Investing in customer loyalty: the moderating role of ...

Serv Bus (2015) 9:17–40 DOI 10.1007/s11628-013-0213-y EMPIRICAL ARTICLE

Investing in customer loyalty: the moderating role of relational characteristics M. S. Balaji

Received: 5 July 2013 / Accepted: 28 October 2013 / Published online: 9 November 2013 Ó Springer-Verlag Berlin Heidelberg 2013

Abstract The purpose of this study is to explore the linkages between relationship investment, relationship quality, and loyalty, as well as the moderating role of relational characteristics of age (length of relationship), density (number of unique relational ties), and dependence (extent of dependence on relationship partnership). Based on the interpersonal perspective, this study extends previous research by incorporating relational characteristics in understanding the effects of relationship investment strategies on loyalty. Responses collected from 381 actual retail banking customers were analyzed using structural equation modeling and hierarchical regression analysis. The results show that in forming customer loyalty, relationship investments, satisfaction, and commitment play a critical role. However, it was found that these relationships are moderated by the relational characteristics. Specifically, the effect of relationship satisfaction on loyalty decreases as the length of the relationship increases. On the contrary, a positive satisfaction and loyalty relationship was observed for high density users. These findings help managers in developing and implementing relationship investment strategies that enhance customer loyalty. Keywords Customer loyalty Relationship investment Satisfaction Relationship age Relationship density Relationship dependence

1 Introduction The concept of relationship investment is increasingly becoming popular in examining interpersonal relationships in both business markets and consumer markets. Prior empirical studies show that investing in relationships can lead to higher M. S. Balaji (&) Department of Marketing, Taylor’s Business School, Taylor’s University, No. 1, Jalan Taylor’s, 47500 Subang Jaya, Selangor, Malaysia e-mail: [email protected]; [email protected]

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firm performance (Palmatier et al. 2006), greater shareholder value (Stahl et al. 2003), increased retention (Ahmad and Buttle 2001), and strategic partnerships (Christopher and Ju¨ttner 2000). In an article, Palmatier et al. (2007) argue that relationship investments are critical for exchange performance as they foster cooperation, commitment, and close relationship. Consequently, firms assign considerable resources (investments) to relationship marketing strategies aimed at increasing relationship satisfaction and loyalty. As De Wulf et al. (2001) point out, in a firm– customer relationship, customers feel compelled to respond to the relationship investment strategies by increasing their loyalty and affiliation toward the firm. Thus, high levels of relationship investments lead to greater relationship quality and loyalty (De Wulf et al. 2001), which eventually leads to lower switching behavior, greater sales revenue, and higher relative market share (De Ruyter et al. 2001). Researchers have suggested that effectiveness of relationship marketing strategies may vary depending on the relational characteristics; this suggests a need for further research that explicitly examines the factors that moderate the investment–loyalty relationship. Furthermore, there have been recent calls in the marketing literature for deeper insights into factors that may affect relationship outcomes (Seiders et al. 2005; Dagger and O’Brien 2010; Kumar et al. 2013). Drawing on the interpersonal relationship perspective (Berscheid et al. 1989), this study proposes three relational characteristics namely age (the duration of time the relationship existed), density (the number of unique relational ties between the firm and the customer), and dependence (extent of dependence on relationship partnership) that may impact the effectiveness of relationship investment strategies. Few prior empirical studies that have examined the impact of relationship age on behavioral outcomes have produced contrasting results. Therefore, this study aims to advance the relationship marketing and strategic marketing literature by addressing the following questions: how relationship investments lead to increased quality perceptions and loyalty; and what relational characteristics affect the relationship between investments and loyalty. The objectives of this research study are threefold. First, this paper aims to examine how customers’ assessment of the firms’ relationship investment strategies affect their perception of relationship quality and loyalty. In doing so, the second objective of this study proposes relationship quality as a disaggregate model consisting of trust, relationship satisfaction and relationship commitment. It is argued that conceptualizing relationship quality as distinct components will help managers better understand the role of each of these components in affecting loyalty. This approach integrates two research streams of relationship marketing literature. The first is the mediating role of trust and commitment in affecting relationship outcomes (Aurier and N’Goala 2010) and the second is the development process of customer relationships (Reinartz et al. 2004). The last objective of this study examines the moderating role of relationship age, density, and dependence in the linkages between investment, quality, and loyalty. This extends the previous research by showing that the investment–loyalty relationship is influenced by the nature of firm–customer relationships. The rest of the paper is organized the following way. The next section begins with a review of extant literature. This review consists of four sections: the effects of relationship investment; interrelationships between the relationship quality

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Investment–quality–loyalty and relationship characteristics

19

components; the relationship between quality components and loyalty; and the moderating role of relational characteristics. Subsequently the research methods for testing the hypotheses are discussed. This is followed by the discussion of the findings, discussion of implications. The paper concludes with the limitations and future research directions.

