2006 Iarep Parigi Paper

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2006 Iarep Parigi Paper

  1. 1. THE IMPACT OF DECOYS AND BACKGROUND INFORMATION ON CONSUMERS PREFERENCES AND DECISION MAKING Fabio Buoncristiano1 Daniele Scarpi2 University of Bologna University of Bologna (IT) INTRODUCTION The process through which consumers form their preferences resembles more a work of architecture and building, starting from certain base-values, rather than an archaeological work of digging for bringing to light pre-existent values and preferences characterising each choice made by the consumer. On one hand it has been recognised there are limits in the cognitive capability of individuals, and there is no such thing as a “perfectly rational” decision-maker. On the other hand, literature has well documented that in conditions of uncertainty, complexity and ambiguity individuals often choose the options which make the choice task easier (Novemsky et al., 2004; Sheng et al., 2005). It is precisely in this frame that context dependent effects can (and do) take place. That is to say, in a nutshell, that the composition and the framing of the choice set influences the final choice: a simple sentence, yet it revolutions traditional (classic) economics, and encounters resistances in numerous academic groups dealing with choice-problems (e.g. industrial economists, financial analysts, marketers). Inevitably putting into communication psychologists and economists, it constitutes a fascinating exchange opportunity which yet sounds fearsome to someone, and vaguely contradictory to the stiffest of old-school economists. Nonetheless, since a decade or more, a great deal of attention has been paid to the analysis of context effects, which in very recent times has got great momentum. THEORETICAL FOUNDATION Background effect “The same circle appears large when surrounded by small circles and small when surrounded by larger ones” (Simonson and Tversky, 1993, p. 281). Whilst choosing, consumer compare the option they might buy with the other alternatives currently available in the choice set, but also with the alternatives they remember from their past experience. Thus, there emerges the importance of the so-called contrast effect, which could highlight the importance of certain information/attributes (e.g. Simonson and Tversky, 1992; Tversky and Simonson, 1993). This effect basically implies that the same product could be perceived differently in a different contest. To better understand the influence of the contrast effect, one could consider previous experience as the ability to recall from memory specific information. The literature presents various experiments, whose results highlight that background context effects can significantly influence consumer preferences: the contrast between past experience and current choice task usually induces consumers to choose more “extreme” options than without such a contrast. For instance, Simonson and Tversky (1992) create choice contexts 1 Via Cleto Tomba 10; 40127 Bologna Italy. E-mail: fabiobuoncristiano@virgilio.it Tel. 0039-(0)51-366698 Fax 0039-(0)51-246 411 2 Via Capo di Lucca 34; 40126 Bologna; Italy. E-mail: scarpi@economia.unibo.it Tel. 0039-(0)51-209 8087 Fax 0039-(0)51-246 411
  2. 2. where the trade off between attributes is expensive (expensive background) and economic (inexpensive background) considering personal computers with different prices for one more KB memory. This way, they test whether consumers with an expensive background would choose the PC with the largest memory, whereas consumers with an inexpensive background would choose the more pricey PC. Similar experiments have verified that the decision maker computes the trade off between the attributes of the first choice set (the background set), and then extends (replicates) it also to the subsequent choice set (the current set). Priester, Dholakia and Fleming (2004) have further shown that the background effect emerges more clearly when consumers undergo a greater cognitive effort. In the empirical part of this study, we will complicate the original experimental design by Simonson and Tversky (1992), analysing not only the consequences of the background effect (expensive vs. inexpensive background), but also the interaction between backgrounds and phantom options. Phantom Options In a typical choice task a consumer has to choose between two or more options, which do not fully dominate each other. For instance, in buying an engagement ring, the relevant attributes could be the size of the diamond (carats), and its clarity (included vs. loupe-clean diamond). Accordingly, the choice could be between a ring with a bigger but less clean diamond, and a ring with a smaller yet cleaner diamond. Information the consumers perceive as clearly uninformative about the desired benefits (e.g. the diamond comes from Tanzania) should have no effect on the choice task, according to the classic theory. Similarly, options which are known to be unavailable (i.e. phantom options) should not influence decisions. This is known as the independence of irrelevant alternatives (Luce, 1959): adding to the choice task an alternative which is not available, will have no impact on the ratio of choice probabilities among the original alternatives. However, subsequent literature has shown that irrelevant alternatives do have an effect on consumers’ preferences, systematically violating Luce’s assumption (see e.g. Praktanis and Farquhar, 1992; Potter and Beach, 1994). Considering out-of-stock-out products as phantom options, our contribution bridges the traditional studies of the effects of phantoms (e.g. Carpenter et al., 1994; Janiszewski, 2002) with studies of consumers’ response to stockouts (e.g. Fitzsimons, 2000). Thus, in this analysis, the irrelevant information is no more related to a really available alternative, but to a non-available additional alternative, which is therefore a phantom and a stock-out at the same time. In other words, consumers are not confronted with irrelevant information such as “and this diamond was mined in Tanzania”, but with irrelevant information such as “this other ring you see here has already been sold”.
