The Self-Monitoring Concept: a Consumer Behavior Perspective

ABSTRACT - The impact of the self-monitoring construct on the attitude-behavior relationship was examined under conditions of social pressure in a student setting. The experimental results, consistent with those in social psychology, suggest an explanation for variations in attitude-behavior consistency across consumers in the presence of situational cues.


Jacques A. Nantel and William Strahle (1986) ,"The Self-Monitoring Concept: a Consumer Behavior Perspective", in NA - Advances in Consumer Research Volume 13, eds. Richard J. Lutz, Provo, UT : Association for Consumer Research, Pages: 83-87.

Advances in Consumer Research Volume 13, 1986      Pages 83-87


Jacques A. Nantel, Ecole des H.E.C., Montreal

William Strahle, Indiana University

[The authors are grateful to Richard Olshavsky, Robert Smith, and Russel Fazio for their helpful comments.]


The impact of the self-monitoring construct on the attitude-behavior relationship was examined under conditions of social pressure in a student setting. The experimental results, consistent with those in social psychology, suggest an explanation for variations in attitude-behavior consistency across consumers in the presence of situational cues.


Beginning with Lapiere's (1934) classic article, the use of the attitude construct to explain and predict behavior has been under constant attack in both the psychological and marketing literature (Bettman 1981). In an extensive review of this literature, Wicker (1969) reported that the majority of studies linking attitudes to behavior showed correlation coefficients only in the order of .30. Be concluded that "... as a whole, these studies suggest that it is considerably more likely that attitudes will be unrelated or only slightly related to overt behaviors than that attitudes will be closely related to actions" (1969, D. 65).

In marketing, the use of the attitude construct has been influenced by Fishbein's (1967) conceptualization which has generated an important stream of research. The research ranges from theoretical and measurement issues (Bennett and Harrell 1975, Bettman, Capon, and Lutz 1975, Mazis, Ahtola, and Klippel 1975, Ryan and Bonfield 1975) to more practical issues such as advertising (Dover and Olson 1977, Smith and Swinyard 1983). Overall, the Fishbein model has been of most interest for marketing and consumer behavior researchers mainly because product evaluation is frequently viewed as a multiattribute decision process (Wilkie and Pessimer 1973).

While Fishbein's extended model can offer satisfactory insights about how attitudes might influence behavioral intentions, it still suffers somewhat from an inability to adequately predict behavior (Songer-Nocks 1976a, 1976b). In fact, closing the 1976 JESP debate (a total of four articles and replies), Azjen and Fishbein (1976a, 1976b) themselves concluded:

"... consistent with our theory, the prediction of behavior from attitudinal and normative components was limited only by the strength of the intention-behavior relationship. Clearly then what does need to be further specified are those factors that limit the prediction of behavior from intentions." (1976b, p. 593).

Several of these moderating variables have since been identified (for a comprehensive summary see Zanna and Olson 1982; and Zanna and Fazio 1982), and one of the most important was found to be the manner in which the attitude was initially formed. Fazio and Zanna (1981) proposed that when attitudes are formed on the basis of previous behaviors, the attitude-behavior relationship should show greater consistency than when they were not. This relationship has been previously tested in a consumer behavior context (Smith and Swinyard 1983). However, a second factor proposed by Snyder and Tanke (1976), self-monitoring, has thus far been overlooked as a possible moderator for the attitude-behavior relationship. Our objectives are. therefore: to review the self-monitoring construct and to test for its mediating role in both the attitude-behavioral intention and the behavioral intention-behavior relationships.


Snyder (1974) suggests that self-monitoring represents the degree to which individuals have the ability to be sensitive to the situational cues which guide their behavior in a variety of situations. Using as a theoretical basis the human need for social appropriateness which had been shown by Ash (1946, 1951, 1956) to have an impact on behavior, his intention was to provide an adequate operationalization for the construct.

The construct itself is defined as follows:

"The self-monitoring individual (read high self-monitor) is one who, out of a concern for social appropriateness, is particularly sensitive to the expression and self-presentation of others in a social situation and uses these cues as guidelines for monitoring his own self-presentation" (Snyder 1974, p. 528).

In the same vein,

"Non self-monitoring individuals (read low self-monitors) have less concern for the appropriateness of their social behavior and attend less to situational cues as guides to their social behavior" (Snyder and Monson 1975, p. 637).

These definitions suggest that the attitude-behavior intention as well as the behavioral intention-behavior relationships may be moderated by the extent to which individuals use social cues as guides to behavior and that the self-monitoring construct can be used to categorize individuals according to their degree of sensitivity to those cues.