2 Literature review We present a conceptual framework in Fig. 1 that proposes relationship investment, trust, satisfaction, and commitment as antecedents to loyalty. Further, we suggest that this relationship is moderated by relationship age, density, and dependence. We operationalise relationship quality as the overall strength of firm–customer relationship comprising of three components namely trust, satisfaction, and commitment (Garbarino and Johnson 1999). By operationalizing relationship quality into disaggregate components; we examine the interrelationships among trust, satisfaction, and commitment in affecting loyalty. This model is consistent with the De Wulf et al. (2001) work that customers reciprocate favorably to the firms’ relationship strategies. In the section that follows, we propose the relationship between the constructs and describe their effects. 2.1 Effect of relationship investment on trust, satisfaction, commitment, and loyalty Prior research studies suggest that effectiveness of relationship marketing strategies depends on the resources committed by the firm in the customer relationship. According to Kim et al. (2008, p. 508) relationship investment refers to a customer’s overall perception of the extent to which a retailer actively makes efforts that are intended to retain regular customers. Such investments create expectations of reciprocity through financial, social, and structural ties and motivate the parties to strengthen their relationships (Hsieh et al. 2005). We use the investment model (Rusbult 1980) one of the leading theoretical frameworks in understanding the role of relationship investment in enhancing relationship quality and loyalty. The investment model suggests that the decision to remain in or voluntarily end the relationship is predicted by subjective experiences (Drigotas and Rusbult 1992). These subjective experiences involve future-orientated thoughts, emotions, obligations, and satisfaction with the firm that contribute to the stay/leave relationship decision. According to Rusbult (1980), relationship investment is one of the key elements that determine the propensity to maintain and feel committed to the relationship. The investment of resources signal the firm’s intended relational efforts and their expected benefits to the customers. As trust is the central condition of any enduring relationship (Morgan and Hunt 1994), it is believed that relationship investments may increase the trust toward the firm. For instance, Rafiq et al. (2012) find empirical support for the positive relationship between e-perceived relationship investment and e-trust among online shoppers. Relationship satisfaction refers to the

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H4

Trust H7

H1 H5

Relationship Investment H10

H2

H11 H12

Relationship Satisfaction H6

H8

Relationship Loyalty

H9

H3

Relationship Commitment

Relational Characteristics • Age • Density • Dependence

H10-12

Fig. 1 Conceptual framework for examining the moderating role of relationship characteristics among investment, quality, and loyalty

emotions/feelings experienced by the customer in a relationship. The De Wulf et al. (2001) study show that customers tend to be more satisfied with firms that make efforts toward them. Also, Wang and Head’s (2007) study in the e-shopping context found that customers tend to evaluate firms with high relationship investments as more trustworthy and satisfied. Since relationship satisfaction is influenced by the extent to which the firm meets the expectations, we believe that firms with high relationship investment provide greater customer benefits and relationship satisfaction. Morgan and Hunt (1994) suggest that relationship commitment is directly influenced by the benefits accrued by the firm’s investments to customers. Similarly, Sung and Choi (2010) find that the customer’s commitment toward the relationship is related to the perceived size of the investments. That is, greater the relationship investment, greater would be the relationship commitment. Thus, relationship investments signal longer-term focus and reflect the firm’s commitment to the customer relationship. Such pledges create continuity expectations that might help maintain and strengthen firm–customer relationship. Alejandro et al. (2011) in their study find that dealers’ relationship-specific investments are directly related to dealers’ loyalty. In addition, greater relationship-specific investments made

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customers perceive the dealers as less opportunistic leading to greater firm performance. Based on the above discussion we hypothesize that: H 1: H 2: H 3: H 4:

Relationship investment is positively related to trust. Relationship investment is positively related to relationship satisfaction. Relationship investment is positively related to relationship commitment. Relationship investment is positively related to loyalty.

2.2 Interrelationships between trust, satisfaction, and commitment Trust and satisfaction are suggested to be two key concepts in relationship marketing. Prior literature provides some empirical evidence of the relationship between trust and satisfaction. For example, Grewal et al. (2001) and Balasubramanian et al. (2003) in their studies have shown that trust directly impacted satisfaction. Moreover, the trust–satisfaction relationship is supported by the cognitive consistency theory which states that people attempt to behave in a consistent manner so as to be in a pleasant psychological state (Fraedrich and Ferrell 1992). Thus, we can expect that relationship satisfaction would be greater in the presence of customer trusting beliefs. Drawing on Hennig-Thurau et al. (2002) and Fullerton (2011), it was postulated that relationship satisfaction would positively influence customers’ commitment to the relationship. As satisfaction is related to meeting customer needs, and repeated realization of these needs can lead to affectionate bonds with the firm (Vlachos et al. 2010), we propose a positive relationship between satisfaction and commitment. Also, a dis-satisfied customer may feel betrayed and become emotionally frustrated and distressed with the firm. Based on the above discussion we hypothesize that: H5: Trust is positively related to relationship satisfaction. H6: Relationship satisfaction is positively related to relationship commitment. 2.3 Effects of trust, satisfaction, and commitment on loyalty Prior research studies have shown that increased trust toward the firm leads to more favorable attitudes and firm–customer relationship (Harris and Goode 2004; Xie and Peng 2009). In a study on fixed line telephones, Ranaweera and Prabhu (2003) examined the effects of trust, satisfaction, and switching barriers on customer retention. They found that trust complements satisfaction at its high-end in affecting customer retention. Similarly, Karjaluoto et al. (2012) in a continuous service provider setting find empirical evidence for the positive relationship between trust and loyalty. This is because, when consumers consistently receive competent service, they perceive the service offering as high value leading to long-term relationships. A number of prior studies have proposed a positive relationship between satisfaction and loyalty. For example, Lai et al. (2009) showed that customer satisfaction has the greatest effect on loyalty when considered along with value and image. Furthermore, the investment model (Rusbult 1980) presents that the satisfied customers’ commitment to the relationship could be influenced by the quality of

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alternatives. In a study, Ping (1994) found that channel customers with low satisfaction levels and high alternative channel attractiveness experienced greater exit intentions. As dissatisfied customers are likely to search for alternatives, they might resist forming a longer-term relationship with the current service provider. These customers are more likely to yield to the competitor’s overturns and thereby decrease their dependence on the service provider (Davis-Sramek et al. 2009). According to Hur et al. (2013), commitment is a key component in building customer loyalty. Commitment exists when the partners believe that maintaining long-term relationship is important for obtaining the desired outcomes. In a study, Caceres and Paparoidamis (2007) found that commitment is positively related to business loyalty. Based on the above discussion we hypothesize that: H7: Trust is positively related to loyalty. H8: Relationship satisfaction is positively related to loyalty. H9: Relationship commitment is positively related to loyalty.

