Managers are recognizing the importance of cultivating loyal customers to increase sales and customer share (Zeithaml 2000; Zeithaml et al. 1996). This is illustrated by the number of companies that presently have loyalty programs in place. A wide variety of such loyalty programs exists throughout the world (Kumar and Reinartz 2006; Shugan 2005; Yi and Jeon 2003). Reports show that retail loyalty programs are growing at 11% per year, that frequent flyer miles could be considered the world’s second largest currency after the U.S. dollar (The Economist 2002), and that co-branded airline customer loyalty cards generated more than 4 billion U.S. dollars in annual revenue for the top seven legacy airlines (Beirne 2008).

From a company’s perspective, one goal of introducing loyalty programs in times of severe competition is to increase or at least maintain customer loyalty for important (e.g., profitable) customers. Loyalty programs function as switching barriers that have been shown to influence customer loyalty positively (Evanschitzky and Wunderlich 2006; Jones at al. 2000; Patterson and Smith 2003). However, extant research suggests that loyalty programs may increase loyalty toward the program rather than loyalty toward the company (Dowling and Uncles 1997; Meyer-Waarden 2007; Yi and Jeon 2003).

Increasing loyalty to a loyalty program, rather than to the company behind the program, can pose serious challenges for companies participating in programs with partner networks that include competitors. A substantial number of loyalty programs are not firm-specific but rather span across competing firms. In fact, in the largest retail loyalty programs in Europe (e.g., Payback and HappyPoints, both with well over 30 million members and well over 10 billion Euros transaction volume per year), multiple competing providers are operating under the banner of a single loyalty program. Therefore, loyalty toward a particular retailer could be very different from loyalty toward the loyalty program. This makes it challenging for firms operating under such conditions to manage properly their affiliation with a loyalty program.

If customer loyalty to the selling firm is based on elements inextricably linked to a specific loyalty program, then “loyalty” should fade if the program is no longer attractive. Some portion of a customer’s perceived loyalty toward the firm could actually be program-specific loyalty, wherein the loyalty is directed specifically toward a loyalty program, independent of the customer’s affiliation with the selling firm. Such arguments would question, for example, whether customers who are loyal to the “OneWorld” loyalty program are necessarily loyal to American Airlines, which is one of the competing firms in the loyalty program. In fact, it may well be that customers remain loyal within the program but do not necessarily remain loyal toward a particular provider. Within-program loyalty might render the intended positive effects of such a program irrelevant for a particular participating firm and would seriously question the substantial investments required for companies to maintain membership of loyalty programs.

In light of these unexplored aspects of loyalty programs, our research contributes to the literature in several ways. We set out to enhance understanding of the construct “program loyalty” as compared to “company loyalty.” We propose a definition, a measure, and an assessment of its antecedents. More importantly, our key contribution is to investigate the impact of program loyalty and company loyalty on preference, intention, and behavioral outcomes. In order to do this, we utilize a wide range of outcome measures—share of wallet, share of visits, willingness to pay a price premium, and future sales—using survey as well as actual purchase data for 5,189 members of a multi-firm loyalty program involving a large European retailer. It is worth noting that the loyalty program in our study combines competing providers in a single program, making switching providers within the program a serious concern for participating companies. Most importantly, we investigate the differential effects of program loyalty and company loyalty on preference, intention, and behavioral outcomes in multi-firm loyalty programs. Specifically, we investigate the marginal relative impact of program loyalty and company loyalty on desired customer outcomes.

In order to deliver on these intended contributions, the paper is organized as follows. We begin by discussing two distinct types of loyalty, namely loyalty to a loyalty program (“program loyalty”) and loyalty to the company brand (“company loyalty”). Then we hypothesize the impact of both types of loyalty on customer preferences, including share of wallet and share of visits, self-reported intentions such as willingness to pay a price premium, and objective future purchase behavior. We conclude our paper by discussing the findings for managers, the key one being that company loyalty seems to attract customers to a particular provider whereas program loyalty ensures that once inside the store customers actually spend more money.

Theoretical background

When customers are considered “loyal,” that loyalty might be directed toward the brand (Yi and Jeon 2003), the loyalty program (Dowling and Uncles 1997; Yi and Jeon 2003), channel intermediaries (Verhoef et al. 2007), or employees (Beatty et al. 1996; Reynolds and Beatty 1999). In particular, Yi and Jeon (2003) conceptualize customer loyalty as being divided into program loyalty and (company) brand loyalty. They define program loyalty as having a positive attitude toward the benefits of the loyalty program, whereas they understand brand loyalty as having a positive attitude toward the company brand.

