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Behavioral Consequences of Affect: Combining Evaluative and Regulatory Properties


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BEHAVIORAL CONSEQUENCES OF AFFECT: COMBINING EVALUATIVE AND REGULATORY PROPERTIES By EDUARDO BITTENCOURT ANDRADE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Eduardo Bittencourt Andrade

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This dissertation is dedicated to my parents.

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ACKNOWLEDGMENTS First of all, I would like to thank Joel Cohen, who supervised this dissertation with an incredible sense of balance between academic guidance and intellectual freedom. I also extend thanks to Chris Janiszewski, Richard Lutz, and Margaret Bradley, for their support. I thank also Alan dAstous (from HEC Montreal), who introduced me to the Marketing Department at the University of Florida. The Warrington College of Business Administration provided me with financial assistance in completing this dissertation, for which Im grateful. Even far away from Gainesville, my family and close friends managed to provide me the emotional support needed to keep the discipline and resolve throughout these long years. I thank them all. iv

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TABLE OF CONTENTS Page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT.......................................................................................................................ix CHAPTER 1 INTRODUCTION........................................................................................................1 Affect............................................................................................................................3 Mediational Processes..................................................................................................4 Behavior........................................................................................................................5 2 THEORETICAL BACKGROUND..............................................................................7 Affective Evaluation (Affects Informational Role).....................................................8 Affect-Regulation (Affects Motivational Role)..........................................................9 3 INTEGRATIVE MODEL OF AFFECTIVE BEHAVIOR (IMAB)..........................12 4 INITIAL EVIDENCE SUPPORTING THE MODEL: THE IMPACT OF AFFECT ON HELPING, RISK TAKING, AND EATING BEHAVIOR.................................20 Helping.......................................................................................................................20 Positive Affect and Helping................................................................................21 Negative Affect and Helping...............................................................................22 Theoretical Integration........................................................................................25 Risk-Taking................................................................................................................26 Negative Affect and Risk-Taking........................................................................27 Positive Affect and Risk-Taking.........................................................................30 Eating Behavior..........................................................................................................31 Negative Affect and Eating Behavior..................................................................32 Positive Affect and Eating Behavior...................................................................34 Summary.....................................................................................................................36 v

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5 TESTING IMAB: PREDICTIONS............................................................................39 6 EXPERIMENT 1........................................................................................................42 Method........................................................................................................................42 Subjects and Design............................................................................................42 Procedure.............................................................................................................42 Affect Manipulation............................................................................................43 Replicates............................................................................................................44 Product Benefits Manipulation............................................................................45 Dependent Measure.............................................................................................46 Results.........................................................................................................................46 Manipulation Checks...........................................................................................46 Purchase Intention...............................................................................................47 Discussion...................................................................................................................49 7 EXPERIMENT 2........................................................................................................51 Method........................................................................................................................52 Subjects and Design............................................................................................52 Procedure.............................................................................................................53 Results.........................................................................................................................54 Manipulation Checks...........................................................................................54 Purchase Intention...............................................................................................54 Affective Evaluation Mechanism........................................................................56 Affect-Regulation Mechanism............................................................................58 Discussion...................................................................................................................59 8 EXPERIMENT 3........................................................................................................61 Method........................................................................................................................62 Subjects and Design............................................................................................62 Procedure.............................................................................................................62 Results.........................................................................................................................64 Manipulation Checks...........................................................................................64 Eating Behavior...................................................................................................65 Affect-Regulation Mechanism............................................................................66 Discussion...................................................................................................................69 9 GENERAL DISCUSSION.........................................................................................71 LIST OF REFERENCES...................................................................................................77 BIOGRAPHICAL SKETCH.............................................................................................87 vi

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LIST OF TABLES Table page 1. Influence of affective evaluation on behavioral intention.............................................57 2. Influence of acknowledged use of chocolate to lift mood on behavioral intention.......58 3. Protective influence of overeating-based guilt feelings on behavior.............................67 vii

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LIST OF FIGURES Figure page 1. Behavioral consequences of affective states...................................................................2 2. Predictions for experiments 1 and 2..............................................................................40 3. Behavioral intentions toward coffee and cereal (collapsed)..........................................47 4. Behavioral intentions toward chocolate........................................................................55 5. Amount of chocolate consumed....................................................................................65 6. Amount of chocolate consumed within the positive affect /mood-threatening condition the impact of expected feelings of guilt after overeating......................................68 viii

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy BEHAVIORAL CONSEQUENCES OF AFFECT: COMBINING EVALUATIVE AND REGULATORY PROPERITES By Eduardo Bittencourt Andrade May 2004 Chair: Joel B. Cohen Major Department: Marketing The impact of affect on consumer behavior is well documented; however, no unique pattern of behavior can be expected from a valenced affective state. While one can find support for the intuitive congruency-type of hypothesis in which positive mood facilitates action or increases purchase intention and negative mood inhibits action or decreases purchase intentions, it is now clear that significant exceptions exist. A negative mood may well encourage action whereas positive mood can inhibit action. Most importantly, the models available in the literature have focused primarily on explaining one or two effects (e.g., when negative affect encourages behavior), leading to a lack of an overarching theory capable of accounting for the sometimes mitigating and sometimes encouraging impact of positive and negative affective states on behavior and behavioral intentions. An integrative model of affective behavior (IMAB) is therefore proposed, in which the informational and regulatory properties (mechanisms) of current and anticipated ix

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affective states simultaneously mediate the impact of affective states on behavior. A broadly-based review of the affect-behavior literature demonstrates that relying on a single mechanism produces, in the aggregate, an ambiguous and often conflicting account of affect's role in guiding behavior. Three research streams well known in the literature (helping, risk-taking, and eating behavior) are reviewed, and their apparent inconsistencies are elucidated under the proposed model. Finally, in a series of three experiments, critical moderating variables associated with each of the two mechanisms (affective evaluation and affect-regulation) are investigated, providing strong initial support for the model. x

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CHAPTER 1 INTRODUCTION The impact of affect on consumer behavior is well documented (see Bagozzi, Gopinath, and Nyer 1999; Cohen and Areni 1991 for reviews). However, no unique pattern of behavior can be expected from a valenced affective state. While one can find support for the intuitive congruency-type of hypothesis in which positive mood facilitates action or increases purchase intention, and negative mood inhibits action or decreases purchase intentions, it is now clear that significant exceptions exist. A negative mood may well encourage action, whereas positive mood can inhibit action. For example, positive feelings increase purchase intentions (Brown, Homer, and Inman 1998), but also decrease risk-taking if the odds are too high (Isen and Geva 1987). Bad moods mitigate consumers willingness to go to a movie when they have a hedonic goal in mind (Pham 1998), but also increase impulsive consumption (Tice, Bratslavsky, and Baumeister, 2001). As Bagozzi and colleagues (1999) summarized, Sometimes emotions spur one onto action; at other times emotions inhibit or constrain action. But only recently have researchers devoted much attention to studying how this occurs (p. 199). Starting with the assumption that affect-behavior relationships can be, at the aggregate level, categorized into four groups (Figure 1), it becomes clear that the current most cited models available have focused primarily on explaining one or two cells of this 2x2 matrix. For instance, mood-congruency and affect-as-information conceptualizations can explain when a positive (negative) mood facilitates (inhibits) action (Cells 1 and 4), but cannot account for the impact of negative mood on purchase/consumption increases 1

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2 (Cell 3). Zillmanns (1988) mood-management theory can explain the latter, suggesting that people in a bad mood are more likely to act in an attempt to feel better. However, it does not explain, for instance, why people in a bad mood are sometimes less likely to act, even if the behavioral activity is not expected to put them in a worse mood (Cell 4). BEHAVIORAL CONSEQUENCE Facilitates Action (Approach Behavior) Inhibits Action (Protective Behavior) POSITIVE Findings (e.g.) Advertising and Purchase Intention (Brown et al. 1998) Hedonic Goals and Purchase Intention (Pham 1998) Theories Mood-Congruency Hypothesis (Isen et al. 1978; Bower 1981) Affect-as-Information Hypothesis (Schwarz and Clore 1983) Mood-Maintenance Hypothesis (Isen 1984, 2000; Clark and Isen 1982) CELL 1 Findings (e.g.) Risk-taking with low prob. of winning (Arkes et al. 1988) Helping when negative stimuli are salient (Isen and Simmonds 1978) Theories Mood-Maintenance Hypothesis (Isen 1984, 2000; Clark and Isen 1982) Hedonic Contingency Hypothesis (Wegener and Petty 1994) CELL 2 AFFECT NEGATIVE Findings (e.g.) Impulsive Behavior (Tice et al. 2001) Music and Purchase Intention (Alpert and Alpert 1986) Theories Mood-Management Theory (Zillmann 1988) Negative State Relief Model (Cialdini et al. 1973) CELL 3 Findings (e.g.) Helping Children (Kenrick and Cialdini 1976) Food Intake Men (Macht et al. 2002) Theories Mood-Congruency Hypothesis (Bower 1981; Isen et al. 1978) Affect-as-Information Hypothesis (Schwarz and Clore 1983) CELL 4 Figure 1. Behavioral consequences of affective states

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3 Our study proposes a parsimonious integrative model with three main goals in mind. First, it enables us to explain the inhibiting and stimulating consequences of both positive and negative affective states on behavior, which has not been yet seen in the literature. Second, based on the assumption that two main interdependent mechanisms (i.e., affective evaluation and affect-regulation) are under operation, our study identifies the critical moderating variables that mitigate/stimulate each mechanism and eventually guide behavior. As the literature has focused more often on the evaluation aspect of current affective states (Bower 1981; Gorn, Pham, and Sin 2001; Pham 1998; Schwarz and Clore 1983), special attention is given to the affect-regulation mechanism, its assumptions, and its internal and environmental contingencies. Finally, the model also demonstrates when regulation-type hypotheses (e.g., mood-maintenance) seem more or less appropriate as a theoretical account for the behavioral consequences of affect. Three major components define the scope of analysis of the model to be advanced: the independent variable (affect), the mediating processes (affective evaluation and affect-regulation), and the dependent variable (behavior and behavioral intentions). Affect Affect is defined as positively or negatively valenced subjective reactions that a person experiences at a given point in time (Wyer, Clore, and Isbell 1999, p. 3). Thus, affect represents the conceptual umbrella for both mood and emotions. Although distinctions between mood and emotions vary somewhat, researchers tend to agree that the source of the affective experience represents a critical distinction. While subjects experiencing emotions are consciously aware that emotions emanates from some source, subjects experiencing moods are not. For example, whereas people are in a bad or good mood, they are angry at someone/something (Schwarz and Clore 1996). Secondly,

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4 emotions have also been categorized in terms of in intensity, duration, and/or cognitive participation (Ortony, Clore, and Collins 1988). Moods, however, represent a unique (usually indivisible) positive or negative state. This implies that specific types of emotions are likely to trigger different sets of behavior, depending on their arousing or cognitive properties (Raghunathan and Pham 1999). For instance, depressed people (compared to subjects who are simply in a bad mood) may behave differently when a mood repair opportunity presents itself, since the chronic properties of the former probably mitigate the impact of affect-regulation on behavior. Although it is beyond the scope of our model to predict the impact of specific types of emotions, the proposed model does recognize the uniqueness of each type of emotion in producing behavioral consequences. Mediational Processes We believe it is useful to make the simplifying assumption that affect can potentially mediate evaluative and behavioral patterns at three different levels of processing (Cohen and Areni 1991; Pham et al. 2001). At the most basic level (Type I-Affect), affective information is conveyed via sensory-motor programs critical to bio-regulation. For example, bodily information is captured by the peripheral nervous system and sent to the central nervous system, which sends back signals that help regulate organs and biorhythmic activities. Subjects are usually unaware of these automatic mechanisms. A mid level of analysis (Type II-Affect) refers to basic affective reactions learned through conditioning, such as fear responses or alertness triggered by danger identification. This type of information may follow a low road, departing from the thalamus (where sensory information is processed) to the amygdala (responsible for triggering emotional/fearful responses). As minimal cortical processing takes place,

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5 individuals have no more than a rough representation of the stimulus, but are capable of reacting fairly quickly to it (LeDoux 1996). Finally, affective information can be processed at a higher level (Type III-Affect), involving subjective appraisal of the stimulus. In this case, affective information requires significant participation of the neocortex, where most of the cognitive functions operate, before any behavioral activity takes place. Though recognizing the importance of all three levels of psychophysiological processes, our model focuses on the impact of affect on behavior via a deliberate cognitive process, rather than an automatic affective reaction. The propositions and empirical evidence to be discussed focus on cognitive processes in which individuals deliberately use affect as a signal to evaluate the environment around them (affective evaluation mechanism) and/or to regulate their affective experiences (affect-regulation mechanism). Behavior Finally, two major factors led to the adoption of behavior and behavioral intentions as the main dependent measures. First, our primary objective is to understand the impact of affect on behavior. While there is extensive research on the impact of affect on judgments/attitudes, the impact of affect on behavior (and behavioral intentions) has received considerably less attention (Forgas 2002). Moreover, as we will see, the theories rooted in affective evaluations (Isen et al. 1978; Bower 1981; Schwarz 1990), cannot by themselves account for the impact of affect on behavior. Second, since compelling evidence suggests strong correlation between intentions and behavior (Eagly and Chaiken 1993), and many of the available studies have investigated the impact of affect

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6 on behavioral intentions rather than on final action, both types of dependent measures are adopted.

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CHAPTER 2 THEORETICAL BACKGROUND Historically, the relationship between affect and its behavioral consequences has been examined from three different perspectives, each highlighting one major property of affect. The first, and probably the oldest tradition, stresses the disrupting properties of affect on judgment and behavior. Brown and Farber (1951) advocated that hunger produced an affective impact (frustration) if the goal were not reached. Two drives were hypothesized to operate in parallel, hungerthe relevant drive, and frustrationthe irrelevant drive. People were seen as striving for the relevant goal while avoiding the negative effects of frustration, the irrelevant drive. Using the same rationale, action control theory (Kuhl and Beckmann 1984) considered frustration a competing tendency that must be controlled for. Finally, negative emotions have been hypothesized to disrupt rational behavior and promote self-regulation failure (Leith and Baumeister 1996). Affect (typically negative affect) is therefore reduced to a disrupting psychological mechanism leading to negative cognitive and behavioral outcomes. A critique of the disruptive perspective begins with the fact that feelings are essentially treated as maladaptive or irrational (and therefore to be avoided) components of human nature. This perspective implies that individuals goals can best be met by controlling for internal and environmental emotional temptations. This conception overlooks the functional aspects of feelings, and constrains our understanding of the importance of feelings to judgment and behavior (Carver and Scheier 1996; Fridja 1999; Pham et al. 2001). Although it is clear that under certain circumstances affect does 7

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8 have disruptive and non-adaptive consequences, feelings are usually indispensable for optimal decision-making, and . the absence of emotions and feeling is no less damaging, no less capable of compromising the rationality that makes us human. . . (Damasio 1994, p. xii). More recently, multiple theoretical accounts have proposed adaptive-type explanations for the impact of affect on behavior and behavioral intentions. Mood-congruency (Bower 1981), affect-as-information (Schwarz 1990; Schwarz and Clore 1983), mood as input (Martin et al. 1993; Martin and Stoner 1996), affect infusion (Forgas 1995), mood-maintenance (Isen 1984; Isen 2000), mood-management (Zillmann 1988) and hedonic contingency (Wegener and Petty 1994) are among the theories focusing on understanding the mediating processes through which current and anticipated affective states influence judgment, intentions, and/or behavior. Although varying in terms of predictions and scope of analysis, it is possible to categorize them at a more basic level. Based on the main underlying mechanism that links affect to behavioral consequences, the theories above can be classified into two groups: affective evaluation and affect-regulation. Affective Evaluation (Affects Informational Role) The first group emphasizes the evaluative properties of affect. It incorporates theories in which affect has been assumed to influence evaluative judgments, and eventually behavior, in either a direct or indirect fashion. The affect-as-information hypothesis (Schwarz 1990, Schwarz and Clore 1983) and the mood-congruency hypothesis (Isen et al. 1978; Bower 1981) are probably the most cited and investigated

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9 accounts. 1 Affect-as-information proposes that individuals directly assess their current affective states and deliberately use them during any evaluative process. The mood-congruency hypothesis proposes an indirect influence of affect, suggesting that affective states make affectively congruent material more accessible in memory, which leads to changes in evaluation. Evidence has supported both affect-as-information (Pham 1998; Pham et al. 2001) and mood-congruency (Goldberg and Gorn 1987; Isen et al. 1978; see also Forgas 1995). Most importantly, although the proposed mediating process varies, the affect-as-information and mood-congruency hypotheses make similar predictions. In general, positive affect is expected to lead to a more favorable evaluation of the environment, which stimulates proactive activities (increased purchase intention); whereas negative affect is expected to lead to a less favorable evaluation of the environment, which inhibits action (decreased purchase intention). In summary, a general affective evaluation mechanism (affect-as-information and/or mood-congruency) can explain Cells 1 and 4 of Figure 1. But it does not explain a negative (positive) mood increasing (decreasing) such behavioral intentions (Cells 3 and 2). Affect-Regulation (Affects Motivational Role) Theories based on affect-regulation assume that positive affect represents a goal, a desired state unto itself; and that people spontaneously attempt to achieve this ideal affective state or protect it once the state has been attained. For example, Zillmanns 1 Affective states are also known to influence evaluation through changes in processing style. Positive mood leads to top-down processes while negative mood triggers bottom-up processes (Martin and Clore 2001), which may have strong influences on evaluation (Barone, Miniard, and Romeo 2000). The present research does not focus on this particular issue. Thus, a deliberate attempt is made throughout the experiments (e.g., experimental control over the amount of time people are exposed to product information) to minimize the impact of processing style on the effects of interest.

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10 mood-management theory (1988) suggests that respondents may be willing to act in anticipation of the mood-lifting consequences of such behavior (Zillmann 1988). Indeed, Tice et al. (2001) showed that people deliberately use food consumption as a tactic for mood repair. Similarly, motivation for upward affect-regulation has also been suggested as a main motivator for self-gifting (Mick and DeMoss 1990a, 1990b; Luomala and Laaksonen 1997), pro-social behavior (Bagozzi and Moore 1994), and difficult trade-offs avoidance (Luce 1998). Therefore, based on the framework proposed in Figure 1, mood-management has focused mostly on explaining when negative affect encourages action (Cell 3 of Figure 1). A regulatory explanation is also used to explain when respondents in a good mood become conservative (that is, less willing to act). Research on helping, and especially risk taking, shows that when the situational cues threaten respondents affective states, those in a positive mood are less likely to take risks or help others (Cell 2 of Figure 1). As happy people have more to lose, they become more protective of their current affective states than those experiencing a more neutral affective state (see Isen 2000 for a review). Good mood has in fact been shown to lead to a protective type of behavior when the negative features of the product were made salient (Kahn and Isen 1993), when risks of losing were too high (Isen and Geva 1987), and when the helping task could produce negative feelings (Isen and Simmonds 1978). Wegener and Pettys (1994) hedonic contingency hypothesis provides similar predictions with a slightly different (i.e., event sampling) rationale. Their theory suggests that since happy people are already in or closer to the ideal affective state, there are fewer (more) behavioral options in the environment that can make them feel better (worse). As a result, they become less willing to take

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11 chances and, therefore, more protective. In a series of studies, the authors showed that when instructed to make a choice among several movies, happy respondents were more attentive to the mood-lifting attributes of the movies than were those in neutral or negative affect conditions. Affect-regulation, in essence, predicts when people are likely to move to a more positive affective state; and also explains when they are likely to try to protect a currently positive affective state. That is, a combined version of existing affect-regulation theories (i.e., mood-management and mood-maintenance or hedonic contingency) can explain when a bad mood stimulates action (Cell 3) as well as when a good mood inhibits action (Cell 2), the two cells of Figure 1 unaccounted for by theories based on an affective evaluation mechanism. It is worth noting that the regulatory properties have also been used to account for the impact of positive affect on behavioral encouragement (Cell 1). The mood-maintenance hypothesis (Clark and Isen 1982) claims that people may perform a mood-lifting behavior (e.g., to help) in an attempt to keep the good mood. However, no direct evidence of this mediating process has been provided, and this effect may be due to evaluation instead of regulation. This issue is further addressed later. It is clear therefore that integrating the evaluative and regulatory properties of affect is essential to account for all four categories of the affect-behavior matrix shown in Figure 1. The Integrative Model of Affective Behavior (IMAB) attempts to accomplish this goal.

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CHAPTER 3 INTEGRATIVE MODEL OF AFFECTIVE BEHAVIOR (IMAB) The first step to developing a model that addresses the four combinations of the affect-behavior relationship shown in Figure 1 is to integrate (at the mediational level) the evaluative and regulatory properties of affective states. The integrative model of affective behavior (IMAB) proposes that affective evaluation and affect-regulation operate in tandem as soon as a valenced affective state is activated. Proposition 1: Peoples affective states influence behavior continuously via two parallel mechanisms: affective evaluation (both direct and indirect processes) and affect-regulation. Strong evidence in the literature supports the behavioral influence of each mechanism (Isen 1984; Forgas 2002; Martin and Clore 2001). However, the literature is relatively silent regarding their parallel effects (but see Gendolla 2000 and Nygren et al. 1996 for exceptions). The affective evaluation mechanism assumes that current affective states (positive or negative) are likely to bias any evaluative judgment, and eventually behavior. Three rather complementary hypotheses have emerged to account for the processes underlying affective evaluations: one direct process (affect-as-information) and two indirect processes (mood-congruency and processing styles). First, the affect-as-information hypothesis proposes that affect itself may provide unique information that will be directly retrieved during evaluation (Schwarz and Clore 1983). Individuals ask themselves How do I feel about it?, and use this information to make evaluative judgments. Recently, 12

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13 Pham et al. (2001) moved a step further, indicating that affect is more than a heuristic cue, as the initial hypothesis would propose. According to these authors, affect-as-information is potentially faster, more reliable, and more predictive of subsequent thoughts compared to cold, reason-based types of information. Second, the mood-congruency hypothesis states that concepts congruent with an individuals current affective state may become more accessible (Bower 1981). As evaluation typically requires a retrieval process, the likelihood of using mood congruent concepts during an evaluative judgment increases, thereby biasing judgment. Third, the processing style bias suggests that affect influences peoples information-processing approaches, such as focus of attention, categorization, and analytical vs. creative processing (Barone, Miniard, and Romeo 2000; Forgas 1995; Schwarz and Clore 1996). For instance, positive affect fosters the use of accessible information, relying more on top-down, expectation-driven processes; whereas negative affect leads people to rely on more data-driven aspects (Clore et al. 2001). Similarly, those in a good (bad) mood form broader (narrower) categories (Isen 2000). As a result, in some circumstances, the impact of affect on evaluation may occur simply as a result of an individuals changes in processing style during the evaluative process. In short, affect is assumed to influence evaluation directly (as information) or indirectly (via changes in the accessibility of mood congruent material or through variations in processing styles). Notice that the mood-congruency hypothesis and the affect-as-information hypothesis provide similar predictions (Martin and Clore 2001). Positive (negative) affect leads to more positive (negative) evaluations, thereby mediating subjects intentions and behavior (Pham 1998). However, this is the case as long as no

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14 misattribution error is made salient. In fact, the affect-as-information hypothesis suggests that as soon as people become aware of their current affective states, they tend to control for any affective influence on judgments; whereas the mood-congruency hypothesis would predict the identical pattern across levels of awareness. Although all three paths may lead affect to influence behavior, the model proposed here emphasizes the more reliably demonstrated affect-as-information and/or mood congruent effects (for reviews, see Forgas 1995; Schwarz 1990). As the predictions are usually the same across these two mechanisms, we do not attempt to disentangle these competing/complementary explanations, since our objective is merely to fully incorporate affective evaluation effects. Whereas the affective evaluation mechanism focuses on the informative value of affect and its mood congruent effects, the affect-regulation mechanism focuses on the motivational aspect of specific affective states. For example, a current (or expected) negative (positive) mood may motivate individuals to improve (or protect) their actual affective state. In other words, affect has informational and goal properties that may influence behavioral decisions. Several research streams have devoted attention to this phenomenon. Tice, Bratslavsky, and Baumeister (2001) demonstrated that people deliberately use food consumption as a tactic for mood repair. Luce (1998) showed that in an attempt to regulate a current or expected negative affective state, people systematically avoided difficult tradeoffs, thereby making less-optimal choices. Similarly, motivation for upward affect-regulation has also led subjects in a bad mood to increase helping (Bagozzi and Moore 1994; Schaller and Cialdini 1990) and self-gifting (Luomala and Laaksonen 1997;

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15 Mick and DeMoss 1990). Finally, presumably in an attempt to protect a current affective state, subjects in a good mood were less likely to take risks compared to respondents in a control condition (Isen and Geva 1987). In short, we assume that there are two different pathways through which affect can influence or bias behavior. However, further assuming simultaneous activity of both types and interaction between these two processes before action, we seek to identify the circumstances that lead one mechanism to be more likely to predominate. What are the situational and internal cues that stimulate (vs. lessen) the impact of affect within these two mechanisms? Proposition 2: The affective evaluation mechanism assumes that affect can acquire the properties of information as well as alter the accessibility of mood congruent information in memory. Its impact is therefore contingent peoples current affective state and on the availability and use of competing diagnostic information about the target during the evaluative process. As affect can acquire informational properties, the quantity and quality of competing information available (as well as the context in which this information is used) influences the extent to which affect influences evaluation, and eventually behavior (Martin and Stoner 1996). As the amount of other available diagnostic information decreases, the impact of affect on behavior via the affective evaluation mechanism strengthens (Martin and Clore 2001). There is some evidence in the literature that the use of affect-as-information varies based on its diagnosticity. Pham (1998) showed that the use of affect is clearly context-dependent, varying also in terms of its quality (i.e., representativeness and relevance) during the evaluative process. He showed that subjects

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16 primed with a hedonic (vs. utilitarian) motive were more willing to use affect as information while assessing purchase intentions. Moreover, the quantity of competing information has also been shown to produce a significant impact on the affective evaluation mechanism. Siemer and Reisenzein (1998) showed that by mitigating peoples access to additional information through a time constraint and/or a secondary parallel task, the effects of mood on judgment increased. Finally, ambiguous information has also led to an increase in the impact of affect on evaluation and/or behavioral intention (Gorn et al. 2001; Isen and Shalker 1982; Miniard, Bhatla, and Sirdeshmukh 1992). Although affect may act as a heuristic (Schwarz 1990) by providing unique information to rely on when no additional information is made available or when the task is too complex, affective influence can occur via substantive changes in content. If the task promotes substantive thinking, it may well increase peoples likelihood of accessing mood congruent material, thereby leading to a strong affective evaluation change (Forgas 1995). In this case, mood congruent retrieval (instead of affect-as-information) may best represent the operative affective evaluation mechanism (Forgas 1992; Forgas 1993). In summary, the IMAB proposes that the affective evaluation mechanism intensifies as subjects current affective state is directly used as information during the evaluative process (due to ease/speed of access or lack of more diagnostic information) and/or indirectly used (via recall of mood congruent material). Proposition 3: The affect-regulation mechanism assumes that affect can acquire the properties of a goal. Its impact on behavior is therefore contingent on peoples current affective states, on their anticipated affective states as a result of action, on the

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17 presence of competing/complementary goals, and on peoples willingness and skills to achieve the goal. The model assumes that affect-regulation (i.e., pursuit or protection of positive feeling states) is potentially active and may even overcome the affective evaluation mechanism depending on internal and environmental contingencies. First, peoples current affective state is critical for affect-regulation to operate, since those experiencing negative affective states will have a stronger motivation to regulate (upward) their unsatisfactory feelings (Parrott and Sabini 1990), whereas those experiencing positive affect will have a stronger motivation to protect their current pleasant feelings (Isen and Geva 1987). The model implies, therefore, that valenced states (i.e., strong signals) are more likely to trigger the affect-regulation mechanism than neutral states (i.e., weak signals). For people to be strongly motivated to change or protect their current feelings, there must be a strong affective signal, which indicates that the individual is experiencing a desired (positive) or undesired (negative) affective state. Cohen and Andrade (2004) provided initial evidence of such an effect. Second, people must anticipate an affective change (upward or downward) as a result of action. In other words, the behavioral activity must be expected to lift or damage peoples current feelings (Cialdini and Kenrick 1976; Isen and Geva 1987; Raghunathan and Pham 1999). Notice that this assumption implies that affect-regulation is a function of peoples intuitive theories about the affective consequences of specific behaviors (e.g., I believe that eating chocolate makes me feel better). Third, competing (or cooperative) goals work in tandem to determine the impact and direction of an individuals current affective state. If more personally relevant or salient goals are operative, individuals may delay upward affect

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18 regulation and retain a negative affective state in order to achieve a competing goal (Cohen and Andrade 2004) or neutralize negative and positive affect in an attempt to enhance response flexibility and improve performance (Erber and Erber 2001). Finally, individuals must have the willingness and skills to regulate their current affective state. Studies examining chronic affective states, for instance, have shown that depressed people do not perceive themselves as capable of regulating upward their current negative affective states (Davidson et al. 2002; Kanfer and Zeiss 1983), which may prevent any mood-lifting attempt. If these conditions are met, affect-regulation is likely to direct behavior, potentially overcoming the opposing effects of the affective evaluation mechanism. In summary, the models main predictors for individuals in a positive affective state, are that, (1) the affective evaluation mechanism should stimulate action (e.g., producing an increase in purchase intention), particularly when no strong competing diagnostic information about the behavior/environment is available; however, (2) when the behavior presents a potential threat to peoples positive feelings (and the other previously mentioned contingencies are met), then the affect-regulation mechanism is likely to dominate the informational properties of the initial positive state. In this case, positive affect should inhibit action (e.g., producing a decrease in purchase intentions) as people attempt to protect their current affective states. For individuals experiencing a negative affective state, the model predicts that (3) the affective evaluation mechanism should inhibit action (e.g., producing a decrease in purchase intentions), particularly when no strong competing diagnostic information about the behavior/environment is available; however, (4) when the behavior presents a

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19 potential mood-lifting benefit to peoples negative feelings (and the other previously mentioned contingencies are met), then the affect-regulation mechanism is likely to direct behavior. In this case, negative affect should stimulate action (e.g., producing an increase in purchase intentions) as people attempt to improve their current feelings. 2 2 Remember that as the IMAB focuses initially on general negative and positive states, it does not make predictions for specific types of emotions. However, it does recognize that emotions vary in terms of affect-regulation and affective evaluation tendencies, which eventually bias behavior. For instance, it is known that whereas sadness increases risk-taking, anxiety decreases it (Raghunathan and Pham 1999). Similarly, while helping increases as a function of sadness (Baumann, Cialdini, and Kenrick 1981), it may well decrease as a result of frustration.

