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UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FACTORS OF CONSUMER’S CHOICES: A REVEALED PREFERENCE ANALYSIS FOR 3IN1 COFFEE BY NGUYEN VAN VIEN MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2016 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FACTORS OF CONSUMER’S CHOICES: A REVEALED PREFERENCE ANALYSIS FOR 3IN1 COFFEE A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN VAN VIEN Academic Supervisor: TRUONG DANG THUY HO CHI MINH CITY, November 2016 ACKNOWLEDGEMENT I would first like to thank my thesis supervisor Dr. Truong Dang Thuy of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics. He consistently allowed this paper to be my own work, but steered me in the right the direction whenever he thought I needed it. I acknowledge the contribution of Dr. Nguyen Ba Thanh (IUH) as the second reader of this thesis, and I am gratefully indebted to him for his very valuable advices on building idea for this thesis. I would like to express my gratitude to the VNP officers who were involved in my thesis process by updating thesis schedule and providing good condition for my research process. Without their passionate participation, the thesis process could not have been successfully conducted. Finally, thanks are also due to my classmates for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you. Nguyen Van Vien Ho Chi Minh City, November 2016 Page i ABSTRACT 3in1 coffee is known as an important product of instant coffee market in Vietnam, especially in Ho Chi Minh City. The reason of that comes from the benefits which 3in1 coffee brings to consumers in term of convenience, product quality, and appropriate price. In the above context of 3in1 coffee market, the main objective of this study is to identify the determinants of consumer’s choices in 3in1 coffee market such as price, main ingredients, packaging, manufacturer, discount, and weight promotion. This study is a practical research with the basis of random utility theory. Specifically, empirical result is produced from the estimation of conditional logit model for the dataset which is collected from consumers in Ho Chi Minh City in 2016. The survey process relies on the revealed preference method with several additional hypothetical scenarios. The main finding of this study emphasizes the importance of main ingredients, packaging, and manufacturers of 3in1 coffee in consumer’s choices. It is recognized that price may not matter consumer’s choices. However, consumers love discount and weight promotion. In addition, several manufacturers enjoy positive marginal utility of price for consumers while the others enjoy the negative one. On the other hand, price changes may give small effects on choice probability of 3in1 coffee products. According to those empirical findings, implications have been employed for manufacturers in order to understand more about 3in1 coffee market, widen their market share, and increase their profits. Page ii TABLE OF CONTENT Chapter Page Acknowledgement ........................................................................................................ i Abstract ........................................................................................................................ ii Table of content .......................................................................................................... iii List of tables ..................................................................................................................v List of figures .............................................................................................................. vi 1. Introduction ...................................................................................................................1 1.1. Research problem ..................................................................................................1 1.2. Research objective .................................................................................................2 1.3. Scope of study ........................................................................................................3 1.4. Thesis structure ......................................................................................................3 2. Literature review ...........................................................................................................4 2.1. Random utility theory ............................................................................................4 2.2. Random utility model forms ...................................................................................9 2.3. Random utility model for beverage or food .........................................................11 2.4. The investigation of coffee’s attribute .................................................................12 2.5. Consumer’s social-demographic characteristics ..................................................16 3. Research methodology ................................................................................................18 3.1. Revealed preference method ................................................................................18 3.2. Attributes of coffee ..............................................................................................20 3.3. Choice set .............................................................................................................21 3.4. Questionnaire .......................................................................................................23 3.5. Survey process .....................................................................................................24 3.6. Model specification...............................................................................................25 4. Data and empirical result ............................................................................................30 4.1. Data ......................................................................................................................30 4.2. Empirical result ....................................................................................................39 4.2.1. Determinants of consumer’s choices for 3in1 coffee ..................................39 Page iii 4.2.2. Price and consumer’s utility of 3in1 coffee by manufacturers ...................46 4.2.3. Price change and choice probability ...........................................................49 4.2.4. Marginal utility of price for respondents ....................................................52 4.2.5. Discount and weight promotion and consumer’s utility for 3in1 coffee ..............................................................................................54 4.2.6. Manufacturers, social-demographic characteristics and consumer’s choices for 3in1 coffee ...............................................................................................55 5. Conclusion ..................................................................................................................58 Reference ................................................................................................................... vii Appendix ..................................................................................................................... xi Page iv LIST OF TABLES Table 2.1. Importance of factor on consumer’s coffee preferences ..................................14 Table 3.1. List of suggested attributes ..............................................................................21 Table 3.2. Volume share and value share of main manufacturers ....................................22 Table 3.3. List of all available 3in1 coffee products .........................................................23 Table 3.4. Variable description .........................................................................................26 Table 4.1. District, super-market, and the number of respondents ...................................31 Table 4.2. Descriptive statistics of the sample ..................................................................32 Table 4.3. Frequency of social-demographic characteristics ............................................34 Table 4.4. Factors of consumer’s choices for 3in1 coffee ................................................42 Table 4.5. Marginal utility of price for respondents by alternative ..................................53 Table A.1. All 19 alternatives and their attributes ........................................................... xii Table A.2. Price fluctuation among super-markets ........................................................ xxi Table A.3. Consumer’s choices change among various choice scenarios ..................... xxii Table A.4. Regression result of equation (3.1) and (3.3) (specific choice set) ............. xxiii Table A.5. Regression result of equation (3.1) and (3.3) (single variable) ................... xxiv Page v LIST OF FIGURES Figure 4.1. Frequency of choice of each alternative by gender ........................................35 Figure 4.2. Alternative and gender, occupation, income, and frequency of 3in1 coffee consumption of respondents ..............................................37 Figure 4.3. Change of consumer’s choices in various choice scenarios ...........................38 Figure 4.4. Price and consumer’s utility by manufacturer ................................................48 Figure 4.5. Price changes and choice probabilities of alternative 2, 8, 15, 18 in case of all choice scenarios ...........................................................................................51 Figure A.1. All 19 alternatives for survey process ........................................................... xi Figure A.2. Consumer’s utility along current price values .............................................xxv Page vi CHAPTER 1: INTRODUCTION 1.1. Research problem In recent years, coffee is an important product of industry sector, agriculture sector, and service sector in Vietnam. According to historical data of International Coffee Organization (ICO), beside Brazil, Vietnam is one of the key countries in coffee production and consumption in the world with 19.4 percent of total coffee production, and 4.5 percent of domestic consumption. Annual yield of Vietnam coffee production increases by 21 times in the period 1990 – 2014 (ICO, 2015). The volume of coffee export in Vietnam contributes more than 2.4 percent of GDP in 2012 (Vietnam Ministry of Industry and Trade, 2012). Based on the report of AC Nielsen (2015), 3in1 coffee market in Vietnam are contributed by many manufacturers of which five main manufacturers are Vinacafe, Nestlé, Trung Nguyen, Fes Vietnam, and Tran Quang. In 2015, total volume share of five main manufacturers is 88 percent (Vinacafe: 38%, Nestlé: 19%, Trung Nguyen: 14.6%, Fes Vietnam: 4.2%, Tran Quang: 12.2%) compared to about 99 percent in 2014. Moreover, in term of value share, 3in1 coffee products comprise 83 percent of total value of instant coffee market. In term of package, bag, box, and sachet are three main kinds of package with 99.9 percent of volume share. Therefore, it is concluded that total demand of 3in1 coffee is relatively high compared to other instant coffee products and the competition among manufacturers is also intense in order to capture more market share. Due to the high demand of consumers, especially young consumers, many manufacturers have diversified their 3in1 coffee products in term of brands, prices, segments, packages, pack size, promotion, main ingredients. For example, five main manufacturers including Vinacafe, Nestlé, Trung Nguyen, Fes Vietnam, and Tran Quang provide 27 different kinds of 3in1 coffee in term of main ingredients, packaging, and brands. Moreover, the competition among these manufacturers is also reflected in the aspect of prices and promotions. Price increase could help manufacturers enjoy the benefits from the increase of profit; however, they may also suffer the decrease of quantity sold. Beside price change, manufacturers could conduct promotion activities in order to increase the number of consumers who know about their products or their brand names. Promotion activities are conducted through many forms such Page 1 as weight promotion, additional sachets, or a gift of related product, for example, spoon, plastic cup, or glass cup. Thus, two important questions are raised that: (1) What are important factors which affect consumer’s choices?, and (2) Do price changes and promotions possibly help one manufacturer to gain consumers from the others? 