Our interest in the self-monitoring concept itself stems from the partial explanation it has provided for the typically low correlation observed between attitudes and behavior in other contexts (see, for example, Fazio and Zanna 1981; Snyder 1974; 1982; Snyder and Tanke 1976; Zanna and Olson 1982; and Zanna and Fazio 1982). In a recent article, Azjen, Tisko and White (1932) measured subjects' attitudes toward smoking marijuana, as well as their perception of the social norms concerning that activity. The results, which were integrated using Fishbein's extended model, revealed a significantly greater amount of consistency between the attitudes and behaviors of low self-monitors than for the high self-monitors--providing support for previous findings.


In the past, marketing studies have produced conflicting results in the use of Fishbein's extended model:

B = BI = (A act) w0 + (NB.MC) w1 (1)

where B = Behavior' BI = Behavior intention; A act = Attitude toward the act; NB = Normative belief (that is, the degree of belief that others expect or do not expect the individual to perform a specific act); MC = Motivation to comply or not comply with the expectations of others; and wo and w1 are weights to be generated by a regression.

In a review of 35 marketing studies using Fishbein's extended motel, Ryan and Bonfield (1975) found multiple correlation coefficients varying between .24 and .81 and beta weights varying between .03 and .68. These authors suggest that the efficiency of the fishbein model, and more specifically the social norm component, might well be a function of the products studied as well as of the degree of attitude centrality (Ryan and Bonfield 1975; see also: Glassman and Fitzhenry 1976). Based an the self-monitoring perspective, a third factor could be added to the ones proposed by Ryan and Bonfield. That is, if high self-monitors are more sensitive to their external environment, then their behavioral intentions should be affected to a greater degree by their social norms than the behavioral intentions of the low self-monitors.

Consistent with our position, it could be argued that the attitude towards a behavior would predict the intentions of high self-monitors as well as those of low self-monitors, assuming that the behavioral relevance of such attitudes is apparent to both types of people.

Finally, given that an individual has formed a behavior intention, if the situation is to be performed in the presence of social pressure (situational cues), we would expect that the degree of consistency between BI and B would be greater for low than for high self-monitors. From the preceding discussion the following formal hypotheses can be stated:

H1: The impact of social norms (SE) on behavioral intentions (BI) should be greater for high than for low self-monitoring individuals.

H2: There should be no significant difference in the impact of attitudes on behavioral intentions between the high and low self-monitoring individuals.

H3: Under conditions of social pressure, low self-monitoring individuals will show a greater degree of consistency between their behavioral intentions and their behavior than high self-monitoring individuals.


Ninety-four undergraduate students from Indiana University participated in the study. About three weeks prior to the experiment, subjects were asked to complete the Snyder self-monitoring scale. The initial questionnaire was presented by the principal investigator as part of a study whose objective was to assess the reliability of the 25-item scale (and provide tracking information). Three weeks later the same subjects were probed via a follow-up questionnaire regarding their attitudes, perceptions of social norms and their behavioral intentions towards the purchase of the Time-Life musical collection (presented in the form of an advertisement).

After collecting the questionnaires, the students were told that, in order to thank them for their cooperation, they would have the opportunity to purchase the music collection at a discount of $2 off the regular price ($29.96). To take advantage of the opportunity, the interested students had to raise their hands and ask for a special form to be used to send them a $2 coupon applicable to the purchase of the Time-Life music collection.

Following a pre-test of four products, the music collection was retained as the target product. The criterion used to select the target product was that no more than 30% of the pre-test sample (N=30) should indicate a favorable attitude towards it. The purpose of using such a product towards which the majority of subjects were to have an unfavorable attitude, was to generate a sufficient level of social pressure for those individuals who would be forced to display a public interest in acquiring the product. That is, since the experiment was to be conducted in a classroom setting, it was assumed that if the majority of the students were not to raise their hands, indicating their lack of interest for the product, it would generate a certain degree of social pressure for the minority of subjects who might be interested in buying the product. Our decision to use students in a classroom setting was based on the earlier work of Park and Lessig (1977) who found that students - particularly in a classroom setting - were extremely sensitive to their reference group. Too, such a procedure implies that the subjects are "forced" to participate, and in our design it was crucial to avoid using only "volunteer" subjects since they would seem more likely to be low self-monitoring individuals (Rosenthal and Rosnow 1979). Thus it was important to control for the strength of the situational cues that the experiment was to generate, and we felt that if at least 70% of the participants were not to raise their hands it would result in a situation such that those high self-monitoring subjects who might otherwise have been interested in "buying" the product would probably refrain from doing so because of the situational cues provided by their environment. Conversely, it was assumed that those same situational cues would not affect the behavior of the low-self-monitoring individuals who intended to acquire the music collection.


