THE EFFECTS OF MOOD AND OPENNESSTOFEELING TRAIT ON CHOICE

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The Roles of Mood and Open-to-Feeling Trait on People’s Risk Taking Tendency

The Effects of Mood and Openness-to-Feeling Trait on Choice

Shih-Chieh Chuang

Department of Business Administration, Chao-yang University of Technology ,Taiwan

Chwen-Li Chang

Department of Business Administration, Chao-yang University of Technology ,Taiwan

Corresponding should be addressed to Chwen-Li Chang, Department of Business Administration, Chao-yang University of Technology, 168 Gifeng E. Rd., Wufeng, Taichung County, Taiwan; e-mail: [email protected] ; TEL: 886-4-23323000 ext. 4688; Fax: 886-4-2374233; Mobil: +886-936896068

The Effects of Mood and Openness-to-Feeling Trait on Choice

Abstract

How do mood states influence risk-taking and choice? Two experiments are conducted to demonstrate and explain the relationship of mood, risk-taking, and choice. Study 1 shows that participants in negative mood condition were more likely systematically to take risk-taking behavior than when in a positive mood, and the mood effect was moderated by openness to feeling (OF) in the individual personality. Study 2 mainly examined the effect of mood condition on individual choice. The results of Study 2 met our prediction that participants in positive mood were likely to having a higher share of an option that is “average” on all dimensions than for the negative mood condition. Study 2 also found that the mood effect was moderated by openness to feeling personality. The effects are discussed in relation to the literature on mood, risk-taking, and choice.

Key words: Mood; Openness-to-feeling; Risk-taking Tendency

The Effects of Mood and Openness-to-Feeling Trait on Choice

How do positive and negative moods influence an individual’s willingness to take risks? The behavioral decision making and social psychology literature suggest that people in a negative mood are more likely to take risks than those in a positive mood during gambling, strategical decisions, and lottery tasks (Arkes, Herren, & Isen 1988; Isen & Patrick, 1983; Kuvaas & Kaufmann, 2004; Mittal & Ross, 1998). However, there has been little research on the relationship of mood and risk-taking in typical every day contexts, such as buying new shoe, that don’t involve gambling or probability. It is necessary then for us to explore the relationship between mood and risk-taking in every day decision making. Since people are not always confronted by gambling and lottery tasks, the relationship of mood and risk-taking from previous studies may have only limited relevance to every day choices, which normally are also made in the face of uncertainty and ambiguity. Therefore, the present study attempts to understand the relationship between mood and risk-taking in every day contexts. Of course, not all people are equally susceptible to such mood influences; there are likely to be significant individual differences between individuals in regards to their openness to their own feelings.

The current paper reports two experiments that required people in happy or sad moods to offer their preferences regarding risk-taking in 13 kinds of scenarios. It is predicted that people in a positive mood will have a tendency to choose a higher risk option than those in a negative mood. It also is predicted that these temporary mood effects would be considerably greater for individuals who score high on certain traits, such as openness to feelings, than those who score low on this measure. Additionally, some of the psychological mechanisms likely to be responsible for these effects will be briefly considered.

The Effect of Mood on Risk-Taking

The increasingly comprehensive literature on mood and social cognition that 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; Bless, 2001; Bower, 1981; Clore, Schwarz, & Conway, 1994; Forgas & Ciarrochi, 2001; Kauvass &　Kaufmann, 2004). The associated research on mood and risk-taking was carried out early on by Isen and her colleagues (Isen & Patrick, 1983). Their work found that positive moods (moods induced by small gifts) yield risk-averse behavior and that negative moods produce risk-taking behavior in gambling and lottery tasks. The results are consistent with later studies (Mittal & Ross 1998; Kuvass & Kaufmann, 2004).

Such results are interpreted in terms of two motivation factors, mood-maintenance hypothesis and the information processing factor. First, mood-maintenance hypothesis (Isen & Patrick, 1983), states that people under a positive mood are motivated to desire maintaining 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 via a desire to maintain a positive mood state or mitigate a negative mood state.

