Consumer Learning as a Determinant of a Loyalty Program's ...

3 downloads 50253 Views 463KB Size Report
Email: [email protected]. This research has been financially .... for managers when they assess the performance of their marketing programs ...... benefit from it more and became the stationery store's best customers (Liu 2007).
Consumer Learning as a Determinant of a Loyalty Program’s Effectiveness: a Behaviorist and Long-term Perspective

Jean FRISOU Hélène YILDIZ

Author's manuscript, submitted before the revisions

Jean Frisou is professor and researcher in marketing at INSEEC Business School - email: [email protected]. He is a member of INSEEC Business School Research Laboratory, 31 Quai de Seine, 75019 Paris. Hélène Yildiz is assistant professor at Nancy 1 University and affiliated researcher at INSEEC Business School Research Laboratory, 31 Quai de Seine, 75019 Paris. Email: [email protected]. This research has been financially supported by INSEEC Research Laboratory. The authors would like to thank the company DOING GROUPE NEYRIAL, and in particular MarieViolette Gellet and Laurence Cote for making available the sample group data, which enabled the empirical study to be implemented.

1

Consumer Learning as a Determinant of a Loyalty Program’s Effectiveness: a Behaviorist and Long-term Perspective Abstract Companies expect their loyalty programs to be effective. But there are two considerations to be taken into account. From a static standpoint, a program’s effectiveness merges with its results; and from a dynamic standpoint, it is constructed from the interactions between the program and its users. This second approach, which we favor, goes beyond simply noting that a program is effective: it enables us to understand why it is effective. We argue that a loyalty program’s effectiveness depends on how the consumer learns to use its rules. Our proposed theoretical framework was tested on the behavioral trajectories of 1380 individuals observed over a four-year period. These were customers of a stationery store, and members of a multi-store loyalty program involving 33 other retailers. The tendency of customers to spend more in the stationery store over four years became increasingly pronounced as they learned how to accumulate points in all stores participating in the program and asked for these to be redeemed in the stationery store. Nevertheless, only 26% of the stationery store customers had an increasing spending trajectory in this store. This finding suggests to firms that a loyalty program’s effectiveness does not depend on the program alone. To obtain the expected loyalty behaviors, firms should take specific measures to help their customers familiarize themselves with the program’s rules.

Keywords: Loyalty program’s effectiveness, operant learning, tendency to spend, tendency to accumulate points, tendency to redeem points for rewards

2

A key question facing firms is knowing whether their loyalty programs do in fact produce the intended results. Despite the efforts of researchers to provide an answer to this question, the results obtained are still very mixed. Thus the pioneering study by Sharp and Sharp (1997) reveals a low impact of loyalty programs on buying behavior. Moreover Mauri (2003) and Allaway, Gooner, Berkowitz, and Davis (2006) show that a high proportion of a loyalty program’s members do not remain loyal to it. Other studies, however, draw more positive conclusions. Some loyalty programs enhance customer retention (Verhoef 2003), increase their share of wallet (Verhoef 2003; Meyer-Waarden 2007) and prolong the duration of the relationship (Meyer-Waarden 2007).

Such divergent findings have reinforced researchers’ skepticism and given rise to a succession of questions: “Do rewards really create loyalty?” (O’Brien and Jones 1995), “Do customer loyalty programs really work?” (Dowling and Uncles 1997), “Do reward programs build loyalty for services?” (Keh and Lee 2006), “Do loyalty programs really enhance behavioral loyalty?” (Leenheer, van Heerde , Bijmolt, Smidts 2007), “Brand loyalty programs: Are they shams?” (Shugan 2005). Such questions seem to attribute the effectiveness of programs to the programs themselves and ignore the nevertheless determining roles of the customers using them.

To go beyond this somewhat reductive perspective, in this study we make three contributions that distinguish it from those of our predecessors. The first contribution is to change how we think about a program’s effectiveness. We propose approaching the effectiveness of loyalty programs not only on the basis of their “a priori” effects and their “a posteriori” results, but according to the way in which each participant learns how to use them.

