Working Paper DRAFT : February 2002 Self-Prediction Based on Strength of Intention

When trying to predict one’s future behavior, a natural starting point is an evaluation of one’s intentions. If self-predictions regarding some target behavior begin with an assessment of the strength of one’s intention to carry out the behavior, previous research on the psychology of judgment implies that (a) predictions will tend to be as extreme as the impression of intention strength on which they are based, even when intention strength is imperfectly correlated with behavior; and that (b) adjustment in light of other factors known to influence behavior will tend to be insufficient. Consequently, self-predictions are expected to overweight the strength of current intentions when predicting future behavior, and to underweight factors that influence the ease with which intentions are translated into action as well as instability in intention strength over time. Because intentions typically reflect one’s goals and aspirations, this analysis implies that self-predictions based on strength of intention will tend to be overly optimistic. Previous research as well as several new preliminary studies consistent with this analysis are described. Self-Prediction Based on Strength of Intention 3 People’s predictions regarding their future behavior play a critical role in everyday decision making. In deciding whether to open a self-directed retirement savings account, for example, an individual must anticipate whether he or she will indeed invest an annual lump sum as intended. Likewise, a patient’s decision to begin a course of antibiotic medication (as opposed to an alternative course of treatment) may hinge on a self-assessment of the likelihood of adhering to the medication schedule (Kaplan & Simon, 1990). Although individuals obviously have access to an enormous amount of information about themselves, self-predictions of future behavior are not always accurate (e.g., Osberg & Shrauger, 1986; Vallone, Griffin, Lin, & Ross, 1990), even for types of behavior that are largely under the individual’s control (e.g., Epley & Dunning, 2001), and poor decisions can be the result. To predict one’s future behavior, it is natural to begin with an evaluation of one’s intentions with regard to the behavior. All else being equal, the stronger one’s intention to carry out a course of action, the more likely it is that the action in fact will be carried out. Reliance on intention strength as a basis for self-prediction, then, will often yield accurate predictions. But it can also produce systematic bias. In particular, it is argued in this paper that self-predictions typically place too much weight on strength of intention, and consequently underweight other factors, unrelated to intention strength, that influence behavior. As a result, because intentions generally reflect goals and aspirations, self-predictions are often too optimistic. To motivate the analysis which follows, several examples are first offered in which reliance on intention strength as a basis for self-prediction may lead to poor decision outcomes. All of these observations are drawn from the domain of consumer behavior, where the evaluation of products and services often implicitly requires an assessment of the consumer’s likely future behavior, but examples from other domains (e.g., health decisions) are also readily generated. Many product purchase decisions are contingent on a prediction of how likely and frequently one is to use the product if purchased. It appears that consumers frequently base such predictions on good intentions that do not always translate into behavior. Exercise equipment is the classic example. According to a 1997 survey by the Sporting Goods Manufacturers Association, of the nearly 50 million American households in which home exercise equipment can be found, the equipment is not regularly used in over one-third of the households. In one Wall Street Journal survey (Dolan, 1989), nearly three-quarters of people who owned home exercise equipment reported that they did not use the equipment as much as they planned. Self-Prediction Based on Strength of Intention 4 Anecdotal evidence suggests that, despite the great expectations they produce, various kitchen gadgets and appliances also often go unused and are eventually relegated to the basement to preserve counter space. Purchasing decisions can be influenced by product features that are never used, such as a camcorders’s fade-in and fade-out controls and superimposed title capabilities (Simonson, Carmen, & O’Curry, 1994). Consumers have also been observed to overestimate their future use of services (e.g., local telephone service; internet home food delivery), and consequently prefer flat-rate billing (Kridel, Lehman, & Weisman, 1993; Nunes, 2000) even when they would be better off, given their actual level of use, paying on a per-use (i.e., measured service) basis. Another example of the consequences of erroneous self-predictions is found in