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CHAPTER 4 INITIAL EVIDENCE SUPPORTING THE MODEL: THE IMPACT OF AFFECT ON HELPING, RISK TAKING, AND EATING BEHAVIOR The impact of affect on behavior has been investigated in several different research streams within the psychology literature. Thus, to provide initial evidence for the model basics propositions, the impact of affect on helping, risk taking, and eating behavior is reviewed in an attempt to assess IMABs ability to account for the results and resolve some of the apparent inconsistencies. Helping Prevalent finding in the helping literature is that current affective states influence individuals willingness to help. However, the effects do not follow a single pattern (for reviews, see Batson 1990; Salovey, Mayer, and Rosenhan 1991; Schaller and Cialdini 1990). Researchers tend to agree that the relationship between positive mood and helping is, in general, well established; and that positive mood increases peoples propensity to help (Isen, Clark, and Schwartz 1976; Isen and Levin 1972; Levin and Isen 1975). However, there is some evidence that the opposite may also be true; thus a decrease in helping due to individuals positive feelings (Isen and Simmonds 1978). The impact of negative affect on helping is also bi-directional. Negative mood sometimes increases helping (Cialdini et al. 1973; Cunningham, Steinberg, and Grev 1980; Manucia, Bauman, and Cialdini 1984) and sometimes decreases helping (Berkowitz 1972; Berkowitz and Connor 1966; Isen 1970). Several hypotheses have been proposed to account for these inconsistent patterns, however the underlying mechanisms seem to vary almost as 20

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21 much as the results themselves: positive mood-maintenance, guilt reduction, negative state relief, aversive arousal reduction, positive affective priming, and negative affective priming (Batson 1990; Salovey et al. 1991). The main findings and their respective explanations are reviewed below. Then we show how the proposed IMAB offers a more integrative and parsimonious account of this body of work. The framework proposed in Figure 1 is used to categorize the effects. Positive Affect and Helping In a field study, Isen and Levin (1972) showed that subjects who found a dime in the coin return of a public telephone were subsequently more willing to pick up papers dropped off in front of them by a confederate (Study 2). Similarly, after manipulating mood through false feedback, Isen (1970) showed that happy (sad) students were more (less) willing to give money to the Junior High Air-Conditioning Fund. Indeed, the positive impact of good mood on prosocial behavior is quite robust (Aderman 1972; Berkowitz and Connor 1966; Levin and Isen 1975; Moore, Underwood, and Rosenhan 1973). Two underlying mechanisms leading to this effect have been advanced; one cognitive (i.e., priming effects), and one motivational (i.e., positive mood-maintenance). The cognitive/priming explanation is based essentially on mood-congruency effects (Isen et al. 1976; Isen, Clark, Shalker, and Karp 1978), through which positive information became more accessible during evaluation and influenced behavior (Clark and Waddel 1983). The competing motivational explanation for the effects of positive affective states on helping adopts a regulatory process approach. It has been proposed that people in a good mood try to remain in their current affective states and, therefore, will be more willing to help (Isen 1984; Levin and Isen 1975). This hypothesis, however, has not

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22 found direct empirical support in the literature. As Schaller and Cialdini (1990) summarized evidence is scarce that happy subjects help as a means to maintain positive moods (p. 282). Indeed, when mood-maintenance plays a role, it may reduce rather than increase helping (since the beneficial affective consequences of trying to help may not be clear). Although direct empirical evidence of the actual mediating processes is still lacking, researchers tend to agree that biases in evaluative judgment can play a role on peoples propensity to help (Batson 1990; Salovey et al. 1991; Schaller and Cialdini 1990). However, as no study has contrasted affect-as-information vs. mood-congruency mechanisms, and both predict the same effects, it seems premature to claim which process (if not both) is responsible for the impact of positive mood on helping. Little evidence is available showing that being in a good mood can decrease helping. However, Isen and Simmonds (1978) found that when the helping scenario displays situational cues that threaten subjects current positive mood (e.g., a demanding helping task), these individuals were indeed less likely to help than subjects in a neural mood. The authors suggested that a challenging helping task may have led happy subjects to anticipate negative affect and triggered a self-protective regulatory mechanism. This type of effect will be further elaborated under the risk-taking literature review, where the impact of positive affect on behavioral discouragement is well established. Negative Affect and Helping Studies showing that negative affect increases helping have generated several related hypotheses to account for the underlying mechanisms, such as guilt reduction (Carlsmith and Gross 1969; Regan, Williams, and Sparling 1972), negative mood relief (Bauman, Cialdini, and Kenrick 1981; Cialdini et al. 1973; Cialdini and Kenrick 1976; Manucia et al. 1984), and aversive arousal reduction (Piliavin et al. 1981, 1982).

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23 Although adopting different research approaches, they all share the basic assumption that upward affect-regulation is at the core of peoples disposition to help. Helping is conceived to be an affect-regulation strategy aiming at achievement of this somewhat superordinate goal. Cialdini and colleagues were among the first to categorize helping as a mood repair strategy. Cialdini et al. (1973) showed that subjects in bad mood were more likely to help in response to another persons request than those in control conditions. Most importantly, as soon as rewarding hedonic benefits were interposed between the mood manipulation and the help request (i.e., an unexpected monetary reward or approval for task performance), the effects of negative affect on helping disappeared. The authors asserted that helping, monetary reward, and positive feedback perform a similar functional goal, bad mood relief (see also Baumann et al. 1981). Manucia et al. (1984) provided further, and perhaps even more compelling, evidence implicating an upward affect-regulation strategy as the mediating mechanism linking being in a bad mood to helping. After instantiation of positive, neutral, or negative affective states, subjects were asked to take a placebo pill. Half the subjects were informed that this pill would freeze their current affective states for a while. The authors predicted that if helping was used as a mood repair strategy, subjects in the frozen bad mood condition should help no more than those in control conditions and significantly less compared to the non-frozen bad mood condition. The results confirmed the predictions. Subjects in a bad mood who were told about the freezing effects of the drug helped much less (compared to those in the non-frozen bad mood condition), and similar to those in the neutral condition.

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24 There is also evidence that negative affect can also decrease helping under certain circumstances (Cialdini and Kenrick 1976; Isen 1970; Isen, Horn, and Rosenhan 1973; Moore et al. 1973). However, purely cognitive approaches have been used to account for operative mediating processes. Similar to positive moods, negative moods are also known to prime congruent thoughts: Thoughts of deprivation, helplessness, and uselessness may become especially available, rendering such sad and self-focused individuals less likely to help. . . (Salovey et al. 1991, p. 222). In summary, whereas upward affect-regulation is typically identified as responsible for instigating people to help (Cell 3), affective evaluation is identified as playing a major role when opposite results are found (Cell 4). At best this strikes us as a marriage of convenience rather than a systematic and balanced explanation. Moreover, there has been a failure to find evidence consistent with affect-regulation (i.e., helping increase) in certain studies where negative mood had been induced. However, in studies where a negative mood decreased helping, children were used as subjects (Cialdini and Kenrick 1976; Isen et al. 1973; Moore et al. 1973). It is possible that for children helping is simply not perceived as an effective affect-regulation strategy. In that case, affective evaluation should have a stronger impact. Indeed, Cialdini and Kenrick (1976) showed that age and levels of socialization are critical moderating variables. In one experiment, the authors found an interaction between age (6-8, 10-12 and15-18 years) and mood on helping. Fifteen to eighteen year old subjects in a bad mood helped more than those in the other two conditions, who turned out to behave similarly to one another. As the authors predicted, individuals whose socialization process is still incipient do not perceive helping as self-gratifying. In short, young

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25 subjects in a bad mood do not help because altruistic behaviors are not perceived as a viable affect-regulation strategy. As a result, the negative thoughts elicited by a bad mood operate to reduce helping. Theoretical Integration The helping literature shows that positive and negative affect can stimulate or discourage helping depending on situational cues available in the environment (i.e., the four Cells of Figure 1), however, a combination of several theories have been required to account for these effects. IMABs main claim (proposition 1) is that, under the basic assumption that two parallel mechanisms underlie the impact of affect on behavior, situational cues and internal affective signals determine which mechanism will prevail. As a result, a single model can reconcile these effects. Positive affect leads to helping increase (Cell 1) via an affective evaluation mechanism Proposition 2. That is, a positive mood biases (positively) subjects evaluations of the helping task either via affect-as-information and/or mood-congruency , both of which should increase subjects willingness to help (Isen 1970; Isen and Levin 1972; Moore et al. 1973). However, IMAB also requires us to consider affect-regulation Proposition 3-, so that when situational cues lead subjects to anticipate negative affect, affect-regulation becomes the dominant mechanism, and behavior is discouraged (Cell 2). The reason for such protective reaction is that subjects in a positive mood have more to lose compared to control conditions. That was the case in Isen and Simmonds (1978) study, where the helping task was mood-threatening, probably leading subjects to speculate about the negative consequences of helping on their current positive affective state. Although this type of effect is rather sporadic in the helping literature, the

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26 study of risk-taking, as we will see, offers consistent theoretical and empirical evidence of the impact of anticipated negative affect on behavior. For subjects in a negative mood, upward affect-regulation is usually a reasonably important motivator and is likely to dominate the impact of the affective evaluation mechanism. As a result, individuals attempt to improve their current negative affective states (Cell 3), with several studies providing evidence of a helping increase for sad subjects (Bauman et al. 1981; Cialdini et al. 1973; Cialdini and Kenrick 1976; Manucia et al. 1984). However, as with any other goal, affect-regulation is contingent on other moderating variables, such as subjects recognition of the stimulus as an effective upward affect-regulation strategy Proposition 3. When sad subjects were incapable of perceiving the mood lifting benefits of helping, affect-regulation was mitigated, and the affective evaluation mechanism (i.e., negative evaluation of the environment) led to a decrease in helping (Cell 4). That was the case when children were used in the experiments (Cialdini and Kenrick 1976; Moore et al. 1973). In short, the proposed IMAB accounts for the bulk of effects in the helping literature, by suggesting that (1) positive affect increases helping via affective evaluation (i.e., priming effects and/or affect-as-information); (2) positive affect decreases helping via affect-regulation when accompanied by mood-threatening cues; (3) negative affect increases helping via (upward) affect-regulation when mood-lifting benefits are made available; and (4) negative affect decreases helping via negative affective evaluation providing subjects are unable to perceive the mood-lifting benefits of helping. Risk-Taking Understanding the impact of affect on risk perception and risk-taking can thus provide additional insights into the behavioral consequences of affect and its mediating

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27 processes. Johnson and Tversky (1983) found that when asked to evaluate the subjective probability of positive future events, subjects in positive moods reported a higher subjective probability (compared to control respondents), and a much higher subjective probability compared to subjects in a negative mood. The opposite was true when they were asked to evaluate the subjective probability of negative future events. In this case, subjects in negative moods reported the highest subjective probability (compared to those in neutral moods), and were much higher than subjects in a positive mood. After tracking for cognitive processes (thought listing), Wright and Bower (1992) showed that individuals focused more on mood congruent information during the assessment of subjective probabilities. The correlation between affective state and expected outcomes is currently well established (Loewenstein et al. 2001). Thus, based on prevailing evidence and on the assumption that people will act rationally, one would think that subjects in bad mood, who tend to perceive a situation as riskier, should be less inclined toward risk-taking. The opposite should be true for subjects experiencing a positive affective state. Individuals in good moods, who usually perceive a safer environment, should be more prone to risk-taking. Yet, findings in the literature do not fully confirm either of these two predictions. Although the results are rather consistent as to the impact of affect on risk perception (Constans and Mathews 1993; Johnson and Tversky 1983; Mayer et al. 1992; Pietromonaco and Rook 1987; Wright and Bower 1992), yet to be resolved is why the impact of affect on risk-taking does not follow the predicted rational pattern. Negative Affect and Risk-Taking Negative affective states have been shown to increase risk-taking (Gehring and Willoughby 2002; Leith and Baumeister 1996; Mano 1992, 1994; Mittal and Ross 1998;

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28 Raghunathan and Pham 1999), though this seems counterintuitive (since we know that negative affect leads to an increase in risk perception, Johnson and Tversky 1983; Wright and Bower 1992). Two sets of hypotheses have been developed to explain such effects: affect-regulation (e.g., mood repair Raghunathan and Pham 1999) and affective disruption (e.g., restricted attentional capacity Mano 1992). Leith and Baumeister (1996) adopted an affect as disruption perspective to account for this pattern of results. Since negative feelings may disrupt peoples ability to properly/rationally make accurate evaluations, such interference could lead subjects to choose the poorer/riskier option. In a series of six studies the authors showed that embarrassment, anger, and unpleasant arousing feelings led to an increase in risk-taking. Since, in one of the studies, sadness did not differ from the neutral conditions, the authors used this null effect to conclude that the risky tendencies are limited to unpleasant moods accompanied by high arousal (p. 1250). This hypothesis has not found much support in the literature. To assess the impact of different emotions on risk-taking, Raghunathan and Pham (1999) investigated the impact of sadness (low arousal) and anxiety (high arousal). Contrary to the affect disruption prediction, when presented with two gamble options in a consumer decision task (low risk-low payoff vs. high risk-high pay off), sad subjects preferred the riskier alternative with a higher payoff (compared to anxious subjects), who turned out to be strongly risk-averse. The authors suggested that different goals are primed for sad vs. anxious people: sad subjects focusing on reward replacement (mood repair) whereas anxious subjects focused on uncertainty reduction. Sad subjects thus perceived the high risk-high payoff option as more attractive (i.e., mood-lifting), whereas

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29 anxious subjects preferred the low risk-low payoff alternative (i.e., it can reduce my uncertainty). This rationale is in line with the Eysenck and colleagues studies in which anxiety has led to attentional and interpretational biases. Based on Eysencks (1992) cognitive theory of trait anxiety, it has been found that highly anxious people have an attentional bias toward threat-related words and also interpret ambiguous information as more threatening (Eysenck, MacLeod, and Mathews 1987). Derakshan and Eysenck (1997) also found that highly anxious people display an interpretative bias for their own behavior in social situations the behavior is perceived as more anxious. Raghunathan and Phams and Eysenck and colleagues findings converge with the IMAB and highlight a critical assumption of the model, the interdependence of the affective evaluation and the affect-regulation mechanisms Proposition 1. Anxious people appear to reinterpret any risky action, making it more negatively arousing and causing whatever mood-lifting benefits that might be associated with a high-risk bet to dissipate, thereby mitigating the impact of the affect-regulation mechanism. Simultaneously, anxious subjects arrive at a rather pessimistic and threatening assessment of the environment, which further strengthens the impact of the affective evaluation mechanism. The impact of strong negative affective evaluation combined with the absence of upward affect-regulation forces leads to risk-averse behavioral patterns (Cell 4). However, when people experience sadness, the mood-lifting benefits of similar risk-taking may remain stable or even intensify, offsetting the negative impact of affective evaluation on risk perceptions, and leading people to choose more risk-prone behaviors (Cell 3). Thus, the type of affective state being experienced may well produce different interpretations of, or attention to, upward affect-regulation opportunities that may be

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30 available Proposition 2, making the affect-regulation mechanism either more or less influential Proposition 3. Positive Affect and Risk-Taking Kahn and Isen (1993) showed that being in a positive mood (compared to a neutral mood) stimulated individuals to seek more variety among otherwise safe and enjoyable food products. Arkes and colleagues (1988) also demonstrated that happy respondents (compared to subjects in a neutral mood) were more willing to pay for lottery tickets (Study 1). These and similar results show that people in a good mood are apparently more prone to risk-taking. Since we know that people in a good mood are more optimistic (Johnson and Tversky 1983; Wright and Bower 1992), this pattern might be labeled as rather intuitive. However, not all findings have shown this riskprone behavior among happy people. Kahn and Isen, for example, showed that the increase of variety-seeking behavior for happy subjects disappeared as soon as a products negative features were included or made salient in the choice context. Similarly, Arkes and colleagues also showed that whereas happy subjects (vs. a control group) displayed risk-prone behavior in a pleasure-seeking situation (i.e., buying lottery tickets), they exhibited a risk-averse pattern in a loss avoidance situation (i.e., buying insurance). It has been hypothesized that subjects in a positive mood are more risk seeking than subjects in a neutral mood providing the potential losses are not salient or too high (Nygren et al. 1996). Research on this topic has used a motivational rationale to account for the findings: people in a good mood facing mood-threatening stimuli become more self-protective of their current feelings, thereby discouraging risky behaviors that may lead them to feel bad. The pattern of results is fully consistent with IMAB, which predicts that affect-regulation is activated not only when subjects experience negative feelings, but also when

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31 subjects anticipate negative feelings Proposition 3. Thus, when losses are likely (e.g., high-risk condition), people in a good mood face a greater relative loss than people in a neutral mood (Isen and Geva 1987). Such anticipatory negative emotional reactions reduce the likelihood of engaging in risky behavior to achieve affect-regulation goals, and hence counteract the impact of positive affect-based evaluations. Nygren et al. (1996) used the seemingly contradictory expression cautious optimism to underscore the dual and, here, opposing mechanisms at work. They summarize their first study by saying that, on the one hand, positive affect participants significantly overestimated the probabilities, but on the other hand, were less likely to gamble than were controls when a real loss was possible (p. 59). In IMAB terms, whereas optimism is a result of affective evaluations (i.e., affective priming effects), caution represents a consequence of affect-regulation, triggered by anticipated negative affect. In summary, being in a good mood may promote both risk-averse (Cell 2) as well as risk seeking behavior (Cell 1). The outcome depends on mediating effects linked primarily to affect-regulation, which have been shown to be contingent on the presence of mood-threatening stimulus and its subjective likelihood of triggering downward affect-regulation. When no threats are made salient affective evaluation leads to risk prone behaviors Proposition 2 -, whereas when environmental cues signal threats affect-regulation goals are activated Proposition 3 promoting negative mood avoidance through risk-averse behaviors. Eating Behavior The impact of emotion on eating behavior has been widely investigated (for reviews, see Canetti, Bachar, and Betty 2002; Christensen 1993; Greeno and Wing 1994). Researchers interests vary significantly; from the effects of stress on psychopathological

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32 behaviors (e.g., obesity and bulimia) to normal influences of mild mood swings on food preferences (e.g., cravings for sweets, carbohydrates, etc.); from tail-pinch stressors and animal eating responses to unpleasant movies and human propensity to eat snack food. As our analysis and proposed model focuses on the impact of mild affective states on everyday behavior, we will concentrate on how negative and positive affective states influence normal food intake. Consistent with the evidence reviewed above, the first conclusion to be drawn from this body of research is that affect does not lead to a unique behavioral consequence. Positive and negative affective states may well stimulate or discourage food intake. Negative Affect and Eating Behavior There are a far greater number of studies dealing with the impact of negative affect (compared to positive affect) on eating behavior. The underlying assumption in most of the literature is that food acts as mood-regulator, lifting subjects current affective state after intake (Bruch 1973; Kaplan and Kaplan 1957; Morris and Reilly 1987; Polivy and Herman 1976; Thayer 1989). Thus, (compared to a control condition), negative affect is expected to encourage eating behavior. However, the results have been shown to vary as a function of several moderating variables such as gender and food type. Until the 1990s, the general impact of negative affect, especially stress, had been surprisingly limited to animal research. Those dealing directly with humans concentrated more on the interaction between affect and individual differences on eating behavior (see Greeno and Wing 1994 for a review). Most recently, however, new studies have emerged in which broader and more fundamental conclusions can be drawn about the consequences of negative affect on peoples eating behavior.

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33 Grunberg and Straub (1992) exposed subjects to a film about industrial accidents (negative affect) or a pleasant travelogue (control) while having snack foods available in the room (dependent measure). They found that eating consumption increased as a result of negative affect, but only among women. The results actually reversed for male subjects, who reduced the amount of food intake as a consequence of negative affect. Although the authors did not advance a systematic theoretical explanation for the mediating effects, the results have proven quite robust. Whereas negative affect tends to increase food intake among women (Macht 1999; Patel and Schlundt 2001; Weinstein, Shide, and Rolls 1997; Willner et al. 1998), this effect is either canceled (Pine 1985) or reversed among men (Abramson and Wunderlich 1972; Macht, Roth, and Ellgring 2002; Reznick and Balch 1977). A potential explanation for such variation is that strategic affect-regulation through food intake is stronger in women than men (Macht 1999; Steptoe, Pollard, and Wardle 1995). Men may not become more attracted to food, or at least certain types of food, as their affective states worsen. Although a variety of biological and psychological explanations have been offered to explain why food intake does not increase among men, they cannot explain why bad feelings lead men to reduce food intake. As we have shown in the other streams of research, pursuing a single explanatory mechanism may be at the root of the apparent inconsistency. In this case, however, the main explanatory mechanism has been affect-regulation rather than affective evaluation. Once again, IMAB proposes that understanding the interaction between affective evaluation and affect-regulation is critical to explain the bi-directional pattern. Whereas upward affect-regulation accounts for the increase in food intake as a result of negative

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34 affect (Cell 3), negative affective evaluation is likely to explain food intake inhibition (Cell 4). According to IMABs third proposition, affect-regulation, as a goal, is contingent on the peoples underlying theories about the affective consequences of action (i.e., Is this behavior mood-lifting/threatening?). If men (vs. women) are less likely to perceive certain types of food as mood-lifters (e.g., Eating chocolate does not make feel better), the impact of affect-regulation will be mitigated and affective evaluation will be most likely to drive the effects (i.e., reduce eating). Our multiple mediator analysis also implies that if the type of food is not perceived as a mood-lifter negative affect is expected to decrease eating. Consistent with Proposition 3, Oliver and Wardle (1998) showed that whereas stress increased the consumption of snack-type foods (perceived both as quick energy products and treats) it decreased the consumption of typical meal-type foods (fruits and vegetables, meat and fish). Relatedly, Willner and Healy (1994) showed that after negative affect induction subjects lowered their own evaluation of cheese in terms of pleasantness and desirability (see also Macht et al., 2002), again suggesting that affective behavior toward food with no subjective mood-lifting attributes is mostly directed by the affective evaluation mechanism. So, as bad feelings produce a worsening evaluation of focal objects such as food, eating should decline. Positive Affect and Eating Behavior Since eating disorders (obesity, binge eating, bulimia, anorexia, etc.), which are normally associated with negative affect, have been at the forefront of research done in the field from the 70s through the 90s, only recently have researchers devoted attention to the consequences of positive affect. The general pattern of results suggests that positive mood stimulates eating (Cools, Schotte, and McNally 1992; Macht 1999; Macht et al. 2002; Patel and Schlundt 2001 upper-left corner of Figure 1), though null effects have

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35 also been reported (Frost et al. 1982; Schmitz 1996). Based on two-week food diaries, Patel and Schlundt (2001) found that (compared to a control condition) obese women increased food intake while experiencing both positive and negative affect. Contrary to the authors expectations of an interaction between mood and social context (eating alone vs. eating in a social context), the impact of valenced moods occurred under both social context scenarios. No explanation was provided to account for the results. Macht and colleagues (2002) provided a compelling mood congruent explanation for such effects. They showed that male subjects experiencing positive (vs. negative) affect provided higher ratings on two general dimensions for chocolates they were eating: affective responses to chocolate (e.g., taste pleasantness) and motivation to eat (e.g., appetite). The authors suggested that the positive impact of affect on food intake was probably a result of mood congruent effects during subjects internal and external evaluation. Consistent with IMABs basic propositions, our analysis of prior research suggests that positive affect probably stimulates eating behavior via the affective evaluation mechanism, though little has been done to isolate mood congruent effects from affect-as-information. Finally, to the best of our knowledge, no study has shown that people in a good mood reduced food intake (Cell 2). However, according to IMAB, this pattern of results is likely when negative consequences of eating become salient. For instance, happy people (compared to a neutral mood condition), should be less likely to eat chocolates if negative nutrition facts are highlighted (i.e., fat product and/or subsequent feelings of guilt). In summary, as with research on helping, the potential negative consequences of positive affect are largely unexplored in the eating behavior literature.