  3. 3. Literature on phantoms has usually dealt just with the effects of a new, irrelevant attribute (e.g. Carpenter et al., 1994), and has usually focused on the informativeness principle of communication (Clark, 1985), distinguishing between the semantic and the pragmatic component of communication (Harris and Monaco, 1987). In a nutshell, the irrelevant information influences consumers’ decisions because it differentiates the target product from the others (Carpenter et al., 1994; Brown and Carpenter, 2000), although promotions on such irrelevant attributes are less likely to produce an effect (Simonson, Carmon and O’Curry, 1994). In the case we investigate here, there is no new-attribute, but rather a whole new (phantom) product. A number of researchers dealing with phantoms have dealt indirectly with consumer response to stockouts (e.g. Farquhar and Praktanis, 1987; Praktanis and Farquhar, 1992). This article deals directly with stockouts, through an examination of the stockout’s contrast and attribute importance. In fact, research has dealt with numerous different kinds of phantoms: for instance, Cialdini at al. (1978), Joule et al. (1989), Praktanis and Farquhar (1992), Highhouse (1996) and Doyle et al. (1999) all investigate irrelevant alternatives of different nature, such as phantoms adding a new attribute, phantoms close to the real options, phantoms far from real options, phantoms averaging the real options, and so on. The results of these studies greatly vary in size and direction due to the different kind of the considered phantoms (and also due to methodological issues). Thus, we believe it is important to treat stockouts not as phantoms in general, but as a very unique kind of phantoms per se, analysing their specific effects on consumers’ evaluations. Furthermore, in the existing literature there seem to be ambiguity with regards to the “irrelevant” information. In fact, as put forward by Janiszewski (2002), the information presented in the studies of Carpenter et al. (1994), Simonson et al. (1994), Brown and Carpenter (2000) was objectively irrelevant, but subjectively relevant, as subjects did not perceive the information as irrelevant, and knowingly relied on it. This is connected to the so- called “principle of relevance” (Sperber and Wilson, 1986) or “maxim of relation” conversational norm (Grice, 1975): in a nutshell, all information is relevant for the goal of the conversation. On the other hand, Praktanis and Farquhar (1992) introduce a distinction between known and unknown phantoms: known phantoms are recognized as illusory options, whereas unknown phantoms are believed to be real options until the consumers tries to purchase them. Thus, known phantoms provide information which is objectively and subjectively irrelevant, whereas unknown phantoms provide information which is objectively irrelevant, but subjectively relevant. Unfortunately, neither Praktanis-Farquhar (1992) nor the subsequent empirical analyses of phantoms empirically test the effects of known and unknown phantoms. We think the time has come to do it, and provide a first attempt. AIMS OF THE RESEARCH Unfortunately, there is no empirical analysis testing the effects of known and unknown phantoms. Therefore, although the literature makes some interesting points, there is no way to say anything about their relevance. The objective of this study is to analyse the combined effects of background effects and phantom options, specifically addressing Janiszewski’s (2002) and Praktanis-Farquhar’s (1992) issue of known against unknown phantoms. As far as we know, this is the very first attempt to do it, and answers the calls by the most recent literature (see Novemsky et al. 2004, Sheng et al. 2005) for studying the interactions between different “irrational” phenomena.
  4. 4. One of the main problems with this body of studies is the disproportion between the numerous, sophisticated, interesting, potentially relevant suggestions, and the relatively few empirical analyses. Therefore, this articles attempts at providing an empirical analysis. A second objective, directly descending from the main one, is to provide useful implications for retailers, especially as 1-in the real world (i.e. out of a laboratory settings) no context effects happens to be alone, and 2-background and phantoms are probably the most frequent context effects from a managerial point of view (e.g. the prices in the last catalogue set a background, and no firm can avoid having some stock-outs at certain times). HYPOTHESES According to the literature, adding to a choice set an asymmetrically dominated alternative (i.e. a decoy) provides stimuli leading to an attraction effect, which favours the purchase of the option which is closest to the decoy and dominates it (Huber et al. 1982; Simonson e Tversky 1992). At the same time, consumers could have background information, and the literature has already assessed that background information can be relevant in the decision making process. Although until now the attraction effect and the background effect have always been considered independently in the literature, we hypothesize there could be an interaction between them. Thus, our first research question asks what happens when consumers have background information which provides stimuli in conflict with the stimuli provided by the decoy. We suggest that, in this case, the attraction effect will exert a different strength than without conflicting background information. Thus, we advance the following hypotheses: • H1: due to the introduction of a decoy in the choice set, there is an attraction effect, which leads to a preference for the option closest to the decoy (and dominating the decoy) • H2: the effect hypothesized in H1 exerts a different strength when the background information is conflicting with the decoy, and when it is not. In particular, we are going to verify which of these two hypotheses will be supported: • H2a: the attraction effect hypothesized in H1 will be stronger when consumers have background information conflicting with the decoy Vs. • H2b: the attraction effect hypothesized in H1 will be stronger when consumers have background information not conflicting with the decoy The second research questions asks about the difference between a decoy option which is really available, and a decoy option which is a phantom 3. We expect the attraction effect to take place also in this case (see H1), nonetheless we hypothesize that the nature of the decoy trigging the attraction effect will impact the strength of such effect. More specifically we hypothesize the following: • H3: the attraction effect hypothesized in H1 exerts a different strength when consumers evaluate a choice set with a real decoy as opposed to a choice set with a phantom decoy. 3 Referring to the classification by Farquhar and Pratkanis (1992) we consider the case of a known phantom, i.e. consumers know the option is not available for choice.
