Process Tracing of Affective Reactions Elicited By Commercials: Assessing the Explanatory Power of the Outputs of the Feelings Monitor and of the Coding of Facial Expressions

ABSTRACT - In this research we use 2 complementary non verbal methods to measure the affective reactions elicited by commercials: the feelings monitor and the coding of facial expressions of emotions. We investigate the impact of variables coming out these two methods on 2 measures of advertising effectiveness: attitude toward the ad (Aad) and attitude toward the brand (AB). We replicate results obtained by Baumgartner et al. (1997) and for the first time the impact of coding facial expressions of emotions on Aad and AB is clearly assessed.


Christian Derbaix and Ingrid Poncin (1999) ,"Process Tracing of Affective Reactions Elicited By Commercials: Assessing the Explanatory Power of the Outputs of the Feelings Monitor and of the Coding of Facial Expressions", in E - European Advances in Consumer Research Volume 4, eds. Bernard Dubois, Tina M. Lowrey, and L. J. Shrum, Marc Vanhuele, Provo, UT : Association for Consumer Research, Pages: 166-173.

European Advances in Consumer Research Volume 4, 1999      Pages 166-173


Christian Derbaix, LABACC, FUCAM, Belgium

Ingrid Poncin, LABACC, FUCAM, Belgium


In this research we use 2 complementary non verbal methods to measure the affective reactions elicited by commercials: the feelings monitor and the coding of facial expressions of emotions. We investigate the impact of variables coming out these two methods on 2 measures of advertising effectiveness: attitude toward the ad (Aad) and attitude toward the brand (AB). We replicate results obtained by Baumgartner et al. (1997) and for the first time the impact of coding facial expressions of emotions on Aad and AB is clearly assessed.


Since the seminal and controversial work of Zajonc (1980), a rich body of empirical studies in marketing has been evoted to understanding how ad-evoked feelings influence advertising responses (e.g. Batra and Ray, 1986; Edell and Burke, 1987; Burke and Edell, 1989; Derbaix, 1995). In these studies it has been established that the affective responses, as subjective states of the consumer, can be clearly distinguished from evaluative judgments of advertisements (Edell and Burke, 1987; Holbrook and Batra, 1987) and impact on Aad and AB generally considered as dependent variables.

In this paper, we bring to the fore a more micro perspective by focusing on the dynamics of the ad-evoked affective reactions. As stressed by several authors (e.g. Vanden Abeele and Maclachlan, 1994), affective reactions are rather volatile; they respond to, and change with, variations in external conditions. So their essential properties (intensity, valence and content; Kroeber-Riel, 1982) can vary in the course of a single commercial.


Post exposure verbal responses are undoubtedly the most often used approaches to assess affective reactions elicited by commercials. They are easy to administer, have the capacity to tap affective reactions but require that the respondent provide a summary of an experience that extends over 30, 45 or even 60 seconds. Nobody knows exactly what do provide these self-report measures: a modal, an average, a final reaction or an unclear mixture of all these reactions ? "The changeability of affective reactions means that a single measure purporting to cover the entire commercial may be very misleading" (Fenwick and Rice, 1991, p.24).

Interruptions in order to grasp the dynamics of the reactions are not the solution. Indeed, when the subject is responding to a TV ad, interruptions can be obtrusive, disrupting the dynamics of the responses.

"Process tracing methods are a better solution to the extent that they offer the opportunity to examine patterns of response to transient external stimuli over time" (Vanden Abeele and Maclachlan, 1994, p.597) and can help identify the precise features of the ad that might inhibit or enhance that ad’s effectiveness. So, measurement of affective reactions should be as contemporaneous as possible with the advertisement eliciting these reactions. Such methods are real-time methods. "Real-time methods provide rich, process oriented measures of subject reactions to dynamic messages. They provide the opportunity to perform individual level diagnostics on the process of interest. They are preferable to post exposure measures in that individuals are unable to provide retrospective responses about early points in a communication without bias due to having seen the rest of the message" (Hughes, 1991). Instead of overall reactions to ads, real-time responses to transitory elements of ads are of interest. They may reveal how the commercial is processed and hence contribute to a better understanding of its effectiveness (Boyd and Hughes, 1992; Fenwick and Rice, 1991; Hughes, 1992; Rothschild et al., 1988; Stewart and Furse, 1982). The warmth-monitor is a well-known example of such methods (Aaker et al., 1986). However the study achieved by Vanden Abeele and Maclachlan (1994) highlighted questions regarding this construct’s meaning ("warmth") and valid measurement.

