The influence of social cues on framing effect.
Problem 1: Imagine that the US is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequences of the programs is as follows: If Program A is adopted, 200 people will be saved. If Program B is adopted, there is one-third probability that 600 people will be saved and two-thirds probability that no people will be saved. Which of the two programs would you favor?
Problem 2: Given the same scenario, if Program C is adopted, 400 people will die. If Program D is adopted, there is one-third probability that nobody will die and two-thirds probability that 600 people will die. Which of the two programs would you favor?
Framing effects are often explained using prospect theory (Tversky & Kahneman, 1992), according to which people encode possible choice options as gains and losses and tend to be risk averse when choosing among prospects perceived as gains but risk seeking when choosing among prospects seen as losses. Later researchers have shown that framing effect can be affected by many factors (Bless, Betsch, & Franzen, 1998; Kuhberger, 1998; Levin, Gaeth, Schreiber, & Lauriola, 2002). Wang (1996), using the Asian disease problem, found that the number of lives at risk had an impact on framing effect. Within the human life domain, the number of lives at risk and the context of one's own kinsfolk versus strangers affect framing effect. Wang, Simons, and Bredart (2001) presumed that different cultural contexts might lead to different understanding of the same scenario, and social cues may play a role in framing effect. Zhang and Miao (2008) adopted the same task with Chinese participants, and found that the social cue of number of lives at risk affected participants' risk preference. Compared with the context of a large number of lives at risk, participants were more sensitive to contexts involving a small number of lives at risk.
In the above studies, the number of lives at risk might involve or evoke the decision maker's kinship concept. The social cue that is derived from the number of lives at risk is essentially the decision maker's subjective interpretation and comprehension about the relationship between people at risk and the decision maker him/herself (Wang, 1996; Wang et al., 2001; Zhang & Miao, 2008). In a simplified decision-making context, decision makers will have time to activate their specific understanding of the relationship. In light of the above literature, I predicted that the social cue of relatives, friends, or strangers' lives being in danger, would weaken the role of framing and further influence the occurrence of a framing effect.
In this study I tested the above hypothesis by conducting two framing effect experiments in different social distance contexts showing different social cues. The lives at risk were described as either a relative/friend of the decision maker or a stranger, and the scenarios were displayed on computers so that reaction time could also be recorded. I aimed to investigate whether or not the relationship between people at risk and decision makers, commonly regarded as an important social cue, has an influence on participants' risk preference. I also tested the information processing of decision making under different frames.
Experiment 1: Framing Effect Experiment Within a Relative/Friend Context
Participants were tested one at a time, at the same computer in the same room. The Asian disease problem was presented on a computer screen so that participants' reaction time (RT) could be recorded. Scenarios and options were the same as those described in Problems 1 and 2 in the introduction to this paper. In this experiment, the 600 lives at risk in the Asian disease problem were described as decision maker's relatives or friends in his/her hometown.
Participants in this experiment were 204 Chinese undergraduates aged between 18 and 23 years (M = 20.5, SD = 1.9). A between-subjects design was adopted, with 111 undergraduates (57 males, 54 females) allocated to the positive (Problem 1) frame condition and 93 undergraduates (46 males, 47 females) allocated to the negative (Problem 2) frame condition.
Participants' choices in positive or negative frames within the relative/friend context were examined.
As shown in Table 1, in the positive frame condition, there was no significant difference between the two choice groups, [chi square] = 0.009, df = 1, p = .924. By contrast, in the negative frame condition, a majority of participants tended to be risk seeking, [chi square] = 11.710, df = 1, p < .001.
The Crosstabs chi square ([chi square]) test was used to analyze jointly the participants' choice in positive or negative frame conditions. It was revealed that participants' decisions were significantly different in the two conditions, [chi square] = 6.225, df = 1, p = .013. However, the expected framing effect, in which participants choose the sure option in the positive framing condition, but choose the risky choice in the negative framing condition, did not occur in this experiment. The preference shift phenomenon (unidirectional framing effect), which means participants in both groups tend to be risk seeking but participants in the negative frame condition are more likely to be so, also did not occur. The results indicated that framing could change participants' risk preference to some extent, but the relative/friend relationship as a social cue weakened this effect.