  5. 5. In particular, we are going to verify which of these two hypotheses will be supported: • H3a: the attraction effect hypothesized in H1 will be stronger when consumers face a real decoy than when they face a phantom decoy. Vs. • H3b: the attraction effect hypothesized in H1 will be stronger when consumers face a phantom decoy than when they face a real decoy. METHOD The methodological aspects underlying virtually all research about context effects are usually very easy in terms of the mathematical tools employed, but rather complicated in terms of the research design. In fact, on one hand hypotheses usually require to simply count how many people made what choice, and to check whether there are significant differences: mathematical models, structural equation models, mixture models and the like are very seldom encountered and needed. On the other hand, the experiments have to be carefully designed, and groups and data need to be kept in rigorous order to be subsequently combined and recombined. Although this premise would be worth further considerations about the philosophical nature of these studies’ Gestaltung contrary to the majority of today’s marketing research (where the main problems often is the mathematic underlying the testing of the hypotheses), we will proceed straightforward to the description of the research design. The simple statistical tools used for testing the hypotheses will be briefly reported alongside the results. Data collection The large majority of the empirical analyses about context effects uses laboratory settings, simulations, and convenience samples of very small size (i.e. 50-70 students with a paper and pencil questionnaire). Indeed there is a huge debate about the appropriateness of student samples (see e.g. Lynch 1982, Calder and Tybout 1999), and the question is left unanswered whether a sample of non-students would have provided the same results. In our analysis we use a large sample of consumers (about 1100) collected on the Internet through the creation of a website simulating the sale of products (refer to the next paragraph for more details about product category). The site stated it was a sale simulation, not a real sale. On one hand, all previous studies of context effects have always used simulations: our research does not deviate from the body of literature with regards to this aspect. Furthermore, decision making is hypothetical in its very core, as making a decision implies anticipating hypothetical states of the world and considering feelings that we do not yet have. On the other hand, there is broad support in the literature that in certain cases hypothetical and real choices lead to the same end-results (Knetsch and Sinden 1984, Harrison 1994, Camerer 1995, Wiseman and Levin 1996, Beattie and Loomes 1997, Camerer and Hogarth 1999; Holt and Laury 2000, Kuuhberger et al. 2002). Unfortunately, some of the simulated situations considered in previous literature violate the requirements for equality between real and hypothetical choices, which are fulfilled in this study (see Kuuhberger et al. 2002 for a review). Internet users where individually contacted through an MIRC script and invited to visit the web-site. The message was circulated in all the main Italian chat-rooms for all ages and jobs (nearly all chat rooms were titled such as for instance “housewives chat” or “40-50y chat” and
  6. 6. the like). The message stated the invitation was not for a commercial purpose, but for a research conducted by a north-Italian University, and that the data collected would have been anonymous and not sold to any company. Response rate was surprisingly high (about 75%): total usable sample size is about 1100. This allowed for a mixed sample in terms of gender, age, and job: about 40% females, mean and median age about 30, the sample comprises people doing all sort of jobs, and jobless people (i.e. not only high-wage hi-tech jobs). At the same time, the sample is all made up by Italian consumers, as the chat rooms, the message and the website all were in Italian language (Italian only). This ensured us against the possibility of biases in the results due to cross-cultural or country-of-origin effects (see Triandis 1995 and Hofstede 1996 for more details). Respondents visiting the website first chose between two Options (these two options built-up the background), then respondents were automatically directed to another web-page and had to choose between other three options (these three options were the target set). Fifty percent of the initial two options were constructed so to built an expensive background, and 50% were constructed so to built an inexpensive background. Which background would appear, was randomised by a software. Each respondent received one background only, and the website was designed so to make it impossible 1-to receive both backgrounds, 2-to compile more than one questionnaire, 3-to compile the same questionnaire more than once. The three options following the background, that is the three options forming the target set, contained a decoy. In some cases the decoy was real, in other case it was illusory (e.g. a known phantom) (50% chance each, randomised by the software). Virtually all respondents compiled the whole questionnaire (i.e. choose between the two options forming the background and between the subsequent three options forming the target set). Finally, the website automatically recorded how much time each respondent took to choose: this information was secretly stored alongside the answers. Product category We consider MP3 players: previous research has often used MP3 players (see e.g. Chernev, 2005; Paxton, Hoeffler and Zhao, 2005), or products which can be considered their “ancestors” (e.g. Lehman and Pan 1994 use stereo speakers, Simonson and Tversky 1992 use AM Players). MP3 players present numerous features which suit the design of this analysis: 1-they are well known to the sample which, browsing online, has a certain familiarity with technological devices; 2-they are easy to be described in