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36 Summary Our review shows that IMABs three propositions can account for the observed consequences of affect on behavior and behavioral intentions across three different bodies of literature: helping, risk-taking, and eating behavior. Behavioral stimulation for people experiencing positive affect (Cell 1) may occur mostly as a result of the affective evaluation mechanism affect-as-information and/or mood congruent effects and there is a paucity of evidence to support mood-maintenance models of affect-regulation. As long as no aversive or threatening cues become salient in the environment, happy people perceive a safer environment (i.e., bring positive thoughts to mind) and therefore become more likely to help, to take risks in gambles, and to eat. When mood-threatening cues are made salient, the affect-regulation mechanism activates and may lead to behavioral discouragement for people experiencing positive affect (Cell 2). As happy people are more sensitive to potential negative affective consequences, since they have more to lose, and bad feelings can be anticipated, behavioral discouragement takes place when negative aspects or consequences become salient. This explains a decrease in helping when the task is mood-threatening as well as risk avoidance when the odds are too high. Happy people thus seem more motivated to avoid a negative mood than to maintain positive feelings, since there is no salient threat likely to instantiate this motivation. On the negative side of the affective spectrum, the affective evaluation mechanism seems to drive the impact of negative affect on behavioral mitigation (Cell 4). People perceive a more intimidating environment (i.e., bring more negative thoughts to mind) and become less likely to help, to take risks, and to eat. This is most likely to be the case, however, only when the affect-regulation mechanism is inactive or blocked. Blocking or

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37 mitigating effects can be a result of subjects inability to perceive specific behavior as an effective mood-lifting opportunity. That was the case among children (vs. adults) facing a helping opportunity, anxious (vs. sad) people facing a risky-high payoff opportunity, and men (vs. women) facing an eating opportunity. When the behavior is perceived to be an effective upward affect-regulation strategy, there are no stronger competing goals in the environment, and people are willing/capable of mood improvement, enactment becomes more likely for those experiencing negative affect via the affect-regulation mechanism, often counteracting the impact of the affective evaluation mechanism (Cell 3). In such situations people attempt to improve their current negative affective states, and this accounts for observed increases in helping, risk-taking, and eating behavior under these conditions. In short, combining the hitherto separately considered affect evaluation and affect-regulation mechanisms in the integrative model of affective behavior provides the following parsimonious account of this substantial literature. Behavioral stimulation for subjects in a positive mood and behavioral mitigation for subjects in a bad mood seems to be mediated mainly by the affective evaluation mechanism (affect-as-information and/or mood-congruency effects), whereas behavioral stimulation for subjects in a bad mood and behavioral mitigation for subjects in a good mood is likely to be mainly mediated by the affect-regulation mechanism. Such conclusion is, however, premature, since, to the best of my knowledge, no single study has addressed the impact of positive, neutral, and negative affective states on behavior, while providing a direct assessment of the two main mediating processes under operation. In the next chapters, the results of three experiments are presented in an

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38 attempt to accomplish this goal and offer stronger empirical support for the integrative model of affective behavior.

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CHAPTER 5 TESTING IMAB: PREDICTIONS To test IMAB propositions, a series of three experiments was conducted. Experiments 1 and 2 show (1) that positive (negative) affect stimulates (inhibits) purchase intentions via the affective evaluation mechanism (Cells 1 and 4 of Figure 1), and (2) that negative affect stimulates purchase intentions via affect-regulation (Cell 3 of Figure 1). Experiment 3 addresses the remaining effect (Cell 2 of Figure 1), and demonstrates the circumstances in which positive affect may inhibit action (i.e., consumption). It also replicates Cell 1 (i.e., when positive affect increases consumption) with an actual behavioral measure. All three experiments focused on two critical moderating variables: peoples current affective states and the availability of mood-lifting (experiments 1 and 2) or mood-threatening (experiment 3) cues in the environment. The other situational contingencies are held constant throughout all the conditions across the three experiments. Below, we describe the predictions for experiments 1 and 2. Predictions for experiment 3 will be described in Chapter 8. In a consumption scenario where people do not possess enough information about the product and the environment does not provide people with an opportunity to upwardly regulate their affective states, IMAB predicts that the affective evaluation mechanism should influence purchase intentions. Affect congruency principles it is hypothesized that people in a negative (positive) mood will have a more negative evaluation of the environment and/or the product, which makes them less (more) likely to purchase a particular product, compared to a control condition (Figure 2 white bars). 39

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40 However, if the product presents mood-lifting benefits, those experiencing negative affect have an opportunity to upwardly regulate their current affective state, which can lead them to increase their purchase intentions despite the opposing influence of the affective evaluation mechanism. On the positive side of the affective spectrum, people are already experiencing something approaching the ideal affective state. Thus, the mood-lifting benefits of a product are not expected to have a strong effect on purchase intention. However, as the affective evaluation mechanism is also active, people are expected to provide a more positive evaluation of the product/environment, which should increase purchase intentions. In short, when they are presented with the opportunity to purchase a mood-lifting product, the combination of affect-regulation for those experiencing negative feelings and affective evaluation for those experiencing positive feelings, leads to a U shape pattern (Figure 2 black bars). 01234567NegativeNeutral Positive AffectBehavioral Intentions mood-lifting benefits no mood-lifting benefits Figure 2. Predictions for experiments 1 and 2 Notice that those in the neutral affect condition are not expected to be strongly sensitive to the mood-lifting benefits of the product. As they are also at some distance from the ideal affective state, one could expect these respondents to be willing to improve

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41 their current feelings in order to achieve the ideal affective state. However, affect-regulation has been shown to be a function of both the affective discrepancy between current and ideal states and, importantly, the strength of the affective signal (Cohen and Andrade 2004). Thus, since neutral affect provides weak signals, affect-regulation goals may not be instantiated, and the regulatory process may not unfold. IMAB incorporates this assumption.

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CHAPTER 6 EXPERIMENT 1 Marketers have recently highlighted their products mood-lifting benefits in an attempt to persuade consumers. Kelloggs, for instance, displays a web page called Breakfast and Mood, where it describes the affect-regulation benefits of eating breakfast cereal in the morning. If the impact of mood-lifting cues are contingent on peoples current and anticipated feelings, as proposed by IMAB, varying respondents current feelings (negative, neutral, positive) and providing them with an opportunity to purchase a mood-lifting versus a non mood-lifting product should allow us to test IMABs predictions described above. Experiment one adopts this procedural approach. Method Subjects and Design Two hundred eighty-four undergraduate students from a southeastern university participated in the experiment in exchange for course credit. The study adopted a two (product benefits: mood-lifting vs. non mood-lifting) by three (affective state: negative vs. neutral vs. positive) by two (replicate: coffee vs. cereal) between subjects design. Respondents were randomly assigned to one of the twelve conditions. Procedure After they entered the lab, respondents were instructed to choose one of the computers and start the first of the two independent short studies they were about to perform. Study 1 represented the affect manipulation while Study 2 captured the product benefit manipulation, followed by the main dependent measure. After presenting 42

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43 students with an informed consent form, the first screen of study 1 introduced the cover story. Respondents were told that as the number of Internet classes was increasing, the university had decided to investigate the impact on memory of material transmitted over the web. Respondents were instructed to watch a video on the web and describe a real life experience similar to that watched in the film (affect manipulation). Then, they were asked to rate the video (manipulation check). As soon as they finished study 1 a new screen introduced the cover page of the second study. An informed consent form was once again provided to the students in order to convey the idea of two independent studies. The next screen indicated that the study attempted to assess peoples willingness to buy a specific product in a given consumption scenario. They were presented with a consumption scenario in which the product (coffee vs. cereal), and its benefits (mood-lifting vs. non mood-lifting) varied across conditions. After 15 seconds spent reading the information, respondents were provided with a nine-point scale where they indicated their purchase intentions (dependent measure). On the next page, they were asked about the information earlier presented in the purchasing scenario (manipulation check for the product benefits manipulation). Finally, they described the purposes of studies 1 and 2 (hypothesis guessing check) and were later debriefed regarding the affect manipulation in order to eliminate any residual effects. Affect Manipulation To vary respondents affective states, a combined technique was used (movie plus personal real life description). This same technique has been successfully used in both the general affect literature and in consumer research on affect (Cohen and Andrade 2004; see also Westermann et al. 1996 for a meta-analysis). Respondents watched five minutes of either a sad sequence of the movie Top Gun (negative affect), a documentary about

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44 Italy (neutral affect) or a happy sequence of the movie American Pie 2 (positive affect). The second step of the induction asked respondents in the valenced affect conditions to describe a real life experience that produced the same feelings as those derived from the video. In the neutral affect condition respondents were asked to describe a real life experience that was similar in content to the scenes watched in the video. Finally, respondents were asked to give their opinion about the video (manipulation check). Ten items were presented in a nine-point semantic differential scale format. Three of the 10 items were designed to assess respondents current affective states (I felt sad-I felt happy, Its depressing-Its upbeat, Created a negative mood-Created a positive mood). The order of the items was randomized to avoid order effects. Replicates The products were selected based on published findings, consumer beliefs, and companies attempts to link their products to mood-lifting qualities. Caffeine and carbohydrates have been shown to produce a positive impact on mood and/or arousal (Smith, Clark, and Gallagher 1999), and descriptive studies have shown, for instance, that drinking coffee or other caffeinated products is one of peoples common mood repair strategies (Thayer 1996). Also, companies selling caffeine (e.g., coffee shops) and carbohydrates (e.g., cereal companies) have explicitly mentioned their products mood-lifting attributes. As already mentioned, one of Kelloggs websites, for instance, contains a section called Breakfast and Mood, in which the mood-regulating properties of breakfast cereals are highlighted: Several studies have shown that consuming high levels of carbohydrates is associated with better mood. As most breakfast cereals are high in carbohydrates, this offers one possible reason why eating a high carbohydrate breakfast is associated with improved mood.

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45 Product Benefits Manipulation Respondents were presented with a short scenario in which they had to imagine themselves in a purchasing environment where, after reading the information available about the product, purchase intentions were to be indicated. For those exposed to the coffee scenario, they were asked to imagine themselves in a coffee shop with a friend who planned to buy a cup of coffee. While they waited in line, he/she (the respondent) started reading a brochure about the benefits of caffeine. In the mood-lifting condition, the brochure stated that . . Popular culture is in fact correct. A research study conducted at UCLA showed that caffeine works as an effective mood regulator. Taken in small doseslike a cup or two a day, caffeine is a very effective mood lifter. In the no mood-lifting condition respondents were presented with the following statement . . A recent study conducted at UCLA showed that coffee is indeed a healthy product. Taken in small doses like a cup or two a day -, caffeine reduces chances of heart failures as well as neurological diseases. Those exposed to the breakfast cereal were asked to imagine themselves in a nearby supermarket. In the cereal aisle one particular cereal box had a message printed on the back. In the mood-lifting condition, the message was the following: Several studies have shown that consuming high levels of carbohydrates is associated with better mood. As cereal is high in carbohydrates, people who eat cereal have a happier diet! In the condition where no mood-lifting benefits were highlighted, the message contained the following statements: It is widely known that carbohydrates are an important nutrient. As cereal is high in carbohydrates, don't miss this opportunity to get those much needed nutrients every morning! Across all conditions students no information about the brand was provided.

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46 Dependent Measure Students had to wait at least 15 seconds before the purchase intention scale appeared on the screen, which allowed some control for unwanted processing style effects. As respondents in a negative (vs. positive) mood are usually more careful in their analysis (Schwarz 2001), we wanted to make it easy for respondents to read the information regardless of their affective states. After reading all the information about the product benefits, they were presented (at the bottom of the screen) with a nine-point scale on which they indicated their willingness to buy the product (9 = I would definitely buy it). Results Manipulation Checks Eight students provided a rudimentary guess of the purpose of the study. They were deleted from the sample. After checking for reliability ( = .92), the three affect-related items were collapsed to form the affect index. The affect manipulation produced a significant main effect on peoples affective states (F(2, 273) = 301.70, p < .001). Pairwise comparisons showed that compared to the neutral affect condition (M = 6.2), respondents in the negative affect condition experienced more negative feelings (M = 2.9; F(1, 190) = 339.49, p < .001), whereas respondents in the positive affect condition evaluated their affective state more positively (M = 7.5; F(1, 185) = 40.29, p < .001). To assess the product benefits manipulation, respondents were asked, at the end of the second study, to indicate (using a nine-point scale item) the extent to which they agreed with the statement that the product contained mood-lifting benefits. As expected, those in the mood-lifting condition perceived the products on average as more mood-lifting (M = 5.4) than those in the non mood-lifting condition (M = 4.0; F(1, 274) =

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47 33.21, p < .001). Moreover, the pattern remained the same across all affect conditions (F < 1). Purchase Intention Product replicate did not interact with product benefit and affective state (F(2, 264) = .99, p = .37), product benefit (F(1, 264) = 1.06, p = .30) or affective state (F(2, 264) = .52, p = .59), so data were collapsed across product replicates (i.e., coffee and cereal). All subsequent analyses followed a three (affect: negative vs. neutral vs. positive) by two (product attributes: mood-lifting vs. no mood-lifting) between respondents design. As predicted, respondents affective state interacted with the product benefits highlighted in the shopping scenario (F(2, 264) = 11.38, p < .001)(Figure 3). 5.184.225.123.74.95.12234567Negative Neutral Positive AffectPurchase Intentions mood-lifting benefits non mood-lifting benefits Figure 3. Behavioral intentions toward coffee and cereal (collapsed) According to IMAB, the impact of affect on behavior via the affective evaluation mechanism should be strongest when respondents do not have enough diagnostic information to make their evaluations and when there are no affect-regulation

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48 opportunities in the environment (i.e., non mood-lifting condition). In this type of scenario, a monotonic increase in behavioral intentions should emerge when feelings move from negative, to neutral, to positive, as a result of the use of affect-as-information and/or mood-congruency during the evaluation process. The results confirmed the models predictions. When respondents were presented with a brand of coffee or cereal with no mood-lifting benefits, there was a monotonic increase on purchase intentions as a function of experienced affective state (F(1, 143) = 9.57, p < .005). Pairwise comparisons showed that respondents experiencing negative affect were more reluctant to buy the product (M = 3.7) compared to respondents in a neutral affect condition (M = 4.9; F(1, 101) = 8.08, p = .005), as well as compared to respondents experiencing positive affect (M = 5.1; F(1, 85) = 9.60, p < .005). However, there was no difference between the neutral and positive affect condition (F < 1). IMAB hypothesizes that a U shaped pattern should emerge when respondents are asked to evaluate a product with mood-lifting benefits. In this case, respondents in a negative mood faced an affect-regulation opportunity. As a result, the affect-regulation mechanism was expected to increase purchase intentions despite the fact that the affective evaluation mechanism, by itself, would tend to lower purchase assessment judgments. As expected, when respondents were presented with a brand of cereal or coffee with mood-lifting benefits, their purchase intentions exhibited a quadratic form (F(1, 127) = 4.66, p < .05). Pairwise comparisons showed that when facing a product with mood-lifting benefits, respondents experiencing negative affect were more willing to buy the product (M = 5.2) than those in a neutral mood (M = 4.2; F(1, 87) = 3.78, p = .05). Respondents

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49 experiencing positive affect (M = 5.1) marginally increased their purchase intentions compared to a neutral condition (F(1, 83) = 3.12, p = .08). Discussion The findings provide initial support for our hypotheses. The expected monotonic increase in purchase intentions towards an unknown brand of coffee or cereal emerged as respondents feelings improved. This pattern, importantly, is contingent on the availability of affect-regulation opportunities. When the scenario highlighted the mood-lifting benefits of the product, then the predicted U shape curve emerged. As expected, respondents in a valenced affective state (positive or negative) were more willing to purchase the product than those in a neutral affective state, but for different reasons. When respondents experienced negative affect there was a tendency toward upward affect-regulation, and respondents approached the stimulus that might help them accomplish this goal. When respondents were in a positive affective state, they also increased their purchase intention, most likely due to a more positive evaluation of the product/purchase scenario. Notice that the theoretical accounts available in the literature cannot explain this combination of effects, which incorporate three out of the four categories of the affect-behavior relationship described in Figure 1 (Cells 1, 3, and 4). It is worth noting that although the results provided general support for the proposed model, positive affect (compared to the neutral affect condition) led to an increase in purchase intentions within the mood-lifting condition, but not within the non mood-lifting condition. Therefore, drawing the conclusion that respondents in a positive affective state were insensitive to mood-lifting benefits is premature. On the one hand, it is possible that affect-regulation may have also played a role on the positive side of the affective spectrum, since the difference between neutral and positive affect condition was

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50 significant only among those facing a mood-lifting opportunity. On the other hand, it is also possible that a heavy handed mood-lifting manipulation could have made respondents within the mood-lifting conditions more sensitive to their feelings, thereby making affect-mediated effects more likely. A less intrusive manipulation, where no explicit description of the mood-lifting properties of the product is highlighted, should eliminate this focus of attention bias and produce similar effects for the mood-lifting and non mood-lifting conditions when positive and neutral affect conditions are contrasted. Also, as experiment 1 employs only an indirect assessment of mediating processes, it is difficult to detect when the two mechanisms operate simultaneously. More direct assessments may help to disentangle the effects of evaluation and regulation when both can predict the same outcome (i.e., increase in purchase intentions for those experiencing positive affect). By the same token, we cannot determine the extent to which affective evaluation and affect-regulation interacted when sad people faced an affect-regulation opportunity. One might wonder whether heightening the activity of the affect-regulation mechanism switches off a less intense affective evaluation effect or, on the other hand, whether affect-regulation simply increases the weight of the products mood-lifting benefits.

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CHAPTER 7 EXPERIMENT 2 Experiment 2 attempted to replicate the first experiment, while addressing the concerns discussed above. First, to address a possible focus of attention bias (i.e., the likelihood that respondents in the mood-lifting conditions became more responsive to feelings), we looked for a scenario in which peoples intrinsic perceptions of mood-lifting properties of the product could vary without any sort of explicit manipulation. The helping literature has provided precedent for such a procedure. Cialdini and Kenrick (1976) showed that whereas adults in a bad mood are more willing to help others (probably in an attempt to regulate their negative mood), the effects reverse among children. Their explanation is that adults are more likely to perceive the mood-lifting benefits associated with helping, whereas children are yet to learn such associations. Following the same rationale, research on eating behavior shows that men and women have different beliefs about food intake in general, and chocolate in particular. First, women are more likely to use food consumption as a mood-lifting alternative than are men (Macht 1999; Steptoe, Pollard, and Wardle 1995). Also, evidence suggests that women are more likely than men to perceive chocolate as a mood-lifting product (Benton, Greenfield, and Morgan 1998; Grunberg and Straub 1992). If this is the case, then women in a negative affective state should be more willing to try a piece of chocolate (compared to a neutral affect condition), as a result of the affect-regulation mechanism. Notice that this manipulation relies on one of the basic tenets of the model, in which the impact of the affect-regulation mechanism is contingent on peoples 51

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52 intuitive theories about the affective consequences of their behavior (e.g., the mood-lifting benefits of chocolate). Among men, who are not as likely to perceive the affect-regulation opportunities of chocolate, negative affect should lead to a decrease in behavioral intentions compared to the neutral affect condition (via the affective evaluation mechanism). For those already experiencing positive affect, both women and men should be equally willing to taste a piece of chocolate (via the affective evaluation mechanism). In other words, across the three levels of affect, a U shape pattern is expected among women due to a combination of affect-regulation within the negative affect condition and affective evaluation within the positive affect condition, whereas a monotonic increase is expected among men due mainly to the impact of the affective evaluation mechanism. Second, to provide more direct evidence about the two mediating processes hypothesized by IMAB, an open-ended question asked respondents to explain their indicated behavioral intentions. Additional items at the end of the experiment also examined the independent impact of the evaluative and regulatory mechanisms. Finally, a new neutral affect condition was used to generate a more symmetrical difference across the three levels of affective states. Method Subjects and Design One hundred fifty-one undergraduate students from a southeastern university participated in the experiment in exchange for course credit. The study adopted a two (gender: men vs. women) by three (affective state: negative vs. neutral vs. positive) between subjects design. Respondents were randomly assigned to one of the six conditions.

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53 Procedure The procedure was similar to that presented in experiment 1. A two-studies cover story was introduced. Study 1 manipulated respondents affective states. A new video (Documentary about John Nash) was presented to students in the neutral affect condition to get a more neutral point on the scale. The other two videos were the same as in the first experiment. In the second study respondents were informed that a foreign company was about to introduce a new chocolate in the American market. The gist of the story was that as sampling promotions in supermarkets turn out to be quite expensive, the company had decided to use a new marketing tool, a so-called Virtual Sampling Promotion. Respondents were then instructed to imagine themselves in a real sampling promotion scenario. A picture of chocolate bars was presented, and they were asked to indicate the extent to which they would try the product. To attach some sort of cost to the behavioral activity, since these were free products, respondents were told to imagine that they would have to answer a 6-minute survey if they decided to taste it. After indicating their willingness to try the product along a 9-point scale (9 = I would definitely try it), respondents were asked to explain their indicated behavioral intentions. Respondents were then presented with a series of items, which assessed the manipulations and the impact of the mediating processes. To confirm the assumption that women are more likely than men to perceive eating chocolate as an affect-regulation opportunity, peoples instrumental use of chocolate was assessed with a single item I eat chocolate to feel better. Another item assessed the impact of affective evaluation on the cost associated with the behavior (i.e., answering the survey) I was a bit concerned that it might take too long to answer the questionnaire. Finally, to check for potential confounding, additional items checked the

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54 extent to which participants were hungry and thirsty, whether or not they had heard of the particular brand before, and if the product had made them think of other products that could have influenced their evaluations. Results Manipulation Checks Six students provided a rudimentary guess of the purpose of the experiment. They were deleted from the sample. After checking for reliability ( = .91), the three affect-related items were collapsed to form the affect index. The affect manipulation produced a significant main effect on peoples prior affective state (F(2, 142) = 124.69, p < .001). Pairwise comparisons showed that compared to the neutral affect condition (M = 5.2), respondents in the negative affect condition experienced more negative feelings (M = 3.3; F(1, 98) = 46.76, p < .001), whereas respondents in the positive affect condition evaluated their affective state more positively (M = 7.8; F(1, 98) = 84.98, p < .001). Finally, the assumption that women are more likely to perceive chocolate as a mood-lifting product was supported. Female respondents were much more likely to acknowledge eating chocolate to feel better than were male respondents (M women = 5.7 vs. M men = 2.7; F(1, 143) = 59.34, p < 001). Purchase Intention As hypothesized, gender and affective state produced a significant interaction (F(2, 139) = 5.09, p < .01). Most importantly, breaking down the analysis by gender, a monotonic increase was observed among men F(1, 80) = 15.29, p < .001), whereas a U shape pattern emerged among women (F(1, 59) = 9.97, p < .005)(Figure 4).

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55 7.385.967.674.795.917.18345678Negative Neutral Positive AffectWillingess to try "Novo" Women Men Figure 4. Behavioral intentions toward chocolate IMAB proposes that as men are less likely to perceive the mood-lifting benefits of eating chocolate, the affective evaluation mechanism should direct their behavior, leading male respondents in positive (negative) affect to increase (decrease) their behavioral intentions, compared to the neutral affect condition. Pairwise comparisons indicated that, when facing an opportunity to try a piece of chocolate, men in a sad mood (M = 4.8) seemed less willing to do so than those in the neutral affect condition (M = 5.9; F(1, 54) = 2.76, p = .10). The opposite was true for those in the positive affect condition, who indicated higher intentions (M = 7.2) to try the chocolate in the same hypothetical scenario (F(1, 57) = 5.6, p < .05). Among women, the affect-regulation mechanism was hypothesized to have a strong influence on behavioral intentions, since this group is more likely (as confirmed by the manipulation checks) to perceive eating chocolate as an affect-regulation opportunity. In accordance with IMABs predictions, women experiencing negative affect indicated stronger intentions to taste the chocolate (M = 7.4) compared to those in the neutral affect

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56 condition (M = 6.0; F(1, 42) = 5.8, p < .05) Women experiencing positive affect also exhibited stronger behavioral intentions compared to those in the neutral affect condition (M = 7.7; F(1, 39) = 6.9, p < .05). IMAB proposes, however, that the latter difference is due to affective evaluation rather than regulation. Since male respondents, who reported not using chocolate as an affect-regulation device, displayed similar patterns on the positive side of the affective spectrum, it seems plausible to suggest that evaluation (instead of regulation) represented the main mediating process for women under positive affect. Affective Evaluation Mechanism Further evidence regarding mediating processes was obtained with the open-ended question and the additional items. IMAB proposes that the affective evaluation mechanism leads people experiencing negative (positive) affect to perceive the costs/risks associated with any behavior as more (less) negative. Remember that across all conditions an element of cost, orthogonal to the product, was included. Those choosing to taste the chocolate were told they had to answer a 6-minute survey. Therefore, if the affective evaluation mechanism is operating, respondents experiencing negative affect should consider this cost element more carefully than those experiencing positive affect. As already mentioned, one item assessed peoples concern about the length of the survey. The results show that affect produced a main effect on respondents concerns about costs (i.e., survey length) associated with the tasting the chocolate (F(2, 142) = 3.28, p < .05). Although they were explicitly told that the survey would take precisely six minutes, sad respondents (M = 6.4) were more concerned that the survey might take too long than those in the neutral (M = 5.2) and positive (M = 4.9) affect conditions (F(1, 98) = 4.12, p < .05; F(1, 88) = 6.23, p < .05, respectively). The difference between neutral and positive

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57 affect conditions was non significant (F < 1). Also important is the fact that no interaction between gender and affect emerged (F < 1). Thus, men and women seemed similarly influenced by the affective evaluation mechanism. The open-ended question also permitted tracking the impact of the affective evaluation mechanism by counting the number of people who mentioned the survey in their justification of their behavioral intentions. The impact of affect was quite evident. Only 9% of respondents in the positive affect condition mentioned the survey in the justification, compared to 33% of respondents in the neutral affect condition (Z = 3.14, p < .01, one tailed test) and 38% in the negative affect conditions (Z = 3.45, p < .01, one tailed test). Once again, the pattern remained the same across gender (table 1). Table 1. Influence of affective evaluation on behavioral intention AFFECT GENDER Negative Neutral Positive Women 33.3% a 43.5% a 5.3% b Men 41.7% a 21.9% b* 11.1% b Gender Collapsed 37.8% a 32.7% a 8.9% b Note: Different superscripts indicate significant differences at p < .05. The asterisk indicates marginal significance (p < .10). Combined, these results suggest, first, that affective evaluation mitigates (enhances) the perceived costs associated with the survey for respondents in a positive (vs. negative) affective state, which can explain why both men and women were more inclined to try the chocolate while experiencing positive affect. Second, these results also imply that if this mechanism were to operate in isolation, women in the negative affect condition should be less inclined to try the chocolate. Since women experiencing negative affect were, on the contrary, more willing to try the chocolate than those in the

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58 neutral affect condition (and equivalent to those experiencing positive affect), another opposing mechanism must have reversed the effects. According to the IMAB, affect-regulation can produce such effects. Affect-Regulation Mechanism According to the model, affect regulation is contingent on peoples beliefs about the affective consequences of a particular behavior (e.g., I eat chocolate because I know it will make me feel better). Although, in general, women were found to eat chocolate to feel better much more often than men, there was a certain degree of variance within each gender condition. Thus, we should expect a decrease in behavioral intentions for those women who acknowledged they were less likely to eat chocolate to feel better, and an increase in behavioral intentions for those men who acknowledged they were more likely to eat chocolate to feel better. A series of 6 bivariate Pearson correlations (one per condition) between respondents acknowledged use of chocolate as a mood-lifting product and their respective intentions to taste the chocolate was conducted. The correlation should be high (low) in the conditions where affect-regulation is expected to have a strong (weak) influence. Table 2 summarizes the results. Table 2. Influence of acknowledged use of chocolate to lift mood on behavioral intention AFFECT GENDER Negative Neutral Positive Women 0.49** 0.04 0.04 Men 0.37* 0.28 0.14 Note: Double-asterisks indicate Pearson correlation significant at p < .05 level (2-tailed). Single asterisks indicate Pearson correlation significant at p < .10 level (2-tailed).