mail-in product rebates. Manufacturers report that offering such rebates frequently stimulates retail sales, but it seems that a surprisingly small fraction of the rebate coupons are actually redeemed (estimates range from 5% up to 20% for small to moderate-sized rebates; Bulkeley, 1998). One possible explanation for this observation, consistent with the present analysis, is that consumers overestimate at the time of purchase the likelihood that they will complete and mail the rebate coupon before it expires (Loewenstein & Schkade, 1999). A final example concerns credit card adoption decisions. The apparent failure of competition in the credit card industry to drive down high interest rates is, arguably, puzzling in light of the number of firms competing in the market. Ausubel (1991) argues that the “stickiness” of credit card interest rates is attributable to suboptimal consumer decision making in which individuals underestimate the probability that they will carry an outstanding balance and consequently incur interest charges. As a result, they are not sufficiently sensitive to interest rates when choosing a credit card. Ausubel supports this claim with a comparison of data from two surveys. A consumer survey reveals that nearly half (47%) of surveyed consumers state that they tend to pay their credit card balance in full each month, but a survey of credit card companies indicates that nearly three-quarters of active credit card accounts incur interest charges in any given month, suggesting that there is a substantial subset of consumers who fail to pay off their outstanding balance each month despite their intentions to do so. Consistent with this analysis, a recent longitudinal study of credit card adoption and use (Yang, Livia, & Qui, 2001) identified a subset of “wishful thinkers” (consumers who indicate that they intend to pay off their outstanding balance each month but who also report carrying such balances on a regular Self-Prediction Based on Strength of Intention 5 basis) and showed that their credit card adoption decisions are driven primarily by annual fees and are largely insensitive to interest rates even though this group routinely carries outstanding balances. Such examples suggest that, in the domain of consumer decision making and presumably other domains as well, systematic biases in self-prediction of future behavior can impose substantial costs. Intention-Based Prediction For behaviors that are largely under an individual’s control, it stands to reason that whether or not the individual intends to engage in some behavior should be a primary determinant of whether or not the behavior occurs. Thus in predicting whether the individual will engage in the behavior, it is only natural to base the prediction on an evaluation of the individual’s intention to carry out the behavior. Indeed, the intuition that the stronger the intention, the more likely the behavior is generally borne out by an extensive program of research based on the theory of planned behavior (e.g., Ajzen, 1991; Ajzen & Madden, 1986). In the present analysis, intentions are assumed to be tied to specific behaviors or activities, and can vary in strength, that is, the extent to which the individual feels committed (at a given point in time) to carrying out that behavior or activity. In this sense, an intention (e.g., to exercise 3 days a week) is more specific than a personal goal (e.g., to lose weight) in that it is tied to the particular means by which the goal will be achieved. Such intentions are generally less specific than plans, which consistent of a temporal chain of situated events (e.g., going to the health club after work three days a week, with aerobic exercise on Mondays and Fridays and weight-lifting on Wednesdays) leading to the target outcome with which the intention is associated (cf. Gollwitzer & Brandstaetter, 1997). The accuracy of predictions based on strength of intention depends on the predictive validity of intention strength, that is, the correlation between intention strength and actual behavior. Common sense may dictate that one’s actions are generally determined by one’s intentions, but folk wisdom also tells us that certain actions may occur (or fail to occur) despite one’s best intentions. Researchers studying attitudes have long been frustrated by low attitude-behavior correlations (e.g., Wicker, 1969). One possible explanation for this observation is that intentions, as determined by attitudes, may not always translate readily into behavior. Indeed, while the theory of planned behavior implicates intentions as the mediator between attitudes and behavior, research conducted within this framework indicates that intentions typically account Self-Prediction Based on Strength of Intention 6 for only about 20% to 40% of variance in actual behavior (Ajzen, 1991; Gollwitzer, 1999; Ouellette & Wood, 1998; Sutton, 1998). In one study (Netemeyer & Burton, 1990), for example, self-rated intentions to participate in an election to be held one week la