terms of a few key attributes; 3-they are one the few products which are successfully sold online all around the world, even in countries where e-commerce is less widespread. Furthermore, Mp3 players appear to better reflect the identity between hypothetical and real choices according to the pertinent literature (see e.g. Kuuhberger et al. 2002). We therefore believe these features contribute to make MP3 players a proper choice for the empirical analysis. Experimental design: Subjects were asked to choose among different MP3 players varied on two attributes: price (Euro) and memory (Gigabyte). We consider the cases of target sets with and without a real/phantom decoys, with and without expensive/inexpensive backgrounds. To test H1 we compare a control group (no decoy) with four other groups (real decoy emphasizing price; real decoy emphasizing disk-space; phantom decoy emphasizing price; phantom decoy emphasizing disk-space). This will allow to see whether there is an attraction effect, its direction and its strength. To test H2 we compare a control group (background but no decoy) with four other groups (non contrasting information and a decoy emphasizing price; non contrasting information and a decoy emphasizing disk-space; contrasting information and a decoy emphasizing price;
  7. 7. contrasting information and a decoy emphasizing disk-space). This procedure is run one time for the case of the expensive background (1 control group plus 4 groups), and one time for the case of the inexpensive background (again 1 control group plus 4 groups). This will allow to see the role of contrasting vs. non contrasting information, and to compare both the backgrounds and the decoys. To test H3 we compare two groups for the expensive background: one has a real decoy, the other one has a phantom decoy. Similarly, we then compare two groups for the inexpensive background: one has a real decoy, the other one has a phantom decoy. This will allow to assess the role of the decoy. The distinction between the real decoy and the phantom decoy was made by writing “currently unavailable” next to the phantom decoy. The appendix provides a summary of the various groups and describes which Mp3 readers were included in each group. RESULTS H1 hypothesized that introducing a decoy in the choice set would generate an attraction effect, leading to a preference for the option closest to the decoy. H1 has been tested comparing G1 (control group) with G2 and G3 regarding the real decoys, and comparing G1 (control group) with G4 and G5 regarding the phantom decoys. The data support H1 for both kind of decoys. Results are summarized in Table 1a and Table 1b: Table 1a – Attraction effect: real decoys G1 G2 G3 (no decoy) Decoy on price - 4.86% - (4Gb/180€) Option C 53.68% 71.84% 26.66% (4Gb/175€) Option D 46.32% 23.30% 63.34% (7Gb/220€) Decoy on space - - 10.00% (7Gb/225€) Table 1b – Attraction effect: phantom decoys G1 G4 G5 (no decoy) Decoy on price - UNAVAILABLE - (5Gb/170€) Option C 53.68% 72.38% 35.10% (4Gb/175€) Option D 46.32% 27.62% 64.90% (7Gb/220€) Decoy on space - - UNAVAILABLE (8Gb/215€) In the control group, preferences are about equally split between C and D, as none of them dominates the other. Introducing a decoy on price in G2 and G4, there is an attraction effect
  8. 8. towards the pricey option (C). Introducing a decoy on disk-space in G3 and G5, there is an attraction towards the option with the largest disk-space (D). These shifts in preferences are in the hypothesised direction and are statistically significant (p<.05) for both real and phantom decoys. Significance has been assessed using a t-test in proportions. H2 suggested that the attraction effect generated by the decoy would exert a different strength when the background information conflicts with the decoy, and when the background information is harmonious with the decoy. To test H2 we compare a group receiving conflicting information from the background and the decoy, with a group receiving harmonious information. We do this comparison for the expensive background with real decoy (G8 vs. G8b), for the expensive background with phantom decoy (G10 vs. G10b), for the inexpensive background with real decoy (G9 vs. G9b), and for the inexpensive background with phantom decoy (G11 vs. G11b). Results are summarized in Table 2a and 2b. Table 2a – Interaction between expensive background and decoy G6 G8 G8b G10 G10b (no decoy) (real decoy) (real decoy) (phantom decoy) (phantom decoy) Decoy on price - 7.61% - UNAVAILABLE Option C 29.33% 75.24% 34.15% 89.09% 32.44% Option D 70.67% 17.15% 56.10% 10.91% 67.56% Decoy on space - - 9.75% - UNAVAILABLE Table 2b – Interaction between inexpensive background and decoy G7 G9 G9b G11 G11b (no decoy) (real decoy) (real decoy) (phantom decoy) (phantom decoy) Decoy on price - - 2.5% - UNAVAILABLE Option C 68.05% 21.49% 72.5% 21.79% 62.85% Option D 31.95% 72.88% 25% 78.21% 37.15% Decoy on space - 5.63% - UNAVAILABLE - Comparing any group with the control groups, one can see an attraction effect always took place thanks to the introduction of a decoy. However, more specifically, in G8, the expensive background directs attention towards the MP3 player with the largest disk-space, whereas the decoy directs attention towards the MP3 player with the lowest price. Instead, in G8b both the background and the decoy direct attention towards the Mp3 player with the largest disk-space. The resulting final choices for G8 are significantly different than for G8b (p<0.01). Similar results emerge with regards to the introduction of a phantom decoy (p<0.01). In G9, the inexpensive background directs attention towards the MP3 player with the lowest price, whereas the decoy directs attention towards the MP3 player with the largest disk-space. Instead, in G9b both the background and the decoy direct attention towards the Mp3 player with the lowest price. The resulting final choices for G9 are significantly different than for G9b (p<0.01). Similar results emerge with regards to the introduction of a phantom decoy (p<0.01).