As shown in Table 2, participants in the positive frame group made decisions more swiftly, with less decision-making time than that of participants in the negative frame group, t = -7.325, df = 94.984, p < .001, showing framing affects individuals' information processing in the decision-making process.
Experiment 2: Framing Effect Experiment Without Relative/Friend Context
In this experiment, the 600 lives at risk in the Asian disease problem were described as strangers, in a remote district, whom the decision maker had never met. The procedure was otherwise identical to that used in Experiment 1.
Participants in this experiment were 208 Chinese undergraduates aged between 18 and 24 years (M = 20.5, SD = 2.3). A between-subjects design was again adopted, with 104 undergraduates (51 males, 53 females) allocated to the positive frame condition and 104 undergraduates (52 males, 52 females) allocated to the negative frame condition.
Participants' choices in a positive or negative frame without the relative/friend context were examined.
As shown in Table 3, the number of participants choosing Option A was significantly greater than that of participants choosing Option B in the positive frame condition, [chi square] = 4.654, df = 1, p = .031. This indicated that people showed risk aversion under the condition of positive framing. However, in the negative frame condition, more people tended to be risk seeking; the difference also reached a marginally significant level, [chi square] = 3.846, df = 1, p = .050.
The Crosstabs [chi square] test was used to analyze jointly the participants' choices in positive or negative frame conditions. It was revealed that participants' decisions were significantly different in the two different framing groups, [chi square] = 8.482, df = 1, p = .004, showing there was a significant difference under different framing conditions in the stranger context. The results indicate that when there was no social cue (such as when there was a relative/friend relationship between the lives at risk and the decision maker), the role frame played was obvious; positive/negative framing reversed the decision maker's risky preference, leading to the occurrence of a framing effect.
As in Experiment 1, participants in the positive frame group made choices more quickly, with their decision-making time significantly less than that of participants in the negative frame group, t = -8.022, df = 154.767, p = .001 (see Table 4).
The results in my two experiments show that when there is a relative/friend relationship between the lives at risk and decision makers, the impact that frame has on an individual's risk preference is weakened, resulting in the absence of a framing effect. By contrast, when there is no relationship between the lives at risk and decision makers (i.e., they were strangers), a framing effect occurs.
Wang (1996) changed the number of lives at risk in the decision-making problem, finding that when the number of lives at risk was 6,000, 600, 60, or 6, the framing effect varied. He also found that with regard to the Asian disease problem, the reference point at which a framing effect began to occur in China was different from that in America. In America, a framing effect appeared when the number of lives at risk was 600, while in China the reference point was 6,000. Wang assumed that this discrepancy might be attributed to the different concepts of kinship in different cultures. In China, the concept of family was associated with numerous individuals, and the number of relatives or friends was greater; therefore, their range of kinship was greater too. So when the number of lives at risk was 6, 60, or 600, the decision makers might know of the individuals whose lives are at risk and, as a consequence, they are more likely to select the risky choice. In the US, by contrast, 600 individuals exceeds the number of people involved in the decision makers' concept of kinship. As a result, they might be relatively ready to judge the 600 lives at risk to be people unknown to them. Wang et al. (2001) further explained that the reference point difference can be attributed to cultural differences. Zhang and Miao (2008) also suggested that the number of lives at risk is a social cue which affects participants' risk preference, and that participants are more sensitive to the small (vs. large) group context.