2.4 The moderating role of relational characteristics Relational characteristics refer to the nature and extent of relationship between the firm and its customers (Seiders et al. 2005). Research on the moderating role of relational characteristics on relationship investment and loyalty has so far been limited. Table 1 summarizes the research studies that examined the moderating effects of relational characteristics. We report the studies only for the moderating effects of relational characteristics; we do not include the studies that investigated the moderating role of customer characteristics. A review of Table 1 shows that prior studies have largely investigated the moderating role of relationship age or length. Further, the effects of relationship age on key behavioral outcomes have produced contradictory findings. Thus, this study makes significant contributions by investigating the moderating role of relationship age between investment, quality, and loyalty linkages; and exploring previously unexamined relational characteristics of relationship density and dependence. 2.5 Relationship age Relationship age refers to the amount of time the customer and the firm had known each other. Firm–customer relationships evolve over time with experience. Relationship age is positively related to the market value creation (Galbreath 2002), corporate reputation (Bartikowski et al. 2011), and profitability (Reinartz and Kumar 2003). We believe that the effects of relationship investment on trust, satisfaction, and commitment may increase along with the age of the relationship. As relationship progresses, customers gain more information and become knowledgeable about the firms’ offerings. This makes the customers more confident in their evaluation of the firm’s relationship efforts (Palmatier et al. 2006). Moreover, the benefits accrued to the customers’ increase as the relationship age progresses.

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Relationship duration

Relationship length

Insurance 2300

Small business owners 677

Insurance 2300

Apparel Retail 945

Mobile services 461

Multiple services 591

Apparel 634

Verhoef et al. (2001)

Coulter and Coulter (2002)

Verhoef et al. (2002)

Seiders et al. (2005)

Raimondo and Costabile (2008)

Dagger et al. (2009)

De Cannie`re et al. (2010)

Relationship strength

Relationship duration

Relationship age

Relationship age Relationship program participation

Relationship age

Relational orientation

Theater company 401

Garbarino and Johnson (1999)

Relational characteristics

Context and sample

Authors

Table 1 Summary of key literature of relational characteristics

Regression analysis

Regression analysis

Structural equation modeling

Regression analysis

Hierarchical regression analysis

Hierarchical regression analysis

Probit model

Structural equation modeling

Method

Relationship strength and relationship quality interaction had a negative effect on buying intentions. However, buying intentions had greater effect on purchase behavior among customers with a strong relationship with the service provider

The interaction between relationship duration and contact frequency on relationship strength is negative and significant. Thus, as relationship age increases the contact frequency enhances relationship strength and vice versa

Relationship age negatively affects the relationship between satisfaction and behavioral loyalty. Relationship age did not have significant effect in the trust and loyalty relationship

Relationship age and relationship program participation did not have significant effects in the relationship between customer satisfaction and repurchase intentions, and behavior

The effect of satisfaction and affective commitment on number of services purchased is dependent on the relationship age. The effect of satisfaction and commitment on services purchase enhances with relationship age. However, relationship age did not affect customer referral

The effect of person-related characteristics namely similarity, empathy, and politeness on trust would decrease as the length of relationship increases. The effect of other-related characteristics of competence, customization, reliability, and promptness on trust increase with relationship length

The positive effects of satisfaction on cross-buying increases as relationship duration with the customer increases. However, no effect of relationship duration was observed for the payment equity and cross-buying relationship

The relationship between satisfaction, trust, and commitment is affected by the relational orientation of the customers. For customers with high relational orientation, overall satisfaction had a greater effect on loyalty than with low relational orientation

Key findings

Investment–quality–loyalty and relationship characteristics 23

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Context and sample

Multiple services 376

Hairstyling industry 279

High technology, materials and industrial products 380

Telecom services 394

Authors

Dagger and O’Brien (2010)

Wang and Wu (2012)

Palmatier et al. (2013)

Ranaweera and Menon (2013)

Table 1 continued

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Relationship age

Relationship length Relationship stage

Relationship length

Relationship age

Relational characteristics

Regression analysis

Hierarchical linear model

Multigroup analysis

Multigroup analysis

Method

Relationship age moderates the effect of satisfaction such that as satisfaction increases newer customers generate more positive word-of-mouth than longterm customers

The positive effects of trust and communication on relationship velocity reduced as relationship age increases. However, the effect on investment on commitment increased as relationship aged. Industry turbulence had a significant interaction with communication on commitment velocity

The antecedents of customer loyalty significantly differ across short-term and longer-term relationship groups. Corporate image has a greater effect on loyalty for longer-term relationship groups than short-term relationship groups

Relationship age affected the relationship of satisfaction and trust on loyalty. While satisfaction had a greater effect on loyalty for novice user, trust had a significant effect on loyalty for expert users