Second, the information processing factor from Schwarz (1990, 2001) and his colleagues (Schwarz & Clore, 1983, 1988), argued that the experience of negative mood indicates a threat to the achievement of desired goals and that the situation calls for systematic and attentive processing, whereas a positive mood signals that the situation is safe, and general knowledge constructs are a sufficient basis for the current situation. Thus, people will look for mood as an indicator of whether they know enough when the goal is accurate judgment decision-making (Bless, 2001; Schwarz, 1990, 2001; Schwarz & Clore, 1983, 1988). For example, Forgas (1998) found that effects of induced mood on attribution errors were strongly related to changes in information-processing style. Subjects in sad moods performed better, making more effective use of memory for task information than did the controls, whereas those in happy moods made more errors and recalled less.

Although most of this evidence comes from social judgments, the evidence may also have relevance for decision making and risk, particularly for the comparison effects for positive vs. negative moods. From a mood repairing perspective, people in negative moods may be expected to choose risky options to give themselves a chance of obtaining the positive outcome that might improve their state of mind. If a negative mood acts to increase systematic processing, then the risky option may be more likely. Conversely, positive mood acts as heuristic processing as well as risk-averser resulting in the choice of the safe option being more likely.

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 every day decision making. For example, the widespread use of gambling and lottery tasks provide an effective way of defining rational behavior, but they may have only limited relevance to every day choices, which normally have to be made in the face of uncertainty and ambiguity. In our current study, we adopt an approach based on Hockey, Maule, Clough, & Bdzola,. (2000) and their use of 13 sets of real life scenarios to explore our prediction that positive moods are likened to risk-aversers, but negative moods are like risk-takers.

H1: Mood states will influence risk-taking such that people in a negative mood state will take higher risks than when in a positive mood state.

The of Personality Variable in Moderating Mood Effect

There are some researches that assert that the effect of mood on information processing and judgment does not yield; when thinking is dominated by a motive state, open, and constructive processing is absent (Berkowitz et al, 2000; Erber & Erber, 1994; Forgas, 1990, 1991; Forgas & Ciarrochi, 2001; Kuvaas &　Kaufmann, 2004). For example, Kuvaas &　Kaufmann, (2004) 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 whose 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 & Ciarrochi (2001) found that subjects who scored high on the openness-to-feeling 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 (2000) 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 were thus these individuals more likely to approach the bargaining task from a predetermined, motivated perspective. In another research, Ciarrochi & 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 should be less likely to prevail.

In this study, we assert that openness to feelings (OF) as an obvious personality variable is likely to influence mood effects on risk-taking. Costa & McCrae (1985) developed a reliable scale measuring this construct, the Openness to Feelings (OF) scale, and assessing the extent that people are receptive to their inner feelings and believe that that such feelings are important in their lives. The effect of mood should be moderated by OF in which 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.

This study mainly explores the premise that personality traits, such as OF, will moderate the judgmental consequences of temporary moods, producing a significant interaction between OF and mood. It expected that those who value and trust their feelings (score high on openness to feelings) are supported to be extra influenced in the mood effect. Conversely, those scoring low on the OF measure should show less likely capability to produce the mood effect as an opposite effect. Because low-OF individuals habitually discount their feelings, they should use a strategy based on that fact and information presentation that they consider (Berkowitz et al., 2000; Forgas & Ciarrochi, 2001; Martin, 2000). Thus,

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.

Study 1: The Effect of Mood and Openness to Feelings on Risk-Taking

The purpose of this study was to test whether openness to feelings (OF) will influence the mood effect on risk-taking. The mood state of participants was experimentally manipulated, OF scoring was measured from the OF scale, and its effect on risk-taking was observed.

Participants

Participants were 82 EMBA students (40 women and 38 men, mean age 29.2 years, age range 21-53) enrolled in marketing management course. They were paid about $3 for their participation.

Design

Half the participants were induced to feel a happy mood (positive), and the other participants were induced to feel a sad mood (negative). The 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 & Tversky (1983) and also used by Mittal & Ross (1998) and Kauvass &　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 (see Appendix A). According to Mittal & Ross (1998) and Kauvass &　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 & Ciarrochi (2001). The OF scale is an eight-items measure that assesses the extent that 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 every day life and representing a wide range of situation (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. In order to obtain a more sensitive measure of choice, respondents were asked to indicate their strength of commitment to the selected option on a 10-point scale (from “definitely A” to “definitely B”). This scale provided a graded measure of riskiness, rather than the dichotomous index of risk choices. The higher values of riskiness refer to increased endorsement of the safe alternative. Finally, participants completed 13 set scenarios across a wide range of situations. Two examples of scenarios are shown in Appendix B.

