ASSOCIATION FOR CONSUMER RESEARCH

A fairly common form of managing consumer price expectations is when companies use numbers in their marketing communications (e.g., product model numbers, numbers in product or place names, and numbers on license plates). In some cases, increasing numbers reflect better items. In others, they do not. For instance, a Canon A530 is not superior to an A460 digital camera. Likewise, Nokia 6110 is inferior to Nokia 6102. Though an average consumer is not knowledgeable about those numbers and how they are assigned to specific models (Gunasti and Ross Jr. 2010), we argue that they are there to serve a strategic mission. Those numbers potentially act as attempts at price guidance. Consider BMW that uses car images with a European license plate displayed prominently in its print ads in Europe where the licenses are all in the format “L LL XXXX” where Ls are letters and Xs are numbers. There is clearly a correlation between the numbers in the license plates and the price of the cars. Specifically, if a zero is added to the number on the plate, the resulting number seems to be generally higher than the price for low and mid-priced models and generally lower than the price for high priced models. It is almost as if there is a not so subtle attempt at price guidance in the choice of numbers for the license plates. This paper explores the effect of seemingly random numbers in marketing communications on consumers’ willingness to pay (WTP). More specifically, we examine the differential effect of incidental environmental anchors (IEA) on consumers’ WTP as a function of the product category. We suggest that although consumers are not aware of the link between the numbers and the price of the product, not only do they base their WTP on the random number but also adjust the numbers to resemble acceptable prices for a given product category. Taking a context-dependent view of anchoring, we suggest that consumers alter and adapt the anchors to fit them into appropriate value range and that range changes from context to another. In a series of experimental studies, we test and demonstrate that individuals’ price expectations, thus their WTPs, are affected by those ‘adapted’ incidental numbers. CONCEPTUAL FRAMEWORK Incidental Environmental Anchoring The idea that use of numbers as anchors to manage individuals’ numerical estimates is not new. We have known for some time that when participants are given a number as an anchor and then asked to provide estimates, they are influenced by the random initial value (Tversky and Kahneman 1974). Selective accessibility framework argues that anchoring occurs in such cases because when individuals are asked a comparative question (e.g., whether the Mississippi river is shorter or longer than 70 miles), they take it as a hypothesis (Is the Mississippi river 70 miles long ? ) and seek for anchor-consistent information (Mochon and Frederick 2013). Therefore, estimates are assimilated toward the anchor. Even in cases when the numerical value is not supplied by the experimenter but rather exists in the environment, numbers seem to influence judgments. For example, Krishna et al. (2006) show that consumers’ price estimates are higher for a camera if they are exposed to an extreme-priced product in the catalog. Their results suggest that one extreme cue can affect the reservation price for many related items and many product categories. In the above examples, either the experimenter or the context supplies anchors that affect subsequent judgments. If a number is provided when there is a need to make numerical judgments, consumers retrieve anchor-consistent knowledge from memory, making the estimates close to the anchor. While a substantial body of research suggests that consumers’ numeric decisions are affected by experimenter or context-induced numbers that are activated before the decision is made, relatively less attention has been devoted to understanding when and how totally incidental numbers impact valuations. A recent exception is research by Critcher and Gilovich (2008) who show that even jersey numbers on football players affect the probability estimates of sacking the quarterback. Participants who saw the player wearing number 94 estimated his probability of sacking the quarterback in his next game higher than those who saw him wearing number 54. Numerical priming essentially suggests that any number in the environment at the moment of judgment may serve as an anchor. A minimal amount of attention to incidental numbers is sufficient to anchor numerical answers to unrelated questions (Wilson, Houston, Etling and Brekke 1996). Although Critcher and Gilovich (2008) provide important insights as to how incidental environmental anchoring (IEA) operates, their research does not discuss the potential anchor values that should be employed for different product categories to get intended reactions. The present research tries to fill this gap in the literature. Central to our hypothesis is that a consumer’s WTP is affected by incidental numbers that are in no way related to the price of the product. We posit and demonstrate that anchors exert context-specific effects in consumer-generated anchors. Context-dependent Effects on Consumer Valuations Individuals’ judgments are affected by the context of concomitant stimuli. Past research has shown that numerical values may take different meanings depending on the context and that the same number may lead to different evaluations (Parducci 1965). For instance, numerical values can be represented in alternative units and the type of unit used may impact consumers’ perceptions and judgments (Pandelaere, Briers, and Lembregts 2011; Zhang and and Schwarz 2012). The unit effect postulates that when individuals come across unfamiliar units (e.g., temperatures in the unfamiliar Celsius scale for Americans); they tend to translate those units into a familiar unit (e.g., Fahrenheit) to be able to make sense of the numbers. Since this conversion is rather difficult, consumers are likely to guesstimate rather than calculate the exact values. Pandelaere, Briers, and Lembregts (2011) argue that this imprecise conversion may render consumers vulnerable to anchoring effects. For instance, consumers tend to spend less in a foreign country if the value of that currency is lower than the value of their own currency (Raghubir and Srivastava 2002). Frederick and Mochon (2012) have also alluded that context has a big impact on the ways in which anchoring operates. If individuals are to make estimates on two different items employing the same response scale, they tend to change the use of the scale to reflect the change in the items. Therefore, in a way, anchoring effects are due to the distortion of a particular response scale. For instance, when a 70 mile anchor is presented, estimates of the length of the Mississippi Advances in Consumer Research (Volume 42) / 295 river are lower because individuals use a different number on the miles scale. In a similar vein, respondents who were first asked to estimate the weight of a wolf later judged ‘1000 lbs’ as bigger, pointing out larger animals when asked to specify an animal that weighs around 1000 lbs. The scale distortion theory of anchoring contends that numeric anchors do not affect the beliefs about the stimulus; rather the use of the scale is altered and adapted to fit the context. Following this logic, we argue that anchoring effect is contextdependent and taking previous research on context and unit effects a step further suggest that individuals not only change the use of the scale to fit the context but alter the numbers altogether to fit into new domains. That is, the same numerical value may serve as a lower bound for item X whereas it is taken as an upper bound for item Y. Hence, a customer may interpret 20 as an upper bound when evaluated in ounces for drinks but as a lower bound when evaluated in gallons for water tanks. The range of acceptable prices for a given product category determines the way in which the anchor value is used by the individual. Consumers adapt and distort these values to reflect a good enough anchor to base their judgments on. Though the relation between the incidental numbers and the price of the product is unknown by the consumer; when consumers’ WTP is asked, they use the readily available random numbers in their environment to make estimates. However, their use of such numbers is not straightforward. These anchors are malleable. Consumers unconsciously change these values to reflect possible acceptable prices for a given product category. Thus, we formally hypothesize that Hypothesis 1: When acceptable prices for an expensive product are used as anchors, a lower value serves as a lower (an upper) bound for an expensive (a less expensive) product. A higher value serves as an upper (a lower) bound for an expensive (a less expensive) product. Hypothesis 2: When acceptable prices for a less expensive product are used as anchors, a lower value serves as an upper (a lower) bound for an expensive (a less expensive) product. A higher value serves as a lower (an upper) bound for an expensive (a less expensive) product.