Research Interests

Gretchen’s Research Interests

My research goal is to illuminate the psychological processes underlying decision making and to harness these findings in the design of theoretically-motivated, policy-relevant interventions to facilitate healthy and prosocial behavior such as vaccination and blood donation.  My research combines the fields of judgment and decision making and health psychology.  Using both laboratory and field experiments, I test behavioral interventions, simultaneously exploring the theoretical mechanisms of decision making and also yielding policy insights into methods for improving health behavior and health outcomes (Li & Chapman, 2013). Below I summarize several of my recent major research projects.

1. Healthy Defaults

The default effect is the tendency for people to stick with the default – the option one will get if one does not specify otherwise.  For example, in the US, organ donation has a default of non-donor:  If one does not say anything, it is assumed that one does not want to
donate one’s organs. One has to specify one’s donor status explicitly to donate. In contrast, many European countries have a presumed consent default such that if one does not specify otherwise, it is assumed that one does want to be a donor. With funding from NIH (NIA 1R01AG037943-01) I have conducted several studies to examine whether the default effect can be harnessed to nudge people toward the healthier option. In one study (Chapman, Li, Colby, & Yoon, 2010), employees received an email telling them that they could make an appointment for a flu shot (default=no appointment) or saying that they had been automatically scheduled for an appointment which they could cancel (default=appointment). Vaccination rates were higher in the second group. In a related study (Chapman, Li, Leventhal, & Leventhal, under review) clinic patients received an opt-out vaccination letter, and opt-in letter, or no letter.  We examined both whether the default condition affected vaccination at the flu clinic appointments that were the target of the letters but also whether the intervention displaced vaccination from regular doctor’s office visits or from off-site (e.g., pharmacy or workplace) vaccinations.  We found a strong default effect and no evidence of displacement.

In another series of studies (Li, Colby, & Chapman, in revision), we examined the default effect in dietary choices – for example, the type of milk (whole or 2%) used when making cappuccinos. Healthy defaults increase choice for healthy foods, but they also decrease sales.  For example, in a hypothetical scenario experiment, participants were more likely to pick the healthy ingredient (e.g., turkey rather than beef burgers) when it was the default ingredient, but they were also less likely to return to a restaurant that used a healthy default than one that used an unhealthy default.  One possible mechanism for this effect is that healthy defaults decrease feelings of virtue.  Specifically, opting out of an unhealthy default feels more virtuous than accepting a healthy default.  Similarly, sticking with an unhealthy default feels more virtuous than opting out of a healthy default.  Thus, holding constant what one consumes, one will feel more virtuous consuming it in a restaurant that uses an unhealthy default.


2. Goals and Social Comparisons

A key construct from decision research is that decision makers code outcomes as gains or losses relative to a reference point. Goals can act as a reference point, such that falling short of a goal is coded as a loss. In a pair of studies funded by the Robert Wood Johnson Foundation, we asked participants to use a pedometer to track the amount that they walked (Chapman, Colby, Convery, & Coups, 2015).  We examined whether comparison to a reference point made pedometer feedback more effective in motivating walking behavior. In one study, participants were given a low, medium, or high personal goal.  If the magnitudes of these goals are set in accord with the value function from Prospect Theory, then one might expect the medium goal to motivate increased walking more than the low or high goal because it places participants on the steepest portion of the value function.  However, in our study, participants given the high goal walked the most, a result that is consistent with Goal Setting Theory.  It is possible that the high goal in our study (which encouraged participants to double the amount they walked) was not sufficiently high to produce the Prospect Theory pattern.  In a second study, participants in the control condition used a pedometer to track their own individual walking behavior while those in the experimental condition were also told how their performance compared to that of others in the study.  Participants who received social comparison feedback walked more.  This finding is consistent with previous research on social norms and also with the proposal that performance by others serves as a reference point.  Performing worse than others is coded as a loss and is experienced negatively, thus motivating participants to perform as well as others.  These studies indicate that the concept of reference point comparisons can be applied to the science of healthy behavior change


3. Allocation of Scarce Resources

Health decisions are made not only at the individual level, but also at the policy level.  An important decision often faced by health policy makers is how to allocate scarce resources. In collaboration with Meng Li and others (Li, Vietri, Galvani, & Chapman, 2010), I examined how framing health outcomes as lives saved or lived lost influences the metrics that decision makers use to guide their choices.  We asked participants to judge health policies that saved the lives of people of different ages and with different life expectancies.  When the questions were framed in terms of lives saved, participants’ judgments tracked a “years left” metric, favoring policies that resulted in many additional life years.  In contrast, when the questions were framed in terms of lives lost, participants’ judgments tracked a “years lived” metric, favoring policies that benefit young people who have not yet lived a long life.  In more recent work with Helen Colby and Jeff deWitt (Colby, DeWitt, & Chapman, 2015), I examined a different kind of presentation effect on policy decisions.  Participants were asked to indicate which of 12 beneficiaries should receive six available transplant organs.  When the beneficiaries were presented in one unified group, participants allocated the organs quite efficiently – giving them to the beneficiaries who would benefit the most from the transplant.  However, when the beneficiaries were presented in two groups (one group who would receive high benefit from a transplant and one group who would receive low transplant), participants had a tendency to spread the organs across the two groups, and in so doing, sacrificed efficiency.  This result indicates that the presence of groups triggers fairness and equity processes.


