The increasingly comprehensive literature on mood and social cognition has produced strong evidence that mood states play a major role in how people learn, remember, think about risk-taking, and evaluate complex social information (Berkowitz, Jaffee, & Troccoli, 2000; Bower, 1981; Forgas & Ciarrochi, 2001; Kuvass & Kaufmann, 2004). The associated research on mood and risk-taking was carried out initially by Isen and her colleagues (Isen & Patrick, 1983). Their work found that in gambling and lottery tasks, positive moods (moods induced by small gifts) yielded risk-averse behavior and that negative moods produced risk- taking behavior. These results are consistent with those of later studies (Kuvass & Kaufmann, 2004; Mittal & Ross 1998).

A motivational perspective has been proposed to explain the process of the effect of mood on risk-taking. This perspective postulates that people in a positive mood are motivated to maintain the positive mood and to repair the negative mood. In a positive mood, people do not take big risks as doing so increases the potential for large personal losses that might disrupt the positive mood state. Similarly, it can also be asserted that in a negative mood, people will be willing to take higher risks to obtain higher potential gains in the hope of repairing their negative mood state (Mittal & Ross, 1998). Therefore, the influence of mood states on risk-taking is explained by a desire to maintain a positive mood state or mitigate a negative mood state.

Although previous research has provided a valuable empirical and theoretical base for the study of mood effects in risk behavior, that research has a number of limitations for application to everyday decision-making. For example, the widespread use of gambling and lottery tasks provides an effective way of defining rational behavior, but they may have only limited relevance to everyday choices, which normally have to be made in the face of uncertainty and ambiguity. In this study, we adopted an approach based on Hockey, Maule, Clough, and Bdzola (2000) and used 13 sets of real life scenarios to explore our prediction that positive moods will lead to risk aversion, but negative moods will result in risk-taking.
H1: Mood states will influence risk-taking such that people in a negative mood state will take higher risks than those in a positive mood state.

Researchers have also noted a difference between decision-makers in their sensitivity to the influences of transient emotional states (Berkowitz et al., 2000; Forgas, 1990, 1991; Forgas & Ciarrochi, 2001; Kuvaas & Kaufmann, 2004). For example, Kuvaas and Kaufmann found that subjects who received mood- congruent framing information (positive mood/positive framing and negative mood/negative framing) showed significantly better recall and were significantly less overconfident than those who received mood-incongruent framing information (positive mood/negative framing and negative mood/positive framing). Yet this effect was moderated by a decision-maker’s need for cognition and was obtained only among subjects with a lower cognitive processing requirement. Forgas and Ciarrochi found that subjects who scored high on the Openness-to-Feelings (OF) scale were most influenced by their moods on subjective valuation of consumer goods. In contrast, people who scored low on this measure showed the reverse pattern. Berkowitz and his colleagues found that the effect of mood in judgment disappeared when a judge’s attention was directed to his or her internal state. In these studied participants, self-directed attention was sufficient to temporarily reduce openness to feelings and to selectively elicit a controlled, motivated processing strategy, leading the participants to discount and disregard their mood states. Rusting (1998) specifically argued that temporary moods and personality traits have an interactive role in thoughts and judgments. In a series of experiments, Forgas (1998) found that the effect of mood on planned and actual bargaining behaviors was reduced for individuals who scored high on traits such as a need for approval and Machiavellism, and thus these individuals were more likely to approach the bargaining task from a predetermined, motivated perspective. In another study, Ciarrochi and Forgas (1999) found that negative mood produced more negative judgments about a racial outgroup, but only for more self-confident, low trait-anxious people. In contrast, high trait-anxious people adopted a defensive, motivated strategy and showed no effect of mood. These studies suggest that when information processing is dominated by a trait-based motivational objective that constrains the open and constructive use of affectively valenced information, the mood effect is less likely to prevail.

In this study, we asserted that openness to feelings (OF), as an obvious personality variable, is likely to influence mood effects on risk-taking. Costa and McCrae (1985) developed a reliable scale measuring this construct, the Openness-to-Feelings (OF) scale, which assesses the extent to which people are receptive to their inner feelings and believe that such feelings are important in their lives. The effect of mood should be moderated by OF; people low in OF will have a habitual tendency and motivation to discount and control their feelings. Conversely, people high in OF will trust their feelings and be highly influenced by mood.

In this study the premise that personality traits, such as OF, would moderate the judgmental consequences of temporary moods, producing a significant interaction between OF and mood was explored. It was expected that those who value and trust their feelings (score high on openness to feelings) would be highly influenced by the mood effect. Conversely, those scoring low on the OF measure would show less capability to produce the mood effect as an opposite effect. Because low-OF individuals habitually discount their feelings, they are likely to use a strategy based on the facts and information presented to them (Berkowitz et al., 2000; Forgas & Ciarrochi, 2001; Martin, 2000).
H2: Subjects who are induced to a positive mood condition and have high-OF, but not those with low-OF, will be less likely to take higher risks than those subjects who are induced to a negative mood condition.

Half of the sample was induced to feel a happy mood (positive), and the other participants were induced to feel a sad mood (negative). This design was a simple one-factor, two-level, between-subjects design.