Company loyalty is more dependent on customers’ emotional states, since it incorporates the underlying psychological state that reflects the affective nature of the relationship between the individual customer and the provider, leading to favorable attitudes (Gundlach et al. 1995; Kumar et al. 1995). In contrast, program loyalty is more economic in nature. Program-loyal customers might not necessarily develop a favorable attitude toward a provider, but they continue to purchase from the provider, thereby accumulating benefits such as loyalty points. This type of loyalty is similar to what Dick and Basu (1994) call “spurious loyalty” as it is devoid of emotional elements.

Due to the different nature of the two loyalties, we assume that both constructs would have different antecedents. Whereas the more emotionally-driven company loyalty is based mainly on the perceived quality of the relationship a customer has with a company, program loyalty touches upon tangible relational benefits, such as the additional value a customer receives from being a member of the loyalty program and the benefits s/he receives by purchasing from providers within the loyalty program.

We also expect the two loyalties to have different consequences. Surprisingly, the extant literature has not sufficiently investigated the question of whether different types of loyalty (i.e., program versus company loyalty) would lead to different preferences, intentions, and behavioral outcomes. Answering this question would be essential for companies attempting to optimize their investments in loyalty. Moreover, it would highlight potential vulnerabilities in loyalty formation. If program loyalty is a stronger predictor of purchase behavior than company loyalty, then companies would need to carefully assess their dependence on such loyalty programs.

Uncles et al. (2003, p. 304) argue that “when the program is attractive, customers may come to build a relationship with the program rather than the brand.” Tying customers to a loyalty program with tangible benefits might increase their likelihood of repurchasing; however, if an alternative provider offering similar products and services also became a member of the same loyalty program, customers might continue to be loyal to the program—buying from any company participating in the loyalty program—but would not necessarily purchase from the same company. That lack of loyalty toward a specific company poses the risk to a company of being substituted by providers from within the same loyalty program.

Moreover, even if a competing provider was not a member of the same loyalty program, that company might join another loyalty program offering similar benefits. Again, the “loyal” customer (more precisely, “loyal” toward the benefits of a program) might switch to a new provider that offers similar benefits since s/he is not emotionally attached to a particular provider.

Conceptual model and research hypotheses

To facilitate a better understanding of the two types of loyalty and their antecedents and consequences, we develop a conceptual model mainly based on social exchange theory (and the related relationship marketing concept) and equity theory. These two theories in particular are suitable to explain the different nature of the two loyalties. More precisely, social exchange theory shows how affective bonds between customer and provider affect loyalty to the provider. Relationship marketing in particular is concerned with explaining how trust, commitment, and satisfaction shape positive emotions as well as customer intentions and behavior. Company loyalty is driven by such positive emotions toward a provider brand. Quite differently, program loyalty is driven by economic considerations. Equity theory is capable of explaining how customers make trade-offs between what is received and what is given up.

In line with the aforementioned theories, we conceptualize relationship quality consisting of trust, satisfaction, and commitment as driving company loyalty, and we see relational economic benefits, such as social benefits, special treatment, and overall program value, as driving program loyalty. After briefly discussing the antecedents of the two types of loyalty in order to improve our understanding of the constructs “program loyalty” and “company loyalty,” we develop our key contribution: assessing the differential impact of the two loyalties on preference, intention, and behavioral outcomes.

Drivers of company loyalty

Company commitment

Commitment is a key antecedent of company loyalty (Beatty and Kahle 1988; Evanschitzky et al. 2006). Morgan and Hunt (1994) argue that commitment has long been a central construct in the social exchange literature (Blau 1964; Thibaut and Kelley 1959). Commitment has also been extensively researched in the consumer behavior domain because of its proposed role in leading to important outcomes such as psychological attachment (Verhoef 2003), personal identification (Garbarino and Johnson 1999), and increased price tolerance (Delgado-Ballester and Munuera-Aleman 2001). The concept of commitment is defined by Moorman and colleagues (1992, p. 316) as “an enduring desire to maintain a valued relationship.” If customers desire to maintain a relationship, we would expect them to be company loyal. Therefore, we assume that:

  1. H1:

    Company commitment has a positive impact on company loyalty.

