Consumer Expertise and the Vividness Effect: Implications For Judgment and Inference

John Kim, Oakland University
Frank R. Kardes, University of Cincinnati
Paul M. Herr, Indiana University
ABSTRACT - Prior research has shown that product information is processed less extensively and less diligently as expertise decreases (for a review see Alba and Hutchinson 1987). This finding suggests that novices are more likely to overlook or underutilize important information (underprocessing), and, consequently, novices should be susceptible to a wide variety of inferential biases. However, the inferential biases that are likely to be exhibited by experts have been neglected in judgment and decision research. This study addresses this asymmetry by attempting to identify inferential biases that are more likely to be manifested by experts than- by novices. Specifically, we suggest that novices are more likely to underprocess information, whereas experts are more likely to overprocess information. Consistent with this hypothesis, the results indicate that one type of overprocessing bias, the vividness effect, is more pronounced for experts than for novices.
[ to cite ]:
John Kim, Frank R. Kardes, and Paul M. Herr (1991) ,"Consumer Expertise and the Vividness Effect: Implications For Judgment and Inference", in NA - Advances in Consumer Research Volume 18, eds. Rebecca H. Holman and Michael R. Solomon, Provo, UT : Association for Consumer Research, Pages: 90-93.

Advances in Consumer Research Volume 18, 1991      Pages 90-93

CONSUMER EXPERTISE AND THE VIVIDNESS EFFECT: IMPLICATIONS FOR JUDGMENT AND INFERENCE

John Kim, Oakland University

Frank R. Kardes, University of Cincinnati

Paul M. Herr, Indiana University

ABSTRACT -

Prior research has shown that product information is processed less extensively and less diligently as expertise decreases (for a review see Alba and Hutchinson 1987). This finding suggests that novices are more likely to overlook or underutilize important information (underprocessing), and, consequently, novices should be susceptible to a wide variety of inferential biases. However, the inferential biases that are likely to be exhibited by experts have been neglected in judgment and decision research. This study addresses this asymmetry by attempting to identify inferential biases that are more likely to be manifested by experts than- by novices. Specifically, we suggest that novices are more likely to underprocess information, whereas experts are more likely to overprocess information. Consistent with this hypothesis, the results indicate that one type of overprocessing bias, the vividness effect, is more pronounced for experts than for novices.

CONSUMER EXPERTISE AND INFERENTIAL JUDGMENT

Consumers are frequently depicted as cognitive misers who use simplifying heuristics (e.g., Kahneman et al. 1982), peripheral cues (e.g., Petty et al. 1983), and other shortcuts designed to reduce the amount of cognitive effort required for judgment and choice tasks. Simplifying strategies are especially prevalent when issue involvement, cognitive capacity, and/or expertise is low. Under these circumstances, cognitive shortcuts enable consumers to make complex judgments and decisions on the basis of limited information. These strategies are useful because they permit consumers to make decisions quickly and easily, but they are potentially harmful because important information is often overlooked or underutilized

Underprocessing Biases

Research on minimal information processing (underprocessing) has shown that people are susceptible to a wide variety of inferential biases when important information is overlooked or underutilized. Attributional biases (Ross and Fletcher 1985), biased assimilation (Ha and Hoch 1489, Hoch and Ha 1986, Lee et al. 1987), nonregressive judgment (Cox and Summers 1987), insensitivity to the reliability and validity of information (Kahneman et al. 1982), and other biases are common when consumers rely too heavily on simplifying strategies.

One factor that influences the extent and the intensity of information processing is expertise, which is defined as "the ability to perform product-related tasks successfully" (Alba and Hutchinson 1987, p. 411). Novices are likely to underprocess information because they lack the cognitive resources required to construe the inferential implications of a large set of product-related information. As expertise increases, however, the ability to process larger sets of information also increases. Moreover, experts are more likely to detect missing information spontaneously and to adjust their judgments accordingly (Kardes, Sanbonmatsu, and Herr 1990). Furthermore, experts are more likely to detect redundancy and to integrate information accordingly (Wallsten and Budescu 1981, 1983). Clearly, experts are able to perform much more sophisticated cognitive operations on much larger sets of information, relative to novices.

Novices -- who lack the cognitive structures and cognitive resources needed to process information extensively and diligently -- are likely to overlook or underutilize important information. Consequently, novices should be susceptible to a wide variety of inferential biases. Although it seems clear that underprocessing should lead to bias and error, it may also be possible to process information too extensively and too intensely (overprocessing). That is, there may be an optimal amount of cognitive effort required to process information (more complex information should require more effort), and when too little or too much effort is allocated to an information-processing task, bias and error results (Kardes forthcoming). We suggest that novices are likely to underprocess information, whereas experts are likely to overprocess information.

