Negative Emotions in Marketing Research

Negative Emotions in Marketing Research: Affect or Artifact? Barry J. Babin THE UNIVERSITY OF SOUTHERN MISSISSIPPI

William R. Darden LOUISIANA STATE UNIVERSITY

Laurie A. Babin THE UNIVERSITY OF SOUTHERN MISSISSIPPI

How similar are positive and negative affect? Are happiness and unhappiness opposite ends of the same continuum? Empirical marketing research generally reports separate positive and negative self-report consumer emotions. Recent research in social psychology calls this distinction into question and reasserts the bipolarity of human emotions. Despite the recent interest in consumer emotions among marketing researchers, the marketing literature has not addressed the issue directly. Two studies (n 5 334 and n 5 335) are reported that investigate this issue. The results suggest that positive and negative consumer emotions may sometimes, but not always, be distinct, and more importantly, suggest further studies. Additionally, evidence is presented that suggests that the respondent task may moderate correlations between positive and negative consumer emotions. J BUSN RES 1998. 42.271–285.  1998 Elsevier Science Inc.

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motions are “fuels for drives, for all motion, every performance, and any behavioral act’’ (Fonberg, 1986, p. 302). From Democritus to Mill, the very meaning of life has been couched in emotional terms; the seeking of true happiness and the avoidance of pain. Things and activities bring us value to the extent that they provide pleasure immediately or allow us to access future pleasure (Russell and Snodgrass, 1987). As with other human activity, consumer activity is guided by this principle as well. Given marketing’s central focus on consumers, there can be no denying the critically important role played by emotions in defining consumption experiences and influencing consumer reactions. Marketing success, then, is determined to the extent that customers are provided with sought after emotional states and emotional states that are not desired are minimized. Address correspondence to Barry J. Babin, Marketing Department, The University of Southern Mississippi, SS 5091, Hattiesburg, MS 39406-5091. E-mail: [email protected] Journal of Business Research 42, 271–285 (1998)  1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

Basic and applied marketing researchers’ growing interest in the role of emotions has the potential to add much to marketing thought. Despite a growing body of literature directed toward understanding consumer emotions (see Babin, Darden, and Griffin, 1992 and Cohen and Areni, 1991, for reviews), a number of basic issues remain unresolved. Consider a typical customer service setting for example. A customer enters a pleasant restaurant environment and enjoys the ambiance while awaiting service, but rather than getting prompt service, the customer waits over 30 minutes before receiving his/her meal. The house wine is surprisingly pleasing, but the salad has the wrong dressing and the steak is over-cooked. A request for water brings another extraordinary wait as does a demand for the check. On leaving, the customer complains to a floor manager. The floor manager apologizes and offers the customer a free dessert. During this setting the customer has had the opportunity to experience both desirable and undesirable emotional states. The offer of a free desert is a vain attempt at having the customer leave happy. Will it work? Is the customer left feeling delighted, terrible, or both? What are the long-term effects? The answers to these questions are clearly affected by the extent to which positive emotions are distinct from negative emotions. Although the bipolarity issue has sparked attention in the psychological literature (cf., Diener and Emmons, 1984; Russell, 1979), marketing research has yet to give it significant direct attention. Our knowledge of consumption emotions has its conceptual underpinnings largely in the work of several prominent emotion scholars (e.g., Mehrabian and Russell, 1974: Izard, 1977; Plutchik, 1980; Smith and Ellsworth, 1985). Following in these traditions, conceptual work indicates that two relatively pervasive factors underlie consumer emotions (Donovan and Rossiter, 1982; Havlena and Holbrook, 1986, Havlena, Holbrook, and Lehmann, 1989). One factor captures pleasure or emotional tone and is indicated by feelings such as happiISSN 0148-2963/98/$19.00 PII S0148-2963(97)00124-0

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ness or joy on one end and sadness or despair on the other. The second captures more the degree of emotional activation and is represented by feelings like excitement or arousal on one end and calmness or sleepiness on the other. Although previous marketing studies investigate dimensionality, they have normally focused on the difference between emotional tone and intensity. Contrasting this notion of bipolar emotion spaces are numerous applications of various emotion batteries among consumers reporting a “negative” affect dimension indicated by either the presence or absence of feelings such as sadness or annoyance (e.g., Edell and Burke, 1987; Burke and Edell, 1989; Oliver, 1993; Mano and Oliver, 1993; Lacher and Mizerski, 1994; Darden and Babin, 1994; Bagozzi and Moore, 1994). Among these studies, positive items comprise one or more separate factors. Thus, the accumulated knowledge regarding self-report consumer emotions presents conflicting evidence regarding their very nature. This issue is important to theory development and marketing practice. Theoretically, it may become necessary for marketing and consumer behavior models positing an important role for emotions (e.g., Bitner, 1992; Holbrook, 1986) to acknowledge separate rather than bipolar emotional dimensions. This would be consistent with the idea that negative emotions may have separate and disparate effects compared to their positive “counterparts” (Gardner, 1985; Thomas and Diener, 1990). Practically, this issue may determine the validity of many accepted methods of assessing consumer emotions. For example, the common industry “satisfaction” survey may be incapable of detecting consumer dissatisfaction. If the two are opposites, a survey containing only satisfaction items is adequate. If the two are distinct, results from such a survey could be considerably misleading. Further, ascertaining the true validity of emotional tone and activation is an important endeavor as both are relevant to consumer evaluations and the building and decimation of customer relationships (Bagozzi and Moore, 1994; Gardial et al., 1994). Thus, the purpose of this article is to spark interest in the bipolarity debate among marketing researchers. Recent evidence in social psychology presents relatively convincing evidence in favor of a bipolarity view (Green, Goldman, and Salovey, 1993, discussed below). If so, why the large number of studies of consumption emotions reporting a “negative” emotion? While a single article is not likely to resolve this debate in the context of consumption emotions, this article makes an initial effort toward that end. Data are presented that examine the factor structure of consumption-related emotions in two separate contexts. Comparative statistical analyses are undertaken to investigate each paradigm’s validity. The results are discussed and are suggestive of further research.