3

The second contribution is a behaviorist conceptual and theoretical framework that takes account of the interactions between the user and the program. Operant learning, which makes behavior dependent on its consequences, is currently used to explain how reward programs work (Foxall 1997; Taylor and Neslin 2005; Liu 2007). However, the authors continue to view perceptual and cognitive processes as the main factors influencing the programs’ effectiveness (Taylor and Neslin 2005; Keh and Lee 2006; Wirtz, Mattila and Lwin 2007). We propose re-adopting the behaviorist theory of operant learning, while linking a program’s effectiveness to the manifest behaviors of its users (Skinner 1965). By “behaviors”, we understand all behaviors that the program gives rise to in users: not only buying behavior, but also loyalty point accumulation and redemption behaviors, which structure their learning about the program.

The third contribution concerns the methodology used for modeling the learning process. The need to take account of the long term for measuring the effects of loyalty programs is frequently mentioned in the literature (Kopalle and Neslin 2003; Taylor and Neslin 2005; Liu 2007). The latent growth curve models that we use place the emphasis on people’s behavioral trajectories over a number of years, rather than their immediate responses on a specific occasion.

The paper is organized as follows. In the first part, we show that the evolving literature leads us to rethink the effectiveness of loyalty programs both as a process and a result. In the second part, we justify our use of operant learning theory to describe these processes. In the third part we present the research hypotheses that explain the learning processes being investigated. Then, in the fourth part, we test the whole theoretical

4 framework on the basis of two different models. Finally we conclude with a discussion of possible future research and the managerial lessons to be drawn from this study.

Measuring Loyalty Programs’ Performance

Measuring the performance of loyalty programs involves making two choices. The first concerns the concept to be adopted for assessing performance. The second is a question of how to envisage this concept.

Performance as Effectiveness

Assessing loyalty programs is simply one specific instance in the measurement of marketing activities. By “activities”, researchers mean the tactics of operational marketing rather than the underlying strategies (Rust, Ambler, Carpenter, Kumar and Srivastavan 2004; O’Sullivan and Abela (2007). Because loyalty programs are levers of operational marketing, measuring their performance becomes a key issue. But this is not a straightforward task, since three dimensions of the performance of a marketing action are currently offered in the literature.

A first dimension of performance is adaptability. This expresses the degree of fit of the action to the marketing environment. The second dimension is efficiency. This measures the relation between the results of an action and the means used to implement it (Clark 2000; Morgan, Clark, and Gooner 2002). Lastly, the third dimension is effectiveness, which expresses the match between the results obtained and the results expected from the marketing action (Clark 2000; Morgan, Clark, and Gooner 2002). Effectiveness, however, seems to be

5 the generally preferred aspect of performance. It represents the most significant dimension for managers when they assess the performance of their marketing programs (Clark 2000). But researchers also use it to measure the performance of loyalty programs (Sharp and Sharp 1997; Wirtz, Mattila and Lwin 2007; Liu 2007; Leenheer, van Heerde, Bijmolt and Smidts 2007).

The Two Perspectives of Effectiveness

Although effectiveness has become a generally agreed criterion for measuring the performance of loyalty programs, there are at least two other ways of viewing this. In the one, effectiveness is measured at the end of the marketing operation (Clark 2000). A loyalty program is then effective if it produces the expected results. In the other, effectiveness is measured as the marketing operation takes place. It is assimilated to a process that aims to optimize the results of the operation (Kahn and Myers 2005). From this standpoint a loyalty program is effective if the monitoring and control of its functioning enables the best results to be obtained (Figure 1). --------------------------Insert Figure 1 --------------------------Within the perspective of effectiveness seen as a “result”, researchers leave aside the way in which the loyalty program operates and simply make sure that the targeted objectives are attained. Their methods are based on three types of comparison (Liu 2007): comparison of the results of firms or brands using a loyalty program and the results of those that do not use them (Sharp and Sharp 1997; Mägi 2003; Leenheer, van Heerde, Bijmolt and Smidts 2007); comparison of the buying behavior of customers belonging to loyalty programs and the behavior of those who do not (Bolton, Kannan, Bramlett 2000; Verhoef 2003); and

6 comparisons between the behavior of people belonging to a loyalty program, from one period to the next (Lai and Bell 2003; Taylor and Neslin 2005). These comparisons are intended to show whether or not a loyalty program has an effect on users’ buying behavior. They provide no explanation as to the causes of any differences that may be noticed.

