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Temptation, Impulsiveness and Committment Daniel Houser Professor of Economics Director, Interdisciplinary Center for Economic Science George Mason University,

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Presentation on theme: "Temptation, Impulsiveness and Committment Daniel Houser Professor of Economics Director, Interdisciplinary Center for Economic Science George Mason University,"— Presentation transcript:

1 Temptation, Impulsiveness and Committment Daniel Houser Professor of Economics Director, Interdisciplinary Center for Economic Science George Mason University, Fairfax, VA

2 Temptation and Impulsiveness

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4 Impulse Buying Impulse buying is a sudden and powerful urge that arises within the consumer to buy immediately (Beatty and Ferrell 1998; Rook 1987). Impulsive purchasing is defined as involving spontaneous and unreflective desires to buy, without thoughtful consideration of why and for what reason a person should have the product (Rook 1987; Rook and Fisher 1995; Verplanken and Herabadi 2001).

5 Why Investigate Impulse Buying? Impulse buying is easier now than it has ever been. –In past times, one would usually need to wait several hours between seeing a product advertised and actually purchasing the product. This can be useful in reducing the impact of impulsivity on purchase decisions. –Now cash machines, television shopping and the internet make it easy to respond to any urge immediately.

6 Some Evidence Self-regulatory resources play an important role in affecting many types of behaviors (Baumeister et. al,1998; Baumeister and Ciarocco 2000; and many others, see Vohs 2006 for review.) Overeating Procrastination Underachievement Vohs and Heatherton (2000) find that dieters sitting next to a bowl of candy are subsequently less able to perform arithmetic as well as dieters who were seated away from a bowl of candy. Exerting self-control on one task renders it harder to exert self-control on a subsequent task.

7 The famous “marshmallow test.” Mischel and Ebbeson, with 4-year-old subjects. “Here is a marshmallow for you. I have to leave the lab for 10 minutes. If you can refrain from eating the marshmallow until I return, you can have a second marshmallow.” Results put children into three categories: –Some children did wait for the delayed reward. A predictor of later academic success! –Many children chose to take the lesser reward immediately. –A third group of children waited several minutes, only to end up eating the marshmallow before the researcher returned.

8 The Marshmallow Experiment Attention to the rewards strongly influenced the outcomes in the experiment. –Children who managed to distract themselves from the marshmallow (or other reward) were much more likely to “pass” the marshmallow test. –Follow-up research found that putting the marshmallow inside a desk drawer helped the subjects become much more successful at waiting.

9 Marshmallow Experiment with Aduluts? Goal is to discover a naturally occurring economic environment where: –Temptation plays an important role –Effects of exposure to temptation can be tested

10 Checking Out Temptation: A Natural Experiment at the Grocery Register Daniel Houser, George Mason University David H. Reiley, University of Arizona Michael B. Urbancic, UC-Berkeley

11 Grocery-store innovations have increased the time and attention customers spend with products. 1800s: General stores kept goods behind the counter. –Individual consumers presented their shopping list to the clerk. –Simple product packaging, for the clerk’s benefit only.

12 1916: Self-service stores invented. –At first, cramped shelves through which customers navigated one-way through a predetermined pattern. –Consumer packaging became important. 1936: Shopping carts invented. ­Carts (along with automobiles) increased the feasible size of grocery purchases. ­Customers could spend much more time comfortably browsing. Previously, only hand-carried baskets were available.

13 Behavioral psychologist John Watson made a second career of consulting on product placement in stores, including “impulse items” at the checkout counter.

14 Predictions to Test Assuming different individuals are characterized by different cases, the model predicts the following. (i) the frequency of tempting purchases increases as exposure duration increases (ii) some people will not purchase tempting goods even with long exposure (iii) some people will purchase tempting goods even with short exposure (iv) If there is uncertainty regarding exposure duration then the model predicts delay in consumption just as observed in the marshmallow task.

15 We made over 2800 direct observations of customers at the checkout aisle to test predictions (i)-(iv). Three stores: –Store 1: a large national grocery chain, located in a middle- income area of the city. (2042 observations) –Store 2: a more upscale chain store, located in a wealthier part of town. (423 observations) –Store 3: a local, independent grocery in a lower-income neighborhood. (326 observations) During spring 2002, undergraduate research assistants watched and recorded 2827 observations of customers in grocery checkout aisles in Tucson, Arizona.