  9. 9. In conclusion, data show that when there is a decisional conflict between background and decoy, the attraction effect reduces the importance of the background (i.e. the decoy wins). Hypothesis H2a is therefore confirmed: the attraction effect is stronger when consumers have background information conflicting with the decoy. H3 finally, suggested that the attraction effect exerts a different strength when consumers evaluate a choice set with a real decoy as opposed to a choice set with a phantom decoy. The hypothesis has been tested confronting two groups with the same background but with a different decoy (real vs. phantom). Table 3 summarises the findings: Table 3 – the role of the decoy Target Set Expensive Background Inexpensive Background G8 G10 G9 G11 (real decoy) (phantom decoy) (real decoy) (phantom decoy) Decoy on price 7.61% UNAVAILABLE Option C 75.24% 89.09% 21.49% 21.79% Option D 17.15% 10.91% 72.88% 78.21% Decoy on space 5.63% UNAVAILABLE The data support H3: 75.24% chooses option C in the target set when the decoy is real, whilst 89.09% chooses C when the decoy is phantom (p<0.05). Similar considerations hold true for with regards to option D (72.88% vs. 78.21%). We therefore conclude that the attraction effects exert a different strength when consumers evaluate a choice set with a real decoy as opposed to a choice set with a phantom decoy. In particular, the attraction effect is stronger when the decoy is phantom: this evidence provides support for H3b. At the same time, these results also highlight that such effect is background-dependent, as the superior strength of the phantom decoy is emphasized in the case of an expensive background. In fact, the difference in “attraction power” between real and phantom decoy is about 14% (89-75) in the expensive background; it is only about 5% (78-73) in the inexpensive background. The next session discusses the findings from both a theoretical and a managerial perspective. DISCUSSION Testing H1 has shown how adding a third option to the choice set raises the likelihood of choosing the alternative with similar or dominant attributes. This phenomenon has been observed in two different choice context: 1-when the decoy is asymmetrically dominated by product in the target set and is really available to the consumers; 2-when the decoy dominates the products in the target set, but is a phantom. In both situations the results exhibit a substantial shift in preferences. Adding a decoy into the choice set influences more the alternatives similar to the decoy than the products dissimilar from the decoy (Tversky, 1972) Furthermore, the decoy might add one more reason to choose the less pricey product, as it
  10. 10. drives attention to the price attribute (Simonson, 1989). This helps the consumer to justify the final choice to the other (e.g. her referents) and to herself, as the target product is clearly superior to the decoy and costs less. Another explanation of the attraction effect may be due to the concept of range: adding a decoy increases the range of variation of the price attribute, and therefore lowers the relative importance of the difference in price between the other two products (Huber et al., 1982): on one hand the relative importance of price of the two non- decoy options is lessened, whereas on the other hand the relative importance of the other attribute is enhanced. Tversky and Kahneman (1991) have shown that consumers evaluate the risk of making a certain decision in terms of shift from a certain reference point (or starting point). A decoy which is dominated by one option, but not by the other, might become the reference point. The other alternative will be therefore evaluated as losses or gains, in the way pointed out by the well known loss-aversion principle (losses are weighted more heavily than gains, and this leads to an S-shaped utility function). Thus, adding a decoy could underline the convenience of moving from one alternative to the other. With regards to the attraction effect originated by a phantom option, we asked respondents to state their preference in a choice context characterized by the presence of a stockout (Fitzsimons, 2000). Although the results we obtain in this case are similar to those obtained with a real decoy, it is worth noticing that some of the explanations the literature provides for the attraction effect are not valid anymore when such effect originates due to a phantom decoy. For instance, the range effect does not seem a plausible explanation in this case, as in our experiment the introduction of a phantom decoy leads to a greater range of variation for both attributes (price and disk-space), thus it does not reduce the difference in convenience for the two real options. As the phantom decoy dominates the real alternatives, the range explanation has to be rejected. On the contrary, we believe that the loss aversion principle (Tversky and