Company trust

Moorman et al. (1992, p. 315) define trust as a “willingness to rely on an exchange partner in whom one has confidence.” Trust has been studied widely in the social exchange literature (Fox 1974), as well as in services marketing (Berry and Parasuraman 1991), and in research on strategic alliances (Sherman 1992), organizational theory (Bradach and Eccles 1989), and retailing, where Berry (1993, p. 1) proposes that “trust is the basis for loyalty.” Many authors have found a positive relationship between trust and loyalty. An empirical study by Delgado-Ballester and Munuera-Aleman (2001) found that trust had a positive impact on customer loyalty, moderated by customer involvement. Chaudhuri and Holbrook (2001), Sirdeshmukh et al. (2002), and Harris and Goode (2004) found that trust is related to both behavioral loyalty and attitudinal loyalty. Hence, we hypothesize that:

  1. H2:

    Company trust has a positive impact on company loyalty.

Company satisfaction

Anderson et al. (1994) define satisfaction as an overall evaluation based on the total purchase and consumption experience with a good or service over time. The link between satisfaction and loyalty has been examined in many studies, with loyalty seen as either a repurchase intention (Anderson and Sullivan 1993; Cronin and Taylor 1992; Oliver 1980), an emotional bond (Bloemer and Kasper 1995), or a deeply held commitment (Oliver 1997). A meta-analytic review of the literature (Szymanski and Henard 2001) as well as recent research reveals satisfaction as one of the enduring antecedents of loyalty (e.g., Harris and Goode 2004). On these grounds, we assume:

  1. H3:

    Company satisfaction has a positive impact on company loyalty.

Drivers of program loyalty

Social benefits of the program

Social benefits were the first in importance among the three relational benefits identified by Gwinner et al. (1998) and focus on the relationship itself rather than on the outcome (or result) of transactions. Social relationship concepts (such as liking, tolerance, and respect) have been found to be influential in the development of loyalty (Goodwin and Gremler 1996). Goodwin (1997) and Goodwin and Gremler (1996) suggest that social benefits are positively related to the customer’s commitment to the relationship. Berry (1995) suggests that social bonds between customers and employees can be used to foster customer loyalty. Similarly, Oliver (1999) maintains that customers who are part of a social organization are more motivated to maintain loyalty with the organization. Hennig-Thurau et al. (2002) found a direct connection between social benefits and loyalty. Blau (1964) and De Wulf et al. (2001) argue that social programs intended to personalize customer relationships may generate more sustainable competitive advantage than financial programs, since social bonds are difficult for competitors to duplicate and may lead the customer to reciprocate in order to “balance the debts.” Hence, we propose:

  1. H4:

    Social benefits have a positive impact on program loyalty.

Special treatment offered by the program

With the idea of increasing customer loyalty to the program, organizations occasionally award members non-price-related special treatment benefits, such as gifts, wedding or birthday cards, and privileges. Gwinner et al. (1998) demonstrated that special treatment benefits are valued by customers as important drivers of loyalty to a particular program. Hennig-Thurau et al. (2002) found special treatment benefits to have an indirect impact on word-of-mouth communication via commitment. Additionally, Fournier et al. (1998) pointed out that customers motivated by special treatment may be loyal only until competitors offer higher rewards. Hence, we hypothesize the following:

  1. H5:

    Special treatment benefits have a positive impact on program loyalty.

Perceived value of the program

Based on equity theory (Adams 1963, 1965; Ajzen 1982), Zeithaml (1988) defined the perceived value of an offer as the consumer’s overall assessment of the utility of a product (or service) based on the perceptions of what is received and what is given. From a consumer’s point of view, perceived costs include monetary payments, expenditure of time, and any feelings of stress. By contrast, value refers to their evaluation of these costs and sacrifices against what they obtained. Consumers often assess the value of the product by comparing the company’s offerings with those of its competitors (Yang and Peterson 2004). Perceived value has been cited as one of the ways a company can generate customer loyalty (Parasuraman and Grewal 2000) and has also been found to be a major contributor to purchase intentions (Yang and Peterson 2004). The perceived value of a loyalty program has been tied to the success of that program by a number of researchers (Dowling and Uncles 1997; O’Brien and Jones 1995; Wendlandt and Schrader 2007). Yi and Jeon (2003) found that value perceptions about the loyalty program were significantly related to program loyalty. Therefore we hypothesize that:

  1. H6:

    Value perception has a positive impact on program loyalty.

Intentional and behavioral consequences of loyalty

Both company as well as program loyalty are believed to have a positive impact on customer intentions, preferences, and actual behavior. Intended behavior and preference are measured through price premium, share of wallet, and share of visits.