In the conception of effectiveness viewed as a process and a result, researchers focus their attention on the functioning of the loyalty program. They are interested in the psychological processes liable to influence users’ behavior. Thus Taylor and Neslin (2005) point to the psychological pressure of loyalty points on the user. Keh and Lee (2006) examine the link between rewards and the user’s buying behavior. Wirtz, Mattila and Lwin (2007) look at the effect on the user’s buying behavior of the program’s attractiveness and switching costs to another program. Nevertheless, while these studies situate effectiveness within the program’s functioning, it is still the program that, through its intrinsic properties, is the main source of effectiveness. It seems to us to be important to change this view of things, by showing that a program’s effectiveness also has extrinsic causes. It depends on what the user does with it.

Theoretical Background Researchers locate the main sources of the effectiveness of loyalty programs in the reward schemes accompanying them. But two aspects of the question are of particular interest: on the one hand, how customer loyalty is rewarded, and on the other, what kind of responses elicit such rewards. These two aspects are closely linked, since customer responses vary according to the type of reward (Dowling and Uncles 1997).

Loyalty Rewarded

7

A loyalty program is defined as an integrated system of marketing operations with the aim of making customers subscribing to it more loyal (Leenheer, van Heerde, Bijmolt and Smidts 2007). Such operations involve offering rewards to customers according to the frequency and volume of their purchases (Yi and Jeon 2003). Loyalty programs and reward programs are thus often synonymous in the literature (Taylor and Neslin 2005); Keh and Lee 2007); Wirtz, Mattila and Lwin 2007).

Nevertheless, the idea of winning customers’ loyalty by promising them rewards is not limited to loyalty programs. In recent years, companies have attempted to develop customer loyalty by incorporating into their offering the promise of a higher quality of products or services (Zeithaml, Berry, and Parasuraman 1998). Loyalty programs have the same goal, but their way of achieving it differs. The promised reward is not part and parcel of the product, but is external to it and is obtained followed a succession of purchases (e.g. a gift or a discount). The literature offers three classifications of reward schemes. In the first, there is a posited opposition between direct and indirect rewards (Dowling and Uncles 1997; Yi and Jeon 2003; Keh and Lee (2007). A direct reward is a benefit that supports the value proposition of the product or service. Free flights on US Airways obtained with US Airways Dividend Miles are direct rewards. Conversely, a reward is indirect if it has no direct link with the product or service provided. In the American Express Membership Rewards program, the gifts (e.g. high-tech products, gourmet products, holidays, etc.) obtained by paying with their credit card are indirect rewards.

8 The second classification makes a distinction between immediate and delayed rewards. Immediate rewards are obtained after making the first purchase, whereas delayed rewards apply only after a number of purchases have been made (Dowling and Uncles 1997; Yi and Jeon 2003; Keh and Lee (2007). The rewards offered by Etihad Airways, which come into effect from the first air mile flown, are immediate rewards, while the free flights with American Airlines obtained by exchanging air miles accumulated over a period of time are delayed rewards.

The third classification is based on the nature of the rewards, with a distinction being made between tangible and intangible rewards. Tangible rewards consist of discounts, purchase vouchers and various gifts of a material nature. Conversely, intangible rewards provide the consumer with non-material benefits, such as personalized information or the feeling of being a favored customer (Roehm, Pullins and Roehm Jr 2002, Leenheer, van Heerde, Bijmolt and Smidts 2007).

The Effects of Rewards

The marketing literature distinguishes various effects produced by rewards, including effects on buying behavior as well as how the loyalty program is perceived. The first studies concluded that loyalty programs had a limited effect on buying behavior (Sharp and Sharp 1997; 1999), but more recent work confirms that this effect is substantial (Taylor and Neslin (2005; Leenheer, van Heerde, Bijmolt and Smidts 20072007; Meyer-Waarden 2007).

The program’s value as perceived by the consumer is also influenced by rewards (O’Brien and Jones 1995; Yi and Jeon 2003). In situations of high consumer involvement, Yi

9 and Jeon (2003) show that direct rewards have a greater effect on the program’s perceived value than indirect rewards. They also show that in a low involvement situation, the effect of immediate rewards on the program’s perceived value is stronger than that of delayed rewards.