Mood manipulation Mood state was manipulated by having participants read a positively or a negatively valenced story adapted from Johnson and Tversky (1983) and also used by Mittal and Ross (1998) and Kuvaas and Kaufmann (2004). The positive story describes a student who is lucky enough to be accepted into medical school with a scholarship, while the negative story describes another student’s struggle with leukemia. According to Mittal and Ross (1998) and Kuvaas and Kaufmann (2004), this method of inducing mood closely resembles the kind of situations that managers might encounter in a real business setting. After reading the story, subjects were asked, “How happy do you feel right now?” and “How enjoyable was it to be in this situation?” to rate their current mood (α = .84). The scales were described with end point 0 = extremely unhappy/bad to 7 = extremely happy/good.

Openness to Feelings (OF) Measures In this section, participants completed the OF scale derived from Forgas and Ciarrochi (2001). The OF scale is an eight- item measure that assesses the extent to which people are receptive to their inner feelings and believe such feelings are important in their lives, such as, “How I feel about things is important to me” and “I seldom pay much attention to my feelings of the moment.” Subjects rated a eight-item measure on a 5-point agree- disagree scale. The OF scale’s reliability was 0.83 (α = 0.83). Subjects were divided into high- and low-OF groups based on a median split.

Dependent variables Personal Risk Inventory (PRI) was carried out measuring the tendency of risk-taking and was developed from Hockey et al. (2000). PRI was designed to be typical of choice situations frequently confronted by individuals in everyday life and representing a wide range of situations (e.g., legal, health, social, moral, financial). Participants were instructed to imagine how they would feel in each situation, and to choose which of two actions (A or B) they would take. A was identified as a “risky” option and B represented a “safe” option. Finally, participants completed 13 set scenarios across a wide range of situations.

When the participants arrived alone at the laboratory, an experimenter told them that the experiment would take 10-30 minutes and that they would be given $3 for their participation. Those who were interested in participating answered a few questions including their school affiliation, age, and year in school. All participants were told that, on the basis of the information they provided, they fit the desired profile for the experiment. This procedure was followed to reduce demand effects.

At the beginning of the experiment, participants were required to follow a procedure of mood inducement, and were asked to read either a happy or sad story and complete two measures regarding mood states as discussed above. They did not see or talk with each other. They completed 13 scenarios across various situations for individual preference in risk-taking and OF-rated measurement. Finally, they received remuneration and left the laboratory.

In general, consumers often engage in purchase decisions under risk, for example, the purchase of a new pair of athletic shoes may involve great economic, social, and performance danger. People may consider a choice of options that vary in the degree of risk. One of two or more alternative courses of action will offer both the greater perceived risk and greater potential benefit. Another alternative may be less risky with more potential reward. Identifying the best option from an available set is often difficult, because choosing one option implies that other options and their attractive features should be foregone (e.g., Bettman, Luce, & Payne, 1998; Festinger 1964). This choice depends on how great a risk people are willing to take. Thus, exploring and understanding the factors that influence consumer tendency for risk-taking is an important topic for research in marketing. This experiment demonstrated that mood states may influence an individual’s preference for risk-taking, and an OF personality may moderate the mood effect on the individual’s preference for risk-taking and therefore, his/her consumer decision. The results show that mood states will influence risk-taking so that people in a negative mood state will take higher risks than those in a positive mood state. Positive mood individuals are motivated to choose a safer option in order to maintain a positive mood, whereas negative mood individuals are motivated to choose a risky option to repair the negative mood by inducing a positive mood.

However, the mood effect is influenced by openness to feelings as a moderator. The results supported the hypothesis that people who are induced into a positive mood condition and have high-OF, but not those with low-OF, will be less likely to take higher risks than people who are induced into a negative mood condition. This finding theoretically explains that the mood effect should be eliminated when people openly engage their feelings. In low-OF, people may discount their feelings and employ motivated processing strategies to correct for such affectively loaded information. As a result, they can eliminate the mood effect. Several studies have shown how such motivated attempts to discount feelings can lead to correcting potential mood biases (Berkowitz et al., 2000; Forgas & Ciarrochi, 2001; Kuvaas & Kaufmann, 2004). This account is also consistent with convergent evidence showing a relationship between mood effects and personal characteristics, such as high self-esteem, Machiavellism, neuroticism, need for cognition, social desirability, or extroversion (Forgas, 1998; Kuvaas & Kaufmann, 2004; Rusting, 1998; Rusting & Nolen-Hoeksema, 1998). In this case, motivated mood-maintenance strategies used by happy, low-OF individuals apparently involved discounting the mood effect.

Finally, the potential limitation of this work should be kept in mind. In this study undergraduate students were employed as subjects to explain the relationship between induced mood and consumer decision making. The experimental results may be restricted and may not apply to the wider population. Additionally, the methodology employed in our experiments, in which reading a story in study was manipulated to induce mood, would limit the robustness of our theoretical framework. Perhaps other forms of inducement could also be used, including those that produce stronger effects, for example, watching a film. Future research should investigate additional elements of consumer decision in light of mood, using real consumers and real settings.