Special Session Summary the Antecedents and Consequences of Choice Deferral


Joel Huber (1995) ,"Special Session Summary the Antecedents and Consequences of Choice Deferral", in NA - Advances in Consumer Research Volume 22, eds. Frank R. Kardes and Mita Sujan, Provo, UT : Association for Consumer Research, Pages: 114-115.

Advances in Consumer Research Volume 22, 1995      Pages 114-115



Joel Huber, Duke University

While a great many studies have explored the determinants of purchase decisions, relatively few have studied what brings a consumer to put off making a decision and fewer still the consequence of that indecision. There are two reasons why indecision has received relatively little attention. First, it is difficult to determine from sources such as scanner data when a deferral decision has been made. For example, how can one know whether a shopper leaving the supermarket without a box of cake mix has rejected all of the offerings, has simply not been in the market at all, has intentionally put off the purchase to ask for a spouse's advice on available brands or to otherwise obtain more brand information, or perhaps put off the purchase because this shopping trip was already straining the shopping budget? Second, while the factors that affect choice within a set generally can be adequately accounted for by the attributes of the alternatives, the decision to put off buying, say, a personal computer for home, is far more dependent on external factors such as income constraints, risk attitude, and the response to a neighbor's purchase.

Given the difficulty of defining and conducting research in the area, it is appropriate that the four papers in this session examine choice deferral through a variety of methodological lenses, each focusing on a different aspect of the phenomenon. The paragraphs below describe the research frameworks of each of these papers and summarize their major findings with respect to the deferral decision.

Robert J. Meyer's paper "Deciding Not to Decide in Sequential Search Among Choice Sets," examines choice deferral in an experimental gaming context. This procedure has advantages in that it is possible to contrast human behavior with optimal policy. MBA students are asked try to find the best backpacking tent as a function of its price, quality and weight. Each is shown a number of tents in a store and can choose one of those offered or move on. While searching is costless, once a store has been left, its options are no longer available, thereby creating an opportunity cost to delaying purchase. Respondents are given prior information about the number of stores in their shopping tour and the expected number of alternatives in each store.

The study uncovers several interesting findings with respect to the impact of set size, past set quality, and within-set contrast effects on choice deferral. Concerning set size, the study shows a general (and unexpected) bias towards shopping at too many stores compared to optimal, and particularly so in environments in which stores stock only one alternative. Thus, there is a particular aversion to making a final decision in lone-alternative stores. The study also finds that a high relative quality in the last store increases the likelihood of deferring choice in the current store. This makes sense. It would be particular hard to settle for an alternative worse than one rejected at an earlier store. Generally, the quality of offerings at the last store are likely to form important anchors in evaluating the quality of the current store.

Importantly, and in contrast with results of Tversky and Shaffir (1992) and Dahr (1992), the study does not find a within-store contrast effect. Neither the difference between the best and second best alternative in a store nor the presence of a dominant option significantly increases the likelihood to stop search. Thus these two aspects of choice difficulty appear not to affect choice deferral in this shopping game.

Another paper, "The Meaning of a 'None' response in Commercial Studies using Choice-Based Conjoint" by Huber and Pinnell is also relevant to this issue of the impact of choice difficulty on decision deferral. Choice-based conjoint is similar to full profile conjoint, except that instead of ranking or rating individual profiles the respondent is asked to choose one from a small set of experimentally manipulated profiles. Choice deferral is commonly assessed by the inclusion of a "none" alternative, indicating that none of the alternatives in a given set is acceptable. Jon Pinnell at IntelliQuest has conducted more than 20 commercial studies of studies which include this default alternative. These studies enable the authors to determine the impact of choice difficulty, absolute choice quality, and respondent characteristics in bringing about the decision deferral in choice-based conjoint.

Choice difficulty is operationalized as an inverse measure of the standard deviation in the utilities of an individual's choice set, the smaller the variation in utilities the more difficult the decision. Logistic regression predicts choice of "none" as a function of the average quality of a choice set and the standard deviation of that quality (Huber and Pinnell 1994). In a study of over 300 computer buyers they show that the logistic weight predicting "none" is far greater for the standard deviation than the average utility of the alternatives in each set. This provides evidence for the importance of choice difficulty on choice-based conjoint. However, attempts to replicate the study with five other choice sets found greater weight for average quality over variability, reversing the finding in the first study that variability is more important than the mean. In these latter data sets, choice difficulty remains statistically significant, but appears to be a relatively unimportant element in the selection of the default.