Nevertheless, the impact of rewards does not necessarily benefit the brand. The perceived value of a loyalty program depends more on the attraction of the rewards than the brand itself (Yi and Jeon 2003). Thus such programs are more liable to create loyalty to the program than loyalty to the brand. The brand does not therefore gain greater protection from its competitors through a loyalty program. In the customer’s mind, the loyalty program is dissociated from the brand and itself is subject to competition from other, more attractive programs (Wirtz, Mattila, and Lwin 2007).

These studies clearly show the many effects of rewards on users’ attitudes and buying behavior. But they do not explain in sufficient detail the underlying psychological processes. Theories derived from social psychology offer perspectives that can deepen our understanding of these phenomena.

Psychological Theories of Rewards

We shall limit our presentation to the three main contributions of social psychology dealing with the effect of rewards on human behavior: the theory of operant learning, the theory of personal commitment and the theory of cognitive evaluation.

Skinner’s (1965) theory of operant learning was first put forward to explain the effect of sales promotions (Rothshild and Gaidis 1981), then to explain the mechanisms of loyalty programs (Foxall 1996, 1997). This psychological theory is based on a straightforward idea.

10 When an individual manifests a behavior in the presence of a stimulus and receives a reward or punishment, this stimulus becomes discriminative. In similar situations, the presence of the stimulus increases the probability that the individual will repeat the behavior if it has previously been rewarded, and will decrease the probability if it has been penalized (contingencies of reinforcement). Reward programs are based on the same principle. Points gained are indirect rewards functioning as secondary reinforcers (Foxall 1996). The reductions or discounts obtained in exchange for the points gained play the role of primary reinforcers (Foxall 1996). The loss of points is a penalty, and encourages the customer to ask for his points to be redeemed within the given time frame (negative reinforcement). Loyalty is therefore not automatically produced by the program, but depends on the ability of the consumer to learn the often complex and changing rules of the system. In the theory of commitment, the rewards offered to induce a particular action tend, on the contrary, to reduce the probability that it will be repeated. This theory, which has been corroborated by the facts on many occasions, asserts that carrying out a relatively easy, freely consented to and publicly exhibited action commits its perpetrator to repeat it subsequently (Kiesler 1971). Belonging to a loyalty program corresponds to this type of action. It calls for very little effort on the part of the customer – the provision of personal information, for example –, is visible to all and agreed to without pressure. The commitment conditions identified by Kiesler (1971) are all present, and these encourage the customer to continue in a relationship the principle of which he has freely accepted (Yildiz 2007). The individual feels committed because he can attribute his participation in the program to himself. Kiesler (1971) nevertheless showed that when an action is rewarded, the individual no longer attributed this action to himself but to the rewards he obtains in exchange. The commitment effect then tends to disappear. The rewards received can therefore reduce users’ commitment to a program over time and can account for their progressive withdrawal.

11

Finally, the theory of cognitive evaluation maintains that people’s intrinsic motivations lie in their need for autonomy and competence (Deci Ryan and Koestner 1999). According to whether or not the rewards obtained satisfy these needs, they respectively increase or reduce people’s motivation to repeat the rewarded behavior. If, when an individual carries out a task, he gets a reward independent of the task, he does not feel controlled by this reward. His autonomy and competence needs are preserved and his motivation to repeat the behavior is unchanged. On the other hand, if the reward depends on the task, the individual can experience this as a restraint on his behavior. His need for autonomy and competence will be thwarted and his motivation to continue acting in the same way will be reduced (Deci, Ryan and Koestner 1999; Jordan 1986). For example, a participant in a loyalty program whose rules are too complicated can feel controlled by the rewards and abandon the program because he feels his autonomy is compromised (Shugan 2005). Conversely, if the rules allocating points call on the user’s astuteness, he can then experience a feeling of competence that will encourage him to continue with the program.

Theoretical Framework Explaining the effectiveness of loyalty programs involves finding the causes of their successes and failures. Therefore we shall exclude at the outset the theory of commitment, for which rewards can result only in the failure of programs and not their success. We shall also ignore the theory of cognitive evaluation, since its findings are not clearly established in the literature (Eisenberg, Pierce and Cameron 1999). The theory of operant learning is the most commonly adopted in marketing research (Foxall 1996, 1997; Liu 2007), and enables both the positive and negative effects of loyalty programs to be accounted for, according to whether the user’s buying behavior brings him rewards or penalties.