1.2. Research objective According to Batsell and Louviere (1991), experimental methods relied on the framework of both econometric analysis and psychometric analysis and it is the most popular method to do researches about consumer’s preferences. Experimental methods explain consumer’s preferences through the process of identifying the range of significant factors, generating hypothetical profiles, collecting consumer’s choices, and analyzing choice data. The datasets of experimental methods are collected from two main survey methods: revealed preference and stated preference methods. Two main survey methods provide a wide application in understanding consumer’s preferences. For example, Durevall (2007) investigated that decreasing price of coffee has less impact on coffee demand in the long term due to the combination of consumer’s preferences and population structure in Sweden. In addition, Wolf et al. (2011) suggested that the interaction of product attributes also have significant impact on consumer’s preferences, beside prices. Therefore, the aim of this study is to achieve three research objectives: (1) Identifying the key determinants of consumer’s choices for 3in1 coffee, (2) Determining relationship between price and consumer’s utility for 3in1 coffee, (3) Evaluating the impact of discount, promotion, and price changes on consumer’s choices. First, beside price, several factors are claimed to be reliably important and influence the consumer’s choices. Thus, identifying these factors provides deeply understanding about consumer’s preference in order to suggest both implication for instant coffee market and development strategy for manufacturers. Second, it is said that determining the relationship between price and consumer’s utility plays an important role in finding out the effect of price change on consumer’s utility or choice probability of products for each manufacturer. This finding gives manufacturers an evaluation about their advantages or disadvantages to get higher profits in the 3in1 coffee market compared to the competitors. Finally, this study also Page 2 considers the impact of discount, promotion, and price change on consumer’s choices. Since 3in1 coffee market is oligopoly in Vietnam, any change of coffee attributes of one brand will have significant impact on its quantity sold. Therefore, manufacturers could optimize their marketing activities for capturing more market shares. 1.3. Scope of study This study is a practical research which relies on the basis of random utility theory. The data collection of this study is conducted in super-markets in Ho Chi Minh City in 2016 by applying revealed preference method with the addition of several hypothetical choice scenarios. Due to the limitation of finance and time span, a small sample of 197 respondents who are 3in1 coffee consumers is collected. Each respondent is assumed to face all 19 surveyed alternatives in actual choice scenario, so the dataset of this study is treated as panel dataset. The contribution of this study is to investigate the association between consumer’s choices and coffee attributes such as price, main ingredients of coffee, packaging, and manufacturers by using the revealed preference method. From that, producers could understand more about the significance of several attributes, which may have heavy contribution to consumer’s choices. Moreover, the relationship between prices and consumer’s utility could help producers to evaluate the trust of consumers to their brands when market changes in term of prices, promotions, and discounts. 1.4. Thesis structure The remaining of this study includes four chapters. Chapter 2 presents literature review, which comprises theoretical review and empirical review. Chapter 3 presents research methodology, which describes the questionnaire design, survey process, and empirical model. Chapter 4 presents the data description, regression result, and discussion. Chapter 5 summarizes the conclusion, the implication, the limitation, and the further research direction of this study. Page 3 CHAPTER 2: LITERATURE REVIEW In this chapter, the theoretical and empirical review have been summarized in order to provide the research framework for this study. In particular, this chapter concentrates on: (1) the literature of random utility theory in term of a brief history, basic assumptions, random utility model and its estimation; (2) several random utility model forms and their application; (3) empirical result of random utility model for food and beverage; (4) relationship between coffee’s attribute and consumer’s choice; (5) relationship between individual characteristics and consumer’s preferences. 