510950 research-article2014
PSSXXX10.1177/0956797613510950Schwartz et al.Healthier by Precommitment
Research Article Psychological Science 2014, Vol. 25(2) 538–546 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797613510950 pss.sagepub.com
Healthier by Precommitment Janet Schwartz1, Daniel Mochon1, Lauren Wyper2, Josiase Maroba2, Deepak Patel2, and Dan Ariely3
1 Department of Marketing, A. B. Freeman School of Business, Tulane University; 2Discovery Vitality, Sandton, South Africa; and 3Department of Marketing, Fuqua School of Business, Duke University
Abstract We tested a voluntary self-control commitment device to help grocery shoppers make healthier food purchases. Participants, who were already enrolled in a large-scale incentive program that discounts the price of eligible groceries by 25%, were offered the chance to put their discount on the line. Agreeing households pledged that they would increase their purchases of healthy food by 5 percentage points above their household baseline for each of 6 months. If they reached that goal, their discount was awarded as usual; otherwise, their discount was forfeited for that month. Thirty-six percent of households that were offered the binding commitment agreed; they subsequently showed an average 3.5-percentage-point increase in healthy grocery items purchased in each of the 6 months; households that declined the commitment and control-group households that were given a hypothetical option to precommit did not show such an increase. These results suggest that self-aware consumers will seize opportunities to create restrictive choice environments for themselves, even at some risk of financial loss. Keywords self-control, health, rewards Received 6/11/13; Revision accepted 10/8/13
Governments and private companies are increasingly offering incentive programs to encourage healthier lifestyles (e.g., Patient Protection and Affordable Care Act, 2010). Motivated consumers will undoubtedly be drawn to these programs with good intentions of eating healthier foods, smoking less, and exercising more. A long stream of research from the behavioral sciences, however, suggests that translating good intentions into real behavior requires precisely the type of persistent selfcontrol that proves to be quite challenging (e.g., Muraven & Baumeister, 2000) and might not be fully achieved by rewards alone. This raises an important question regarding what types of incentive designs are necessary to activate and maintain ongoing self-control. Financial incentives are a long-standing and attractive approach to improving self-control, particularly in situations in which restraint is needed in the present, the deleterious effects of poor choices are cumulative rather than immediately punishing, and the rewards are intangible or delayed (Metcalfe & Mischel, 1999). This may be especially true in the health domain, where the immediate gratification of any one instance of overeating, drinking, or smoking is pitted against an elusive reward of
(maybe) better future health (e.g., Frederick, Loewenstein, & O’Donoghue, 2002). Incentives can make abstaining more immediately rewarding, and thus better align the goals of one’s current and future selves. Indeed, recent research has shown that weight loss ( John, Norton, Fassbender, & Volpp, 2011; Volpp, John, et al., 2008), smoking cessation (Volpp et al., 2006; Volpp et al., 2009), exercise (Charness & Gneezy, 2009), and medication adherence (Volpp, Loewenstein, et al., 2008) can be substantially improved with the use of financial incentives. In some cases, the effects have been shown to last even after the incentive has been removed, which suggests that incentives can provide an important catalyst to habit formation and do not necessarily have to be in place long term (Charness & Gneezy, 2009; Volpp et al., 2009). Other studies, however, have shown that once incentives are removed, bad behaviors can return (e.g., John et al., 2011; Volpp, John, et al., 2008; Volpp, Loewenstein, et al., Corresponding Author: Janet Schwartz, Tulane University, 7 McAlister Dr., New Orleans, LA 70118 E-mail: [email protected]
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Healthier by Precommitment 539 2008). If financial incentives can successfully curb behavior but must be maintained, questions arise about whether pure financial rewards are feasible in the long term (Gneezy, Meier, & Rey-Biel, 2011). At the same time, this uncertainty provides an opportunity to empirically investigate other methods of improving self-control. One promising intervention approach stems from the insight that some people are sophisticated (or selfaware) about their self-control problems (O’Donoghue & Rabin, 1999, 2001) and may seize opportunities to limit their behavior (Ariely & Wertenbroch, 2002). For example, consumers may prefer to pay a premium for smaller packages of cigarettes rather than receive a bulk discount (Wertenbroch, 1998), and when given the option, may choose smaller portions in fast-food meals, even when they cost the same as larger portions (Schwartz, Riis, Elbel, & Ariely, 2012). Such behavior, though not strictly rational, signals that people seek to avoid the temptation that comes with an abundance of vices. Research in the domain of self-awareness and restricted choices has also examined more binding cases of precommitment involving financial deposit contracts (participants deposit their own money and forfeit it if they do not meet their goals) to boost both savings and health-related behaviors. Ashraf, Karlan, and Yin (2006) found significant demand for a commitment device that offered no incentive for deposits, and no possibility of early withdrawals, among people opening savings accounts. Those who precommitted to making deposits had significantly greater savings 6 and 12 months after opening their accounts than did those who had declined the contract and a control group. Smoking-cessation research has shown that smokers who commit to a self-funded 6-month financial deposit contract are less likely to be smoking 12 months later, compared with smokers who make no such commitment (Giné, Karlan, & Zinman, 2010; see also Donatelle et al., 2004). Taken together, these results suggest that incentive designs can help self-aware people resist temptation via threat or risk of financial loss and further intimate that they will welcome this approach. The current study examined whether precommitment can have the effect of helping people purchase healthier food above and beyond the effects of a financial incentive to do so. We asked two key questions: Is there demand for a commitment device in a large-scale incentive program and, if so, can such a device change people’s behavior? We tested the efficacy of a commitment device for ongoing healthy-food purchases in a randomized controlled field experiment conducted in collaboration with Discovery Health in South Africa. A defining feature of Discovery Health is the Vitality program, an incentive program that rewards people for a variety of healthy behaviors, including purchasing healthy food. One effective (Sturm, An, Segal, & Patel, 2013) member
benefit is a monthly 25% cash-back bonus awarded to members’ credit-card statements for all healthy food items (e.g., most fruit, vegetables, fat-free dairy products, lean meats, and whole grains) purchased the previous month at South Africa’s Pick n Pay grocery chain—one of the nation’s largest. Despite the program’s popularity and generous incentive, the HealthyFood benefit appears to be underutilized. On average, only 31% of eligible grocery purchases (i.e., food items) are from the healthyfood category. Although a traditional economics approach to this problem might be to further increase the financial incentive, we instead leveraged an important insight from behavioral science. We tested whether offering a commitment device with the potential of a loss and no additional incentive beyond what households were already getting would help participating households improve the health quality of their purchases. Households were invited to put their monthly credit-card cash-back bonus on the line with a precommitment to a 5-percentage-point increase in healthy food items purchased, relative to their own historical baseline, for each of the following 6 months. The 5-percentage-point increase was chosen as the goal because we wanted members to commit to a change they felt was attainable through some amount of effort, but was not so drastic as to result in a sure loss. Households that met the goal in a given month kept their monthly cash-back bonus; a failure to achieve this goal meant that they gave up their bonus for that month. In other words, the program offered the threat of a negative consequence without any positive incentive, aside from the opportunity to exercise better self-control when grocery shopping.
Design and Procedure Household selection criteria The sample consisted of 6,570 households meeting inclusion criteria set to ensure that their grocery food shopping was trackable over the study period. Specifically, we selected only households that actively participated in the HealthyFood program during 3 months of the preintervention period ( January 1, 2012, through June 30, 2012); had a Discovery Health–issued credit card on which they received the monthly HealthyFood cash-back bonus from the Vitality program (which could be reversed if the goal was not met); and spent at least 500 South African rands (R500, or ~$56 U.S.) per month on groceries using their Discovery Health credit card, with 75% or more of this grocery spending being at Pick n Pay. This ensured that the sample was limited to relatively frequent Pick n Pay shoppers who had a relatively stable baseline percentage of healthy food purchases.