16 Observations included an array of descriptive and quantitative statistics. Location, day of the week, time of day Length of time spent in line (until checkout began) Whether or not an impulse item was purchased (binary variable; multiple impulse items counted the same as a single item—at least one impulse purchase) Some demographic data (always gender & kids, sometimes race & age)

17 Following are some descriptive statistics of the observations: Store 1Store 2Store 3Aggregate TotalPurchasesTotalPurchasesTotalPurchasesTotalPurchases Males *53 (6.1%) (4.2%) (9.2%) (6.4%) Females (9.7%) (5.4%) (14.5%) (9.4%) Total (8.2%)42321 (5.0%)36241 (11.3%) (8.1%) Distribution of Sex and Purchases by Store (N = 2827) Distribution of Observations Where Children Were Present and Purchases by Store (N = 2827) Store 1Store 2Store 3Aggregate TotalPurchasesTotalPurchasesTotalPurchasesTotalPurchases With Males 526 (11.5%)40 (0%)72.5 (35.7%)638.5 (13.5%) With Females (19.1%)244 (16.7%)226.5 (29.5%) (20.1%) Overall (17.1%)284 (14.3%)299 (31.0%)25046 (18.4%) *Note: In each of the above tables groups of customers of mixed gender were treated as an appropriately proportioned fractional sex observation. Though the percentages above suggest that females were more likely to make impulse purchases, t-tests show that there is no significant difference due to gender. Instead, this effect reflects the fact that 74.8% of observations with children involved female customers.

18 As in the marshmallow test, customers often waited before picking up an impulse item in the checkout aisle. The behavior seen above is consistent with temptation theory.

19 The data directly suggest that time spent in line may affect the incidence of impulse purchases. Time in Line for All Observations and Given Purchase, by Store (N = 2827, times in mm:ss) Store 1Store 2Store 3Aggregate All Given PurchaseAll Given PurchaseAll Given PurchaseAll Given Purchase Min Time0:030:290:26 0:090:210:030:21 Max Time18:3514:0316:247:2012:426:4518:3514:03 Mean Time4:295:402:282:442:383:103:564:57 Mean time in line given purchase is a full minute (25.8%) longer than the mean for all observations, suggesting that longer wait times influence positively the frequency of impulse purchases. Could the direction of causation be the opposite of what we think? No, because we measure time until the cashier begins to ring up one’s purchases. Spending time to pick up an item would not increase my wait time, though it might possibly increase the wait time of those who come after me.

20 Logistic regressions confirm the positive effect of time in line on the frequency of impulse purchases. FEMALEKIDSSTOR2STOR3TIME FEM * TIME KIDS * TIME STR2* TIME STR3 * TIME FEM * KIDSConstant Reg I ** 0.174** ** Reg II ** **0.220** ** Reg III * **0.220** ** * Significant at the 5% level ** Significant at the 1% level Dependent variable: Purchase of an impulse item (0/1). Note the positive coefficient on time in line. The presence of kids also tends to increase the purchase probability. Kids and females tend to reduce the impact of time on purchase relative to males, though these effects are not statistically significant. Store 3 has more impulse purchases. Probit and linear-probability specifications produce qualitatively similar results. Standard errors in italics

21 implications for both academics and the grocery industry This study quantifies the effect of increased time in line on impulse purchases. A measurable, real-world implication of temptation theory. Though we did not attempt to measure intention, the choice data suggest that some purchasers changed their decisions and behavior over time due to temptation. Future research might benefit from choice data with surveys of impulse- item purchasers. These results may have concrete applications for grocers, especially since impulse items often earn stores their highest profit margins. Stores may wish to staff checkout aisles so that customers spend slightly longer before reaching the register (though not if it drives customers to competing stores). Since kids tend to increase impulse purchases, stores may wish to encourage the presence of children with their parents on shopping trips. Or, can stores distinguish themselves by having checkout lanes without these items?

