The debate between theorists who believe that outcomes and probability can be predicted with certainty and consumer researchers who believe that it cannot may be considered to be a question of philosophy (Mitchell, 1999). One argument is that the objective probability of a certain outcome and its associated consequences exist independently of perception and measurement, and therefore risk is objective and can, theoretically, be calculated. Conchar et al (2004, p. 419), for example, states that ‘risk can be conceptualised as an objective characteristic of a given situation’. The other argument is that the extent and level of risk is entirely dependent on the perceiver and therefore wholly subjective (Conchar et al, 2004). Many researchers in the field of consumer psychology who prioritise subjective (often called ‘perceived’) risk, concede that objective risk does exist, but it is rarely possible to measure it (Mitchell, 1999). However, whether risk can be measured and incorporated into a decision making model has been researched for decades, indicating firstly its contentious nature and secondly the importance to marketers and business managers.
Economic theorists such as von Neumann and Morgenstern (1944) discuss the ‘rational consumer’ who obtains complete information before making an accurate judgement which maximises the utility of a purchase decision. Von Neumann and Morgenstern (1944) conceived the Expected Utility
60 (EU) theory to describe choice preferences in decision making situations involving risk, which centres on mathematical calculations of the consequences of alternatives. In his paper in 1982, Shoemaker described this theory as the major paradigm in decision making since the Second World War, such is the esteem that the model has been held in. EU theory is an evolution of Expected Value (EV) theory where simple probability/reward calculations can be used to predict decisions (in theory). To provide a simple example of EV theory, consider a gambler who calculates the chance of a £1,000 pay out as 1 in 10 and the cost of the bet is £25. Under these circumstances, the bet represents good value and the theory would assume that the gambler accepts the risk in return for the potential reward. Increasing the complexity of EV theory is the notion that the gambler may be faced with more than one option to choose between. In addition to the option above there may be another bet with a reward of £2,000, a chance of success of 50 to 1 and the cost of the bet is £15. It is fairly simple to calculate that the gambler will still choose the first option, but the more options that require consideration, the more complex the decision making process and the closer we get to our limitations in processing power. Again, the tourism industry provides an excellent opportunity for research in this field due to the near limitless combination of choice alternatives available to consumers.
EU theory adds a level of sophistication to EV theory as it helps to explain why the gambler may actually select the option with the higher potential reward rather than the highest probability of reward. Firstly, it implicitly includes risk (or risk aversion) and secondly the desired outcome is expressed in terms of its subjective utility rather than numerical value, thus allowing for personal preferences. Regardless of these two concessions, according to von Neumann and Morgenstern (1944), decisions can still be calculated and predicted using formulas that include a utility function. A person will choose one option over other alternatives if the utility function of that option exceeds the utility function of the remaining options. The utility function can be described as the expected utility of the outcomes weighted based on the probability of their outcomes. This function, also called the von Neumann-Morgenstern function is a theorem based on five axioms:
1. Independence; where outcomes that are ranked according to preference do not change their
rank regardless of the probability attached to its outcome. For example, person may prefer to spend a week skiing in resort A than resort B because it has a more beautiful surroundings and more suitable ski runs, but it may be that resort B is at higher altitude and therefore has
61 a better chance of having sufficient snow. Regardless of this probability, the person prefers resort A.
2. Monotony; where the decision maker knows the probabilities of achieving a ‘desired
outcome’ that is associated with each alternative. The decision maker is predisposed to choosing the alternative with a higher probability of achieving that ‘desired outcome’ than an alternative with a lower probability. The alternatives in question have the same ‘desired outcome’, but there is more than one possible way of achieving that outcome. A simple example may be online versus face to face payment methods. Empirical studies (see Athiyaman, 2002 for an example) have shown that many people purchasing holidays or holiday elements deem the security risk associated with the internet too great and prefer to pay an agent or operator directly even though, in both cases, the desired outcome is the same.
3. Completeness; where an individual has discrete and well established preferences over
alternatives and the alternatives are ranked in terms of those preferences. Preferences incorporate the desired outcomes and their probabilities.
4. Transitivity; where alternative 1 is preferable to alternative 2, so therefore 1 must also
necessarily be preferable to alternative 3. This axiom also identifies that an individual makes selection decisions based on completeness.
5. Continuity; where if there is an alternative and probability combination that is desirable, and
an alternative and probability combination that is undesirable, there must be an alternative and probability combination where the decision maker is indifferent to the alternative. Taking a skiing holiday as an illustrative example again, if good conditions can be guaranteed, the tourist may want to go skiing. If conditions are guaranteed to be poor, the tourist does not want to go skiing. Between the two extremes, there exists a scenario where the tourist is indifferent between whether to go or not.
An accurate prediction of the result of the decision making process can be made based on ‘the weighted sums obtained by adding the utility values of outcomes multiplied by their respective probabilities’ (Mongin, 1997). Although Expected Utility theory is most commonly applied to economics and politics, it can be used in any decision making context. Moutinho (1987, p. 29) describes the Utility of a tourist destination as the ‘function of estimated utilities of the attributes that comprise it’ Sheluga et al (1979) state that the utility of a destination can be described as:
62
U
jk= f(u
1jk’ u
2jk’...u
njk)
where Ujk is the utility of product j for tourist k and Unjk is the utility of the ith attribute of product j for tourist k (i = 1, 2,...n). Most of the literature involving EU theory is largely formulaic in nature (see Machina, 1982; Loomes and Sugden, 1982; Kahneman and Tversky, 1979; Hey and Orme, 1994; Dubra et al, 2004), thereby giving credence to the argument that risk can be calculated and decisions can be based on the calculations.