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Experimental design and procedure This field experiment employed a 2 (condition: treatment vs. control) × 2 (time: pre- vs. postintervention) factorial design with condition as the manipulated betweengroups factor and time as the longitudinal repeated measures factor. Selected households were randomly assigned to the conditions: One third were assigned to the control condition, and two thirds were assigned to the treatment condition. Oversampling the treatment condition ensured that a sufficient number of households would consent to the precommitment (see Background in the Supplemental Material available online for additional information about the Vitality program and survey questions). On June 19, 2012, all 6,570 households were invited by e-mail to complete a survey about their grocery-shopping habits in exchange for the chance to win a R1,000 (~$113 U.S.) gift certificate to Pick n Pay. This e-mail provided the household’s baseline healthy shopping percentage, which was the average percentage of food items (i.e., excluding nonfood items like cleaning supplies) qualifying for the HealthyFood cash-back bonus during 3 of the preintervention months (February–April, 2012), and a survey link. Recipients in the treatment group who followed the survey link were taken to a different, condition-specific, consent form and series of questions than were those in the control group. Specifically, the treatment group was told that their survey responses could change their Vitality benefit for a period of time, whereas the control group was told that their survey responses would not change their Vitality benefit. After giving their consent to participate in the survey, participants in both groups were introduced to precommitment as one strategy for improving self-control and achieving their health goals. Participants in both groups were told that Vitality was considering offering a “Precommitment Programme” and were asked if they would be interested in trying it for 6 months, with the option of discontinuing their participation after the 1st month if the program was not right for them. Allowing participants to opt out after the binding 1st month had the advantage of limiting the financial risk to a forfeiture of a single month’s cash-back bonus. This gave sophisticated consumers the chance to recognize and seize the opportunity, but not be burdened by it. After the program was described, the participants were again given their household’s baseline percentage of healthy foods and asked if they would be willing to commit to an increase of 5 percentage points over that baseline for each of the following 6 months under the condition of forfeiting their cash-back bonus in any month that they failed to achieve the goal. For the control group, this was a hypothetical commitment, but for the treatment group, the commitment was real and binding.
Exposing both groups to the precommitment idea and gauging their interest ensured that any observed differences between the groups were not due to mere exposure to a self-control strategy, but rather were due to an actual binding financial commitment. Consenting households in the treatment group received an e-mail from the study administrator that specified their start date and provided a link to the study’s Frequently Asked Questions (FAQ) page. After each monthly cycle was complete, each household’s percentage of healthy foods purchased was calculated, its goal attainment was measured relative to the baseline, and the appropriate feedback was given by e-mail. Successful households kept their cash-back bonus and were encouraged to continue their healthy behavior in the following month. Unsuccessful households forfeited their cashback bonus (noted as a line-item reversal on their creditcard statement) and were encouraged to try again the next month. Finally, participants’ survey responses were recorded in a data file that was merged with their Pick n Pay and credit-card data from the pre- and postintervention periods via a household-unique identifier. For the duration of the pre- and postintervention periods, automated procedures reported the relevant data for all 6,570 households. In addition to itemizing the HealthyFood items, this system designates the remaining food items purchased at Pick n Pay as neutral or unhealthy. Members are unaware of the neutral- and unhealthy-food itemizations, which appear only in the Vitality system and were available for analysis purposes.
Results Sixty two percent of households who received the invitation e-mail began the survey. The percentage of households starting the survey was uninfluenced by experimental condition, χ2(1, N = 4,073) = 0.66, p = .42. Thus, we limited the initial analyses to those 4,073 households (see Table 1 for descriptive statistics).
Commitment choice A total of 632 households (36% of treatment households that actively made a choice) chose to precommit to the program. Thus, there appears to be demand for a commitment device in a large incentive program, even if there is no extra incentive and only the threat of a loss. The baseline percentage of healthy foods for households agreeing to the precommitment did not differ significantly from the baseline percentage for households that were in the treatment condition but did not precommit or were in the control condition, F(2, 4070) = 0.41, p = .67. This suggests that precommitment had wide appeal, and
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Healthier by Precommitment 541 Table 1. Mean Percentages and Numbers of Food Items Purchased, Mean Amounts Spent on Those Items, and Likelihood of Meeting the Goal (Being 5 Percentage Points Above the Baseline) Before and After the Intervention Preintervention (January–June 2012)
Measure Healthy items (%) Neutral items (%) Unhealthy items (%) Number of items Number of healthy items Number of neutral items Number of unhealthy items Total amount spenta Amount spent on healthy itemsa Amount spent on neutral itemsa Amount spent on unhealthy itemsa Likelihood of meeting goal
Postintervention (July–December 2012)
Control group (n = 1,329)
Noncommitted group (n = 2,112)
Committed group (n = 632)
Control group (n = 1,307)
Noncommitted group (n = 2,099)
Committed group (n = 623)
32.14 (12.26) 47.85 (9.39) 20.01 (8.53) 81.21 (44.63) 26.34 (17.58) 38.76 (22.80) 16.11 (11.21) 1,850.51 (1,092.43) 505.14 (350.19) 1,044.87 (668.30) 300.50 (213.93) .22 (.18)
31.77 (11.86) 48.05 (9.42) 20.18 (8.66) 86.92 (47.65) 28.18 (19.41) 41.22 (23.35) 17.51 (12.18) 1,972.02 (1,161.09) 533.06 (386.11) 1,111.05 (681.69) 327.91 (251.24) .21 (.18)
32.00 (11.97) 48.28 (9.22) 19.72 (8.85) 79.32 (47.05) 25.79 (19.39) 38.02 (22.61) 15.51 (11.39) 1,832.11 (1,193.29) 498.82 (399.02) 1,036.30 (684.36) 296.99 (240.61) .23 (.19)
30.30 (12.54) 49.98 (9.96) 19.72 (9.07) 77.59 (47.55) 24.06 (17.89) 38.56 (24.40) 14.98 (11.18) 1,822.26 (1,181.13) 476.94 (369.51) 1,054.57 (720.43) 290.75 (223.73) .23 (.27)
29.99 (12.13) 50.26 (9.73) 19.75 (9.01) 83.96 (52.87) 25.84 (19.84) 41.68 (26.69) 16.44 (12.83) 1,960.48 (1,288.05) 504.80 (394.92) 1,139.87 (778.82) 315.80 (253.88) .22 (.26)
33.37 (15.38) 48.39 (11.72) 18.24 (10.63) 72.76 (51.24) 24.49 (20.40) 35.33 (25.99) 12.95 (11.06) 1,732.17 (1,342.97) 490.98 (440.33) 983.56 (811.37) 257.63 (229.43) .34 (.33)
Note: Standard deviations are given in parentheses. a These amounts are in South African rands.
was not limited to participants who were already doing well.
Intervention As there were no differences between groups in the percentage of healthy food purchases at the introduction of the intervention, we focused on the three groups who started the survey: committed (treatment-group participants who made a binding commitment), noncommitted (treatment-group participants who declined to make the binding commitment), and control (hypothetical commitment option only). Comparison of the committed group with the noncommitted and control groups shows the importance of making a binding financial commitment as opposed to mere exposure to the idea. This is an important comparison because improvement among the committed households but not among the noncommitted and control households would suggest that the threat of a loss must be real in order for it to work. Approximately
15% of committed households dropped out some time after the 1st month, but for analysis purposes were retained in that group for the study’s full duration. We first examined whether the commitment device helped committed households improve the percentage of healthy items purchased relative to the noncommitted and control households. Figure 1 shows the average percentage of healthy foods purchased for the three groups over the 1-year study period ( January–December 2012). We tested the effect of precommitment on the percentage of healthy, neutral, and unhealthy items purchased with a series of random-effects linear regressions with the assumption of compound symmetry on the covariance matrix. We specified the independent predictor variables of group (control vs. noncommitted vs. committed), a dummy variable indicating whether data were for the 6 months preintervention ( January–June) or the 6 months postintervention ( July–December), and the key Group × Postintervention interactions. Table 2 shows the results, with the control group specified as the reference group.
