Recurring Goals: The Effect of Divisibility and. Goal Attainment on Motivation

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1 Recurring Goals: The Effect of Divisibility and Goal Attainment on Motivation XAVIER DRÈZE JOSEPH C. NUNES * DRAFT Please do not circulate without consent of the authors. 1

2 * Xavier Drèze is Assistant Professor of Marketing, the Wharton School of the University of Pennsylvania, Philadelphia Joseph C. Nunes is Associate Professor of Marketing, Marshall School of Business, University of Southern California, Los Angeles, CA Both authors contributed equally and are listed in alphabetical order. Questions should be directed to either Xavier Drèze at or Joseph C. Nunes at 2

3 ABSTRACT The growth in popularity of loyalty programs has given rise to hundreds of alternative currencies (e.g., frequent flier miles, credit card points) firms issue to consumers to reward specific purchase behavior. Yet how consumers can spend these currencies is limited by the firm, which decides how many rewards to offer and at what levels. The number of unique exchange opportunities for a currency is its divisibility. A reward level that appear attainable to consumers can serve as a recurring goal that even after being attained, can serve as an objective towards which consumers continue striving. This research shows the importance of divisibility in designing effective loyalty programs, whereby too little divisibility presents a an award level that is too challenging while too much divisibility presents too lofty a reward level that fail to serve as a goal. This research documents how success within a recurring goal framework engenders feelings of self-efficacy that elevates effort in successive attempts. Although it diminishes, an increase in motivation continues to occur even after several successes. 3

4 Whether it is as mundane an endeavor as catching an early train or as exceptional as training to run a marathon, people frequently work towards goals that once attained continue serving as goals to which they aspire. Hectic commuters frequently scramble each morning to make it to the station for the first train out. Dedicated runners often begin training for the next race the day after they finish the previous one. In many cases, reaching a goal does not lead to the cessation of the activities that led to its attainment; in fact, it may inspire the person to work even harder. When reaching a goal is not an end unto itself, success provides valuable feedback regarding the likelihood of future success. If I did it once, I can do it again is a common mantra reflecting how success affects a person s perceptions of his or her own abilities. Yet even if people believe they can do it again, do previous successes affect how hard they work towards the goal the next time? This research focuses on the effects of goal attainment on people s interest in, and effort towards, reaching the same goal again. More specifically, we ask whether success impacts motivation on consecutive attempts to complete the identical task for the same reward. We are concerned with a specific type of task, one which people attempt repeatedly, with the same outcome as the goal and the same reward as the incentive. This is what we refer to as a recurring goal. A limitation of past research on goal pursuit and motivation is a common assumption that goal attainment is a singular endeavor, such as losing weight or learning a language. Knowledge of results, a common form of feedback, typically occurs before the goal is attained and extrinsic rewards are purposefully absent. In this work, we explore the impact of goal attainment as feedback and its impact on successive attempts towards the same goal. Recurring goals are of particular interest to 4

5 marketers given that many loyalty programs offer identical awards for a specific amount of purchase activity. For example, consider the Baby Club, tested by the grocery store chain ABCO. Participating consumers received a Baby Buck for every dollar spent on baby products (Dreze and Hoch 1998). The program offered a $10 voucher for every $100 in baby products purchased by its members. After earning the desired reward (i.e. $10 voucher), the customer faced the decision of whether or not to continue consolidating his or her purchases with ABCO. The ability of loyalty program awards to serve as goals and motivate consumers depends in large part on the number of rewards offered and the amount of purchase activity required to earn a particular reward, which is under the control of the firm. It is likely the results of the test would have been different had ABCO offered a $5 voucher in exchange for 50 Baby Bucks, or $100 for 1,000 Baby Bucks. The number of unique exchange opportunities available utilizing a particular currency is what we refer to as divisibility. A reward program s divisibility is of critical importance to firms that utilize alternative currencies to create idiosyncratic reward structures (e.g. frequent flier miles, Hilton Honor points, AmEx Reward points). Consumers accumulate, budget, and spend alternative currencies much like they would traditional paper money (Drèze and Nunes 2004). Money, however, is much different in that it is almost always perfectly divisible. One can just as easily spend $500, $50 or 50 cents. Economists consider money divisible when in particular, a money holder can exchange any fraction of his money holdings (Shi 1997). Alternative currencies are neither universally accepted, nor can they be spent in any increment (e.g. American does not accept United miles, and opportunities to 5