Overprocessing Biases

Most of the research that has been conducted to date has focused on underprocessing biases. However, a few studies have shown that people sometimes overutilize information that they would have been better off without. For example, judgments and decisions tend to be influenced by irrelevant analogies, if these analogies are accessible from memory (Gilovich 1981). Moreover, people tend to weigh behavioral information too heavily, without sufficiently accounting for the context in which the behavior was observed (Gilbert and Krull 1988).

In both of these cases, irrelevant information was presented in a vivid manner. Vividly presented information is inherently interesting, attention-drawing, and thought-provoking, and, consequently, vividly (as opposed to pallidly) presented information tends to be overutilized (for reviews see Kisielius and Sternthal 1986; Nisbett and Ross 1980).

However, the vividness effect is not as ubiquitous as one would expect (Taylor and Thompson 1982), and, consequently, recent research has focused on the boundary conditions of the phenomenon. For example, strong vividness effects on judgment are most likely when a large amount of information is available and when elaborative processing is likely (McGill and Anand 1989). When a relatively large set of information is available, differential attention to vivid versus pallid information is likely. Because greater amounts of attention should be allocated to vivid information, vivid information should have a greater impact on subsequent judgments.

However, recent evidence indicates that differential attention is not sufficient for producing judgmental vividness effects (Shedler and Manis 1986; Taylor and Wood 1983). This finding implies that other factors must also be important. One additional factor that appears to play a key role in the effects of vividly presented information on judgment is cognitive elaboration (Kisielius and Sternthal 1984, 1986; McGill and Anand 1989). Elaborative processing produces rich associative networks that facilitate the retrieval of target information and related information (Anderson 1983). Because vividly presented information facilitates elaborative processing, and because elaborative processing increases the amount of information that is likely to influence judgment, vividly presented information should have a greater impact on judgment.

In contrast, when elaborative processing is unlikely (due to the presence of other cognitive demands and/or due to low levels of prior knowledge in a given domain to guide processing), judgmental vividness effects should not be found. Thus, because elaboration likelihood is greater for experts than for novices (Alba and Hutchinson 1987), it was predicted that judgmental vividness effects should be more pronounced for experts than for novices.

METHOD

Subjects and Design

Eighty four undergraduates were randomly assigned to conditions in a 2 (vividly or pallidly presented information) X 2 (positive or negative valence) factorial design. Subjects were categorized as experts or as novices on the basis of their scores on an product knowledge inventory patterned after Sujan (1985). This instrument consisted of ten multiple-choice questions about the target product class, personal computers (e.g., What is the CPU? How many bits are there in a byte? What is the function of the ROM?). A median-split was performed on subjects' scores on this inventory, and experts answered more questions correctly, relative to novices (Ms = 8.22 vs. 3.52 out of a possible 10), F(1, 78) = 3.86, p = .053.

Stimuli

Subjects received a description of a new personal computer. The description was "condensed from Consumer Reports" and contained a summary of standard features (held constant across conditions), a rating (3rd or 17th best out of 20), and favorable or unfavorable (e.g., 640 or 512 KB) attribute information (memory, monitor, keyboard, hard drive system, printer port, graphics, and clock speed).

After exposure to the product description, subjects received a favorable or an unfavorable testimonial: "It's the best [worst] computer I've ever owned. It's really easy [hard] to use, and I haven't had a single problem [had nothing but problems] with it." The testimonial was presented either in a vivid face-to-face manner by a confederate posing as an experimental participant, or in a pallid printed transcription from Consumer Digest In contrast to several previous studies (for reviews see Kisielius and Sternthal 1986, Taylor and Thompson 1982), the content of the testimonial was held constant to avoid confounding vividness with amount of information, type of information (e.g., base rate vs. case information), or other characteristics (e.g., novelty, redundancy, ambiguity) of information. Only manner of presentation (vivid or pallid) was varied. Finally, the testimonial and the product description were always evaluatively inconsistent to permit assessment of the relative impact of vivid versus pallid information.

Measures

Subjects rated the target product on three 11-point scales ranging from 0 to 10 (bad/good, favorable/unfavorable, desirable/undesirable). These ratings were averaged to form a single brand attitude index (Cronbach's alpha = .92, p < .001).

RESULTS

Brand attitude favorability as a function of information vividness, valence, and expertise is presented in Table 1. A 2 X 2 X 2 between-subjects analysis of variance performed on brand attitude favorability yielded a significant vividness by valence interaction, F(1, 72) = 102.68, p < .001. The predicted vividness by valence by expertise interaction was marginally significant, F(1, 72) = 3.89, p = .052. No significant main effects were obtained.