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Schwartz et al.
542 Control
Noncommitted
Committed
Healthy Food Items Purchased (%)
36 35 34 33 32 31 30 29 28 27 26
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Fig. 1. Mean percentage of healthy food items purchased by group and month.
We found no significant difference between the three groups at baseline in the percentage of healthy, unhealthy, or neutral food items purchased. However, we observed the predicted Committed Group × Postintervention interaction, showing that the committed group bought a significantly higher percentage of healthy items during the 6-month commitment period relative to the control group. They increased the healthy items in their monthly food basket by an average of 3.52 percentage points (Table 2). Moreover, this increase came as a function of a decrease in both the percentage of neutral items and the percentage of unhealthy items purchased. Noncommitted
households behaved no differently than control households after the intervention. Note that the postintervention coefficient in Table 2 shows that the percentage of healthy foods purchased was lower postintervention than preintervention and reflects a general decreasing trend in healthy food purchases (see Fig. 1). To ensure that the intervention did not cause healthy food purchasing to decrease, we examined each month separately and found that the percentage of healthy purchases was greatest in January (summertime, when produce is more widely available; also, New Year’s resolutions may cause healthy behavior
Table 2. Results of Random-Effects Linear and Logistic Regressions: Effects of the Intervention on Percentages of Healthy, Unhealthy, and Neutral Items Bought and on the Probability of Meeting the Goal Predictor Intercept Postintervention Noncommitted group Committed group Noncommitted Group × Postintervention Committed Group × Postintervention
Percentage of healthy items
Percentage of neutral items
Percentage of unhealthy items
Likelihood of meeting goal
32.13*** (0.33) –1.84*** (0.16) –0.34 (0.42) –0.17 (0.58) 0.09 (0.21)
47.86*** (0.25) 2.17*** (0.16) 0.18 (0.32) 0.44 (0.45) –0.03 (0.21)
20.00*** (0.23) –0.34* (0.14) 0.16 (0.30) –0.27 (0.41) –0.05 (0.18)
–1.27*** (0.03) 0.08* (0.04) –0.07* (0.04) 0.05 (0.05) –0.01 (0.05)
3.52*** (0.29)
–2.22*** (0.29)
–1.30*** (0.25)
0.52*** (0.07)
Note: n = 4,073 for all analyses. In these analyses, the control group was specified as the reference group. The Committed Group × Postintervention coefficient was also significantly different from the Noncommitted Group × Postintervention coefficient for all four regressions (p < .001). The results were unchanged when households that did not open the survey were included in the analyses. The results were also unchanged when fixed effects rather than random effects were used to control for household-level effects. Similar results were obtained in regressions predicting the numbers of items purchased and the amount of money spent (see Methods and Results, Table S1, in the Supplemental Material). *p < .05. ***p < .001. Downloaded from pss.sagepub.com at COLUMBIA UNIV on March 2, 2015
Healthier by Precommitment 543 to peak in January) and steadily decreased throughout the year. The coefficients for June and July revealed lower percentages relative to January, but not a lower percentage for July than for June (see Methods and Results, Table S2, in the Supplemental Material). If the intervention had negatively affected healthy purchases, one would expect there to have been a significant difference between June and July. We observed similar effects when we looked at the likelihood of meeting the goal (i.e., achieving a 5percentage-point increase in healthy items over the household’s baseline). Likelihood of goal attainment was significantly higher postintervention among households that had made a financially binding commitment than among control households. There was no difference between the control group and the noncommitted group with respect to postintervention goal achievement. This is a critical finding because it means that participants in the control group showed no effect of the hypothetical commitment that 56% of them had endorsed. That is, a financially binding commitment appears to have been crucial to increasing the percentage of healthy items purchased.
Treatment versus selection effects An advantage to the Vitality and Pick n Pay automated data-capture and -reporting system was that data from the sample households were available for the study’s duration, regardless of random assignment to condition, whether the survey was opened, and whether a commitment (binding or hypothetical) was made. This allowed us to further, though not fully, isolate treatment and selection effects (limitations to this analysis and an
alternative approach are discussed in Methods and Results in the Supplemental Material) by dividing the sample into six groups: nonresponding households in the control group (the survey was never opened), noncommitted households in the control group (the survey was opened but the hypothetical commitment was declined), committed households in the control group (the survey was opened and the respondent endorsed the hypothetical commitment), nonresponding households in the treatment group (the survey was never opened), noncommitted households in the treatment group (the survey was opened but the binding commitment was declined), and committed households in the treatment group (the survey was opened and a binding commitment was made). We again tested the effect of precommitment on the percentage of healthy, neutral, and unhealthy items purchased with a series of randomeffects linear regressions specifying the independent predictor variables of group (as just specified), the postintervention dummy variable (as in the prior analyses), and the key Group × Postintervention interactions. Table 3 shows the results, with the nonresponding control group specified as the reference group. We found selection effects among the four groups of households that responded to the survey. These groups showed a higher average baseline percentage of healthy purchases than did the control-group nonresponders. The Group × Postintervention interactions showed that despite being healthier at the start of the intervention than the nonresponders, none of these responder groups improved except for the treatment-group committed households. This further suggests that in order for precommitment to be effective as a self-control strategy, there must be an actual commitment with the threat of a
Table 3. Results of Random-Effects Linear Regressions Isolating Treatment and Selection Effects Predictor
Percentage of healthy items
Percentage of neutral items
Percentage of unhealthy items
Intercept Postintervention Noncommitted control group Committed control group Nonresponding treatment group Noncommitted treatment group Committed treatment group Noncommitted Control Group × Postintervention Committed Control Group × Postintervention Nonresponding Treatment Group × Postintervention Noncommitted Treatment Group × Postintervention Committed Treatment Group × Postintervention
30.02*** (0.40) –1.74*** (0.21) 1.67** (0.60) 2.57*** (0.61) –0.09 (0.49) 1.77*** (0.48) 1.95** (0.61) –0.17 (0.31) –0.03 (0.32) 0.34 (0.26) –0.01 (0.25) 3.42*** (0.32)
48.85*** (0.31) 2.17*** (0.21) –0.68 (0.47) –1.30** (0.47) 0.31 (0.38) –0.81* (0.37) –0.55 (0.48) –0.07 (0.32) 0.07 (0.32) –0.64* (0.26) –0.03 (0.25) –2.22*** (0.32)
21.13*** (0.30) –0.43* (0.19) –0.99* (0.45) –1.28** (0.45) –0.23 (0.37) –0.97** (0.35) –1.40** (0.46) 0.23 (0.27) –0.05 (0.28) 0.30 (0.23) 0.04 (0.22) –1.22*** (0.28)
Note: N = 6,570 for all analyses. In these analyses, the nonresponding control group was specified as the reference group. *p < .05. **p < .01. ***p < .001.
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Schwartz et al.
544 real loss. In stark comparison, the control-group committed households had the highest baseline percentage of healthy purchases but ultimately showed no Group × Postintervention interaction despite their hypothetical commitment to the 5-percentage-point increase. This suggests that simply indicating interest in precommitment as a self-control strategy does not improve behavior. The comparison of households that did and did not open the survey also suggests that there is no effect of merely exposing people to the idea of precommitment, as neither group showed any improvement over time. Again, only a binding commitment affected behavior. Finally, we examined the overall effect of random assignment to the two conditions (treatment vs. control) and found that treatment-group households had a significantly higher percentage of healthy purchases than did control-group households for the study’s 6 months. This pattern of results was identical when this intention-totreat analysis was limited only to households that opened the survey (see Methods and Results, Tables S3A and S3B, in the Supplemental Material).
