Differential Effects of Subjective Knowledge, Objective Knowledge, and Usage Experience on Decision...

JOURNAL OF CONSUMER PSYCHOLOGY, 4(2), 153- 180 Copyright O 1995, Lawrence Erlbaum Associates, Inc.

Differential Effects of Subjective Knowledge, Objective Knowledge, and Usage Experience on Decision

Making: An Exploratory Investigation

P. S. Raju University of Louisville

Subhash C. Lonial University of Louisville

W. Glynn Mangold Murray State University

Product knowledge has been recognized as an important factor in the research on consumer decision making. It has also been acknowledged that there are different types of knowledge. In this article, we examine the impact of three types of knowledge-subjective knowledge, objective knowledge, and usage experience-on selected aspects of consumer decision making. Effects are examined within the context of an electronic shopping scenario in which subjects selected a VCR brand based on brand and attribute information that could be accessed through a personal computer. Results indicate that, consistent with real-world experience, the three types of knowledge are correlated with each other. How- ever, their effects on attribute importances, information search, and perceived decision outcomes vary by level and type of knowledge. We discuss these effects and their implications for future research.

Product knowledge, long recognized as a key concept in information-processing and decision-making research, has been examined in many research studies over the past several years (see Bettman & Park, 1980; Brucks, 1985; Johnson & Russo, 1984; Rao & Monroe, 1988; Sujan, 1985). Most of these researchers

Requests for reprints should be sent to P. S. Raju, College of Business and Public Adminis- tration, University of Louisville, Louisville, KY 40292.

154 RAW, LONIAL, MANGOLD

.greed that there are different types of product knowledge. For example, Brucks (1985) noted:

The measures of consumer product class knowledge used in previous studies fall into three categories. The first measures an individual's perception of how much s/he knows. . . . The second category measures the amount, type, or organization of what an individual actually has stored in memory. . . . The third category measures the amount of purchasing or usage experience with the product. (p. 1)

These three types of knowledge (termed subjective knowledge, objective knowledge, and usage experience) are generally considered distinct even though they are often positively correlated with one another. The feeling of knowing (subjective knowledge) plays an important role in memory and problem solving (Metcalfe, 1986; Schacter, 1983). Many factors (e.g., the level of knowledge itself) contribute to the discrepancy between subjective knowledge (sometimes termed self-perceived knowledge) and actual knowledge (Park, Gardner, & Thukral, 1988). Park and Lessig (1981) argued that subjective knowledge is a combination of knowledge and self-confidence and, therefore, may have a somewhat different effect on information processing than objective knowledge. Although some researchers have argued that usage experience is not knowledge per se, a certain type of knowledge does accrue with continued usage of a product. Also, different individuals may learn different aspects and amounts of product knowledge from similar usage experiences. Hence, the effects of usage experience on decision making may be different from those of subjective and objective knowledge (Alba & Hutchinson, 1987; Brucks, 1985).

The distinct nature of the three types of knowledge is reflected in their measurement. Subjective knowledge has typically been measured by subjects' self-reports of their knowledge of a product category or domain (Brucks, 1985; Raju & Reilly, 1980; Rao & Monroe, 1988). Objective knowledge has generally been assessed using objective tests of an individual's extent of knowledge about a product or domain. The number of objective test questions answered cor- rectly is one available index of objective knowledge (Johnson & Russo, 1984). In addition, usage experience has been assessed by subjects' self-reported experience with the product category or domain. For example, Bettman and Park (1980) categorized subjects into knowledge classes based on whether they had used, owned, or searched for information about microwave ovens.

All three types of knowledge may have significant effects on various aspects of the decision process. Park and Lessig (1981) and Rao and Monroe (1988) suggested that knowledge could influence the preference for extrinsic (price, warranty, etc.) versus intrinsic (internal performance-related) attributes in a purchase decision. Knowledge may also influence information search, although the precise nature of this relation is not well understood given inconsistent results in past research (see Bettman & Park, 1980; Punj & Staelin, 1983). Finally, the decision-making literature suggests that different types of

EFFECTS OF KNOWLEDGE TYPES ON DECISION MAKING 155

knowledge are likely to have different effects on decision outcomes, such as choice satisfaction, perceived task complexity, and perceived confusion during task performance (Brucks, 1985; Park & Lessig, 1981). Thus, the literature shows that knowledge affects the entire decision process, from attribute selection through search to perceived decision outcomes. Further, the three distinct types of knowledge (subjective knowledge, objective knowledge and usage experience) are likely to have different effects on the decision process.

Despite the recognition that product knowledge has a significant impact on consumer decision making, many gaps remain in our understanding of the relation. In addition, there are no reported studies that compare all three types of knowledge simultaneously in terms of their effects on several aspects of the decision process. In this research, we aimed to shed some light on the issue. Specifically, we examined the effects of the different knowledge types on (a) the perceived importances of intrinsic and extrinsic attributes, (b) actual and perceived search, and (c) selected self-perceived outcomes of a decision task.

These objectives are accomplished in the context of a purchase decision task in which subjects selected a brand of VCR based on information provided on various attributes for each of several brands. The next section provides a review of the relevant literature and develops several pertinent hypotheses. Subsequent sections describe the methodology employed, present the analysis and results, and discuss the implications and conclusions drawn.

BACKGROUND LITERATURE

Knowledge and Attribute Importances

Park and Lessig (1981) proposed that familiarity should influence the type of information used in product evaluations. They hypothesized that low-familiarity consumers, who are likely to be unfamiliar with the functional attributes of a product, should rely more on nonfunctional attributes (e.g., price) than on functional attributes. Further, because high-familiarity consumers should be able to infer the functional attributes from the nonfunctional attributes, they too should rely more on the latter. Consequently, moderately familiar consumers should rely more on functional attributes than either low- or high-familiarity consumers. Park and Lessig's results were consistent with these arguments.

Rao and Monroe (1988) examined the impact of product knowledge on price-quality relations. Like Park and Lessig (1981), they proposed that low- familiarity consumers are more likely to use an extrinsic attribute (e.g., price) as an indicator of quality, whereas moderately familiar consumers are more likely to use intrinsic (i.e., functional) attributes to judge quality. However, unlike Park and Lessig, Rao and Monroe proposed that high-familiarity consumers (who can discern relations between the extrinsic and intrinsic attributes) may use either price or intrinsic attributes to judge quality depending on the "diagnostic value of the extrinsic information" (p. 255) in a given situation.

156 RAJU, LONIAL, MANGOLD

If a product is known to exhibit a positive price-quality relation, high-familiarity consumers are more likely to rely on price. However, if the price-quality association is weak, they are likely to rely on the intrinsic attributes. These propositions were confirmed by their results.