22 Can temptation be controlled by using a commitment device?

23 Temptation and Commitment in the Laboratory Daniel Houser ∙ Daniel Schunk Joachim Winter ∙ Erte Xiao

24 Background Long literature on individual decision-making in dynamic choice situations Recent focus on temptation A critical feature of most theory is the possibility to “commit” to avoid a temptation There is little empirical data to inform the theory

25 Purpose of this paper To design a laboratory experiment that includes a good that is tempting in formal sense. To investigate how commitment costs affect decisions to consume tempting goods. To learn whether established measures of psychological disposition help to predict how people behave in tempting situations.

26 Definition of a tempting good Gul and Pesendorfer (2001, Econometrica) argue that “Set Betweenness” describes the preferences of an individual who struggles with temptation. Standard decision maker: x > y  {x} ~ {x,y} > {y} Gul and Pesendorfer: y is a temptation if we observe some individuals for whom {x} > {x,y}. Set Betweenness Axiom: {x} ≥ {x,y} ≥ {y}

27 Specific Goals (1) Design a laboratory experiment in which “Set Betweenness” is satisfied. – Come up with two alternatives x and y, each of which can be freely chosen, but such that some subjects will pay to avoid having y in their choice set. – That is, some subjects will make a costly commitment to x and avoid the tempting good y.

28 Specific Goals (2) Study the link between commitment and consumption of the tempting good. – Characterize the effect of commitment. Is the tempting good consumed less, in aggregate, when commitment is less expensive? – Connect behavior to scores on personality surveys.

29 Instructions Thank you for coming. You have already earned five dollars for arriving on time. These instructions explain how you can earn more money during the experiment. Today’s experiment involves counting. From time to time you will see displayed on your computer screen nine digits, either zeros or ones. Your task is to count the number of ones, and report that number in a box provided. You will have 15 seconds to provide an answer. Not providing an answer, or providing an incorrect answer, is counted as a mistake. If at the end of the experiment you have made mistakes on less than 30% of the counting tasks, then you earn $15 in addition to your show-up fee. If at the end of the experiment you have made mistakes on more than 30% of the counting tasks, then you earn $3 in addition to your show-up fee. This experiment will end at different times for different participants. Please do not leave the room, talk or otherwise distract other participants in any way until you are told that all participants have completed the experiment and you have exited the laboratory.

30 120 minutes of boredom Between the counting tasks, subjects face an empty screen with only a digital clock. The time between counting tasks is equally likely to be 1min, 2min, or 3min. The counting experiment lasts for 120 minutes. The experiment consists of three phases, but subjects were not informed (but this at the beginning of the experiments).

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33 Phase 0 Lasts 30 minutes, 15 counting tasks. Subjects count… 1, 2, or 3 minutes elapse between counting tasks… Subjects stare at blank screen between tasks…

34 Phase 0 After 30 minutes, at the end of phase 0: Some subjects have earned $8 (thus, $7 left to earn) Some subjects have earned $10 (thus $5 left to earn)

35 Phase 1 Lasts 45 minutes (12 counting tasks) Counting screens as before. “Temptation screen” (see next slide) is present 6 times, offering these choices: -Continue: Have a chance to earn the maximum $15 (i.e. $5 or $7 more) and keep the option to surf. -Commit: Have a chance to earn the maximum $15 (i.e. $5 or $7 more) but without the option to surf. The cost for this commitment is either $0 or $1. -Surf: Go to the internet.

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37 Phase 1 At the end of phase 1 subjects are told they have earned either $10 to that point (leaving $5 left to earn) or $15 (no more earning possible.) The $15 treatment is a check to ensure that our task is not more pleasurable than surfing. [Finding: All subjects for whom no more earnings were possible immediately went surfing.]

38 Phase 2 Lasts 45 minutes (12 counting tasks). Only for those who didn’t choose to surf at some point in the second period. Subjects who committed only see counting screen. Subjects who continued without commitment see counting and temptation screens.

39 The phases of the experiment

40 Hypotheses H0Surfing is a tempting item for some subjects. That is, some subjects pay a positive value to remove the temptation screens from their choice set. H1Allowing for decision error, most surfing and commitment decisions occur at the first opportunity, and then monotonically decline. H2In phase 2, when the benefit to counting is lower (sometimes zero), there are a greater number of decisions to surf. H3The frequency of commitment is lower when the commitment cost is higher.