Discussion Research from the behavioral sciences has shown that many people are aware that their self-control problems can interfere with their goals and are willing to make a one-time decision that precommits them to a better course of action. The goal of this study was to test the viability of a novel commitment device that specifically targeted better nutrition in a large-scale health incentive program. Members were invited to precommit to purchasing a greater percentage of healthy foods each month at the risk of losing an existing 25% discount if they were unable to meet this goal, and with no additional incentive or bonus. This intervention differs from other precommitment interventions in that it did not involve a traditional deposit contract or specify an end goal such as a determined amount of weight loss or smoking cessation, but rather targeted improvements in shopping behavior that would ideally improve health outcomes if sustained. As in previous research (Ashraf et al., 2006, Giné et al., 2010), we found that there was significant demand for a commitment device and that the precommitment strategy helped people engage in healthier behavior. The results presented here thus point to a promising approach to help people who have taken the initiative to enroll in health and wellness programs stay on track. From a policy perspective, precommitment is costeffective, because it can improve behavior without any additional incentive. This is an attractive alternative to increasing incentive amounts, which may not be feasible long term or even effective. In addition, a pure commitment device with no additional incentive is unappealing
to people who are already doing well because improvement is harder to achieve at these levels and because a bigger loss is at stake. Thus, commitment devices can help underperformers do better, without overrewarding individuals at the top end of the distribution. An increase in incentive amounts, in contrast, would give bigger rewards (e.g., discounts in the HealthyFood program) to the already successful and not necessarily motivate underperformers. Our participants were asked to commit to a 5-percentage-point increase, essentially a handful of items, in their healthy food purchasing each month. This proved quite challenging, as in any given month only one third of the committed households met their goal. This outcome raises an important question about how much improvement people are willing to commit to before the option feels too risky. A much higher goal may have led to lower rates of precommitment, and without binding commitment, improvement is unlikely. Another consideration is whether people work only to satisfy a specified, and perhaps arbitrary, goal. Committed households consistently showed an average monthly 3.5-percentagepoint increase after committing to a 5-percentage-point increase; perhaps they would have shown a 7-percentage-point increase had we set a goal of a 10-percentagepoint increase. This is one of many practical and empirical questions to be addressed by future research. Another area of investigation could focus on the possibility of making people more sophisticated about their self-control problems and, in such moments of recognition, set guardrails for themselves that keep them moving along the path of most resistance. A helpful feature of the current intervention was that although the commitment was financially binding, it was not overly restrictive. People could test the boundaries of their sophistication but were allowed to opt out after the 1st month if precommitment was not working for them (though only 15% dropped out at some point during the study). Helping people become more sophisticated and offering “light” commitment devices may prove to be an important tool in helping people activate and maintain self-control in a variety of health domains, including nutrition, smoking cessation, exercise, and medication adherence. As is the case with any field experiment, our study has limitations. First, we do not know how participants fared once the precommitment ended, though the results of this study and previous research suggest that less healthy shopping habits would reemerge once the threat of a loss was lifted. However, an advantage of this particular commitment device is that if it were offered as a real product, it would not have to be removed—members could choose to keep it in place indefinitely. The optimal design and longevity of commitment devices presents another exciting avenue for future research. Second, there was
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Healthier by Precommitment 545 always the possibility that members could purchase unhealthy food items elsewhere or even at Pick n Pay but with cash or a credit card not registered with the Vitality program; such purchases would have gone undetected. We believe the study’s design minimized this possibility by allowing households to opt out after the 1st month, as it is more cumbersome to shop at separate stores or segregate grocery baskets and payments than it is to e-mail a request to discontinue. Moreover, the temptation to “cheat” by these strategies was further minimized by the fact that there was no additional incentive offered; the best any household could do by artificially inflating its percentage of healthy food purchases was to increase transaction costs in order to get the 25% discount that Vitality was always willing to pay. A final limitation is that although it is assumed that financial incentives targeting behavioral change will produce better outcomes, we were unable to measure health outcomes. Thus, we could not determine whether better shopping habits produced significant changes in participants’ health status. Moreover, accurate measurement of the potential benefits of precommitment would require manipulating variables such as the length of the commitment, the size of the goal, and the penalties for failing to meet the goal. Such manipulations were beyond the scope of the current work, which was focused on testing the viability and effectiveness of a commitment device on a large scale. We hope that future work will examine these important practical questions. We close by noting the challenges of behavioral change, and how resistant people are to positive change despite the myriad forces designed to help them be more aware, better educated, and financially motivated to engage in activities that facilitate self-control, and by extension, better outcomes. In many cases, these approaches are welcomed and help establish positive attitudes and intentions to change behavior. Gym memberships, Weight Watchers, and Smoke Enders are commercially successful programs that capitalize on such optimism. Research suggests that self-control challenges are annoyingly persistent, however, and people will continue to struggle as good intentions do not translate into better behaviors. Here, we have shown that there is some hope, as many people recognize their self-control problems and, in such moments of self-awareness, may also welcome an opportunity to create an environment that makes it easier to do what is right. Author Contributions All authors contributed to the study concept and design. Study administration and participant feedback were administered by L. Wyper and D. Patel. Automated archival data extraction was performed by J. Maroba. J. Schwartz and D. Mochon performed the data analysis and statistical interpretation. J. Schwartz and
D. Mochon drafted the manuscript; critical revisions were provided by D. Patel and D. Ariely. All authors approved the final version of the manuscript for submission.
Acknowledgments We thank Shaun Matisonn and Craig Nossel for their assistance executing the research. We are also grateful to Pierre Chandon, Gretchen Chapman, Dan Goldstein, Tulane’s Health Policy Working Group members, and Carnegie Mellon’s Center for Behavioral Decision Research seminar attendees for helpful comments and suggestions and to Luke Nowlan for research assistance.
Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.
Supplemental Material Additional supporting information may be found at http://pss .sagepub.com/content/by/supplemental-data
References Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13, 219–224. Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. The Quarterly Journal of Economics, 121, 635–672. Charness, G., & Gneezy, U. (2009). Incentives to exercise. Econometrica, 77, 909–931. Donatelle, R. J., Hudson, D., Dobie, S., Goodall, A., Hunsberger, M., & Oswald, K. (2004). Incentives in smoking cessation: Status of the field and implications for research and practice with pregnant smokers. Nicotine & Tobacco Research, 6(Suppl. 2), S163–S179. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40, 351–401. Giné, X., Karlan, D., & Zinman, J. (2010). Put your money where your butt is: A commitment contract for smoking cessation. American Economic Journal: Applied Economics, 2(4), 213–235. Gneezy, U., Meier, S., & Rey-Biel, P. (2011). When and why incentives (don’t) work to modify behavior. The Journal of Economic Perspectives, 25(4), 191–209. John, L. K., Norton, L., Fassbender, J. E., & Volpp, K. G. (2011). Financial incentives for extended weight loss: A randomized, controlled trial. Journal of General Internal Medicine, 26, 621–626. Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106, 3–19. Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: Does self-control resemble a muscle? Psychological Bulletin, 126, 247–259.