Research Questions Given the mixed bag of results across studies of consumer emotions, two separate studies were conducted aimed at ad-

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dressing the discrepancies directly. Several related research questions are raised by previous research. Each question considered here centers on the bipolarity of consumption emotions. Space precludes a more thorough literature review. However, a cursory examination of the marketing literature suggests significant numbers of empirical studies reporting a bipolar positive-negative emotion factor (e.g., Bateson and Hui, 1992; Hui and Bateson, 1991; Pavlechak, Antil, and Munch, 1988; Donovan and Rossiter, 1982) while others report distinct positive and negative emotion factors (e.g., Edell and Burke, 1987; Stayman and Batra, 1991; Allen, Machleit, and Kleine, 1992). Surprisingly, many studies involving consumer emotions reveal little detail concerning measurement quality. Analytical measurement tools such as confirmatory factor analysis are rarely detailed, and only a few studies report any type of factor loadings (e.g., Holbrook and Batra, 1987; Burke and Edell, 1989; Mano and Oliver, 1993). First, the fact that some studies have produced a bipolar pleasure-displeasure factor and others, using very similar emotional descriptors, have produced a unipolar negative affect factor is investigated. The Russell (1979) view strongly suggests that operationalizations theorizing a bipolar pleasure dimension will be empirically superior to any other view. In contrast, the “negative” affect view would suggest that separate “negative” and “positive” emotion dimensions will provide the best empirical fit. Watson and Tellegen (1985, 1988), relying heavily on early work by Izard (1972), provide a conceptual basis for this disparate argument. Therefore, the first research question involves a comparison of these two approaches in consumption-related contexts. RQ1: Does a bipolar theory of consumption-related emotions fit better than the “independent” negative and positive emotion theory? Second, the mere frequency with which marketing researchers have reported a distinct “negative” emotion factor suggests a belief that it is somewhat unique if not independent. While interfactor correlations are not reported in many of these studies, Mano and Oliver (1993, Table 2) report a correlation of only 2.11 between “pleasantness” and “unpleasantness.” Clearly, a notion that positive emotions and negative emotions are independent contradicts a substantial amount of theory into general emotions (e.g., Mehrabian and Russell, 1974; Russell, 1979; Plutchik, 1980; Smith and Ellsworth, 1985) and consumer emotions (Donovan and Rossiter, 1982; Havlena and Holbrook, 1986; Holbrook, 1986). Thus, the second research question addresses the distinctiveness of negative and positive emotions in a consumption-related context. RQ2: Are negative and positive consumption-related emotions independent (i.e., uncorrelated), distinct (i.e., correlated less than 21), or mirror images (i.e., correlated 21)? Third, proponents of a bipolar theory argue that those

Negative Emotions

empirically finding distinct negative and/or positive emotions do so as the result of a statistical artifact. The argument suggests that there is an unaccounted for systematic source of covariation among items comprising the emotion battery that causes distortion of correlations between “negative” and “positive” emotions (Bentler, 1966; Russell, 1979; Russell and Snodgrass, 1987). Green et al. (1993, Study 2) present data showing that the correlation between multi-item “happy” and “sad” scales increases from 2.40 (the observed correlation) to 2.92 when allowance is made for the additional systematic covariance (they also provide a mathematical rationale for the correlation between positive and negative mood to be generally attenuated). Common sources of nonemotional systematic covariance are in an overrepresentation of negative or positive items among the battery or in the fact that a common measurement instrument is generally used. They conclude that reported correlations between positive and negative mood states are more statistical artifact than a valid representation. Thus, the third research question examines the distinctiveness of positive and negative emotions while attempting to control for an additional source of systematic covariation. RQ3: Does the addition of a common source of systematic covariance affect the correlations between positive and negative emotion terms? Finally, the psychological debate concerning the distinctiveness of positive and negative emotions largely has evolved around differences in positive and negative mood (see Diener and Emmons, 1984). For example, individuals’ moods may influence information processing and thus, their subsequent recall. A study of individuals’ accuracy in recalling emotions demonstrated that the frequency of positive emotions is generally underestimated (Thomas and Diener, 1990), suggesting relatively greater recall of negative emotions. Marketing researchers have assessed consumer emotions both through recall of previous events (e.g., Sujan, Bettman, and Baumgartner, 1993; Darden and Babin, 1994) and during or immediately following a consumption experience (e.g., Holbrook et al., 1984; Donovan and Rossiter, 1982; Dawson, Bloch, and Ridgway, 1990). Thus, there is the possibility that contextual factors such as this may influence the distinctiveness between positive and negative emotions. Further, studies of subject mood over various time periods suggest that the correlation between positive and negative affect varies with the richness of a task (Diener and Emmons, 1984). For example, daily mood measures, assessed over longer periods of time (i.e., months), generally produce correlations lower in magnitude than do measures assessing subject recall of mood in a much more concrete fashion (i.e., “what was your mood Monday?”). Thus, research contexts that involve fairly vivid and multifarious respondent tasks may report negative and positive emotion correlations of lower magnitude than would more concrete respondent tasks. The fourth research question explores the potential of moderation due to

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research context (i.e., stimulus-based versus memory-based task). The extent of this moderation may even include different factor structures across different contexts as has been suggested for attitudinal data (Batra, 1986). RQ4: Does the correlation between positive and negative emotions vary with respondent task?

Study 1 Consumption emotions play a key role in the perceptual positioning and meaning of retail stores (Grossbart et al., 1975: Kotler, 1974; Markin, Lillis, and Narayana, 1976). A place’s “affective quality” is generally considered to be its emotional connotation augmenting any physical representation present in memory (Russell and Pratt, 1980). Russell and Pratt (1980) provide an emotion inventory allowing a place’s affective quality to be assessed and report correlations among “bipolar” affective quality scales. For example, exciting and gloomy and pleasant and unpleasant scales exhibited attenuated correlations of 2.78 and 2.79, respectively. Arguably, correcting these for random measurement error would produce correlations not significantly different from 21. These factors showed little correlation with the other factors comprising affective quality: relaxing-distressing and arousing-sleepy, respectively. Thus, affective quality can be described completely in one of two arbitrary bipolar spaces as shown in Figure 1. The applicability of this concept to retail shoppers has been demonstrated in a study using student respondents (Darden and Babin, 1994). However, this study reported smaller correlations (magnitude) between positive and negative emotional meanings than those stated above. Study 1’s approach is similar in that it assesses consumers’ emotional connotations of familiar retail stores.