6 redeem 1,200 miles are not readily available). Thus, alternative currencies are unlike legal tender in that their use is constrained; they are far from perfectly divisible. The construct of divisibility is important theoretically because it dictates when and how a reward might serve as a recurring goal. For example, all else equal, a program in which awards are available at 50 point increments (i.e. 50, 100, 150 etc.) is more divisible than a program where awards are available at only 100-point increments. However, a program in which awards are available at 50- and 100-point increments is not more divisible than a program where awards are available at only 50-point increments. Adding another reward at 75 points would increase divisibility, while adding a reward for 150 points would not. In short, divisibility addresses the variety of spending opportunities within a loyalty program. Marketing managers are confronted with the task of determining how divisible a rewards program should be. Most often, as divisibility increases, the size of the reward decreases such that the total payoff (i.e. reward) remains constant; a change occurs only in the rate at which it is distributed. In this research, we argue that increasing divisibility can allow for increased loyalty especially among those with low asset levels in the alternative currency, while too much divisibility can be de-motivating as lofty reward levels may fail to effectively serve as goals. We show that divisibility affects how easy or difficult a reward is to acquire and that success can serve as performance feedback, impacts motivation in future attempts. In our framework, reaching a reward level is synonymous with attaining a recurring goal. Success early on provides positive feedback about the individual s aptitude for a particular task. We find this feedback enhances feelings of self-efficacy, which encourages repetition while bolstering effort in successive undertakings. Consequently, 6

7 marketers who design reward structures with well-designed recurring goals (i.e. rewards) will benefit from the impact early successes have on increasing subsequent effort and thus encouraging what most consider customer loyalty. The remainder of this paper is organized as follows. A review of the literature summarizes the relevant findings regarding goals, feedback and motivation. Study 1 reveals the importance of divisibility, or the number of rewards and the level at which they are awarded, on the ability of a loyalty program to inspire consumers to shop at a particular store. Study 2 documents the impact of success within a recurring goal framework on effort, revealing how the benefit of increased divisibility found in Study 1 positively impacts people s effort in successive attempts at reaching the same goal. Study 3 examines the underlying cause of the increased motivation found in Study 2, documenting how goal attainment impacts self-perceptions of individual aptitude for a particular task. Study 4 documents these effects using data from a real world frequent flier program. The results illustrate empirically how effort increases as fliers redeem successive rewards. We conclude by pointing out some of the limitations of this work and suggesting avenues for future research. Rewards, Goals, Feedback and Effort Much of what is known with respect to goal attainment and motivation emerged from the behaviorist movement at the beginning of the century. Thorndike s (1927, 1931) classic theory of learning focused psychologists attention on rewards and punishment as feedback. His Law of Effect stated that behaviors that are rewarded tend to reoccur, while his Law of Exercise stated that the connection between a behavior and its consequence is 7

8 made stronger through repetition. Thorndike lost confidence in his Law of Exercise after concluding that without feedback, repetition does not lead to improved performance. Thorndike s work was the impetus for a stream of research which followed exploring the impact of knowledge of performance as feedback. That work was summarized in Ammons (1956) influential review of the feedback literature, which concluded the most common effect of knowledge of performance is to increase motivation (p285). More than 100 articles since refer to Ammons statement as if it were fact, yet to many his conclusion too simplistic given what we know today about feedback and motivation. In their 1996 review of the feedback literature, Kluger and DeNisi (1996) assert that Ammons claim was based on evidence collected informally and inferred from other findings (Ammons 1956, p. 285). They argue Ammons equated positive attitudes in response to receiving feedback information with actual task motivation, while presenting no evidence of the latter (Kluger and DeNisi, p. 256). The empirical evidence was found to be lacking, and research since has found the relationship to be far more complicated. This is due in part to how one defines feedback. Feedback typically comes in one of two broad forms: outcome feedback, referring to performance outcomes, and process feedback, which refers to the manner in which an individual implements a task strategy (Earley, Northcraft, Lee and Lituchy 1990). Goal setting theory (Latham and Lock 1991) asserts that people compare outcome feedback to a goal, resulting in a feedback sign (positive or negative). In the simple case, effort is presumed to increase when the feedback sign is negative (suggesting a shortfall), and decrease or be maintained when the sign is positive. However, a positive feedback sign may signal an opportunity for self-enhancement. Accomplishments that strengthen self- 8

9 efficaciousness are likely to lead performers to raise their standards and consequently improve future performance. And performance accomplishments are an especially influential form of efficacy information because they are based on personal mastery experiences (Bandura 1977). In fact, Bandura and Cervone (1983) argue that goal attainment that matches or surpasses personal standards creates self-satisfaction that serves as positive inducements for further pursuits. In summary, success can serve as positive feedback that increases perceptions of self-efficacy, which, if elevated, increases motivation the next time the same task is encountered. We should point out that in most goal-setting studies the term goal refers to attaining a specific standard of proficiency at the task rather than the successful completion of a task (Locke, Saari, Shaw and Latham 1981). These studies explore how outcome feedback (e.g. 10 successes in two minutes) affects improvements in people s skills. In this research, the goal is an achievement level accompanied by an extrinsic reward, and our interest lies in changes in effort rather than proficiency. In a consumer context, it is typically less likely that individuals can become more proficient at attaining purchasing goals. In addition, research on how individuals set goals for themselves is abundant, while research exploring how to design a goal framework to best engage others is scarce. We are the first to explore how the structure of reward levels, or divisibility, impacts goal attainment within a recurring goal framework, and the resulting impact on future motivation. Recall that divisibility holds total payoff (i.e. reward) constant but changes the rate at which it is distributed. In this way we hold the cost to the firm constant; it is not the size of the reward, but the frequency of distribution that changes. Therefore, when we 9