Simple effect tests were performed to interpret the interactions while controlling for the compounding of alpha. When the testimonial was favorable (and the specific attribute information was unfavorable), more favorable brand attitudes were formed when the testimonial was presented in a vivid (face-to-face) as opposed to a pallid (printed) manner (p < .001). In contrast, when the testimonial was unfavorable (and the specific attribute information was favorable), less favorable brand attitudes were formed when the testimonial was presented in a vivid as opposed to a pallid manner (p < .001). As predicted, this pattern was more pronounced for experts than for novices. As Table 1 indicates, mean differences in product evaluations following exposure to vivid versus pallid testimonials were greater for experts (Ms = 3.96 and -3.50 for positive and negative testimonials, respectively) than for novices (Ms = 2.95 and -1 .90).

TABLE 1

EFFECTS OF INFORMATION VIVIDNESS, VALENCE, AND EXPERTISE ON PRODUCT EVALUATIONS

DISCUSSION

The manner in which information is presented has a strong effect on product evaluations, even when information content is held constant. Although vividly (as opposed to pallidly) presented information influences the judgments of both experts and novices, it has a greater impact on the judgments of experts. Of course, this effect is unwarranted because manner of presentation does not influence the reliability or validity of information. Hence, vivid information is weighed heavily in judgment, even when more important but less vivid information is available.

The present results also illustrate the paradox of the expert rather nicely. Although experts are able to learn, use, and remember more relevant information than novices (Alba and Hutchinson 1987), they are also likely to read too much into information of low or marginal probative value. Experts tend to process information more extensively and more deeply, and, consequently, experts tend to generate a richer and more elaborate associative network for a given piece of information. When experts (who are able to elaborate extensively) are exposed to vivid information (that is easy to elaborate on), a very rich associative network is formed.

What effects on memory and judgment are produced by a rich associative network? First, elaborative processing tends to increase recall (Anderson 1983). An elaborate network contains not only the target information, but also other related information. When a retrieval cue activates some related information, this information can then be used to infer the target information (Reder 1988; Walker 1986). Second, elaborative processing tends to increase judgment polarization (Tesser 1978). When missing information is correlated with presented information, presented information can be used to draw inferences about omissions. These inferences increase the amount of information that can be used as inputs for judgment, and as the amount of information available for judgment increases, judgmental extremity and confidence also increases (the set-size effect, see Anderson 1981).

Although the results are consistent with the hypothesis that novices are more susceptible to underprocessing biases, whereas experts are more likely to exhibit overprocessing biases, alternative interpretations are possible. For example, although we controlled for the amount of information presented and for information novelty, redundancy, ambiguity, coherence, etc. by holding information constant, source effects are possible. For example, a fellow undergraduate may be a more credible source, compared to an unknown individual interviewed by a consumer magazine. Furthermore, perceptions of a source may vary as a function of the level of expertise of the perceiver. Future research should control for possible source effects.

Finally, it should been emphasized that most of the research that has been conducted to date on inferential biases has focused on underprocessing biases. When too little cognitive effort is allocated to an information-processing task, important information is likely to be overlooked or underutilized and a number of systematic errors are likely to result. Novices are especially susceptible to this class of inferential errors. However, when too much effort is allocated to a cognitive task, several interesting overprocessing errors are likely to occur. Unfortunately, less is known about this class of inferential errors. The results of the present study indicate that the vividness effect is more pronounced for experts than for novices. Future research should investigate whether or not experts are more susceptible to other overprocessing errors as well. Overprocessing errors such as the correspondence bias (Gilbert and Krull 1988), the use of irrelevant analogies (Gilovich 1981), the perseverance effect (Ross et al. 1975), and the dilution effect (Nisbett et al. 1981) may also be more pronounced for experts than for novices.

REFERENCES

Alba, Joseph W. and J. Wesley Hutchinson (1987), "Dimensions of Consumer Expertise," Journal of Consumer Research, 13 (March), 411-454.

Anderson, John R. (1983), The Architecture of Cognition, Cambridge, MA: Harvard University Press.

Anderson, Norman H. (1981), Foundations of Information Integration Theory, New York: Academic Press.

Cow, Anthony D. and John O. Summers (1987), "Heuristics and Biases in the Intuitive Projection of Retail Sales," Journal of Marketing Research, 24 (August), 290-297.

Gilbert, Daniel T. and Douglas S. Krull (1988), "Seeing Less and Knowing More: The Benefits of Perceptual Ignorance," Journal of Personality and Social Psychology, 54 (February), 193-202.

Gilovich, Thomas (1981), "Seeing the Past in the Present: The Effect of Associations to Familiar Events on Judgments and Decisions," Journal of Personality and Social Psychology, 40 (May), 797-808.

Ha, Young-Won and Stephen J. Hoch (1989), "Ambiguity, Processing Strategy, and Advertising-Evidence Interactions," Journal of Consumer Research, 16 (December), 354-360.