Sample Potential respondents were recruited by undergraduate marketing research students. Each student recruited five volunteers to participate in a marketing research project. Students were given instructions regarding the demographic characteristics of potential respondents in an effort to obtain a sample similar to that of the typical retail consumers in this particular city. This procedure allowed us to collect data away from the store environment so that it is purely recall-based, and it also provided each student with data that could be used in various course assignments. Numerous interview sessions were set up over a two-week period so survey instruments could be completed at a respondent’s convenience. In all, 355 respondents participated. Respondents were randomly assigned to rate one of nine local nonfood retail stores. The stores used as stimuli were selected based on pretests of each’s expected familiarity and in an effort to insure variance. Thus, up-scale department stores and discounters were both included. All the stores were located in and around

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Figure 1. Affective quality space (Russell and Pratt, 1980).

the city’s dominant retail mall. Respondents rated the affective quality of the assigned store using the 40 affect items comprising the Russell and Pratt (1980) inventory. An 8-point scale (1 5 strongly disagree to 8 5 strongly agree) was used to assess the extent to which a store made them feel each of the items listed. Demographic information and information relevant to shopping involvement (not used in this study) also was collected. A total of 334 usable questionnaires were obtained through this procedure.

Analyses and Results Maximum likelihood confirmatory factor analysis (CFA) was used to examine the alternative theoretical structures and to obtain interfactor correlation estimates. Table 1 shows correlations among items used in the analyses. Since affective quality is defined by two sets of arbitrary dimensions, only the 20 items comprising the exciting-gloomy and relaxing-distressing dimensions were selected (see Table 2). Duplicate analyses using items comprising the alternative dimensions provided similar results and are not discussed in detail here. Table 3

summarizes parallel analyses using the pleasing-displeasing and arousing-sleepy dimensions. Table 2 reports factor loadings, scale statistics, and F coefficients representing the correlation between factors. Fit statistics for alternative models are included in Table 4. Although not directly relevant to any specific research question, unidimensional and null model results are provided for reference. First, fitting the 20 items into independent bipolar dimensions as prescribed in Russell and Pratt (1980) produced acceptable construct reliabilities (.92 and .85, respectively) and generally acceptable loadings (all p , .001). The x 2 fit statistic representing this model is 1,431.0 with 170 degrees of freedom producing a comparative fit index (CFI) of .68 (Bentler, 1990). The CFI is equivalent to the relative noncentrality index (RNI) in all cases where the model x2 exceeds the model degrees of freedom. It is relatively insensitive to model complexity and sample size making it suitable for comparing alternative theoretical structures (Gerbing and Anderson, 1992; Marsh, 1994). However, the assumption of “independent’’ dimensions is RQ1: BIPOLAR OR UNIPOLAR?

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Table 1. Correlations Used in Analyses X1

X2

Exhilarating Sensational Stimulating Exciting Interesting Dreary Dull Unstimulating Monotonous Boring Frenzied Tense Hectic Panicky Rushed Tranquil Serene Peaceful Restful Calm

1.00 .59 .64 .81 .54 2.42 2.52 2.44 2.40 2.54 .27 .11 .19 .13 .13 2.16 2.04 2.01 2.10 2.19

1.00 .65 .55 .54 2.36 2.43 2.37 2.40 2.44 .11 .05 .14 .08 .17 2.07 .04 2.05 2.07 2.17

Content Happy Satisfied Pleased Depressed Unhappy Unsatisfied Annoyed Sluggish Dull Sleepy Unaroused Frenzied Excited Stimulated Aroused

1.00 .55 .67 .57 2.19 2.31 2.30 2.24 2.15 2.25 2.08 2.28 2.01 .35 .33 .36

X1

Correlations among Exciting, Gloomy, Relaxing, and Distressing Subscales (Study 1) X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

1.00 .60 1.00 .61 .58 1.00 2.43 2.50 2.46 1.00 2.52 2.63 2.56 .73 1.00 2.52 2.47 2.46 .55 .66 2.36 2.48 2.45 .52 .64 2.50 2.55 2.49 .57 .64 .03 .18 .07 .15 .05 2.01 .09 .01 .17 .10 .04 .21 .14 .06 .00 2.03 .07 2.03 .23 .18 .03 .20 .14 .11 2.02 2.01 2.24 2.11 .23 .31 .02 2.12 2.09 .11 .23 .08 2.10 2.05 .06 .14 2.03 2.22 2.10 .20 .32 2.15 2.28 2.16 .25 .32 Correlations among Sixteen X2 X3 X4 X5 X6 X7

1.00 .54 .70 2.18 2.39 2.23 2.32 2.22 2.27 2.16 2.30 .11 .55 .40 .46

1.00 .70 2.20 2.34 2.37 2.24 2.18 2.22 2.17 2.31 .05 .45 .43 .39

1.00 2.15 2.35 2.33 2.32 2.18 2.24 2.17 2.32 .07 .50 .43 .43

1.00 .56 .56 .53 .40 .39 .31 .28 .27 2.10 2.15 2.08

1.00 .53 .59 .47 .55 .41 .43 .16 2.29 2.23 2.18

1.00 .54 .38 .43 .27 .45 .15 2.32 2.30 2.27

1.00 .60 1.00 .53 .52 1.00 .17 .19 .02 1.00 .20 .24 .05 .54 1.00 .09 .07 2.04 .64 .55 1.00 .24 .28 .14 .60 .61 .47 1.00 .03 .06 2.06 .45 .60 .63 .44 1.00 .27 .28 .23 2.15 2.16 2.29 2.02 2.29 1.00 .21 .22 .16 2.15 2.13 2.30 2.01 2.33 .66 1.00 .08 .12 .12 2.21 2.26 2.41 2.20 2.43 .55 .57 1.00 .21 .22 .27 2.13 2.23 2.31 2.08 2.39 .61 .62 .63 1.00 .28 .30 .31 2.16 2.27 2.40 2.15 2.41 .53 .47 .52 .54 1.00 Mehrabian and Russell Pleasure and Arousal Items (Study 2) X8 X9 X10 X11 X12 X13 X14 X15 X16

1.00 .40 .52 .34 .39 .22 2.30 2.25 2.20

1.00 .59 .58 .47 .17 2.16 2.16 2.17

arguably overly restrictive so an alternative bipolar model was fit allowing correlation between the exciting-gloomy and relaxing-distressing dimensions. The fit improved slightly (x2 5 1,415.3; df 5 169, CFI 5 .68, see Table 4) with loadings and scale statistics virtually unchanged (Table 2). The interfactor correlation estimate (F 2,1 5 .24) is significant (t 5 4. 19, p , .001) but well below an amount that would question discriminant validity (Fornell and Larcker, 1981; Anderson and Gerbing, 1988). Next, taking a unipolar view, an a priori two-factor model was fit hypothesizing separate but correlated positive and negative affective quality factors. The x 2 fit statistic representing this model is 1,997.0 with 169 degrees of freedom, and the CFI is .54. While the construct reliabilities (.76 and .83, respectively) are acceptable, the variance extracted in each