10 increase the frequency at which we reward consumer there is a proportional decrease in the reward size. One might expect that, ceteris paribus, the customer would prefer smaller increments paid out more frequently. Mento, Cartledge, and Locke (1980) have shown that individuals are more likely to work towards a goal when they have high rather than low expectation of reaching it. The long-standing belief in the goal-setting literature is that there exists a positive, linear relationship between goal difficulty and performance, assuming people believe they can reach the goal (Locke 1968). Yet more attainable rewards are not necessarily more motivating. If one makes a program too divisible, then the goals are unlikely to be challenging enough, and the rewards not large enough (Locke, Shaw, Saari and Latham 1981; Pritchard and Curtis 1973), to motivate consumers. This leads to Hypotheses 1a and 1b: H1a: Increasing divisibility will increase persistence for consumers for whom the reward becomes attainable. H1b: There is an upper bound such that too much divisibility will result in too frequent and too small of rewards which no longer serve as motivating goals. We test these hypotheses directly in Study 1. STUDY 1 THE RIGHT AMOUNT OF DIVISIBILITY In this study, we explore consumers reactions to differing degrees of goal difficulty or attainability. Study 1 illustrates how the efficacy of recurring goals diminishes with either too little or too much divisibility. Too little divisibility presents a task that is too 10

11 challenging such that there is little hope of succeeding, while too much divisibility presents tasks that are non-challenging, such that they fail to serve as goals. Method Respondents. Participants in this study were 300 undergraduate business students at a major West Coast university who completed the survey voluntarily. Seven respondents failed to answer the questions resulting in 293 usable surveys. Stimuli and design. We utilized a 3 (divisibility: reward every $100, $500, or $1,000 spent) x 4 (accumulated spending: $25, $425, $525, or $925) between subjects, full factorial design. Participants were asked to imagine that they had moved to a new city and there were two large grocery stores near their house carrying essentially the same assortment of goods. The stores were no different in terms of price, layout, cleanliness, or service. However, respondents were told the two grocers did differ significantly in their frequent shopper programs. Store B offers a 10% cash refund on every dollar spent at the store, which is issued immediately. For example, if a shopper spends $50, they would receive a $5 refund immediately (i.e., thereby only paying $45). Store A offers cash back at various increments. These increments or reward levels varied according to the degree of divisibility ($100, $500, or $1,000). It is important to note that the cumulative rewards offered by the two stores are identical; only the frequency at which the reward is dispersed differs. The first question in the study asked them which store they believed they would shop at more frequently. The study went on to describe their progress towards the goal at 11

12 Store A. It explained that: one store is near the freeway, which you often take to work, while the other is closer to a main surface street, which you take to and from work instead of the freeway when you leave during rush hour. While they are equal distances from your house, they are in opposite directions. As a result, you have shopped at both stores repeatedly during the past six months, as convenience has often been a factor. This scenario was consistent with the metropolitan community in which most students resided. We then manipulated accumulated spending by stating: at store A you have currently accumulated $25 [$425, $525, or $925] in purchases. (Remember, when you reach an accumulated spending of $100 [$500, $1,000] they will refund $10 [$50, $100].) The scenario then told them that it was the weekend (rush hour would not be a factor) and asked them to imagine that they were heading out to buy $50 in groceries for a party they were planning that evening. They were asked to indicate at which store they would shop. There are three fundamental predictions we make based on our theorizing. First, in accordance with the goal gradient effect (Hull 1932, Brown 1948, Nunes and Drèze 2006), when the only reward available is at $1,000, consumers will become increasingly inclined to go to Store A as their accumulated spending increases from $25 up to $925. In other words, the value of each dollar in accumulated spending increases as they near the $1,000 goal. Second, in accordance with Hypothesis 1, by inserting a sub-goal at $500, we expect the propensity to visit Store A (i.e., continue progress towards the goal) to increase for those who were far from the $1,000 goal. In particular, we expect those with an accumulated spending level of $425 to be more likely to visit store A when rewards are issued for every $500 spent than when they are issued for every $1,000 spent. In addition, changing the payoff scheme as such should also make shopping at Store A more 12