In this study I hypothesized that the social cue of lives at risk being relatives, friends, or strangers, would weaken the role of framing, and further influence the occurrence of a framing effect. I tested this hypothesis by conducting two framing effect experiments in different social distance contexts. The results showed that different numbers of lives at risk influenced framing effect because, under the simplified situation with no time limitation, decision makers made different associations to the relationship between lives at risk and themselves. When the social cue was interpreted as the person at risk being someone close to them, or their relative or friend, individuals tended to opt for the risky choice in both framing conditions. On this occasion, the effect of framing was weakened and no framing effect occurred. On the other hand, when the social cue was interpreted as someone with whom they had no relationship, or someone in a remote area whom they had never met, frame played an obvious role.
The results showed that when making risky decisions in social contexts, people rely on social cues and these cues may weaken the effect of framing. In the kinship context the participants were risk seeking regardless of framing. My study results confirm the finding of Wang et al. (2001) that the kinship cue fosters a risk-seeking preference. The results support kin selection, rather than prospect, theory. Participants would rather take a risk to save all their relatives/friends under both conditions. This is especially true for participants from China, with a collectivist society, who are more risk seeking, as they are more likely to receive help from relatives and, consequently, are less risk averse than are people in an individualistic society such as America (Hsee & Weber, 1999). Further, my study results show that framing affects individuals' information processing speed in their decision-making process; these results are consistent with those of Huangfu and Zhu (2014).
In this study I found that social cues influence the occurrence of framing effect but fail to explain the mechanism of this phenomenon. Moreover, I examined framing effect as influenced by the positive or negative nature of the task. Individual differences should be considered in future research.
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Hsee, C. K., & Weber, E. U. (1999). Cross-national differences in risk preference and lay predictions. Journal of Behavioral Decision Making, 12, 165-179. http://doi.org/d3pzd5
Huangfu, G., & Zhu, L. (2014). A new look at the robustness of the framing effect: Cognitive processing. Social Behavior and Personality: An international journal, 42, 37-44. http:// doi.org/r5s
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Levin, I. P., Gaeth, G. J., Schreiber, J., & Lauriola, M. (2002). A new look at framing effects: Distribution of effect sizes, individual differences, and independence of types of effects. Organizational Behavior & Human Decision Processes, 88, 411-429. http://doi.org/cqkwg5
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Wang, X. T., Simons, F., & Bredart, S. (2001). Social cues and verbal framing in risky choice. Journal of Behavioral Decision Making, 14, 1-15. http://doi.org/dmgtcq
Zhang, Y., & Miao, D. (2008). Social cues and framing effects in risky decisions among Chinese military students. Asian Journal of Social Psychology, 11, 241-246. http://doi.org/bxnkhd
Gang Huangfu, School of Economics and Management, Beihang University.
This study was supported by research grant NSFC 71072022 and China 973 project (2010CB8339004). Correspondence concerning this article should be addressed to: Gang Huangfu, School of Economics and Management, Beihang University, 37 Xueyuan Road, Chaoyang District, Haidian, Beijing 100191, People's Republic of China. Email: firstname.lastname@example.org
Table 1. Participants' Choice in Positive or Negative Framing Conditions Within the Relative/Friend Context Choice Positive framing Negative framing "Sure thing" choice A 55 (50%) 30 (32%) Risky choice B 56 (50%) 63 (68%) Total 111 (100%) 93 (100%) Table 2. Participants' Reaction Times in Positive or Negative Framing Conditions Within the Relative/Friend Context Condition N M SD SE Reaction Positive frame 111 9.730 2.5685 0.2438 time Negative frame 93 23.873 18.4718 1.9154 Table 3. Participants' Choice in Positive or Negative Framing Conditions Within the Stranger Context Choice Positive frame Negative frame N (%) N (%) "Sure thing" choice 63 (61%) 42 (40%) Risky choice 41 (39%) 62 (60%) Total 104 (100%) 104 (100%) Table 4. Participants' Reaction Times in Positive or Negative Framing Conditions Within the Stranger Context Group N M SD SE Reaction Positive frame 104 12.737 5.0463 .4948 time Negative frame 104 21.351 9.7197 .9531
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|Publication:||Social Behavior and Personality: An International Journal|
|Date:||Apr 1, 2014|
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