1.00 .43 .50 .15 2.25 2.22 2.23

1.00 .41 .16 2.17 2.15 2.20

1.00 2.03 1.00 2.41 .28 1.00 2.43 .20 .74 1.00 2.47 .31 .70 .65 1.00

factor (.318 and .362, respectivley) is considerably less than 50%, indicating problems with scale convergence. Further, the correlation estimate (F2,1 5 2.72) squared (.52) exceeds each considerably, raising doubts about scale discrimination (Fornell and Larcker, 1981). Despite these poor results, the model fits significantly better than a one-factor model (which assumes the correlation of 21 required for bipolarity), suggesting an alternative dimensionality. However, the two-factor unipolar results are clearly inferior compared to a two-factor bipolar representation. The next analyses addressed the possibility that each specific 5-item affective quality scale represents a unique factor as suggested in an earlier consumer-related application (Darden and Babin, 1994). A four-factor model hypothesizing separate but correlated, unipolar exciting, gloomy, relaxing,

Variance Extracted Construct Reliability

Correlations: row 2 row 3 row 4

Exhilarating Sensational Stimulating Exciting Interesting Dreary Dull Unstimulating Monotonous Boring Frenzied Tense Hectic Panicky Rushed Tranquil Serene Peaceful Restful Calm

Indicator

Correlated Unipolar Three Factors

4-Dimension Unipolar Model

5-Dimension ‘‘Nonrandom’’ Error Model

0.84

0.76

0.82

20.72

0.21 0.10 0.20 0.09 0.20

0.85

0.39 0.46 0.60 0.31 0.62 20.70 20.70 20.75 20.75 20.69

0.87 0.69 0.74 0.88 0.69

53.0% 37.9% 31.8%

0.24

0.76 0.65 0.73 0.80 0.71 20.70 20.82 20.70 20.66 20.73

0.83

36.2%

0.41 0.32 0.23 0.39 0.43

0.76 0.89 0.74 0.72 0.73

0.89

61.1%

20.75 20.21

0.86 0.70 0.76 0.87 0.70

0.88

60.3%

0.26

0.77 0.91 0.74 0.72 0.73

0.85

37.7%

0.71 0.70 0.76 0.76 0.69 20.38 20.44 20.59 20.30 20.61

0.89

61.2%

20.76 0.20 20.17

0.86 0.70 0.76 0.87 0.70

0.88

60.5%

0.15 0.37

0.77 0.89 0.76 0.74 0.73

0.86

55.2%

20.42

0.75 0.76 0.79 0.69 0.73

0.87

57.4%

0.78 0.76 0.75 0.81 0.69

0.87

56.7%

20.92 0.13 20.29

0.81 0.66 0.73 0.84 0.70

0.86

55.0%

0.06 0.30

0.73 0.85 0.72 0.69 0.72

0.83

49.4%

20.59

0.67 0.71 0.78 0.60 0.74

0.84

51.3%

0.73 0.71 0.72 0.76 0.66

0.53

5.3%

0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Dim. 1 Dim. 2 Positive Negative Positive Negative Rel/Dist Exciting Gloomy Relaxing Distressing Exciting Gloomy Relaxing Distressing Error

Correlated Bipolar

28.0%

0.74 0.63 0.70 0.79 0.69 20.71 20.84 20.71 20.68 20.74 0.04 20.03 0.12 20.09 0.13 20.33 20.23 20.17 20.31 20.39

Unidimensional

Table 2. Affective Quality Maximum Likelihood Confirmatory Factor Analyses Results (Study 1)

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0.88

0.91

Construct Reliability

0.92

52.7%

59.7% 52.9%

Variance Extracted

20.67

1,723.1 169 df 1,492.6

0.80

0.84 0.80 0.53 0.73 0.55

Chi-square

Correlations: row 2 row 3 row 4

20.87 20.78 20.59 20.75 20.61 0.10 0.68 0.60 0.69 0.23

0.80 0.67 0.85 0.71 0.70

0.85

38.9%

169 df

0.14 0.70 0.47 0.57 0.33

0.85 0.82 0.60 0.71 0.67

0.88

60.0%

20.62 0.57

0.87 0.72 0.87 0.74 0.66

0.97

60.1%

1,289.4

20.71

0.84 0.83 0.64 0.80 0.75

0.66 0.59 0.74 0.55 0.57 20.81 20.79 20.60 20.76 20.71

Pleasant Nice Pleasing Pretty Beautiful Unsatisfying Displeasing Repulsive Unpleasant Uncomfortable Inactive Drowsy Idle Lazy Slow Intense Arousing Active Alive Forceful

0.85

40.3%

167 df

20.87 20.79 20.60 20.76 20.62 0.10 0.67 0.60 0.68 0.22

Dull/ Arous.

Three Factors

Dim. 1 Dim. 2 Positive Negative Positive Negative

Correlated Unipolar

Indicator

Correlated Bipolar

0.88

59.6%

20.62 0.45 20.52

0.87 0.71 0.87 0.73 0.66

0.88

60.2%

926.60

20.48 0.84

0.84 0.83 0.64 0.79 0.75

Pleasing Repulsing

0.77

71.3%

164 df

20.62

0.88 0.81 0.70 0.86 0.95

Dull

0.77

45.6%

0.22 0.70 0.86 0.95 0.30

Arousing

4-Dimension Unipolar Model

Table 3. Affective Quality CFA Results Using Pleasing, Displeasing, Aroused, Unaroused Subscales

0.83

49.8%

876.00

20.90 0.38 20.70

0.78 0.66 0.81 0.65 0.60

0.85

52.7%

163 df

20.64 0.80

0.81 0.78 0.58 0.74 0.69

Pleasing Repulsing

0.72

40.5%

20.75

0.13 0.66 0.83 0.91 0.21

Dull

0.84

51.4%

0.87 0.79 0.56 0.73 0.58

Arousing

0.64

8.0%

20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28 20.28

Error

5-Dimension ‘‘Nonrandom’’ Error Model

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Table 4. Fit Statistics for Alternative Measurement Models