Kahnemann, 1991) provides a possible explanation of the attraction effect even when such effect is trigged by a phantom decoy. The dominant options are more attractive as they are more similar (in price and in disk-space) to the reference point represented by the phantom decoy. The losses a consumer perceives comparing the phantom decoy to the real alternatives are small. In other terms, there is no need (and no reason) to suppose that the real decoy will act as a reference point to the consumer, but the phantom decoy will not. A further reason might be the “scarcity - attractiveness effect” (Farquhar and Pratkanis, 1992). According to this principle, the non-availability of a product raises the perceived importance of the attribute where such product is stronger, establishing a sense of uniqueness. Our results confirm that adding to the choice set a price-dominating phantom decoy significantly lowers the preference for the disk-space dominating alternative: being pricey becomes more attracting than having a large disk-space. Our results show that modifications in the choice set influence the final decision. They also show that, before discarding the phantom option, consumers take into consideration its information, although it is irrelevant information because such option is unavailable. The consumers use the information derived from the phantom option to evaluate the real options, and make use of heuristics to simplify the choice task. When the available alternatives have some positive attribute (e.g. lower price) and some other negative attributes (e.g. lower quality) compared to other available alternatives, consumers make use of the information derived from the phantom decoy to solve such decisional conflict and to solve the trade-off between attributes. With regards to the background effect, our findings are in line with the mainstream of the literature and one just need to compare the shift in preferences from the control group (G1) to the experimental groups (G6 and G7) to see the background’s role. The respondents who
  11. 11. learn an expensive trade off between attributes in the background set, choose in the target set the Mp3 player with the highest quality. On the contrary, the respondents who learn a pricey trade off between attributes in the background set, choose the Mp3 player with the lowest price. Thus, consumers learn and evaluate from past experience, and their future decisional behaviour will be influenced by the background set they experience. We have also investigate the interaction between background and attraction effect, considering information deriving from the background but conflicting with information deriving from the actual context, in order to understand which source is more important to the consumer. Our findings lead to conclusions that the decoy (real and phantom) has a prominent role in influencing choice: the background effect is superset by the attraction effect. An explanation could be that the decoy simplifies the choice task in cases where it is difficult to identify a trade-off between attributes, in that the decoy makes comparisons become easier and more clear cut. Introducing a third option in the choice set modifies the structure of the context and limits the application of rules learned from experience. In fact, it might cost the consumer a great effort to recall from memory a specific comparison between specific attributes and, at the same time, to evaluate the information she is getting from the actual choice set. In this perspective, the decoy appears to minimise the cognitive effort produced by the presence of a conflict between stimuli derived from the past background and stimuli derived from the actual choice set. Focusing the attention of the decision maker upon one attribute only, the decoy limits the amount of information to be processed and simplifies choice. Our study also concentrates upon the role of experience as moderator of attraction. Results suggest that the magnitude of the attraction effect depends upon the difficulty of the choice task. Consumers make an increased use of the decoy as reference point (both of real and the phantom decoy) inasmuch the choice task becomes more difficult. The data make it possible to see that the attraction effect is stronger in groups which had past experiences conflicting with the present informational stimuli deriving from the target set. Furthermore, individuals who received some background information are more tied to the context than individuals who did not get any background information. We believe such results are important, as they witness that consumers make use of simpler choice strategies when choice becomes more difficult. Practitioner should therefore pay attention also from the background their potential customers come from, and should take into consideration that suddenly changing a choice context by –for instance- adding a decoy or changing