In Search of the Elusive Consumer Inference

Sarah Fisher Gardial, University of Tennessee
David W. Schumann, University of Tennessee
ABSTRACT - A critical question raised by the papers in this session is how consumers respond to missing information. More specifically, when might we expect consumers to make inferences to fill in informational gaps in the environment? A framework is proposed which highlights the conditional nature of consumer inference making. This framework serves to 1) integrate the somewhat piecemeal research questions and methodologies which have characterized inference research to date and 2) suggest areas for future research.
[ to cite ]:
Sarah Fisher Gardial and David W. Schumann (1990) ,"In Search of the Elusive Consumer Inference", in NA - Advances in Consumer Research Volume 17, eds. Marvin E. Goldberg, Gerald Gorn, and Richard W. Pollay, Provo, UT : Association for Consumer Research, Pages: 283-287.

Advances in Consumer Research Volume 17, 1990    Pages 283-287


Sarah Fisher Gardial, University of Tennessee

David W. Schumann, University of Tennessee


A critical question raised by the papers in this session is how consumers respond to missing information. More specifically, when might we expect consumers to make inferences to fill in informational gaps in the environment? A framework is proposed which highlights the conditional nature of consumer inference making. This framework serves to 1) integrate the somewhat piecemeal research questions and methodologies which have characterized inference research to date and 2) suggest areas for future research.


How consumers respond to informational deficiencies in the environment is a fundamental issue for those interested in consumer decision making. The number of recent studies concerning missing information, in general, and consumer inference making, in particular, attests to the amount of interest and attention which has recently been generated around the topic. However, despite the amount of research which has been forthcoming, we still seem to be grappling with some basic issues regarding inference making. How often does it occur? What types of consumers are most likely to make inferences? When is inference making a preferred decision (sub)strategy?

To some extent, researchers may be drawn to this issue because of its intuitive appeal. We all know that we can and do make inferences about all kinds of consumer products. We also know that most consumers have limited time to deal with the multitude of gaps in the informational environment, and that inference making may be an efficient way to fill in missing data. However, knowing that the phenomenon exists, and being able to actually isolate and understand it are totally different issues.

To some extent, it's like hunting an elusive jungle creature. We may see its tracks, and we may often encounter strong evidence that the beast has passed through. But we haven't been able to trap it. The scent is heavy on the trail, but the creature is too crafty or evasive to be caught. The main problem is knowing when and where the creature is going to appear. If we could only isolate some of the predictable aspects of its behavior, we could intercept it more successfully.

Such is consumer inference research. While we are sure the phenomenon exists, much of our lack of success in measuring and understanding inference making may be because we are not clear about the conditions under which it will appear. Two of the papers in this session address this issue ("The Effects of Missing Information on Decision Strategy Selection" by Sandra Burke and "Inferences About Missing Attributes: Contingencies Affecting the Use of Alternative Information Sources" by Carolyn Simmons and Nancy Leonard). These papers, as well as other research, can be viewed within a broader framework that suggests when inference making may be used to fill in missing data in the consumer environment. The framework suggests a logical sequence of decision points which may lead the consumer toward or away from inference making. In addition, it suggests a list of potential mediating factors which must be taken into account at each decision point. Hopefully, it provides a mechanism for integrating and explaining much of what we know, and don't know, about consumer inference making to date.


In order to address when consumers make inferences, it is necessary to identify other alternative strategies or the missing information problem. Consumers don't have to make inferences at all. It is a matter of when they choose that course of action over others which are available.

There are at least four alternative strategies which the consumer may adopt when missing information is encountered. The first option, probably more likely for high risk decisions, is that the consumer may delay the decision until the missing information is obtained. We must keep in mind that in laboratory settings consumers will be faithful subjects and follow our instructions. If they are asked to make decisions with incomplete data, they will do so. However, in the real world they have the option to defer decisions. Many types of consumer product decisions may lead to this alternative, including those with high financial or psychological costs, those for novice consumers, or those with long-term or personal consequences, e.g., buying a house or medicines. It is easy to imagine this as a viable and frequent alternative to inference making.

Second, the consumer may simply ignore missing data (Zwick 1988). That is, acknowledging that missing data can lead to a suboptimal decision, the consumer may simply decide to continue with the current decision strategy while operating within the constraints of less information. Whether the consumer is using a compensatory, hierarchical, lexicographic or other decision framework, each can be pursued using only available attribute information. Some researchers suggest that there may be some evaluative discounting due to the absence of information (Meyer 1981), but an attitude may be formed nonetheless. Given the low risk associated with many consumer decisions and possible preferences for particular decision strategies, this seems to be a plausible response. In fact, consumers may become quite good at ignoring what is not available and constraining the decision to the most accessible, least effortful information at hand.