The experimental questionnaire contained three main sections, with the first measuring subject attitudes using a multi-attribute expectancy-value model. Belief strength (E), and attribute evaluation (v) were collected for each of five attributes selected on the basis of pre-test relevance ratings. Consistent with previous operationalizations (Fishbein and Azjen 1975; Smith and Swinyard 1983), belief strength (E) was measured on a 7-point scale which asked subjects to indicate, "Row likely do you think it is that the Time-Life musical collection is (product attribute i)?" Attribute evaluations (V) were measured on 7-point scale (from "extremely bad" to "extremely good") by asking subjects "If you were considering buying a musical collection, how would you evaluate one that offers (product attribute i)?" In order to obtain an estimate of the subject's attitude (EV) toward the target product, his/her scale values were multiplied and summed over all five attributes.

In order to test our hypotheses, a conceptually parsimonious measurement of the subjects' motivation to comply to social norms was necessary. As initially specified in Fishbein's extended motel (Equation l), the tendency to comply to normative pressures is (SN) = NB*MC, where NB is the normative belief component and SC is the motivation to comply. In order to operationalize the SN construct, Glassman and Fitzhenry (1977) tested different measurement approaches by varying the level of specificity of the different components, Their results indicate that the use of a single question such as "most of the people whose opinion is important to me would think that the (behavior) is ," is quite acceptable. In fact, this is consistent with Fishbein's (1973) position that the MC component may not improve predictability as only positive evaluated others tend to be elicited. Thus, we elected to measure subjects' social norms by using a 7-point adjectival scale (ranging from "extremely bad" to "extremely good") as response categories to the question "most of the people whose opinion is important to me would think that the purchase of the Time-Life musical collection is          ."

Behavioral intentions were measured as responses on a 7-point likelihood scale (ranging from "zero-likelihood" to "certain") to the quest$on "Row likely or unlikely is it that you would buy the Time-Life musical collection?" Finally, the completed forms that subjects had to ask for and return in order to receive the $2 discount coupon were used as the measure of behavior.


The following analyses are based on 94 usable sets of data representing subjects who had completed and signed both questionnaires. Subjects' self-monitoring scores varied from 5 (low self-monitoring) to 21 (high self-monitoring) out of a possible range from O to 25, with a mean of 12.46 and a standard deviation of 3.52. Following Snyder (1974), subjects with a self-monitoring score greater than or equal to 15 (N=28, 29.7%) were classified as being high self-monitors, and those with a score less than or equal to 10 (N=30, 32%) were classified as being low self-monitors

We first tested for any differences in the reported attitudes toward the musical collection per se between the two types of self-monitors, that is, whether the target product had more appeal for one group than for another. This seemed crucial since any initial differences between the attitudes held by the two kinds of self-monitors could confound the rest of the analyses. A one-way ANOVA was performed testing for differences in attitude scores (EV) between the high and the low self-monitors, and this analysis revealed that the extent to which an individual is self-monitor hat no apparent effect on his/her attitude toward the musical collection (E.S.M. i=2.1, N=28; L.S.M. x=l.9, N=30), F(1,56)=0.57, p=.811).

Next, in order to test the first two hypotheses, subjects' attitudes (EV) and social norms (SN) were combined using Fishbein's extended model and were used to generate an overall regression equation. Two similar regression equations were then generated, the first using only the scores of the 30 low self-monitors and the second using only the scores of the 28 high self-monitors. The results presented in Table l show some support for our use of the Fishbein's extended model.



While both components of the model contributed significantly to the explanation of behavioral intentions when using the total sample, of somewhat more interest is the comparison between the low and the high self-monitoring regressions. Here the social norm component (SN) was significant only for the high self-monitoring subjects (E.S.M., p=.026; L.S.M., p=.602) suggesting that situational cues (normative pressures) have an impact on the formation of behavioral intentions only for high self-monitors. While this doesn't mean that only the high self-monitors perceive social norms, it does mean that only the high self-monitors use them as guides for their behavior. This last point is an important one since use of an ANOVA revealed no differences in the reporting of social norms between the low and the high self-monitoring subjects (E.S.M. x=.22; L.S.M. x-17), F(1,56)-.l, p=.970) - a finding quite similar to that of Azjen, Tisko and White (1982). Thus the first hypothesis predicted that the impact of social norms (SN) on behavioral intentions would be greater for high than for low self-monitoring individuals, and our results failed to disconfirm this hypothesis.