Customers’ willingness to pay a price premium is an important financial outcome for a firm as it measures the average percentage premium that the buyer would pay to purchase from a focal provider as opposed to another provider offering the same products. Share of wallet is a measure of how shoppers divide their purchases across competing stores. We define it as the percentage of the value of purchases by a customer at the retailer to the total value of purchases at all other retailers used by the customer. Share of visits reflects a customer’s preference of shopping at a particular store. It can be defined as the ratio of shopping trips a customer does to a particular retailer and the total number of shopping trips. It is worth noting that share of wallet considers the amount spent, whereas share of visits looks at the number of shopping trips, irrespective of the amount spent per trip. Clearly, intentions and preferences are different from actual purchase behavior, even though the former ones are frequently used as proxies for behavior.

Existing research on the attitudinal and behavioral consequences of loyalty programs could broadly be grouped into three streams. The first stream relates to the behavioral consequences of loyalty programs, largely disregarding attitudinal antecedents. For example, using consumer panels, Meyer-Waarden (2007) examined the effects of loyalty programs on lifetime duration and customer share of wallet at the store level and found that loyalty programs have positive effects on these variables. Taylor and Neslin (2005) found that reward programs contribute to profit. Similarly, Leenheer et al. (2007) found a small positive effect for loyalty program membership on share of wallet.

The second stream of research acknowledges attitudinal antecedents but relies on self-reported behavioral measures. For example, Wirtz et al. (2007) provide empirical evidence supporting the effectiveness of reward programs on psychological attachment toward the company (attitudinal loyalty) and credit card usage (share of wallet). Liu (2007) found that the loyalty program of a convenience store franchise had positive effects on purchase frequencies and transaction sizes, and it caused customers to become more loyal to the store.

The third research stream looks at the impact of loyalty programs on firm profitability and thereby disregards individual-level outcomes such as customer loyalty. For instance, Palmatier and Gopalakrishna (2005) examined three types of relationship marketing programs (financial, social, and structural) to determine which offers positive economic returns to the firm in a business-to-business setting. They found that loyalty programs that create relational bonds through personalized treatment of customers have the strongest impact on firm profitability.

Based on past research, we hypothesize that both program and company loyalty could lead to an increase in purchase intention and to ongoing business with the provider. Hence:

  1. H7:

    Company loyalty has a positive impact on (a) future purchase behavior, (b) price premium, (c) share of wallet, and (d) share of visits.

  2. H8:

    Program loyalty has a positive impact on (a) future purchase behavior, (b) price premium, (c) share of wallet, and (d) share of visits.

Both company loyalty and program loyalty are believed to have an impact on intended behavior and preference (i.e., price premium, share of wallet, share of visits), as well as on actual behavior (i.e., future purchase behavior). The question of which type of loyalty might have a higher relative impact on intention, preference, and behavior is still to be answered.

Company loyalty is understood as a favorable attitude toward the focal provider. It is likely to produce positive bonding with the provider, based on emotional attachments. Company loyalty motivates customers to stay in a long-term relationship and, by so doing, contributes to the feelings of attachment, identification, or even partnership with the provider (Fullerton 2003). Company loyalty incorporates the underlying psychological state that reflects the affective nature of the relationship between the individual customer and the provider (Gundlach et al. 1995; Kumar et al. 1995). The identification that the customer feels toward the company is likely to be translated into positive feelings about the company (Harrison-Walker 2001). Thus, the emotional attachment and positive feelings toward the company are likely to lead to a desire to maintain the relationship and, consequently, an intention to act upon it by developing positive purchase intentions (Dick and Basu 1994; Fullerton 2003).

However, situational factors might still prevent company-loyal customers from actually purchasing from a particular provider. In particular, economic incentives might cause customers to switch their favorite provider. One such incentive might be the benefits derived from being a member of a loyalty program. This benefit acts as a switching barrier, substantially increasing the costs of purchasing when the customer is not a member of that provider’s loyalty program. Results from the switching literature support this argument by demonstrating that switching costs give customers a strong incentive to continue buying from the same provider, even if other providers are offering identical products or services (e.g., Beggs and Klemperer 1992). We would therefore assume that company-loyal customers will develop positive intentions; however, high levels of company loyalty might not necessarily lead to higher purchase behavior.

Quite the opposite effects can be expected from customers with high levels of program loyalty. Loyalty toward a loyalty program can be considered a strong economic incentive to purchase from a particular provider. Customers might not necessarily develop a favorable attitude toward that provider, but they value the benefits and hence purchase from the provider to further gain or accumulate these benefits. Even though these customers might display rather low levels of company loyalty, their more rational assessment of the benefits of purchasing from a provider they are affiliated with through a loyalty program might positively impact their actual behavior much more than favorable intentions or preferences towards the company. For program-loyal customers, it is possible that even without intent to purchase from a particular provider, they will in the end purchase from that provider because of its affiliation with the loyalty program. The desire for obtaining the additional benefits available through membership of the loyalty program might outweigh the potentially negative or less favorable feelings toward the company.