Focusing on product categories in which extrinsic information is relevant for assessing product quality, Rao and Sieben (1992) found that examination of extrinsic information relative to intrinsic information first decreased and then increased with growing levels of prior knowledge. Their findings suggest that, although low-knowledge consumers may place greater relative weight on extrinsic (vs. intrinsic) attributes, this relative weight should be generally lower for consumers with moderate knowledge. In some cases (see Rao & Monroe, 1988), moderate-knowledge consumers may even perceive the intrinsic attributes to be relatively more important than the extrinsic attributes.

Although the set of findings for low- and moderate-knowledge consumers are generally consistent, the findings for high-knowledge consumers are equiv- ocal. Although the situationally contingent predictions (Rao & Monroe, 1988; Rao & Sieben, 1992) are plausible accounts, another reasonable postulate is that high-knowledge consumers (who understand the task better) may attend to both intrinsic performance-type attributes as well as to extrinsic attributes (e.g., price and warranties). Thus, high-knowledge consumers may weigh intrinsic and extrinsic attributes more evenly than those with low and moderate knowledge. Note also that in the Rao and Sieben study, attention to intrinsic attributes did not vary as a function of prior knowledge, and the relative importance findings were driven by the patterns observed for extrinsic attributes. Hence, they may have observed more evenly divided attention to extrinsic and intrinsic attributes for the high-knowledge group had their findings cor- roborated the theoretical expectation that attention to intrinsic attributes should increase with prior knowledge.

Will these relations between knowledge and attribute importances be different for subjective knowledge, objective knowledge, and usage experience? Past research is silent on the potential nature of these differences. One may antici- pate different degrees of weight shifts between extrinsic and intrinsic attributes by knowledge level for subjective knowledge, objective knowledge, and usage experience. However, meaningful interpretation of such differences would rest on commensurate scaling of the levels of the different knowledge types. We, therefore, postulate that the pattern of main effects of the three knowledge types will be essentially similar in Hypothesis (H)l.

H1: The relative importance of extrinsic and intrinsic attributes will vary by level of knowledge. Extrinsic attributes will be most important compared to intrinsic attributes for the low-knowledge group. This relative importance of extrinsic over intrinsic attributes will be lower for the moderate-knowledge group. For the high-knowl-


edge group, both types of attributes will be equally important. This pattern of relations will be similar for subjective knowledge, objective knowledge, and usage experience.

In this study, we also explored the higher order interactive effects of the three knowledge types on the relative importance of extrinsic and intrinsic attributes. However, in the absence of past research on differences among the main effects, it may be somewhat premature to hypothesize specific patterns for the interactive effects among the knowledge types. Hence, we offer no formal hypothesis for these interactive effects, although these effects are explored in the discussions.

Knowledge and Information Search

Many studies have examined the relation between product knowledge and external search. Bettman and Park (1980) proposed an inverted-U relation between knowledge and search. They argued that both low- and high-knowledge consumers are more likely to rely on prior knowledge and experience (i.e., less external search), whereas moderate-knowledge consumers are more likely to rely on increased external search. Johnson and Russo (1984) claimed that the relation between knowledge and search depends on the decision strategy. When rating alternatives, as in a judgment task, more knowledgeable consumers use their superior encoding ability and search for and encode more information than those who are less knowledgeable. Existing knowledge encourages one to seek more information in this situation and leads to a positive monotonic relation between knowledge and search. However, in choice tasks, one may observe an inverted-U relation between familiarity and search. In this case, the information selection skills of highly knowledgeable consumers lower their level of external search.

Punj and Staelin (1983) distinguished between usable prior information and prior memory structure. The former construct is the quantity of relevant external information, such as that contained in a Brand x Attribute matrix. The latter construct represents the consumer's knowledge of the buying process and the product category in general (akin to the type of knowledge considered in this article). Punj and Staelin postulated that such prior product category knowledge makes it easier to comprehend new information and, therefore, should increase information search. However, their results were not consistent with this prediction. Other studies have found either a negative relation between knowledge and search (Newman & Staelin, 1971, 1972) or no relation at all (Claxton, Fry, & Portis, 1974). In sum, as Brucks (1985) and Fiske, Luebbehusen, Miyazaki, and Urbany (1993) noted, the findings pertain- ing to knowledge and information searchlusage have been inconsistent.

Although researchers agree that the moderate-knowledge consumers would


engage in more external search than low-knowledge consumers, they disagree regarding the search behavior of high-knowledge consumers (Bettman & Park, 1980; Johnson & Russo, 1984; Punj & Staelin, 1983). Are high-knowledge subjects likely to search more (a positive monotonic relation) or less (an inverted-U relation) than moderate-knowledge subjects? In this article, we suggest that this relation may depend on the type of knowledge considered. Consumers with high levels of objective knowledge may be more discriminat- ing about certain attributes, and those with high-usage experience may focus on specific usage-related product dimensions. Such selectivity may lower external search relative to moderate-knowledge consumers and produce an inverted-U relation between knowledge and search.

Contrarily, consumers possessing high subjective knowledge may engage in equal or greater amounts of external search than moderate-knowledge subjects. First, the increased confidence associated with higher subjective knowledge may encourage these consumers to search for more information. Second, during task performance, some of these consumers may find that they possess less actual knowledge than their self-reported subjective knowledge. As a result, they may actually need more information than if their actual knowledge matched their self-report. Thus, as posed in H2, subjective knowledge may show a positive monotonic relation with search.

H2: The amount of information search will vary by the type of knowledge. For subjective knowledge, search will show a positive monotonic relation with knowledge level. For objective knowledge and usage experience, the amount of search will show an inverted-U relation with knowledge levels.

Knowledge and Decision Outcomes

After a decision task has been completed, consumers may subjectively assess various decision outcomes. These assessments may include a perceived need for more information, perceptions of task complexity, or a sense of confusion during choice, as well as feelings of confidence and satisfaction related to the choice. Such assessments may be critical determinants of postdecision behavior. Intuitively, one may expect that such self-perceived decision outcomes would be influenced by the level and type of knowledge. Park and Lessig (1981) conceptualized subjective knowledge as being a combination of knowledge and self-confidence and suggested that many decision outcomes are perhaps simi- larly impacted by both of these elements. Therefore, one would expect that subjective knowledge will be monotonically related to perceptions of decision outcomes. For example, Park and Lessig found evidence that confidence in the decision was significantly higher for high-familiarity subjects relative to low- familiarity subjects.


Objective knowledge should exhibit a similar monotone relation with these self-perceived outcome variables. However, the relation may be weaker because it is not reinforced by the confidence associated with subjective knowledge. One may expect that the relation between the outcome variables and usage experience would also be monotonic. However, as Brucks (1985) noted, usage experience may be less directly related to aspects of behavior because the same experience can lead to different types and amounts of knowledge across individuals. Hence, one may expect greater variance in the the relation between usage experience and decision outcomes. Therefore, as posed in H3, this relation may be the weakest relative to those for subjective and objective knowledge.