12

Background

The impact of loyalty programs has been mainly studied in relation to changes in users’ buying behavior (Sharp and Sharp 1997, 1999; Taylor and Neslin 2005; MeyerWaarden 2007). However, the use of a loyalty program gives rise to behaviors other than purchasing. A loyalty program is comparable to a board game, where the consumer has to learn the rules in order to maximize his rewards. As well as buying behavior, learning to play the loyalty program game involves two other forms of behavior – accumulating points and redeeming points – that we must define and conceptualize.

Foxall (1996 p 286) considers accumulation to be a “planned acquisition of a series of reinforcers which have some hedonic content but which are principally informational”. Accumulating points thus presupposes learning the rules of the program in order to apply them better and win maximum points. The customer will, for example, make his purchases on Tuesday rather than Saturday, because on Tuesday the points gained are doubled or trebled. The bonus points inform the customer positively as to his ability to manage the program successfully (Foxall 1996 p283). Points redemption behavior can be seen in a similar way as the planned acquisition of hedonic or utilitarian reinforcers. It consists of converting the points gained (intangible rewards) into reductions or gifts (tangible rewards).

Concepts and Definitions

The conceptualization of the behaviors involved in learning a program has to resolve two problems. On the one hand, such behaviors are oriented toward the long term and their definition should take account of this temporal aspect (Liu 2007; Meyer-Warden 2007). On

13 the other hand, product purchases and the acquisition or redemption of points are moves by the consumer that often come up against obstacles (Bagozzi and Warshaw 1990). A stockout in a store or forgetting one’s loyalty card prevent these behaviors achieving their aim. Thus behavior achieved and observed must be distinguished from the tendency that guides it. This latent tendency is defined as a “probability of response” which produces effective behavior if there is no obstacle in the customer’s environment that conflicts with it (Coutu 1949; Skinner 1965).

Taylor and Neslin (2005) examine two stages in learning a loyalty program. During the first, the consumer is subject to the pressure of points until such time as the desired gift or discount is obtained. The program is deemed to be effective if the buying behavior is maintained or increases after this reward has been obtained. Our longitudinal approach covers several years, but also makes a distinction between two periods. The first year, or initial period, is an adaptation stage during which the customer familiarizes himself with the loyalty program. He receives his first rewards and establishes his initial use levels. In subsequent years, or the development period, the consumer adjusts his initial use levels (Liu 2007), either maintaining them or increasing them if his learning has been successful, or reducing them if it has failed. The tendency to increase purchases over time represents an effort to control on the part of the consumer, in order to overcome the obstacles arising in purchasing situations (Oliver, 1999).

We therefore define the tendency to loyalty behavior as the customer’s propensity to maintain or increase his initial level of expenditure from one period to the next despite the obstacles he encounters (Oliver, 1999). Similarly, we define the tendency to accumulate points as the customer’s propensity to maintain or increase the expected initial level of points

14 from one period to the next. Finally, we define the tendency to redeem points for rewards as the customer’s propensity to maintain or increase over time the initial level of his redemption demands.

Research Hypotheses

Our research hypotheses concern the relations between the accumulation of points, the redemption of points, and purchasing, both during the initial adaptation period and in the consolidation period.

In the initial period, the points (secondary, conditioned reinforcers) control buying behavior because the customer associates them with tangible delayed rewards (backup reinforcers). During this adaptation period, we therefore expect a positive effect from the customer’s anticipated points level on his intended level of expenditure in the stores he frequents. Hence the hypothesis:

H1 – In the initial period, the level of points expected by a customer has a positive effect on the level of expenditure he anticipates making in each of the stores participating in the program.

But the rules of a loyalty program also set the time limits beyond which the points gained by the customer are lost. These rules exert pressure through the points pushing the customer to act in order to preserve his points capital (Taylor and Neslin 2005). Redeeming them avoids the risk of losing them and brings the customer a sense of relief that is equivalent to a reward (Skinner 1965). This negative reinforcement of redemption behavior is all the stronger the higher the initial expected level of points. Hence the second hypothesis:

15 H2 – In the initial period, the points level expected by the customer has a positive effect on the redemption level that he envisages in each of the stores participating in the program.

Redeeming points also brings the customer tangible rewards which reinforce his buying behavior in the store which gives him these rewards. Many researchers emphasize the determining role of these primary reinforcers which shape buying behavior (Nord and Peter 1980; Rothschild and Gaidis 1981; Foxall 1996, 1997; Sharp and Sharp 1997; Liu 2007). Hence our third hypothesis:

H3 – In the initial period, the redemption level envisaged by a customer in a store has a positive effect on the initial level of expenditure he intends making in this store.