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59 As can be observed, the strongest effect (i.e., the only significant result at p < .05) emerged when female respondents were experiencing negative affect, the condition in which affect-regulation was hypothesized to direct behavioral intentions. In other words, intentions to try the chocolate were lowered for female respondents who acknowledged not using chocolate to regulate their affective states. A marginal effect was also found among men in the negative affect condition, suggesting that those men who are more likely to eat chocolate to lift their mood were more willing to try the chocolate. Among those experiencing neutral or positive affect, there were no significant correlations, which rules out regulatory influences of affect beyond the negative side of the affective spectrum. These results not only challenge the mood-maintenance type of explanation for those experiencing positive affect but also support IMABs assumption that affect-regulation is less likely within neutral affect conditions. Discussion By relying on peoples perceptions of the products mood-lifting properties rather than using a more heavy handed manipulation, experiment 2 successfully replicated the earlier results while eliminating the focus of attention bias inherent in the first experiment. Most importantly, it also shed additional light on the two mediating processes assumed to link affect to behavioral intentions. First, among respondents experiencing positive affect, the affective evaluation mechanism seems to have a predominant role, since the impact of the survey (i.e., cost associated with the behavior) weakened as respondents feelings improved. Moreover, affect-regulation seems inactive for happy respondents since, unlike for sad respondents, (1) there was no correlation between respondents acknowledged use of chocolate as a mood-lifting option and their respective behavioral intentions and (2) intentions to try the

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60 chocolate also increased among male respondents, who, based on self reports, were in general unlikely to eat chocolate in an attempt to regulate their mood. Second, affect-regulation seems to have a predominant role among the women who experienced negative affect, since their behavioral intentions increased (compared to those in neutral conditions), and there was a significant positive correlation between the use of chocolate as a mood-lifting product and their behavioral intentions. In other words, behavioral intentions were higher for women who were more likely to perceive eating chocolate as an affect-regulation opportunity. Notice that these results also support the assumption that as neutral affective states trigger weak signals, the discrepancy between the current and the ideal affective state becomes less salient, which results in minimal affect-regulation tendencies. Finally, the interaction between the two mechanisms can be observed in negative affect conditions. Independent of gender, the costs associated with the survey increased in the negative (positive) affect condition, which indicated the negative impact of the affective evaluation mechanism. Nonetheless, female respondents were highly willing to taste the chocolate due to its regulatory properties, as indicated by the correlational analysis. Such opposing patterns suggest, therefore, that affect-regulation does not necessarily switch off the affective evaluation mechanism, but instead dominates its opposing effect, probably by increasing the weight given to a products mood-lifting benefits.

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CHAPTER 8 EXPERIMENT 3 So far, IMAB has demonstrated and explained three out of the four types of effects presented in Figure 1. The potential inhibitory consequences of positive affect (i.e., Cell 3) remain to be examined. Can positive affect mitigate peoples willingness to eat chocolate? According to model, the affect-regulation mechanism is more likely to guide behavior if people can anticipate the affective consequences of the behavioral activity. When people in a bad mood anticipate that their behavior will make them feel better, they become more likely to act. Experiments one and two confirmed that hypothesis. It follows that when people in a good mood anticipate that their behavior will make them feel worse, they should become less likely to act than those in the control condition, simply because they have more to lose. Research in risk taking has provided support for this rationale (see Isen 2000 for a review). Experiment 3 focuses therefore on the facilitatory (Cell 1) and the inhibitory (Cell 2) behavioral consequences of positive affect. A major procedural change (compared to the previous experiments) is that a direct behavioral measure rather than intention is used as the main dependent measure. Therefore, respondents faced actual, instead of projected, behavioral consequences throughout the experiment. Similar to experiment 2, chocolate represents the target stimulus; however, in this final experiment actual consumption is assessed. Popular culture as well as the scholarly research largely support the assumption that chocolate consumption can be associated with a posteriori negative affect, notably feelings of guilt (Benton et al. 1998; Cramer and Hartleib 2001; Macdiarmid and 61

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62 Hetherington 1995). Thus, to create a mood-threatening cue condition that may lead people experiencing positive affect to be less likely to act, a group of respondents is reminded of the negative consequences of the particular behavior (i.e., fat and fat calories associated with chocolate consumption). IMAB predicts that happy respondents facing a potential affective threat (subsequent feelings of guilt) will eat less chocolate (compared to the respective neutral affect condition), due to the protective response of the affect-regulation mechanism. When no mood-threatening cues are made salient, happy respondents are expected to consume more chocolate than the control condition as a result of the affective evaluation mechanism (i.e., a better evaluation of the environment). Since the fat components of chocolate are likely to be perceived as a negative attribute by both men and women, gender should not interact with the other two factors (i.e., affect and mood-threatening cue). Method Subjects and Design One hundred sixty-seven undergraduate students from a southeastern university participated in the experiment in exchange for course credit. The study adopted a two (affective state: neutral and positive) by two (mood-threatening cue: salient vs. not salient) between subjects design. Respondents were randomly assigned to one of the four conditions. Procedure To keep the affect manipulation similar to the previous studies, the cover story focused on the impact of video on memory, but it added information identifying potential interactive effects of food consumption. As in experiments 1 and 2 respondents were presented with a five-minute program (an episode of Friends vs. a documentary about

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63 John Nash), and were asked to recall a personal experience related to film (affect manipulation) as well as to evaluate the video. Three items embedded into the scale assessed respondents current affective state (affect manipulation check). Then respondents were instructed to open an envelope placed to the left of the computer monitor. Inside was 89-gram package of M&Ms. A five-minute sequence of the previous movie was then presented on the screen and respondents task was simply to watch the video while eating as many or as few M&Ms as they wanted. Food consumption while watching a movie represents a common data collection procedure in the eating behavior literature (Cools, Schotte, and McNally 1992; Grunberg and Straub 1992). Each M&M package was weighed before and after the experiment to ensure an accurate measure. The amount of M&Ms consumed became the dependent variable. To manipulate the mood-threatening cue associated with eating behavior an additional instruction page was given to one subgroup before they started watching the video and eating the chocolate candies. These respondents were informed that as different experimental groups tasted different products, the experimenter had to control for the number of calories consumed. Therefore, respondents should look for the nutrition facts table on the M&M package and enter the number of calories and fat calories of this particular product on the screen. In the control group, no nutrition information about the product was requested from respondents. After watching the second part of the video, respondents were instructed to put the M&M package back into the envelope and answer some final questions/fill out some scales. An additional memory type of question was asked to complete the cover story followed by another affect manipulation check, also embedded into the movie evaluation

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64 scale. Then, in an attempt to gather additional insights into peoples motivations, an open-ended question about the potential reasons that stimulated or impeded their actions (i.e., chocolate consumption) followed. Similar to experiment 2, respondents also responded to items referring to their general consumption habits and perceived relationships between mood and chocolate consumption. To assess the impact of potential covariates, a Restraint Questionnaire (Herman and Polivy 1975; Polivy, Herman, and Warsh 1978), was presented, which assesses peoples chronic attempt to keep strict control over their eating behavior. Respondents body mass index (BMI) was also calculated, and the time of data collection was also recorded. All experimental sessions took place between 2:00 pm and 4:30 pm. Results Manipulation Checks. Two students provided a rudimentary guess as to the purpose of the experiment. They were deleted from the sample. After checking for reliability ( = .88), the three affect-related items were collapsed to form an affect index. The affect manipulation produced a significant main effect on respondents prior affective state. Respondents exposed to first five-minute exposure of the video plus the description of a personal experience were happier when they watched an episode of friends (M = 7.9) than when they watched a documentary about John Nash (M = 5.6; F(1, 163) = 161.32, p <.001). The mood-threatening cue manipulation was assessed by the number of respondents who entered the calories and fat calories components of their respective M&M package in the requested blank spaces. As expected, all respondents in the salient mood-threatening cue condition correctly followed the instructions. Neither the body mass index nor the Restraint Scale produced any impact on eating behavior and were not incorporated in

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65 further analyses. Similarly, eating behavior remained the same regardless of the time of the experiment (2pm, 3pm, or 4pm). Gender produced a marginal simple main effect on chocolate consumption, with male respondents eating more (M = 26.3) than female respondents (M = 21.8; F(1, 163) = 3.70, p <.10). However, as predicted, no three-way interaction was observed (F(1, 157) = 2.59, p >.10). The interactions between gender and affective state and gender and mood-threatening cue were also non significant (F<1). Eating Behavior As hypothesized, mood-threatening cue (salient vs. not salient) and affective state (positive vs. neutral) produced a significant interaction (F(1, 161) = 8.25, p =.005)(Figure 5). 23.3323.5230.9518.055101520253035non salient moodthreatening cuessalient moodthreatening cuesgrams of M&Ms consumed neutral affect positive affect Figure 5. Amount of chocolate consumed When respondents were asked to eat as much or as little chocolate as they wanted and no mood-threatening cues associated with the product/behavior were highlighted, respondents experiencing positive affect ate more M&Ms (M =30.9) than those in the neutral affect condition (M = 23.3; F(1, 81) = 4.42, p <.05). The results reversed when

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66 the calories and fat calories associated with M&Ms (i.e., the mood-threatening cues of the product/behavior) were made salient prior to consumption. In this case, those in the positive affect condition ate significantly fewer M&Ms (M = 18.0) than did those in the control condition (M = 23.5; F(1, 80) = 3.96, p =.05). This interaction not only replicates the impact of positive affect on action stimulation (Cell 1) with a direct behavioral measure, but also, and most importantly, demonstrates that positive affect can also lead to inhibitory actions (i.e., less consumption) when the behavior threatens peoples current affective state (Cell 2). Affect-Regulation Mechanism IMAB proposes that peoples anticipated (post behavior) affective state can promote or inhibit consumption via the affect-regulation mechanism. Therefore, respondents experiencing positive feelings, having more to lose than those in a control condition, should eat less chocolate when the mood-threatening consequences of the behavior are made salient. This rationale is based on the assumption that chocolate can trigger guilt feelings a posteriori and that people can anticipate them (Benton et al. 1998; Cramer and Hartleib 2001; Macdiarmid and Hetherington 1995), which makes happy people more conservative in their actions, in an attempt to protect the current pleasant affective state. To assess the impact of guilt, respondents were asked (at the end of the experiment) to indicate the extent to which they generally had guilt feelings after overeating (a four point scale from never to always). This item was extracted from the Restraint Questionnaire (Herman and Polivy 1975; Polivy et al. 1978). A correlational analysis (within each of the four cells) between the grams of chocolate consumed and peoples

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67 acknowledgement of experiencing guilt feelings after overeating was then performed. Table 3 summarizes the results. Table 3. Protective influence of overeating-based guilt feelings on behavior AFFECT MOOD-THREATENING CUE Neutral Positive Yes -.12 -.30* No -.01 .07 Note: The asterisk indicates Pearson correlation significant at p < .10 level (2-tailed). When the mood-threatening cues associated with the product/behavior were not made salient (i.e., cells at the bottom row), respondents guilt feelings after overeating did not correlate with the amount of chocolate consumed. Similarly, when people experienced neutral affect, thereby having little to lose and/or a weak signal (i.e., cells at the left column), the correlation was also non significant. However, when respondents experiencing positive affect, thereby having a lot to lose and/or a strong signal, are given the mood-threatening cues for chocolate consumption, a marginally significant negative correlation emerged (r = -.30, p = .064). In other words, within this condition, those who often or always feel guilty after overeating (M = 16.2) tended to eat significantly less chocolate compared to those who rarely or never experience these negative feelings after overeating (M = 21.3, F(1, 36) = 3.10, p < .10)(Figure 6).

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68 10121416182022NeverRarelyOftenAlwaysFeelings of Guilt after OvereatingGrams of M&Ms Consumed Figure 6. Amount of chocolate consumed within the positive affect /mood-threatening condition the impact of expected feelings of guilt after overeating Within the positive affect no mood-threatening cue condition, an increase in chocolate consumption emerged compared to the respective neutral affect condition. Although IMAB predicts this impact to be mostly driven by the affective evaluation mechanism, others (Clark and Isen 1982) suggest that mood-maintenance (i.e., affect-regulation) could also be responsible for the effects. In experiment 2, this alternative explanation was ruled out by showing that within the positive affect conditions there is no correlation between respondents acknowledgement of the use of chocolate as a mood-lifting strategy (i.e., I eat chocolate to feel better) and their willingness to try the chocolate. In experiment 3 two items were used to test this potential correlation (i.e., I eat chocolate to cheer myself up; Eating chocolate when Im happy helps me to maintain my good mood). If a positive correlation emerged between chocolate consumption and these two items, then one can claim there is initial evidence for affect-regulation mechanism. If no correlation exists, affect-regulation probably did not play a major role. None of the items correlated with chocolate consumption (p > .50 for both items), which replicates the results presented in experiment 2 using an actual behavioral

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69 measure. In other words, the extent to which respondents consume chocolate as a mood-lifting or mood-maintenance strategy did not lead them to eat more or less. Therefore, as predicted by IMAB, the affect-regulation mechanism seems not to have a major impact on the increase of chocolate consumption in the absence of mood-threatening cues. Instead this effect was probably guided by a more positive evaluation of the product/behavior. Discussion The main purpose of the third experiment was to assess the extent to which positive affect can inhibit behavior (Cell 2 of Figure 1). IMAB predicts that this can occur once mood-threatening cues are made salient, thereby leading happy individuals to adopt a more conservative response in an attempt to protect their current enjoyable feelings. Using actual chocolate consumption, it was shown that respondents experiencing positive affect ate less chocolate (compared to the neutral affect condition), once the fat and fat calories of the product were made salient prior to consumption. Moreover, this effect seemed to intensify (vs. weaken) among respondents who acknowledge being more (less) likely to feel guilty after overeating. Notice that, similar to experiment 2, this effect is based on IMABs assumption that the affect-regulation mechanism is contingent on people intuitive theories about the affective consequences of their respective behavior. In experiment 2, those within the negative affect condition who expected to feel better after the behavior were more likely to try the chocolate. In experiment 3, the reverse rationale applied. Those within the positive affect condition who expected to feel worse after the behavior were less likely to eat chocolate. When the mood-threatening cues (i.e., calories and fat calories of M&Ms) were not highlighted, happy respondents ate more chocolate than did those in the neutral affect

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70 condition. Moreover, within this particular Cell there was no correlation between peoples acknowledged use of chocolate for mood-lifting or mood-maintenance purposes and the grams of chocolate consumed. Thus, similar to experiment 2, and as predicted, affect-regulation was not responsible for the effect, suggesting that affective evaluation was the operative mechanism.

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CHAPTER 9 GENERAL DISCUSSION The behavioral consequences of affect can be categorized into four groups: positive affect encouraging action, positive affect inhibiting action, negative affect encouraging action, and negative affect inhibiting action. The several theories and hypotheses developed since the early 1970s have focused prominently on one, or at most, two of these four potential effects (Figure 1), thereby providing no parsimonious explanation of how affect influences behavior. To fill this theoretical gap an integrative model of affective behavior (IMAB) was proposed, in which two well established properties of affect (i.e., evaluative/informational and regulatory/motivational) represent the potentially opposing mediating mechanisms responsible for the behavioral consequences of individuals current affective states. Moderating variables attached to each of these mechanisms determine which mediating process prevails and consequently guides behavior. In a series of three experiments, it was demonstrated that peoples current affective state represents a critical moderator for both the affective evaluation and affect-regulation mechanisms, where valenced affective states tend to lead to stronger behavioral effects compared to control conditions. Most importantly, the expected mood changing characteristic of the behavioral activity (i.e., the mood-lifting cues in experiments 1 and 2 and mood-threatening cues in experiment 3) and its interaction with peoples current affective state help to determine the extent to which the affect-regulation mechanism will prevail over the affective evaluation mechanism, and eventually direct behavior. 71

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72 Rooted in the mood-congruency and affect-as-information hypotheses, the affective evaluation mechanism predicts that people will congruently evaluate any behavioral activity. As a result of a congruent evaluation, happy (sad) people become more (less) likely to act, especially when there is no competing, more diagnostic information available in the environment. Experiment 1 (within non mood-lifting conditions) and 2 (among men) confirmed the effect. Experiment 3 (within non mood-threatening conditions) further replicates it using a direct behavioral measure. These affective evaluation-mediated effects characterize Cells 1 and 4 of Figure 1. The affect-regulation mechanism, however, may come into play as a counteracting force if certain conditions are met. For those experiencing negative feelings while facing a potential mood-lifting opportunity (i.e., having their affective state salient in memory (Cohen and Andrade 2004) and having much to gain (affectively) via the behavioral activity, affect-regulation encourages action, overcoming the opposing impact of the affective evaluation mechanism. Experiments 1 (within the mood-lifting conditions) and 2 (among women) confirm these predictions. Respondents under such conditions indicated they were more likely to purchase coffee and breakfast cereal (experiment 1), and to taste chocolate in an imaginary situation (experiment 2) than those in the respective neutral affect conditions. For those experiencing positive affect while facing a mood-threatening scenario, (i.e., having their affective state salient in memory, but also having more to lose), affect-regulation discourages action. This can overcome the stimulating effects of the affective evaluation mechanism. Experiment 3 confirms this prediction by showing that when happy respondents are required to monitor the calories and fat calories associated with chocolate prior to consumption they eat less chocolate

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73 than those in the neutral affect condition. These affect-regulation-mediated effects characterize Cells 3 and 2 of Figure 1. IMAB incorporates the basic rationale of two groups of theories already well established in the literature, but that have followed independent routes. One the one hand, it fully incorporates the basic assumptions advanced by the affect-as-information hypothesis into the affective evaluation mechanism. By the same token, it combines central aspects of several of the affect-regulation types of theories into a newer and more comprehensive affect-regulation mechanism. As a result of such integration IMAB has the ability to account for a broader array of effects described in the literature. However, it does not necessarily invalidate other accounts. For instance, the fact that the affect-as-information or mood-congruency hypotheses cannot explain the impact of negative affect on the increase of purchase intentions does not make these theories inaccurate in any sense; it simply shows that there are some effects that go beyond the scope of these models. Put simply, Cells 2 and 3-types of effects do not provide empirical evidence against theories focused on explaining the effects of Cells 1 and 4. In this sense, IMAB complements more than competes against the current models, and it may be preferable on the basis of offering a more parsimonious and internally consistent account. Nevertheless, the proposed theory does provide a competing explanation for behavior that has been linked to several existing theories, such as the mood-maintenance hypothesis (Clark and Isen 1982). Mood-maintenance proposes that peoples willingness to maintain their current affective state will lead them not only to protect themselves against mood-threatening behavior (Cell 2), but also to promote mood-lifting behavior, such as helping (Cell 1). Whereas IMAB recognizes and empirically confirms the former,

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74 it raises questions as to the latter. It was shown that the increase in intentions (experiment 2) and actual consumption (experiment 3) of a mood-lifting product among happy people (compared to the neutral affect condition) were most likely driven by peoples positive assessment of the environment (i.e., affective evaluation), rather than a systematic attempt to act in order to keep a current positive affective state (i.e., affect-regulation). In experiment 2, it was shown that men in the positive affect condition were more likely to eat chocolate than in the control condition, despite the fact that they did not perceive chocolate as a mood-lifting product. Also, a correlation between the extent to which chocolate is used as a mood-lifting strategy and respondents willingness to try the product did not emerge for both men and women within the positive and neutral affect conditions, but did appear for both men and women within the negative affect condition. Finally, in experiment 3 there was an increase in actual chocolate consumption from neutral to positive affect conditions. However, peoples self-rated use of chocolate to keep their good mood or to improve it did not correlate with actual consumption. Since existing empirical evidence for mood-maintenance under Cell 1 (i.e., promote a mood-lifting action to keep a good mood) is entirely consistent with the IMAB and does not require a separate mood-maintenance heuristic, IMAB dominates mood-maintenance as an explanation for this type of behavior. As shown throughout this research, a better/safer evaluation of the environment rather than the need to keep a good mood seems more likely to lead people experiencing positive affect to be more active. However, when it comes to mood-threatening scenarios, IMAB fully converges with the mood-maintenance hypothesis (Isen 2000 for a review), in which people become more

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75 sensitive and more protective of their behavior (compared to a control condition), because they have more to lose (Cell 3). In integrating these two mechanisms, the model also advances theory also by explicitly identifying the critical moderating variables attached to each mechanism, particularly for affect-regulation. IMAB is also able to account for the weak/null effects of neutral affect compared to polarized affective states. The three experiments show that people in a more neutral affective state are usually weakly sensitive to available mood-lifting or mood-threatening cues. Based on recent evidence and theoretical propositions (Cohen and Andrade 2004), IMAB suggests that affect-regulation is not only a function of the discrepancy between the current and desired affective state, but also of the strength of the affective signal, which makes the existing discrepancy more or less salient. As neutral affective states provide weak signals, the discrepancy is not highlighted and the motivation for change becomes much weaker compared to polarized affective states. Once the discrepancy is made salient, expected affective changes (i.e., from peoples intuitive theories as to the mood-lifting and mood-threatening properties of the product/behavior) are critical to affect-regulation and follow the same theoretical rationale for both negative and positive affective states. Finally, IMAB points to a tempting conclusion that, compared to a control condition (i.e., neutral affect), the affective evaluation mechanism is more likely to influence behavior when we find happy people more willing to act (Cell 1) and sad people less willing to act (Cell 4), whereas the affect-regulation mechanism is more likely to have influenced behavior when we find sad people more willing to act (Cell 3) and happy people less willing to act (Cell 2). However, it is possible that under certain

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76 circumstances, regulation and evaluation may combine to promote or inhibit behavior, although as to which contingencies are required remains an open question. Also, the presumed interdependence of affective evaluation and affect-regulation implies that some moderating variables can well have an impact on both mechanisms at the same time. For instance, lack of diagnostic information about the behavior/environment tends to instantiate the affective evaluation mechanism. However, it may also mitigate the impact of the affect-regulation mechanism if this information modifies the perceived mood changing properties of the behavior. Moreover, some of the moderating variables described by the model (e.g., availability of competing goals, peoples beliefs about their affect-regulation skills) are also to be explored. Finally, more specific emotions can also be incorporated into the model. Raghunathan and Pham (1999) showed that anxious people are less likely to take risks compared to sad people. In IMAB terms, the reason is simply that anxious people do not perceive risk-taking as a potential upward affect-regulation opportunity whereas sad people do. In short, specific emotions may also be an interesting way of enhancing or inhibiting the impact of the affective mediating processes on behavior.

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PAGE 97

BIOGRAPHICAL SKETCH I graduated in Business Administration at the Universidade Federal de Santa Catarina in Florianopolis (SC) Brazil, in December 1993. A few years later, I received my masters degree in business (marketing option) from the Universite de Montreal HEC School. My interests in marketing and, particularly, in consumer behavior led me to move to the University of Florida to pursue a Ph.D. in the marketing department. I will be joining the Haas School of Business at the University of California-Berkeley as an Assistant Professor of Marketing in July 2004. 87


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BEHAVIORAL CONSEQUENCES OF AFFECT: COMBINING EVALUATIVE AND
REGULATORY PROPERTIES















By

EDUARDO BITTENCOURT ANDRADE


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2004

































Copyright 2004

by

Eduardo Bittencourt Andrade


























This dissertation is dedicated to my parents.















ACKNOWLEDGMENTS

First of all, I would like to thank Joel Cohen, who supervised this dissertation with

an incredible sense of balance between academic guidance and intellectual freedom. I

also extend thanks to Chris Janiszewski, Richard Lutz, and Margaret Bradley, for their

support. I thank also Alan d'Astous (from HEC Montreal), who introduced me to the

Marketing Department at the University of Florida. The Warrington College of Business

Administration provided me with financial assistance in completing this dissertation, for

which I'm grateful. Even far away from Gainesville, my family and close friends

managed to provide me the emotional support needed to keep the discipline and resolve

throughout these long years. I thank them all.
