H3: Subjective knowledge, objective knowledge, and usage experience will each exhibit significant monotonic effects on self-perceived decision outcomes. However, the relations will generally be the strongest for subjective knowledge, followed by objective knowledge, and the weakest for usage experience.

METHODOLOGY

Sample

A total of 138 subjects (students in a medium-size metropolitan university located in the midwestern United States) participated in the study as part of a classroom exercise. Data from 11 subjects were unusable due to computer malfunctions. Another 11 subjects indicated that they did not have a VCR at home and, therefore, could not respond to the usage experience questions. Hence, the analyses are based on a net sample size of 116. Missing data also reduced the effective number of observations in a few of the analyses. These are reflected in somewhat fewer degrees of freedom reported on the specific tests described in the analyses section.

The subjects ranged in age from 19 to 45 (M = 25.17). Eighty-two percent were employed either full time or part time in occupations ranging from skilled labor to middle management. Men constituted 53.2% of the subjects, and 76% were married. These characteristics were consistent with the university's student profile which includes a large number of nontraditional students. In fact, the subjects were more representative of the general population than a typical student sample.

Procedures

Data were collected in a computer room that had 18 personal computers, each equipped with a hard disk, a 3 'h in. floppy disk drive, a mouse, a keyboard, and a color monitor. Sessions included between 5 and 17 subjects. Subjects


were told that the study was being conducted to assess how consumers would react in a futuristic environment where product information may be provided through personal computers. They were informed that they would examine information on various brands of VCRs and select a brand to buy. Subjects were instructed to use the information just as they would if they were actually in the market to purchase a VCR. Such purchase scenarios are becoming increasingly common as consumers gain greater access to electronic market services ("Cashing In," 1995, p. 78).

Data Collection

After signing an informed consent form, subjects completed a preexperiment questionnaire. This included questions designed to assess key variables for the study, such as knowledge, use, and ownership of VCRs, attribute importances, and subjects' general feelings about the VCR product category.' These variables are discussed in greater detail later in the analysis section. Next, subjects were shown how to access a demonstration data base containing brand and attribute information for a practice product category (five automobile brands and five attributes). This procedure taught subjects how to use the computer, the software, and the mouse to access brand and attribute information.

After the subjects were comfortable with the method for accessing brand and attribute information, they were asked to decide which VCR they would purchase. The software was designed so that various VCR brands and attributes were displayed simultaneously on the monitor screen. Subjects accessed the information on a particular attribute for a particular brand using the mouse to point and click on that brand and attribute in any order. The computer then displayed the information. Real brands and attributes from Consumer Reports magazine were used ("Home Entertainment", 1991, pp. 176-177). The study included two information load conditions. Approxi- mately half the subjects received access to information on 15 VCR brands and 15 VCR attributes (high load). The other half were given access to information only on five brands and five attributes (low load).2 Sessions were randomly assigned to one of the two load conditions, and subjects were not told their

'Careful consideration was given to the timing of this premeasurement. Following prior researchers, knowledge, attribute importances, and so on were measured prior to the choice task (Brucks, 1985; Rao & Monroe, 1988). Thus, every subject was exposed to the same amount of information through the questionnaire. Hence, any premeasurement bias should have been evenly distributed across subjects and should not have influenced the comparisons between high-, moderate-, and low-knowledge subjects.

2Pretesting with students indicated that price and picture quality were considered by almost everyone in the selection of a VCR. Hence, these two attributes were chosen. The other three attributes were chosen randomly from the remaining 13 attributes. Informal consultations with some students confirmed that the 5 attributes chosen offered a realistic profile of VCR brands.


assignment prior to the start of the choice task. The load manipulation is not the focus of this article and will not be discussed f ~ r t h e r . ~

Subjects completed a second questionnaire after they had selected a brand of VCR. This questionnaire obtained data on perceived decision outcomes, use of various types of information, and demographics. Subjects were then de- briefed and released.

Stimuli

The selection of a product for the study was guided by four principal considerations: (a) The product profiles in Consumer Reports Magazine must have at least 15 brands and 15 attributes so the high-load condition could be met, (b) consumers should display considerable variation in knowledge so the high- and low-knowledge conditions could be met, (c) the product should be equally familiar to both men and women to avoid gender bias, and (d) the product should not appeal only to a narrow segment of the population, so that subjects could be found relatively easily. Based on these considerations and informal discussions with students (the relevant subject population), the VCR category was selected from about 10 product categories (televisions, camcorders, tele- phone answering machines, vacuum cleaners, binoculars, etc.)

MEASUREMENT OF KNOWLEDGE AND USAGE EXPERIENCE

Self-report measures have been commonly used in the literature to measure various types of knowledge (Cole, Gaeth, & Singh, 1986). The three knowledge constructs were operationalized based on a review of several well cited knowledge studies in the consumer behavior literature (see Appendix A for a sum- mary). Note that many previous studies have used either aggregate or composite measures of knowledge and have rarely measured and examined the three types of knowledge separately. For instance, Johnson and Russo (1984) used an aggregate familiarity measure. Rao and Monroe (1988) and Rao and Sieben (1992) are among those who have used composite measures of product familiarity. Sujan (1985) used only an objective knowledge measure, Bettman and Park (1980) and Park and Lessig (1981) used a predominantly usage based measure, and Brucks (1985) used both subjective and objective knowledge measures. Our measurement approach was designed to permit separate exami- nations of the three knowledge types.

)Because subjects signed up randomly for the sessions, the two different information load conditions should be evenly distributed across knowledge levels. Nonsignificant chi-square values confirmed this expectation for each of the three knowledge levels.


Subjective Knowledge

Subjects provided self-ratings on (a) ability to use a VCR for recording a TV program on tape, (b) ability to judge the quality of a VCR, and (c) their understanding of various attributeslfeatures of VCRs. These data were scored on 5-point scales ranging from very poor (1) to very good (5). They also responded to three short scenarios. Two of these described situations that related to their perceived ability to hook up a VCR to the TV using the instruction booklet and to set up the VCR to record a program on very short notice. The responses were measured with 5-point scales ranging from very easy (1) to very dzficult (5). The third scenario measured their perceived ability to offer on-the-spot advice to others about buying a VCR; this was also rated on a 5-point scale ranging from very incapable (1) to very capable (5). The total score (after appropriate scale reversals) on all six items was used as the subjective knowledge measure and could, therefore, range from a minimum of 6 to a maximum of 30. Cronbach's alpha for the six items was 0.83, indicating good internal consistency. In addition, exploratory factor analysis indicated only one factor (using the eigenvalue greater than 1 criterion). All six items loaded highly on this one factor, which explained 55.1% of the total variance. Thus, the evidence suggests that all six items represented a single aspect of knowledge.

Objective Knowledge

An acknowledged faculty expert on VCRs designed several multiple-choice questions about VCRs, of which we selected the best 15 questions. These questions measured subjects' general knowledge about VCRs, technical jargon relating to VCRs, and various attributes and features of VCRs (Appendix B). Objective knowledge was operationalized as the number of correct answers to the 15 questions. Scores could range from a minimum of 0 to a maximum of 15. An alpha value of 0.72 (items coded dichotomously as right or wrong) indicated good internal consistency among the items.