In subsequent years, the success of the program depends on how in the long term the customer’s initial use levels change (Liu 2007). He has learned which behaviors are best remunerated in terms of points. Accumulating points, redeeming points, and the amount spent on purchases should therefore progressively increase over the initial levels. Liu (2007) reveals an increase in the frequency and scale of purchases during the twenty four months after joining a program. We therefore have good grounds for extending to the long term the hypotheses advanced for the initial use levels. Hence the three following additional hypotheses:

H4 – A customer’s tendency to accumulate points in all the stores participating in the program has a positive effect on his tendency to make changes in his expenditure in each of the stores he frequents. H5 – A customer’s tendency to accumulate points in all the stores participating in the program has a positive effect on his tendency to make changes in his redeeming of points in each of the stores he frequents.

16

H6 – A customer’s tendency to ask for his points to be redeemed in a store participating in the program has a positive effect on his tendency to make changes in his expenditure in that store.

Another significant finding reported in the literature concerns the heterogeneity of behavior among a program’s users. Loyalty programs seem to work on some customers and not on others. Different user profiles have even been distinguished (Mauri 2003; Allaway, Gooner, Berkowitz, and Davis (2006); Lewis 2004; Liu 2007). Liu (2007) explains these differences with reference to learning the program. According to this author, the most loyal customers, and the most profitable to the company, are those who accumulate points and claim their redemption. Hence the final hypothesis:

H7 – Customers with a strong tendency to accumulate and redeem points have on average a tendency to increase their initial level of expenditure to a greater extent than other customers.

Data and Methodology Data Collection

The data used in this study concern members of a multi-business loyalty program. This reward scheme covers 33 stores in a medium-sized French town near a metropolitan region with a million inhabitants. The participating stores belong to various sectors in retail distribution (e.g. food, clothing, personal services, IT, stationery, etc.). The cohort studied is made up of the customers of a stationery store which joined the program in 2001. This store was chosen because of its great involvement as a driver of the loyalty program. The sample represents the entire cohort, and comprises 1380 people of whom 54% are women and 46%

17 men. These percentages should be treated with caution, since each loyalty card can be used by any member of the family. The principle of the program is simple. Every €3 spent in any of the 33 participant stores gives the bearer of the loyalty card one loyalty point. When 100 points are accumulated, the bearer has the right to a reduction or a purchase voucher worth €10. Any points not converted into rewards within a year are lost. We have three series of individual data for the years 2002 to 2005: (1) the total number of points won in all the stores, (2) the total number of points redeemed in the stationery store (direct tangible rewards), (3) total expenditure in the stationery store. The main descriptive statistics pertaining to these data are summarized in Table 1. --------------------------Insert Table 1 ---------------------------

Operationalization of Constructs

The operationalization of the concept of tendency, applied to buying behavior, points accumulation behavior and points redemption behavior, is implemented by means of latent growth curve models (Duncan, Duncan, Strycker, Li and Alpert 1999; Kaplan, 2000; Bollen and Curran 2006). Although we considered the tendencies pertaining to three distinct behaviors (accumulation, buying, redemption), the way of operationalizing them is the same and will be presented by taking buying behavior as an example.

In a given purchasing situation, the buying behavior observed can be split up into two components. One is an underlying, non-observable or latent buying behavior, the other an

18 adaptation behavior due to the situation’s positive or negative influence on buying behavior (Frisou 2005). We therefore have the following identity: Observed buying behavior = non-observable underlying buying behavior + adaptation to the situation behavior. If, for reasons of simplification, we accept a linear development of buying behavior, buying behavior observed in the long term can be represented by two trajectories: a manifest trajectory of the consumer’s repeated expenditure, represented by the dotted line (Figure 2A); and a non-observable latent trajectory of the underlying or probable buying behavior, represented by the solid straight line (Figure 2-A). The gap observed in each period between these two lines (e.g. i1) represents the customer’s adaptation to the purchasing situation (e.g. stockout, anticipated purchase, etc.) For example, in the event of a stockout, the consumer will switch to another brand. --------------------------Insert Figure 2 --------------------------The measurement model of repeated buying behavior comprises two latent factors (Figure 2-B). These are interpreted as chronometric common factors. The latent factor  represents the initial level of customers’ expenditure during the first period “t=0”. The latent factor  expresses customers’ long-term latent tendency to change the initial level of expenditure  from one period to the next.In mathematical terms,  is an “intercept” and a “slope”. The amount of observed expenditure “eit” for customer “i”, at each period “t”, is written: (1) eit = i +  I * t + it In the first period t = 0, equation (1) takes the following form (2):