When assessing the relative impact of both types of loyalty on behavior and intentions, the above arguments suggest that the more emotion-based company loyalty would be a stronger driver of positive intentions and preferences, while program loyalty is more directly related to actual behavior because of its economic nature, directly incentivizing a particular behavior irrespective of having a favorable attitude. Therefore we hypothesize the following:

  1. H9:

    The relative impact of program loyalty on (a) future purchase behavior is stronger than the impact of company loyalty on this behavior.

  2. H10:

    The relative impact of program loyalty on (b) price premium, (c) share of wallet, and (d) share of visits is weaker than the impact of company loyalty on these variables.

Figure 1 summarizes the conceptual model of this research.

Fig. 1
figure 1

Conceptual model

Full size image

Methodology

Sampling and data collection

We drew our sample from a large European retailer that is a member of a multi-firm loyalty program in which competing companies are under the banner of the same loyalty program. Hence, there is substantial within-program competition. The retail context is characterized by medium levels of involvement and information asymmetry between customer and company employees: normally, customers are not experts in the product categories involved and therefore depend on advice from service employees.

Having been granted access to the customer transaction data of the retail chain, we chose all customers from the database as the overall population for this study. We randomly selected 20,000 customers and mailed each the questionnaire, cover letter, and pre-paid return envelope. A total of 5,189 respondents returned usable questionnaires, resulting in a response rate of 25.9%. We then matched transaction data to the survey data, based on each customer’s loyalty program identification number. We compared selected items from early and late respondents and saw no signs of non-response bias (Armstrong and Overton 1977). We also found no behavioral differences (e.g., purchase frequency, average amount spent per visit, sales) between participants of the survey (n = 5,189), the selected sampling population (n = 20,000), and the overall population. Based on these findings, we conclude that non-response bias is not an issue in our study.

Measures

We developed the items for measuring the constructs of the study by drawing on prior research. The initial item pool was tested in qualitative interviews, focus-group discussions, and a pre-test among 500 customers of the same retailer (who did not participate in the main study). This procedure led to the final survey instrument for the main study. Multi-item seven-point Likert scales anchored at 1 = strongly disagree (very unsatisfied/poorest value) and 7 = strongly agree (very satisfied/best value) were used. We measured the following constructs:

  • Company Loyalty is understood as a positive attitude toward the provider (Yi and Jeon 2003) and is measured with three items adapted from Zeithaml et al. (1996) (Alpha = .802, construct reliability (CR) = .846, average variance extracted (AVE) = .648). It is important to note that all items are solely related to loyalty toward that particular retailer.

  • Company Satisfaction is the overall evaluation of the relationship a customer has with the store. It is measured with two items (De Wulf et al. 2001) (Alpha = n.a., CR = .825, AVE = .702).

  • Company Trust, also in line with De Wulf et al. (2001), is measured with two items assessing the overall feeling of trust in the provider (Alpha = n.a., CR = .909, AVE = .833).

  • Company Commitment is understood as the emotional attachment to the provider. It is measured with three items from De Wulf et al. (2001) (Alpha = .867, CR = .877, AVE = .704).

  • Program Loyalty is defined, in line with Yi and Jeon (2003), as having a favorable attitude toward the loyalty program. It is measured with three items (Alpha = .918, CR = .921, AVE = .797).

  • Program Special Treatment, according to Hennig-Thurau et al. (2002), is understood as benefits such as discounts or better prices which customers receive only because they are members of the loyalty program. It is measured with four items (Alpha = .901, CR = .905, AVE = .706).

  • Program Social Benefits, also in line with Hennig-Thurau et al. (2002), is understood as arising from being part of a community of members, such as being recognized in or being familiar with the store. It is measured with five items (Alpha = .817, CR = .857, AVE = .549).

  • Program Value is seen by Yi and Jeon (2003) as the favorable perception of the value coming from the program in terms of cash value, convenience value, and aspirational value. It is measured with four items (Alpha = .792, CR = .824, AVE = .610).

  • Future Sales are measured by transaction data coming from the retailer’s database. In line with suggestions from the literature (e.g., Bolton et al. 2000; Vogel et al. 2008), we aggregate the sales for the six months following the survey and discount them to the date of the survey with a discount rate of 15%, as suggested by Reinartz and Kumar (2003).