Usage Experience

Subjects rated their frequency of use of the VCR for both recording and playing tapes. Seven-point scales ranging from don't use (I) to use over 16 times per month (7) were used. The total score for these two frequency items (range from 0 to 12) was taken as the measure of usage experience. Because both usage experience and subjective knowledge were measured with self-report scales, it was necessary to ensure that the two measures were not representing a single latent construct. An exploratory factor analysis was used after combin- ing the six subjective knowledge variables with the two usage experience varia-


bles. This analysis revealed two separate factors (using the eigenvalue greater than 1 criterion); the six subjective knowledge variables loaded on the first factor, and the two usage experience variables loaded on the second factor. The total variance explained by both factors was 64%.

Subjects were then divided into three approximately equal-size groups of low-, moderate-, and high-knowledge participants based on the distribution of scores for each knowledge measure. F tests and pairwise comparisons using Tukey's B test confirmed that each pair of group means was significantly different for each type of knowledge (Table 1). Cross tabulations among the three measures revealed that approximately 38% of the subjects were classified in the same group (low, moderate, or high) irrespective of the type of measure used.

Relations Among the Knowledge Types

The three knowledge types were significantly correlated (p < .05) with each other. The correlation between subjective knowledge and objective knowledge was fairly high (0.589). Usage experience was also significantly correlated with both subjective and objective knowledge, although the correlations were lower (0.307 and 0.258, respectively). These correlations are consistent with past findings. Cole, Gaeth, and Singh (1986), using the product category of board games, found correlations ranging from 0.47 to 0.56 between subjective and

TABLE 1 Knowledge and Usage Experience Group Statistics

Knowledge Type/Level Size Range M SD

Subjective Knowledge Low Medium High

Total Objective Knowledge

Low Medium High

Total Usage Experience

Low Medium High

Total --

Note. For subjective knowledge, objective knowledge, and usage experience, the F test for differences between group means was highly significant. In addition, the Tukey B test for pairwise comparison between means was significant in every case.


objective knowledge, from 0.39 to 0.63 between subjective knowledge and usage experience, and from 0.34 and 0.77 between objective knowledge and usage experience. Feick, Park, and Mothersbaugh (1992) reported correlations ranging from 0.4 to 0.6 between subjective knowledge and objective knowledge. They also reported that ownership/usage experience was more closely linked with subjective knowledge than with objective knowledge. Thus, our findings add to the documentation supporting the apparent empirical generali- zation regarding the correlations among the knowledge types (see also Brucks, 1985).

Knowledge and Involvement

Some prior studies (Park & Lessig, 1981; Sujan, 1985) have suggested that the knowledge effects may be confounded with the effects of product category interest or involvement. To examine this possibility, subjects in this study were also asked questions relating to their interest and involvement with VCRs. An involvement measure (coefficient alpha = 0.722) was constructed by combin- ing three scales that measured general interest in the VCR product category, importance of choosing the best brand of VCR, and how enjoyable the experience of choosing a VCR would be. This measure was significantly (p < .05) correlated with the knowledge variables. The correlations were 0.536, 0.534, and 0.365 for subjective knowledge, objective knowledge, and usage experience, respectively. These numbers are consistent with Sujan's finding of a significant correlation of 0.52 between involvement and objective knowledge. Separate analyses indicated that the results reported for subjective knowledge, objective knowledge, and usage experience in our study were not moderated by involvement.

ANALYSIS AND RESULTS


In the preexperiment questionnaire, subjects rated the importance of 15 salient VCR attributes on 5-point scales ranging from not at all important (1) to very important (5). These attributes were obtained from Consumer Reports' evaluation of VCR brands ("Home Entertainment," 1991, pp. 176-177). For analysis purposes, we characterized these attributes as being either intrinsic or extrinsic. Physical design characteristics and performance attributes were treated as intrinsic, whereas attributes external to the physical product and unrelated to its performance were classified as extrinsic (see Olson, 1977, or Rao & Monroe, 1988, for similar categorizations). Of the 15 attributes, 12 were categorized as intrinsic (i.e., those dealing directly with the audio, video, or


TABLE 2 Correlations of Subjective Knowledge, Objective Knowledge, and Usage Experience With

Attribute Importances

Attribute Subjective Objective Usage Knowledge Knowledge Experience

Intrinsic Programming-no. of days .199* ,226, .390* Dynamic range-audio ,231 * .214* ,073 Ease of remote control - ,070 - ,020 ,100 Ease of programming - .209* - ,149 ,152 Flutter-audio .217* .241* ,111 Picture quality ,091 - ,077 - ,088 Sensitivity ,073 ,061 .045 Frequency of response-audio .272* .23 1 * ,025 Visibility-remote .139 ,101 .I54 Programming-no. of events ,059 ,079 .287* Selectivity ,136 ,199 ,133 Layout of remote ,059 ,027 .072

Extrinsic Price - .229* - .258* -.I24 Warranty (parts) - .207* - .212* - ,142 Warranty (labor) - ,080 - ,135 - ,174

*Significant at the .05 level.

physical design characteristics of VCRs). The 3 remaining attributes (price, warranty [parts], and warranty [labor]) were categorized as extrinsic attrib- u t e ~ . ~

Table 2 lists the attributes and shows the correlations between each attribute's importance rating and the subjective knowledge, objective knowledge, and usage experience measures. The pattern of correlations is similar for subjective and objective knowledge. As knowledge increases, the importances of many of the intrinsic attributes increase as well. The intrinsic attributes with significant positive correlations with subjective and objective knowledge are programming-number of days, dynamic range-audio, flutter-audio, and frequency response-audio. Selectivity also has positive correlations, but only the correlation with objective knowledge is significant. Ease of programming is the only intrinsic attribute that shows a significant negative correlation with subjective knowledge. Two of the three extrinsic attributes (i.e., warranty

'We independently categorized the attributes as either intrinsic or extrinsic, which resulted in 100% agreement. The attribute list and the definitions of intrinsic and extrinsic were given to a convenience sample of five of our faculty colleagues who were unaware of our Hs. Four of the five categorized the attributes exactly as we did. The fifth had a philosophical disagreement with the scheme itself and categorized all attributes as intrinsic.


[parts] and price) show significant negative correlations with both subjective and objective knowledge. The correlations for warranty (labor) are also negative but not significant. These correlation patterns suggest low- (high-) knowledge consumers tend to place greater importance on extrinsic (intrinsic) attributes.

The pattern of correlations for usage experience is, however, strikingly different. Only two attributes, programming-number of days and programming-number of events, have statistically significant correlations (both positive) with usage experience. It makes sense that heavy users of the VCR place greater importance on programming-related intrinsic attributes. Also, the overall pattern of results (for the three knowledge types) is consistent with the fact that subjective and objective knowledge have a higher correlation with each other than either knowledge type with usage experience.

