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Article Excerpt
Introduction
In many service situations, consumers are transported from one state to another over a certain interval of time. For example, an airline may transport passengers from Denver to Boston over four hours, or a supplier of machine tools may undertake to deliver and install a new assembly line of 80 machine tools over 20 weeks. In both cases, the service moves the consumer (the passenger, or the assembly line) from a start state (Denver, or the absence of the assembly line) to a destination state (Boston, of the existence of a new line) over a specified period of time (four hours, or 20 weeks, respectively; we refer to this as the elapsed time). In these services, the goal of the consumer is to attain the destination state without much regard to the intermediate states.
Given their goal-oriented nature, the most basic evaluation of such services might use a total cost approach in which the evaluation is a function of the cost of time involved (i.e., a function of elapsed time, Becker 1965) and any additional costs incurred. These additional costs could be monetary (i.e., the price paid) or nonmonetary (e.g., the hassle of changing planes). Consumers should be indifferent between services in which each element of cost is held constant. However, we argue in this paper that in addition to cost factors like the elapsed time and price, the evaluation of services is influenced by the path characteristics of the service. Specifically, certain path characteristics convey a greater perceived progress towards the destination, and a belief that the elapsed time is utilized efficiently. This perceived progress towards the destination represents an additional variable that influences consumer choice.
Note that the comparison of two paths in which an identical output (i.e., distance traveled or number of machines installed) is produced in an identical elapsed time might lead to a conclusion that the mathematical rate of progress, as viewed over the entire path, is identical (cf. Allen 1997). However, we propose that the perceived progress as experienced during the path also influences consumer judgment and decision making. We use the term virtual progress to capture this perception of progress. It is not our intention to argue that the effects of virtual progress are either unreal or irrational; we simply use the word "virtual" to capture a source of progress that may not be immediately obvious from a simple mathematical representation of the service situation.
In the rest of this paper, we first review relevant literature, present ah analytical model of consumer choice, and derive predictions about specific relationships between path characteristics and choice. Second, we present the results of six experiments designed to test these predictions and to highlight the importance of virtual progress. The first five experiments study choices between services in which the elapsed time and price is held constant, but in which the path characteristics vary. In a final experiment, we show that virtual progress is not merely an artifactual phenomenon that only comes into prominence when elapsed time is held constant, but that consumers are actually willing to choose longer paths that have a higher degree of perceived progress. Finally, we conclude with a general discussion and propose directions for future research.
The Effect of Path Characteristics on Evaluations--A Model of Consumer Choice
Recent research in behavioral decision making suggests that sequences of events create consumer experiences (Ariely 1998, Ariely and Carmon 2000, Carmon and Kahneman 1996). Research has also shown that the evaluation of such experiences is not greatly influenced by the actual duration of the experience (Fredrickson and Kahneman 1993) or by the final outcome (Hsee and Abelson 1991), but rather by some defining features or gestalt characteristics of the experiences (Ariely and Carmon 2000, Kahneman et al. 1993). These features include the relative value of the outcome as compared to its past values (Loewenstein and Prelec 1993), the rate of change of the outcome (Hsee and Abelson 1991), the peak intensity of the experience (Ariely 1998), and the affective experience at the end of the sequence (Ariely and Carmon 2000). We identify another path characteristic that is especially relevant for goal-oriented services--the perceived progress towards the destination. Prior research suggests that the achievement of subtasks towards the attainment of a goal often signals a sense of progress that contributes to feelings of well-being and high morale in individuals (Brunstein 1993, Cantor and Kihlstrom 1987). Theories of motivation (Deci and Ryan 1985) suggest that people like to be in situations in which they are constantly making progress towards their goal, and further, that progress enhances psychological well-being (Sheldon and Kasser 1995, 1998). This suggests that in situations where consumers are focussed on the goal of reaching the destination, they actively choose activities that will help them attain this goal (Locke and Latham 1990, Sheldon and Kasser 1998).
Modeling Consumer Choice
We first consider service alternatives that have identical prices, and start (O) and destination (D) locations. The elapsed time T represents the time needed to traverse this distance. We represent the opportunity costs of elapsed time T as c(T) where c'(T) > (Becker 1965, Soman 2001).