19 (2) ei0 = i + I0 . i would therefore be equal to the initial level of expenditure ei0 if the behavior due to the purchasing situation I0 had no effect on the consumer’s choice (i.e. I0 = 0). For the following periods (i.e. t = 1, 2, 3), the levels of expenditure are given by equation (1). In this equation i represents the initial level of expenditure aimed for at t = 0 by consumer “i”, i * t the linear variation of the initial level of his expenditure during period t, and it the positive or negative influence of the purchasing situation on his expenditure in period “t”. If i  0, the buying tendency is increasing or unchanged and corresponds to a tendency to loyal behavior. If i < 0, the buying tendency is decreasing and corresponds to disloyal behavior. i and i characterize the latent buying trajectory of consumer “i” in the program. The following equations (3) and (4) complete the specification of the measurement model. i =  +  i i =  +  I In equation (3) the initial level of expenditure i of consumer “i” is a function of the mean of all the consumers’ initial expenditure levels () and of a random component specific to consumer “i”,  i. Similarly the latent tendency i of consumer “i” to change his initial level of expenditure is a function of the mean of all consumers’ tendencies () and of a random component specific to consumer “i”,  i.

Equations (1) and (2) define level 1 of the model (“within person model”) and equations (3) and (4) define level 2 of the model (“between person model”) (Curran 2000). In Figure 2A we show the latent and observed trajectories of individual “i”. In Figure 2B, we reproduce the measurement model’s path diagram of the tendency associated with these curves. This measurement model also applies to points accumulation and redemption

20 behaviors. A growing tendency to accumulate points means that the loyalty program member is increasingly well able to use the program’s rules. Similarly a growing tendency to redeem points means that the customer is increasingly well able to manage his points account.

Models and Results Testing the research hypotheses was carried out using two distinct models. The first is a multivariate model combining three latent growth curves (M1), and allows the first six hypotheses to be tested. The second is a multi-group model applied to the single latent curve of buying behavior (M2), and allows the seventh hypothesis to be tested. To make this paper easier to read, we present the mathematical aspects of models M1 and M2 in annexes A and B. A preliminary analysis of the data collected did not enable their multinormality to be established. The Mardia-based kappa index was estimated at 6.403, while it should be around zero if the data were multinormal. The violation of the multivariate normality hypothesis affects the estimation of the chi-square and standard errors (Bollen 1989). To overcome this problem we estimated the two models with Muthén and Muthén’s (2004) appropriate MLMV estimator, which produces a scaled chi-square and robust standard errors (Satorra and Bentler 1988). We re-estimated the standard errors of the parameters with a 1000-sample bootstrap.

. First Model: The Learning Process

The first structural model M1 describes the operant learning process defined by the first six hypotheses. The model is estimated with the data available from the 1380 individuals making up the cohort. Its path-diagram is shown in Figure 3.

21

--------------------------Insert Figure 3 --------------------------The model’s goodness-of-fit is acceptable, on the basis of the cut-off criteria for fit indices accepted in the literature (Hu and Bentler 1999). The estimated value of ² is 13.985, with 5 degrees of freedom and a p-value estimated at 0.016. The estimations of the CFI and TLI indices come out at 0.94 and 0.90 respectively. They are slightly lower than the minimum threshold recommended for these indices ( 0.95). The estimation of the SRMR (0.056) is higher but close to the maximum recommended threshold (< 0.05) and that of the RMSEA (0.036) is lower than the generally accepted maximum threshold (< 0.05). The estimations of the structural model M1 parameters are shown in Table 1. --------------------------Insert Table 2 --------------------------Hypothesis H1 predicts that the expected initial points level (iphas a positive effect on the expected initial level of expenditure (ie  The results show a positive and statistically significant standardized coefficient, which confirms this hypothesis (p1 = 0.258, p