicas existentes para abordar este tipo de mercado é conhecida por Análise Conjunta Baseada em Escolhas (Choice Based Conjoint Analysis), que permite estimar as utilidades atribuídas para cada característica dos produtos, prevendo o desejo de cada produto gerado pela combinação dos seus atributos, possibilitando, assim, simular como as decisões de uma amostra de respondentes seriam distribuídas em um mercado simulado entre dois ou mais produtos competidores. Esses modelos, porém, limitam-se a maximizar a receita individual de cada produto, de forma isolada, não levando em conta a possibilidade das firmas terem interesses em maximizar a soma de dois ou mais produtos de forma conjunta. Isso se torna necessário, por exemplo, quando uma empresa comercializa dois produtos que competem no mesmo mercado. Com o objetivo de maximizar a receita conjunta de dois ou mais produtos, foi desenvolvido um método alternativo, baseado em Programação Não-Linar, que foi aplicado em uma cidade brasileira e em um país centro-americano. A comparação dos resultados do modelo desenvolvido com os do modelo tradicional evidencia que o modelo desenvolvido apresenta melhores resultados – soma das receitas das firmas de interesse – gerando uma taxa de crescimento na receita 3% maior, no caso brasileiro e 75% maior no estudo centro-americano. O modelo desenvolvido pode ser adaptado e utilizado em outros mercados oligopolistas ou para otimizar diferentes funções-objetivo. / The Brazilian Higher Education private market shows high levels of concentration and can be considered an oligopoly. Therefore, one can study it as a Game Theory problem. Choice Based Conjoint Analysis – a technic that can be used to approach this kind of market – can estimates the utilities of each products’ features and predict the desire of each product generated by the combination of its attributes. Such technic can simulate how the decisions of a sample of respondents would be distributed among the products of a market made of two or more competitor. These models, however, only maximize the revenues of individual products, not considering the possibility of firms wanting to maximize the sum of the revenue of two or more products. This is useful, for instance, when a company trends two or more products that compete in the same market. An alternative method, based on nonlinear programming, was developed, in order to maximize the conjoint revenue of two or more products and it was applied in a Brazilian city and in a Central American country. Comparing both models – the traditional versus the developed one –, we can see that the developed model shows better outcomes – ie, sum of both companies’ revenues – resulting in a revenue increase rate 3% higher in the Brazilian case and 75% higher in the Central American study. This model can be fitted to other oligopolistic markets or to optimize others objective functions.

O mercado de Educação Superior privado no Brasil apresenta altos índices de concentração, caracterizando-se como um oligopólio, podendo, portanto, ser estudado sob a ótica da Teoria dos Jogos. Uma das técnicas existentes para abordar este tipo de mercado é conhecida por Análise Conjunta Baseada em Escolhas (Choice Based Conjoint Analysis), que permite estimar as utilidades atribuídas para cada característica dos produtos, prevendo o desejo de cada produto gerado pela combinação dos seus atributos, possibilitando, assim, simular como as decisões de uma amostra de respondentes seriam distribuídas em um mercado simulado entre dois ou mais produtos competidores. Esses modelos, porém, limitam-se a maximizar a receita individual de cada produto, de forma isolada, não levando em conta a possibilidade das firmas terem interesses em maximizar a soma de dois ou mais produtos de forma conjunta. Isso se torna necessário, por exemplo, quando uma empresa comercializa dois produtos que competem no mesmo mercado. Com o objetivo de maximizar a receita conjunta de dois ou mais produtos, foi desenvolvido um método alternativo, baseado em Programação Não-Linar, que foi aplicado em uma cidade brasileira e em um país centro-americano. A comparação dos resultados do modelo desenvolvido com os do modelo tradicional evidencia que o modelo desenvolvido apresenta melhores resultados – soma das receitas das firmas de interesse – gerando uma taxa de crescimento na receita 3% maior, no caso brasileiro e 75% maior no estudo centro-americano. O modelo desenvolvido pode ser adaptado e utilizado em outros mercados oligopolistas ou para otimizar diferentes funções-objetivo. / The Brazilian Higher Education private market shows high levels of concentration and can be considered an oligopoly. Therefore, one can study it as a Game Theory problem. Choice Based Conjoint Analysis – a technic that can be used to approach this kind of market – can estimates the utilities of each products’ features and predict the desire of each product generated by the combination of its attributes. Such technic can simulate how the decisions of a sample of respondents would be distributed among the products of a market made of two or more competitor. These models, however, only maximize the revenues of individual products, not considering the possibility of firms wanting to maximize the sum of the revenue of two or more products. This is useful, for instance, when a company trends two or more products that compete in the same market. An alternative method, based on nonlinear programming, was developed, in order to maximize the conjoint revenue of two or more products and it was applied in a Brazilian city and in a Central American country. Comparing both models – the traditional versus the developed one –, we can see that the developed model shows better outcomes – ie, sum of both companies’ revenues – resulting in a revenue increase rate 3% higher in the Brazilian case and 75% higher in the Central American study. This model can be fitted to other oligopolistic markets or to optimize others objective functions.