13 attractive at the $425 accumulated spending level (in anticipation of the goal) than at the $525 accumulated spending level (after achieving one goal and far from the second). Third, in accordance with hypothesis 1b, we expect a small increase in divisibility (i.e., going from rewards issued for every $1,000 to every $500) to increase the overall attractiveness of the program. Conversely, we expect the large increase in divisibility (from rewards issued for every $1,000 to every $100) to negatively impact the overall attractiveness of the program, as the rewards are no longer viewed as goals worth striving for. Results Four of the 293 respondents (1.4%) indicated that they would regularly visit Store A suggesting that Store B was the preferred option in the absence of any accumulated spending. Figure 1 depicts the reported probability of going to Store A for each of the three reward levels ($1,000, $500 and $100) and each of the four levels of accumulated spending ($25, $425, $525, $925). As is evident from the graph, the likelihood of going to Store A is affected by both the level of reward and accumulated spending. As predicted, the likelihood of going to Store A increases monotonically in the $1,000 reward condition. Tests of proportion revealed that those who spent $525 were more likely to choose Store A than those who spent only $25 (PA 525 = 28% versus PA 25 =4%, χ 2 = 3.9, p <.05), and those who spent $925 were more likely to go to Store A than those who spent $525 (PA 925 = 100% versus PA 525 = 28%, χ 2 = 10.2, p <.01). Although the likelihood of choosing Store A in the $425 condition (16%) falls halfway between those 13

14 who spent $25 (4%) and those who spent $525 (28%), neither difference is statistically significant. Nevertheless, the overall pattern of results supports our first prediction. Insert Figure 1 about here With regards to our second prediction, by increasing the divisibility, we increased the likelihood of going to Store A for those with $425 in accumulated spending from 16% when the reward level was at $1,000 to 76% when it was reduced to $500 (χ 2 = 15.3, p <.01). The likelihood of going to Store A was also larger for those who spent $425 and had not yet achieved the $500 goal than for those who spent $525 and had earned the reward at $500, but were now $475 away from earning another $50 (PA 425 = 76% versus PA 525 = 44%, χ 2 = 5.1, p <.05). The average likelihood of going to Store A was largest (57%) when divisibility was moderate, and shoppers were rewarded for every $500 they spent. The average likelihood was smallest (11%) when the divisibility was increased to reward shoppers for every $100 they spent. The loyalty program that rewarded shoppers for each $1,000 they spent fell in between at 36%. In other words, overall the $500 reward level induced a higher likelihood of visiting Store A than either the $1,000 reward level (χ 2 = 8.6, p <.01) or the $100 reward level (χ 2 = 15.2, p <.01). Discussion Study 1 clearly shows the importance of divisibility - the number of rewards and the level at which they are awarded - on the ability of a loyalty program to incite consumers to shop at a particular store. While the overall amount a shopper would 14

15 receive remains constant for anyone spending $1,000, two effects are at play here. If one makes the program too divisible (but not perfectly, as in Store B) then the rewards structure is unlikely to be attractive enough to induce loyalty. In contrast, if the program is not divisible enough, it may offer great incentives, but only for those who are already committed to the program, and thus will be de-motivating for relative newcomers. An adequate balance must be struck between the two forces. Another point made salient by Study 1 is that loyalty programs benefit from a strong memory effect people take into consideration whether they have successfully reached the goal in the past. Indeed, in the absence of a memory effect, one would expect that in the $500 reward condition the percentage of respondents who would travel to Store A would be equal in the $25 and $525 accumulated spending conditions (each $475 away from earning $50). Likewise, we would expect the percentages to be equal in the $425 and $925 accumulated spending conditions (each $75 away from earning $50). This is not what occurred. Instead, those who accumulated $525 in spending were more likely to choose Store A than those who accumulated $25 in spending (44% versus 12.5% respectively, χ 2 =5.35, p <.05). Similarly, those who accumulated $925 in spending were marginally more likely to choose Store A than those who accumulated $425 in spending (96% versus 76% respectively, χ 2 = 3.11, p <.10). It is important to remember that this effect did not occur in the $100 reward condition indicating that consumers do not seem to derive any secondary benefits from achieving easy goals. These results are consistent with our theorizing that goalattainment, which is a function of divisibility, provides a self-enhancing opportunity that increases motivation for recurring goals. It is worth pointing out that in this study 15

16 providing a figure for accumulated spending that implied respondents had successfully reached the reward level previously was enough to boost commitment towards Store A. We should also highlight the relative size of the effect. For example, at the $525 level, the respondents in the $500 condition were actually more likely to go to Store A then the respondents in the $1,000 condition, even though their accumulated spending is lower ($25 vs. $525). It appears success acted as a motivator in Study 1. These findings lead us to Hypotheses 2a and 2b: H2a: Successful goal attainment will lead to an increase in motivation in successive attempts at a recurring goal. H2b: The impact of success will attenuate with repeated successes. We test these hypotheses directly in Study 2. STUDY 2 THE IMPACT OF SUCCESS ON MOTIVATION In Study 2, we investigate the impact of goal attainment on an individual s motivation to pursue the identical goal again. To do so, we utilize the same scenario as in Study 1, but vary the number of times the respondent has succeeded. In this way, we directly test the relationship between success and motivation to continue shopping at the same store, and examine the impact of repeated successes (i.e. more than one). Certainly, one success may be expected to impact motivation, as one learns that goal attainment is feasible. However, if a second or tenth success were to impact motivation, this would be 16

17 more reflective of self-enhancement, or an updating of one s owns abilities with the particular task structure. Method Respondents. Participants in this study were 80 graduate business students at a major West Coast university who completed the survey voluntarily. Three of the studies were either incomplete or unusable, leaving us with 77 data points. Stimuli and design. We utilized a single factor with four levels of accumulated spending ($25, $1,025, $2,025, or $10,025) between subjects design. The scenario was identical to that of Study 1 except the reward level was held constant at $1,000 whereby shoppers would be rewarded with a $100 rebate for every $1,000 in spending. In each condition, respondents were $975 in spending away from their goal. The only thing that changed was whether they had yet to achieve success ($25), had achieved success once ($1,025), twice ($2,025) or ten times ($10,025). The dependent variable was store choice, just as it was in study 1. In line with hypothesis 2a, we expect the propensity to travel to store A to increase with each success (i.e. 0, 1, 2 and 10 respectively); the probability for those who have spent $10,025 will be greater than for those who have spent $2,025, and so on. However, we do not expect the effect to go on forever. Rather, we expect any increase in the percentage saying they would travel to Store A to decrease in line with Hypothesis 2b. 17

18 Results The first question in the study asked them which store they believed they would shop at more frequently. Only 4% of respondents said they would frequent Store A where they would need to spend $1,000 before earning cash back rather than Store B where they would receive a 10% cash refund on every dollar spent. This provides a baseline for our four data points. Figure 2 depicts the reported probability of going to Store A for each of the four levels of accumulated spending ($25, $1,025, $2,025, or $10,025). To test hypotheses 2a and 2b we ran a logistic regression using accumulated spending and spending-squared as independent variables. The regression is significant (χ 2 = 15.94, p <.01). In support of hypothesis 2a the coefficient for accumulated is positive and significant (χ 2 = 5.4, p <.05). In support of Hypothesis 2b, the coefficient for the squared term is significant and negative (χ2 = 3.9, p <.05). Insert Figure 2 about here Discussion In Study 2, we find goal attainment has a positive effect on subsequent effort, but that the increase in motivation diminishes as the number of successes increases. In this study, the total reward remains constant - $100 for each $1,000 in spending yet respondents were more likely to steer their purchases towards Store A as the number of successful redemptions increased. In Studies 1 and 2 the effects are consistent with our theorizing that success in attaining a goal serves as feedback that increases motivation. However, we theorize that success acts as positive feedback to boosts self-efficacy. It is 18

19 this self-efficacy that increases people s perceived likelihood of success in the future, and in turn, effort. This leads us to Hypothesis 3: H3: Successful goal attainment within a series of recurring goals increases one s perceived ability of reaching the same goal again. In Study 3, we test the linkage between goal attainment, self-enhancement or selfperceptions of aptitude, and motivation. We also replicate the results of Study 2 utilizing a laboratory task, but in a more natural environment, in which respondents exerted actual effort and earned real rewards. STUDY 3: THE EFFECT OF GOAL-ATTAINMENT ON SELF-PERCEPTIONS Study 3 was designed to gauge how divisibility, or the frequency at which rewards are distributed, impacts perceptions of self-efficacy. The study was presented as a game whereby the respondents goal was to determine whether the experimenter was lying by judging his or her facial expression. Method Subjects. Participants were 70 undergraduate business students at a West Coast university who participated in this along with several other studies for course credit. Stimuli and design. Respondents were brought into the lab under the premise that they were participating in a study on their ability to interpret facial expressions. It was explained that in business just as in poker, people make judgments as to the credibility of a statement based on subtle facial cues. The study proceeded as follows. Each respondent 19

20 was presented with the 8 of diamonds from a standard deck of cards. The experimenter explained that he or she would draw a card randomly from the deck (hidden behind a screen), and no matter what card was drawn, would tell the respondent that the card was higher than the eight of diamonds. This implied the card drawn was either a 9, 10, jack, queen king or ace. The respondent s task was to determine whether the experimenter was telling the truth (i.e. it was indeed higher) or a lie (the card was actually a 2, 3 4, 5, 6 or 7). As there are an equal number of cards below and above the 8, on average their success rate should equal 50%. That is, it was explained, unless the person can detect a lie by the experimenter s facial expression. They were told expert poker players can identify when a person is bluffing two-thirds of the time, or in 66% of trials, on average. The design was a single factor experiment with two conditions: 10 trials or 30 trials (cards drawn). Those in the 10-trial (30-trial) condition were told that if they exceeded 66% correct, or were successful in at least 7 out of 10 trials (21 of 30) they would receive a $4 ($12) reward. Before commencing the task, respondents answered several questions regarding their perceived ability to determine whether someone is lying based on facial cues and their perceived likelihood of success. Both groups answered the same questions after completing 10 trials (a successful completion for those in the 10- trial condition). Those in the 10-trial condition were asked to repeat the task and both groups completed the questionnaire after 20 trials (2 successful completions and 2/3rds progress respectively). Respondents rated their ability to determine whether someone is bluffing and their ability to read facial cues on an 11-point scale (-5 indicating much worse than average and +5 indicating much better than average). They then rated how likely they believed they were to score 66% or better, and thus perform as well as 20

21 professional poker players, on a 7-point scale where 1 indicated not at all likely and 7 indicated extremely likely. The experimenter utilized a screen to hide the cards drawn from the deck, which unbeknownst to the respondent included a hidden pocket containing a pre-ordained sequence of cards. This was done to insure participants got 7 of their first 10 responses, and 7 of their second 10 responses correct. Accordingly, those in the 10-trial condition completed the task successfully twice while those in the 30-trial condition experienced the exact same sequence of success and failure in terms of trials, but were only one-third and two-thirds of the way along when asked to assess the effect of feedback on their aptitude. Objectively, all respondents received identical feedback in terms of the number of trials they completed successfully. All that differed was the frequency at which they were rewarded the task divisibility ($4 after 10 trials or $12 after 30 trials). We predicted, in accordance with Hypothesis 3, that those in the more divisible (10-trial) condition who reached the goal and received a reward would experience a greater change in their perceptions of self-efficacy. Results Three participants in the 10-trial condition did not want to perform the task a second time; hence we have only 32 responses for post 20 evaluation measures in that condition. Because the measures utilized different scales (-5 to +5 for Bluffing and Facial and 1 to 7 for Success ), we standardized the data before starting the analysis. Further, we collapsed the three measures into one given the high degree of agreement between them (α =.83). 21

22 To test for effective randomization, we compared the pre-measurement (selfassessments before beginning the task) across conditions. We do not find any significant differences across cells (M 10,pre = -.49 vs. M 30,pre = -.64, F =.44, n.s.). Our within-subject design allows us to normalize each subjects score by subtracting each subject s pre-task measurement rating (their self-assessment before receiving any feedback) from later measurements (after 10 and after 20 trials) before making any comparisons across conditions. The average changes in ratings are shown in Figure 3. Insert Figure 3 about here An ANOVA reveals a change in perceptions of self-efficacy that varied across conditions (M 10 =.70 vs. M 30 =.43, F = 13.68, p <.01), a significant change in these perceptions as respondents completed more trials (M pre vs. M +10 vs. M +20, F = 60.26, p <.01) as well as a significant interaction between the two (F = 3.74, p <.05). Individual contrasts show that there is a significant improvement in perceived ability after 10 trials in both conditions (M 10,pre = 0.0 vs. M 10,+10 =.92, p <.01; M 30,pre = 0.0 vs. M 30,+10 =.59, p <.01). As predicted, this increase is larger for those who viewed 70% correct after 10 trials as completing the task successfully (M 10,+10 =.92 vs. M 30,+10 =.59, p <.01). The gap in self-perceptions widens further after 20 trials (2 successful completions vs. 2/3 completion); M 10,+20 = 1.17 vs. M 30,+20 =.69, p <.01). Further, the changes in ratings from 10 to 20 trials is marginally significant in the 10-trial condition (M 10,+10 =.92 vs. M 10,+20 = 1.17, p =.056) while it is not in the 30-trial condition (M 30,+10 =.59 vs. M 30,+20 =.69, p =.42). Finally, it is worth noting that the change for those in 30-trial condition 22

23 after 20 trials was less than the change for those in the 10-trial condition after only 10 trials, and this difference was marginally significant (M 30,+20 =.69 vs. M 10,+10 =.92, p =.068). Discussion In this study, we examined the impact of divisibility as it pertains to perceptions of self-efficacy. When the task was divided such that rewards were distributed more frequently, and goals reframed at lower thresholds, we find equivalent rates of success lead to much greater changes in self-perceptions. Successfully completing 7 of 10 trials did far more to boost both one s perceptions of their own face-reading skills and their perceived likelihood of success in the future when the task was complete and a fraction of the reward ($4 of $12) was awarded. Thus, we find unequivocal support for hypothesis 3. STUDY 4: RECURRING GOALS, SUCCESS AND EFFORT IN THE REAL WORLD In Study 4 we again examine the impact of goal attainment on an individual s motivation to pursue the identical goal again. However, we investigate consumers response to recurring goals empirically in a real world setting. To do so, we utilize frequent flier program data obtained from a major international airline. The data include information about 8.5 million flights taken by 1.46 million program members over the course of a 2-year period (an average of about 6 flights per person). The data include all flights on that carrier by program members, who were pre-screened for having redeemed at least one award flight between May 2004 and May We limited our analysis to 23

24 fliers who had redeemed miles for rewards in order to maximize the likelihood that participants viewed a free flight as a worthwhile goal. We utilize this data to test the motivational effects of recurring goals (i.e. rewards) after repeated successes. In Study s 1 and 2, people preferred Store A given the choice between two stores. In this study, people can decide not to fly. In Study 3, respondents who successfully reached their goal once were overwhelmingly inclined to attempt the same task a second time. However, the cost was minimal (a matter of minutes) and the game was fixed such that we could not compare effort exerted between the first and second 10 trials. In this study, we observe the behavior of people spending real money and can compare effort directly after repeated successes to determine how effort changes in response to goal-attainment. Further, we validate the goal gradient effect documented for loyalty program rewards for recurring goals (Nunes and Drèze 2006). Within our proposed framework, we consider a decrease in inter-purchase time on the same carrier a proxy for consumer s increased motivation to reach the goal. Each flight taken serves as a consumption occasion, for which the flyer must decide which airline to patronize. However, our data include travel on only one airline; we cannot observe airline choice directly. Yet we can gauge fliers purchase activity indirectly. We expect to observe a decrease in inter-purchase times when an individual directs a greater share of wallet the focal airline, or increases his or her purchase amount. It is important to point out that whether an increase in patronage comes from stealing share or an increase in primary demand is irrelevant as both indicate an increase in effort. 24

25 The focus of this study is to determine whether a relationship exists between the completion of a goal (i.e. award redemption) and the subsequent rate of purchase (i.e. a reduction in inter-purchase times). To be consistent with our earlier findings, we would expect inter-purchase times to decrease following successive reward redemptions. If fliers update self-perceptions concerning how likely they are to earn free flights on the focal airline (given differences across competitors on traits such as routing and flight times, etc.) with each reward redeemed, they should change their purchase behavior by increasing their patronage. DATA ANALYSIS & RESULTS To model the impact of reward redemption on the interval between flights, we fit a hazard rate model with the number of days elapsed between consecutive flights serving as the dependent variable. The independent variables include the number of awards redeemed up until that point (NAWARD) and its squared value (NAWARD2), the latter serving to accommodate a potential leveling of the effect. To account for the anticipated goal gradient effect, we also include the number of flights taken since the last award (NFLIGHT) and its squared value (NFLIGHT2). Because the data cover only two years of activity, we cannot determine how many awards a member might have redeemed (NAWARD and NAWARD2) before May This introduces a potential bias into these measures. To offset this bias, we include the number of years a traveler has been a member of the program and its squared term (NYEAR, NYEAR2) as a proxy for the number of previous awards. This also accounts for any kind of experience effects. 25

26 Not knowing whether or when an award was redeemed before the beginning of our data collection period (May 2004) introduces potential measurement error for NFLIGHT and NFLIGHT2. This poses a problem with respect to the number of flights taken by an individual before we observe his or her first award redemption. We eliminate this bias by examining only those flights taken after a redemption, which limits our analysis to only those travelers with at least one award redemption during the data collection period. Given our focus on the impact of success on motivation, as well as the size of the data set and the robustness of the results, we do not view this as a significant concern. With regard to the last recorded flight of any individual traveler, we do not know the exact elapsed time between that flight and the traveler s next flight, which would have occurred after May Rather then simply eliminate that observation, we code it as a right censored observation using the last day of data collection as the censoring point. Roughly 17% of our observations are censored. Finally, we have information about the status level for each traveler. The airline utilizes a 3-tiered program: Tier 1 = 25,000-49,999 miles per year, Tier 2 = 50,000-99,999 miles per year, Tier 3 = 100,000 miles or more per year. One would expect higher-tiered travelers to fly more frequently (although they may also reach higher status by traveling longer distances rather than more frequently). Thus we include three tier dummies in the model. The model was fitted using PROC LIFEREG in SAS. The parameter estimates and corresponding statistics are shown in Table 1. The results support our theorizing in that each successive award redemption reduces inter-travel time by about 20% (i.e., exp(-.2627) =.77). We also find support for the goal gradient effect in that each additional 26