We next incorporate the effects of perceived progress. We characterize a service path as a series of velocities {[v.sub.t], t [member of] [0, T]} where velocity [v.sub.t] is a measure of the progress towards destination at any given time t. Velocity [v.sub.t] can represent the speed of airplanes, the rate at which machine tools are delivered, or the rate of change in a patient's blood glucose level, and it can be positive, zero, or negative. A positive velocity transports the consumer closer to the destination (resulting in positive displacement), zero velocity keeps the consumer at the same location (idle period), and a negative velocity (a flight traveling in the opposite direction) moves the consumer away from the destination (negative displacement). (1)
Prior research shows that people anticipate utilities from future events (cf. Elster and Loewenstein 1992). Specifically, we propose that consumers evaluating a path anticipate gaining some value u([v.sub.t]) from the velocity [v.sub.t] at any time t, and that this value is a function of the difference between the actual velocity and a reference velocity (Loewenstein and Prelec 1993). Further, research has shown that future outcomes have a lower impact than current outcomes because people tend to undervalue--or discount--the future (Liberman and Trope 1998, Rachlin and Raineri 1992). We capture this by a discounting function such that [u.sub.0]([v.sub.t]), the present anticipated value arising from time t, equals [u([v.sub.t]).sub.[delta]t] ([delta] = discounting factor, [less than or equal to] [delta] [less than or equal to] 1). When [delta] = 1, there is no discounting and future outcomes carry as much weight as present outcomes, and when [delta] = 0, future outcomes play no role and only the current outcome matters. Recent research infers implicit discount rates for the decision weights associated with delayed attributes and suggests that typical values of [delta] are in the range of 0.8-0.95 (see Loewenstein and Prelec 1992). Generally, it appears that individuals care about future events, but undervalue these future events relative to the present (Soman 2002).
We further propose that values are generated for each instant during the service and that the valuation of the entire service (U) is the simple aggregation of such values plus a negative value associated with the length of the elapsed time. We use the notation "U" rather than "V" to represent value to avoid confusion with the notation for velocity. Therefore, the predicted value is
(1) U = [[integral].sup.T.sub.0] [u([v.sub.t]).sub.[delta]t] dt - c(T).
Note that service valuation also decreases with the elapsed time T. Equation (1) represents our basic model of consumer decision making. (2)
Anticipated Value from Progress
We model the anticipated value arising from the perceived progress towards destination through the deviation of a service route from the consumer's expected progress over time. A rich literature has documented that utility (or value) of ah outcome is evaluated with reference to some underlying expected level of that outcome (e.g., Kahneman and Tversky 1979). In this spirit, we argue that a consumer would obtain a positive value when progressing faster than expected, but receive a negative value when moving slower than expected. Let [R.sub.t] be the expected rate of progress at time t. We propose that this is a constant; i.e. [R.sub.t] = v where t [member of] [0, T]. This is consistent with Loewenstein and Prelec (1993), who suggest that consumers develop their reference based on a uniform path from start to destination state (i.e., v = v = D/T). We also validate this assumption using a series of verbal protocols where subjects were asked to think aloud as they evaluated a number of services with different path characteristics. One of the most prominent themes that emerged from this analysis was a tendency to start the evaluation by computing the average velocity (see also Flint 1998). Even when evaluating a path singly, subjects tended to compare it with some internally generated "control" path of uniform velocity. Thus, while we do not impose restrictions on the value of v, past research and our protocol analysis suggest that v is a good approximation for v.
Let g(*) and l(*) represent gain (velocity greater than reference) and loss (velocity less than reference) function, respectively. Then,
(2) g[v.sub.t] = |[v.sub.t] - v| + ([v.sub.t] - v)/ 2,
(3) l[v.sub.t] = |[v.sub.t] - v| + ([v.sub.t] - v])/ 2.
If a consumer has a gain, then loss l([v.sub.t]) = 0. Similarly, if he experiences a loss, then g([v.sub.t]) = 0. We can then write a consumer's predicted value from velocity [v.sub.t] as
(4) u[v.sub.t] = {g[v.sub.t] if [v.sub.t] [greater than or equal to] v, -[beta]l[v.sub.t] if [v.sub.t] < v,
where [beta] is the coefficient of loss aversion (Tversky and Kahneman 1991). Consistent with loss aversion, we posit that [beta] > 1; that is, losses loom larger than gains in decision making. (3) Note that if there are no gains or losses (i.e., the actual service path is identical to the "reference" path), our proposed model is simplified to the special case where only the elapsed time matters in the evaluation of the service.
Figure 1 represents consumer decision making using a schematic representation of the model. The first panel shows a reference path (dotted line) and the actual path (solid line), while the second panel shows the corresponding velocity profiles. Gains and losses are represented by the shaded areas and labeled as G and L, respectively. The third panel shows the effect of intertemporal discounting--areas that are further away from the time of decision making are shrunk by a greater degree than areas that are closer. The resulting sum of the shrunken areas ([G.sub.d1] + [G.sub.d2] - [L.sub.d1] - [L.sub.d2]) represents the contribution of path characteristics to the final evaluation of the service path (the final evaluation also includes the negative value associated with the elapsed time). Note that this simple schematized version of the model treats time discretely in terms of four segments of the service, while the model in Equation (1) treats time as a continuous variable.
[FIGURE 1 OMITTED]
In this paper, we are particularly interested in path characteristics that hinder progress, and we refer to these as obstacles. Two such obstacles are (a) the presence of idle time during the path, and (b) movement away from the final destination (or the presence of negative displacement).
(a) Presence of Idle Time. A growing body of literature in marketing suggests that consumers are averse to waiting before...
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