Key findings

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This reduces the perceived risk and provides customers with the sense of security in their relationship with the firm (Dagger and O’Brien 2010). Accordingly, we argue that the effects of relationship investments on trust, satisfaction, and commitment will be moderated by relationship age. Based on the above discussion, we hypothesize that: H10a–c: The effect of relationship investment on (a) trust, (b) satisfaction, and (c) commitment significantly increases along with relationship age. Empirical results present contradictory findings for the role of relationship age in affecting satisfaction and loyalty. In this study, we argue that as customers do not possess sufficient information during the early stages of relationship they rely on satisfaction judgments in loyalty formation. This is because satisfaction is felt immediately and customers often rely on product/service features that are easy to evaluate during the earlier stages of the relationship (Dagger and O’Brien 2010). On the contrary, as relationship age increases customers develop extensive knowledge structures that allow them to evaluate the firm offerings and the relationship efforts more accurately. Moreover, Raimondo and Costabile (2008) argue that satisfaction may not be sufficient for building loyalty at the later stages of the relationship. Based on the above discussion, we hypothesize that: H10d: The effect of relationship satisfaction on loyalty decreases along with relationship age. 2.6 Relationship density Relationship density refers to the number of relational ties or interconnectedness between the firm and the customer (Palmatier 2008). The relational ties between the customer and firm form the channels for transfer of resources. Zhou et al. (2008) identify these resources as comprising of information sharing, market exchanges, joint planning and operations, and commitment to work closely with each other. As the number of relational ties grow the interactions between the firm and the customer increases. These enhanced interactions enable the firm to acquire critical customer information, and thereby enables them to respond to the market changes in a timely manner (Jap and Ganesan 2000). Moreover, firms with a wide breadth of contacts can uncover customer needs and identify opportunities that enable them to build strong relationships. It was argued that as the number of satisfactory firm–customer interactions increase it leads to greater trust and stronger commitment to the relationship (Morgan and Hunt 1994). Overall, a firm with more relational ties might be more efficient in meeting customer needs and thereby enhance satisfaction (Gassenheimer et al. 1995). Based on the above discussion, we hypothesize that: H11a–c: The effect of relationship investment on (a) trust, (b) satisfaction, and (c) commitment significantly increases along with relationship density. Morgan and Hunt (1994) argue that, as the number of relational ties increase, satisfied interactions lead to longer relationship duration. From a social exchange

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perspective, the frequency, and intensity of contacts allow customers to form an impression of the firm’s relationship efforts and benefits (Venkatesan et al. 2007). Furthermore, satisfied relational ties influence customer evaluation of the firm and its offerings leading to greater commitment to the relationship. Based on the above discussion, we hypothesize that: H11d: The effect of relationship satisfaction on loyalty significantly increases with relationship density. 2.7 Relationship dependence Relationship dependence refers to the extent to which the parties depend on the relationship partnership. Bendapudi and Berry (1997) describe relationship dependence as a constraint-based relationship in which either party believes that exiting the relationship would result in greater economic or psychological costs. In this study, we considered relationship dependence in examining the investment–loyalty linkage as previous literature has acknowledged the importance of dependencies in various dyadic relationships, including firm–customer relationships and business relationships (Gronroos 1990; Proença and de Castro 2007). While various relationship issues have been emphasized in previous literature, relationship dependence issues have not been so frequently researched in spite of its relevance in buyer–seller relationships. According to resource dependence theory (Krapfel et al. 1991), dependence of the parties in the relationship is determined by the magnitude and importance of the exchange outcomes. An interdependent relationship creates greater trust and high switching costs making it difficult in replacing the incumbent firm (Berman et al. 1999; Chiou and Shen 2006). Moreover, trust becomes important in contexts where dependency (i.e., risk) exists. Dwyer et al. (1987) argue that relationship commitment increases as the dependence of the firm and customer in the relationship partnership increases. When the firms make specific relationship investments, customers may evaluate them favorably and perceive the relationship as long-term. This increases their confidence in the partner thereby making them committed to the relationship (Gutie´rrez et al. 2004). Based on the above discussion, we hypothesize that: H12a–c: The effect of relationship investment on (a) trust, (b) satisfaction, and (c) commitment significantly increases along with relationship dependence. Relationship dependence is a prerequisite for establishing and maintaining a long-term relationship with the customer. Anderson and Narus (1990) argue that greater relationship dependence shows greater interest by the parties in sustaining the relationship. Furthermore, firm–customer relationship dependence indicates the level of difficulty in accessing alternate source of valued outcomes (Waheed and Gaur 2012). Based on the above discussion, we hypothesize that: H12d: The effect of relationship satisfaction on loyalty significantly increases along the relationship dependence.

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3 Method 3.1 The setting To determine the tenability of hypothesized relationships the study was carried out in the retail banking context. There are various reasons for considering retail banking in this study. First, the retail banking depicts personalized service encounters between a customer and a service provider. Such personalized service encounters contribute to the individuation of the customer through high interaction, decision control, and personal attention (Surprenant and Solomon 1987). Second, the relationship marketing practices are widespread in the banking industry (Gilbert and Choi 2003). Finally, the economic reforms, the policy changes, and the introduction of new technology channels have significantly transformed the retail banking (Zhao et al. 2010). Consequently, it makes relationship marketing all the more urgent and important in the retail banking context. Thus, the scope of the study is limited to the retail banking industry. The focus of the research would be on customer’s perception of relationship marketing practices in the retail banks. Figure 1 presents the conceptual hypothesized model. 3.2 Questionnaire A questionnaire with all construct items measured on a five-point scale was developed. Following the pretest with three academicians and five students, the questionnaire was improved by rewording some items and removing the confusing items. As common method biases can have potentially serious effects in selfreported data, procedural remedies as recommended by Podsakoff et al. (2003) were followed. First, the respondent’s evaluation apprehension was reduced by assuring them of anonymity and informing them that there was no right or wrong answer. Second, different response formats were used for measurement of constructs. For example, relationship satisfaction was measured using semantic differential scale while other constructs were measured using the Likert rating scale. Third, the construct items were counterbalanced to control for priming effects and mood biases. Finally, well-established measures were used to reduce ambiguity and improve the validity of the measurement items. 3.3 Measures Following the Verhoef et al. (2002) study, relationship age was measured as the interval between the time of measurement and the starting date of the relationship with the retail bank (primary bank) in years. Relationship density refers to the interpersonal ties between the parties. Based on the Palmatier’s (2008) study, relationship density was measured as the wide breadth of contacts between the customer and the firm. Consequently, relationship density was measured using a single item measure of different product/services brought from the primary bank. Relationship dependence measures the extent to which the customers depend on the service provider for his banking needs. This was measured by single-item ‘‘whether

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customers use multiple banks for meeting their banking needs.’’ In measuring relationship characteristics, this study followed a standard procedure recommended by Wildt and Ahtola (1978) by presenting the relationship characteristics questions (relationship age, density, and dependence) in the beginning of the questionnaire. This reduces the influence of emotions experienced by the respondents during the rating of the questionnaire. Following the relationship characteristics questions, the respondents were asked to recall their past relationship encounters with their primary bank and respond to the reminder of the questionnaire. Relationship investment was measured using three items based on De Wulf et al. (2001) study which taps into the bank’s efforts to improve customer loyalty. Trust was measured via seven items adapted from Twing-Kwong et al. (2013) cognitive trust scale used in the retailing context. Relationship satisfaction refers to the customer’s satisfaction with the relationship and was measured using five-item semantic differential scale adapted from Jones and Suh (2000) and Ndubisi and Wah (2005). Relationship commitment was measured using three-items developed by Fullerton (2011). The loyalty scale consisted of three-items, namely ‘‘say positive things,’’ ‘‘encourage friends and relatives,’’ and ‘‘use for future investment needs’’ adapted from the Bettencourt’s (1997) study. 3.4 Sample and data collection Responses were collected from actual retail banking customers in India. Appropriate instructions were given to the respondents to consider their primary bank in rating the questionnaire items. Data was collected using convenience sampling method with a structured questionnaire. Respondents were customers using retail banking services in a cosmopolitan city in India. A total of 381 usable responses from actual retail banking customers were collected. In this survey, 63 % of the respondents were males and 37 % females. There were 27 (7.1 %) respondents who were less than 21 years of age; 179 (46.9 %) who were between 22 and 30; 87 (22.8 %) who were between 31 and 40 years; 65 (17.2 %) who were between 41 and 50 years; and 23 (6 %) who were over the age of 50. The sample had a high proportion of respondents educated at the graduate level (61.9 %). Almost 57.5 % (219) of the respondents were employed and remaining 42.5 % (162) of the respondents were housewife, retired, studying, and unemployed. In this survey, the average relationship age of the respondent with their primary bank was 4.7 years. The relationship age was re-coded as short-term and long-term user based on the number of years of relationship with the bank. There is very little theory in guiding the choice of the relevant time frames for categorizing customers on relationship age in the marketing literature (Ranaweera and Menon 2013). Further, development of relationship goes through different stages of exploration, expansion, and commitment (Scanzoni 1979). As the timeframes are highly contextual, we used the median-split procedure to categorize respondents into shortterm users and long-term customers for purpose of moderation analysis. Although median-split arbitrarily identifies short-term and long-term customers, several prior studies have used this procedure in examining relationship age (Verhoef et al. 2002; Dagger et al. 2009).

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Regarding the relationship density, 65 % of the respondents indicated that they bought more than one product/service from their primary bank in the last 2 years. There were 358 (94 %) respondents having savings accounts; 110 (29 %) respondents having a credit card; 30 (7.9 %) respondents having demat account; 75 (19.6 %) having recurring/fixed deposit; 59 (15.4 %) respondents having personal/car/home loans; 42 (11 %) respondents having investment products; and 95 (24.9 %) respondents having other bank products/services. Consequently, respondents were categorized as low-density and high-density users based on the number of products/services the respondents have bought from their primary bank. Respondents having one product/service with their primary bank were coded as low-density (n = 186) users, while those with two or more products/services were coded as high-density users (n = 195). It is interesting to note that almost 51 % of the respondents have more than one banking provider. The average number of banks the respondents did business with was 1.7 banks. Relationship dependence was coded as less-dependent and highdependent users depending on the banking behavior. Multiple bank users were coded as low-dependent users while single bank users were coded as highdependent users. 3.5 Measurement properties As prior literature indicates that relationship quality can be operationalized as both higher-order construct and as disaggregate model, we performed a confirmatory factor analysis to test the validity of these models. The fit statistics indicated that the disaggregate model had better fit with the data compared to the higher-order construct. The higher-order relationship quality model provide fit statistics with v2/df = 3.28, GFI = 0.91, CFI = 0.94, TLI = 0.93, and RMSEA = 0.078. However, the disaggregate model provided a better fit with v2/df = 2.75, GFI = 0.94, CFI = 0.96, TLI = 0.95, and RMSEA = 0.068. The findings provided validity for the disaggregate model of relationship quality employed in this study. As the validity of the relationship quality was established, we continued to examine the measurement properties of the conceptual model proposed in this study. Initially, an exploratory factor analysis (EFA) was used to examine the factor structure. Following the EFA, items with low loadings and/or cross loadings were identified for deletion. Direct Oblimin rotation was employed to extract the factor structure. The factor structure extracted was similar to the initial conceptualization expect for the two trust items which had low factor loading and high cross loading. Subsequently these items were screened out from further analysis. A confirmatory factor analysis (CFA) with AMOS 16.0, employing maximum likelihood estimation procedure was carried out. Covariance matrix was used as input in examining the model fit statistics of the hypothesized model. The measurement model provided a good fit with v2 = 354.3 (p \ 0.000) v2/ df = 2.605, GFI = 0.91, CFI = 0.95, SRMR = 0.047, and RMSEA = 0.06. The fit statistics indicated good psychometric properties of the measures (Bagozzi and

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Table 2 Confirmatory factor analysis Items

Factor loadings

T value

Construct reliability

Variance extracted

Make efforts to increase loyalty

0.72

–a

0.79

0.60

Make efforts to improve its ties

0.81

11.69

Cares about keeping its customers

0.79

10.75 0.86

0.57

0.87

0.53

0.87

0.69

0.78

0.55

Relationship investment

Trust Confidence in honesty of the bank

0.78

Rely on being informed by the bank

0.77

14.32

–

Bank behaves in a trustworthy manner

0.71

12.89

Trust this bank

0.82

14.31

High level of confidence in the relationship

0.70

13.10

Satisfaction Satisfied/dissatisfied

0.72

Pleased/annoyed

0.73

– 15.65

Favorable/unfavorable

0.70

12.23

Good/bad

0.72

12.73

Happy/unhappy

0.76

13.45

Commitment Emotionally attached

0.84

Strong sense of identification

0.89

19.70

–

Great deal of personal meaning

0.76

16.67

Loyalty

a

Say positive things

0.72

Encourage friends and relatives

0.77

12.54

–

Use for future investment needs

0.73

12.17

Marker

Yi 2012). The standardized regression weights for all the indicators were greater than 0.7 demonstrating the unidimensionality of the measures. Further, the t values for the measurement items were found to be significant and large (t values between 10.75 and 19.70) providing evidence of convergent validity (see Table 2). The construct reliabilities ranged from 0.78 for loyalty to 0.87 for relationship satisfaction. The average variance extracted ranged from 0.53 to 0.69, well above the threshold level of 0.5 as recommended by Bagozzi and Yi (2012). Discriminant validity was assessed by comparing the average variance extracted for each construct with the squared correlation between that construct and any other construct (Fornell and Larcker 1981). As seen in Table 3, the average variance extracted of all constructs was higher than the squared correlation between the constructs providing evidence of discriminant validity.

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Table 3 Test results on discriminant validity Relationship investment Relationship investment Trust

(0.60) 0.36***

Trust

Satisfaction

Commitment

Loyalty

0.13

0.26

0.18

0.22

(0.57)

0.35

0.21

0.18

0.32

0.24

Satisfaction

0.51***

0.59***

Commitment

0.42***

0.47***

(0.53) 0.57***

Loyalty

0.46***

0.43***

0.49***

(0.69) 0.51***

0.26 (0.55)

The values in diagonal (in italics) represent the average variance extracted by the construct. The lower part of the diagonal represents the correlation between the constructs while the upper part of the diagonal represents the squared correlation (variance shared) between the constructs * p \ 0.10; ** p \ 0.05; *** p \ 0.01

3.6 Testing for common method bias Common method bias was assessed using two approaches. In the first approach, Harman’s one factor test (Podsakoff et al. 2003) was carried out. In this approach, all the variables are modeled as indicators of a single factor. The underlying assumption is that if common method bias exists then the single factor model with all indicators would have better fit statistics than the conceptualized five-factor model. The results show that one factor test with v2 = 1,363.48 (p \ 0.000) v2/ df = 8.97, GFI = 0.68, CFI = 0.67, SRMR = 0.09, and RMSEA = 0.14 had a poor fit compared to the five-factor model with v2 = 356.21 (p \ 0.000) v2/ df = 2.600, GFI = 0.91, CFI = 0.95, SRMR = 0.047, and RMSEA = 0.06. This suggests that common method bias is not a problem in this study. We used another approach by Lindell and Whitney (2001) to validate the above findings. Lindell and Whitney (2001) suggest examining the second-smallest correlation among the variables for estimating the presence of common method bias. In this study, the second smallest correlation was 0.152 between the bank behaves in a trustworthy manner (trust 3) and makes efforts to increase loyalty (relationship investment 1). The small size of the correlation provides further support that common method bias is not an issue in this study.

4 Results 4.1 Hypothesis testing Analysis of the path estimates reveals that all hypothesized paths are significant except the relationship between trust and loyalty (see Fig. 2). Thus, H7 was not supported. Consequently, the structural model was re-estimated with this path deleted (v2 = 357.1, p \ 0.000). The change in Chi square of 0.9 was not significant at p \ 0.05. The re-estimated model explained 49 % of the variance in loyalty. In terms of the effects, relationship investment had a significant effect on the relationship quality constructs. However, relationship investment was strongly

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0.21***

Trust 0.7n.s.

0.42***

2

0.54*** Relationship Investment

0.36**

R = 0.17

Relationship Satisfaction

0.30***

Relationship Loyalty

R2 = 0.58 0.57***

R2 = 0.49 0.31***

0.16**

Relationship Commitment R2 = 0.47

*p< 0.10, **p< 0.05, ***p< 0.01

Fig. 2 Results of the path estimates analysis

related to trust (b = 0.42, p \ 0.01) than satisfaction (b = 0.36, p \ 0.01) and commitment (b = 0.16, p \ 0.05). This provides support for hypotheses H1, H2, and H3. Further, relationship investment (b = 0.21, p \ 0.01), relationship satisfaction (b = 0.31, p \ 0.01), and commitment (b = 0.30, p \ 0.01) make significant contributions to loyalty. This result provides support for H4, H8, and H9. Hypothesis H5 was supported as trust had a positive and significant effect on relationship satisfaction (b = 0.54, p \ 0.01). Further, relationship satisfaction had a significant and positive effect on commitment (b = 0.57, p \ 0.01), providing support for H6. To test hypotheses H10–12, hierarchical regression analysis was carried out separately with each relationship quality construct as dependent variable. The variables are mean-centered to overcome the possible problem of multi-collinearity (Cohen et al. 1983). A two-step procedure was used with the direct effects of the predictor variables being estimated in the first step followed with the interaction effects in the later step. Table 4 presents the interaction effects of relational characteristics on relationship quality and loyalty constructs. The results of the moderated hierarchical regression analysis show positive and significant interaction effect of relationship age and relationship investment on trust (b = 0.16, p \ 0.05) and satisfaction (b = 0.18, p \ 0.01). This provides support for hypotheses H10a and H10b. The findings reveal that the effect of relationship investment on trust and satisfaction is greater for long-term users than short-term users.

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Table 4 Hierarchical regression analysis results Trust a

b

p value

Satisfaction

Commitment

Loyalty

b

b

b

p value

p value

p value

Relationship investment Investment 9 age

0.16

\0.05

0.18

\0.01

0.07

n.s.

Investment 9 density

0.10

n.s.

0.13

\0.05

0.03

n.s.

-0.10

n.s.

-0.13

\0.10

0.14

n.s.

Investment 9 dependency Relationship satisfaction

-0.12

\0.10

Satisfaction 9 density

0.24

\0.01

Satisfaction 9 dependency

0.03

n.s.

Satisfaction 9 age

a

Standardized coefficient

As shown in Table 4, the interaction effect of relationship density and investment has a positive and significant effect on satisfaction (b = 0.13, p \ 0.05). This provides support for H11b. Thus, the effect of relationship investment on satisfaction is enhanced with relationship density. Hypothesis H12a–c examined the role of relationship dependence in the investment–quality relationship. Results of the hierarchical regression analysis show that relationship dependence has a negative and a moderate effect on satisfaction (b = -0.13, p \ 0.10). This indicates that the investment–satisfaction relationship is greater for single banking users (highdependent users) than multiple banking users (low-dependent users). This provides support for H12b. The results of the hierarchical regression analysis provide moderate support for the hypothesis H10d. The interaction of relationship age and satisfaction has a negative and moderating influence on relationship loyalty. Thus, satisfaction has a greater effect on loyalty for short-term users than long-term users. On the contrary, relationship density was found to positively moderate the effect of satisfaction on loyalty. This supports the hypothesis H11d. The hypothesis H12d was not supported as relationship dependence did not moderate the effect of satisfaction on loyalty. 4.2 Alternative model testing We examined an alternative model drawing on the Morgan and Hunt’s (1994) study theorization that trust and commitment are the key mediating constructs in a successful relationship exchange. On the basis of this, we tested a model where trust and commitment were modeled as mediators of satisfaction and loyalty. The re-specified model fit statistics suggested poorer fit compared with the main model [v2 = 404.66 (p \ 0.000) v2/df = 2.890, GFI = 0.89, CFI = 0.92, SRMR = 0.050, and RMSEA = 0.07]. Further, as suggested by Bozdogan (1987), we used AIC (Akaike’s Information Criteria) and CAIC (Consistent AIC) in comparing the two models. For the main model, AIC (461.13) and CAIC (718.16) had smaller values compared to the re-specified model (AIC = 504.66, CAIC = 751.80) indicating a better fit for the

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main model. The results indicate that the main model with relationship quality variables mediating the relationship between relationship investment and loyalty variables is superior to the alternative model with trust and commitment as mediating variables in predicting loyalty.

5 Discussion The marketing concept, which proposes that investment in different types of relationship activities increase profitability, is based on the assumption that relationship marketing leads to development of valuable and beneficial long-term customer relationships. In challenging this assumption, the current study proposes that relational characteristics would moderate the relationship between investment, quality, and loyalty. First, this study contributes to the existing literature by specifying how relationship investment can guide the firm–customer relationship. Overall, relationship investment was found to have a positive yet differential effect on trust, satisfaction, and commitment. This could be explained by the customers’ evaluation of investment and trust predominantly through cognitive considerations while satisfaction and commitment through affective considerations. This is an important finding as it indicates that it pays for the firm to invest in relationship marketing activities as it enhances relationship quality (H1–H3) and loyalty (H4). Further, it substantiates the premise of social exchange theory that relationship marketing strategies contribute to mutual obligation and beneficial relationships. Second, we provide empirical evidence for the conceptualization of relationship quality construct. While previous research has examined relationship quality, both as global construct (Ndubisi and Wah 2005) and as disaggregate model (Vesel and Zabkar 2010), the present study examines the relative performance of both models. The findings show that customers can distinguish between the different elements of relationship quality and that the disaggregate model of relationship quality provides a superior fit than the aggregate model. The disaggregate model allows for better understanding of the association between investment and different relationship quality elements; thereby enhancing the ability to make better investment decision. Third, the study contributes to the debate on whether trust affects satisfaction (Deng et al. 2010) or whether satisfaction leads to the development of trust (Kim et al. 2009). The results suggest that trust is an essential prerequisite to relationship satisfaction. This is supported by the poorer performance of the alternative model where satisfaction was conceptualized as an antecedent to trust. A surprising finding was the non-significant relationship between trust and loyalty. The results show that trust–loyalty relationship is fully mediated through satisfaction and commitment. We believe that this trust–loyalty-mediated relationship is robust and that future research should examine this in other contexts to generalize the findings. Finally, the study finds support for the moderating role of relational characteristics of age, density, and dependence in the relationship between investment and quality. Consistent with the study’s expectations and extent literature, the findings suggest that relationship investment leads to greater trust and satisfaction as

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relationship age increases. Further, investment was found to have a greater effect on satisfaction when customers have high relationship density than low relationship density with the firm. As the number of firm–customer ties and interconnectedness is greater with high density users, relationship investment leads to greater satisfaction. On the contrary, relationship dependence was found to have a negative moderate effect in the investment–satisfaction relationship. Thus, relationship investment leads to greater satisfaction levels among single-bank users than multibank users. Similarly, we observed that relationship age and density moderated the linkage between satisfaction and loyalty. The negative interaction of relationship age and satisfaction on loyalty is consistent with findings of Dagger and O’Brien (2010) that short-term users rely on satisfaction in determining their loyalty. The positive moderating influence of relationship density and satisfaction on loyalty indicates that satisfaction enhances loyalty for customers with greater number of relational ties with the firm. The study results show that relationship dependence did not moderate the satisfaction and loyalty relationship. This could be attributed to the measurement of relationship dependence. Fink et al. (2011) propose that relationship exchange benefits occur in conditions where mutual dependence between the firm and the customer exists. While the present study measured the perceived customer dependence future research could examine the extent of mutual dependence in examining the relational outcomes.

6 Implications The current study provides several implications for marketing researchers, academicians, and practitioners. In general, the empirical model might provide some new insights about the antecedents and consequences of relationship quality and loyalty which can provide managers a valuable tool to assess both current and potential relationships with their customers. More specifically, the relationship managers can use the study findings in three ways. First, the findings show that customer loyalty will develop if relationship investment, satisfaction, and commitment are well managed. From the strategic point of view, this is a key finding as it may show possible areas of achieving competitive advantage and developing profitable customer relationships. Thus, managers may want to wisely invest in relationship activities/programs to build loyalty by making most interactions with their customers satisfying. If the firms can build a ‘‘relational capital’’ through their investment, then this might act as a barrier for customers to exit the relationship. The study findings also indicate that increasing relationship investment can work to the firm’s advantage through its influence on trust and commitment. This is consistent with the Wilson’s (1995) proposition that partners are less likely to invest in relationship if they do not perceive the other party to be trustworthy. Thus, relationship marketing practices may be more successful when the managers focus on the psychological process underlying the relationship between the investment, quality, and loyalty.

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Second, we observe that relationship investment enhances trust, satisfaction and commitment toward the firm. Further, relationship investment had a differential effect on relationship quality elements. The effects of investment on relationship quality elements suggest that managers should focus on relationship strategies that build trust and satisfaction. While commitment is important and cannot be ignored, it is less valuable than trust and satisfaction as determinants of investment strategies. This helps managers target their investment efforts more precisely in order to improve relationship quality. Third, the findings indicate that the relationship between investment, trust, satisfaction, commitment, and loyalty vary as the function of relational characteristics of age, density, and dependence. The key implication of this finding is that managers should not direct their investment strategies in building a relationship with all customers. Rather they need to adjust their investment strategies depending on the customers’ experience, number of products bought, and use of multiple banks. Specifically, the results emphasize firms to invest in relationship strategies to enhance satisfaction and trust in the later stages of the relationship. With respect to the effects of relationship density, firms could consider customers’ with multiple products/services as more inclined to maintaining a relationship. Thus, high density customers are more satisfied than low density customers with investment activities/ programs. Finally, the study provides evidence for the negative effect of relationship dependence on relationship quality. Managers should be aware of investing in relationship marketing for multibank users as it leads to negative consequences on satisfaction. The relational age was found to negatively moderate the relationship between satisfaction and loyalty. On the contrary, satisfaction was found to enhance loyalty as relationship density increases. Taken together, the results confirm that while retaining customers could enhance relationship quality, it might not make them more loyal. Thus, relationship managers should focus on improving interactions with customers. This confirms the central role of interaction in building long-term relationships with customers (Gronroos 1990). Finally, the moderating effects of relationship characteristics might help managers in segmenting customers into less loyal and high loyal groups. Firms can identify high loyal groups and then try to promote long-term relationships with these customers.

7 Limitations and future research directions Some limitations of this study must be noted. First the study utilized a cross-section design for relationship age, density, and dependence. Even though this is not different from the design used by prior studies, it would be interesting to investigate the effects of relational characteristics using a longitudinal design by tracking same group of customers from their initial encounters to later stages of a relationship. Second, this study considered relationship quality elements of trust, satisfaction, and commitment in examining the investment–loyalty relationship. A noteworthy direction for future research would be to address the influence of investment on other relationship quality elements such as product/service related quality,

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communication, power, and market orientation. Even though we recognize that relationship investment contributes to these elements, we did not study them in this research for reasons of parsimony. Third, we examined the research hypotheses in the personalized services context; future research could examine the differences in the relationship between investment and quality across different settings. Lovelock (1983) offers a classification typology in which services are classified based on tangibility, customization, contact, supply–demand fluctuations, and delivery method. Examining whether the relationship between investment and quality elements differ across the service types would be of interest for managers and researchers. For services that are highly intangible, building trust is important in enhancing loyalty. Thus, investment in relationship strategies that convey trust might be critical in building customer loyalty. Finally, this study considered relational characteristics in examining the moderation effects on the association between investment and relationship quality. Future research could benefit from examining the effects of customer characteristics such as age, income, and gender and firm characteristics such as size, market, reputation, and orientation on relationship investment and quality. Understanding the effects of customer and firm characteristics is needed to know how different moderators impact the investment decisions and loyalty. Despite these limitations, the findings of the study are robust and can be extended for further researchers in different settings and varied perspectives (e.g., managers).

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