  • Willingness to Pay a Price Premium is conceptualized as an intention and measured with three items based on Netemeyer et al. (2004) (Alpha = .857, CR = .873, AVE = .699).

  • Share of Wallet indicates preference and is measured with a single item asking respondents to estimate what percentage of their total expenditures on products in a given category was spent in this store.

  • Share of Visits is also a measure of preference. We used a single item asking respondents how often out of ten visits they go to this store when they intend to purchase products in this category.

Results of confirmatory factor analyses (CFA) suggest valid and reliable scales. In addition, discriminant validity of the constructs was assessed (Fornell and Larcker 1981). The average variance extracted (AVE) for each construct exceeds the shared variance with all other constructs. Hence, we conclude sufficient reliability and validity for the measures in this study. Results of reliability and validity tests can be found in Table 1. The scale items are provided in the Appendix.Footnote 1

Table 1 Correlations and psychometric properties
Full size table

As we are interested in assessing marginal effects, we chose 3SLS (Greene 2007) and not structural equation modeling (SEM). However, when replicating the analysis with SEM, substantive findings remain unchanged. Having used 3SLS, our results can be read in such a way that managers would be able to assess the impact of a change (e.g., a 1-unit change) in the loyalty drivers, and consequently in the two types of loyalty, on behavioral outcomes. For analytical purposes, we averaged all indicators of each scale to construct a single-item measure. Since we used three-stage least square (3SLS) multiple regression analysis, we also assessed multicollinearity, as suggested by Hair et al. (2006) and Rust et al. (2004). Results indicate that it is not a significant issue in our data (all variance inflation factors [VIFs] are well below 3, from 1.213 for “program special treatment” to 2.557 for “company satisfaction”). Since we have data on all attitudinal constructs from just one source (i.e., the customer), we also assessed common method bias along the lines of Podsakoff et al. (2003). We performed a Harman’s single factor test and ran competing CFA-models as suggested by Podsakoff et al. (2003). Results ascertained that common method bias was not a serious issue in our study.

Results

Model results

The correlation statistics (Table 1) show that, as expected, company and program loyalty are two distinct types of loyalty. The correlation between the two constructs is .371, meaning that they share only limited variation (about 13%). Clearly, we identified two distinct potential drivers of intentions, preference, and behavior.Footnote 2

Next, we tested the hypotheses using three-stage least square (3SLS) regression analysis (Greene 2007). The final results are shown in Table 2 (note that 1A-1D are alternative models in which the dependent variable in equation 1 is different).

Table 2 3SLS-regression results (unstandardized coefficients)
Full size table

As can be seen, both company loyalty (CL) and program loyalty (PL) are driven by the expected antecedents derived in H1-H3 and H4-H6, respectively. As for company loyalty, commitment (CC;γ between .320 and .335, p < .01) and trust (CT;γ between .301 and .311, p < .01) seem to be slightly more important drivers than satisfaction (CS;γ between .265 and .295, p < .01), together explaining about 69% of the variance in company loyalty, indicating good predictive power for the model.

Clearly the strongest predictor of program loyalty is the value offered by the program (PV;γ between .617 and .628, p < .01). Both social benefits (SB;γ between .117 and .120, p < .01) and special treatments (ST;γ between .129 and .144, p < .01) are significant, yet less important, predictors. This result shows that in line with our theoretical argument, program loyalty is mainly driven by the cost/benefit calculations of customers estimating their economic gain from loyalty program membership; it reinforces our assumption that program loyalty is a calculative type of loyalty, based on economic benefits. Again, good predictive power of the model can be observed: the three predictors of program loyalty explain about 59% of its variation.

We also find that both company loyalty and program loyalty are significant drivers of future sales (CL:γ = 46.332, p < .01; PL: γ = 80.316, p < .01; r-square = .080), price premium (CL:γ = .591, p < .01; PL: γ = .401, p < .01; r-square = .228), and share of wallet (CL:γ = 4.554, p < .01; PL: γ = 3.299, p < .01; r-square = .289), while only company loyalty significantly influences share of visits (CL:γ = .981, p < .01; PL: γ = .021, p > .1; r-square = .292). This largely confirms H7 and H8.

It is important to note that three of these four dependent variables (price premium, share of wallet, and share of visits) are survey-based perceptual measures of intention and preference. When assessing the relative impact of company and program loyalty on actual behavior and intention/preference, we see that the three perceptual variables are predominantly predicted by company loyalty. They show higher coefficients than program loyalty across all three models. Conversely, program loyalty is a significantly stronger driver of future sales than company loyalty, displayed by a larger regression coefficient (Table 2).