Procedure

When the participants arrived alone at the laboratory, an experimenter told participants that the experiment would take 10-30 min. and that they would be given $3 for their participation in exchange for their time. The researcher asked those who were interested to participate 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 they were asked to read an either happy or sad story and completed two measures regarding mood states as we discussed above; they did not see and talk with each other. Consequently, they completed 13 kinds of scenarios across various situations for individual preference in risk-taking and OF rated measurement. Finally, they received remuneration and left the laboratory.

Results

Manipulation Checks of Mood

Subjects in positive mood condition (M _positive=4.9, SD=0.77) felt happier than in negative mood condition (M _negative=3.1, SD=0.99) immediately after a mood was induced, t (80)=8.89, p<0.05. This result confirmed the effectiveness of mood manipulation.

Effects of mood states

In Study 1, we demonstrate that OF might influence the relationship of mood and risk behavior. The logit model analysis will be used to examine our prediction that subjects who are induced to a positive mood condition and have high-OF, but not those with low-OF, will be less likely to choose a safe option than subjects who are induced to a negative mood condition. The dependent variable was a 0-1 dummy variable, where 1 denotes the choice of a safe option (B). The independent variables include (1) a mood dummy variable that was a 0-1 variable, where 0 denotes positive mood and 1 represents a negative mood; (2) the openness to feelings dummy variable was a 0-1 variable, where 0 denotes high-OF scores and 1 represents low-OF scores.

Participants, on average across 13 set scenarios, for choosing the mean share of the safe option, were 52% regardless of mood and OF condition. The logit model showed that there was a significant main effect in the mood effect, t=2.01, p< .05, and participants who were induced to experience a positive mood (59%) were a higher grouping than those who were induced to feel a negative mood (45%) for choosing the mean share of the safe options across 13 set of scenarios regardless of OF condition. However, when OF was added to this model, there was a significant interaction between mood and OF for choosing a safe option, t=2.32, p< .05. As shown in Table 1, the effect of mood states was observed only among the high-OF group, 22%(M _{positive= 68%}- M _{negative
= 46%)}, and the mood status effect appeared to have no effect among low-OF subjects, 6% (M _{positive= 50%}- M _{negative = 44%}).

In addition, the degree of commitment to the selected option also was used to examine the tendency for participants in risk-taking. As shown Table 1, an ANOVA showed that the main effect of the mood was statistically significant, F (1, 78)=67.78, p< .05, and there was significant interaction between mood and OF for tendency to take a risky option across 13 set scenarios, F (1,78)= 6.912, p< .05. In average, across 13 set of scenarios, the effect of mood on the positive mood condition has a higher value than the negative mood in a tendency of risky option found apparently merely in high-OF, whereas low-OF has no mood effect. The two analyses supported H1 and H2.

Insert Table 1 about here

The findings from the experiment show that the preference of risk-taking may be influenced by mood states and OF. In addressing the Study 1 research question raised earlier, the resulting demonstrated that the subjects in a positive mood are likely to engage in risk-taking behavior than in negative mood was supported in high-OF subjects than in low-OF subjects. Next, we will use the result from Study 1 to understand how mood state will influence consumer decision-making.

Study 2: The Effect of Moods and Openness to Feelings on Share of All-average Option

Individual choice, in general, involves the choice of option, one option, termed “ “mixed options”, which has advantages on some dimensions and disadvantages on others for attribution of commodity, and another option, termed “all-average option”, which has average values with respect to attributions of the commodity. For example, when renting an apartment, in a choice between two apartments, the all-average option might have average values with respect to safety, price, space, and landlord relations. Conversely, a second apartment might have superior (above-average) value based on safety and space and inferior values on price and landlord relations.