Chi-square Degrees of freedom GFI CFI PRNI RMSR

A

B One Factor

C Correlated Bipolar

D Correlated Unipolar

E Three Factors

Null 4,137.4 190 0.80 na na na

2,308.8 170 0.46 0.46 0.41 0.20

1,415.3 169 0.57 0.68 0.60 0.13

1,997.0 169 0.49 0.54 0.48 0.19

1,183.2 167 0.65 0.74 0.65 0.13

F Four Factors 571.1 164 0.85 0.90 0.77 0.07

G Four Factors Plus “Error” 570.7 163 0.85 0.90 0.77 0.07

Note: GFI 5 goodness of fit index, CFI 5 comparative fit index, PRNI 5 parsimony relative noncentrality index, and RMSR 5 root mean square residual. Model Descriptions: A 5 Null model B 5 Unidimensional “Affective Quality” Factor C 5 Correlated Bipolar Exciting and Relaxing Factors (Russell and Pratt 1980) D 5 Correlated Unipolar Positive and Negative Factors E 5 Separate Exciting (pos), Gloomy (neg), and bipolar Relaxing-Distressing factors F 5 Unique factors for each AQ scale: Exciting, Gloomy, Relaxing, Distressing G 5 Addition of a common nonrandom error factor

and distressing factors was tested. This model produced a x2 fit statistic of 571.7 with 164 degrees of freedom yielding a CFI of .90. All factor loadings are highly significant, and both the construct reliabilities and variance extracted estimates are supportive. Thus, this model is superior to both a two-factor bipolar or unipolar model and provides an adequate fit for testing theory (Carmines and McIver, 1981; Anderson and Gerbing, 1992). Among the four-factor model, factors most clearly representing positive and negative affective quality are exciting and gloomy. The correlation estimate between these two factors is 2.76 (t 5 225.2; p , .001) suggesting highly related factors. However, its square (.57) does not exceed the variance extracted in either the exciting or gloomy factor (.612 and .605, respectively), providing evidence of scale discrimination (Fornell and Larcker, 1981). Further, a CFA model constraining this coefficient to 21 (suggesting a total lack of discrimination) provided a significantly worse fit than did the lesser constrained model (xdiff2 5 230.2, df 5 1, p , .001). The second highest F coefficient is 2.42 (t5 7.76; p , .001) and represents the correlation between relaxing and distressing scales, previously hypothesized as bipolar (Russell and Pratt, 1980). No further analyses were conducted since the squared estimate is clearly below each variance extracted percentage. To this point, the four correlated unipolar factors model appears most valid. It provides a considerably better fit than either two-dimensional alternative considered and provides sufficient evidence of both construct and discriminant validity. However, few studies involving consumption-related emotion terms report confirmatory results like those presented here. Previous applications generally provide only exploratory factor results (e.g., Burke and Edell, 1989: Mano and Oliver, 1993) or an overall fit measure (e.g., Westbrook, 1987). Thus, the detailed analyses provided here may prove more insightful. RQ2: SCALE INTERRELATIONS.

Additional analyses were conducted to examine the possibility that an additional source of nonrandom covariance is present among the indicators and suppresses the magnitude of the correlation between positive and negative factors (Green et al., 1993). A fifth factor was added to the four-factor model to account for this nonrandom error. If the nonrandom error is due to the use of a common measurement device (Green et al., 1993), the factor should affect each item identically. Thus, the factor loadings on this fifth factor were constrained to be equal to one another. This model’s x2 statistic (Table 4) is 570.7 with 163 degrees of freedom yielding a CFI of .90. However, the interfactor correlation estimates address scale discrimination more directly. Adding the fifth constant factor to the model caused the correlation estimates between factors to change as expected (Green et al., 1993). Specifically, the correlation between exciting and gloomy changes from 2.76 to 2.92. Since its square (.83) exceeds the variance extracted in both the exciting and gloomy scale, an additional analysis of discriminant validity was undertaken. As in the four-factor model, an additional CFA model was fit that constrained the correlation between exciting and gloomy to 21.0. This model produced a x2 statistic of 579.0 with 165 degrees of freedom. Thus, the x2 difference statistic (8.3, df 5 1, p , .01) is significant, suggesting that the two factors remain distinct but highly correlated. RQ3: BIPOLARITY IS MASKED BY NONRANDOM ERROR.

Discussion An exhaustive series of measurement models presents data relevant to three research questions. The data suggest that a two-factor bipolar model provides a superior fit than does a two-factor positive and negative emotion model. However, neither of these models stood up to the more specific four factor model positing distinct unipolar dimensions. Although this provides some support for the view that positive and

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negative consumption affect is separate, a model allowing for an additional source of nonrandom covariance among items supports higher correlation among factors. Results of discrimination between the exciting and gloomy factor proved mixed. While the coefficient’s square failed to exceed the variance extracted estimates (Fornell and Larcker, 1981), a less stringent test of discriminability suggested the two scales are somewhat distinct (Anderson and Gerbing, 1988).

Study 2 The results of Study 1 are somewhat mixed and do not address the possibility that the research context may moderate the correlation between positive and negative consumption emotions. Further, if the fifth factor representing an additional source of systematic covariance biases the correlations to the extent that bipolarity is masked, this effect should be observed regardless of the study’s context. While the first study involved a somewhat discrete task involving subject recall (memorybased), this study involves a somewhat richer task by assessing consumer emotions while still within a consumption environment (stimulus-based).

Sample Potential respondents were intercepted while shopping at one of the nine retail stores providing stimuli for Study 1. Again, multiple stores were included to insure variance. The procedures followed closely the methodology outlined in other studies of in-store consumer emotions (e.g., Dawson et al., 1990; Babin, Darden, and Griffin, 1994). Survey administrators were given instructions on respondent selection, randomly assigned to one of the nine stores, and provided with some preliminary screening questions. These questions precluded from the sample respondents not actually shopping or who had been in the store less than 10 minutes (Dawson et al., 1990; Donovan and Rossiter, 1982). The final screening question asked potential respondents their willingness to complete a short survey instrument. This particular mall contains a market research firm so that shopper intercepts are a common occurrence. Respondents meeting the screening criteria were given the survey materials and provided with a comfortable place to sit. The intentions were to obtain a similar number of respondents compared to Study 1. Three-hundred forty respondents filled out questionnaires. Three-hundred thirty-five contained complete information and were used in analyses.