TABLE OF CONTENTS

Page

A C K N O W L E D G M E N T S ................................................................................................. iv

L IST O F T A B L E S ......................... ........ ...................... .. .. .. ...... ............. .. vii

LIST O F FIG U RE S .......................................................................... viii

ABSTRACT .............. .......................................... ix

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

A ffe c t ................................................................................. 3
M ediational Processes ........................................ .......................... .4

2 THEORETICAL BACKGROUND .................. ...... ...........................
2 THEORETICAL BACKGROUND.......................................7

Affective Evaluation (Affect's Informational Role)................. ............................8
Affect-Regulation (Affect's Motivational Role) ...................................................9

3 INTEGRATIVE MODEL OF AFFECTIVE BEHAVIOR (IMAB)..........................12

4 INITIAL EVIDENCE SUPPORTING THE MODEL: THE IMPACT OF AFFECT
ON HELPING, RISK TAKING, AND EATING BEHAVIOR..............................20

H elp in g ............................................................... ................ 2 0
Positive A effect and H helping ........................................ .......................... 21
N negative A effect and H elping..................................... ......................... .. ......... 22
Theoretical Integration ............................................... ............................. 25
R isk -T making ................................................... .............................. 2 6
Negative Affect and Risk-Taking...................... .... ......................... 27
Positive A effect and Risk-Taking ........................................ ...... ............... 30
E ating B eh av ior ................................................................... ................ ... 3 1
N negative Affect and Eating Behavior............................................................... 32
Positive Affect and Eating Behavior ..........................................................34
S u m m ary ...................................... .................................................. 3 6









5 TESTIN G IM AB : PRED ICTION S .................................. ..........................................39

6 EXPERIM ENT 1 .................................... ..... .......... ....... ..... 42

M eth o d .................. ...................................................................................4 2
Subjects an d D esign .................................................................................42
Procedure ................ ............................... 42
A effect M manipulation ................................................... ............................... 43
R ep lic ate s ............................................................4 4
Product B benefits M anipulation......................................... ......................... 45
D ep en dent M easu re ......... .........................................................................4 6
Results .............................................. ....... .................... 46
M anipu lation C h eck s........................................... ...........................................4 6
Purchase Intention ............................. .................... .. ........ .. .............47
Discussion ......................... ...................... 49

7 E X PE R IM E N T 2 ......... ......... ...... ....................................................... 51

M eth o d .............. ...................................................................................... 52
Subjects an d D esign .................................................................................52
Procedure ................ ............................... 53
Results .............................................. ....... .................... 54
Manipulation Checks.................... ..............................54
Purchase Intention ........................... ...... .. .. ...... ...............54
Affective Evaluation Mechanism ................... ........... ................. 56
A ffect-R regulation M echanism ........................................ ......................... 58
D discussion ......... ............................................................................................... 59

8 EXPERIM ENT 3 .................................... ..... .......... ....... ..... 61

M ethod .............. .......................................................................................... ......... 62
Subjects an d D esign .................................................................................62
Procedure ................ ............................... 62
Results .............................................. ....... .................... 64
M manipulation C hecks ......... ................ ........... ....... ................... ............... 64
E atin g B eh av io r ............ ................................................................ ......... ......... 6 5
A ffect-R regulation M echanism ........................................ ......................... 66
D discussion ......... ...............................................................................................69

9 GENERAL DISCU SSION .......................................................... .............. 71

L IST O F R E FE R E N C E S ............................................................................. .............. 77

B IO G R A PH IC A L SK E TCH ...................................................................... ..................87
















LIST OF TABLES

Table pge

1. Influence of affective evaluation on behavioral intention .......................................... 57

2. Influence of acknowledged use of chocolate to lift mood on behavioral intention.......58

3. Protective influence of overeating-based guilt feelings on behavior.............................67
















LIST OF FIGURES


Figure pge

1. Behavioral consequences of affective states...................................... ....................... 2

2. Predictions for experim ents 1 and 2....................................... ......................... 40

3. Behavioral intentions toward coffee and cereal (collapsed)........................................47

4. Behavioral intentions tow ard chocolate................................... ......................... 55

5. A m ount of chocolate consum ed......................................................... ............... 65

6. Amount of chocolate consumed within the positive affect /mood-threatening condition
the impact of expected feelings of guilt after overeating ............. ...............68















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

BEHAVIORAL CONSEQUENCES OF AFFECT: COMBINING EVALUATIVE AND
REGULATORY PROPERITES

By

Eduardo Bittencourt Andrade

May 2004

Chair: Joel B. Cohen
Major Department: Marketing

The impact of affect on consumer behavior is well documented; however, no

unique pattern of behavior can be expected from a valenced affective state. While one

can find support for the intuitive congruency-type of hypothesis in which positive mood

facilitates action or increases purchase intention and negative mood inhibits action or

decreases purchase intentions, it is now clear that significant exceptions exist. A negative

mood may well encourage action whereas positive mood can inhibit action. Most

importantly, the models available in the literature have focused primarily on explaining

one or two effects (e.g., when negative affect encourages behavior), leading to a lack of

an overarching theory capable of accounting for the sometimes mitigating and sometimes

encouraging impact of positive and negative affective states on behavior and behavioral

intentions.

An integrative model of affective behavior (IMAB) is therefore proposed, in which

the informational and regulatory properties (mechanisms) of current and anticipated









affective states simultaneously mediate the impact of affective states on behavior. A

broadly-based review of the affect-behavior literature demonstrates that relying on a

single mechanism produces, in the aggregate, an ambiguous and often conflicting account

of affect's role in guiding behavior. Three research streams well known in the literature

(helping, risk-taking, and eating behavior) are reviewed, and their apparent

inconsistencies are elucidated under the proposed model. Finally, in a series of three

experiments, critical moderating variables associated with each of the two mechanisms

(affective evaluation and affect-regulation) are investigated, providing strong initial

support for the model.














CHAPTER 1
INTRODUCTION

The impact of affect on consumer behavior is well documented (see Bagozzi,

Gopinath, and Nyer 1999; Cohen and Areni 1991 for reviews). However, no unique

pattern of behavior can be expected from a valenced affective state. While one can find

support for the intuitive congruency-type of hypothesis in which positive mood facilitates

action or increases purchase intention, and negative mood inhibits action or decreases

purchase intentions, it is now clear that significant exceptions exist. A negative mood

may well encourage action, whereas positive mood can inhibit action. For example,

positive feelings increase purchase intentions (Brown, Homer, and Inman 1998), but also

decrease risk-taking if the odds are too high (Isen and Geva 1987). Bad moods mitigate

consumers' willingness to go to a movie when they have a hedonic goal in mind (Pham

1998), but also increase impulsive consumption (Tice, Bratslavsky, and Baumeister,

2001). As Bagozzi and colleagues (1999) summarized, "Sometimes emotions spur one

onto action; at other times emotions inhibit or constrain action. But only recently have

researchers devoted much attention to studying how this occurs" (p. 199).

Starting with the assumption that affect-behavior relationships can be, at the

aggregate level, categorized into four groups (Figure 1), it becomes clear that the current

most cited models available have focused primarily on explaining one or two cells of this

2x2 matrix. For instance, mood-congruency and affect-as-information conceptualizations

can explain when a positive (negative) mood facilitates (inhibits) action (Cells 1 and 4),

but cannot account for the impact of negative mood on purchase/consumption increases










(Cell 3). Zillmann's (1988) mood-management theory can explain the latter, suggesting

that people in a bad mood are more likely to act in an attempt to feel better. However, it

does not explain, for instance, why people in a bad mood are sometimes less likely to act,

even if the behavioral activity is not expected to put them in a worse mood (Cell 4).


BEHAVIORAL CONSEQUENCE


Facilitates Action
(Approach Behavior)


Findings (e.g.)

Advertising and Purchase Intention (Brown
et al. 1998)
Hedonic Goals and Purchase Intention
(Pham 1998)


Theories

Mood-Congruency Hypothesis (Isen et al.
1978; Bower 1981)
Affect-as-Information Hypothesis
(Schwarz and Clore 1983)
Mood-Maintenance Hypothesis (Isen 1984,
2000; Clark and Isen 1982)
CELL 1

Findings (e.g.)

Impulsive Behavior (Tice et al. 2001)
Music and Purchase Intention (Alpert and
Alpert 1986)


Theories

Mood-Management Theory (Zillmann
1988)
Negative State Relief Model (Cialdini et al.
1973)

CELL 3


Inhibits Action
(Protective Behavior)


Findings (e.g.)

Risk-taking with low prob. of winning
(Arkes et al. 1988)
Helping when negative stimuli are salient
(Isen and Simmonds 1978)


Theories

Mood-Maintenance Hypothesis (Isen 1984,
2000; Clark and Isen 1982)
Hedonic Contingency Hypothesis
(Wegener and Petty 1994)


CELL 2

Findings (e.g.)

Helping Children (Kenrick and Cialdini
1976)
Food Intake Men (Macht et al. 2002)


Theories

Mood-Congruency Hypothesis (Bower
1981; Isen et al. 1978)
Affect-as-Information Hypothesis
(Schwarz and Clore 1983)

CELL 4


Figure 1. Behavioral consequences of affective states









Our study proposes a parsimonious integrative model with three main goals in

mind. First, it enables us to explain the inhibiting and stimulating consequences of both

positive and negative affective states on behavior, which has not been yet seen in the

literature. Second, based on the assumption that two main interdependent mechanisms

(i.e., affective evaluation and affect-regulation) are under operation, our study identifies

the critical moderating variables that mitigate/stimulate each mechanism and eventually

guide behavior. As the literature has focused more often on the evaluation aspect of

current affective states (Bower 1981; Gorn, Pham, and Sin 2001; Pham 1998; Schwarz

and Clore 1983), special attention is given to the affect-regulation mechanism, its

assumptions, and its internal and environmental contingencies. Finally, the model also

demonstrates when regulation-type hypotheses (e.g., mood-maintenance) seem more or

less appropriate as a theoretical account for the behavioral consequences of affect.

Three major components define the scope of analysis of the model to be advanced:

the independent variable (affect), the mediating processes (affective evaluation and

affect-regulation), and the dependent variable (behavior and behavioral intentions).

Affect

Affect is defined as "positively or negatively valenced subjective reactions that a

person experiences at a given point in time" (Wyer, Clore, and Isbell 1999, p. 3). Thus,

affect represents the conceptual umbrella for both mood and emotions. Although

distinctions between mood and emotions vary somewhat, researchers tend to agree that

the source of the affective experience represents a critical distinction. While subjects

experiencing emotions are consciously aware that emotions emanates from some source,

subjects experiencing moods are not. For example, whereas people are in a bad or good

mood, they are angry at someone/something (Schwarz and Clore 1996). Secondly,









emotions have also been categorized in terms of in intensity, duration, and/or cognitive

participation (Ortony, Clore, and Collins 1988). Moods, however, represent a unique

(usually indivisible) positive or negative state. This implies that specific types of

emotions are likely to trigger different sets of behavior, depending on their arousing or

cognitive properties (Raghunathan and Pham 1999). For instance, depressed people

(compared to subjects who are simply in a bad mood) may behave differently when a

mood repair opportunity presents itself, since the chronic properties of the former

probably mitigate the impact of affect-regulation on behavior. Although it is beyond the

scope of our model to predict the impact of specific types of emotions, the proposed

model does recognize the uniqueness of each type of emotion in producing behavioral

consequences.

Mediational Processes

We believe it is useful to make the simplifying assumption that affect can

potentially mediate evaluative and behavioral patterns at three different levels of

processing (Cohen and Areni 1991; Pham et al. 2001). At the most basic level (Type I-

Affect), affective information is conveyed via sensory-motor programs critical to bio-

regulation. For example, bodily information is captured by the peripheral nervous system

and sent to the central nervous system, which sends back signals that help regulate organs

and biorhythmic activities. Subjects are usually unaware of these automatic mechanisms.

A mid level of analysis (Type II-Affect) refers to basic affective reactions learned

through conditioning, such as fear responses or alertness triggered by danger

identification. This type of information may follow a "low road," departing from the

thalamus (where sensory information is processed) to the amygdala (responsible for

triggering emotional/fearful responses). As minimal cortical processing takes place,









individuals have no more than a rough representation of the stimulus, but are capable of

reacting fairly quickly to it (LeDoux 1996). Finally, affective information can be

processed at a higher level (Type III-Affect), involving subjective appraisal of the

stimulus. In this case, affective information requires significant participation of the

neocortex, where most of the cognitive functions operate, before any behavioral activity

takes place.

Though recognizing the importance of all three levels of psychophysiological

processes, our model focuses on the impact of affect on behavior via a deliberate

cognitive process, rather than an automatic affective reaction. The propositions and

empirical evidence to be discussed focus on cognitive processes in which individuals

deliberately use affect as a signal to evaluate the environment around them (affective

evaluation mechanism) and/or to regulate their affective experiences (affect-regulation

mechanism).

Behavior

Finally, two major factors led to the adoption of behavior and behavioral intentions

as the main dependent measures. First, our primary objective is to understand the impact

of affect on behavior. While there is extensive research on the impact of affect on

judgments/attitudes, the impact of affect on behavior (and behavioral intentions) has

received considerably less attention (Forgas 2002). Moreover, as we will see, the theories

rooted in affective evaluations (Isen et al. 1978; Bower 1981; Schwarz 1990), cannot by

themselves account for the impact of affect on behavior. Second, since compelling

evidence suggests strong correlation between intentions and behavior (Eagly and

Chaiken 1993), and many of the available studies have investigated the impact of affect






6


on behavioral intentions rather than on final action, both types of dependent measures are

adopted.














CHAPTER 2
THEORETICAL BACKGROUND

Historically, the relationship between affect and its behavioral consequences has

been examined from three different perspectives, each highlighting one major property of

affect. The first, and probably the oldest tradition, stresses the disrupting properties of

affect on judgment and behavior. Brown and Farber (1951) advocated that hunger

produced an affective impact (frustration) if the goal were not reached. Two drives were

hypothesized to operate in parallel, hunger-the "relevant drive," and frustration-the

"irrelevant drive." People were seen as striving for the relevant goal while avoiding the

negative effects of frustration, the irrelevant drive. Using the same rationale, action

control theory (Kuhl and Beckmann 1984) considered frustration a "competing

tendency" that must be controlled for. Finally, negative emotions have been hypothesized

to disrupt "rational" behavior and promote "self-regulation failure" (Leith and

Baumeister 1996). Affect (typically negative affect) is therefore reduced to a disrupting

psychological mechanism leading to negative cognitive and behavioral outcomes.

A critique of the disruptive perspective begins with the fact that feelings are

essentially treated as maladaptive or irrational (and therefore "to be avoided")

components of human nature. This perspective implies that individuals' goals can best be

met by controlling for internal and environmental "emotional temptations." This

conception overlooks the functional aspects of feelings, and constrains our understanding

of the importance of feelings to judgment and behavior (Carver and Scheier 1996; Fridja

1999; Pham et al. 2001). Although it is clear that under certain circumstances affect does









have disruptive and non-adaptive consequences, feelings are usually indispensable for

optimal decision-making, and .. the absence of emotions and feeling is no less

damaging, no less capable of compromising the rationality that makes us human. ... ."

(Damasio 1994, p. xii).

More recently, multiple theoretical accounts have proposed adaptive-type

explanations for the impact of affect on behavior and behavioral intentions. Mood-

congruency (Bower 1981), affect-as-information (Schwarz 1990; Schwarz and Clore

1983), mood as input (Martin et al. 1993; Martin and Stoner 1996), affect infusion

(Forgas 1995), mood-maintenance (Isen 1984; Isen 2000), mood-management (Zillmann

1988) and hedonic contingency (Wegener and Petty 1994) are among the theories

focusing on understanding the mediating processes through which current and anticipated

affective states influence judgment, intentions, and/or behavior. Although varying in

terms of predictions and scope of analysis, it is possible to categorize them at a more

basic level. Based on the main underlying mechanism that links affect to behavioral

consequences, the theories above can be classified into two groups: affective evaluation

and affect-regulation.

Affective Evaluation (Affect's Informational Role)

The first group emphasizes the evaluative properties of affect. It incorporates

theories in which affect has been assumed to influence evaluative judgments, and

eventually behavior, in either a direct or indirect fashion. The affect-as-information

hypothesis (Schwarz 1990, Schwarz and Clore 1983) and the mood-congruency

hypothesis (Isen et al. 1978; Bower 1981) are probably the most cited and investigated









accounts. 1Affect-as-information proposes that individuals directly assess their current

affective states and deliberately use them during any evaluative process. The mood-

congruency hypothesis proposes an indirect influence of affect, suggesting that affective

states make effectively congruent material more accessible in memory, which leads to

changes in evaluation. Evidence has supported both affect-as-information (Pham 1998;

Pham et al. 2001) and mood-congruency (Goldberg and Gorn 1987; Isen et al. 1978; see

also Forgas 1995).

Most importantly, although the proposed mediating process varies, the affect-as-

information and mood-congruency hypotheses make similar predictions. In general,

positive affect is expected to lead to a more favorable evaluation of the environment,

which stimulates proactive activities (increased purchase intention); whereas negative

affect is expected to lead to a less favorable evaluation of the environment, which inhibits

action (decreased purchase intention). In summary, a general affective evaluation

mechanism (affect-as-information and/or mood-congruency) can explain Cells 1 and 4 of

Figure 1. But it does not explain a negative (positive) mood increasing (decreasing) such

behavioral intentions (Cells 3 and 2).

Affect-Regulation (Affect's Motivational Role)

Theories based on affect-regulation assume that positive affect represents a goal, a

desired state unto itself; and that people spontaneously attempt to achieve this "ideal"

affective state or protect it once the state has been attained. For example, Zillmann's


1 Affective states are also known to influence evaluation through changes in processing style. Positive
mood leads to top-down processes while negative mood triggers bottom-up processes (Martin and Clore
2001), which may have strong influences on evaluation (Barone, Miniard, and Romeo 2000). The present
research does not focus on this particular issue. Thus, a deliberate attempt is made throughout the
experiments (e.g., experimental control over the amount of time people are exposed to product information)
to minimize the impact of processing style on the effects of interest.









mood-management theory (1988) suggests that respondents may be willing to act in

anticipation of the mood-lifting consequences of such behavior (Zillmann 1988). Indeed,

Tice et al. (2001) showed that people deliberately use food consumption as a tactic for

mood repair. Similarly, motivation for upward affect-regulation has also been suggested

as a main motivator for self-gifting (Mick and DeMoss 1990a, 1990b; Luomala and

Laaksonen 1997), pro-social behavior (Bagozzi and Moore 1994), and difficult trade-offs

avoidance (Luce 1998). Therefore, based on the framework proposed in Figure 1, mood-

management has focused mostly on explaining when negative affect encourages action

(Cell 3 of Figure 1).

A regulatory explanation is also used to explain when respondents in a good mood

become conservative (that is, less willing to act). Research on helping, and especially risk

taking, shows that when the situational cues threaten respondents' affective states, those

in a positive mood are less likely to take risks or help others (Cell 2 of Figure 1). As

happy people have more to lose, they become more protective of their current affective

states than those experiencing a more neutral affective state (see Isen 2000 for a review).

Good mood has in fact been shown to lead to a protective type of behavior when the

negative features of the product were made salient (Kahn and Isen 1993), when risks of

losing were too high (Isen and Geva 1987), and when the helping task could produce

negative feelings (Isen and Simmonds 1978). Wegener and Petty's (1994) hedonic

contingency hypothesis provides similar predictions with a slightly different (i.e., event

sampling) rationale. Their theory suggests that since happy people are already in or closer

to the ideal affective state, there are fewer (more) behavioral options in the environment

that can make them feel better (worse). As a result, they become less willing to take









chances and, therefore, more protective. In a series of studies, the authors showed that

when instructed to make a choice among several movies, happy respondents were more

attentive to the mood-lifting attributes of the movies than were those in neutral or

negative affect conditions.

Affect-regulation, in essence, predicts when people are likely to move to a more

positive affective state; and also explains when they are likely to try to protect a currently

positive affective state. That is, a combined version of existing affect-regulation theories

(i.e., mood-management and mood-maintenance or hedonic contingency) can explain

when a bad mood stimulates action (Cell 3) as well as when a good mood inhibits action

(Cell 2), the two cells of Figure 1 unaccounted for by theories based on an affective

evaluation mechanism. It is worth noting that the regulatory properties have also been

used to account for the impact of positive affect on behavioral encouragement (Cell 1).

The mood-maintenance hypothesis (Clark and Isen 1982) claims that people may perform

a mood-lifting behavior (e.g., to help) in an attempt to keep the good mood. However, no

direct evidence of this mediating process has been provided, and this effect may be due to

evaluation instead of regulation. This issue is further addressed later.

It is clear therefore that integrating the evaluative and regulatory properties of

affect is essential to account for all four categories of the affect-behavior matrix shown in

Figure 1. The Integrative Model of Affective Behavior (IMAB) attempts to accomplish

this goal.














CHAPTER 3
INTEGRATIVE MODEL OF AFFECTIVE BEHAVIOR (IMAB)

The first step to developing a model that addresses the four combinations of the

affect-behavior relationship shown in Figure 1 is to integrate (at the mediational level)

the evaluative and regulatory properties of affective states. The integrative model of

affective behavior (IMAB) proposes that affective evaluation and affect-regulation

operate in tandem as soon as a valenced affective state is activated.

Proposition 1: People 's affective states influence behavior continuously via two

parallel mechanisms: affective evaluation (both direct and indirect processes) and affect-

regulation.

Strong evidence in the literature supports the behavioral influence of each

mechanism (Isen 1984; Forgas 2002; Martin and Clore 2001). However, the literature is

relatively silent regarding their parallel effects (but see Gendolla 2000 and Nygren et al.

1996 for exceptions).

The affective evaluation mechanism assumes that current affective states (positive

or negative) are likely to bias any evaluative judgment, and eventually behavior. Three

rather complementary hypotheses have emerged to account for the processes underlying

affective evaluations: one direct process (affect-as-information) and two indirect

processes (mood-congruency and processing styles). First, the affect-as-information

hypothesis proposes that affect itself may provide unique information that will be directly

retrieved during evaluation (Schwarz and Clore 1983). Individuals ask themselves "How

do I feel about it?", and use this information to make evaluative judgments. Recently,









Pham et al. (2001) moved a step further, indicating that affect is more than a heuristic

cue, as the initial hypothesis would propose. According to these authors, affect-as-

information is potentially faster, more reliable, and more predictive of subsequent

thoughts compared to "cold," reason-based types of information. Second, the mood-

congruency hypothesis states that concepts congruent with an individual's current

affective state may become more accessible (Bower 1981). As evaluation typically

requires a retrieval process, the likelihood of using mood congruent concepts during an

evaluative judgment increases, thereby biasing judgment. Third, the processing style bias

suggests that affect influences people's information-processing approaches, such as focus

of attention, categorization, and analytical vs. creative processing (Barone, Miniard, and

Romeo 2000; Forgas 1995; Schwarz and Clore 1996). For instance, positive affect fosters

the use of accessible information, relying more on top-down, expectation-driven

processes; whereas negative affect leads people to rely on more data-driven aspects

(Clore et al. 2001). Similarly, those in a good (bad) mood form broader (narrower)

categories (Isen 2000). As a result, in some circumstances, the impact of affect on

evaluation may occur simply as a result of an individual's changes in processing style

during the evaluative process.

In short, affect is assumed to influence evaluation directly (as information) or

indirectly (via changes in the accessibility of mood congruent material or through

variations in processing styles). Notice that the mood-congruency hypothesis and the

affect-as-information hypothesis provide similar predictions (Martin and Clore 2001).

Positive (negative) affect leads to more positive (negative) evaluations, thereby mediating

subjects' intentions and behavior (Pham 1998). However, this is the case as long as no









misattribution error is made salient. In fact, the affect-as-information hypothesis suggests

that as soon as people become aware of their current affective states, they tend to control

for any affective influence on judgments; whereas the mood-congruency hypothesis

would predict the identical pattern across levels of awareness.

Although all three paths may lead affect to influence behavior, the model proposed

here emphasizes the more reliably demonstrated affect-as-information and/or mood

congruent effects (for reviews, see Forgas 1995; Schwarz 1990). As the predictions are

usually the same across these two mechanisms, we do not attempt to disentangle these

competing/complementary explanations, since our objective is merely to fully incorporate

affective evaluation effects.

Whereas the affective evaluation mechanism focuses on the informative value of

affect and its mood congruent effects, the affect-regulation mechanism focuses on the

motivational aspect of specific affective states. For example, a current (or expected)

negative (positive) mood may motivate individuals to improve (or protect) their actual

affective state. In other words, affect has informational and goal properties that may

influence behavioral decisions.

Several research streams have devoted attention to this phenomenon. Tice,

Bratslavsky, and Baumeister (2001) demonstrated that people deliberately use food

consumption as a tactic for mood repair. Luce (1998) showed that in an attempt to

regulate a current or expected negative affective state, people systematically avoided

difficult tradeoffs, thereby making less-optimal choices. Similarly, motivation for upward

affect-regulation has also led subjects in a bad mood to increase helping (Bagozzi and

Moore 1994; Schaller and Cialdini 1990) and self-gifting (Luomala and Laaksonen 1997;









Mick and DeMoss 1990). Finally, presumably in an attempt to protect a current affective

state, subjects in a good mood were less likely to take risks compared to respondents in a

control condition (Isen and Geva 1987).

In short, we assume that there are two different pathways through which affect can

influence or bias behavior. However, further assuming simultaneous activity of both

types and interaction between these two processes before action, we seek to identify the

circumstances that lead one mechanism to be more likely to predominate. What are the

situational and internal cues that stimulate (vs. lessen) the impact of affect within these

two mechanisms?

Proposition 2: The affective evaluation mechanism assumes that affect can acquire

the properties of information as well as alter the accessibility of mood congruent

information in memory. Its impact is therefore contingent people 's current affective state

and on the availability and use of competing diagnostic information about the target

during the evaluative process.

As affect can acquire informational properties, the quantity and quality of

competing information available (as well as the context in which this information is used)

influences the extent to which affect influences evaluation, and eventually behavior

(Martin and Stoner 1996). As the amount of other available diagnostic information

decreases, the impact of affect on behavior via the affective evaluation mechanism

strengthens (Martin and Clore 2001). There is some evidence in the literature that the use

of affect-as-information varies based on its diagnosticity. Pham (1998) showed that the

use of affect is clearly context-dependent, varying also in terms of its quality (i.e.,

representativeness and relevance) during the evaluative process. He showed that subjects









primed with a hedonic (vs. utilitarian) motive were more willing to use affect as

information while assessing purchase intentions. Moreover, the quantity of competing

information has also been shown to produce a significant impact on the affective

evaluation mechanism. Siemer and Reisenzein (1998) showed that by mitigating people's

access to additional information through a time constraint and/or a secondary parallel

task, the effects of mood on judgment increased. Finally, ambiguous information has also

led to an increase in the impact of affect on evaluation and/or behavioral intention (Gorn

et al. 2001; Isen and Shalker 1982; Miniard, Bhatla, and Sirdeshmukh 1992).

Although affect may act as a heuristic (Schwarz 1990) by providing unique

information to rely on when no additional information is made available or when the task

is too complex, affective influence can occur via substantive changes in content. If the

task promotes substantive thinking, it may well increase people's likelihood of accessing

mood congruent material, thereby leading to a strong affective evaluation change (Forgas

1995). In this case, mood congruent retrieval (instead of affect-as-information) may best

represent the operative affective evaluation mechanism (Forgas 1992; Forgas 1993).

In summary, the IMAB proposes that the affective evaluation mechanism

intensifies as subjects' current affective state is directly used as information during the

evaluative process (due to ease/speed of access or lack of more diagnostic information)

and/or indirectly used (via recall of mood congruent material).

Proposition 3: The affect-regulation mechanism assumes that affect can acquire

the properties of a goal. Its impact on behavior is therefore contingent on people's

current affective states, on their anticipated affective states as a result of action, on the









presence of competing/complementary goals, and on people 's willingness and skills to

achieve the goal.