2.1. Random utility theory Probabilistic choice theories were the important parts in psychology. They were developed to explain the inconsistency and non-transition of individuals’ preferences in experimental observations (Luce & Suppes, 1965). The inconsistency and non-transition of individuals’ preferences could be reflected through choice situations when individuals do not choose same alternatives in different choice situations or different sets of alternatives. Thurstone (1927) introduced the “law of comparative judgment” to apply to “the comparison of physical stimulus intensities and qualitative comparative judgments such as those of excellence of specimens in an educational scale, and the measurement of such psychological values as a series of opinions on disputed public issues”. The process, in which individuals react differently to several stimuli to be suitable to their demand, is called the discriminal process. Although various stimuli are judged by the same individual, their discriminal processes are different. The difference between two alternatives is measured by a scale, which is called discriminal difference. With specific specimen, discriminal processes are distributed by the standard deviation, which is called discriminal dispersion. According to Thurstone (1927), each specimen, which is chosen by the individuals, is described by two components: a scale value, and a discriminal dispersion. These two values could be determined. In addition, in term of scale value, that value comprises two components: an origin with its specific unit of measurement, and the unknown correlation between discriminal deviations of two different stimuli. Thurstone (1927) also assumed that the unknown correlation is constant for the whole series of stimuli. Page 4 Based on the “law of comparative judgment”, in order to measure and explain determinants of individual choice, constant utility and random utility approaches were introduced by Luce and Suppes (1965). Constant utility approach, which was first introduced by Luce (1959), based on the assumption of fixed utilities of alternatives. It meant that individuals did not choose alternative with the highest utility. Choice probabilities for decision makers are expressed by a function in which utilities of alternatives are parameters. On the other hand, Marschak (1960) first introduced an economic viewpoint of random utility approach which relied on Thurstone’s “law of comparative judgment” (1927). Random utility approach based on the perspective that individuals choose alternative with the highest utility and utility is treated as a function of attributes plus a random component. Although Marschak (1960) interpreted Thurstone’s “law of comparative judgment” in the economics field, McFadden (1974a) introduced the general procedure in order to apply random utility theory for analyzing qualitative choice behavior. From that, this theory is widely reviewed in the research of Danganzo (1979), Hensher and Button (2000), Train (2009). According to McFadden (1974a), conditional logit analysis is appropriate economic analysis method for consumer’s choices behavior. The research of consumer’s choice behavior comprises three main problems: (1) type of alternative and sets of available alternatives to consumers, (2) observable attributes of alternatives to consumers, and (3) identifying the model of consumer’s choice and behavior. Each alternative gives a stimulus to decision makers, which economists considered as utility. It is assumed that respondents are rational and they choose alternative which has the highest perceived utility. In addition, perceived utility is treated as a function of two main components: deterministic component and stochastic component. The general hypothesis of the random utility theory is that individuals are rational decisionmakers, and they try to maximize their utility when facing a choice between multiple (mutually exclusive) alternatives. In other word, they compare the utilities of alternatives, which they face and choose the alternative with the highest utility. Luce (1959) introduced an important axiom of random utility approach, which expressed that the presence or absence of additional alternative did not influence the relative odds of chosen alternative over the second one. McFadden (1974a) formalized this axiom into three below assumptions: Page 5 (1) Independence of irrelevant alternatives (IIA): the relative ratio of choice probability of one alternative over choice probability of another alternative is affected in equally proportion by the presence of the other alternatives. (2) Positivity: choice probabilities of all alternatives in all possible alternative sets are positive. (3) Irrelevance of alternative set effect: a weak identifying restriction. According to McFadden (1974a), the perceived utility U comprises two components: the systematic utility V and the error term  . The systematic utility represents the utility, which is perceived by decision makers in the same purchasing context. Alternatives and attributes are known as important components to describe different purchasing contexts. The error term represents the unknown deviation of utility perceived by decision makers from the utility. Specifically, the error term captures the effects of all unobservable factors. The relationship between the perceived utility U , the systematic utility V , and the error term  is expressed by the below equation: U V  (2.1) In the viewpoint of researchers, they could not observe the utility U of decision makers. The researchers could observe the characteristics of decision makers and the attributes of alternatives which are faced by decision makers. From that, they could estimate the choice probabilities. According to McFadden (1981), choice probabilities have to satisfy two conditions: (1) choice probabilities are non-negative and sum to one; and (2) choice probabilities depend on both observable attributes of alternatives and characteristics of decision makers. Moreover, researchers do not know the error term  which captures unobservable factors that affect utility of decision makers and are not included in the systematic utility V . Thus, researchers treat the error term  as random component. The distribution of the error term  mainly depends on researcher’s consideration of the deterministic component V . Suppose that, decision makers try to maximize their utility, and their utility is described by the utility function: U ( s , x )  V ( s , x )   ( s, x ) (2.2) Page 6 where s are measured attributes, x is chosen alternative from the alternative set by decision makers. Moreover, utility component V is the function of measured attributes S : V  1S1   2 S2  ...   