Measures The first part of the questionnaire contained items assessing emotions experienced by respondents while shopping. The questionnaire contained an inventory of emotion terms based on the Mehrabian and Russell (1974) pleasure and arousal scales. These scales have provided the basis for assessing consumer emotions in numerous previous marketing studies (e.g.,

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Donovan and Rossiter, 1982; Holbrook et al., 1984; Dawson et al., 1990; Babin et al., 1994) and have been validated as capable of capturing consumer emotions (Havlena and Holbrook, 1986). Different versions of the pleasure and arousal inventories have been used varying slightly the emotions included and the scale format. Since this study involves in-store consumer emotions, the items included for use here were taken from the semantic differential scales applied in Donovan and Rossiter (1982). However, rather than using a semantic differential format, a six-point (0 5 did not feel at all to 5 5 felt very much) scale was used (cf., Holbrook and Batra, 1987; Mano and Oliver, 1993). Thus, where a single semantic differential included an emotion and its a priori counterpart (e.g., satisfiedunsatisfied), two items are used here. Table 5 provides a list of the 16 items used. An equal number of items are a priori positive and negative thus avoiding bias due to a disproportionate number of positive or negative items. Additional data were collected pertaining to respondent demographics and purchases made but are not used here.

Analyses and Results The procedures follow those of Study 1 closely. Maximum likelihood CFA is used to examine the data. Table 5 shows parameter estimates and scale statistics across alternative measurement models. Overall fit statistics are provided in Table 6. RQ1: BIPOLAR OR UNIPOLAR? We investigated the bipolar versus unipolar alternatives using several alternative conceptualizations. First, items were arranged into a two-factor bipolar model assuming distinct but correlated pleasant-unpleasant and aroused-sleepy factors. The x2 overall fit of this statistic for this model is 1,109.3 with 103 degrees of freedom. The model CFI is .62. While this overall fit is not generally supportive, the scale reliabilities are acceptable (.84 and .77, respectively), all loadings are significant, and the model is clearly superior to either a null or one-factor model. Alternatively, a model positing two separate unipolar factors, each comprised of a priori positive and negative items, was fit. The x2 fit statistic for this model is 729.6 with 103 degrees of freedom, and the model CFI is .77. The scale reliabilities are good (.86 and .87, respectively), all loadings are significant, and the variance extracted measures approach 50% (these might be increased by dropping one or two items from the battery, at this point however, scale items matching the source items are maintained). Thus, comparatively speaking, this two-factor unipolar model appears preferable to the original conceptualization. Further, a two-factor unipolar model assuming independent positive and negative emotion factors provided a better fit (x2 5 799.9, df 5 104, CFI 5 .74) than did the bipolar alternative. However, an alternative operationalization would be to comprise a four-factor model consisting of distinct pleasure, displeasure, aroused, and unaroused factors. This model pro-

33.5%

0.89

Construct Reliability

0.60 0.68 0.67 0.71 20.43 20.63 20.59 20.57 20.47 20.55 20.41 20.62 0.03 0.69 0.63 0.62

Variance Extracted

Correlations: Row 2 Row 3 Row 4

Content Happy Satisfied Pleased Depressed Unhappy Unsatisfied Annoyed Sluggish Dull Sleepy Unaroused Frenzied Excited Stimulated Aroused

Indicator

Unidimensional

Correlated Unipolar Three Factors

0.31 0.38 0.30 0.58 20.23 20.86 20.81 20.79 20.50

0.17 0.76 0.68 0.68

0.65 0.76 0.74 0.79

0.84

0.77

0.86

40.5% 34.0% 46.1%

20.70

0.70 0.76 0.78 0.82 20.35 20.54 20.51 20.47

0.87

45.7%

0.65 0.78 0.68 0.72 0.66 0.72 0.56 0.62

0.87

62.5%

20.48 20.67

0.72 0.77 0.80 0.86

0.32

55.2%

0.47

0.71 0.77 0.73 0.75

0.78

0.70 0.80 0.71 0.75

0.87

0.83

55.0%

20.48 20.39 0.78 0.65 20.36 34.2% 49.8%

0.31 0.39 0.30 0.58 20.23 20.86 20.81 20.79

0.71 0.77 0.80 0.86

0.80

50.0%

20.38

0.76 0.77 0.63 0.66

Sleepy

0.81

54.4%

0.29 0.90 0.82 0.78

Arousing

4-Dimension Unipolar Model

Dim. 1 Dim. 2 Positive Negative Pleased Unhappy Aroused Pleased Unhappy

Correlated Bipolar

Table 5. PAD Items’ Confirmatory Factor Analysis Results (Study 2)

0.60 0.75 0.68 0.69

0.82

53.7%

0.78

46.5%

20.74 20.64 0.75 0.60 20.60

0.67 0.72 0.75 0.80

0.74

41.9%

20.64

0.65 0.69 0.55 0.69

Sleepy

0.82

61.0%

na 0.84 0.78 0.72

Arousing

0.60

8.4%

0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29

Error

5-Dimension “Nonrandom” Error Model Pleased Unhappy

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Table 6. Fit Statistics for Alternative Measurement Models

Chi-square Degrees of freedom GFI CFI PRNI RMSR

A Null

B One Factor

C Correlated Bipolar

D Correlated Unipolar

E Three Factors

F Four Factors

G Four Factors Plus “Error”

2,792.6 120 0.33 na na na

1,288.5 104 0.57 0.56 0.48 0.14

1,109.3 103 0.59 0.62 0.53 0.16

729.6 103 0.75 0.77 0.66 0.10

777.2 101 0.72 0.75 0.63 0.14

371.6 98 0.88 0.90 0.73 0.08

301.4* 83 0.90 0.92 0.72 0.06

Note; GFI5goodness of fit index, GFI5comparative fit index, PRNI5parsimony relative noncentrality index, and RMSR5root mean square residual. Model Descriptions: A5Null model B5Unidimensional “Emotion” Factor C5Correlated Bipolar Pleasure and Arousal Factors (e.g., Donovan and Rossiter 1974) D5Correlated Unipolar Positive and Negative Factors E5Separate pleased (pos), displeased (neg), and bipolar arousal factors F5Unique factors: Pleased, Displeased, Aroused, Unaroused G5Addition of a common nonrandom error factor, one variable dropped