The third alternative, suggested in the Burke paper, is for the consumer to actually change the decision strategy to one which better accommodates missing information. In particular, she suggests that the consumer may move away from attribute based strategies, requiring between brand comparisons, toward strategies which are 1) more alternative based (within brand) or 2) hierarchical strategies using only available information. While also a plausible alternative, it is important to note that such a change requires both that the consumer is aware of alternative strategies and is amenable to their use. In other words, the consumer must not have a strong preference for attribute based strategies.

The fourth alternative to missing information is inference making. The preceding three alternatives (and possibly others not described) will be collectively termed "other" alternative strategies for the remainder of the discussion. The realization of "other" strategies reduces the question to a very simple one: when will consumers choose inference making over other strategies. In short, if we can isolate the conditions which would lead the consumer toward alternative four (or, conversely, toward alternatives one through three), then we will be a great deal closer to capturing inferences in our research and determining their influence on day to day consumer decisions.


The following is a brief overview of the proposed framework (see Figure 1). Research will be occasionally referenced to note where previous knowledge may be brought to bear on different parts of the framework. However, this paper will by no means attempt to provide a thorough review of the state of inference research to date. Rather, its intent to show where the two presented papers in this session may be viewed in a larger context, and to identify issues which are ripe for future research.

The central part of the framework consists of four critical questions. These four questions are those most likely to impact whether the consumer pursues an inference strategy versus whether an alternative "other" strategy is more attractive. Note that there is no attempt to indicate which alternative strategy will be used, that is another research question. Here we are only interested in identifying when inference making is the likely response.

The framework also lists some of the many possible mediating factors determining the response to the four questions. This is only a partial list, some of which have been suggested by previous research, others which are more speculative but may be fruitful areas for further study. They have been grouped into three broad categories representing individual,- task and information configuration differences.

Finally, two "outcome" issues are identified: 1) how are the inferences actually made and 2) what impact do they have on subsequent consumer decisions. Each of these topics could well be expanded into sub-frameworks of equal length and complexity. For now, these issues will be relegated to others to develop. They are listed here to acknowledge that understanding the conditions under which inferences are made is only a limited first step in integrating our knowledge of inference making into the larger consumer decision making context.

The Four Critical Questions

The first question is the most obvious: Is the missing data perceived? Obviously, consumers must perceive that attribute information is unavailable before they can attempt to infer it. Consumers cannot be expected to know all of the potential attributes to considered in evaluating a product. Thus, the likelihood that any one particular unavailable attribute is even noticed may be small. This is certainly possible given the demands of information overload or the possibility of a novice consumer.

In fact, available evidence suggests that this may be the case. Much of the existing inference research explicitly prompted inference making (e.g., Meyer 1981, Huber and McCann 1982, Kardes 1986, Johnson and Levin 1985). However, recently some studies have found greater inference making under prompted (cued) than unprompted conditions (Zwick 1988, Ford and Smith 1987). Although it is possible that in the latter condition missing data was noted but ignored, these results also suggest that at times consumers may be unaware of missing information.

A second question, noted in the Simmon/Leonard paper, relates to this issue. That is, once noted, does the consumer assume that information is missing or that an attribute is missing. For instance, a consumer may notice that warranty information is not available. In this case, she might assume that 1) the product has a warranty which is simply not mentioned or 2) that the product does not come with a warranty. Obviously, if the latter is assumed there will be no incentive to make an inference about the warranty. Therefore, perceiving missing information is a two-step function: it requires both that the product attribute is noted and that it is assumed to be available elsewhere.

Looking at the list of potential mediating factors, it is interesting to speculate about factors which may cause the consumer to never notice missing information. This may be likely for consumers with low product knowledge/experience or those with a low need for cognition. Task factors, such as time pressure or a long time lapse between information presentation and the decision may also inhibit missing information perception. Finally, the information configuration may facilitate or inhibit the perception of missing information, e.g., whether the information is presented by brand, attribute, or brand-attribute matrices (Zwick 1988) or whether realistic ad configurations are used (Gardial and Biehal 1989).

The second question is one to which very little research attention has been given. Is the missing data perceived to be important to the decision (Hansen and Zinkhan)? Typical inference research has not allowed the decision maker to select which information s/he wishes to use to evaluate the product. These studies seem to assume that all information provided to the decision maker (and none which is not provided) is important to the decision at hand. Clearly consumers come to decisions with idiosyncratic decision criteria. Those who have high product. knowledge may have especially well developed schemas for brand evaluation. Thus, even if information is noted to be missing, it may not be considered critical to the decision. Because of the effort involved in inferring missing information, it is unlikely that consumers will infer missing, but relatively unimportant, attributes.