Indeed, previous research showed that the all-average option is more likely to be selected when people are concerned about being evaluated and criticized by others, which suggest that such an option is perceived as a safer choice when neither had a decisive advantage (Simonson & Nowlis, 2000) However, consistent with our assertion that positive mood is as a risk-averse and that negative mood is as a risk-taker, people who are induced to yield a positive mood, in order to maintain a positive mood, are likely to select the all-average option as a safer choice than those who are induced to yield a negative mood. On the other hand, people in a positive mood chose an all-average option as a safe choice that will be rarely criticized by others, because the all-average option is “average” in all dimensions. Conversely, people in a negative mood will be likely to choose a more mixed option than the all-average option, because the mixed option provides a hope of “repairing” the negative mood. More important, for high-OF people who enhance and weight self-feeling, they will attach importance to maintaining the positive feeling and be willing to choose a safer option when in a positive mood. Conversely, low-OF people who discount and control their feelings, are lowly influenced by mood and will be less likely to choose a safer option, showing an opposite effect related to high-OF. Thus:

H3: subjects who are induced to a positive mood and have high-OF, but not those with low-OF, will be tendency to produce a higher share of an all-average option than subjects who are induced to a negative mood.

Method

In Study 2, we will examine the prediction that subjects who are introduced to positive mood will choose a higher share of all-average options than who are introduced to an induced negative mood, and whether the mood effect is moderated by openness to feelings.

Participants were 127 advanced undergraduate and MBA students enrolled in a marketing management course. They were paid about $2 to participate in the study. The experimenter, at the beginning of the experiment, told participants that the researcher was interested in understanding how moods influence consumer choice, and the task involved making (hypothetical) purchase decisions in several product categories. It was emphasized that there were no right or wrong answers and that the participants should consider only their personal preferences.

The experiment was conducted in a classroom setting where the 127 participants were randomly assigned to either positive or negative mood conditions with approximately 64 participants in each group. Then, each participant was required to test preference in three categories: Restaurant, resting apartment, and health clubs. The stimuli used to test the hypotheses were similar to other studies that have examined choice between all-average and mixed options (Dhar, Nowlis, & Sherman, 2000; Dhar & Simonson, 2003; Shafir, 1993; Simonson & Nowlis 2000). For example, in renting apartment, with a choice between two apartments, the all-average option might have average values with respect to safety, price, space, and landlord relations. Conversely, a second apartment might have superior (above-average) value on safety and space and inferior values on price and landlord relations.

Results

Manipulation Checks of Mood

The positive mood conditional group (M=4.71) felt happier than the group in the negative mood condition (M=3.57) and were supported after the mood being induced immediately, t(125)=5.65, p<0.05, and the result confirmed the effectiveness of mood manipulation.

Examination of the Preference

Subjects who made choices in three product categories for choosing an all-average option on average were 54%, and 46% in choosing the mixed option. The logit analysis showed a main effect due to mood states, t=2.01, p< .05, and not a main effect due to openness to feelings, t=1.2, p> .1. As shown in Table 2, participants in a positive mood condition for choosing the mean share of the all-average option was 16%(M _{positive= 62%}- M _{negative = 46%)}higher than in a negative mood condition. However, the interaction between mood state and OF was significant, t=4.2, p< .001, The effect of mood states was observed only among the high-OF group, 29% (M _{positive= 72%}- M _{negative
= 43%)}, and mood states effect appeared to have no effect among low-OF subjects 2% (M _{positive= 51%}- M _{negative = 49%),}and indicating that the effect of mood states was greater among high-OF subject.. The direction of the effect was the same in the three categories and consistent with H3.

Insert Table. 2 about here

In summary, based on our proposition that happiness is a take-averser and that sadness is a risk-taker, subjects who were induced in a positive mood were less likely to be engaged in risk-taking and consequently will likely have a higher share of all-average option than those in a negative mood, in order to maintain, continue or keep a positive mood. Conversely, negative mood as a risk-taker will likely choose a high share in a mixed option, because the mixed option provides a hope of “repairing” the negative mood relating to the all-average option. However, when consideration of openness to feelings was added to the relationship of moods and risk-taking as moderator, positive mood subjects who were more likely to choose the all-average option as safer were found only apparently in the high-OF. Conversely, low-OF showed an opposite effect. Thus, the result of the study supported H3.