41 Data 88 subjects, equal numbers in each treatment. Subjects are GMU undergrads recruited using standard procedures – 42% female – 37% soc. science, 35% econ/business, 17% nat. science, 11% others Subjects were in the lab for 2.5 to 3 hours, earned: – $5 for participation. – $3 to $15 for the temptation task, less any commitment costs. – $3 for the survey.

42 Results: {count} ≥ {count, surf} ≥ {surf} 32 subjects (36%) chose to commit to counting for the entire two hours. 10 of these subjects paid to make this commitment. 38 subjects (43% of all) succumbed to the temptation. 11 of those succumbed right at the beginning. 18 subjects (21%) neither committed, nor succumbed to the temptation (i.e. resisted without commitment). Counting: High accuracy rates, did not vary over treatments, or with time. No subject below 70% correct.

43 Results: commitment cost Commitment is less frequent when there is a cost to it Fraction of subjects who chose to commit (at first opportunity)

44 Results: value of commitment Commitment is more likely when its value is higher Fraction of subjects who chose to commit (at first opportunity)

45 Results: consumption decisions Consumption of the tempting good is independent of commitment costs. Fraction of subjects who chose to surf (at first opportunity)

46 Results: consumption decisions Consumption of the tempting good is less frequent when its cost is higher. Fraction of subjects who chose to surf (at first opportunity)

47 Psychological Measures – Big 5 [10 items], Costa et al. (1980): 5 personality dimensions – Need for Cognition [18 items], Cacioppo et al. (1984) Tendency to engage in effortful cognitive tasks – CFC [8 items], Strathman et al. (1994) Tendency to consider the future – Mach IV scale [20 items], Christie (1970) “Machiavellism”  not analyzed here Four disposition measures are elicited for each subject:  Do these measures help to predict behavior in tempting situations?

48 Results: Commitment Decision Commitment cost and remaining value of resisting temptation are significantly related to commitment decision. Probit regression Number of obs = 88 Pseudo R2 = Commitment Decision | Coef. P>|z| Remaining value | Commitment cost | Female | NatScience-Major | EconBusiness-Major | SocScience-Major | CFC-Score | NC-Score | Big5-Extraversion | Big5-Agreeableness| Big5-Conscientiousness | Big5-Neuroticism | Big5-Openness | Constant | Psychometric dispositional measures are not related to commitment decision.

49 Results: Succumbing to Temptation Commitment cost and remaining value of resisting temptation are not related to “succumbing to temptation”. Probit regression Number of obs = 88 Pseudo R2 = Succumbing to Temptation | Coef. P>|z| Remaining value | Commitment cost | Female | NatScience-Major | EconBusiness-Major | SocScience-Major | CFC-Score | NC-Score | Big5-Extraversion | Big5-Agreeableness | Big5-Conscientiousness | Big5-Neuroticism | Big5-Openness | Constant | CFC-score: The higher your consideration for future consequences, the less likely you succumb to the temptation.

50 Summary Our laboratory design includes a good that satisfies “Set Betweenness”: Some subjects pay a commitment cost to avoid temptation. Commitment is sensitive to its cost, but the likelihood of succumbing to temptation is not related to commitment costs. This is predicted by the G-P model. Patterns of commitment and consumption over time are also in line with G-P predictions. Dispositional measures are correlated with behavior in situations that require self-control.

51 Temptation at Work: A Field Experiment on Willpower and Productivity Alessandro Bucciol University of Amsterdam and Netspar Daniel Houser George Mason University Marco Piovesan University of Copenhagen

52 MOTIVATION Temptations are a largely unavoidable part of life. Resisting temptation is usually seen as a virtue. However, delaying gratification can detrimentally impact performance on subsequent tasks (Vohs and Heatherton, 2000) We develop a simple model connecting temptation, willpower and worker productivity. We test that model using a field experiment with children 52August, 2010

53 53 GOAL Examine the role of willpower in determining the effect of a prohibited tempting item on work productivity Novelty of our approach: –Labor output is the outcome variable –Productivity is rewarded –Participants are children August, 2010

54 54 CLOSELY RELATED LITERATURE Baumeister et al, (1998) Vohs and Heatherton (2000) Burger, Charness & Lynham (2009) Bryan, Karlan and Nelson (2009) August, 2010