a price can have effects going beyond expectations, and probably also going in a different direction than expected. Future research could try to investigate more clearly the role of time, analysing how fast the background information fades away in the mind of the consumers, and what happens when a consumer has more past experiences stratified in his life (i.e. whether the last or the first background is more important). Beside verifying the relation between the context effects, we also aimed at understanding how the consumer interprets and uses information to make a decision. We have therefore investigated the different impact a real and a phantom decoy might exert on consumers’ evaluations, to verify whether consumers rely more upon “relevant” (i.e. real) information or “irrelevant” (i.e. phantom) information. Experimental data show that making a decision with phantom alternatives simplifies the choice task: the attraction effect is stronger when a phantom decoy rather than a real decoy is added to the choice set. A choice context with three options, one of which is asymmetrically dominated and really available, represents a more complex choice task in terms of required cognitive effort, because it has more information to
  12. 12. be processed. In other words, consumers can more easily make decisions when the third option is unavailable, as the choice task requires a lesser level of information processing. The implications deriving from these considerations are mainly related to the crafting of the choice set. As the composition of the choice set has a key influence on the final choice, practitioners should pay great care to what options they should present to the consumers. They should also take into consideration that deciding to use a decoy to influence final choice is not enough of a strategy, as the nature of the decoy (real vs. phantom) plays a key role and can significantly affect final choices. Our results do furthermore show that context effects do interact, sometimes in synergy and sometimes in conflict, and should therefore not considered and analysed separately, but together. In particular, the background and the attraction effect play a key role in shifting consumers preferences and work together in the formation of individual preferences, thus providing support to the idea that there is a strong tie between past experiences and present choice contexts, and that the decoy plays its role being nested inside such interplay. LIMITATIONS AND CONCLUSIONS We acknowledge the limitations of the present analysis, but we believe it provides a contribution towards a deeper and more systematic understanding of context effects, and in particular, towards the comprehension of the interaction between background effect and phantom options. This has been done with the intent to provide a first answer to the numerous calls in the literature for empirical analyses, and in light of the even more numerous calls for better samples. We welcome further research proceeding along this path, which we believe to be both fascinating and fruitful, as it bears managerial implications of key relevance, dealing as it deals with how people behave and choose, believe and buy. REFERENCES Beattie, I. & Loomes, G. (1997) “The impact of the incentives upon risky choice experiments” in Journal of Risky and Uncertainty, 14, pp. 155 – 168. Brown, C.L. and Carpenter G.S. (2000), “Why Is the Trivial Important? A Reasons-Based Account for the Ef-fects of Trivial Attributes on Choice,” Journal of ConsumerResearch, 26 (4), 372–385. Camerer, C. (1995) “Individual decision making”, in Kagel, J.H. & Roth, A.E. (eds.): Handbook of Experimental Economics, 587-703. Princeton, NJ, Princeton University Press. Camerer, C. & Hogarth R.M. (1999) “The effects of financial incentives in experiments: a review and capital-labor-production framework”, in Journal of Risky and Uncertainty, 19, pp. 7 – 42. Carpenter, G.S., Glazer, R. and Nakamoto K. (1994) “Meaningful brands from meaningless differentiation: the dependence on irrelevant attributes” in Journal of Marketing Research, Vol. 31, pp. 339 – 350. Chernev, A. (2005) “Context effects without a context: attribute balance as a reason for choice” in Journal of Consumer Research, Vol.32, pp. 213 – 223. Cialdini, R., Cacioppo J., Bassett, R. and Miller, J. (1978) “Low-ball procedure for producing compliance: commitment then cost” in Journal of Personality and Social Psychology, 36, pp. 463 - 476.