General Discussion

In general, consumer often engage in a purchase decision under risk, for example, the purchase of a new pair of athletic shoes offers great possible economic, social, and performance danger. People may consider a choice of options under different risks. One of two or more alternative courses of action must offer both the greater perceived risk and greater potential benefit. Another alternative probably is less risk with more potential reward. Identifying the best option from an available set is often difficult, because choosing one option implies that the other option and its attractive features should be foregone (e.g. Bettman, Luce, & Payne, 1998; Festinger 1964;). This choice depends on how much people have a willingness to take risk. Thus, exploring and understanding the factors that influence consumer tendency for risk-taking is an important researched topic in marketing. The serial experiments demonstrated that mood states might influence an individual’s preference for risk-taking, and an OF personality might moderate the mood effect on individual’s preference for risk-taking and consumer decision. These results include several interesting conceptual and theoretical implications for the understanding of the relationship of mood, openness to feelings, and consumer decision.

The result of Study 1 found 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. Theoretically, the effect of mood of Study 1 can be viewed from two perspectives. First is the information processing perspective, and second is the motivational perspective. The motivational perspective postulates that those in positive moods are motivated to maintain their moods, whereas those in negative mood are motivated to repair their negative moods. According to this perspective, positive mood individuals would be motivated to choose a safer option in order to maintain a positive mood, whereas negative mood individuals would be motivated to choose a risky option probably to repair the negative mood with a positive mood. The information processing perspective postulates that people in a negative mood state process information more systematically as smarter, whereas those in a positive mood process information more heuristically (Forgas, 1995). Previous studies found that people in a negative mood are less susceptible to framing effect, priming effect, and stereotype than those in a positive mood (Fiedler, 2000; Mittal & Ross, 1998). Thus, information processing might explain this result.

However, the mood effect is influenced by openness to feelings as a moderator. The result of Study 1 supported our H2 that people who are induced into a positive mood condition and have high-OF, but not those with low-OF, will less likely 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 engage their feelings in the open. 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 showed 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 that mood effects are reduced or eliminated whenever personal characteristics, such as high selfesteem, Machiavellism, neuroticism, need for cognition, social desirability, or extroversion, provide a relevant impetus to engage in a mutated thing ( Forgas, 1998; Kuvaas &　Kaufmann, 2004; Rusting, 1998; Rusting & Nolen-Hoehsema, 1998). In this case, motivated mood-maintenance strategies used by happy, low-OF individuals apparently involved discounting the mood effect.

Study 2 found that subjects in negative mood condition as risk-takers likely choose a mixed option because the mixed option provides the hope of repairing their negative states (Mittal & Ross, 1998), whereas subjects in a positive mood condition as risk averser are less likely to choose a mixed option because choosing the mixed option under a positive mood condition increases the potential for large personal losses that might disrupt the positive mood state. This result should be consistent with previous studies (Hockey et al., 2000; Mano, 1992, 1994; Mittal & Ross, 1998). However, when OF is considered for this model, subjects who are induced into a positive mood and high-OF, but not those with low-OF, will tend to choose the high share of the all-average option more than subjects who were induced to a negative mood. This finding is also supported in that a high-OF increase and a low-OF decrease the mood effect in any choice of two options involving various risks.

Implications for Decision Making

A person’s life in the every day world is rife with mood states, and there is no avoiding these states. However, do these mood states influence consumer decisions? What is the nature of this influence? When individuals in the studies were asked about the impact of mood on a consumer decision, they generally responded by stating that mood states should be minimized. For marketing managers, this study implies that providing marketing tactics is more effective for negative mood than for positive mood. Marketing tactics should concentrate on negative mood as a marketing segment. Additionally, the results suggest that people scoring high on OF may derive additional psychological benefit from risk-aversers when they are in a good mood. Negative mood in turn could produce a marked incidence of taking higher risk behavior. Thus, the mood effect in positive and negative may be of interest to management and psychologists, and the role of specific traits, such as openness to feelings in moderating these effects, should also be of considerable applied interest.

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

Three other directions are important for extending these results. First, previous research has found that individual’s differences as a need for cognition (NFC) might influence the degree of risk-taking (Kuvaas & Kaufmann, 2004), so it is worthwhile to note whether individual differences (e.g., NFC) might influence the relationship between mood states and consumer decision as a moderate effect. Second, relating to the method of mood inducement, it is necessary to demonstrate whether a different method of mood inducement might have more consistent conclusions regarding our findings for the relationship of mood states and consumer decision. Eventually, in addition to valence, the role of arousal should also be considered when examining consumer decision making.