At this point, we formally examined H1 which postulated that the importance of the extrinsic attributes (relative to the intrinsic attributes) will vary by level of knowledge. The pattern was expected to be similar for the different types of knowledge. The dependent variable for this analysis was constructed as follows. For each subject, average scores were first calculated for the importance ratings assigned to the 3 extrinsic attributes (IMPEXT) and to the 12 intrinsic attributes (IMPINT). Thereafter, the difference between the IMPEXT and IMPINT scores (RELIMP) was used as the measure of the relative importance of the extrinsic over the intrinsic attributes. The mean IMPEXT, IM- PINT, and RELIMP scores are shown in Table 3 by level for each of the three different types of knowledge. A positive (negative) RELIMP score implies that

TABLE 3 Attribute Importances by Type and Level of Knowledge

Level

Knowledge Type Low Medium High

Subjective Knowledge IMPEXT 4.431 IMPINT 3.601 RELIMP 0.830

Objective Knowledge IMPEXT 4.467 IMPINT 3.622 RELIMP 0.845

Usage Experience IMPEXT 4.457 4.099 4.135 IMPINT 3.586 3.713 3.883 RELIMP 0.871 0.386 0.252

Note. The overall means for the variables across all subjects were 4.222 for IMPEXT, 3.734 for IMPINT, and 0.488 for RELIMP.


extrinsic attributes are on average more (less) important than the intrinsic attributes. A RELIMP score of 0 implies that the two sets of attributes are on average equally important.

An analysis of variance (ANOVA) was conducted with RELIMP (the dependent variable) specified as a function of the Subjective Knowledge, Objec- tive Knowledge, and Usage Experience factors (each at three levels-high, moderate, and low). The two-way interactions of these factors were also included in the model. Because the design was unbalanced, the General Linear Models (GLM) procedure in SAS was used. The three-way interaction was excluded due to inadequate cell sizes. Based on Type I11 sums of squares (which examine the incremental variance explained by the Focal factor, condi- tional on other factors being included in the model), the only main effect explaining significant incremental variance was that of Usage Experience, F(2, 90) = 4.04, p = .02. The two-way interaction between Subjective Knowledge and Usage Experience was also significant, F(4,90) = 2.79, p = .03, and that between Subjective and Objective Knowledge approached significance, F(4, 90) = 2.17, p < .08.

The means in Table 3 suggest similar patterns and magnitudes of main effects for the three knowledge types. Surprisingly, however, only Usage Expe- rience explained significant incremental variance. The data show that the relative importance of extrinsic over intrinsic attributes (RELIMP) is highest for subjects with low Usage Experience, somewhat lower for subjects with moderate Usage Experience, and lowest for subjects with high Usage Experi- ence. This pattern is generally consistent with H1.

The Type I sums of squares (which examine the variance explained by the Focal factor when it is the first factor entered in the model) showed that, although Subjective Knowledge and Objective Knowledge each explained significant variance in RELIMP, F(2, 90) = 5.46, p < .01, and F(2, 90) = 3.95, p < .05, respectively, much of this explained variance was shared with each other and with other factors. Consistent with the previously noted high correlation between the two measures, neither Subjective Knowledge nor Objective Knowledge explains significant incremental variance (p > .30 and p > .lo, respectively). Thus, these data do not allow claims of reliable and independent main effects for Subjective and Objective Knowledge. Thus, H1 is not sup- ported, even though the means are ordered as predicted.

Next, we performed an exploratory analysis of the two-way interactions. The two-way interaction between Subjective and Objective Knowledge approached significance. The relevant means are shown in Table 4. Low Objec- tive Knowledge results in RELIMP (extrinsic over intrinsic) scores significantly greater than 0 (p < .05) irrespective of the level of Subjective Knowledge. Also, when Subjective Knowledge is high, RELIMP declines monotonically from low to high levels of Objective Knowledge (p < .01). However, this pattern does not hold either for medium or for low levels of


TABLE 4 Relative Importance of Extrinsic and Intrinsic Attributes (RELIMP) by Knowledge Type

and Level

Subjective Knowledge Level

Low Medium High

Objective Knowledge Low 0.975(1)* 0.590(4)* 1.247(7)* Medium 0.596(2)* 0.200(5) 0.591(8)* High 0.790(3) 0.500(6) - 0.061(9)

Usage Experience Low 0.952(1)* 0.713(4)* 0.972(7)* Medium 0.405(2) 0.513(5)* 0.084(8) High 0.976(3)* 0.121(6) - 0.083(9)

*Means significantly different from 0, p < .05. Numbers in parentheses are used to identify the cells for discussion purposes.

Subjective Knowledge. In both these cases, the RELIMP score is significantly different from 0 (p < .05) when Objective Knowledge is low. However, the pattern of means for the moderate- and high-Objective Knowledge subjects are neither ordered as predicted nor significantly different from each other (all ps > .lo). Note that the RELIMP score is high (0.975) when Subjective and Objective Knowledge are both low. However, when Subjective and Objective Knowledge are at either medium or high levels, the RELIMP scores (0.200 and -0.061, respectively) are not different from 0 (p > .20). In other words, subjects who were low on both Subjective and Objective Knowledge assigned greater importance to extrinsic attributes relative to intrinsic attributes. Those with either moderate or high levels of both Subjective and Objective Knowl- edge tended to weigh the two attribute sets approximately equally.

Subjective Knowledge and Usage Experience also show a significant two- way interaction (see Table 4 for relevant means). Low Usage Experience results in mean RELIMP scores significantly greater than 0 (p < .05) irrespective of the level of Subjective Knowledge. With increasing levels of Usage Experience, the mean RELIMP scores declines monotonically for high and medium levels of Subjective Knowledge. However, the pattern of RELIMP scores are different when the level of Subjective Knowledge is low. Of these subjects, those with both low and high levels of Usage Experience assigned significantly greater weights to the extrinsic attributes relative to the intrinsic attributes. Subjects with moderate levels of Usage Experience weighted intrinsic and extrinsic attributes about evenly.

These findings are based on simultaneous consideration of subjects' levels of the three different types of knowledge. The results for low-knowledge sub-


jects are consistent with prior findings (e.g., Rao & Monroe, 1988) in that they assigned higher relative weights to extrinsic than intrinsic attributes. For moderate-knowledge subjects, however, the relative importance of extrinsic and intrinsic attributes remained unchanged although the weights shifted somewhat toward the intrinsic attributes. High-knowledge subjects tended to weight extrinsic and intrinsic attributes approximately equally. Moreover, the exploratory analyses of the two-way interactions suggest that the relative importance weights that an individual assigns to extrinsic and intrinsic attributes may depend on the specific combination of his or her levels of subjective knowledge, objective knowledge, and usage experience.