Again, mediating factors may effect this question. For instance, the higher the perceived risk associated with a decision, the more thorough a consumer might be in his/her evaluation. Under this circumstance, the list of attributes considered important may be expanded and more exhaustive than when the decision carries less risk. In addition, attribute importance may change depending on whether the consumer is choosing between brands or simply evaluating individual brands. Differences caused by between-brand (choice) versus within brand (evaluation) decisions strategies may impact the perception of attribute importance. Simmons (1986) has suggested that inference making may be more common to choice than evaluation. Also, the amount of available/missing information may impact this issue. If a great deal of information is generally available, the consumer may be more likely to overlook the absence of even important attributes. As suggested by Burke (1989), attribute redundancy may also play a part. Even if a particularly "important" attribute is missing (e.g., repair costs over time), the availability of an attribute with redundant implications (e.g., warranty) may reduce the need for the former's inference.

The third question is perhaps the most critical of the four: does the consumer have the ability to fill in the data? Even if the motivation to make inferences is present (questions one and two), inability to do so stops the process at this point. The answer to this question may also have a significant impact on the fourth question. If a consumer has sufficient ability to make an inference, e.g., an "expert," s/he may also perceive little effort is required to do so, although this does not always have to be the case.

Several researchers have suggested that product experience is positively related to inference making ability (Sujan and Dekleva 1987, Gardial and Biehal 1989, Olson 1978, Alba and Hutchinson 1987). In the Simmons/Leonard paper in this session, it is quite possible that students were unfamiliar with purchasing refrigerators and carpet cleaners, thus hampering their ability to make inferences. Others have suggested that ability to make inferences is a function of what information is available to the consumer, and how "redundant" or how closely correlated the available and unavailable information is (Burke 1989, Ford and Smith 1987). It is also possible that the availability of visual along with verbal cues may increase consumers ability to make inferences (Smith 1988, Chattopadhyay and Alba 1988).

The final question is that posed by Burke: is the effort to make the inference worth it to the consumer? Presumably, if it is, inference making will occur. Otherwise, alternative strategies will be pursued. In her paper, Burke identifies some of the factors which might mediate the consumers' perceived effort. That list is contained within the larger list of mediating factors in this framework.

It is interesting to note how "late" in the framework the effort issue comes into play. While Burke asks an important and worthwhile question, it only becomes a consideration when the three previous preconditions (questions) have all been answered in the affirmative. Thus, it is important to note where her piece of the puzzle fits in to the larger context.

Other Framework Issues

Four issues relating to the interpretation and use of the framework are worth noting. First, the four questions are presented in a somewhat logical sequence, i.e., it is unlikely that the consumer would ever encounter question number two if s/he failed to answer question number one in a positive fashion. Likewise, question number four is moot unless question number three is answered in the affirmative.

Second, note that multiple mediating factors may drive the response at any level of the framework. In addition, it is perfectly possible that one type of mediating factor may drive the response to one question (e.g., prior knowledge may have a significant impact on question three), while a completely different factor may impact another question (e.g., time constraints may impact question four). Thus, the presence of mediating factors significantly complicates the model (and may explain some of our difficulty in explaining the inference making phenomenon).

Third, inferences may only be made if all four of the critical questions are answered in the affirmative. If the response to any of the four is negative, then the consumer will probably resort to other alternative strategies. The complexity of the framework to this point, and the numerous potential conditions which would encourage "other" alternative strategies, may leave the impression that inference making is a relatively rare phenomenon and not worthy of our research attention. However, most consumer decisions are relatively repetitive, low risk, and familiar ones for consumers. In addition, realistic advertising constraints (cost, length and consumer attention spans) dictate that only partial product information is conveyed to the consumers. For these reasons, consumers should frequently encounter missing information and frequently have the ability to make inferences as a consequence. The more product experience consumers have, the more likely they are to notice missing information, to have the ability to make inferences, and to require relatively little effort to do so.

Even in a world of "cognitive misers," inference making may be so routine and effortless that consumers often create information beyond that which is available to them, while at the same time ignoring some available information. One could even posit that inference making is a simplifying strategy, i.e., a few pieces of quickly gathered product information may be expanded into a more complete profile. Thus, despite the reasons why inference might not be made, there is probably st ample opportunity for this phenomenon to affect a wide range of consumer decisions.

Finally, successfully bringing the consumer to the point of making an inference explains only part of the inference phenomenon. Several additional questions are then raised, including how many inferences are made (Gardial and Biehal 1987, Chattopadhyay and Alba 1988), what types of inferential processes are used (Simmons 1986, Zwick 1988), and the nature of the inferences that are made. E addition, once these inferences are produced, how do they impact subsequent decisions (Kardes 1986, Johnson and Levin 1985)? These questions are important in their own right, and illustrate how much more has to be learned in order to approach a complete picture of the consumer inference process.


Like stalking the elusive jungle creature, researching the consumer inference making phenomenon is a challenging proposition. The framework proposed here is one which may help researchers integrate and assimilate the sometimes disparate evidence that has been reported in the literature. In addition, it will hopefully move the research stream forward in a systematic way, highlighting what is known and unknown, and suggesting the need to understand the many and subtle complexities of this phenomenon.


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