duced a x2 goodness of fit statistic of 371.6 with 98 degrees of freedom and a CFI of.90. All factor loadings are significant and the variance extracted measures range from .498 to .550. The overall results and scale statistics are generally supportive of this model (Carmines and McIver, 1981; Fornell and Larcker, 1981; Anderson and Gerbing, 1992). The only weak point in model fit is a low loading on the “frenzied” item (.29, t 5 5.07; p , .001). Relatively speaking, the overall fit is clearly superior to any of the alternatives discussed above. Thus, it provides some support for the superiority of unipolar consumption emotions. The second research question is similar to the first, but it directs specific attention to the discriminant validity between unipolar factors. Methodologically, a model could provide acceptable overall fit but contain constructs so highly correlated that they are not unique. Our attention turned to the four interfactor correlation estimates obtained in the above analysis. The correlation of most theoretical interest is that between the four-item pleasure and displeasure factors. The phi coefficient representing this correlation is negative (F2,1 5 2.48) and significant (t 5 29.36; p , .001), suggesting correlated factors. However, the variance extracted in both the pleasure and displeasure factor (.498 and .550, respectively) exceeds the square of the correlation estimate (.23) considerably, suggesting adequate discrimination. Interestingly, two other correlation estimates exceed the correlation between pleasure and displeasure. Among these, the estimate between the unaroused and unhappy factors (F 3,2 5 .78; t 5 22.2; p , .001) causes the most concern over discriminant validity since its square (.6l) exceeds either relevant variance extracted measure. So, an additional analysis of discriminant validity was conducted by comparing the fit of the four-factor model above to that of an identical model constraining F3,2 to unity. The fit of this model proved signifiRQ2: SCALE INTERCORRELATIONS.

cantly worse than the lesser constrained model suggesting adequate discrimination and distinct factors (Gerbing and Anderson, 1988). Thus, this analysis provides no support for bipolar consumer emotions. RQ3: ADDITION OF NONRANDOM ERROR. Analysis of the affective quality data showed that the correlation between positive affective quality (pleasure) and negative affective quality (displeasure) increased in magnitude once an additional source of nonrandom error covariation was added to the model (Green et al., 1993). Thus, this model was replicated here by introducing a fifth constant source of covariance to the fourfactor model with one exception. Since the item “frenzied” received such a low loading, it was dropped from this analysis. The five-factor noncongeneric model produced a x2 goodness of fit residual of 301.4 with 83 degrees of freedom. The model CFI is .92. Again, including the fifth factor caused variation among interfactor correlation estimates. The correlation estimate between pleasure and displeasure changed to 2.74 (t 5 212.94; p , .001), calling into question scale discrimination. Thus, a further model was tested constraining this factor to 21. The x2 difference statistic between these two models (x2 5 232.7; df 5 1; p , .001) supports discriminability between factors (Gerbing and Anderson, 1988). Identical analyses investigating discrimination among other factors provided identical results. Interestingly, in both Studies 1 and 2, the correlation between the a priori positive and negative emotion factors were affected more by the introduction of the fifth factor than were any other correlation estimates. Consistent with results of Study 1, addition of a nonrandom “error” factor was associated with changes in the interfactor correlation estimates. Despite the increase, however, scale discrimination is still present. Thus, although the error factor may “mask” some correlation, a case can still be made for distinct positive and negative affect factors.

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The effect of contextual variation on the correlation between positive and negative emotions can be explored by comparing results from Study 1 and Study 2. Specifically, the magnitude of correlation between memorybased affective quality positive and negative emotion terms is expected to be higher than that between the stimulus-based in-store positive and negative emotions. The rationale is that the in-store task is more complex and rich than the simple recall task. The most relevant correlation to compare is F2,1 in both studies. Specifically, the “corrected” correlation between “exciting” and “gloomy” is 2.92 in Study 1 while the correlation between “pleasure” and “displeasure” is 2.74 in Study 2. Thus, using the same stores as stimuli, very similar emotional batteries produced correlations that varied when the respondent task was altered. While this provides some support for contextual variation, it could be argued that differences in the correlation estimates are due to differences in emotional indicators across the two studies. We investigated this possibility by reestimating twofactor CFA models for each data set (representing only a priori positive and negative factor items) using items common to both emotion batteries although perhaps appearing in a different form (e.g., “pleased” and “pleasing”). This resulted in two sixitem CFA models with three items each indicating pleasant and unpleasant emotions. The results proved generally consistent with those reported above. In terms of overall model results, two-factor models, presupposing distinct but related positive and negative emotion factors, provided far better fit than did unidimensional alternatives. Most relevant to the issue of contextual variation however, the correlation estimate between positive and negative factors using Study 1 data is 2.64 (t 5 216.1) without correcting for nonrandom measurement error; this coefficient increases in magnitude to 2.86 (t 5 220.1) with the addition of a systematic error factor. The same correlation estimates using the stimulus-based Study 2 data are 2.53 (t 5 210.1) and 2.79 (t 5 213.3), respectively. Thus, although not a conclusive comparison, results remain consistent with RQ4. RQ4: CONTEXTUAL VARIATION.

Discussion The in-store consumer emotion data results can be compared to the affective quality data reported earlier. Contrasting results from Study 1, an hypothesized two-factor model positing bipolar pleasure and arousal dimensions did not perform as well as a two-factor positive and negative emotion model. This is particularly surprising given that the positive and negative terms are extracted from semantic differentials that placed them on opposite ends of the same scale (e.g., happy– unhappy). However, other results were generally consistent across studies. In both Study 1 and Study 2, measurement models positing specific unipolar factors provided the best comparative and most defensible congeneric fits. The addition of a fifth source of nonrandom measurement error covariance

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suggested that the correlations between positive and negative emotions may be higher than previously reported. Further, some evidence is presented suggesting that the distinctiveness between positive and negative consumptionrelated emotions is not constant. Study 1 involved subject recall of a retail store and produced stronger correlation between positive and negative emotion than did Study 2 involving subjects’ feelings while still in the consumption experience environment. Consistent with this finding, the relative fit of the theorybased two-factor bipolar fit was better in Study 1 than in Study 2 (compared to a two-factor unipolar model). Although “corrected’’ correlations in both studies failed the relatively strict Fornell and Larcker (l981) test, stimulus-based emotions (Study 2) easily passed the Anderson and Gerbing (1988) tests for scale discrimination. The differences in correlation estimates across both contexts suggest that emotions that are purely memory-based, assessing events occurring some time ago, may be more bipolar that those assessed in the presence of the emotion-evoking stimulus. While strong conclusions cannot be drawn from this particular finding, it does point out the need for additional study directed more specifically at the effect of study context on consumption emotions.