4. Psychology of Vaccination

I have a longstanding interest in factors that drive vaccination decisions (Betsch, Böhm, & Chapman, 2015) because vaccination decisions can be a window onto a number of fundamental psychological phenomena including time discounting and prosociality. In collaboration with Alison Galvani at Yale, I have examined how vaccination decisions by an individual are affected by the decisions and outcomes of others.  Because of herd immunity, one person’s vaccination protects others with whom that person is in contact.  This provides an opportunity for free riding or for prosocial vaccination. In the case of seasonal influenza, mortality is highest among the elderly, but young people are primarily responsible for spreading the virus.  Thus, the elderly can be protected by vaccination of the young, but achieving that outcome requires the young to act prosocially.  In a laboratory experiment funded by NSF (SBE-0624098), participants played “young” or “elderly” roles and decided on each round of the game whether to pay points to vaccinate in order to reduce their own and others’ risk of infection.  When players were paid according to their own point balances, their behavior was consistent with self interest, with elderly players getting vaccinated more than young players.  However, when players were paid according to the group point balance, their behavior was consistent with a prosocial motive, with young players getting vaccinated more than elderly players, resulting in lower rates of infection and higher payouts.  These results suggest that individuals will engage in preventive behavior to help others when the incentives are right (G. B. Chapman et al., 2012).

In subsequent work, we examined prosocial motives for vaccination more explicitly.  Because vaccination protects not only the person vaccinated, but also the vaccinated individual’s contacts, protecting others from infection can serve as a motivation for vaccination.  In NSF-funded research (SES-1227306), we found that messages about identified flu victims, relative to a control message, increase sympathy, pro-sociality, and intention to vaccinate.  This pattern was consistent across the eight countries included in the internet survey (Li, Taylor, Atkins, Chapman, & Galvani, under review).


5. Prosocial Behavior

Whereas vaccination has both self-beneficial and prosocial benefits, other behaviors such as blood donation have only prosocial benefits. My current NSF grant (SES-1528614) examines prosocial behavior directly. One benefit from engaging in a virtuous act is the signal that an agent’s act sends to others about her type.  If agents have private knowledge about the type of person they are (e.g., prosocial vs. self-interested), and it is valuable for them to make this information known, they will take actions that signal this information.  Consequently, receiving a self-interest incentive for a prosocial act (e.g., being offered cash for blood donation) could discourage or “crowd out” the prosocial behavior because it dilutes the signal that the act usually sends, both to others and to the agent herself.  In one set of field studies we are sending potential blood donors different types of messages about gift card incentives for blood donation and tracking the number of blood donations that result from each condition.  In some related lab studies we manipulate whether participants in a social dilemma game receive a visual signal that may allow them to coordinate with the other member of their group to achieve cooperation.


 

Each of the projects described above is motivated by the goal to produce policy recommendations for improving health and welfare by encouraging vaccination, healthy eating, physical activity, or prosocial behavior such as blood donation.  In order to find interventions that work and to understand the contexts in which they are effective, we need to understand the basic decision processes underlying these behaviors.  My studies test specific theoretical mechanisms that give rise to behavior change.  Although laboratory studies and questionnaires are often the settings for homing in on mechanism, applied contexts and field studies can also provide opportune testbeds for hypotheses about fundamental principles of decision making in addition to providing real world relevance.

 

References

Betsch, C., Böhm, R., & Chapman, G. (2015). Using Behavioral Insights to Increase Vaccination Policy Effectiveness. Policy Insights from the Behavioral and Brain Sciences.

Chapman, G. B., Colby, H., Convery, K., & Coups, E. J. (2015). Goals and Social Comparisons Promote Walking Behavior. Medical Decision Making, 3–9. http://doi.org/10.1177/0272989X15592156

Chapman, G. B., Li, M., Colby, H., & Yoon, H. (2010). Opting in vs opting out of influenza vaccination. JAMA : The Journal of the American Medical Association, 304(1), 43–44. http://doi.org/10.1001/jama.2010.892

Chapman, G. B., Li, M., Vietri, J., Ibuka, Y., Thomas, D., Yoon, H., & Galvani,  a. P. (2012). Using Game Theory to Examine Incentives in Influenza Vaccination Behavior. Psychological Science, 23, 1008–1015. http://doi.org/10.1177/0956797612437606

Colby, H., DeWitt, J., & Chapman, G. B. (2015). Grouping Promotes Equality: The Effect of Recipient Grouping on Allocation of Limited Medical Resources. Psychological Science. http://doi.org/10.1177/0956797615583978

Li, M., & Chapman, G. B. (2013). Nudge to Health: Harnessing Decision Research to Promote Health Behavior. Social and Personality Psychology Compass, 7, 187–198. http://doi.org/10.1111/spc3.12019

Li, M., Vietri, J., Galvani, A. P., & Chapman, G. B. (2010). How do people value life? Psychological Science : A Journal of the American Psychological Society / APS, 21(December 2009), 163–167. http://doi.org/10.1177/0956797609357707

 

 

Advertisements

Department of Psychology, Rutgers, the state university of New Jersey

%d bloggers like this: