Relationship Marketing in Consumer markets

Building sustainable customer relationships through loyalty programs: a structural modeling investigation Sanjai K Parahoo Postgraduate Program Director, e-School of Business and Quality Management, Hamdan Bin Mohammed e-University, Dubai, UAE, [email protected]; [email protected] Conference Track: Marketing- CRM ABSTRACT This study investigates the impact of involvement and commitment, on firm-customer relationships within customer loyalty programs. A conceptual model of loyalty is designed, and using structural equation modeling, is empirically examined using data collected from 445 members of global loyalty programs. The fit of both the measurement and structural models are statistically significant and satisfactory, confirming that involvement and commitment sustain loyalty. In addition, quality and value are found to directly influence loyalty. Therefore, firms implementing loyalty programs should focus on defining win-win partnerships with their customers based on equity, and develop attractive offers to motivate and sustain their customers within the relationship. This study contributes to relationship marketing in consumer markets through a predictive model of loyalty, generic with regard to industry and nationality of respondents, thereby mapping the nature of global loyalty programs, whereas previous studies have tended to focus on a specific industry using national samples of respondents.

Keywords: customer loyalty; loyalty program, involvement, commitment, service quality, relationship marketing.

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Building sustainable customer relationships through loyalty programs: a structural modeling approach

1.0

Introduction

Customer loyalty programs are booming (Dorotic et al, 2011), with memberships estimated at over 1.3 billion in the USA(Ferguson and Hlavinka, 2007), and with Canada, UK and Australia being classified as other mature markets (Lacey and Sneath, 2006).Loyalty programs have been defined as: as an integrated system of marketing actions that aims to make customers more loyal by developing relationships with them (Sharp and Sharp, 1997). They vary in their complexity from mere stamp-based discount schemes tomore formal and elaborate programs involving customer databases. The focus here will be on the later ones,pioneered byairline frequent flyer programs, and subsequently widely adopted by firms in other industries (e.g. hotel, credit card, retail, etc.). These firmsattempt to maintain or increase the output from their profitable customers through a value-added, interactive long-term relationship that is sustained by its mutually beneficial nature.

The rapid growth of loyalty programs has drawn research attention to them (Gomez et al., 2006), with studies often examining the programs’ effect on loyalty and their critical success factors in various industry settings (Stauss et al., 2005). However, due to a lack of understanding of their drivers and inadequate generalizable conclusions across preceding studies, the impact of loyalty programs has beenmixed (Dorotic, et al., 2011),thus the debate on their effectiveness is far from settled (Liu, 2007). In this context, customer loyalty rates in industry are reported to be low. As an illustration, in the retail sector where loyalty programs are widespread,while70 percent of customers report to be “loyal” to their favorite retailers, up to 85 percent of them would be willing to shop elsewhere if properly enticed (Accenture cited by Hoffman and Lowitz, 2008).

This state of affairs may derive from the factthat loyalty createdwithinloyalty programs represents a deviation from that in current management literature based ondeveloping an affective preference for a brand (e.g. Dick and Basu, 1984). Conversely, in a loyalty program environment, the dynamics that would motivate customers to restrict their future purchase choices are based on behavioral reciprocity within a rather calculative mutually beneficial relationship, insteadof necessarily involving an affective bond with a specificbrand. A typical illustration could be when the primary motivatorfora customer in selecting an airline is to earn frequent flyer miles, rather than an affective preference forthe airline. Therefore, marketers need to recognize this situation and consequently base their strategies on achieving and sustaining such behavioral loyalty; a situation requiring a far deeper understanding of perceptions and attitudes of customers towards loyalty programs (Gomez et al., 2006).

This study therefore investigatedthe major variables that influence the stability and sustainability of the firm-customer relationships in loyalty programs. It complements recent research in this field by designing and empirically testinga structural model that incorporates two customer characteristics, involvement and commitment, as well as quality and value, and the respective effects of these variables on loyalty. With the presence of strategic alliances among global firms, loyalty programs now operate across industries and geographical markets. Therefore, the study model developed must be generic as regards nationality of respondents and industry offering programs.

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Literature review and design of conceptual model

Drawing from a review of the extant literature, the pertinent variables involved in establishing firmcustomer relationships will sequentially be identified,and theirinter-relationships proposed, starting from the output variable, customer loyalty.

2.1

Dependent variables

2.1.1 Customer Loyalty Customer loyalty is the outcome variable of the study model and it has been defined as a customer’s intention or predisposition to purchase from the same firm again (Edvardsson et al., 2000). The motivation for understanding and improving loyalty emerges from its empirically validated relations with profitability (e.g. Turel and Serenko, 2006). Since the 1990’s, customer loyalty has beenconceptualized as an attitudinal construct (Bloemer et al., 1999), and anotable contribution proposeda framework with three attitudinal antecedents (cognitive, affective, and conative) of loyalty (Dick and Basu 1994).

Such a perspective motivated marketers to attempt to develop a strong attitudinal preference for their brands,inspired by the success of mega brands such as Apple, Google, and IBM, each individually valued above USD 100 billion(Bradnz, 2011).However, the loyalty developed within loyalty programs isbased on a different premise, and the value of such programs is determined by: cash value,choice of redemption options,aspirational value,relevance, andconvenience (O’Brien and Jones, 1995). Customer loyalty therefore seems to derive from pragmatic considerations based on reciprocity, within a calculative, win-win and long-term relationship, rather than on affective loyalty (Bolton et al., 2000). As a result, customer decision to maintain the relationship and not to shift to a competitor, would be guided by a feeling of intelligence and pride of having achieved something without having to pay the normal price (Kivetz and Simonson, 2002), with apayback resting onthe continuity of the relationship exceedingthat of defection. Loyalty will therefore be conceptualized in this study from a behavioral perspective.

2.1.2 Perceived service quality and perceived value Customer satisfaction has been defined as the ‘customer’s fulfilment response’ (Oliver 1997). For this reason, many firms concentrate on delivering quality and value to achieve customer fulfilment and hence satisfaction. It may therefore be considered that modelling satisfaction as the output of quality and value would lead to a tautology. Perceived service quality and perceived value are therefore selected to represent an elaborate conceptualization of customer satisfaction, in the study model.

There is strong research evidence that service quality influences the behavioral intentions of customers or has an indirect influence on such intentions, mediated through customer satisfaction (Cronin et al., 2000, Lewis and Soureli, 2006). Similarly, perceived value has often been hypothesized to be a driver of satisfaction and loyalty (e.g. Lewis and Soureli, 2006; Li and Yang, 2008), where it has been argued that customers receiving less than expected performances may still be satisfied if they paid less for the service (Patterson and Spreng, 1997).Specifically, in a loyalty program environment, customer satisfaction has been found to support loyalty (Mueller and Pietrzyck, 2004), while in financial services customer satisfaction has been found to lead to behavioral loyalty (Liang et al., 2009; Lewis and Soureli, 2006). Furthermore, the relative attractiveness of a reward program has been found to have a positive impact on behavioral loyalty (Wirtz et al., 2007).

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Based on the above discussion, the following hypotheses may be postulated, and the relationships are graphically represented in figure 1: H1: Perceived value has a positive influence on customer loyalty, and H2: Perceived service quality has a positive influence on customer loyalty.

Further, it may be expected that perceived service quality would be positively related with perceived value (Lewis and Soureli, 2006). In a loyalty program environment, when a customer receives higher service due to his membership status (e.g. access to airport lounge), this is expected to lead to a positive influence on perceived value by increasing benefits derived from the product/service at constant cost (see Zeithaml, 1988). A service quality- value relationship has been empirically determined (e.g. Zins, 2001). Thus: H3: Perceived service quality has a positive influence on perceived value.

2.2

Independent variables

It has been proposed that understanding customer characteristics is important to managers both as supportto tailor their service delivery, and as a means to better segment customers (Baker, et al., 2009). In this regard, twocustomer characteristics determining the success of relationships emerge from the literature:  Involvement (e.g. Tu and Olsen, 2010; Hansen et al., 2010; and Wu, et al., 2011);  Commitment (Lacey, 2009; Bloemer and Odekerken-Schröder, 2003; Garbarino and Johnson, 1999; and Morgan and Hunt, 1994);

2.2.1 Involvement Involvement has been defined (Rothschild, 1984) as “an unobservable state of motivation, arousal or interest”, and considered as the relational variable most predictive of purchase behavior (Evrard and Aurier, 1996). It has been proposed that consumer behavior research can be can be made more insightful and actionable by introducing key elements of motivation research (Pincus, 2004), and this is supported by studies that have shown that higher involvement results in greater elaboration (Celsi and Olson, 1988; Petty et al., 1983), leading to more carefully reasoned appraisals of a product/service offer (Hansen, et al., 2010). Involvement thus works towards motivating a loyalty program member to display the rigor to optimize on the appropriate and timely choice of rewards and benefits, thereby leading to higher service quality, such as access to an airport lounge or airline class upgrade. Although, high-involvement customers are (theoretically) likelyto derive value from loyalty programs (Gordon et al., 1998), in practice this only occurs over a long time frame. For example, a free airline ticket may be earnedby a program member after seven or more regular flights have been purchased. For average customers, this would probably span a period exceeding2 years,thereby not representing any short-term gratification linked to their involvement.For this reason, involvement is not expected to directly influence perceived value.However, it has been argued that a total customer experience is created by active involvement between a firm and its customers, which supported the development of loyalty (Mascarenhas et al, 2006; Wu et al., 2011), and enhanced participation in loyalty program relationships (Varki and Wong, 2003). Accordingly the following hypotheses may be proposed: H4: Involvement has a positive influence on perceived service quality H5: Involvement has a positive influence on customer loyalty

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2.2.2 Commitment Commitment has been defined as (Morgan and Hunt, 1994): an exchange partner’s belief that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it; that is the committed party believes the relationship is worth working on to ensure that it endures indefinitely.