So we are looking for an ideal (?) measure of affective reactions. Such a measure has to be continuous or at least in real-time, as spontaneous as possible, objective, disguised, assessing intensity, valence and content, metric if possible in order to allow subsequent statistics and of course valid and reliable. Reviewing the extant literature on the measurement of affective reactions did not pinpoint such an ideal (Derbaix and Poncin, 1998). However two measurement tools appeared to be particularly attractive as well as complementary.

Baumgartner, Sujan and Padgett (1997) assessed moment-to-moment affective responses with a computerized procedure they called the "eelings monitor".

"Initially, the computer screen displays the anchors for the feeling scale, which range from "strong negative feelings" to "neutral" to "strong positive feelings". By pressing the left mouse button, the participant activates the cursor at the start of each advertisement. The cursor moves down the screen at a constant speed and participants can control only the cursor’s horizontal position by moving the mouse to the left or right and indicating to what extent the advertisement elicits positive or negative feelings at any given moment. The computer displays the charted affect pattern on the screen and automatically records responses every second. The computer is programmed so that the screen with the feelings monitor disappears after a commercial is over. Just before the beginning of a new advertisement, a start signal appears on the television screen and by pressing the left mouse button, the participant activates the feelings monitor and starts the cursor moving down the screen from the neutral position" (Baumgartner, Sujan and Padgett, 1997, p.222)

The ability to register affective responses almost continuously, its attractive simplicity and low cost were criteria for its selection for our study. This procedureBclearly inspired from the one used with the warmth monitor - has also the benefits of the directness of the task and of a well-exercised behavior (similar to the drawing of a line). However it does not precise the content of the affective response, a criticism shared by most continuous measures which only allow respondents a selective response range.

The disguised observation of facial expressions of emotions (a motor behavior) offers the possibility of identifying some affective reactions elicited by the ad in real-time and in a non-reactive manner. It has now been accepted that, in some circumstances, the face is home to a system of rapid, emotion-revealing signals (Ekman, 1972; Ekman and Friesen, 1978; Ekman, 1993). Coding facial expressions recorded in a well-known context (in order to know the causal factors, such as in a lab-setting) has also numerous advantages. It needs no retrospection, nor introspection. It also allows the recording of the chronological appearance of emotions. As for the inconveniences, three seem important: the necessity to possess costly equipment to be able to film the face precisely and without being seen, complex decoding which entails long and difficult training and a low probabilily of being able to observe weak affective reactions. As underlined by Derbaix and Bree (1997), the first condition to use a method based on facial expressions of emotions postulated to be universal, is to admit that this thesis is one of the best plausible alternative. In its most restrictive form, this thesis means the existence of separate specific facial configurations corresponding to the same number of separate specific emotions, easily recognized by all humans. In an insightful discussion of the extant literature, Russell (1994) underlined methodological problems found in studies leading to the conclusion of the universality thesis. In our study reported hereunder we tried to minimize these deficiencies. We worked indeed with spontaneous and dynamic facial expressions; we knew the expresser’s context (a lab-setting where the respondent was shown different ads); our judges did not give a code to every facial movements. Moreover partial coding of secretly videotaped faces illustrates the fact that our judges did not interpret facial expressions dichotomously. [Our coders did focus on different areas of the face and then interpret the whole facial expressions on the basis of Ekman, Friesen and Tomkins (1971), Ekman (1972) and Ekman and Friesen (1975) works.] Finally implementing our experimentation (in a pure occidental context) with one respondent at a time and knowing the setting make it possible to disentangle face-emotion knowledge from face-situation knowledge.

The main advantages and drawbacks of the two measurement methods we selected are summarized in the tables 1 and 2.