  13. 13. Clark, H. H. (1985). Language use and language users. In G. Lindzey & E. Aronson (Eds.),Handbook of social psychology (pp. 179-231). New York: Random House. Doyle, J.R., O’Connor, D.J., Reynolds, G. M. and Bottomley, P. A. (1999) “The robustness of the asymmetrically dominated effect: buying frames, phantom alternatives, and in–store purchases”, in Psychology & Marketing, pp. 225 – 243. Farquhar, P. H. & Pratkanis, A. R. (1993) “Decision stucturing with phantom alternatives”, in Management Science, Vol.39, pp. 1214 – 1226. Fitzsimons, G.J. (2000) “Consumer Response to Stockouts”, in Journal of Consumer Research, Vol. 27 (2), pp. 249-266. Grice, H. P. 1975. "Logic and conversation." In Cole, P. and Morgan 1975 41–58. Harris, R. J., & Monaco, G. E. (1978). Psychology of pragmatic implication: Information processing between the lines. Journal of Experimental Psychology: General, 107, 1-22. Harrison G.W. (1994). Expected utility theory and the experimentalists. Empirical Economics, 19, 223–253. Highhouse, S. (1996) “Context-dependent selection: the effects of decoy and phantom job candidates”, in Organizational Behavior and human Decision Processes, Vol. 65, No. 1, January, pp. 68–76. Holt, C.A. & Laury, S.K. (2000), “Risk aversion and incentive effects”, Online Paper, University of Virginia. Joule R.V., Guillox F.. and Weber F. (1989) “The lure: A new compliance procedure”, The Journal of Social Psychology, Vol. 129, n°6, pp.741-749. Knetsch, J.L. & Sinden, J.A. (1984) “Willingness to pay and compensation demanded: experimental evidence of an unexpected disparity in measures of value", in Quarterly Journal of Economics, 99 (3), pp. 507 - 521. Kuuhberger A., Schulte-Mecklenbeck M. and Perner J. (2002) Framing decisions: Hypothetical and real, Organizational Behavior and Human Decision Processes 89, 1162– 1175 Luce, R.D. (1959) “Individual choice behaviour”, New York, John Wiley. Meyvis, T. & Janiszewski, C. (2002) “Consumers’ beliefs about product benefits: the effect of obviously irrelevant product information” in Journal of Consumer Research, 28, pp. 618 - 635. Novemsky, N., Dahr, R., Schwartz, N. and Simonson I. (2004) “The effect of preference fluency on consumer decision making”, Workshop Paper, available at: http://gsbwww.uchicago.edu/ kilts/research/workshop/WorkshopPapers//w06/novemsky.pdf. Paxton, J.G., Hoeffler, S. and Zhao, M. (2005) “When categorization is ambiguous: factors that facilitate the use of a multiple category inference strategy”, in Journal of Consumer Psychology, Vol.15, pp. 127 – 140. Potter, R. E. & Beach, L. R. (1994) “Decision making when the acceptable options become unavailable”, in Organizational Behavior and Human Decision Processes, 57, pp. 468 - 483. Pratkanis, A. R. & Farquhar P. H. (1992) “A brief history of research on phantom alternatives: evidence for seven empirical generalizations about phantoms”, in Basic and Applied Social Psychology, pp. 103 – 122. Priester, J., Dholakia, U.R. and Fleming, M.A. (2004) “When and why the background contrast effect emerges: thought engenders meaning by influencing the perception of applicability”, in Journal of Consumer Research, Vol. 31, pp. 491 – 501. Sheng, S., Parker, A. M. and Nakamoto, K. (2005) “Understanding the mechanism and determinants of compromise effects”, in Psychology & Marketing, Vol. 22, No. 7, July.
  14. 14. Simonson, I. (1989) “Choice Based on Reasons: The Case of Attraction and Compromise Effects”, in Journal of Consumer Research, Vol. 16, pp. 158 – 174. Simonson, I. & Tversky, A. (1992) “Choice in context: tradeoff contrast and extremeness aversion”, in Journal of Marketing Research, Vol. 29, pp. 281 - 295. Simonson, I., Carmon, Z. and O’ Curry, S. (1994) “Experimental evidence on the negative effect of product features and sales promotions on brand choice”, in Marketing Science, 13, pp. 23 – 40. Sperber, D. & Wilson, D. (1986) “Relevance: Communication and cognition”, Blackwell, Oxford. Tversky, A. & Simonson, I. (1993) “Context-dependent Preference”, in Management Science, Vol. 39, No. 10, pp. 1179 – 1189. Wiseman, D. & Levin, I. P. (1996) “Comparing risky decision making under conditions of real and hypothetical consequences”, in Organizational Behavior and Human Decision Processes, 66, pp. 241 - 250. APPENDIX Tab.1 Summary of the experimental groups G1 G2 G3 G4 G5 C)4Gb/175€ C)4Gb/175€ C)4 Gb/175€ C) 4Gb/175€ C) 4Gb/175€ D)7Gb/220€ D)7Gb/220€ D)7Gb/220€ D) 7Gb/220€ D) 7Gb/220€ E)4Gb/180€ E’)7Gb/225€ F) 5Gb/170€ F′)8Gb/215€ UNAVAILABLE UNAVAILABLE G6 G7 G8 G8b G9 A) 2 Gb/90€ A′) 8 Gb/225€ A) 2 Gb/90€ A) 2 Gb/90€ A′) 8 Gb/225€ B) 3 Gb/170€ B′) 20 Gb/250€ B) 3 Gb/170€ B) 3 Gb/170€ B′) 20 Gb/250€ C) 4 Gb/175€ C) 4 Gb/175€ C) 4 Gb/175€ C)4 Gb/175€ C) 4 Gb/175€ D) 7 Gb/220€ D) 7 Gb/ 220€ D) 7 Gb/220€ D)7Gb/220€ D) 7 Gb/220€ E) 4 Gb/180€ E’)7Gb/225€ E′) 7 Gb/225€ G9b G10 G10b G11 G11b A′) 8 Gb/225€ A) 2 Gb/90€ A) 2 Gb/90€ A′) 8 Gb/225€ A′) 8 Gb/225€ B′) 20 Gb/250€ B) 3 Gb/170€ B) 3 Gb/170€ B′) 20 Gb/250€ B′) 20 Gb/250€ C)4Gb/175€ C) 4 Gb/175€ C) 4Gb/175€ C) 4 Gb/175€ C) 4Gb/175€ D)7Gb/220€ D) 7 Gb/220€ D) 7Gb/220€ D) 7 Gb/ 220€ D) 7Gb/220€ E)4Gb/180€ F) 5 Gb/170€ F′)8Gb/215€ F′)8Gb/ 215€ F) 5Gb/170€ UNAVAILABLE UNAVAILABLE UNAVAILABLE UNAVAILABLE Expensive background: A) 2 Gb/90€; B) 3 Gb/170€ Inexpensive background: A′) 8 Gb/225€; B′) 20 Gb/250€

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