To formally test the significance of difference between the coefficients, we created a comparison between the model in which the coefficients of company and program loyalty on the four dependent variables are freely estimated and a model in which equality of the two paths is enforced. The restricted model has one extra degree of freedom (DF). We then compared the chi-square differences (Δχ2, DF = 1) between the free and restricted models and find a significantly poorer fit for the four restricted models (Δχ2 between 5.895 and 209.741). These results lend support to H9 and H10.Footnote 3

In summary, we find strong support for our theoretical model. In particular, the results show that company loyalty influences a customer’s choice to visit a particular provider and to prefer it over competitors, but it is not a strong predictor of purchase behavior. Conversely, program loyalty is a far more important driver of purchase behavior. Figure 2 summarizes the key results of the empirical study.

Fig. 2
figure 2

Key results

Full size image

Our findings raise some important implications for managers and marketing theory alike. We discuss them in the subsequent section.

Discussion

Summary of results and implications

Results obtained from this large-scale empirical study offer relevant insights into drivers of customer intention, preference, and behavior. First, results clearly show that company loyalty and program loyalty are not only conceptually distinct, but they are also empirically different constructs, evidenced by a low correlation of .371. This reinforces the need to assess and manage both types of loyalty separately in an attempt to better understand the customer. As for company loyalty, our study illustrates that trust, satisfaction, and commitment have a significant positive effect on company loyalty, although commitment and trust are relatively more important than satisfaction. These drivers deal with emotions a customer has developed toward the relationship with a particular provider. On the other hand, the strongest driver of program loyalty is the economic value offered by the program. Social benefits and special treatments are significant yet less important drivers of program loyalty. The fact that program loyalty is mainly driven by value shows that it is based on cost/benefit calculations by customers estimating their economic gain from being a member of the program.

These findings illustrate that the common practice of managing “loyalty” by introducing a loyalty program is too simplistic. Customers differentiate between loyalty to a program and loyalty to a company. Program loyalty is largely driven by economic incentives appealing to rational customers, while company loyalty is more emotional in nature, being driven by the quality of the relationship between customer and company.

Results indicate that the two loyalties operate quite differently in such a way that emotion-based company loyalty mainly attracts customers to a particular company (i.e., an outlet of a branded chain store) as opposed to a competitor. Hence, company loyalty is a stronger predictor of share of visits—a measure of preferences—while program loyalty is a non-significant predictor of share of visits. Similarly, although to a lesser degree, company loyalty is a better predictor of share of wallet, which essentially measures the attractiveness of the focal provider compared to competitors. In contrast, we find program loyalty to be of pivotal importance in predicting actual purchase behavior, but less so preference.

These findings suggest that once customers have been attracted to a particular store, program loyalty and the economic rationale it is based upon leads them to be more susceptible to cross- and up-selling opportunities. A simple analysis of the marginal effect of program loyalty on future sales using the case of our retailer provides some indication of the size of these opportunities: a one-unit increase in program loyalty would lead in this case, ceteris paribus, to additional sales of 80.32 Euros per customer. As the retailer in question has a customer base of 1.5 million, it could potentially yield additional sales of up to 120 million Euros. We are not suggesting that these numbers are generalizable to other providers; however, it would be worthwhile for managers to investigate the marginal effects of their loyalty program affiliation on potential future earnings in order to assess the necessity to remain in a given loyalty program.

Based on our study’s results, we caution managers not to assume that loyalty programs automatically lead customers to be loyal to the company. In order to gain long-term benefits from their relationship marketing efforts, managers must consider delivering both emotional and economic benefits to the customer. Providers offering loyalty reward programs devoid of emotional benefits run the risk of losing their customers in the long run.

Our study also shows that both company loyalty and program loyalty are significant drivers of future sales. We see, however, that company loyalty is a significantly stronger driver of price premium, share of wallet, and share of visits than is program loyalty. In contrast, program loyalty is a significantly stronger driver of future sales than is company loyalty. These results are both encouraging and troubling. It is good news for managers that their substantial investments in loyalty programs seem to lead to at least some positive consequences and, more importantly, that they seem to encourage future purchases. However, since customer behavior is mainly driven by the value a particular loyalty program offers, competing providers can simply imitate these benefits and, by so doing, encourage customers to switch to a provider that offers a similar or better loyalty program. Such switching behavior would be likely because customers have not developed a favorable relationship with the providing company. Therefore, switching would be difficult to prevent by any means other than improving the value of one’s own loyalty program. This might result in “loyalty program wars”—similar to “price wars”—leading to profit-deterioration for the provider as a consequence of the costly need to upgrade the benefits of the loyalty program.

