The microeconomic theory of consumer behavior provides the standard approach in modeling choice or consumption decisions. The theory is concerned about how consumers allocate their income and how this determines the demand for various goods and services. It assumes that the consumer is rational and that when faced with a set of feasible alternatives, he/she will at all times choose the most preferred bundle from the set of alternatives (Varian, 1984). The choice of an alternative is based on a decision rule known as utility maximization, which implies that when faced with two or more alternatives, the consumer chooses the alternative that will give him/her the highest possible utility (Ben-Akiva & Lerman, 1985). However, to investigate consumer behavior toward different product alternatives (composed of discrete bundles of attributes) as in the case of this study, the traditional microeconomic theory of consumer behavior cannot be applied because it assumes homogeneous goods and utility as a function of the quantities of goods consumed and not attributes. Instead, discrete choice theories (i.e., Lancaster and random utility theories) provide a better framework for dealing with such discrete choice situations (Ben-Akiva & Lerman, 1985). The theoretical foundations of discrete choice models are rooted in consumer theory developed by Lancaster (1966) and the random utility theory.
8 2.1.1 Lancaster’s consumer theory
Prior to Lancaster’s new approach to consumer theory, the prevailing assumption was that goods are the direct objects of utility (Lancaster, 1966). However, Lancaster’s approach deviates from the traditional microeconomic consumer theory, which indicates that goods are the direct objects of utility (Lancaster, 1966). The main departure of Lancaster’s approach from the traditional microeconomic theory of consumer behavior is that utility is derived from the characteristics that goods possess rather than the goods per se (Lancaster, 1966). Lancaster’s approach assumes that goods have more than one characteristic, and they can be used either singly or in combination to produce different characteristics from which the consumer derives utility. Lancaster (1966) assumed that utility orderings are rank collections of the characteristics that goods possess. In the context of this thesis, for example, chicken meat, the good of interest, can be viewed as a collection of its quality attributes such as the origin, product form, storage form, and the claim.
According to Lancaster (1966), a consumer possesses an ordinal utility function on attributes, U(z) and that will select a situation that maximizes his/her U(z), subject to the budget constraint px ≤ k, where 𝑧 is a vector of the nth attribute that the consumer obtains from the consumption of goods (z1,...,zn), p is a vector of prices for each of the goods, x represents the goods, and k is the consumer’s income. A transformation between the utility function defined on the characteristics-space and the budget constraint defined on the goods-space is represented by the equation system z=Bx, where B is a matrix of constants. Additionally, the non-negativity constraints represented as z, x ≥ 0 are assumed to hold initially. However, the non-negativity constraints may not always be part of the model in some applications. Simplifying the model and assuming a one-to-one correspondence between goods and activities, the consumer choice is given as:
Maximize U(z) subject to px ≤ k
with z=Bx z, x ≥ 0
Meanwhile, Lancaster’s theory assumes that goods are infinitely divisible, regularly purchased, and have low unit value. Nonetheless, many goods are not perfectly divisible, specifically goods that are important to discrete choice applications, which often are not purchased frequently (Louviere, Hensher, & Swait, 2000).
9 2.1.2 Random utility theory
To describe observed inconsistencies in patterns of individual behavior, the random utility theory (RUT) was proposed by Thurstone (1927) and further developed by McFadden (1974) from paired comparisons to multiple comparisons. Like traditional consumer theory, the RUT assumes that an individual will choose an alternative from a set of alternatives that will maximize his/her utility. The idea behind random utility theory is that there is a latent construct known as “utility” for each choice option, which is not observable but only exists in the mind of the decision-maker (Louviere et al., 2010). However, while a decision-maker may have perfect information in terms of his/her utility function, analysts (researchers) do not know what is in the mind of a decision-maker but can observe his/her choices and make inferences about the factors that drive such choices.
Unlike the traditional consumer theory, which assumes deterministic behavior, the RUT indicates that the latent utility individuals derive from a choice object can be decomposed into both deterministic (systematic) and random (unexplained) components (Louviere et al., 2000;
Louviere et al., 2010). The deterministic or systematic component represents the attributes of the choice alternatives and the characteristics of the individual decision-makers that can be observed by the analyst, whereas the random component is the utility contributed by attributes unobserved by the analyst and captures uncertainty or all unidentified factors that influence choices (Louviere et al., 2000; Louviere et al., 2010). Following on from that, the utility that individual n associates with alternative j in the choice set 𝐶𝑛 is given by
Ujn=Vjn+εjn (2.1)
where Ujn is the unobservable utility that individual n associates with choice alternative j, Vjn is the deterministic component of utility that individual n associates with alternative j and 𝜀𝑗𝑛 is the random component associated with individual n and alternative j, capturing the uncertainty. The individual will choose the alternative with the highest utility from the choice set. Therefore, it is possible to predict the probability that individual n will choose alternative j, but not the exact alternative that individual n will select (Louviere et al., 2010). The probability that individual n chooses alternative j from a set of competing options Cn is equal to the probability that the utility of alternative j is greater than the utility associated with alternative 𝑘 after evaluating each alternative in the choice set. This is given as follows:
P(j|Cn) = Prob[(Vjn+εjn) > (Vkn+εkn) ∀j ∈ Cn; j ≠ k] (2.2)
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Rearranging Equation (2.2) to put the observables and unobservable together gives:
P(j|Cn) = Prob[(Vjn-Vkn) > (εkn-εjn) ∀j ∈ Cn;j ≠ k]
= Prob[(εkn-εjn) < (Vjn-Vkn) ∀j ∈ Cn;j ≠ k] (2.3)
By assuming different probability distributions for the unobserved portion of utility, different probabilistic discrete choice models, such as the multinomial logit (MNL) model, can be derived from Equation (2.3). For the researcher, εjn is a random variable and represents the utility contributed by the unobserved attributes (Train, 2009; Louviere et al., 2000). However, this does not imply that individuals maximize utility in a random manner but rather they can be deterministic utility maximizers (Louviere et al., 2000). Randomness occurs since the analyst does not know what is in the mind of each individual but fully observe the set of influencing factors and the complete decision calculus, suggesting that the analyst can only explain choice up to a probability of event selection (Louviere et al., 2000). According to Louviere et al.
(2010), psychologists assume that individuals are not perfect measurement devices, and thus, the random component of the utility can additionally be explained by including sociodemographic or psychological factors that reflect the variability and differences in individual choices and not the choice options per se. Given that these factors are also important in explaining buying behavior, the microeconomic theory (i.e., discrete choice theory) applied in this thesis is extended to take into account other factors that may influence the choice and consumption of chicken meat.