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546 O’Donoghue, T., & Rabin, M. (1999). Doing it now or later. American Economic Review, 89(1), 103–124. O’Donoghue, T., & Rabin, M. (2001). Choice and procrastination. The Quarterly Journal of Economics, 116, 121–160. Patient Protection and Affordable Care Act, 3590 C.F.R. (2010). Schwartz, J., Riis, J., Elbel, B., & Ariely, D. (2012). Inviting consumers to downsize fast-food portions significantly reduces calorie consumption. Health Affairs, 31, 399–407. Sturm, R., An, R., Segal, D., & Patel, D. (2013). A cash-back rebate program for healthy food purchases in South Africa: Results from scanner data. American Journal of Preventive Medicine, 44, 567–572. Volpp, K. G., John, L. K., Troxel, A. B., Norton, L., Fassbender, J., & Loewenstein, G. (2008). Financial incentive–based approaches for weight loss. The Journal of the American Medical Association, 300, 2631–2637.
Volpp, K. G., Levy, A. G., Asch, D. A., Berlin, J. A., Murphy, J. J., Gomez, A., . . . Lerman, C. (2006). A randomized controlled trial of financial incentives for smoking cessation. Cancer Epidemiology Biomarkers & Prevention, 15, 12–18. Volpp, K. G., Loewenstein, G., Troxel, A. B., Doshi, J., Price, M., Laskin, M., & Kimmel, S. E. (2008). A test of financial incentives to improve warfarin adherence. BMC Health Services Research, 8(1), 272. Retrieved from http://www .biomedcentral.com/1472-6963/8/272 Volpp, K. G., Troxel, A. B., Pauly, M. V., Glick, H. A., Puig, A., Asch, D. A., . . . Audrain-McGovern, J. (2009). A randomized, controlled trial of financial incentives for smoking cessation. New England Journal of Medicine, 360, 699–709. Wertenbroch, K. (1998). Consumption self-control by rationing purchase quantities of virtue and vice. Marketing Science, 17, 317–337.
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PSSXXX10.1177/0956797613510950Schwartz et al.Healthier by Precommitment
Research Article Psychological Science 2014, Vol. 25(2) 538–546 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797613510950 pss.sagepub.com
Healthier by Precommitment Janet Schwartz1, Daniel Mochon1, Lauren Wyper2, Josiase Maroba2, Deepak Patel2, and Dan Ariely3
1 Department of Marketing, A. B. Freeman School of Business, Tulane University; 2Discovery Vitality, Sandton, South Africa; and 3Department of Marketing, Fuqua School of Business, Duke University
Abstract We tested a voluntary self-control commitment device to help grocery shoppers make healthier food purchases. Participants, who were already enrolled in a large-scale incentive program that discounts the price of eligible groceries by 25%, were offered the chance to put their discount on the line. Agreeing households pledged that they would increase their purchases of healthy food by 5 percentage points above their household baseline for each of 6 months. If they reached that goal, their discount was awarded as usual; otherwise, their discount was forfeited for that month. Thirty-six percent of households that were offered the binding commitment agreed; they subsequently showed an average 3.5-percentage-point increase in healthy grocery items purchased in each of the 6 months; households that declined the commitment and control-group households that were given a hypothetical option to precommit did not show such an increase. These results suggest that self-aware consumers will seize opportunities to create restrictive choice environments for themselves, even at some risk of financial loss. Keywords self-control, health, rewards Received 6/11/13; Revision accepted 10/8/13
Governments and private companies are increasingly offering incentive programs to encourage healthier lifestyles (e.g., Patient Protection and Affordable Care Act, 2010). Motivated consumers will undoubtedly be drawn to these programs with good intentions of eating healthier foods, smoking less, and exercising more. A long stream of research from the behavioral sciences, however, suggests that translating good intentions into real behavior requires precisely the type of persistent selfcontrol that proves to be quite challenging (e.g., Muraven & Baumeister, 2000) and might not be fully achieved by rewards alone. This raises an important question regarding what types of incentive designs are necessary to activate and maintain ongoing self-control. Financial incentives are a long-standing and attractive approach to improving self-control, particularly in situations in which restraint is needed in the present, the deleterious effects of poor choices are cumulative rather than immediately punishing, and the rewards are intangible or delayed (Metcalfe & Mischel, 1999). This may be especially true in the health domain, where the immediate gratification of any one instance of overeating, drinking, or smoking is pitted against an elusive reward of
(maybe) better future health (e.g., Frederick, Loewenstein, & O’Donoghue, 2002). Incentives can make abstaining more immediately rewarding, and thus better align the goals of one’s current and future selves. Indeed, recent research has shown that weight loss ( John, Norton, Fassbender, & Volpp, 2011; Volpp, John, et al., 2008), smoking cessation (Volpp et al., 2006; Volpp et al., 2009), exercise (Charness & Gneezy, 2009), and medication adherence (Volpp, Loewenstein, et al., 2008) can be substantially improved with the use of financial incentives. In some cases, the effects have been shown to last even after the incentive has been removed, which suggests that incentives can provide an important catalyst to habit formation and do not necessarily have to be in place long term (Charness & Gneezy, 2009; Volpp et al., 2009). Other studies, however, have shown that once incentives are removed, bad behaviors can return (e.g., John et al., 2011; Volpp, John, et al., 2008; Volpp, Loewenstein, et al., Corresponding Author: Janet Schwartz, Tulane University, 7 McAlister Dr., New Orleans, LA 70118 E-mail: [email protected]
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Healthier by Precommitment 539 2008). If financial incentives can successfully curb behavior but must be maintained, questions arise about whether pure financial rewards are feasible in the long term (Gneezy, Meier, & Rey-Biel, 2011). At the same time, this uncertainty provides an opportunity to empirically investigate other methods of improving self-control. One promising intervention approach stems from the insight that some people are sophisticated (or selfaware) about their self-control problems (O’Donoghue & Rabin, 1999, 2001) and may seize opportunities to limit their behavior (Ariely & Wertenbroch, 2002). For example, consumers may prefer to pay a premium for smaller packages of cigarettes rather than receive a bulk discount (Wertenbroch, 1998), and when given the option, may choose smaller portions in fast-food meals, even when they cost the same as larger portions (Schwartz, Riis, Elbel, & Ariely, 2012). Such behavior, though not strictly rational, signals that people seek to avoid the temptation that comes with an abundance of vices. Research in the domain of self-awareness and restricted choices has also examined more binding cases of precommitment involving financial deposit contracts (participants deposit their own money and forfeit it if they do not meet their goals) to boost both savings and health-related behaviors. Ashraf, Karlan, and Yin (2006) found significant demand for a commitment device that offered no incentive for deposits, and no possibility of early withdrawals, among people opening savings accounts. Those who precommitted to making deposits had significantly greater savings 6 and 12 months after opening their accounts than did those who had declined the contract and a control group. Smoking-cessation research has shown that smokers who commit to a self-funded 6-month financial deposit contract are less likely to be smoking 12 months later, compared with smokers who make no such commitment (Giné, Karlan, & Zinman, 2010; see also Donatelle et al., 2004). Taken together, these results suggest that incentive designs can help self-aware people resist temptation via threat or risk of financial loss and further intimate that they will welcome this approach. The current study examined whether precommitment can have the effect of helping people purchase healthier food above and beyond the effects of a financial incentive to do so. We asked two key questions: Is there demand for a commitment device in a large-scale incentive program and, if so, can such a device change people’s behavior? We tested the efficacy of a commitment device for ongoing healthy-food purchases in a randomized controlled field experiment conducted in collaboration with Discovery Health in South Africa. A defining feature of Discovery Health is the Vitality program, an incentive program that rewards people for a variety of healthy behaviors, including purchasing healthy food. One effective (Sturm, An, Segal, & Patel, 2013) member
benefit is a monthly 25% cash-back bonus awarded to members’ credit-card statements for all healthy food items (e.g., most fruit, vegetables, fat-free dairy products, lean meats, and whole grains) purchased the previous month at South Africa’s Pick n Pay grocery chain—one of the nation’s largest. Despite the program’s popularity and generous incentive, the HealthyFood benefit appears to be underutilized. On average, only 31% of eligible grocery purchases (i.e., food items) are from the healthyfood category. Although a traditional economics approach to this problem might be to further increase the financial incentive, we instead leveraged an important insight from behavioral science. We tested whether offering a commitment device with the potential of a loss and no additional incentive beyond what households were already getting would help participating households improve the health quality of their purchases. Households were invited to put their monthly credit-card cash-back bonus on the line with a precommitment to a 5-percentage-point increase in healthy food items purchased, relative to their own historical baseline, for each of the following 6 months. The 5-percentage-point increase was chosen as the goal because we wanted members to commit to a change they felt was attainable through some amount of effort, but was not so drastic as to result in a sure loss. Households that met the goal in a given month kept their monthly cash-back bonus; a failure to achieve this goal meant that they gave up their bonus for that month. In other words, the program offered the threat of a negative consequence without any positive incentive, aside from the opportunity to exercise better self-control when grocery shopping.