The model assumes that affect-regulation (i.e., pursuit or protection of positive

feeling states) is potentially active and may even overcome the affective evaluation

mechanism depending on internal and environmental contingencies. First, people's

current affective state is critical for affect-regulation to operate, since those experiencing

negative affective states will have a stronger motivation to regulate (upward) their

unsatisfactory feelings (Parrott and Sabini 1990), whereas those experiencing positive

affect will have a stronger motivation to protect their current pleasant feelings (Isen and

Geva 1987). The model implies, therefore, that valenced states (i.e., strong signals) are

more likely to trigger the affect-regulation mechanism than neutral states (i.e., weak

signals). For people to be strongly motivated to change or protect their current feelings,

there must be a strong affective signal, which indicates that the individual is experiencing

a desired (positive) or undesired (negative) affective state. Cohen and Andrade (2004)

provided initial evidence of such an effect. Second, people must anticipate an affective

change (upward or downward) as a result of action. In other words, the behavioral

activity must be expected to lift or damage people's current feelings (Cialdini and

Kenrick 1976; Isen and Geva 1987; Raghunathan and Pham 1999). Notice that this

assumption implies that affect-regulation is a function of people's intuitive theories about

the affective consequences of specific behaviors (e.g., "I believe that eating chocolate

makes me feel better"). Third, competing (or cooperative) goals work in tandem to

determine the impact and direction of an individual's current affective state. If more

personally relevant or salient goals are operative, individuals may delay upward affect-









regulation and retain a negative affective state in order to achieve a competing goal

(Cohen and Andrade 2004) or neutralize negative and positive affect in an attempt to

enhance response flexibility and improve performance (Erber and Erber 2001). Finally,

individuals must have the willingness and skills to regulate their current affective state.

Studies examining chronic affective states, for instance, have shown that depressed

people do not perceive themselves as capable of regulating upward their current negative

affective states (Davidson et al. 2002; Kanfer and Zeiss 1983), which may prevent any

mood-lifting attempt. If these conditions are met, affect-regulation is likely to direct

behavior, potentially overcoming the opposing effects of the affective evaluation

mechanism.

In summary, the models' main predictors for individuals in a positive affective

state, are that, (1) the affective evaluation mechanism should stimulate action (e.g.,

producing an increase in purchase intention), particularly when no strong competing

diagnostic information about the behavior/environment is available; however, (2) when

the behavior presents a potential threat to people's positive feelings (and the other

previously mentioned contingencies are met), then the affect-regulation mechanism is

likely to dominate the informational properties of the initial positive state. In this case,

positive affect should inhibit action (e.g., producing a decrease in purchase intentions) as

people attempt to protect their current affective states.

For individuals experiencing a negative affective state, the model predicts that (3)

the affective evaluation mechanism should inhibit action (e.g., producing a decrease in

purchase intentions), particularly when no strong competing diagnostic information about

the behavior/environment is available; however, (4) when the behavior presents a










potential mood-lifting benefit to people's negative feelings (and the other previously

mentioned contingencies are met), then the affect-regulation mechanism is likely to direct

behavior. In this case, negative affect should stimulate action (e.g., producing an increase

in purchase intentions) as people attempt to improve their current feelings. 2









































2 Remember that as the IMAB focuses initially on general negative and positive states, it does not make
predictions for specific types of emotions. However, it does recognize that emotions vary in terms of affect-
regulation and affective evaluation tendencies, which eventually bias behavior. For instance, it is known
that whereas sadness increases risk-taking, anxiety decreases it (Raghunathan and Pham 1999). Similarly,
while helping increases as a function of sadness (Baumann, Cialdini, and Kenrick 1981), it may well
decrease as a result of frustration.














CHAPTER 4
INITIAL EVIDENCE SUPPORTING THE MODEL: THE IMPACT OF AFFECT ON
HELPING, RISK TAKING, AND EATING BEHAVIOR

The impact of affect on behavior has been investigated in several different research

streams within the psychology literature. Thus, to provide initial evidence for the model

basic's propositions, the impact of affect on helping, risk taking, and eating behavior is

reviewed in an attempt to assess IMAB's ability to account for the results and resolve

some of the apparent inconsistencies.

Helping

Prevalent finding in the helping literature is that current affective states influence

individuals' willingness to help. However, the effects do not follow a single pattern (for

reviews, see Batson 1990; Salovey, Mayer, and Rosenhan 1991; Schaller and Cialdini

1990). Researchers tend to agree that the relationship between positive mood and helping

is, in general, well established; and that positive mood increases people's propensity to

help (Isen, Clark, and Schwartz 1976; Isen and Levin 1972; Levin and Isen 1975).

However, there is some evidence that the opposite may also be true; thus a decrease in

helping due to individuals' positive feelings (Isen and Simmonds 1978). The impact of

negative affect on helping is also bi-directional. Negative mood sometimes increases

helping (Cialdini et al. 1973; Cunningham, Steinberg, and Grev 1980; Manucia, Bauman,

and Cialdini 1984) and sometimes decreases helping (Berkowitz 1972; Berkowitz and

Connor 1966; Isen 1970). Several hypotheses have been proposed to account for these

"inconsistent" patterns, however the underlying mechanisms seem to vary almost as









much as the results themselves: positive mood-maintenance, guilt reduction, negative

state relief, aversive arousal reduction, positive affective priming, and negative affective

priming (Batson 1990; Salovey et al. 1991). The main findings and their respective

explanations are reviewed below. Then we show how the proposed IMAB offers a more

integrative and parsimonious account of this body of work. The framework proposed in

Figure 1 is used to categorize the effects.

Positive Affect and Helping

In a field study, Isen and Levin (1972) showed that subjects who found a dime in

the coin return of a public telephone were subsequently more willing to pick up papers

dropped off in front of them by a confederate (Study 2). Similarly, after manipulating

mood through false feedback, Isen (1970) showed that happy (sad) students were more

(less) willing to give money to the "Junior High Air-Conditioning Fund". Indeed, the

positive impact of good mood on prosocial behavior is quite robust (Aderman 1972;

Berkowitz and Connor 1966; Levin and Isen 1975; Moore, Underwood, and Rosenhan

1973). Two underlying mechanisms leading to this effect have been advanced; one

cognitive (i.e., priming effects), and one motivational (i.e., positive mood-maintenance).

The cognitive/priming explanation is based essentially on mood-congruency effects (Isen

et al. 1976; Isen, Clark, Shalker, and Karp 1978), through which positive information

became more accessible during evaluation and influenced behavior (Clark and Waddel

1983).

The competing motivational explanation for the effects of positive affective states

on helping adopts a regulatory process approach. It has been proposed that people in a

good mood try to remain in their current affective states and, therefore, will be more

willing to help (Isen 1984; Levin and Isen 1975). This hypothesis, however, has not









found direct empirical support in the literature. As Schaller and Cialdini (1990)

summarized "evidence is scarce that happy subjects help as a means to maintain positive

moods" (p. 282). Indeed, when mood-maintenance plays a role, it may reduce rather than

increase helping (since the beneficial affective consequences of trying to help may not be

clear). Although direct empirical evidence of the actual mediating processes is still

lacking, researchers tend to agree that biases in evaluative judgment can play a role on

people's propensity to help (Batson 1990; Salovey et al. 1991; Schaller and Cialdini

1990). However, as no study has contrasted affect-as-information vs. mood-congruency

mechanisms, and both predict the same effects, it seems premature to claim which

process (if not both) is responsible for the impact of positive mood on helping.

Little evidence is available showing that being in a good mood can decrease

helping. However, Isen and Simmonds (1978) found that when the helping scenario

displays situational cues that threaten subjects' current positive mood (e.g., a demanding

helping task), these individuals were indeed less likely to help than subjects in a neural

mood. The authors suggested that a challenging helping task may have led happy subjects

to anticipate negative affect and triggered a self-protective regulatory mechanism. This

type of effect will be further elaborated under the risk-taking literature review, where the

impact of positive affect on behavioral discouragement is well established.

Negative Affect and Helping

Studies showing that negative affect increases helping have generated several

related hypotheses to account for the underlying mechanisms, such as guilt reduction

(Carlsmith and Gross 1969; Regan, Williams, and Sparling 1972), negative mood relief

(Bauman, Cialdini, and Kenrick 1981; Cialdini et al. 1973; Cialdini and Kenrick 1976;

Manucia et al. 1984), and aversive arousal reduction (Piliavin et al. 1981, 1982).









Although adopting different research approaches, they all share the basic assumption that

upward affect-regulation is at the core of people's disposition to help. Helping is

conceived to be an affect-regulation strategy aiming at achievement of this somewhat

superordinate goal.

Cialdini and colleagues were among the first to categorize helping as a mood repair

strategy. Cialdini et al. (1973) showed that subjects in bad mood were more likely to help

in response to another person's request than those in control conditions. Most

importantly, as soon as rewarding hedonic benefits were interposed between the mood

manipulation and the help request (i.e., an unexpected monetary reward or approval for

task performance), the effects of negative affect on helping disappeared. The authors

asserted that helping, monetary reward, and positive feedback perform a similar

functional goal, bad mood relief (see also Baumann et al. 1981). Manucia et al. (1984)

provided further, and perhaps even more compelling, evidence implicating an upward

affect-regulation strategy as the mediating mechanism linking being in a bad mood to

helping. After instantiation of positive, neutral, or negative affective states, subjects were

asked to take a placebo pill. Half the subjects were informed that this pill would "freeze"

their current affective states for a while. The authors predicted that if helping was used

as a mood repair strategy, subjects in the "frozen" bad mood condition should help no

more than those in control conditions and significantly less compared to the "non-frozen"

bad mood condition. The results confirmed the predictions. Subjects in a bad mood who

were told about the "freezing" effects of the drug helped much less (compared to those in

the "non-frozen" bad mood condition), and similar to those in the neutral condition.









There is also evidence that negative affect can also decrease helping under certain

circumstances (Cialdini and Kenrick 1976; Isen 1970; Isen, Horn, and Rosenhan 1973;

Moore et al. 1973). However, purely cognitive approaches have been used to account for

operative mediating processes. Similar to positive moods, negative moods are also known

to prime congruent thoughts: "Thoughts of deprivation, helplessness, and uselessness

may become especially available, rendering such sad and self-focused individuals less

likely to help. ." (Salovey et al. 1991, p. 222). In summary, whereas upward affect-

regulation is typically identified as responsible for instigating people to help (Cell 3),

affective evaluation is identified as playing a major role when opposite results are found

(Cell 4). At best this strikes us as a "marriage of convenience" rather than a systematic

and balanced explanation.

Moreover, there has been a failure to find evidence consistent with affect-regulation

(i.e., helping increase) in certain studies where negative mood had been induced.

However, in studies where a negative mood decreased helping, children were used as

subjects (Cialdini and Kenrick 1976; Isen et al. 1973; Moore et al. 1973). It is possible

that for children helping is simply not perceived as an effective affect-regulation strategy.

In that case, affective evaluation should have a stronger impact. Indeed, Cialdini and

Kenrick (1976) showed that age and levels of socialization are critical moderating

variables. In one experiment, the authors found an interaction between age (6-8, 10-12

andl5-18 years) and mood on helping. Fifteen to eighteen year old subjects in a bad

mood helped more than those in the other two conditions, who turned out to behave

similarly to one another. As the authors predicted, individuals whose socialization

process is still incipient do not perceive helping as self-gratifying. In short, young









subjects in a bad mood do not help because altruistic behaviors are not perceived as a

viable affect-regulation strategy. As a result, the negative thoughts elicited by a bad mood

operate to reduce helping.

Theoretical Integration

The helping literature shows that positive and negative affect can stimulate or

discourage helping depending on situational cues available in the environment (i.e., the

four Cells of Figure 1), however, a combination of several theories have been required to

account for these effects. IMAB's main claim (proposition 1) is that, under the basic

assumption that two parallel mechanisms underlie the impact of affect on behavior,

situational cues and internal affective signals determine which mechanism will prevail.

As a result, a single model can reconcile these effects.

Positive affect leads to helping increase (Cell 1) via an affective evaluation

mechanism Proposition 2. That is, a positive mood biases (positively) subjects'

evaluations of the helping task either via affect-as-information and/or mood-

congruency -, both of which should increase subjects' willingness to help (Isen 1970;

Isen and Levin 1972; Moore et al. 1973). However, IMAB also requires us to consider

affect-regulation Proposition 3-, so that when situational cues lead subjects to anticipate

negative affect, affect-regulation becomes the dominant mechanism, and behavior is

discouraged (Cell 2). The reason for such protective reaction is that subjects in a positive

mood have more to lose compared to control conditions. That was the case in Isen and

Simmonds' (1978) study, where the helping task was mood-threatening, probably leading

subjects to speculate about the negative consequences of helping on their current positive

affective state. Although this type of effect is rather sporadic in the helping literature, the









study of risk-taking, as we will see, offers consistent theoretical and empirical evidence

of the impact of anticipated negative affect on behavior.

For subjects in a negative mood, upward affect-regulation is usually a reasonably

important motivator and is likely to dominate the impact of the affective evaluation

mechanism. As a result, individuals attempt to improve their current negative affective

states (Cell 3), with several studies providing evidence of a helping increase for sad

subjects (Bauman et al. 1981; Cialdini et al. 1973; Cialdini and Kenrick 1976; Manucia et

al. 1984). However, as with any other goal, affect-regulation is contingent on other

moderating variables, such as subjects' recognition of the stimulus as an effective upward

affect-regulation strategy Proposition 3. When sad subjects were incapable of

perceiving the mood lifting benefits of helping, affect-regulation was mitigated, and the

affective evaluation mechanism (i.e., negative evaluation of the environment) led to a

decrease in helping (Cell 4). That was the case when children were used in the

experiments (Cialdini and Kenrick 1976; Moore et al. 1973).

In short, the proposed IMAB accounts for the bulk of effects in the helping

literature, by suggesting that (1) positive affect increases helping via affective evaluation

(i.e., priming effects and/or affect-as-information); (2) positive affect decreases helping

via affect-regulation when accompanied by mood-threatening cues; (3) negative affect

increases helping via (upward) affect-regulation when mood-lifting benefits are made

available; and (4) negative affect decreases helping via negative affective evaluation

providing subjects are unable to perceive the mood-lifting benefits of helping.

Risk-Taking

Understanding the impact of affect on risk perception and risk-taking can thus

provide additional insights into the behavioral consequences of affect and its mediating









processes. Johnson and Tversky (1983) found that when asked to evaluate the subjective

probability of positive future events, subjects in positive moods reported a higher

subjective probability (compared to control respondents), and a much higher subjective

probability compared to subjects in a negative mood. The opposite was true when they

were asked to evaluate the subjective probability of negative future events. In this case,

subjects in negative moods reported the highest subjective probability (compared to those

in neutral moods), and were much higher than subjects in a positive mood. After tracking

for cognitive processes (thought listing), Wright and Bower (1992) showed that

individuals focused more on mood congruent information during the assessment of

subjective probabilities. The correlation between affective state and expected outcomes is

currently well established (Loewenstein et al. 2001).

Thus, based on prevailing evidence and on the assumption that people will act

"rationally", one would think that subjects in bad mood, who tend to perceive a situation

as riskier, should be less inclined toward risk-taking. The opposite should be true for

subjects experiencing a positive affective state. Individuals in good moods, who usually

perceive a safer environment, should be more prone to risk-taking. Yet, findings in the

literature do not fully confirm either of these two predictions. Although the results are

rather consistent as to the impact of affect on risk perception (Constans and Mathews

1993; Johnson and Tversky 1983; Mayer et al. 1992; Pietromonaco and Rook 1987;

Wright and Bower 1992), yet to be resolved is why the impact of affect on risk-taking

does not follow the predicted "rational" pattern.

Negative Affect and Risk-Taking

Negative affective states have been shown to increase risk-taking (Gehring and

Willoughby 2002; Leith and Baumeister 1996; Mano 1992, 1994; Mittal and Ross 1998;









Raghunathan and Pham 1999), though this seems counterintuitive (since we know that

negative affect leads to an increase in risk perception, Johnson and Tversky 1983; Wright

and Bower 1992). Two sets of hypotheses have been developed to explain such effects:

affect-regulation (e.g., mood repair Raghunathan and Pham 1999) and affective

disruption (e.g., restricted attentional capacity Mano 1992).

Leith and Baumeister (1996) adopted an "affect as disruption" perspective to

account for this pattern of results. Since negative feelings may disrupt people's ability to

properly/rationally make accurate evaluations, such interference could lead subjects to

choose the "poorer"/riskier option. In a series of six studies the authors showed that

embarrassment, anger, and unpleasant arousing feelings led to an increase in risk-taking.

Since, in one of the studies, sadness did not differ from the neutral conditions, the authors

used this null effect to conclude that the "risky tendencies are limited to unpleasant

moods accompanied by high arousal" (p. 1250). This hypothesis has not found much

support in the literature.

To assess the impact of different emotions on risk-taking, Raghunathan and Pham

(1999) investigated the impact of sadness (low arousal) and anxiety (high arousal).

Contrary to the "affect disruption" prediction, when presented with two gamble options

in a consumer decision task (low risk-low payoff vs. high risk-high pay off), sad subjects

preferred the riskier alternative with a higher payoff (compared to anxious subjects), who

turned out to be strongly risk-averse. The authors suggested that different goals are

primed for sad vs. anxious people: sad subjects focusing on reward replacement (mood

repair) whereas anxious subjects focused on uncertainty reduction. Sad subjects thus

perceived the high risk-high payoff option as more attractive (i.e., mood-lifting), whereas









anxious subjects preferred the low risk-low payoff alternative (i.e., "it can reduce my

uncertainty"). This rationale is in line with the Eysenck and colleagues studies in which

anxiety has led to attentional and interpretational biases. Based on Eysenck's (1992)

cognitive theory of trait anxiety, it has been found that highly anxious people have an

attentional bias toward threat-related words and also interpret ambiguous information as

more threatening (Eysenck, MacLeod, and Mathews 1987). Derakshan and Eysenck

(1997) also found that highly anxious people display an interpretative bias for their own

behavior in social situations the behavior is perceived as more anxious.

Raghunathan and Pham's and Eysenck and colleagues' findings converge with the

IMAB and highlight a critical assumption of the model, the interdependence of the

affective evaluation and the affect-regulation mechanisms Proposition 1. Anxious

people appear to reinterpret any risky action, making it more negatively arousing and

causing whatever mood-lifting benefits that might be associated with a high-risk bet to

dissipate, thereby mitigating the impact of the affect-regulation mechanism.

Simultaneously, anxious subjects arrive at a rather pessimistic and threatening assessment

of the environment, which further strengthens the impact of the affective evaluation

mechanism. The impact of strong negative affective evaluation combined with the

absence of upward affect-regulation forces leads to risk-averse behavioral patterns (Cell

4). However, when people experience sadness, the mood-lifting benefits of similar risk-

taking may remain stable or even intensify, offsetting the negative impact of affective

evaluation on risk perceptions, and leading people to choose more risk-prone behaviors

(Cell 3). Thus, the type of affective state being experienced may well produce different

interpretations of, or attention to, upward affect-regulation opportunities that may be









available Proposition 2, making the affect-regulation mechanism either more or less

influential Proposition 3.

Positive Affect and Risk-Taking

Kahn and Isen (1993) showed that being in a positive mood (compared to a neutral

mood) stimulated individuals to seek more variety among otherwise safe and enjoyable

food products. Arkes and colleagues (1988) also demonstrated that happy respondents

(compared to subjects in a neutral mood) were more willing to pay for lottery tickets

(Study 1). These and similar results show that people in a good mood are apparently more

prone to risk-taking. Since we know that people in a good mood are more optimistic

(Johnson and Tversky 1983; Wright and Bower 1992), this pattern might be labeled as

rather intuitive. However, not all findings have shown this risk-prone behavior among

happy people. Kahn and Isen, for example, showed that the increase of variety-seeking

behavior for happy subjects disappeared as soon as a product's negative features were

included or made salient in the choice context. Similarly, Arkes and colleagues also

showed that whereas happy subjects (vs. a control group) displayed risk-prone behavior

in a pleasure-seeking situation (i.e., buying lottery tickets), they exhibited a risk-averse

pattern in a loss avoidance situation (i.e., buying insurance). It has been hypothesized that

subjects in a positive mood are more risk seeking than subjects in a neutral mood

providing the potential losses are not salient or too high (Nygren et al. 1996). Research

on this topic has used a motivational rationale to account for the findings: people in a

good mood facing mood-threatening stimuli become more self-protective of their current

feelings, thereby discouraging risky behaviors that may lead them to feel bad.

The pattern of results is fully consistent with IMAB, which predicts that affect-

regulation is activated not only when subjects experience negative feelings, but also when









subjects anticipate negative feelings Proposition 3. Thus, when losses are likely (e.g.,

high-risk condition), people in a good mood face a greater relative loss than people in a

neutral mood (Isen and Geva 1987). Such anticipatory negative emotional reactions

reduce the likelihood of engaging in risky behavior to achieve affect-regulation goals,

and hence counteract the impact of positive affect-based evaluations. Nygren et al. (1996)

used the seemingly contradictory expression "cautious optimism" to underscore the dual

and, here, opposing mechanisms at work. They summarize their first study by saying that,

on the one hand, "positive affect participants significantly overestimated the

probabilities...", but on the other hand, "...were less likely to gamble than were controls

when a real loss was possible..." (p. 59). In IMAB terms, whereas "optimism" is a result

of affective evaluations (i.e., affective priming effects), "caution" represents a

consequence of affect-regulation, triggered by anticipated negative affect.

In summary, being in a good mood may promote both risk-averse (Cell 2) as well

as risk seeking behavior (Cell 1). The outcome depends on mediating effects linked

primarily to affect-regulation, which have been shown to be contingent on the presence of

mood-threatening stimulus and its subjective likelihood of triggering downward affect-

regulation. When no "threats" are made salient affective evaluation leads to risk prone

behaviors Proposition 2 -, whereas when environmental cues signal threats affect-

regulation goals are activated Proposition 3 promoting negative mood avoidance

through risk-averse behaviors.

Eating Behavior

The impact of emotion on eating behavior has been widely investigated (for

reviews, see Canetti, Bachar, and Betty 2002; Christensen 1993; Greeno and Wing 1994).

Researchers' interests vary significantly; from the effects of stress on psychopathological









behaviors (e.g., obesity and bulimia) to normal influences of mild mood swings on food

preferences (e.g., cravings for sweets, carbohydrates, etc.); from tail-pinch stressors and

animal eating responses to unpleasant movies and human propensity to eat snack food.

As our analysis and proposed model focuses on the impact of mild affective states

on everyday behavior, we will concentrate on how negative and positive affective states

influence normal food intake. Consistent with the evidence reviewed above, the first

conclusion to be drawn from this body of research is that affect does not lead to a unique

behavioral consequence. Positive and negative affective states may well stimulate or

discourage food intake.

Negative Affect and Eating Behavior

There are a far greater number of studies dealing with the impact of negative affect

(compared to positive affect) on eating behavior. The underlying assumption in most of

the literature is that food acts as mood-regulator, lifting subjects' current affective state

after intake (Bruch 1973; Kaplan and Kaplan 1957; Morris and Reilly 1987; Polivy and

Herman 1976; Thayer 1989). Thus, (compared to a control condition), negative affect is

expected to encourage eating behavior. However, the results have been shown to vary as

a function of several moderating variables such as gender and food type.

Until the 1990s, the general impact of negative affect, especially stress, had been

surprisingly limited to animal research. Those dealing directly with humans concentrated

more on the interaction between affect and individual differences on eating behavior (see

Greeno and Wing 1994 for a review). Most recently, however, new studies have emerged

in which broader and more fundamental conclusions can be drawn about the

consequences of negative affect on people's eating behavior.









Grunberg and Straub (1992) exposed subjects to a film about industrial accidents

(negative affect) or a pleasant travelogue (control) while having snack foods available in

the room (dependent measure). They found that eating consumption increased as a result

of negative affect, but only among women. The results actually reversed for male

subjects, who reduced the amount of food intake as a consequence of negative affect.

Although the authors did not advance a systematic theoretical explanation for the

mediating effects, the results have proven quite robust. Whereas negative affect tends to

increase food intake among women (Macht 1999; Patel and Schlundt 2001; Weinstein,

Shide, and Rolls 1997; Willner et al. 1998), this effect is either canceled (Pine 1985) or

reversed among men (Abramson and Wunderlich 1972; Macht, Roth, and Ellgring 2002;

Reznick and Balch 1977).

A potential explanation for such variation is that strategic affect-regulation through

food intake is stronger in women than men (Macht 1999; Steptoe, Pollard, and Wardle

1995). Men may not become more attracted to food, or at least certain types of food, as

their affective states worsen. Although a variety of biological and psychological

explanations have been offered to explain why food intake does not increase among men,

they cannot explain why bad feelings lead men to reduce food intake.

As we have shown in the other streams of research, pursuing a single explanatory

mechanism may be at the root of the apparent inconsistency. In this case, however, the

main explanatory mechanism has been affect-regulation rather than affective evaluation.

Once again, IMAB proposes that understanding the interaction between affective

evaluation and affect-regulation is critical to explain the bi-directional pattern. Whereas

upward affect-regulation accounts for the increase in food intake as a result of negative









affect (Cell 3), negative affective evaluation is likely to explain food intake inhibition

(Cell 4). According to IMAB's third proposition, affect-regulation, as a goal, is

contingent on the people's underlying theories about the affective consequences of action

(i.e., "Is this behavior mood-lifting/threatening?"). If men (vs. women) are less likely to

perceive certain types of food as mood-lifters (e.g., Eating chocolate does not make feel

better"), the impact of affect-regulation will be mitigated and affective evaluation will be

most likely to drive the effects (i.e., reduce eating). Our multiple mediator analysis also

implies that if the type of food is not perceived as a "mood-lifter" negative affect is

expected to decrease eating. Consistent with Proposition 3, Oliver and Wardle (1998)

showed that whereas stress increased the consumption of snack-type foods (perceived

both as "quick energy" products and "treats") it decreased the consumption of typical

meal-type foods (fruits and vegetables, meat and fish). Relatedly, Willner and Healy

(1994) showed that after negative affect induction subjects lowered their own evaluation

of cheese in terms of pleasantness and desirability (see also Macht et al., 2002), again

suggesting that affective behavior toward food with no subjective mood-lifting attributes

is mostly directed by the affective evaluation mechanism. So, as bad feelings produce a

worsening evaluation of focal objects such as food, eating should decline.

Positive Affect and Eating Behavior

Since eating disorders (obesity, binge eating, bulimia, anorexia, etc.), which are

normally associated with negative affect, have been at the forefront of research done in

the field from the 70s through the 90s, only recently have researchers devoted attention to

the consequences of positive affect. The general pattern of results suggests that positive

mood stimulates eating (Cools, Schotte, and McNally 1992; Macht 1999; Macht et al.

2002; Patel and Schlundt 2001 upper-left corer of Figure 1), though null effects have









also been reported (Frost et al. 1982; Schmitz 1996). Based on two-week food diaries,

Patel and Schlundt (2001) found that (compared to a control condition) obese women

increased food intake while experiencing both positive and negative affect. Contrary to

the authors' expectations of an interaction between mood and social context (eating alone

vs. eating in a social context), the impact of valenced moods occurred under both social

context scenarios. No explanation was provided to account for the results. Macht and

colleagues (2002) provided a compelling mood congruent explanation for such effects.