Three measures of information search were used. The first was a measure of self-perceived information search, and the remaining two were measures of actual information search. Self-perceived information search was operationalized as the subjects' estimate of the proportion of the total information available that they had accessed when making their choice. These responses were coded as follows: < 20% = 1,20%-39% = 2,40%-59% = 3,60%-79% = 4, and 80% or higher = 5.

The computer software tracked the information that subjects accessed. Two measures of actual information search were developed from these data. One measure, total search, was a normalized count of the number of times the subject requested brand-attribute information. The normalization factor was the number of available information items (25 or 225, depending on the load condition). The second measure, unique search, focused only on the number of new or different pieces of information accessed (i.e., ignoring repeated access of the same information). This measure was also normalized as for total search. The two actual search measures were highly correlated (0.916). Total search and unique search were also correlated (0.308 and 0.330, respectively) with the measure of self-perceived search.

Each of these variables was subjected to a separate ANOVA as a function of the three knowledge measures and their two-way interactions. The ANOVAs controlled for the significant effects of the Load factor on each dependent variable. Table 5 shows the means for each search dependent variable by the type and level of the knowledge variables. Examination of the Type I11 sums of squares shows that, after controlling for the significant effects of load, F(1,96) = 7 . 7 2 , ~ < .01, only objective knowledge explained significant incremental variance in self-perceived search, (F(2,96) = 3.30, p < .05. As the means in Table 5 show, subjects with moderate levels of Objective Knowledge showed the highest levels of perceived search, which is consistent with the inverted-U pattern predicted in H2. Although Subjective Knowledge did not


TABLE 5 Knowledge and Information Search

Level


Subjective Knowledge Perceived search Actual search-total Actual search-unique

Objective Knowledge Perceived search Actual search-total Actual search-unique

Usage Experience Perceived search Actual search-total Actual search-unique

Note. The overall means across all subjects for the three variables were 3.655 for perceived search, 0.754 for actual search-total, and 0.505 for actual search-unique.

affect perceived search (in terms of incremental explained variance), the pattern of means showed a positive monotone relation with Subjective Knowledge (H2). Separate ANOVAs showed that the Type I sums of squares were significant for Subjective Knowledge, F(2, 96) = 3.73, p < .03. Thus, although Subjective Knowledge was associated with perceived search, the explained variance was shared with other factors.

Similar ANOVAs for both total search and unique search showed that no factor (besides Load) explained significant incremental variance, F(1, 96) = 178.88,~ < .0001, and F(1,96) = 305.13,~ < .0001, respectively, in these two measures. The Type I sums of squares showed that Subjective Knowledge was significantly associated with the means for the unique search measure, F(2,96) = 10.32, p < .0001, but that the explained variance was shared with other factors. The means in Table 5 indicate that the amount of unique search increased with Subjective Knowledge levels, which is consistent with H2. The unique search means also showed a predicted inverted-U relation with Objec- tive Knowledge and Usage Experience. However, these associations were not statistically significant.

Despite the associations between subjective knowledge and both perceived and actual search, the explained variance for these dependent variables is not incremental to the factor. Consequently, the results are not uniquely attributable to subjective knowledge. Nevertheless, they suggest differential search patterns by type and level of knowledge. This possibility should be explored further via orthogonal manipulations of these types of knowledge.



The relation between knowledge and perceived decision outcomes was examined using a multivariate analysis of variance (MANOVA). The seven dependent variables were desire for more information, perceived task complexity, confusion while performing task, certainty of best choice, likelihood did not get best buy, likelihood other brands were equallbetter, and satisfaction with the purchase decision. These variables were analyzed as a function of the three types of knowledge and their two-way interactions, controlling for the significant effect of Load, Wilks's lambda = 0.767, F(7, 90) = 3.910, p < .001.

Table 6 shows the means for each dependent variable at each of the different levels of the three knowledge type variables. No two-way interaction was

TABLE 6 Knowledge and Decision Outcomes

Level


Subjective Knowledgea Desire for more information 4.629 4.681 5.559 Perceived task complexity 3.914 3.255 2.941 Confusion while performing task* 3.829 2.596 1.971 Certainty of best choice 5.286 5.766 5.824 Likelihood-did not get best buy* 4.000 3.128 2.794 Likelihood-other brands equallbetter* 4.943 4.043 3.588 Satisfaction with purchase decision 5.600 5.915 5.794

Objective Knowledgeb Desire for more information 4.537 4.91 1 5.467 Perceived task complexity 3.585 3.222 3.267 Confusion while performing task 3.220 2.644 2.400 Certainty of best choice 5.439 5.756 5.733 Likelihood-did not get best buy 3.415 3.133 3.367 Likelihood-other brands equallbetter 4.073 4.356 4.067 Satisfaction with purchase decision 5.805 5.822 5.700

Usage ExperienceC Desire for more information* 4.229 5.368 5.093 Perceived task complexity 3.143 3.474 3.442 Confusion while performing task 2.657 2.868 2.814 Certainty of best choice* 5.686 5.974 5.302 Likelihood-did not get best buy 3.171 3.158 3.512 Likelihood-other brands equallbetter 3.943 4.342 4.233 Satisfaction with purchase decision 5.914 5.895 5.581

Nore. The overall mean values for the seven perceived decision outcome variables listed in the same order as in column 1 were 4.922, 3.362, 2.785, 5.638, 3.293, 4.181, and 5.785.

aWilks'slambda = 0.746, F(14, 180) = 2 . 0 2 6 , ~ = ,018. b ~ i l k s ' s lambda = 0.867, F(14, 180) = 0.949, p = ,508. Wilks's lambda = 0.807, F(14, 180) = 1.45 1, p = .134.

*Individual ANOVAs significant at the .05 level.


significant ( p > .lo). Only Subjective Knowledge showed a significant relation with decision outcomes, Wilks's lambda = 0.746, F(14, 180) = 2 .026 ,~ < .02. Neither Objective Knowledge nor usage experience were significant (p > .50 and p > .lo, respectively). Separate follow-up ANOVAs on each dependent variable showed that Subjective Knowledge had strong significant relations (p < .05 on the basis of incremental explained variance) with confusion while performing task, likelihood did not get best buy, and likelihood other brands were equallbetter. The means in Table 6 show that Subjective Knowledge has a monotonic relation with these decision outcomes. The higher the Subjective Knowledge, the lower the perceived confusion while performing the task. Higher Subjective Knowledge was also associated with a lower likelihood of not getting the best buy and of other brands being equal or better than the chosen brand.

Although the MANOVA was not significant for Usage Experience, the ANOVAs on the individual dependent variables showed that Usage Experi- ence had a significant main effect (on the basis of incremental explained vari- a n c e , ~ < .05) on desire for more information and certainty of best choice. The means for the three knowledge levels suggest inverted-U relations between Usage Experience and these decision outcomes. Because desire for more information may be interpreted as motivation to engage in further search, this result is consistent with the inverted-U relation between Usage Experience and search postulated in H2.