Design and Procedure Household selection criteria The sample consisted of 6,570 households meeting inclusion criteria set to ensure that their grocery food shopping was trackable over the study period. Specifically, we selected only households that actively participated in the HealthyFood program during 3 months of the preintervention period ( January 1, 2012, through June 30, 2012); had a Discovery Health–issued credit card on which they received the monthly HealthyFood cash-back bonus from the Vitality program (which could be reversed if the goal was not met); and spent at least 500 South African rands (R500, or ~$56 U.S.) per month on groceries using their Discovery Health credit card, with 75% or more of this grocery spending being at Pick n Pay. This ensured that the sample was limited to relatively frequent Pick n Pay shoppers who had a relatively stable baseline percentage of healthy food purchases.
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Experimental design and procedure This field experiment employed a 2 (condition: treatment vs. control) × 2 (time: pre- vs. postintervention) factorial design with condition as the manipulated betweengroups factor and time as the longitudinal repeated measures factor. Selected households were randomly assigned to the conditions: One third were assigned to the control condition, and two thirds were assigned to the treatment condition. Oversampling the treatment condition ensured that a sufficient number of households would consent to the precommitment (see Background in the Supplemental Material available online for additional information about the Vitality program and survey questions). On June 19, 2012, all 6,570 households were invited by e-mail to complete a survey about their grocery-shopping habits in exchange for the chance to win a R1,000 (~$113 U.S.) gift certificate to Pick n Pay. This e-mail provided the household’s baseline healthy shopping percentage, which was the average percentage of food items (i.e., excluding nonfood items like cleaning supplies) qualifying for the HealthyFood cash-back bonus during 3 of the preintervention months (February–April, 2012), and a survey link. Recipients in the treatment group who followed the survey link were taken to a different, condition-specific, consent form and series of questions than were those in the control group. Specifically, the treatment group was told that their survey responses could change their Vitality benefit for a period of time, whereas the control group was told that their survey responses would not change their Vitality benefit. After giving their consent to participate in the survey, participants in both groups were introduced to precommitment as one strategy for improving self-control and achieving their health goals. Participants in both groups were told that Vitality was considering offering a “Precommitment Programme” and were asked if they would be interested in trying it for 6 months, with the option of discontinuing their participation after the 1st month if the program was not right for them. Allowing participants to opt out after the binding 1st month had the advantage of limiting the financial risk to a forfeiture of a single month’s cash-back bonus. This gave sophisticated consumers the chance to recognize and seize the opportunity, but not be burdened by it. After the program was described, the participants were again given their household’s baseline percentage of healthy foods and asked if they would be willing to commit to an increase of 5 percentage points over that baseline for each of the following 6 months under the condition of forfeiting their cash-back bonus in any month that they failed to achieve the goal. For the control group, this was a hypothetical commitment, but for the treatment group, the commitment was real and binding.
Exposing both groups to the precommitment idea and gauging their interest ensured that any observed differences between the groups were not due to mere exposure to a self-control strategy, but rather were due to an actual binding financial commitment. Consenting households in the treatment group received an e-mail from the study administrator that specified their start date and provided a link to the study’s Frequently Asked Questions (FAQ) page. After each monthly cycle was complete, each household’s percentage of healthy foods purchased was calculated, its goal attainment was measured relative to the baseline, and the appropriate feedback was given by e-mail. Successful households kept their cash-back bonus and were encouraged to continue their healthy behavior in the following month. Unsuccessful households forfeited their cashback bonus (noted as a line-item reversal on their creditcard statement) and were encouraged to try again the next month. Finally, participants’ survey responses were recorded in a data file that was merged with their Pick n Pay and credit-card data from the pre- and postintervention periods via a household-unique identifier. For the duration of the pre- and postintervention periods, automated procedures reported the relevant data for all 6,570 households. In addition to itemizing the HealthyFood items, this system designates the remaining food items purchased at Pick n Pay as neutral or unhealthy. Members are unaware of the neutral- and unhealthy-food itemizations, which appear only in the Vitality system and were available for analysis purposes.
Results Sixty two percent of households who received the invitation e-mail began the survey. The percentage of households starting the survey was uninfluenced by experimental condition, χ2(1, N = 4,073) = 0.66, p = .42. Thus, we limited the initial analyses to those 4,073 households (see Table 1 for descriptive statistics).
Commitment choice A total of 632 households (36% of treatment households that actively made a choice) chose to precommit to the program. Thus, there appears to be demand for a commitment device in a large incentive program, even if there is no extra incentive and only the threat of a loss. The baseline percentage of healthy foods for households agreeing to the precommitment did not differ significantly from the baseline percentage for households that were in the treatment condition but did not precommit or were in the control condition, F(2, 4070) = 0.41, p = .67. This suggests that precommitment had wide appeal, and
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Healthier by Precommitment 541 Table 1. Mean Percentages and Numbers of Food Items Purchased, Mean Amounts Spent on Those Items, and Likelihood of Meeting the Goal (Being 5 Percentage Points Above the Baseline) Before and After the Intervention Preintervention (January–June 2012)
Measure Healthy items (%) Neutral items (%) Unhealthy items (%) Number of items Number of healthy items Number of neutral items Number of unhealthy items Total amount spenta Amount spent on healthy itemsa Amount spent on neutral itemsa Amount spent on unhealthy itemsa Likelihood of meeting goal
Postintervention (July–December 2012)
Control group (n = 1,329)
Noncommitted group (n = 2,112)
Committed group (n = 632)
Control group (n = 1,307)
Noncommitted group (n = 2,099)
Committed group (n = 623)
32.14 (12.26) 47.85 (9.39) 20.01 (8.53) 81.21 (44.63) 26.34 (17.58) 38.76 (22.80) 16.11 (11.21) 1,850.51 (1,092.43) 505.14 (350.19) 1,044.87 (668.30) 300.50 (213.93) .22 (.18)
31.77 (11.86) 48.05 (9.42) 20.18 (8.66) 86.92 (47.65) 28.18 (19.41) 41.22 (23.35) 17.51 (12.18) 1,972.02 (1,161.09) 533.06 (386.11) 1,111.05 (681.69) 327.91 (251.24) .21 (.18)
32.00 (11.97) 48.28 (9.22) 19.72 (8.85) 79.32 (47.05) 25.79 (19.39) 38.02 (22.61) 15.51 (11.39) 1,832.11 (1,193.29) 498.82 (399.02) 1,036.30 (684.36) 296.99 (240.61) .23 (.19)
30.30 (12.54) 49.98 (9.96) 19.72 (9.07) 77.59 (47.55) 24.06 (17.89) 38.56 (24.40) 14.98 (11.18) 1,822.26 (1,181.13) 476.94 (369.51) 1,054.57 (720.43) 290.75 (223.73) .23 (.27)
29.99 (12.13) 50.26 (9.73) 19.75 (9.01) 83.96 (52.87) 25.84 (19.84) 41.68 (26.69) 16.44 (12.83) 1,960.48 (1,288.05) 504.80 (394.92) 1,139.87 (778.82) 315.80 (253.88) .22 (.26)
33.37 (15.38) 48.39 (11.72) 18.24 (10.63) 72.76 (51.24) 24.49 (20.40) 35.33 (25.99) 12.95 (11.06) 1,732.17 (1,342.97) 490.98 (440.33) 983.56 (811.37) 257.63 (229.43) .34 (.33)
Note: Standard deviations are given in parentheses. a These amounts are in South African rands.
was not limited to participants who were already doing well.