They showed that male subjects experiencing positive (vs. negative) affect provided

higher ratings on two general dimensions for chocolates they were eating: affective

responses to chocolate (e.g., taste pleasantness) and motivation to eat (e.g., appetite). The

authors suggested that the positive impact of affect on food intake was probably a result

of mood congruent effects during subject's internal and external evaluation.

Consistent with IMAB's basic propositions, our analysis of prior research suggests

that positive affect probably stimulates eating behavior via the affective evaluation

mechanism, though little has been done to isolate mood congruent effects from affect-as-

information. Finally, to the best of our knowledge, no study has shown that people in a

good mood reduced food intake (Cell 2). However, according to IMAB, this pattern of

results is likely when negative consequences of eating become salient. For instance,

happy people (compared to a neutral mood condition), should be less likely to eat

chocolates if negative nutrition facts are highlighted (i.e., fat product and/or subsequent

feelings of guilt). In summary, as with research on helping, the potential negative

consequences of positive affect are largely unexplored in the eating behavior literature.









Summary

Our review shows that IMAB's three propositions can account for the observed

consequences of affect on behavior and behavioral intentions across three different

bodies of literature: helping, risk-taking, and eating behavior.

Behavioral stimulation for people experiencing positive affect (Cell 1) may occur

mostly as a result of the affective evaluation mechanism affect-as-information and/or

mood congruent effects- and there is a paucity of evidence to support mood-

maintenance models of affect-regulation. As long as no aversive or threatening cues

become salient in the environment, happy people perceive a safer environment (i.e., bring

positive thoughts to mind) and therefore become more likely to help, to take risks in

gambles, and to eat. When mood-threatening cues are made salient, the affect-regulation

mechanism activates and may lead to behavioral discouragement for people experiencing

positive affect (Cell 2). As happy people are more sensitive to potential negative affective

consequences, since they have more to lose, and bad feelings can be anticipated,

behavioral discouragement takes place when negative aspects or consequences become

salient. This explains a decrease in helping when the task is mood-threatening as well as

risk avoidance when the odds are too high. Happy people thus seem more motivated to

avoid a negative mood than to maintain positive feelings, since there is no salient threat

likely to instantiate this motivation.

On the negative side of the affective spectrum, the affective evaluation mechanism

seems to drive the impact of negative affect on behavioral mitigation (Cell 4). People

perceive a more intimidating environment (i.e., bring more negative thoughts to mind)

and become less likely to help, to take risks, and to eat. This is most likely to be the case,

however, only when the affect-regulation mechanism is inactive or blocked. Blocking or









mitigating effects can be a result of subjects' inability to perceive specific behavior as an

effective mood-lifting opportunity. That was the case among children (vs. adults) facing a

helping opportunity, anxious (vs. sad) people facing a risky-high payoff opportunity, and

men (vs. women) facing an eating opportunity. When the behavior is perceived to be an

effective upward affect-regulation strategy, there are no stronger competing goals in the

environment, and people are willing/capable of mood improvement, enactment becomes

more likely for those experiencing negative affect via the affect-regulation mechanism,

often counteracting the impact of the affective evaluation mechanism (Cell 3). In such

situations people attempt to improve their current negative affective states, and this

accounts for observed increases in helping, risk-taking, and eating behavior under these

conditions.

In short, combining the hitherto separately considered affect evaluation and affect-

regulation mechanisms in the integrative model of affective behavior provides the

following parsimonious account of this substantial literature. Behavioral stimulation for

subjects in a positive mood and behavioral mitigation for subjects in a bad mood seems to

be mediated mainly by the affective evaluation mechanism (affect-as-information and/or

mood-congruency effects), whereas behavioral stimulation for subjects in a bad mood

and behavioral mitigation for subjects in a good mood is likely to be mainly mediated by

the affect-regulation mechanism.

Such conclusion is, however, premature, since, to the best of my knowledge, no

single study has addressed the impact of positive, neutral, and negative affective states on

behavior, while providing a direct assessment of the two main mediating processes under

operation. In the next chapters, the results of three experiments are presented in an






38


attempt to accomplish this goal and offer stronger empirical support for the integrative

model of affective behavior.














CHAPTER 5
TESTING IMAB: PREDICTIONS

To test IMAB propositions, a series of three experiments was conducted.

Experiments 1 and 2 show (1) that positive (negative) affect stimulates (inhibits)

purchase intentions via the affective evaluation mechanism (Cells 1 and 4 of Figure 1),

and (2) that negative affect stimulates purchase intentions via affect-regulation (Cell 3 of

Figure 1). Experiment 3 addresses the remaining effect (Cell 2 of Figure 1), and

demonstrates the circumstances in which positive affect may inhibit action (i.e.,

consumption). It also replicates Cell 1 (i.e., when positive affect increases consumption)

with an actual behavioral measure. All three experiments focused on two critical

moderating variables: people's current affective states and the availability of mood-lifting

(experiments 1 and 2) or mood-threatening (experiment 3) cues in the environment. The

other situational contingencies are held constant throughout all the conditions across the

three experiments. Below, we describe the predictions for experiments 1 and 2.

Predictions for experiment 3 will be described in Chapter 8.

In a consumption scenario where people do not possess enough information about

the product and the environment does not provide people with an opportunity to

upwardly regulate their affective states, IMAB predicts that the affective evaluation

mechanism should influence purchase intentions. Affect congruency principles it is

hypothesized that people in a negative (positive) mood will have a more negative

evaluation of the environment and/or the product, which makes them less (more) likely to

purchase a particular product, compared to a control condition (Figure 2 white bars).









However, if the product presents mood-lifting benefits, those experiencing negative affect

have an opportunity to upwardly regulate their current affective state, which can lead

them to increase their purchase intentions despite the opposing influence of the affective

evaluation mechanism. On the positive side of the affective spectrum, people are already

experiencing something approaching the "ideal" affective state. Thus, the mood-lifting

benefits of a product are not expected to have a strong effect on purchase intention.

However, as the affective evaluation mechanism is also active, people are expected to

provide a more positive evaluation of the product/environment, which should increase

purchase intentions. In short, when they are presented with the opportunity to purchase a

mood-lifting product, the combination of affect-regulation for those experiencing

negative feelings and affective evaluation for those experiencing positive feelings, leads

to a "U" shape pattern (Figure 2 black bars).


7 -
6-


4 u mood-lifting benefits
3 no mood-lifting benefits





Negative Neutral Positive
Affect


Figure 2. Predictions for experiments 1 and 2

Notice that those in the neutral affect condition are not expected to be strongly

sensitive to the mood-lifting benefits of the product. As they are also at some distance

from the ideal affective state, one could expect these respondents to be willing to improve






41


their current feelings in order to achieve the ideal affective state. However, affect-

regulation has been shown to be a function of both the affective discrepancy between

current and ideal states and, importantly, the strength of the affective signal (Cohen and

Andrade 2004). Thus, since neutral affect provides weak signals, affect-regulation goals

may not be instantiated, and the regulatory process may not unfold. IMAB incorporates

this assumption.














CHAPTER 6
EXPERIMENT 1

Marketers have recently highlighted their products' mood-lifting benefits in an

attempt to persuade consumers. Kellogg's, for instance, displays a web page called

"Breakfast and Mood", where it describes the affect-regulation benefits of eating

breakfast cereal in the morning. If the impact of mood-lifting cues are contingent on

people's current and anticipated feelings, as proposed by IMAB, varying respondents'

current feelings (negative, neutral, positive) and providing them with an opportunity to

purchase a mood-lifting versus a non mood-lifting product should allow us to test

IMAB's predictions described above. Experiment one adopts this procedural approach.

Method

Subjects and Design

Two hundred eighty-four undergraduate students from a southeastern university

participated in the experiment in exchange for course credit. The study adopted a two

(product benefits: mood-lifting vs. non mood-lifting) by three (affective state: negative

vs. neutral vs. positive) by two (replicate: coffee vs. cereal) between subjects design.

Respondents were randomly assigned to one of the twelve conditions.

Procedure

After they entered the lab, respondents were instructed to choose one of the

computers and start the first of the two independent short studies they were about to

perform. "Study 1" represented the affect manipulation while "Study 2" captured the

product benefit manipulation, followed by the main dependent measure. After presenting









students with an informed consent form, the first screen of study 1 introduced the cover

story. Respondents were told that as the number of Internet classes was increasing, the

university had decided to investigate the impact on memory of material transmitted over

the web. Respondents were instructed to watch a video on the web and describe a real life

experience similar to that watched in the film (affect manipulation). Then, they were

asked to rate the video (manipulation check). As soon as they finished study 1 a new

screen introduced the cover page of the "second" study. An informed consent form was

once again provided to the students in order to convey the idea of two independent

studies. The next screen indicated that the study attempted to assess people's willingness

to buy a specific product in a given consumption scenario. They were presented with a

consumption scenario in which the product (coffee vs. cereal), and its benefits (mood-

lifting vs. non mood-lifting) varied across conditions. After 15 seconds spent reading the

information, respondents were provided with a nine-point scale where they indicated their

purchase intentions (dependent measure). On the next page, they were asked about the

information earlier presented in the purchasing scenario (manipulation check for the

product benefits manipulation). Finally, they described the purposes of studies 1 and 2

(hypothesis guessing check) and were later debriefed regarding the affect manipulation in

order to eliminate any residual effects.

Affect Manipulation

To vary respondents' affective states, a combined technique was used (movie plus

personal real life description). This same technique has been successfully used in both the

general affect literature and in consumer research on affect (Cohen and Andrade 2004;

see also Westermann et al. 1996 for a meta-analysis). Respondents watched five minutes

of either a sad sequence of the movie Top Gun (negative affect), a documentary about









Italy (neutral affect) or a happy sequence of the movie American Pie 2 (positive affect).

The second step of the induction asked respondents in the valenced affect conditions to

describe a real life experience that produced the same feelings as those derived from the

video. In the neutral affect condition respondents were asked to describe a real life

experience that was similar in content to the scenes watched in the video. Finally,

respondents were asked to give their opinion about the video (manipulation check). Ten

items were presented in a nine-point semantic differential scale format. Three of the 10

items were designed to assess respondents' current affective states (I felt sad-I felt happy,

It's depressing-It's upbeat, Created a negative mood-Created a positive mood). The order

of the items was randomized to avoid order effects.

Replicates

The products were selected based on published findings, consumer beliefs, and

companies' attempts to link their products to mood-lifting qualities. Caffeine and

carbohydrates have been shown to produce a positive impact on mood and/or arousal

(Smith, Clark, and Gallagher 1999), and descriptive studies have shown, for instance, that

drinking coffee or other caffeinated products is one of people's common mood repair

strategies (Thayer 1996). Also, companies selling caffeine (e.g., coffee shops) and

carbohydrates (e.g., cereal companies) have explicitly mentioned their products' mood-

lifting attributes. As already mentioned, one of Kellogg's websites, for instance, contains

a section called "Breakfast and Mood", in which the mood-regulating properties of

breakfast cereals are highlighted: "Several studies have shown that consuming high levels

of carbohydrates is associated with better mood. As most breakfast cereals are high in

carbohydrates, this offers one possible reason why eating a high carbohydrate breakfast is

associated with improved mood."









Product Benefits Manipulation

Respondents were presented with a short scenario in which they had to imagine

themselves in a purchasing environment where, after reading the information available

about the product, purchase intentions were to be indicated. For those exposed to the

coffee scenario, they were asked to imagine themselves in a coffee shop with a friend

who planned to buy a cup of coffee. While they waited in line, he/she (the respondent)

started reading a brochure about the benefits of caffeine. In the mood-lifting condition,

the brochure stated that ". Popular culture is in fact correct. A research study

conducted at UCLA showed that caffeine works as an effective mood regulator. Taken in

small doses-like a cup or two a day-, caffeine is a very effective mood lifter." In the

no mood-lifting condition respondents were presented with the following statement "...

A recent study conducted at UCLA showed that coffee is indeed a healthy product. Taken

in small doses like a cup or two a day -, caffeine reduces chances of heart failures as

well as neurological diseases." Those exposed to the breakfast cereal were asked to

imagine themselves in a nearby supermarket. In the cereal aisle one particular cereal box

had a message printed on the back. In the mood-lifting condition, the message was the

following: "Several studies have shown that consuming high levels of carbohydrates is

associated with better mood. As cereal is high in carbohydrates, people who eat cereal

have a 'happier diet!' In the condition where no mood-lifting benefits were

highlighted, the message contained the following statements: "It is widely known that

carbohydrates are an important nutrient. As cereal is high in carbohydrates, don't miss

this opportunity to get those much needed nutrients every morning!" Across all

conditions students no information about the brand was provided.









Dependent Measure

Students had to wait at least 15 seconds before the purchase intention scale

appeared on the screen, which allowed some control for unwanted processing style

effects. As respondents in a negative (vs. positive) mood are usually more careful in their

analysis (Schwarz 2001), we wanted to make it easy for respondents to read the

information regardless of their affective states. After reading all the information about the

product benefits, they were presented (at the bottom of the screen) with a nine-point scale

on which they indicated their willingness to buy the product (9 = I would definitely buy

it).

Results

Manipulation Checks

Eight students provided a rudimentary guess of the purpose of the study. They were

deleted from the sample. After checking for reliability (a = .92), the three affect-related

items were collapsed to form the affect index. The affect manipulation produced a

significant main effect on people's affective states (F(2, 273) = 301.70,p < .001).

Pairwise comparisons showed that compared to the neutral affect condition (M= 6.2),

respondents in the negative affect condition experienced more negative feelings (M= 2.9;

F(1, 190) = 339.49, p < .001), whereas respondents in the positive affect condition

evaluated their affective state more positively (M = 7.5; F(1, 185) = 40.29, p < .001).

To assess the product benefits manipulation, respondents were asked, at the end of

the second study, to indicate (using a nine-point scale item) the extent to which they

agreed with the statement that the product contained mood-lifting benefits. As expected,

those in the mood-lifting condition perceived the products on average as more mood-

lifting (M= 5.4) than those in the non mood-lifting condition (M= 4.0; F(1, 274) =










33.21, p < .001). Moreover, the pattern remained the same across all affect conditions (F

< 1).

Purchase Intention

Product replicate did not interact with product benefit and affective state (F(2, 264)

=.99, p = .37), product benefit (F(1, 264) = 1.06, p = .30) or affective state (F(2, 264) =

.52, p = .59), so data were collapsed across product replicates (i.e., coffee and cereal). All

subsequent analyses followed a three (affect: negative vs. neutral vs. positive) by two

(product attributes: mood-lifting vs. no mood-lifting) between respondents design.

As predicted, respondents' affective state interacted with the product benefits

highlighted in the shopping scenario (F(2, 264) = 11.38, p < .001)(Figure 3).




7 -

6 -
o 5.18 5.125.12
4.9
5 -
S4.22 mood-lifting benefits
4 [0O non mood-lifting benefits
4 -3.7

3

2
Negative Neutral Positive
Affect


Figure 3. Behavioral intentions toward coffee and cereal (collapsed)

According to IMAB, the impact of affect on behavior via the affective evaluation

mechanism should be strongest when respondents do not have enough diagnostic

information to make their evaluations and when there are no affect-regulation









opportunities in the environment (i.e., non mood-lifting condition). In this type of

scenario, a monotonic increase in behavioral intentions should emerge when feelings

move from negative, to neutral, to positive, as a result of the use of affect-as-information

and/or mood-congruency during the evaluation process. The results confirmed the

model's predictions. When respondents were presented with a brand of coffee or cereal

with no mood-lifting benefits, there was a monotonic increase on purchase intentions as a

function of experienced affective state (F(1, 143) = 9.57, p < .005). Pairwise comparisons

showed that respondents experiencing negative affect were more reluctant to buy the

product (M= 3.7) compared to respondents in a neutral affect condition (M= 4.9; F(1,

101) = 8.08, p = .005), as well as compared to respondents experiencing positive affect

(M= 5.1; F(1, 85) = 9.60,p < .005). However, there was no difference between the

neutral and positive affect condition (F < 1).

IMAB hypothesizes that a "U" shaped pattern should emerge when respondents

are asked to evaluate a product with mood-lifting benefits. In this case, respondents in a

negative mood faced an affect-regulation opportunity. As a result, the affect-regulation

mechanism was expected to increase purchase intentions despite the fact that the affective

evaluation mechanism, by itself, would tend to lower purchase assessment judgments. As

expected, when respondents were presented with a brand of cereal or coffee with mood-

lifting benefits, their purchase intentions exhibited a quadratic form (F(1, 127) = 4.66, p

< .05). Pairwise comparisons showed that when facing a product with mood-lifting

benefits, respondents experiencing negative affect were more willing to buy the product

(M= 5.2) than those in a neutral mood (M= 4.2; F(1, 87) = 3.78, p = .05). Respondents









experiencing positive affect (M= 5.1) marginally increased their purchase intentions

compared to a neutral condition (F(1, 83) = 3.12, p = .08).

Discussion

The findings provide initial support for our hypotheses. The expected monotonic

increase in purchase intentions towards an unknown brand of coffee or cereal emerged as

respondents' feelings improved. This pattern, importantly, is contingent on the

availability of affect-regulation opportunities. When the scenario highlighted the mood-

lifting benefits of the product, then the predicted "U" shape curve emerged. As expected,

respondents in a valenced affective state (positive or negative) were more willing to

purchase the product than those in a neutral affective state, but for different reasons.

When respondents experienced negative affect there was a tendency toward upward

affect-regulation, and respondents approached the stimulus that might help them

accomplish this goal. When respondents were in a positive affective state, they also

increased their purchase intention, most likely due to a more positive evaluation of the

product/purchase scenario. Notice that the theoretical accounts available in the literature

cannot explain this combination of effects, which incorporate three out of the four

categories of the affect-behavior relationship described in Figure 1 (Cells 1, 3, and 4).

It is worth noting that although the results provided general support for the

proposed model, positive affect (compared to the neutral affect condition) led to an

increase in purchase intentions within the mood-lifting condition, but not within the non

mood-lifting condition. Therefore, drawing the conclusion that respondents in a positive

affective state were insensitive to mood-lifting benefits is premature. On the one hand, it

is possible that affect-regulation may have also played a role on the positive side of the

affective spectrum, since the difference between neutral and positive affect condition was









significant only among those facing a mood-lifting opportunity. On the other hand, it is

also possible that a "heavy handed" mood-lifting manipulation could have made

respondents within the mood-lifting conditions more sensitive to their feelings, thereby

making affect-mediated effects more likely. A less intrusive manipulation, where no

explicit description of the mood-lifting properties of the product is highlighted, should

eliminate this "focus of attention bias" and produce similar effects for the mood-lifting

and non mood-lifting conditions when positive and neutral affect conditions are

contrasted.

Also, as experiment 1 employs only an indirect assessment of mediating processes,

it is difficult to detect when the two mechanisms operate simultaneously. More direct

assessments may help to disentangle the effects of evaluation and regulation when both

can predict the same outcome (i.e., increase in purchase intentions for those experiencing

positive affect). By the same token, we cannot determine the extent to which affective

evaluation and affect-regulation interacted when sad people faced an affect-regulation

opportunity. One might wonder whether heightening the activity of the affect-regulation

mechanism "switches off' a less intense affective evaluation effect or, on the other hand,

whether affect-regulation simply increases the weight of the product's mood-lifting

benefits.














CHAPTER 7
EXPERIMENT 2

Experiment 2 attempted to replicate the first experiment, while addressing the

concerns discussed above. First, to address a possible focus of attention bias (i.e., the

likelihood that respondents in the mood-lifting conditions became more responsive to

feelings), we looked for a scenario in which people's intrinsic perceptions of mood-lifting

properties of the product could vary without any sort of explicit manipulation. The

helping literature has provided precedent for such a procedure. Cialdini and Kenrick

(1976) showed that whereas adults in a bad mood are more willing to help others

(probably in an attempt to regulate their negative mood), the effects reverse among

children. Their explanation is that adults are more likely to perceive the mood-lifting

benefits associated with helping, whereas children are yet to learn such associations.

Following the same rationale, research on eating behavior shows that men and

women have different beliefs about food intake in general, and chocolate in particular.

First, women are more likely to use food consumption as a mood-lifting alternative than

are men (Macht 1999; Steptoe, Pollard, and Wardle 1995). Also, evidence suggests that

women are more likely than men to perceive chocolate as a mood-lifting product

(Benton, Greenfield, and Morgan 1998; Grunberg and Straub 1992). If this is the case,

then women in a negative affective state should be more willing to try a piece of

chocolate (compared to a neutral affect condition), as a result of the affect-regulation

mechanism. Notice that this manipulation relies on one of the basic tenets of the model,

in which the impact of the affect-regulation mechanism is contingent on people's









intuitive theories about the affective consequences of their behavior (e.g., the mood-

lifting benefits of chocolate).

Among men, who are not as likely to perceive the affect-regulation opportunities of

chocolate, negative affect should lead to a decrease in behavioral intentions compared to

the neutral affect condition (via the affective evaluation mechanism). For those already

experiencing positive affect, both women and men should be equally willing to taste a

piece of chocolate (via the affective evaluation mechanism). In other words, across the

three levels of affect, a "U" shape pattern is expected among women due to a

combination of affect-regulation within the negative affect condition and affective

evaluation within the positive affect condition, whereas a monotonic increase is expected

among men due mainly to the impact of the affective evaluation mechanism.

Second, to provide more direct evidence about the two mediating processes

hypothesized by IMAB, an open-ended question asked respondents to explain their

indicated behavioral intentions. Additional items at the end of the experiment also

examined the independent impact of the evaluative and regulatory mechanisms. Finally, a

new neutral affect condition was used to generate a more symmetrical difference across

the three levels of affective states.

Method

Subjects and Design

One hundred fifty-one undergraduate students from a southeastern university

participated in the experiment in exchange for course credit. The study adopted a two

(gender: men vs. women) by three (affective state: negative vs. neutral vs. positive)

between subjects design. Respondents were randomly assigned to one of the six

conditions.









Procedure

The procedure was similar to that presented in experiment 1. A two-studies cover

story was introduced. "Study 1" manipulated respondents' affective states. A new video

("Documentary about John Nash") was presented to students in the neutral affect

condition to get a more "neutral point" on the scale. The other two videos were the same

as in the first experiment. In the "second study" respondents were informed that a foreign

company was about to introduce a new chocolate in the American market. The gist of the

story was that as sampling promotions in supermarkets turn out to be quite expensive, the

company had decided to use a new marketing tool, a so-called "Virtual Sampling

Promotion". Respondents were then instructed to imagine themselves in a real sampling

promotion scenario. A picture of chocolate bars was presented, and they were asked to

indicate the extent to which they would try the product. To attach some sort of cost to the

behavioral activity, since these were free products, respondents were told to imagine that

they would have to answer a 6-minute survey if they decided to taste it. After indicating

their willingness to try the product along a 9-point scale (9 = I would definitely try it),

respondents were asked to explain their indicated behavioral intentions. Respondents

were then presented with a series of items, which assessed the manipulations and the

impact of the mediating processes.

To confirm the assumption that women are more likely than men to perceive eating

chocolate as an affect-regulation opportunity, people's instrumental use of chocolate was

assessed with a single item "I eat chocolate to feel better". Another item assessed the

impact of affective evaluation on the cost associated with the behavior (i.e., answering

the survey) "I was a bit concerned that it might take too long to answer the

questionnaire". Finally, to check for potential confounding, additional items checked the









extent to which participants were hungry and thirsty, whether or not they had heard of the

particular brand before, and if the product had made them think of other products that

could have influenced their evaluations.

Results

Manipulation Checks

Six students provided a rudimentary guess of the purpose of the experiment. They

were deleted from the sample. After checking for reliability (a = .91), the three affect-

related items were collapsed to form the affect index. The affect manipulation produced a

significant main effect on people's prior affective state (F(2, 142) = 124.69, p < .001).

Pairwise comparisons showed that compared to the neutral affect condition (M= 5.2),

respondents in the negative affect condition experienced more negative feelings (M= 3.3;

F(1, 98) = 46.76, p < .001), whereas respondents in the positive affect condition

evaluated their affective state more positively (M = 7.8; F(1, 98) = 84.98, p < .001).

Finally, the assumption that women are more likely to perceive chocolate as a mood-

lifting product was supported. Female respondents were much more likely to

acknowledge eating chocolate to feel better than were male respondents (Momen = 5.7 vs.

Amen= 2.7; F(1, 143) = 59.34, p < 001).

Purchase Intention

As hypothesized, gender and affective state produced a significant interaction (F(2,

139) = 5.09, p < .01). Most importantly, breaking down the analysis by gender, a

monotonic increase was observed among men F(1, 80) = 15.29, p < .001), whereas a "U"

shape pattern emerged among women (F(1, 59) = 9.97, p < .005)(Figure 4).










8 7.67
7.38 7.18
7-
S5.96 5.91
S6 Women

54.79 O Men
5-

4

3
Negative Neutral Positive
Affect



Figure 4. Behavioral intentions toward chocolate

IMAB proposes that as men are less likely to perceive the mood-lifting benefits of

eating chocolate, the affective evaluation mechanism should direct their behavior, leading

male respondents in positive (negative) affect to increase (decrease) their behavioral

intentions, compared to the neutral affect condition. Pairwise comparisons indicated that,

when facing an opportunity to try a piece of chocolate, men in a sad mood (M= 4.8)

seemed less willing to do so than those in the neutral affect condition (M= 5.9; F(1, 54)

= 2.76, p = .10). The opposite was true for those in the positive affect condition, who

indicated higher intentions (M= 7.2) to try the chocolate in the same hypothetical

scenario (F(1, 57) = 5.6,p < .05).

Among women, the affect-regulation mechanism was hypothesized to have a strong

influence on behavioral intentions, since this group is more likely (as confirmed by the

manipulation checks) to perceive eating chocolate as an affect-regulation opportunity. In

accordance with IMAB's predictions, women experiencing negative affect indicated

stronger intentions to taste the chocolate (M= 7.4) compared to those in the neutral affect









condition (M= 6.0; F(1, 42) = 5.8, p < .05) Women experiencing positive affect also

exhibited stronger behavioral intentions compared to those in the neutral affect condition

(M= 7.7; F(1, 39) = 6.9, p < .05). IMAB proposes, however, that the latter difference is

due to affective evaluation rather than regulation. Since male respondents, who reported

not using chocolate as an affect-regulation device, displayed similar patterns on the

positive side of the affective spectrum, it seems plausible to suggest that evaluation

(instead of regulation) represented the main mediating process for women under positive

affect.

Affective Evaluation Mechanism

Further evidence regarding mediating processes was obtained with the open-ended

question and the additional items. IMAB proposes that the affective evaluation

mechanism leads people experiencing negative (positive) affect to perceive the costs/risks

associated with any behavior as more (less) negative. Remember that across all

conditions an element of cost, orthogonal to the product, was included. Those choosing

to taste the chocolate were told they had to answer a 6-minute survey. Therefore, if the

affective evaluation mechanism is operating, respondents experiencing negative affect

should consider this cost element more carefully than those experiencing positive affect.

As already mentioned, one item assessed people's concern about the length of the survey.

The results show that affect produced a main effect on respondents' concerns about costs

(i.e., survey length) associated with the tasting the chocolate (F(2, 142) = 3.28, p < .05).

Although they were explicitly told that the survey would take precisely six minutes, sad

respondents (M= 6.4) were more concerned that the survey "might take too long" than

those in the neutral (M= 5.2) and positive (M= 4.9) affect conditions (F(1, 98) = 4.12, p

< .05; F(1, 88) = 6.23, p < .05, respectively). The difference between neutral and positive









affect conditions was non significant (F < 1). Also important is the fact that no interaction

between gender and affect emerged (F < 1). Thus, men and women seemed similarly

influenced by the affective evaluation mechanism.

The open-ended question also permitted tracking the impact of the affective

evaluation mechanism by counting the number of people who mentioned the survey in

their justification of their behavioral intentions. The impact of affect was quite evident.

Only 9% of respondents in the positive affect condition mentioned the survey in the

justification, compared to 33% of respondents in the neutral affect condition (Z= 3.14, p

< .01, one tailed test) and 38% in the negative affect conditions (Z= 3.45,p < .01, one

tailed test). Once again, the pattern remained the same across gender (table 1).


Table 1. Influence of affective evaluation on behavioral intention

AFFECT

GENDER Negative Neutral Positive
Women 33.3%a 43.5%a 5.3%b
Men 41.7%a 21.9%* 11.1%
Gender Collapsed 37.8%a 32.7%a 8.9%b

Note: Different superscripts indicate significant differences at p < .05. The asterisk
indicates marginal significance (p < .10).

Combined, these results suggest, first, that affective evaluation mitigates

(enhances) the perceived costs associated with the survey for respondents in a positive

(vs. negative) affective state, which can explain why both men and women were more

inclined to try the chocolate while experiencing positive affect. Second, these results also

imply that if this mechanism were to operate in isolation, women in the negative affect

condition should be less inclined to try the chocolate. Since women experiencing

negative affect were, on the contrary, more willing to try the chocolate than those in the









neutral affect condition (and equivalent to those experiencing positive affect), another

opposing mechanism must have reversed the effects. According to the IMAB, affect-

regulation can produce such effects.

Affect-Regulation Mechanism

According to the model, affect regulation is contingent on people's beliefs about

the affective consequences of a particular behavior (e.g., "I eat chocolate because I know

it will make me feel better"). Although, in general, women were found to eat chocolate to

feel better much more often than men, there was a certain degree of variance within each

gender condition. Thus, we should expect a decrease in behavioral intentions for those

women who acknowledged they were less likely to eat chocolate to feel better, and an

increase in behavioral intentions for those men who acknowledged they were more likely

to eat chocolate to feel better.

A series of 6 bivariate Pearson correlations (one per condition) between

respondents' acknowledged use of chocolate as a mood-lifting product and their

respective intentions to taste the chocolate was conducted. The correlation should be high

(low) in the conditions where affect-regulation is expected to have a strong (weak)

influence. Table 2 summarizes the results.


Table 2. Influence of acknowledged use of chocolate to lift mood on behavioral intention

AFFECT

GENDER Negative Neutral Positive
Women 0.49** 0.04 0.04
Men 0.37* 0.28 0.14

Note: Double-asterisks indicate Pearson correlation significant atp < .05 level (2-tailed).
Single asterisks indicate Pearson correlation significant atp < .10 level (2-tailed).









As can be observed, the strongest effect (i.e., the only significant result at p < .05)

emerged when female respondents were experiencing negative affect, the condition in

which affect-regulation was hypothesized to direct behavioral intentions. In other words,

intentions to try the chocolate were lowered for female respondents who acknowledged

not using chocolate to regulate their affective states. A marginal effect was also found

among men in the negative affect condition, suggesting that those men who are more

likely to eat chocolate to lift their mood were more willing to try the chocolate. Among

those experiencing neutral or positive affect, there were no significant correlations, which

rules out regulatory influences of affect beyond the negative side of the affective

spectrum. These results not only challenge the mood-maintenance type of explanation for

those experiencing positive affect but also support IMAB's assumption that affect-

regulation is less likely within neutral affect conditions.

Discussion

By relying on people's perceptions of the product's mood-lifting properties rather

than using a more "heavy handed" manipulation, experiment 2 successfully replicated the

earlier results while eliminating the focus of attention bias inherent in the first

experiment. Most importantly, it also shed additional light on the two mediating

processes assumed to link affect to behavioral intentions.

First, among respondents experiencing positive affect, the affective evaluation

mechanism seems to have a predominant role, since the impact of the survey (i.e., cost

associated with the behavior) weakened as respondents' feelings improved. Moreover,

affect-regulation seems inactive for happy respondents since, unlike for sad respondents,

(1) there was no correlation between respondents' acknowledged use of chocolate as a

mood-lifting option and their respective behavioral intentions and (2) intentions to try the









chocolate also increased among male respondents, who, based on self reports, were in

general unlikely to eat chocolate in an attempt to regulate their mood.

Second, affect-regulation seems to have a predominant role among the women who

experienced negative affect, since their behavioral intentions increased (compared to

those in neutral conditions), and there was a significant positive correlation between the

use of chocolate as a mood-lifting product and their behavioral intentions. In other

words, behavioral intentions were higher for women who were more likely to perceive

eating chocolate as an affect-regulation opportunity. Notice that these results also support

the assumption that as neutral affective states trigger weak signals, the discrepancy

between the current and the ideal affective state becomes less salient, which results in

minimal affect-regulation tendencies.

Finally, the interaction between the two mechanisms can be observed in negative

affect conditions. Independent of gender, the costs associated with the survey increased in

the negative (positive) affect condition, which indicated the negative impact of the

affective evaluation mechanism. Nonetheless, female respondents were highly willing to

taste the chocolate due to its regulatory properties, as indicated by the correlational

analysis. Such opposing patterns suggest, therefore, that affect-regulation does not

necessarily "switch off' the affective evaluation mechanism, but instead dominates its

opposing effect, probably by increasing the weight given to a product's mood-lifting

benefits.














CHAPTER 8
EXPERIMENT 3

So far, IMAB has demonstrated and explained three out of the four types of effects

presented in Figure 1. The potential inhibitory consequences of positive affect (i.e., Cell

3) remain to be examined. Can positive affect mitigate people's willingness to eat

chocolate? According to model, the affect-regulation mechanism is more likely to guide

behavior if people can anticipate the affective consequences of the behavioral activity.

When people in a bad mood anticipate that their behavior will make them feel better, they

become more likely to act. Experiments one and two confirmed that hypothesis. It

follows that when people in a good mood anticipate that their behavior will make them

feel worse, they should become less likely to act than those in the control condition,

simply because they have more to lose. Research in risk taking has provided support for

this rationale (see Isen 2000 for a review).

Experiment 3 focuses therefore on the facilitatory (Cell 1) and the inhibitory (Cell

2) behavioral consequences of positive affect. A major procedural change (compared to

the previous experiments) is that a direct behavioral measure rather than intention is used

as the main dependent measure. Therefore, respondents faced actual, instead of projected,

behavioral consequences throughout the experiment. Similar to experiment 2, chocolate

represents the target stimulus; however, in this final experiment actual consumption is

assessed. Popular culture as well as the scholarly research largely support the assumption

that chocolate consumption can be associated with aposteriori negative affect, notably

feelings of guilt (Benton et al. 1998; Cramer and Hartleib 2001; Macdiarmid and









Hetherington 1995). Thus, to create a mood-threatening cue condition that may lead

people experiencing positive affect to be less likely to act, a group of respondents is

reminded of the negative consequences of the particular behavior (i.e., fat and fat calories

associated with chocolate consumption). IMAB predicts that happy respondents facing a

potential affective threat (subsequent feelings of guilt) will eat less chocolate (compared

to the respective neutral affect condition), due to the protective response of the affect-

regulation mechanism. When no mood-threatening cues are made salient, happy

respondents are expected to consume more chocolate than the control condition as a

result of the affective evaluation mechanism (i.e., a better evaluation of the environment).

Since the fat components of chocolate are likely to be perceived as a negative attribute by

both men and women, gender should not interact with the other two factors (i.e., affect

and mood-threatening cue).

Method

Subjects and Design

One hundred sixty-seven undergraduate students from a southeastern university

participated in the experiment in exchange for course credit. The study adopted a two

(affective state: neutral and positive) by two (mood-threatening cue: salient vs. not

salient) between subjects design. Respondents were randomly assigned to one of the four

conditions.

Procedure

To keep the affect manipulation similar to the previous studies, the cover story

focused on the impact of video on memory, but it added information identifying potential

interactive effects of food consumption. As in experiments 1 and 2 respondents were

presented with a five-minute program (an episode of"Friends" vs. a documentary about









John Nash), and were asked to recall a personal experience related to film (affect

manipulation) as well as to evaluate the video. Three items embedded into the scale

assessed respondents' current affective state (affect manipulation check). Then

respondents were instructed to open an envelope placed to the left of the computer

monitor. Inside was 89-gram package of M&Ms. A five-minute sequence of the previous

movie was then presented on the screen and respondents' task was simply to watch the

video while eating "as many or as few M&Ms" as they wanted. Food consumption while

watching a movie represents a common data collection procedure in the eating behavior

literature (Cools, Schotte, and McNally 1992; Grunberg and Straub 1992). Each M&M

package was weighed before and after the experiment to ensure an accurate measure. The

amount of M&Ms consumed became the dependent variable.

To manipulate the mood-threatening cue associated with eating behavior an

additional instruction page was given to one subgroup before they started watching the

video and eating the chocolate candies. These respondents were informed that as different

experimental groups tasted different products, the experimenter had to control for the

number of calories consumed. Therefore, respondents should look for the nutrition facts

table on the M&M package and enter the number of calories and fat calories of this

particular product on the screen. In the control group, no nutrition information about the

product was requested from respondents.

After watching the second part of the video, respondents were instructed to put the

M&M package back into the envelope and answer some final questions/fill out some

scales. An additional memory type of question was asked to complete the cover story

followed by another affect manipulation check, also embedded into the movie evaluation









scale. Then, in an attempt to gather additional insights into people's motivations, an

open-ended question about the potential reasons that stimulated or impeded their actions

(i.e., chocolate consumption) followed. Similar to experiment 2, respondents also

responded to items referring to their general consumption habits and perceived

relationships between mood and chocolate consumption. To assess the impact of potential

covariates, a Restraint Questionnaire (Herman and Polivy 1975; Polivy, Herman, and

Warsh 1978), was presented, which assesses people's chronic attempt to keep strict

control over their eating behavior. Respondents' body mass index (BMI) was also

calculated, and the time of data collection was also recorded. All experimental sessions

took place between 2:00 pm and 4:30 pm.

Results

Manipulation Checks.

Two students provided a rudimentary guess as to the purpose of the experiment.

They were deleted from the sample. After checking for reliability (a = .88), the three

affect-related items were collapsed to form an affect index. The affect manipulation

produced a significant main effect on respondents' prior affective state. Respondents

exposed to first five-minute exposure of the video plus the description of a personal

experience were happier when they watched an episode of friends (M= 7.9) than when

they watched a documentary about John Nash (M= 5.6; F(1, 163) = 161.32, p <.001).

The mood-threatening cue manipulation was assessed by the number of respondents who

entered the calories and fat calories components of their respective M&M package in the

requested blank spaces. As expected, all respondents in the salient mood-threatening cue

condition correctly followed the instructions. Neither the body mass index nor the

Restraint Scale produced any impact on eating behavior and were not incorporated in









further analyses. Similarly, eating behavior remained the same regardless of the time of

the experiment (2pm, 3pm, or 4pm). Gender produced a marginal simple main effect on

chocolate consumption, with male respondents eating more (M = 26.3) than female

respondents (M= 21.8; F(1, 163) = 3.70, p <.10). However, as predicted, no three-way

interaction was observed (F(1, 157) = 2.59, p >.10). The interactions between gender and

affective state and gender and mood-threatening cue were also non significant (F<1).

Eating Behavior

As hypothesized, mood-threatening cue (salient vs. not salient) and affective state

(positive vs. neutral) produced a significant interaction (F(1, 161) = 8.25,p =.005)(Figure

5).


35 -
30.95
30 -

S25 23.33 23.52
0
20 18.05 neutral affect
5 20 18.05
Positive affect
S15-

10 -

5
non salient mood salient mood
threatening cues threatening cues


Figure 5. Amount of chocolate consumed

When respondents were asked to eat as much or as little chocolate as they wanted

and no mood-threatening cues associated with the product/behavior were highlighted,

respondents experiencing positive affect ate more M&Ms (M=30.9) than those in the

neutral affect condition (M = 23.3; F(1, 81) = 4.42, p <.05). The results reversed when









the calories and fat calories associated with M&Ms (i.e., the mood-threatening cues of the

product/behavior) were made salient prior to consumption. In this case, those in the

positive affect condition ate significantly fewer M&Ms (M= 18.0) than did those in the

control condition (M= 23.5; F(1, 80) = 3.96, p =.05). This interaction not only replicates

the impact of positive affect on action stimulation (Cell 1) with a direct behavioral

measure, but also, and most importantly, demonstrates that positive affect can also lead to

inhibitory actions (i.e., less consumption) when the behavior threatens people's current

affective state (Cell 2).

Affect-Regulation Mechanism

IMAB proposes that people's anticipated (post behavior) affective state can

promote or inhibit consumption via the affect-regulation mechanism. Therefore,

respondents experiencing positive feelings, having more to lose than those in a control

condition, should eat less chocolate when the mood-threatening consequences of the

behavior are made salient. This rationale is based on the assumption that chocolate can

trigger guilt feelings aposteriori and that people can anticipate them (Benton et al. 1998;

Cramer and Hartleib 2001; Macdiarmid and Hetherington 1995), which makes happy

people more conservative in their actions, in an attempt to protect the current pleasant

affective state.

To assess the impact of guilt, respondents were asked (at the end of the experiment)

to indicate the extent to which they generally had guilt feelings after overeating (a four

point scale from never to always). This item was extracted from the Restraint

Questionnaire (Herman and Polivy 1975; Polivy et al. 1978). A correlational analysis

(within each of the four cells) between the grams of chocolate consumed and people's









acknowledgement of experiencing guilt feelings after overeating was then performed.

Table 3 summarizes the results.


Table 3. Protective influence of overeating-based guilt feelings on behavior


AFFECT
MOOD-THREATENING Ne l
Neutral Positive
CUE
Yes -.12 -.30*
No -.01 .07

Note: The asterisk indicates Pearson correlation significant atp < .10 level (2-tailed).

When the mood-threatening cues associated with the product/behavior were not

made salient (i.e., cells at the bottom row), respondents' guilt feelings after overeating

did not correlate with the amount of chocolate consumed. Similarly, when people

experienced neutral affect, thereby having little to lose and/or a weak signal (i.e., cells at

the left column), the correlation was also non significant. However, when respondents

experiencing positive affect, thereby having a lot to lose and/or a strong signal, are given

the mood-threatening cues for chocolate consumption, a marginally significant negative

correlation emerged (r = -.30, p = .064). In other words, within this condition, those who

often or always feel guilty after overeating (M= 16.2) tended to eat significantly less

chocolate compared to those who rarely or never experience these negative feelings after

overeating (M= 21.3, F(1, 36) = 3.10, p < .10)(Figure 6).











22
= 20
I 18
16
o 14
0
S12
10
Never Rarely Often Always
Feelings of Guilt after Overeating



Figure 6. Amount of chocolate consumed within the positive affect /mood-threatening
condition the impact of expected feelings of guilt after overeating

Within the positive affect no mood-threatening cue condition, an increase in

chocolate consumption emerged compared to the respective neutral affect condition.

Although IMAB predicts this impact to be mostly driven by the affective evaluation

mechanism, others (Clark and Isen 1982) suggest that mood-maintenance (i.e., affect-

regulation) could also be responsible for the effects. In experiment 2, this alternative

explanation was ruled out by showing that within the positive affect conditions there is no

correlation between respondents' acknowledgement of the use of chocolate as a mood-

lifting strategy (i.e., I eat chocolate to feel better) and their willingness to try the

chocolate. In experiment 3 two items were used to test this potential correlation (i.e., "I

eat chocolate to cheer myself up"; "Eating chocolate when I'm happy helps me to

maintain my good mood"). If a positive correlation emerged between chocolate

consumption and these two items, then one can claim there is initial evidence for affect-

regulation mechanism. If no correlation exists, affect-regulation probably did not play a

major role. None of the items correlated with chocolate consumption (p > .50 for both

items), which replicates the results presented in experiment 2 using an actual behavioral









measure. In other words, the extent to which respondents consume chocolate as a mood-

lifting or mood-maintenance strategy did not lead them to eat more or less. Therefore, as

predicted by IMAB, the affect-regulation mechanism seems not to have a major impact

on the increase of chocolate consumption in the absence of mood-threatening cues.

Instead this effect was probably guided by a more positive evaluation of the

product/behavior.

Discussion

The main purpose of the third experiment was to assess the extent to which positive

affect can inhibit behavior (Cell 2 of Figure 1). IMAB predicts that this can occur once

mood-threatening cues are made salient, thereby leading happy individuals to adopt a

more conservative response in an attempt to protect their current enjoyable feelings.

Using actual chocolate consumption, it was shown that respondents experiencing positive

affect ate less chocolate (compared to the neutral affect condition), once the fat and fat

calories of the product were made salient prior to consumption. Moreover, this effect

seemed to intensify (vs. weaken) among respondents who acknowledge being more (less)

likely to feel guilty after overeating. Notice that, similar to experiment 2, this effect is

based on IMAB's assumption that the affect-regulation mechanism is contingent on

people intuitive theories about the affective consequences of their respective behavior. In

experiment 2, those within the negative affect condition who expected to feel better after

the behavior were more likely to try the chocolate. In experiment 3, the reverse rationale

applied. Those within the positive affect condition who expected to feel worse after the

behavior were less likely to eat chocolate.

When the mood-threatening cues (i.e., calories and fat calories of M&Ms) were not

highlighted, happy respondents ate more chocolate than did those in the neutral affect






70


condition. Moreover, within this particular Cell there was no correlation between

people's acknowledged use of chocolate for mood-lifting or mood-maintenance purposes

and the grams of chocolate consumed. Thus, similar to experiment 2, and as predicted,

affect-regulation was not responsible for the effect, suggesting that affective evaluation

was the operative mechanism.














CHAPTER 9
GENERAL DISCUSSION

The behavioral consequences of affect can be categorized into four groups: positive

affect encouraging action, positive affect inhibiting action, negative affect encouraging

action, and negative affect inhibiting action. The several theories and hypotheses

developed since the early 1970's have focused prominently on one, or at most, two of

these four potential effects (Figure 1), thereby providing no parsimonious explanation of

how affect influences behavior. To fill this theoretical gap an integrative model of

affective behavior (IMAB) was proposed, in which two well established properties of

affect (i.e., evaluative/informational and regulatory/motivational) represent the

potentially opposing mediating mechanisms responsible for the behavioral consequences

of individuals' current affective states. Moderating variables attached to each of these

mechanisms determine which mediating process prevails and consequently guides

behavior.

In a series of three experiments, it was demonstrated that people's current affective

state represents a critical moderator for both the affective evaluation and affect-regulation

mechanisms, where valenced affective states tend to lead to stronger behavioral effects

compared to control conditions. Most importantly, the expected mood changing

characteristic of the behavioral activity (i.e., the mood-lifting cues in experiments 1 and 2

and mood-threatening cues in experiment 3) and its interaction with people's current

affective state help to determine the extent to which the affect-regulation mechanism will

prevail over the affective evaluation mechanism, and eventually direct behavior.









Rooted in the mood-congruency and affect-as-information hypotheses, the affective

evaluation mechanism predicts that people will congruently evaluate any behavioral

activity. As a result of a congruent evaluation, happy (sad) people become more (less)

likely to act, especially when there is no competing, more diagnostic information

available in the environment. Experiment 1 (within non mood-lifting conditions) and 2

(among men) confirmed the effect. Experiment 3 (within non mood-threatening

conditions) further replicates it using a direct behavioral measure. These affective

evaluation-mediated effects characterize Cells 1 and 4 of Figure 1.

The affect-regulation mechanism, however, may come into play as a counteracting

force if certain conditions are met. For those experiencing negative feelings while facing

a potential mood-lifting opportunity (i.e., having their affective state salient in memory

(Cohen and Andrade 2004) and having much to gain effectivelyy) via the behavioral

activity, affect-regulation encourages action, overcoming the opposing impact of the

affective evaluation mechanism. Experiments 1 (within the mood-lifting conditions) and

2 (among women) confirm these predictions. Respondents under such conditions

indicated they were more likely to purchase coffee and breakfast cereal (experiment 1),

and to taste chocolate in an imaginary situation (experiment 2) than those in the

respective neutral affect conditions. For those experiencing positive affect while facing a

mood-threatening scenario, (i.e., having their affective state salient in memory, but also

having more to lose), affect-regulation discourages action. This can overcome the

stimulating effects of the affective evaluation mechanism. Experiment 3 confirms this

prediction by showing that when happy respondents are required to monitor the calories

and fat calories associated with chocolate prior to consumption they eat less chocolate









than those in the neutral affect condition. These affect-regulation-mediated effects

characterize Cells 3 and 2 of Figure 1.

IMAB incorporates the basic rationale of two groups of theories already well

established in the literature, but that have followed independent routes. One the one hand,

it fully incorporates the basic assumptions advanced by the affect-as-information

hypothesis into the affective evaluation mechanism. By the same token, it combines

central aspects of several of the affect-regulation types of theories into a newer and more

comprehensive affect-regulation mechanism. As a result of such integration IMAB has

the ability to account for a broader array of effects described in the literature. However, it

does not necessarily invalidate other accounts. For instance, the fact that the affect-as-

information or mood-congruency hypotheses cannot explain the impact of negative affect

on the increase of purchase intentions does not make these theories inaccurate in any

sense; it simply shows that there are some effects that go beyond the scope of these

models. Put simply, Cells 2 and 3-types of effects do not provide empirical evidence

against theories focused on explaining the effects of Cells 1 and 4. In this sense, IMAB

complements more than competes against the current models, and it may be preferable on

the basis of offering a more parsimonious and internally consistent account.

Nevertheless, the proposed theory does provide a competing explanation for

behavior that has been linked to several existing theories, such as the mood-maintenance

hypothesis (Clark and Isen 1982). Mood-maintenance proposes that people's willingness

to maintain their current affective state will lead them not only to protect themselves

against mood-threatening behavior (Cell 2), but also to promote mood-lifting behavior,

such as helping (Cell 1). Whereas IMAB recognizes and empirically confirms the former,









it raises questions as to the latter. It was shown that the increase in intentions

(experiment 2) and actual consumption (experiment 3) of a mood-lifting product among

happy people (compared to the neutral affect condition) were most likely driven by

people's positive assessment of the environment (i.e., affective evaluation), rather than a

systematic attempt to act in order to keep a current positive affective state (i.e., affect-

regulation). In experiment 2, it was shown that men in the positive affect condition were

more likely to eat chocolate than in the control condition, despite the fact that they did

not perceive chocolate as a mood-lifting product. Also, a correlation between the extent

to which chocolate is used as a mood-lifting strategy and respondents willingness to try

the product did not emerge for both men and women within the positive and neutral

affect conditions, but did appear for both men and women within the negative affect

condition. Finally, in experiment 3 there was an increase in actual chocolate consumption

from neutral to positive affect conditions. However, people's self-rated use of chocolate

to keep their good mood or to improve it did not correlate with actual consumption.

Since existing empirical evidence for mood-maintenance under Cell 1 (i.e.,

promote a mood-lifting action to keep a good mood) is entirely consistent with the IMAB

and does not require a separate mood-maintenance heuristic, IMAB dominates mood-

maintenance as an explanation for this type of behavior. As shown throughout this

research, a better/safer evaluation of the environment rather than the need to keep a good

mood seems more likely to lead people experiencing positive affect to be more active.

However, when it comes to mood-threatening scenarios, IMAB fully converges with the

mood-maintenance hypothesis (Isen 2000 for a review), in which people become more









sensitive and more protective of their behavior (compared to a control condition),

because they have more to lose (Cell 3).

In integrating these two mechanisms, the model also advances theory also by

explicitly identifying the critical moderating variables attached to each mechanism,

particularly for affect-regulation. IMAB is also able to account for the weak/null effects

of neutral affect compared to polarized affective states. The three experiments show that

people in a more neutral affective state are usually weakly sensitive to available mood-

lifting or mood-threatening cues. Based on recent evidence and theoretical propositions

(Cohen and Andrade 2004), IMAB suggests that affect-regulation is not only a function

of the discrepancy between the current and desired affective state, but also of the strength

of the affective signal, which makes the existing discrepancy more or less salient. As

neutral affective states provide weak signals, the discrepancy is not highlighted and the

motivation for change becomes much weaker compared to polarized affective states.

Once the discrepancy is made salient, expected affective changes (i.e., from people's

intuitive theories as to the mood-lifting and mood-threatening properties of the

product/behavior) are critical to affect-regulation and follow the same theoretical

rationale for both negative and positive affective states.

Finally, IMAB points to a tempting conclusion that, compared to a control

condition (i.e., neutral affect), the affective evaluation mechanism is more likely to

influence behavior when we find happy people more willing to act (Cell 1) and sad

people less willing to act (Cell 4), whereas the affect-regulation mechanism is more likely

to have influenced behavior when we find sad people more willing to act (Cell 3) and

happy people less willing to act (Cell 2). However, it is possible that under certain









circumstances, regulation and evaluation may combine to promote or inhibit behavior,

although as to which contingencies are required remains an open question. Also, the

presumed interdependence of affective evaluation and affect-regulation implies that some

moderating variables can well have an impact on both mechanisms at the same time. For

instance, lack of diagnostic information about the behavior/environment tends to

instantiate the affective evaluation mechanism. However, it may also mitigate the impact

of the affect-regulation mechanism if this information modifies the perceived mood

changing properties of the behavior. Moreover, some of the moderating variables

described by the model (e.g., availability of competing goals, people's beliefs about their

affect-regulation skills) are also to be explored. Finally, more specific emotions can also

be incorporated into the model. Raghunathan and Pham (1999) showed that anxious

people are less likely to take risks compared to sad people. In IMAB terms, the reason is

simply that anxious people do not perceive risk-taking as a potential upward affect-

regulation opportunity whereas sad people do. In short, specific emotions may also be an

interesting way of enhancing or inhibiting the impact of the affective mediating processes

on behavior.















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BIOGRAPHICAL SKETCH

I graduated in Business Administration at the Universidade Federal de Santa

Catarina in Florianopolis (SC) Brazil, in December 1993. A few years later, I received

my master's degree in business (marketing option) from the Universite de Montreal -

HEC School. My interests in marketing and, particularly, in consumer behavior led me to

move to the University of Florida to pursue a Ph.D. in the marketing department. I will be

joining the Haas School of Business at the University of California-Berkeley as an

Assistant Professor of Marketing in July 2004.