The MANOVA results are partially consistent with H3. As expected, the relation with decision outcomes was strongest for Subjective Knowledge. However, the relations for Objective Knowledge and Usage Experience were not significant.

DISCUSSION

We note some limitations of our study that should be kept in mind when interpreting the results. First, sample size constraints restricted our ability to examine three-way interactive effects among the knowledge types. Second, like other knowledge studies, this study would have benefitted from including more product categories to enable comparisons among them. Improvements could be made in the operationalizing the knowledge constructs and other decision- related variables. For instance, subjects' prior experience with specific brands was not measured, and actual use experience (vs. self-reported use) could have been measured. The decision outcome variables could have been measured in ways other than self-reports.

At the same time, other evidence suggests that our study scored well on internal and external validity criteria. In the postexperiment questionnaire, subjects indicated that they understood the task instructions very well (M =


6.617 on a 7-point scale; 97% of the subjects scored 2 5), and that they were very sincere in completing the questionnaires (M = 6.550; 95% scored 2 5) . Although these data are self-ratings, they lend confidence in the internal validity of the study. Subjects also agreed that their decision process in the study resembled a real-world decision process reasonably well (M = 4.85; 68.3% scored 1 5), thus attesting to acceptable external validity.

Relations Among Knowledge Types

The finding that subjective knowledge, objective knowledge, and usage experience have significant positive correlations with each other corresponds to intuition regarding real-world situations and is consistent with several past studies. The relative magnitudes of the correlations conform to the findings of some studies (Brucks, 1985; Cole et al., 1986; Feick et al., 1992), but they are not entirely consistent with others. For instance, Cole, Gaeth, Chakraborty, and Levin (1992) contended that subjective knowledge and experience-based knowledge may be more closely related than any of the other concept pairs.

Such variation in research findings suggests that the correlations among knowledge types may depend on factors such as the product category studied. For some products (e.g., frequently purchased nondurables for which learning stems from actual usage of various brands), mere usage may increase actual knowledge of the attributes or instill confidence in choice. In such cases, one may expect a high correlation between usage experience and subjective and/or objective knowledge. In contrast, for durables, mere product usage does not ensure either actual knowledge of the attributes or higher confidence in brand choice. In such cases (as with VCRs in our study), the correlation between usage experience and subjective/objective knowledge may be weaker.

Naturally occurring correlations among the three knowledge types poses a problem with separating the effects of each knowledge type on decision making. In this study, although several dependent variables showed strong associations with one or more of the knowledge types, the incremental variance explained by one knowledge type was often not significant when other knowledge types were included as factors in the model. This was especially true in the results for attribute importance and search. Our conclusions are based on the Type I11 sums of squares, and assertions of significance rest on incremental variance explained when the other knowledge variables are included in the model. In future research, the knowledge types could be manipulated ortho- gonally (e.g., Park et al., 1988) to assess their independent effects. Such manipulations may also allow a more precise study of the two-way and three- way interactive effects of the knowledge types. The potential or problems with such orthogonal manipulations need more study. Moreover, there is a possibility that these different types of knowledge are causally related. For example, objective knowledge or usage experience may drive subjective knowledge in


some cases. Such possibilities require additional conceptual and empirical analyses.


The pattern of results for attribute importances was consistent with H1. The relative importance of extrinsic attributes over intrinsic (RELIMP) declined with higher levels of all three types of knowledge. However, using our conserv- ative criterion, the results were significant only for usage experience. Note that, contrary to previous research (Park & Lessig, 1981; Rao & Monoroe, 1988), we did not observe an importance weight reversal between the extrinsic and intrinsic attributes. Thus, the intrinsic attributes were never more important than the extrinsic attributes.

It is reasonable to expect that intrinsic attributes would increase in importance as the level of knowledge increases. However, extrinsic attribues continued to be important at all knowledge levels, suggesting that low-, moderate-, and high-knowledge consumers may be using the extrinsic attributes differ- ently. Low-knowledge consumers may be making naive inferences from the extrinsic to the intrinsic attributes (e.g., high price always implies high quality). In contrast, high-knowledge consumers may use extrinsic attributes with greater discrimination (e.g., judging the context in which a certain price may be acceptable for a brand). Hence, extrinsic attributes such as price and warranty would always remain important to consumers, except that they can be used in different ways depending on knowledge level. Thus, the "diagnostic value of extrinsic information" (Rao & Monroe, 1988, p. 255) would not be exogenously given but would depend on the consumer's knowledge level. The psychological processes underlying such inferences were not addressed here. However, they should be a future research priority (see the use of country of origin information in product evaluation, e.g., Hong & Wyer, 1989; Li & Wyer, 1994).


Although the results did not achieve statistical significance, the ordering of means suggests that subjective knowledge has a positive monotonic relation with search, whereas objective knowledge and usage experience both have inverted-U relations. The literature has postulated both of these patterns for knowledge in general. However, our results suggest that the relation between knowledge level and search may be a function of the knowledge type. Future investigations of these relations must circumvent natural collinearities between subjective knowledge, objective knowledge, and usage experience through orthogonal manipulations of these variables.

Research is also needed on how perceived and actual information search


drive decision processes. Although perceived search showed moderate correlations with the actual search measures, their impact on decision processes and outcomes could vary. Actual search is often of greater interest than perceived search, but in some situations the latter may critically influence consumer perceptions of the effort involved in a choice or purchase situation. This may determine the additional search conducted or affect the overall cost-benefit perception of a transaction.


The results show that subjective knowledge is most closely related to self- reported decision outcomes. Those with greater subjective knowledge are likely to feel less confused and more certain about the quality of their choice. The absence of significant effects for objective knowledge suggests that perhaps it is self-perceived knowledge and confidence, rather than objective knowledge, that influence perceptions of decision outcomes. Of the usage experience results, the inverted-U relation with desire for more information variable is interesting because it is consistent with the results on the relation between usage experience and search.

Although our results suggest that subjective knowledge, with its implicit connotations of confidence, is the primary determinant of perceived decision outcomes, one may argue that confidence may actually be driven by objective knowledge (Peterson & Pitz, 1988) and usage experience. In such cases, one may postulate that subjective knowledge is a consequence of objective knowledge and usage experience and mediates their effects on decision outcomes. Such a perspective needs to be researched and could be approached using path analytic methods.

CONCLUSION

This article takes an initial step toward examining the distinct roles of subjective knowledge, objective knowledge, and usage experience in consumer decision making. Notwithstanding empirical correlations between these three knowledge constructs, the findings demonstrate several distinct effects on the relative importance of extrinsic versus intrinsic attributes, perceived and actual search, and self-reported decision outcomes. These results establish the need for research that parses consumer product knowledge into its different components and explores their independent and interactive effects on decision making.

Finally, note that, in addition to subjective knowledge, objective knowledge, and usage experience, researchers have suggested other typologies that characterize the aspects of knowledge that may be relevant in decision making.