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APPENDIX A

Story used to induce positive mood (Local Student Achieved Lifetime Goal)

It was an exceptionally nice day, John Evans thought, as he walked home from the exam. He felt he did very well on the test. Although his applications to medical schools had been sent out months ago, John thought that these grades still might mater. John’s thought turned to that evening. He was going out to dinner with his girlfriend, at their favorite restaurant. The food there was very good, and he really did enjoy his girlfriend’s company. As he turned the corner, he noticed the mail had come. He anxiously opened the box and took out the mail. Flipping through the envelopes, he saw an envelope with the return address of his first choice of medical school. He was almost afraid to open it, thinking he might already have been rejected. Still it seemed too thick to simply be a rejection. Nervously, he sat down on the steps and opened it. As he read down the page, he realized that it was an acceptance! Not only that, but the chances of financial aid seemed to be very good. He sat back in the sunshine and realized that his date tonight would be a real celebration.

Story used to induce negative mood (Local Student Die of Leukemia)

The recent death of John Graham, 20, gives us an insight into the ordeal of a young cancer victim. Graham, a student at the University, had always considered himself healthy. Since his freshman year his only illness has been a head cold. After his exams he noticed he was feeling tired, but attributed it to overwork in preparing for his tests. Sleep did not help his condition, and Graham now felt exhausted after climbing the two flights of stairs to his dorm room. His girlfriend noticed his condition and mentioned that he seemed less active than usual. He assured her it was nothing, but secretly suspected that he had contracted mononucleosis. When he finally went to the Student Health Center, the doctor seemed very concerned. After seeing the results of blood tests, the physician ordered Graham into the hospital “for a few more tests.” He never left. The diagnosis was an advanced case of leukemia, a cancer of the blood. Intense radiation therapy was tried. This last-ditch effort caused severe side effects that were extremely painful and caused Graham to lose much of his hair. Despite the treatment, the disease spread. Heavy doses of pain-relieving drugs were tried. But even this treatment did not relieve his agony. He lost weight, but it became too painful to ingest food. His acquaintances found it difficult to recognize their friend who only months ago had appeared active and energetic. As the pain become unbearable, he could no longer read or walk through the hospital corridors. All that was left for Graham was intense suffering and two months later, his death.

APPENDIX B

Hospital parking

You have to visit a close relation in the hospital, and you manage to get away from work for an hour at a busy time. As usual, the small visitors’ car parking lot opposite the hospital is full, and you know from experience that you will probably have to wait 15 minutes or so at this time for a space. You could drive into the staff parking, but this is occasionally patrolled by hospital security staff, and you know that cars have been clamped. (explain “clamped” briefly)

You wonder where you should park:

Use staff car parking (B) Use visitors’ car parking

Pub visit

You have been in a new job for a week and enjoy it. On the first Friday, you overhear people talking about visiting a pub together at the end of work. You would like to get to know your colleagues better, but you have not received an invitation to go along with them. You are unsure whether this is just an oversight or a deliberate snub. On your way home, you pass the pub where everyone is meeting, and consider whether you should go straight home or stop in. They may be very pleased to see you, but it may also be embarrassing and make future work with your colleagues less enjoyable.

You wonder what you should do:

Go straight home (B) Stop in at the pub

Table 1. Choosing the mean share of a safe option for experiment 1 (n=82)

Share of Option

% Choosing safe

Option (B)

Degree of commitment to choosing a safe option (B)

High-OF

Positive Mood

Negative Mood

Low-OF

Positive Mood

Negative Mood

7.70

4.06

5.66

4.63

Table 2. Choosing the mean share of an all-average option (n=127)

Share of Option	Positive Mood (N=63)	Negative Mood (N=64)
High-OF	72%	43%
Low-OF	51%	49%
Total	62%	46%

11 EFFECTS OF AUSTENITIZATION TEMPERATURE ON THE MICROSTRUCTURE OF
12 CROP YIELD AND PRICE DISTRIBUTIONAL EFFECTS ON REVENUE
1546 EITHER CAT 25 THE EFFECTS OF ANTIHYPERTENSIVE DRUGS

Tags: choice shih-chieh, first choice, effects, trait, opennesstofeeling, choice