Commitment has been recognized as an essential component for successful long-term relationships (Gabarino and Johnson, 1999; Hausman, 2001), and loyalty programs develop and sustain stronger firmcustomer relationships than would otherwise result without these programs (Lacey, 2009). This commitment of customers to a loyalty program relationship has a positive impact on customers’ purchase intentions (Bloemer and Odekerken-Schröder, 2003; Lacey, 2009). In addition, a commitment-satisfaction relationship has been empirically demonstrated (Hausman, 2001), a finding expected, as a customer displaying commitment will invest sustained time and efforts in his loyalty relationship with a firm in an extended time frame, leading to customer derived value (e.g. free air travel), and service quality (e.g. room upgrade in a hotel).

Accordingly, it is hypothesized: H6: Commitment has a positive effect on perceived value H7: Commitment has a positive effect on perceived service quality H8: Commitment has a positive effect on customer loyalty

The preceding relationships may be represented in a structural model as per figure 1:

H5

Perceived Value

Involvement

H4

H1 H3

H6 Commitment

Customer Loyalty

H7 Perceived Service Quality

H2

H8 Figure 1: Theoretical structural model with proposed hypotheses (all relationships are positive)

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3.0

Methodology

In order to develop appropriate measures for the five study constructs, a scientific methodology was followed using data collected from two independent samples of 46 and 128 respondents.Members of global loyalty programs of both genders and of different nationalities were randomly sampled and interviewed face-to-face in a hotel setting. For conciseness, the major process steps followed are summarizedbelow, and relevantcharacteristicspertaining to the scale items are presented in table 1.

Through an intensive literature review, an initial pool of 59 items encompassing the domain of the five study constructs was constituted (recommendations of Churchill, 1979). The items were adapted to the context of the present study, andfine-tuned after a pilot run with 9 members of loyalty programs.

Using data collected fromthefirst sample of 46 respondents,item-to-total correlation analysis in SPSS was used to purify the pool of items(Gerbing and Anderson, 1988). It has been recommended that an item has a minimum loading of 0.7 on its associated construct (Fornell et al., 1982). Allitems with correlation coefficients exceeding 0.7 were therefore retained for four study constructs (involvement, commitment, value, and loyalty). In the case of service quality scale which,compared to other study constructs,had nearly twice the number of initial items, the cut-off threshold was set at 0.75, this process yieldinga total of 28 measurement items distributed about equally among the different constructs.

At the next stage, using data collected from the independent sample of 128 respondents, a comparative test of uni-dimensionality for each scale was undertaken through confirmatory factor analysis (Gerbing & Anderson, 1988), on the purified scale of 28 items. The fit of the measurement model was successfully confirmed using Lisrel 8.54 (Joreskog and Sorbom, 2003), as illustrated in table 1.The p-values for the five measurement models were all well above 0.05, implying the null hypothesis of no difference between the observed and fitted association matrices could not be rejected, thereby conforming the structure of the five models. In further support of this finding, the fit indices (RMSEA and AGFI) were above the thresholds proposed by Hulland, et al. (1996), and the path loadings from the constructs to their indicators were generally well above 0.70 for all indicators in the different measurement models.

In addition to uni-dimensionality, a measurement scale must demonstrate both reliability and validity.Reliability of a scale demonstrates the consistency with which the instrument measures the concept (Malhotra, 2004, p 267), and a Cronbach’s alphavalue of above 0.70 is usually taken as an indication of internal scale consistency (Nunnally, 1988, p. 96). Excellent alpha values close to 0.9 were obtained for all five scales (refer to table 1), thereby confirming their respective reliabilities.

While validity addresses the extent to which differences in the observed scores reflect true differences among objects on the characteristics being measured (Malhotra, 2004, p 269), there are several types most frequently used including: face, concurrent & construct. To address how well the scale items captured the measurement task (Malhotra, 2004), the current instrument was built by using items from existing construct scales, thereby addressing face validity. Concurrent validity, the degree of correspondence between a measure and a criterion variable existing at the same time as the measure (Bollen, 1989, p. 186), was established by correlating the composite score of each scale with its overall measure, captured separately in the questionnaire. Results showed excellent correlations coefficients (Involvement= 0.77; Commitment=0.94; Value=0.92; Quality=0.86; and Loyalty=0.94; p < 0.01) thereby establishing concurrent validity. Lastly, construct validity was successfully established for the five scales by the high and significant correlation of the composite score of the respective construct with that of a related construct, with: r=0.575, p