n Sn (2.3) where S1,2,...,n are measured attributes, and 1,2,...,n are estimated coefficients. McFadden (1974a) suggested two lemmas in which the value of the error term is independently identically distributed with Weibull (Gnedenko, extreme value) distribution in the first lemma, and with Gumbel distribution (Extreme Value Type I) in the second lemma. Under the condition of the second lemma, McFadden (1974a) proved that the choice probability of one alternative equals the proportion of exponential function of utility of this alternative over the exponential function of utility of remaining alternatives in the alternative set. McFadden’s (1974a) finding about choice probability is expressed by the following equation: P( x | s, B)  eV ( s , x )  eV ( s, y ) (2.4) yB where B is alternative set; x, y are alternatives of alternative set B . Then, the relative odds of choices is expressed by the following equation: log where Pi Vi  Pj V j (2.5) Pi and Pj are probabilities of choosing alternative i and j ; Vi and V j are utilities of alternative i and j . Based on equation (2.3), it is noted that utility component V depends on measured attributes, and the unknown betas of that equation should be estimated in order to calculate the utilities and choice probabilities of alternatives. The betas are estimated by the maximum likelihood estimation (McFadden, 1974a). McFadden (1974a) supposed that all measured attributes which are included in the deterministic utility V are independent with the unobserved component of utility  . Moreover, each respondent’s choice is independent with the others. Based on those assumptions, the choice probability of alternative i for respondent n is: Page 7  (P ) yni ni , (2.6) i where yni  1 if respondent n chooses alternative i and yni  0 if respondent n does not choose alternative i , and yni  0 for all other alternatives. Then, choice probability of each respondent in the sample of N respondents is: N L(  )   ( Pni ) yni , n 1 (2.7) i where  is the vector comprising all coefficients in the equation (2.3). Thus, log-likelihood function is expressed as the following equation: N LL(  )   yni ln Pni n 1 i  x N  e ni   yni ln   xnj n 1 i  e  j      (2.8) N N  x   yni (  xni )   yni ln   e nj n 1 i n 1 i j   .  Then, derivatives of log-likelihood function is: N dLL(  ) d   y n 1 ni i (  xni ) d N   y n 1 ni i ln( e  xnj ) j d (2.9) N   ( yni  Pni ) xni . n 1 i N d LL(  ) d 2 2   ( y n 1 i ni  Pni ) xni d (2.10) N   Pni ( xni2  xni xnj ) n 1 i Page 8 McFadden (1974a) proved that d 2 LL(  ) is always negative with every values of  . It means d 2 that log-likelihood is globally concave. Thus, there is existence of a critical point of  that maximizes log-likelihood value and that critical point of  satisfies the equation: dLL( ) 0 d (2.11) In addition, the predicted probabilities that are calculated from  are closet to the observed choices (McFadden, 1974a). McFadden (1974a) also pointed out the advantages and disadvantages of this estimation method of choice probabilities based on the formula (2.4). In term of advantages, this estimation could interpret the choice probabilities in term of the relative systematic utility of alternatives. Moreover, this estimation could estimate the effect of presence of new alternatives. Specifically, the choice probability of old alternative will proportionally equally decrease by the choice probability of new alternative. In addition, this estimation could estimate the choice alternative effects without replication, and predict the choice behavior from extrapolation of observed alternative sets. On the other hand, McFadden (1974a) pointed out that the limitations of this estimation related to the independence of irrelevant alternatives axiom. Based on that axiom, the alternative sets may include alternatives, which are close substitutes. 2.2. Random utility model forms According to Train (2009), the variation of random utility model forms is derived under the different distribution functions of unobserved factors, which are expressed by the function f ( ) . Train (2009) supposed that a sample of respondents face the same observed utility V , however, the values of unobserved factors are different among respondents. Thus, the function f ( ) reflects the distribution of unobserved utility among respondents who face the same observed utility within a sample. In this section, several popular forms of random utility model, which are widely used in market research, are described. They include logit model, Generalized Extreme Value (GEV) Page 9 Commented [TT1]: Who says this? Are all of them use Extreme Value Type I? What distribution other than that? model, probit model, and mixed logit model. With each form of random utility model, the situation in which the form is applied will be discussed. First, logit model is considered as the most popular and widely used model. Based on the assumption of independence irrelevant alternatives (IIA), Luce (1959) derived the original formula of logit model. Then, that formula was proved to be consistent with the utility maximization by Marschak (1960). Finally, McFadden (1974a) showed a complete economic analysis of logit model by proving that logit model for choice probabilities works under the assumption of extreme value distribution of unobserved factors. That distribution is usually called as Gumbel distribution or Extreme Value Type I. Based on that assumption, the distribution of each unobserved factor for when a decision maker