Our position has consistently been that high and low self-monitors differ in the degree to which they use situational cues rather than in the degree to which they use attitudes to guide their behaviors. However, the beta weights for the attitude component in Table l suggest that the low self-monitoring subjects might be more likely to use their attitudes in forcing their behavioral intentions than do the high self-monitoring subjects (L.S.M. beta=.47; E.S.M. beta=.40). The difference was tested by comparing the partial correlations between Att and BI using Fisher's r to Z transforation for independent samples. The finding of no difference between the two coefficients (even at the .10 level) suggests that the two types of individuals to not differ in their propensity to use their attitudes to guide their behaviors, again replicating the findings of Azjen, Timko and White (1982). Thus, the second hypothesis of no significant differences in the impact of attitudes on behavioral intentions between the high and the low self-monitoring individuals could not be disconfirmed.

Out of 94 subjects, only 20% (N=18) indicated a favorable behavioral intention towards obtaining the target product. These results were consistent with those of the pre-test and were deemed necessary in order to generate a sufficient amount of situational pressure on those who were favorably inclined toward product "purchase." Only 5 subjects requested the discount coupon by raising their hands. Fortunately, none of these subjects had indicated an unfavorable behavioral intention toward buying the product, thus minimizing the possibility that an individual could ask to receive the coupon in order to resell it or to give it to someone else. Under these conditions, since the majority of the subjects indicated an unfavorable behavioral intention and acted consistently by not "buying" the product, a simple correlation between behavioral intention and behavior would be meaningless. Of greater interest is the behavior of those individuals who had a favorable behavioral intention towards product purchase. Our third hypothesis predicted that in the presence of social pressure, low self-monitoring individuals would show a greater degree of consistency between their behavior and their behavioral intentions than high self-monitoring individuals. In order to test this hypothesis, a chi square analysis was performed using the 18 subjects who had indicated a positive behavioral intention toward the purchase of the Time-Life collection. Table 2 shows the configuration of the analytical design. The result offer strong support for the third hypothesis (X2=6.978, df=l, p < .0l0).



With some exceptions, the self-monitoring concept doesn't seem to have been extensively used in consumer behavior. Becherer and Richard (1978) found personality to have a greater impact on behavior among low self-monitors than among high self-monitors. Becherer, Morgan, and Richard (1980) found that low self-monitors were more subject to reference groups' influence than were high self-monitors. Our research is conceptually closer to Snyder's work. The self-monitoring construct is presented here as a variable which can moderate the impact of attitudes. The relationships presented in this paper could be of some theoretical interest for the area of consumer research. Since attitude is one the central constructs proposed in most consumer behavior models a closer look at the factors which can mediate the attitude-behavior relationships seems important. In a recent publication, Smith and Swinyard (1983) have proposed several such mediating variables and have urged for more research along these lines. From a more practical standpoint the present research might also have some relevance. For marketing managers and marketing researchers, interpreting the results of pre-tests of advertisements, products and product concepts that use behavioral intentions as the dependent variables should be made with greater care if a sufficient number of subjects are found to be high self-monitoring individuals.

Although the present paper offers some interesting implications several limitations are worth mentioning. First, in recent years, the self-monitoring scale has come under close scrutiny by many researchers (Briggs, Cheeks, and Buss 1980, Gabrenya and Arkin 1980). These authors suggest that the self-monitoring scale is a multidimensional rather than an unidimensional scale and that further research is needed in order to fully understand the exact nature of the constructs it taps. Along these lines, Lennox and Wolfe (1984) have suggested a revised version of the scale. Second, the SE and BI components of our equation were measured using only one item scales. Although the measures were based on other studies, the use of single measures in the present study is weak from a construct validity standpoint. Finally, the strategy of placing every one in a social pressure condition where nonbehavior supports the hypothesis, although an original one, could have been better if the social pressure condition would have been manipulated.

The main objective of the present research was to generate some interest for the self-monitoring construct. At this point, future research could and should be considered. Very little has been done concerning the sociometric dimensions of the self-monitoring construct. Also, the concept could be of some interest for the researchers interested in the diffusion of innovations. That is, given a certain amount of interest in a product, its adoption could still be jeopardized if there were too few individuals actualizing their intentions and failing to provide favorable situational cues for others in the marketplace. It could also be that those individuals labelled as "innovators" are much more likely to be low self-monitoring individuals, though this remains an empirical question. It is our hope that our findings will sensitize marketing researchers to the use of the self-monitoring concept and generate additional research on its use in consumer behavior.


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Jacques A. Nantel, Ecole des H.E.C., Montreal
William Strahle, Indiana University


NA - Advances in Consumer Research Volume 13 | 1986

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