27 flight flown between awards reduces inter-travel time by 4% (exp(-.0431) =.96). The parameters for the tier dummies are as expected, with Tier 3 travelers flying more frequently then Tier 2; Tier 2 more frequently then Tier 1; and Tier 1 more frequently then regular travelers. Insert Table 1 about here To assist with the interpretation of the squared term parameters in this multiplicative model, we plot the predicted values from the model in Figure 4. In our previous studies, we measured either the likelihood of selecting a store, or the perception of task fit (changes in one s self-efficacy in a particular task). To make the graph comparable to these constructs, we plot 1/[expected inter-travel time] as a function of the number of flights taken. In our data set, award redemption occurs on average after every 12 paid flights. Hence, we predict inter-flight time for a hypothetical traveler who just joined the program (i.e., all variables are initialized at 0) and who redeems an award after every 12 th flight. As shown in Figure 4, the predicted inter-purchase time interval decreases (gets larger) as one gets closer to accumulating enough flights for an award. Further, this interval decreases at an increasing rate with each successive reward redemption (the effort exerted between rewards, reflected by the size of the bars, gets larger with each award redeemed). This pattern of results is remarkably consistent with the pattern from our laboratory findings shown in Figure 1. 27

28 Insert Figure 4 about here DISCUSSION In Study 4, we utilized real world loyalty program data to examine how consumers respond to recurring goals, and how motivation changes in response to goal attainment. We find that the time between flights on the focal airline decreases as one gets closer to redeeming a reward (i.e. the goal gradient effect). This pattern is consistent with respect to the first, second and subsequent reward redemptions. More importantly, the time between purchases continues to decease with each successive reward redeemed. In other words, as shown in Figure 4, consumers exert more effort (purchase tickets at a faster rate overall) following each successive reward redemption, resulting in the observed ascending saw-tooth pattern. GENERAL DISCUSSION AND CONCLUSION The objective of this research was to demonstrate how the effectiveness of a loyalty program in spurring consumers to consolidate purchases depends directly on the number of rewards offered and the amount of an alternative currency required to redeem each reward, what we have labeled a program s divisibility. We adopt a goal-based framework to describe how consumers respond to increasing and decreasing divisibility. We demonstrate that increasing divisibility can allow for increased loyalty among those with low asset levels in the alternative currency, this research also reveals how too much divisibility can be de-motivating as it diminishes the effectiveness of rewards as goals. 28

29 This last result is counter-intuitive, as one would expect a currency to be more highly valued as the exchange options available for that currency increase. In addition, we explore the impact of increased divisibility, or more attainable goal-attainment and reward redemption on motivation. We show how the successful attainment of a goal and the accompanying reward serves to increase consumers motivation in subsequent undertakings, and that this increase in motivation can endure after more than one or two successes. We also show that increasing divisibility, which reframes a larger task as several smaller tasks and disburses a larger reward in smaller increments, increases people s perceptions of self-efficacy to a much greater extent. We argue that the increase in motivation brought on by successfully reaching a goal and earning a reward is due to the change in self-efficacy. Finally, we illustrate these effects using a significant, real-world data set from a major airline s loyalty program. This research is not without its limitations. We suggest a moderate level of divisibility is best, but this level will vary across programs. We do not offer a methodology for determining this level, but only make managers aware of how changes in divisibility (i.e. adding or deleting reward levels) might impact their programs. While exploring how goal-attainment acts as feedback, we did not examine the impact of positive feedback on people s emotional response, such as the feelings of satisfaction and pleasure it should elicit (Isen 1987). It may be that affect regulation, or a process in which the positive affect associated with the reward represents a goal in itself, plays a role. It is not clear, however, whether the positive feelings would encourage individual to try again, or whether individuals would attempt to protect the positive state associate with success by not trying again. Future research could examine the determinants of these opposing 29

30 responses. We should point out that Klein s (1989) integrated feedback model, a cognitive element processes how one s current state compares to the goal, while an effective element dictates one s desire to reduce perceived discrepancies. While we focused on the impact of success on self-efficacy, future research may examine the link between emotional factors and the desire to simply undertake the task again. From a practical perspective, it is worthwhile to note that the divisibility of any alternative currency may not be entirely within the control of the firm issuing that currency. For example, frequent flier miles, the most ubiquitous alternative currency, are becoming interchangeable with several other alternative currencies and some can now be redeemed at numerous second-party vendors. Other firms, such as Southwest Airlines, limit the divisibility of their alternative currency by issuing credits in non-divisible increments (flight segments). 30

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