So, in this research, we rejected verbal measurements whose potential reactivity might induce distortions rlating to social desirability, evaluation apprehension as well as bias towards more cognitive than affective reactions. As appeared in tables 1 and 2, the 2 methods we plan to use appear to be complementary by providingBif used simultaneouslyBthe polarity, the intensity as well as the content of affective reactions. In fact using these 2 methods simultaneously serves to lessen their respective drawbacks. Moreover using the outputs of the feelings monitor as well as the ones of coding facial expressions (2 non-verbal but different methods), as potential explanatory variables of our 2 more macro-constructs (Aad and AB), minimizes the problem of shared method variance encountered when using multiple verbal report methods with question formats perceived to be similar by respondents (Feldman and Lynch, 1988).

Baumgartner, Sujan and Padgett (1997) developed several hypotheses dealing with the integration of moment-to-moment emotional responses into overall ad judgments. The results of the 3 empirical studies they achieved, using the feelings monitor, were essentially that consumers evaluate commercials in terms of proxy measures of the moment-to-moment affect trace. Specifically, they found that consumers prefer commercials that have high peaks (the most extreme experience in the affect trace), end on a strong positive note and exhibit sharp increases in the trend of affective experiences over time. They stressed that the latter result might reflect the same process as the preference for high peaks and ends, because steeper slopes contribute to higher peaks and ends. On the other hand, total ad duration was related only weakly to overall judgments. They also showed that people’s preference for these specific patterns of emotional experience generated by advertisements not only apply to ad liking but also to brand liking and brand recall.


In this research, we tried to replicate the findings of Baumgartner, Sujan and Padgett (1997) but also to assess the explanatory power of the outputs of the coding of facial expressions with respect to the ones of the feelings monitor.


Twenty-four respondents (from 23 to 40 years-old) participated to this study. These participantsBcontacted by study collaborators who were requested to avoid students, friends and relativesBwere unaware of the exact goals of our research. A few days after having been contacted they came in the LAB of our research center. During the commercials the respondent pressed the left mouse button and the computer displays the charted affect pattern on the screen (feelings monitor). These participants were not aware of being filmed by a camera behind a one-way mirror. After the 20 commercials, we revealedBduring the debriefingBthat the respondent’s face had been filmed during the commercials. To use the respondent’s video-taped face, his/her permission was required in writing.


Twenty-five commercials were selected in order to trigger off affective reactions. They came from Switzerland and Quebec where they were regularly aired but simultaneously they were unknown in Belgium, therefore removing the concern of differential familiarity with the stimuli. Almost all of the advertised brands were also unknown in Belgium (where our experiment took place). During a pretesting phase we realized that 5 of the commercials elicited quite different affect traces among our respondents. These 5 commercials were therefore eliminated for the final study.


The choice of ads as units of analysis differs from the prevailing norm in most studies on attitude toward the ad. Such studies have invariably used people rather than adverisements as units of observation. However, for many purposes (and certainly from a managerial point of view), it makes sense to regard the ads themselves as having different "emotional profiles" to which members of the target audience react with a fair degree of homogeneity (Holbrook and Batra, 1987). Indeed in advertising research, the relevant variance seems to be the one stemming from properties of the ads rather than the one coming from systematic and random differences across consumers. In this study as in the one of Baumgartner, Sujan and Padgett (1997), we assume (and check) that individuals respond relatively homogeneously and can therefore be aggregated to create response measures that characterize ads rather than people as the sampling units of interest. This way of working (focusing on the variability between the ads) is called the Among Analysis. As demonstrated by Srinivasan, Vanden Abeele and Butaye (1989), "the correlations in the Among Analysis can be strongly influenced by the lack of representativeness of the stimuli. Moreover the pattern of correlations in the Among Analysis can get distorted by the respondents’s differential familiarity with the stimuli". To avoid these problems, in our study we used unknown ads for novel brands as well as unknown ads for familiar brands in order to elicit the whole spectrum of affective reactions (e.g., surprise), these ads being targeted at both men and women. These advertisements were selected to cover a broad range of products. A pretest had indicated relatively low variance in the ratings of affective responses for the same advertisement (to ensure that the stimulus generated fairly homogeneous affective responses) for 20 out of 25 commercials and high variance across the different ads. So in this study we treated people as replicates.