General Discussion Noted scholars of human emotion have argued conceptually and empirically that the most pervasive latent factor underlying human emotion is a bipolar pleasure-displeasure factor representing emotional tone (Russell, 1979; Russell and Snodgrass, 1987; Smith and Ellsworth, 1985). This view also has its roots in the classic work of Osgood, Suci, and Tannenbaum (1957). Substantive studies, not focusing on the true nature of emotions themselves, however, began revealing multi-item negative and positive emotion factors that did not covary perfectly. Early studies reporting such findings often dealt with affective reactions to politicians or political groups and showed correlations among liking and disliking ranging from 2.4 to 2.54 (see Abelson et al., 1982). Empirical findings like these rekindled a debate in the psychological literature over the complexity of human emotion. Are feelings of happiness and unhappiness opposite ends of the same phenomenon, or are human emotions more complex? Four related research questions were raised concerning the relative validity of a bipolar versus unipolar view of consumer emotions. These questions were examined in two separate consumer contexts. One involved consumer recall of emotions associated with a particular retail store. This would be similar to the study of affective traces which help provide meaning to past consumption experiences (Bower and Cohen, 1982: Cohen and Areni, 1991; Babin et al., 1992). The other context involved assessing consumer emotions while within a serviceproviding environment. This approach has been used previously in marketing studies (e.g., Donovan and Rossiter, 1982; Dawson et al., 1990). Particularly in the latter context,

Negative Emotions

correlations between positive and negative consumer emotions are significantly different than 21. In the former context, although arguably distinct, the corrected correlation was quite high and suggests little uniqueness in a separate negative affect factor. Further, some support was found for an additional noncongeneric source of systematic error covariance such as might be driven by a common method factor. Evidence was presented that suggests that discriminant validity, and therefore the true factor structure, cannot be assessed without accounting for this factor. Specifically, including this factor in the measurement model increased magnitudes of correlations between positive and negative emotion in both studies. Also, evidence of moderation of interfactor correlations was presented by comparing results across studies. Specifically, the less rich memory-based task produced a higher correlation than did a stimulus-based emotion assessment. This is consistent with results from a previous study showing much stronger correlation between positive and negative emotion factors evoked by a highly sexually involving advertisement compared to those evoked by a more mundane ad (Machleit and Wilson, 1988). Clearly, further research is needed to specify better factors contributing to this moderation. However, the variance in correlations suggests conditions may exist where the correlation between positive and negative emotional states is not significantly different than 21. Emotions assessed within a retailing or service-providing environment, most closely matched by Study 2, would not appear to provide such an instance. These results also suggest that correlations among consumer emotions may be moderated across conditions involving stimulus-based versus memory-based affective responses. Let us return to the restaurant scene with which the article opened. What is the result of this emotional mishmash? A bipolar view suggests the resulting emotional state is either happiness or unhappiness. Thus, the offer of dessert on the house may cancel the unhappiness with additional happiness. However, the unipolar view, suggesting somewhat unique positive and negative emotion factors, suggests that the customer may feel significant levels of both happiness and unhappiness. Since, if separate, they can also have unique effects, the outcome of this situation depends on the relative efficacy of positive and negative emotional states. If this customer were to fill out a “satisfaction” survey, he or she might indicate some “satisfaction” on the four-point satisfaction scale provided. Yet, the customer may never return because of the high levels of negative emotion also experienced and unassessed. The main purpose of this article was to stimulate additional research. Given the focus of the article, much more detailed measurement results are reported than can be in most substantive articles. Therefore, the focus is on questions that might not even surface otherwise. The potential of moderation among positive and negative emotion factor correlations suggests similar analyses need be conducted in a wider range of consumption contexts. If the

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supposition that the richness or complexity of the consumption context comprises a moderating condition is valid, research contexts that are relatively brief and uninvolving may produce correlations of such magnitude that a unipolar factor structure is questionable. Thus, exhaustive measurement analyses take on greater importance in relatively uninvolved and simple consumption contexts (e.g., advertising). Conversely, highly involving consumption experiences may expect very low and potentially even positive correlations between a priori positive and negative emotions. For example, some high risk leisure behaviors involve consumers who actively engage fear (Celsi, Rose, and Leigh, 1991). Likewise, some consumers intentionally seek out sad movies and music. Would such a consumer be displeased if the sight of Scarlet standing at the door as Rhett walks away fails to evoke a tear? Future research should also investigate the use of multiple emotion measurement approaches within the same study. Ideally, this would involve something beyond the paper and pencil self-report methodology. However, given their convenience, and the fact that self-reports have received considerable validation against visceral measures (Davidson et al., 1990), the use of multiple self-report methods should not be discounted. Thus, there is a need for the development of multimethod consumer emotion scales. Practically, the results presented here show the danger of relying on simplistic scale validation techniques. While some studies reported in the academic literature offer only exploratory factor results or simply report reliability estimation, the findings from this data suggest that this approach can be very misleading. Note that scale reliabilities for scales in all the measurement models tested here are generally acceptable. In no case does a reliability estimate fall below .7. High reliabilities should be expected given the relatively high interterm correlations common among consumption emotion incidents. However, researchers should be aware that reliabilities do not assess discriminant validity. Thus, marketing applications should report detailed measurement results involving analytical techniques allowing thorough assessment of convergence and discrimination. Further, researchers should consider alternative measurement structures and the effect of common nonrandom error on interfactor correlations. The results of this study also provide empirical support for the inadequacy of the semantic differential to capture consumer emotions. Note that Study 2 involved positive and negative items formerly comprising a single semantic differential item. However, the factors recovered did not resemble that predicted by the semantic differential. Therefore, despite its convenience and ease, its use should be cautioned since a bipolar measurement structure can still be recovered using other scale devices. Further, the relative superiority of multiple discrete emotions over a more general approach assuming one or two emotion factors suggests researchers adopt a more categorical approach to studying emotions and use greater

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theoretical knowledge as to which are most appropriate in a given situation.

Conclusions This article sought to address a gap in the developing theory of consumer emotions. Specifically, why did some researchers posit and/or recover a “negative” emotion factor while others found emotions to be defined along a bipolar continuum ranging from positive to negative emotions? The data presented suggest that feeling a negative emotion does not preclude the occurrence of a positive emotion as a bipolar theory would hypothesize. While the results are somewhat exploratory in nature, the hope is that they will stimulate others to consider this potential conceptual problem and direct their attention toward its resolution.

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