Our findings also indicate that future behavior is driven, to only a relatively small degree, by a feeling of attachment to a particular provider. This is unfortunate, because such a sense of belonging might prevent customers from switching even when a competing offer is equal to or even better than the current provider’s offer. It seems that economic or rational reasons, such as collecting loyalty points and receiving tangible relational benefits, drive customers’ behavior once inside the store much more than does their sense of belonging to a company. It can be concluded that while company loyalty is more likely to attract customers to a provider, program loyalty creates opportunities to cross- and up-sell and by so doing, increases the amount of money spent by customers once inside the store.

Managers who are aware of these effects should reconsider the design of their loyalty programs. As a first consideration, multi-store loyalty programs with competing providers (as is the case in our study as well as many airline and European retail loyalty programs) might not be ideal, because customers can remain loyal to the program without being loyal to a particular provider. Effectively managing within-program competition is critical, and individual companies must determine whether other companies that are partners of the program are competitors in at least some parts of the assortment of products or services offered.

Even if there is no threatening competition within the program, competition from other programs may still pose a risk. In order to prevent customers from switching not only providers but also programs, there must be a clear value proposition. Ideally, such a value proposition should attempt to offer rewards that are likely to increase company as well as program loyalty. For example, while a cash bonus might increase the value of a program, special products and services, extended opening hours, or events for loyalty program members might at the same time create a feeling of belonging to the company. Clearly, matching a percentage cash reward is easy to imitate, but offering a special shopping experience might differentiate one provider from another.

Limitations and future research

Despite its large-scale empirical data, this study has limitations. Our findings may only be relevant to loyalty programs with multiple competitors enrolled. In situations with no intra-program competition, customers might not differentiate between company and program loyalty and consequently, our findings might not apply. Moreover, we have data from only one loyalty program in one industry and in one country, thus potentially limiting its generalizability. We would speculate that results might be generalizable to settings with similar levels of customer involvement (i.e., medium) and substantial intra-program competition. Settings with longer purchase cycles (e.g., consumer durables) could spark higher levels of customer involvement in the purchase, which in turn might render company loyalty more important due to higher risks associated with the purchase. Conversely, low involvement settings (e.g., grocery retailing) might see a relative unimportance of company loyalty for predicting future behavior. However, these speculations can only be tested through diligent replications of our model in different settings (e.g., grocery retailing, consumer durables, cars), different countries, and different types of programs (e.g., with different switching barriers and different levels of within-program competition).

In examining the effectiveness of program loyalty versus company loyalty, we have not taken into account a firm’s business practices and marketing efforts in relation to its competitors. For instance, an improvement in the value of the loyalty program might trigger competitors to improve their own offerings. In case they are members of a competing loyalty program, a possible result might be the aforementioned “loyalty program war,” by which ever improving program benefits would seriously harm retailers’ profitability. At some point, the incremental benefits of the loyalty program to the individual retailer might disappear.

As our study considers only attitudinal data from one point in time, further studies could include longitudinal observation of customer attitudes such as program and company loyalty. Possibly, over time, switching patterns might emerge that would inform retailers when to take particular care of their loyalty program members. It is likely that certain levels of company loyalty and program loyalty might function as a hygiene factor. Apparently, there is no simple trade-off between the two. Further studies would benefit from investigating these issues in more detail.

Another fruitful area of further research would be to analyze the interplay between company loyalty and program loyalty. Our model does not constrain their relationship as it allows both loyalties to correlate. However, rival hypotheses might test whether program loyalty is antecedent to company loyalty or the other way around. Arguments could be made either way, and results would further improve our understanding of loyalty formation.

Our results offer strong evidence concerning the relative importance of company and program loyalty on intention, preference, and behavior, which might be further analyzed in empirical studies using more behavioral indicators such as number of products purchased, the margins for each product, or customer lifetime value. This would further enhance understanding of the consequences of different types of loyalty.

Finally, our findings also suggest that there is only a weak relationship between self-reported attitudes or intentions and actual behavior. Moreover, we find antecedents of behavioral intentions to differ from those of actual behavior. We believe that this is an important finding particularly for academic research, which often relies on self-reported behavior as a proxy for actual behavior. Our findings raise some concerns about this practice as it potentially introduces substantial bias. It is a clear challenge to find more reliable proxies for behavior in the absence of behavioral data.