Intervention As there were no differences between groups in the percentage of healthy food purchases at the introduction of the intervention, we focused on the three groups who started the survey: committed (treatment-group participants who made a binding commitment), noncommitted (treatment-group participants who declined to make the binding commitment), and control (hypothetical commitment option only). Comparison of the committed group with the noncommitted and control groups shows the importance of making a binding financial commitment as opposed to mere exposure to the idea. This is an important comparison because improvement among the committed households but not among the noncommitted and control households would suggest that the threat of a loss must be real in order for it to work. Approximately
15% of committed households dropped out some time after the 1st month, but for analysis purposes were retained in that group for the study’s full duration. We first examined whether the commitment device helped committed households improve the percentage of healthy items purchased relative to the noncommitted and control households. Figure 1 shows the average percentage of healthy foods purchased for the three groups over the 1-year study period ( January–December 2012). We tested the effect of precommitment on the percentage of healthy, neutral, and unhealthy items purchased with a series of random-effects linear regressions with the assumption of compound symmetry on the covariance matrix. We specified the independent predictor variables of group (control vs. noncommitted vs. committed), a dummy variable indicating whether data were for the 6 months preintervention ( January–June) or the 6 months postintervention ( July–December), and the key Group × Postintervention interactions. Table 2 shows the results, with the control group specified as the reference group.
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Schwartz et al.
542 Control
Noncommitted
Committed
Healthy Food Items Purchased (%)
36 35 34 33 32 31 30 29 28 27 26
Ja
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Ap
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Fig. 1. Mean percentage of healthy food items purchased by group and month.
We found no significant difference between the three groups at baseline in the percentage of healthy, unhealthy, or neutral food items purchased. However, we observed the predicted Committed Group × Postintervention interaction, showing that the committed group bought a significantly higher percentage of healthy items during the 6-month commitment period relative to the control group. They increased the healthy items in their monthly food basket by an average of 3.52 percentage points (Table 2). Moreover, this increase came as a function of a decrease in both the percentage of neutral items and the percentage of unhealthy items purchased. Noncommitted
households behaved no differently than control households after the intervention. Note that the postintervention coefficient in Table 2 shows that the percentage of healthy foods purchased was lower postintervention than preintervention and reflects a general decreasing trend in healthy food purchases (see Fig. 1). To ensure that the intervention did not cause healthy food purchasing to decrease, we examined each month separately and found that the percentage of healthy purchases was greatest in January (summertime, when produce is more widely available; also, New Year’s resolutions may cause healthy behavior
Table 2. Results of Random-Effects Linear and Logistic Regressions: Effects of the Intervention on Percentages of Healthy, Unhealthy, and Neutral Items Bought and on the Probability of Meeting the Goal Predictor Intercept Postintervention Noncommitted group Committed group Noncommitted Group × Postintervention Committed Group × Postintervention
Percentage of healthy items
Percentage of neutral items
Percentage of unhealthy items
Likelihood of meeting goal
32.13*** (0.33) –1.84*** (0.16) –0.34 (0.42) –0.17 (0.58) 0.09 (0.21)
47.86*** (0.25) 2.17*** (0.16) 0.18 (0.32) 0.44 (0.45) –0.03 (0.21)
20.00*** (0.23) –0.34* (0.14) 0.16 (0.30) –0.27 (0.41) –0.05 (0.18)
–1.27*** (0.03) 0.08* (0.04) –0.07* (0.04) 0.05 (0.05) –0.01 (0.05)
3.52*** (0.29)
–2.22*** (0.29)
–1.30*** (0.25)
0.52*** (0.07)
Note: n = 4,073 for all analyses. In these analyses, the control group was specified as the reference group. The Committed Group × Postintervention coefficient was also significantly different from the Noncommitted Group × Postintervention coefficient for all four regressions (p < .001). The results were unchanged when households that did not open the survey were included in the analyses. The results were also unchanged when fixed effects rather than random effects were used to control for household-level effects. Similar results were obtained in regressions predicting the numbers of items purchased and the amount of money spent (see Methods and Results, Table S1, in the Supplemental Material). *p < .05. ***p < .001. Downloaded from pss.sagepub.com at COLUMBIA UNIV on March 2, 2015
Healthier by Precommitment 543 to peak in January) and steadily decreased throughout the year. The coefficients for June and July revealed lower percentages relative to January, but not a lower percentage for July than for June (see Methods and Results, Table S2, in the Supplemental Material). If the intervention had negatively affected healthy purchases, one would expect there to have been a significant difference between June and July. We observed similar effects when we looked at the likelihood of meeting the goal (i.e., achieving a 5percentage-point increase in healthy items over the household’s baseline). Likelihood of goal attainment was significantly higher postintervention among households that had made a financially binding commitment than among control households. There was no difference between the control group and the noncommitted group with respect to postintervention goal achievement. This is a critical finding because it means that participants in the control group showed no effect of the hypothetical commitment that 56% of them had endorsed. That is, a financially binding commitment appears to have been crucial to increasing the percentage of healthy items purchased.
Treatment versus selection effects An advantage to the Vitality and Pick n Pay automated data-capture and -reporting system was that data from the sample households were available for the study’s duration, regardless of random assignment to condition, whether the survey was opened, and whether a commitment (binding or hypothetical) was made. This allowed us to further, though not fully, isolate treatment and selection effects (limitations to this analysis and an
alternative approach are discussed in Methods and Results in the Supplemental Material) by dividing the sample into six groups: nonresponding households in the control group (the survey was never opened), noncommitted households in the control group (the survey was opened but the hypothetical commitment was declined), committed households in the control group (the survey was opened and the respondent endorsed the hypothetical commitment), nonresponding households in the treatment group (the survey was never opened), noncommitted households in the treatment group (the survey was opened but the binding commitment was declined), and committed households in the treatment group (the survey was opened and a binding commitment was made). We again tested the effect of precommitment on the percentage of healthy, neutral, and unhealthy items purchased with a series of randomeffects linear regressions specifying the independent predictor variables of group (as just specified), the postintervention dummy variable (as in the prior analyses), and the key Group × Postintervention interactions. Table 3 shows the results, with the nonresponding control group specified as the reference group. We found selection effects among the four groups of households that responded to the survey. These groups showed a higher average baseline percentage of healthy purchases than did the control-group nonresponders. The Group × Postintervention interactions showed that despite being healthier at the start of the intervention than the nonresponders, none of these responder groups improved except for the treatment-group committed households. This further suggests that in order for precommitment to be effective as a self-control strategy, there must be an actual commitment with the threat of a
Table 3. Results of Random-Effects Linear Regressions Isolating Treatment and Selection Effects Predictor
Percentage of healthy items
Percentage of neutral items
Percentage of unhealthy items
Intercept Postintervention Noncommitted control group Committed control group Nonresponding treatment group Noncommitted treatment group Committed treatment group Noncommitted Control Group × Postintervention Committed Control Group × Postintervention Nonresponding Treatment Group × Postintervention Noncommitted Treatment Group × Postintervention Committed Treatment Group × Postintervention
30.02*** (0.40) –1.74*** (0.21) 1.67** (0.60) 2.57*** (0.61) –0.09 (0.49) 1.77*** (0.48) 1.95** (0.61) –0.17 (0.31) –0.03 (0.32) 0.34 (0.26) –0.01 (0.25) 3.42*** (0.32)
48.85*** (0.31) 2.17*** (0.21) –0.68 (0.47) –1.30** (0.47) 0.31 (0.38) –0.81* (0.37) –0.55 (0.48) –0.07 (0.32) 0.07 (0.32) –0.64* (0.26) –0.03 (0.25) –2.22*** (0.32)
21.13*** (0.30) –0.43* (0.19) –0.99* (0.45) –1.28** (0.45) –0.23 (0.37) –0.97** (0.35) –1.40** (0.46) 0.23 (0.27) –0.05 (0.28) 0.30 (0.23) 0.04 (0.22) –1.22*** (0.28)
Note: N = 6,570 for all analyses. In these analyses, the nonresponding control group was specified as the reference group. *p < .05. **p < .01. ***p < .001.