Hoch, Stephen J. and Young-Won Ha (1986), "Consumer Learning: Advertising and the Ambiguity of Product Experience," Journal of Consumer Research, 13 (September), 221-233.

Kahneman, Daniel, Paul Slovic, and Amos Tversky (eds.) (1982), Judgment Under Uncertainty: Heuristics and Biases, New York: Cambridge University Press.

Kardes, Frank R. (forthcoming), "Consumer Inference: Determinants, Consequences, and Implications for Advertising,'' in Advertising Exposure, Memory and Choice, ed. Andrew A. Mitchell, Hillsdale, NJ: Lawrence Erlbaum Associates, in press.

Kardes, Frank R., David M. Sanbonmatsu, and Paul M. Herr (1990), "Consumer Expertise and the Feature-Positive Effect: Implications for Judgment and Inference," in Advances in Consumer Research, Vol. 17, eds. Marvin E. Goldberg, Gerald Gorn, and Richard W. Pollay, Provo, UT: Association for Consumer Research, 351-354.

Kisielius, Jolita and Brian Sternthal (1984), "Detecting and Explaining Vividness Effects in Attitudinal Judgments," Journal of Marketing Research, 21 (February), 54-64.

Kisielius, Jolita and Brian Sternthal (1986), "Examining the Vividness Controversy: An Availability-Valence Interpretation," Journal of Consumer Research, 12 (March), 418431.

Lee, Hanjoon, Frank Acito, and Ralph L. Day (1987), "Evaluation and Use of Marketing Research by Decision Makers: A Behavioral Simulation," Journal of Marketing Research, 24 (May), 187-196.

McGill, Ann L. and Punam Anand (1989), "The Effect of Vivid Attributes on the Evaluation of Alternatives: The Role of Differential Attention and Cognitive Elaboration," Journal of Consumer Research, 16 (September), 188-196.

Nisbett, Richard and Lee Ross (1980), Human Inference: Strategies and Shortcomings of Social Judgment, Englewood Cliffs, NJ: Prentice-Hall.

Nisbett, Richard, Henry Zukier, and Ronald E. Lemley (1981), 'The Dilution Effect: Nondiagnostic Information Weakens the Implications of Diagnostic Information," Cognitive Psychology, 13 (April), 248-277.

Petty, Richard E., John T. Cacioppo, and David Schumann (1983), "Central and Peripheral Routes to Persuasion: Applications to Advertising," Journal of Consumer Research, 10 (September), 135-146.

Reder, Lynne M. (1988), "Strategic Control of Retrieval Strategies," in The Psychology of Learning and Motivation: Advances in Research and Theory, Vol. 22, ed. Gordon H. Bower, New York: Academic Press, 227-259.

Ross, Lee, Mark R. Lepper, and Michael Hubbard (1975), "Perseverance in Self-Perception and Social Perception: Biased Attributional Processes in the Debriefing Paradigm," Journal of Personality and Social Psychology, 32 (November), 880-892.

Ross, Michael and Garth J. O. Fletcher (1985), "Attribution and Social Perception," in The Handbook of Social Psychology, Vol 2, eds. Gardner Lindzey and Elliot Aronson, New York: Random House, 73-122.

Shedler, Jonathan and Melvin Manis (1986), "Can the Availability Heuristic Explain Vividness Effects?" Journal of Personality and Social Psychology, 51 (July), 26-36.

Sujan, Mita (1985), "Consumer Knowledge: Effects on Evaluation Strategies Mediating Consumer Judgments," Journal of Consumer Research, 12 (June), 3146.

Taylor, Shelley E. and Suzanne C. Thompson (1982), "Stalking the Elusive 'Vividness' Effect," Psychological Review, 89 (March), 155-181.

Taylor, Shelley E. and Joanne V. Wood (1983), "The Vividness Effect Making a Mountain out of a Molehill?" in Advances in Consumer Research, Vol. 10, eds. Richard P. Bagozzi and Alice M. Tybout, Ann Arbor, MI: Association for Consumer Research, 540-542.

Tesser, Abraham (1978), "Self-Generated Attitude Change." in Advances in Experimental Social Psychology, Vol. 11, ed. Leonard Berkowitz, New York: Academic Press, 289-338.

Walker, Neff (1986), "Direct Retrieval from Elaborated Memory Traces," Memory & Cognition, 14 (July), 321-328.

Wallsten, Thomas S. and David V. Budescu (1981), "Additivity and Nonadditivity in Judging MMPI Profiles," Journal of Experimental Psychology: Human Perception and Performance, 7 (October), 1096-1109.

Wallsten, Thomas S. and David V. Budescu (1983), "Encoding Subjective Probabilities: A Psychological and Psychometric Review," Management Science, 29 (February), 151-173.

----------------------------------------