For the analysis of the data coming from the feelings monitor, for each commercial, we charted a "mean curve", i.e. a curve on which each point (score) was the average of the momentary affective experience computed for all the subjects, [For several of our ads, one or two "outliers" (subjects) were disregarded on the basis of completely different affect traces. For each ad, we computed Cronbach a in order to check the homogeneity of the affective traces across our respondents. With the exception of the ad for IKEA, all of our alphas for the affect traces were between .673 and .941 with a mean of .865.] second by second. The following variables coming from the feelings monitor were computed for each commercial:

SSM: sum of the scores (points) on the mean curve divided by the length of the commercial.

PE: peak experience (highest score on the mean curve).

EN: end note (final moment) on the mean curve.

TTP: time to peak (i.e. time elapsed before the peak experience).

TAP: time after peak.

For the facial expressions, the outputs of our procedure were computed as follows (for the 4 emotions which were expressed and coded, [Only one respondent exhibited the expected pattern of facial expression for sadness, and 2 respondents for anger and only in the case of one commercial. These respondents were disregarded from our analyses for this commercial.] i.e. Joy=J, Positive Surprise=PS, Negative Surprise=NS and Disgust=D). Taking VALSER (a commercial for a mineral water; 45") as an example the score computed for joy was:

Respondent n¦ 1: joy from seconds 2 to 15 with an intensity of 2; [The intensity for each facial expression of emotion was coded on a 3 points (so from 1 to 3) scale by our 2 coders. In case of disagreement, a third coder was requested to solve the problem.] from 15" to 24" (joy with an intensity of 3); from 38" to 45" (joy with an intensity of 3). So for this respondent the score of joy was (13 (seconds) * 2) + (9 * 3) + (7 * 3)=74. We did the same for all the other respondents. We summed all these scores, then divide by the number of respondents and finally divide by the length of the commercial (in this example 45).



On the basis of all the computed explanatory variables, we first estimated what we called the total affective model (see Table 3):

Aad = constant + SSM + PE + EN + TTP + TAP +  J + PS +NS + D

To pinpoint the significant variables in all the estimated models, a screening procedure (a step-by-step regression in descending order) was applied. This procedure was based on three criteria: value of the t-test; standard/mean of the variable and tolerance. Tolerance is one minus the squared multiple correlation between each predictor and the remaining predictors in the equation. Low values of tolerance indicate that some of the predictors are highly intercorrelated. In short, this led to eliminating one variable at a time and reestimating the equation each time until all the predictors were significant.

Table 4 (hereunder) provides the results of this screening procedure, first applied to the total affective model (left side of the table) then to the facial affective model (right side of the table). Our starting equation for the latter was (taking into account only the variables coming from the coding scheme): Aad=constant + J + PS + NS + D.

Probably due to a very high correlation (.99) between SSM and PE, the sign of the SSM regression coefficient was contrary to our expectations in the total affective model. So we regressed Aad on the sole PE and obtained the results presented in table 5. Consequently the "Peak Experience" appeared in our investigation as the strongest predictor of Aad and therefore as a very good proxy of the affect trace. [When adding SSM to PE, the squared multiple R jumped from .901 to .926. The FADD statistic (Kerlinger and Pedhazur, 1973) was not significant at p=.025. This statistic tests whether the increase in R2 from adding a variable to a model is statistically significant.] As one realizes, the contribution of facial expressions seems more limited at the end of this procedure.

Although the results obtained with the facial affective model are less significant than the ones obtained with the total affective model (pinpointing essentially variables coming from the feelings monitor), they are much more significant than the ones produced in previous advertising studies where facial expressions were coded (e.g. Derbaix, 1995; Derbaix and Bree, 1997). The reasons of the better results obtained here are perhaps:

* the fact that the intensity (noted "subjectively" by the coders but taking into account descriptions differing in intensity of facial expressions of emotion published in Ekman and Friesen (1975); for instance for joy on page 104) was here taken into account (which had not been done before).

* we used the ad as unit of analysis.

Focusing on our other macro construct (AB) and following the same screening procedure led to the results presented in table 6.

These final results are very interesting to the extent that 2 proxy variables of the affect trace (feelings monitor) and a variable emerging from our coding of facial expressions of emotions impact on the attitude toward the brand, a variable more closely linked to managerial interests than Aad. However, one more time we have to record an important collinearity between 2 of our explanatory variables (SSM et EN), this multicollinearity being probably at the origin of the unexpected sign of the SSM coefficient. Let us also stress that another important correlation between explanatory variables (between PE et EN) appeared in our research. Examining the affect traces, we of course found that in many instances, PE and EN were superimposed. This fact justifies that in the last regression ENBafter the screening procedureBremains highly significant "instead" of PE.