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Schwartz et al.
544 real loss. In stark comparison, the control-group committed households had the highest baseline percentage of healthy purchases but ultimately showed no Group × Postintervention interaction despite their hypothetical commitment to the 5-percentage-point increase. This suggests that simply indicating interest in precommitment as a self-control strategy does not improve behavior. The comparison of households that did and did not open the survey also suggests that there is no effect of merely exposing people to the idea of precommitment, as neither group showed any improvement over time. Again, only a binding commitment affected behavior. Finally, we examined the overall effect of random assignment to the two conditions (treatment vs. control) and found that treatment-group households had a significantly higher percentage of healthy purchases than did control-group households for the study’s 6 months. This pattern of results was identical when this intention-totreat analysis was limited only to households that opened the survey (see Methods and Results, Tables S3A and S3B, in the Supplemental Material).
Discussion Research from the behavioral sciences has shown that many people are aware that their self-control problems can interfere with their goals and are willing to make a one-time decision that precommits them to a better course of action. The goal of this study was to test the viability of a novel commitment device that specifically targeted better nutrition in a large-scale health incentive program. Members were invited to precommit to purchasing a greater percentage of healthy foods each month at the risk of losing an existing 25% discount if they were unable to meet this goal, and with no additional incentive or bonus. This intervention differs from other precommitment interventions in that it did not involve a traditional deposit contract or specify an end goal such as a determined amount of weight loss or smoking cessation, but rather targeted improvements in shopping behavior that would ideally improve health outcomes if sustained. As in previous research (Ashraf et al., 2006, Giné et al., 2010), we found that there was significant demand for a commitment device and that the precommitment strategy helped people engage in healthier behavior. The results presented here thus point to a promising approach to help people who have taken the initiative to enroll in health and wellness programs stay on track. From a policy perspective, precommitment is costeffective, because it can improve behavior without any additional incentive. This is an attractive alternative to increasing incentive amounts, which may not be feasible long term or even effective. In addition, a pure commitment device with no additional incentive is unappealing
to people who are already doing well because improvement is harder to achieve at these levels and because a bigger loss is at stake. Thus, commitment devices can help underperformers do better, without overrewarding individuals at the top end of the distribution. An increase in incentive amounts, in contrast, would give bigger rewards (e.g., discounts in the HealthyFood program) to the already successful and not necessarily motivate underperformers. Our participants were asked to commit to a 5-percentage-point increase, essentially a handful of items, in their healthy food purchasing each month. This proved quite challenging, as in any given month only one third of the committed households met their goal. This outcome raises an important question about how much improvement people are willing to commit to before the option feels too risky. A much higher goal may have led to lower rates of precommitment, and without binding commitment, improvement is unlikely. Another consideration is whether people work only to satisfy a specified, and perhaps arbitrary, goal. Committed households consistently showed an average monthly 3.5-percentagepoint increase after committing to a 5-percentage-point increase; perhaps they would have shown a 7-percentage-point increase had we set a goal of a 10-percentagepoint increase. This is one of many practical and empirical questions to be addressed by future research. Another area of investigation could focus on the possibility of making people more sophisticated about their self-control problems and, in such moments of recognition, set guardrails for themselves that keep them moving along the path of most resistance. A helpful feature of the current intervention was that although the commitment was financially binding, it was not overly restrictive. People could test the boundaries of their sophistication but were allowed to opt out after the 1st month if precommitment was not working for them (though only 15% dropped out at some point during the study). Helping people become more sophisticated and offering “light” commitment devices may prove to be an important tool in helping people activate and maintain self-control in a variety of health domains, including nutrition, smoking cessation, exercise, and medication adherence. As is the case with any field experiment, our study has limitations. First, we do not know how participants fared once the precommitment ended, though the results of this study and previous research suggest that less healthy shopping habits would reemerge once the threat of a loss was lifted. However, an advantage of this particular commitment device is that if it were offered as a real product, it would not have to be removed—members could choose to keep it in place indefinitely. The optimal design and longevity of commitment devices presents another exciting avenue for future research. Second, there was
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Healthier by Precommitment 545 always the possibility that members could purchase unhealthy food items elsewhere or even at Pick n Pay but with cash or a credit card not registered with the Vitality program; such purchases would have gone undetected. We believe the study’s design minimized this possibility by allowing households to opt out after the 1st month, as it is more cumbersome to shop at separate stores or segregate grocery baskets and payments than it is to e-mail a request to discontinue. Moreover, the temptation to “cheat” by these strategies was further minimized by the fact that there was no additional incentive offered; the best any household could do by artificially inflating its percentage of healthy food purchases was to increase transaction costs in order to get the 25% discount that Vitality was always willing to pay. A final limitation is that although it is assumed that financial incentives targeting behavioral change will produce better outcomes, we were unable to measure health outcomes. Thus, we could not determine whether better shopping habits produced significant changes in participants’ health status. Moreover, accurate measurement of the potential benefits of precommitment would require manipulating variables such as the length of the commitment, the size of the goal, and the penalties for failing to meet the goal. Such manipulations were beyond the scope of the current work, which was focused on testing the viability and effectiveness of a commitment device on a large scale. We hope that future work will examine these important practical questions. We close by noting the challenges of behavioral change, and how resistant people are to positive change despite the myriad forces designed to help them be more aware, better educated, and financially motivated to engage in activities that facilitate self-control, and by extension, better outcomes. In many cases, these approaches are welcomed and help establish positive attitudes and intentions to change behavior. Gym memberships, Weight Watchers, and Smoke Enders are commercially successful programs that capitalize on such optimism. Research suggests that self-control challenges are annoyingly persistent, however, and people will continue to struggle as good intentions do not translate into better behaviors. Here, we have shown that there is some hope, as many people recognize their self-control problems and, in such moments of self-awareness, may also welcome an opportunity to create an environment that makes it easier to do what is right. Author Contributions All authors contributed to the study concept and design. Study administration and participant feedback were administered by L. Wyper and D. Patel. Automated archival data extraction was performed by J. Maroba. J. Schwartz and D. Mochon performed the data analysis and statistical interpretation. J. Schwartz and
D. Mochon drafted the manuscript; critical revisions were provided by D. Patel and D. Ariely. All authors approved the final version of the manuscript for submission.
Acknowledgments We thank Shaun Matisonn and Craig Nossel for their assistance executing the research. We are also grateful to Pierre Chandon, Gretchen Chapman, Dan Goldstein, Tulane’s Health Policy Working Group members, and Carnegie Mellon’s Center for Behavioral Decision Research seminar attendees for helpful comments and suggestions and to Luke Nowlan for research assistance.
Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.
Supplemental Material Additional supporting information may be found at http://pss .sagepub.com/content/by/supplemental-data
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