How is the "none" response used in commercial studies? It would be very useful if it was a surrogate measure for potential demand measured in speed or volume of purchases. However, that interpretation has not found empirical support. A study comparing respondents' frequency of choosing "none" with their likelihood to purchase a computer within a three month period showed no relationship, and across a number of studies, direct questions measuring purchase likelihood or speed are consistently unrelated to each respondents propensity to use "none". However, while market demand appears not to be usefully related to choice of the default, it is related to the difficulty consumers have with the decision. That is, people who have less authority to make the decision, are in larger firms at which making a computer decision is more difficult, and have less information about it, tend to be more likely to choose "none."

The Dahr and Simonson paper, "The Effect of Forced Choice on Consumer Preferences" reverses the causal focus of the other two papers. Rather than attending to the antecedents of choice deferral, their paper explores the consequences of having a choice option on two well-known context effects. These context effects are the attraction effect (Huber, Payne and Puto 1982), which posits that the addition of an alternative to a choice set dominated by only one alternative will increase the share of the dominating alternative, and the compromise effect (Simonson 1989), which posits that choice share is biased towards the middle alternative in a set of three.

Dhar and Simonson find that the availability of a "None" option differentially affects these two context effects. The attraction effect is made stronger by the presence a default, while the compromise effect is made weaker. The authors speculate that the motivation for compromise is similar to the motive to use "none", both reflect a cautious response to a difficult decision. By contrast, asymmetric dominance of the attraction effect provides a reason (albeit counter-normative) for choosing an object and thus gets one out of the need to use "none." These results begin an important process of elaborating on the different and diverse motives for constructed choices.

The final paper, by Greenleaf and Lehmann, "The Effect of Consumer Delay Time and Delay Reasons on Consumer Satisfaction," also looks at the consequences how long and why consumers delay - in this case on consumer satisfaction. Their project works with retrospective reports. They ask MBA students to think of a recent major decision (over $200) which they had delayed for at least one month before making the decision (Greenleaf and Lehmann 1994). They then are asked to think about the reasons for that delay and their eventual satisfaction with the selection, the process and the product chosen.

The authors propose that the relationship between delay time and satisfaction depends on the reasons why consumers delay. In its main effect, longer delay tends to decrease satisfaction. The reasons why consumers delay also affect satisfaction - some delay reasons tend to increase satisfaction, while others decrease it. However, consumers who delay for some reasons tend to be more satisfied as delay time increases, while consumers who delay for other reasons tend to be less satisfied as delay increases. Thus the answer to the question "What is the impact of delay time on satisfaction?" depends on why consumers are delaying.

For example, consumers tend have higher overall satisfaction when they delay to obtain more information. However, they tend to be less satisfied as the amount of time required to obtain this information increases - presumably because they want the information rapidly and easily and want to get on with selecting a brand. By contrast, delay to wait for prices to drop also tends to increase satisfaction, but here consumers tend to be more satisfied as this delay increases - presumably because it gives them time to find a better price. Such reasoning is speculative, necessarily so given the limitations of retrospective data. However, the ideas from such analysis can provide rich source of hypotheses that can be tested by the experimental methods of the first three papers. Thus, the struggle to understand choice deferral will necessary progress through the interaction of different research modalities.


Dhar, Ravi (1992), "Investigation Context and Task Effects on Deciding to Purchase," Ph.D. Dissertation, University of California, Berkeley

Greenleaf, Eric A., and Donald R. Lehmann (1994) "A Typology of Substantial Delay in Consumer Decision Making," Working paper. Stern School of Business, New York University

Huber, Joel, John W. Payne, and Christopher Puto (1982), "Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis." Journal of Consumer Research, 9 (June), 90-98.

Huber, Joel and Jonathan Pinnell (1994) "The Impact of Set Quality and Decision Difficulty on the Decision to Defer Purchase," Working paper, Fuqua School of Business, Duke University.

Simonson, Itamar (1989), "Choice Based on Reasons: The Case of Attraction and Compromise Effects," Journal of Consumer Research, 16 , 158-174.

Tversky, Amos and Eldar Shaffir (1992), "Choice Under Conflict: The Dynamics of Deferred Decisions," Psychological Science, 3.6 (November), 358-361.



Joel Huber, Duke University


NA - Advances in Consumer Research Volume 22 | 1995

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