Finally, we tested the possible mediating role of Aad with respect to the impact of our affective variables on AB, in accodance with Baron and Kenny’s (1986) method. After the 3 needed regressions, we found that SSM and PE are non significant in explaining AB once Aad is included among the explanatory variables.

Discussion and Conclusion

As clearly shown by our results, 3 of the outputs of the feelings monitor, i.e. the peak experience, the end note and the sum of the scores, were highly significant in our study. Let us stress thatBcontrary to Baumgartner et al. (1997)Bsome portions of our affect traces were on the negative side of the chart and that 3 of the peak experiences and 3 of the end notes (for the same commercials) were negative. The greater impact of the proxy variables of the affect trace with respect to the outputs of our coding of facial expressions can perhaps be explained as follows. Feelings recorded with the monitor as well as Aad and AB are less spontaneous and more cognitive than the facial expressions. So we can not ruled out a "shared method variance" explanation for partially justifying the greater impact on our more macro constructs (Aad and AB) of the variables coming out the feelings monitor. Let us also stress that with the feelings monitor the measurement of the intensity is more direct, does not require the "subjectivity" of coders and is perhaps more precise than the measurement of the intensity with coding facial expressions of emotions. Moreover let usBone more timeBunderline that many commercials do not perhaps have the potential to generate strong affective reactions (i.e. emotions for which there are facial counterparts in the coding systems elaborated by Ekman and his collaborators). Nevertheless, our facial affective model highlights significant variables emerging from our coding schema of facial expressions of emotion.



Due to the simultaneous use of 2 non verbal methods, it is also possible in our investigation to try to interpret, i.e. to label, the affect traces. For the 3 negative peak experiences it was easy to diagnose that disgust was at their origin. On Graph 1 we reproduce the patterns for respondent n¦ 11 and for the VALSER commercial (an unknown ad for an unknown brand of mineral water) coming from the feelings monitor (o) and from the coding of the facial expression of Joy (n). We observe a good similarity between the affect trace recorded by the feelings monitor and a comparable chart we built for joy (rescaling the figures for joy). In a near future we will investigate all these possible pairs of charts, i.e. at least 20 (commercials) by 24 (respondents) charts. These 480 charts are a minimum to the extent that in some cases we have different expressions of emotions appearing chronologically during the same commercial.



Moreover having video-taped not only the facial expressions of emotions but also what appeared on the TV screen, i.e. the commercials, while the respondent was displaying a facial expression of disgust, for instance, it is now possible to find out the exact elements of the commercial which could be at the origin of this emotion. From a practical point of view polarity and intensity of affective reactions are of course crucial but adding content (due to the coding of facial expressions) is undoubtedly an interesting plus. Furthermore we have to be careful with hypotheses regarding the impact of the polarity of affective reactions on Aad and AB. Commercials eliciting sadness, for instance, may well be liked.

A substantial interest in the role of affective reactions and states was sparked by influential studies in the 1980’s and 1990’s (see Brown S.P., P.M. Homer and J.J. Inman (1998) for a synthesis) and have to some extent compete with the study of the higher cognitive processes that have been the mainstay of consumer behavior research. In this study, we replicate the compelling demonstration of Baumgartner et al. (1997) concerning the predictive power of the peak experience and the end note of the affect trace. Moreover and, to our knowledge, for the first time in the marketing literature the impact of facial expressions of emotions n Aad and AB was clearly assessed. Our results can serve as a suggestive point of departure concerning the validity of the affect trace. In a next future we will compareBas in Graph 1Bthe respective graphs emerging from the outputs of our 2 non verbal methods in order to progress in validly interpreting affect traces elicited by ads, investigate sequence effects, effect of repetition on affective reactions and build new explanatory variables applicable in specific cases (i.e., when we have during the same commercial positive and negative affective reactions), ...


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Christian Derbaix, LABACC, FUCAM, Belgium
Ingrid Poncin, LABACC, FUCAM, Belgium


E - European Advances in Consumer Research Volume 4 | 1999

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