55 CHILD DEVELOPMENT Child development literature argues children seven and younger typically find it difficult to delay gratification Children 11 and older have developed delay of gratification strategies (Mischel and Metzner, 1962) 55August, 2010

56 PREDICTIONS Productivity of young children (under age eight) will be detrimentally impacted by resisting a temptation –Resisting temptation depletes willpower, leaving them with less psychic energy to focus on the productivity task Productivity of children age 11 or older will be positively impacted by resisting a temptation –Older children distract themselves from the temptation by focusing on how to perform task better 56August, 2010

57 57 FIELD EXPERIMENT Who Children aged 6-13 Where In the summer camp of CUS Padua (Italy) Outdoors When Two warm days of July 2008 (temperature: o F) 11 sessions between 9.00 am and 5.30 pm local time Groups in each session roughly homogeneous in age How Participants complete a repetitive paper-folding task August, 2010

58 58 1. SPLIT AND INSTRUCTIONS We split each group randomly into two sub- groups: Control Treatment (CT) group Food Treatment (FT) group We seat the two sub-groups separately We provide them with identical instructions on how to complete the game * Subjects were not attending to the “tempting” items while participating in the instructions. We leave them seated in their separate areas for five more minutes – in (FT) need to resist! 5 min. August, 2010

59 59 FOOD TREATMENT Only the sub-group in FT is seated near a table with snacks and drinks Prior to the instructions the children in FT are informed that the snacks and drinks have been reserved for a different event in the same day The children in FT are “tempted” by the snack items only during the final five minutes! August, 2010

60 60 2. REJOIN AND GAME The two sub-groups rejoin and go to a long table to complete the task The task is: –To fold a pre-printed sheet in three parts –Highlight the star –Attach a label –Close the sheet with a paper clip 10 min. August, 2010

61 University of Padua61August, 2010

62 62 THE GAME August, 2010

63 63 REWARD 1 token for each sheet accurately folded At the end of the game each kid receives a certificate showing the number of tokens she won Children use their certificates to get items from a menu of food, ice cream and drinks available at the summer camp’s clubhouse 1 token ≈ 10 eurocents August, 2010

64 64 OBSERVATIONS Prior to conducting the experiment we received informed consent from the parents Acceptance rate: 82.27% In such occasion we collected basic information on each child: Average statistics Whole sample CTFT N. Sheets folded Age % Female % Better at school Number of siblings Body Mass Index (BMI) N. Observations August, 2010

65 65 FINDING #1 The exposure to a prohibited tempting item significantly reduces productivity for children younger than 8, and significantly increases it for children older than 10 CTFTTest (1)Obs.(2)Obs.(1) = (2) Age under ** Age between 8 and Age over Whole sample ** = reject at 5% in favor of (1) > (2) August, 2010

66 66 FINDING #2 The increasing relation between age and FT is robust to demographic and experimental controls N. Sheets folded FT Age under Age over *** Female Better at school N. Siblings0.1536*** BMI FT*Age under * FT*Age over FT*Female0.3423* FT*Better at school FT*N. Siblings * FT*BMI Day * FT*Sessions before break FT*Sessions before meal Constant1.0118*** Alpha N. Observations123 Log-Pseudo-likelihood * = significant at 10% ** = significant at 5% *** = significant at 1% Method: negative binomial regression August, 2010

67 67 FINDING #3 The elasticity of FT on productivity is significantly negative for children younger than 8, and is significantly positive for children older than % 4.72% 12.28% August, 2010

68 68 FINDING #4 The elasticity of FT on productivity is significantly negative for boys, and is significantly positive for girls % 21.18% August, 2010

69 DISCUSSION Temptations are a largely unavoidable part of today’s workplace (e.g., Internet) but are detrimental to productivity Offices prohibit them Employees have to wait until the workday ends The use of willpower to delay gratification can impact performance on subsequent tasks (Vohs and Heatherton, 2000) What is optimal office policy for promoting productivity? 69August, 2010

70 70 SUMMARY Our results are consistent with predictions implied by the child development literature combined with our simple model of temptation’s effect on productivity Resisting temptation reduces productivity among youngest children Older children use the repetitive task to distract themselves from the tempting items Prohibiting a tempting activity may eliminate the productivity cost of the temptation August, 2010

71 71 Thank You! August, 2010


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