For example, Brucks (1986) offered a typology that separates knowledge into components such as terminology, product attributes, and general attribute evaluation. Relative to prior monolithic conceptualizations, such as familiarity and expertise, these emergent taxonomies of knowledge promise a finer grained understanding of how knowledge influences decision processes and outcomes. We hope that the ideas presented here will stimulate further studies of the way to categorize knowledge and how these aspects influence decision processes and outcomes.

ACKNOWLEDGMENTS

We thank the Telecommunications Research Center at the University of Louisville for the grant that facilitated this research.

We are also very grateful to three anonymous Journal of Consumer Psychol- ogy reviewers and the editor for providing several constructive suggestions on an earlier version of this article.

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APPENDIX A

Some Selected Measures of KnowledgeIExperience in the Literature

Johnson and Russo (1984). The product category was automobiles. Product familiarity was operationalized in terms of self-reported prior knowledge, ownership, and usage of automobiles. Subjects recorded their prior knowledge of automobiles, compared to the rest of the population, on a 5-point scale. Ownership was operationalized as the number of cars in the product class owned by the respondent, and usage experience was operational-


ized as the number of cars in the product class in which the respondent had ridden. On the Prior Knowledge Scale, ratings less than or equal to 3 were classified in the low-familiarity group, those between 3 and 4 were classified in the moderate-familiarity group, and those greater than 4 were classified in the high-familiarity group.

Brucks (1985). The product used was sewing machines. Both subjective and objective knowledge were measured. Two scales were used to measure subjective knowledge, one measuring knowledge of sewing machines and the other measuring familiarity with sewing machines. Objective knowledge was measured by a free-response method and a structured method. A set of free- response questions was administered to identify various aspects of product knowledge. Pretest data from this set of questions was used to generate a second set with a relatively structured response format. Scores were generated for knowledge relating to terminology, available attributes, criteria for evaluation, attribute covariation, and usage situations.

Punj and Staelin (1983). The product category was cars. Punj and Staelin considered that there are at least two unique components of prior knowledge. The first (usable prior knowledge) concerns knowledge that is directly associated with the available alternatives. The second (prior memory structure) consists of other relevant information stored in memory that helps a consumer obtain and process newly acquired information. In the study, usable prior knowledge was operationalized as a function of satisfaction with previous car, time since last purchase, and total number of new or used cars bought in the last 10 years. Prior memory structure was a function of total number of new or used cars bought in the last 10 years, car knowledge, and education of the respondent.

Park (1976). Seven different products were used. Product familiarity was operationalized in terms of the three levels of learning in the Howard-Sheth (1969) theory-extensive problem solving, limited problem solving, and rou- tinized response behavior. Subjects were categorized into three levels of familiarity based on their agreement with one of three statements assessing the degree to which the subject had established a set of criteria for product evaluation. The measure was validated by its correlation with usage experience.

Sujan (1985). The product used was cameras. Only objective knowledge was measured. A 15-item multiple-choice scale on cameras was administered to known groups of experts and novices. Results were used to determine a cutoff score for categorizing a person as an expert or a novice.

Rao and Monroe (1988). The product category was women's blazers. The knowledge measured was a combination of objective and subjective knowl-


edge. A 13-question scale measured knowledge of brand names, store names, technical terms, and appropriate usage situations. In addition, a 5-point scale measured self-assessed familiarity. Questions were weighed on level of diffi- culty. Cutoff points were determined to categorize subjects as being low, medium, or high in familiarity.

Bettman and Park (1980) and Park and Lessig (1981). The product category was microwave ovens. Subjects were asked whether they had ever searched for information on, used, or owned a microwave oven. Those who responded negatively to all three questions were assigned to the low prior knowledge and experience group. Subjects who had searched for information or used but not owned a microwave oven were assigned to the moderate group, and subjects who owned a microwave oven were assigned to the high prior knowledge and experience group.

APPENDIX B

Objective Knowledge Questions

Please answer the following questions about VCRs to the best of your ability. In each case, please select only one response by circling the corresponding letter. If you do not know the answer to a particular question, please do not guess. In fact, few people will know the answers to all the following questions. Instead, we prefer that you circle the "don't know" category in that situation.

1. What percentage of VCRs are made in the United States? a. 0%. b. 20%. c. 30%. d. 50%. e. don't know.

2. One recent trend in the VCR market is: a. increased use of bar code programming. b. increased use of LP recording. c, inclusion of most controls on the remote. d. less use of on-screen programming. e. don't know.

3. Hi-fi VCRs, compared to standard mono VCRs: a. provide stereo sound. b. have a better frequency response. c. provide a more quiet background. d. all of the above. e. don't know.

4. Bar code scanners for VCRs: a. are the most popular form of programming for VCRs. b. also require the use of on-screen programming. c. cannot be used in the SP mode. d. are less popular than other programming methods. e. don't know.

5. Which of the following specifications or features are most important for cable TV subscribers?

a. VCR sensitivity. b. VCR selectivity. c. VCR tunability. d. VCR SIN ratio. e. don't know.


6. On standard VHS video recorders, the HQ feature: a. enhances picture quality. b. produces stereo sound. c. allows the use of other brand remotes. d. is needed for on-screen programming. e. don't know.

7. The highest quality VCR in terms of picture resolution available to home consumers is:

a. the original beta format. b. standard VHS. c. VHS-C. d. S-VHS. e. don't know.

8. The most popular VCR tape sold is the: a. T-30. b. T-60. c. T-90. d. T-120. e. don't know.

9. EP or SLP play speed for VCR tape lasts: a. 120 min (2 hr). b. 240 min (4 hr). c. 360 min (6 hr). d. 480 min (8 hr). e. don't know.

10. The fastest tape speed in the VCR is: a. LP. b. SP. c. EP. d. XP. e. don't know.

11. Consumers who primarily buy a VCR to play prerecorded movies should be most concerned with:

a. having all controls on the VCR itself. b. the ability to record up to 8 hr. c. EP playing time. d. picture quality at SP speed. e. don't know.

12. The phrase "time-shifting" refers to: a. recording programs for playing at a later time. b. buying two VCRs so movies may be copied. c. programming a VCR with on-screen mes- sages. d. recording in the LP mode. e, don't know.

13. One serious audio flaw associated with non-Hi-Fi VCRs is: a. due to diagonal recording of audio tracks. b, the possibility of flutter. c. flat frequency response. d. inability to produce a stereo signal. e. don't know.

14. Compared to the laser disc format, the standard home VCR: a. has much less resolution. b. can play stereo sound tracks. c. shows movies in the "letterbox" format. d. uses the CX noise reduction system. e. don't know.

15. The typical standard home VHS VCR has a horizontal resolution of approximately:

a. 50 lines. b. 250 lines. c. 800 lines. d. 1,200 lines. e. don't know.

Accepted by Dipankar Chakravarti.

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