faces J alternatives is expressed by the following equation: f ( nj )    nj  e   nj , (2.12) and the equation for cumulative distribution is: F ( nj )  e e   nj (2.13) . If logit model is applied for two alternatives j and i , and  nj and  ni are identically distributed * * extreme value, then the difference between them  nji (  nji   nj   ni ) follows logistic distribution: * F ( nji ) e *  nji 1 e *  nji . (2.14) Equation (2.14) is applied for binary logit model in case of two alternatives. The extension of logit model when respondents face many alternatives was widely known as multinomial logit model or conditional logit model. Second, Generalized Extreme Value models, which are known as GEV models, are the generalization of standard logit model. GEV models comprise mathematical formulation that describes different characteristic functions. The key characteristic of GEV models is that the distribution of unobserved utility of all alternatives follows a generalized extreme value. Moreover, that distribution allows the correlations among alternatives. The disappearance of all correlations among alternatives will transform GEV models to standard logit model. The Page 10 GEV family includes several models such as nested (or two-level) logit model, and three-level nested logit model. Third, probit model requires the normal distribution of all unobserved components of utility. That requirement may be inappropriate in most of situations. For instance, with estimated coefficients of price variable, that requirement implies that those coefficients follow normal distribution and distribute in both sides of zero. In other word, price variable takes both positive values and negative values. However, it is recognized that probit model could deal with three important limitation points of logit model. First, probit model could present the random taste variation. It means that estimated coefficients could be random among respondents instead of being fixed like regression result of logit model. Second, probit model ignores the independence from irrelevant alternatives. With specific data, researchers could determine an appropriate substitution pattern in order to estimate less parameters and interpret them carefully. Finally, probit model could be applied for panel data in which each respondent could make a choice among different alternatives at different periods of time or choice situations. Fourth, mixed logit model, which was introduced by McFadden and Train (2000), is a model with high level of flexibility. In mixed logit model, choice probability of alternatives is expressed as the following equation: Pni   e xni f (  )d  , x  e nj (2.15) j where f (  ) is a density function of  . In the special case, if f (  ) describes fixed estimated parameters, then f (  )  1 for   b , and f (  )  0 for   b . Therefore, equation (2.15) will become choice probabilities for standard logit model: Pni  ebxni . bx  e nj (2.16) j 2.3. Random utility model for beverage or food Schiffman and Kanuk (2000) defined perception as “the process by which an individual observes, selects, organizes and reacts to environmental stimuli in a meaningful way”. Page 11 Characteristics of products and consumers affect consumer’s preferences. According to Issanchou (1996), these characteristics could be divided into two groups: intrinsic characteristics group and extrinsic characteristics group. Intrinsic characteristics mainly reflect the sensory attributes of products such as appearance, texture, taste, after-taste, odor, aroma, feeling. Extrinsic characteristics mainly reflect the factors that are outside the products, for example, personal attributes (age, gender, income, education), the situational attributes (the price, brand familiarity, environmental attributes, the availability of products). Thus, Cardello (1996) suggested that consumer’s preferences could be investigated through two linkages: (1) intrinsic characteristics and consumer’s preferences, and (2) extrinsic characteristics and consumer’s preferences. Facing with the abundance of products, consumers usually compared various attributes of products. Trade-off problem among attributes appeared in consumer’s choices. Trade-off problem could be analyzed by conjoint analysis, which is a multivariate technique. This technique evaluates purchaser trade-off through the decision-making process of respondents when they face various hypothetical multi-attribute alternatives (American Marketing Association, 1992). Louviere and Hensher (1983) suggested that first choice of alternative brings out the highest utility for consumers. The method in which consumers face multiattribute alternatives and make a choice is likely useful approach for basic and applied consumer research. Because existing profile of available products is limited, hypothetical profiles of products are generated in order to find out the relationship between attributes of products and consumer’s preferences. The hypothetical profiles of products are the combination of various attributes, which have ranges, and several levels. Range of attributes should be wide enough to capture consumer’s potential preferences, and narrow enough to assure the efficiency of estimation and the reality of hypothetical profiles toward to consumers (Bunch et al., 1993). For example, Mtimet and Albisu (2006) concentrated on four attributes of designation of origin (DO) wine including origin, price, wine aging, and the grape variety. By applying the method suggested by Street, Burgess, and Louviere (2005), the final 27 choice sets are chosen with four levels of each attribute. With the same procedure, Lockshin et al. (2006) suggested price, region of origin, brand name, and award as important label information to consumer’s wine choices. 20 choice tasks were generated by combining various levels of above attributes. Page 12
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