SERVICE FAIRNESS, SERVICE QUALITY AND RELATIONSHIP QUALITY EVALUATION EFFECTS ON CUSTOMER LOYALTY

6th Annual Conference of the

EuroMed Academy of Business

Confronting Contemporary Business Challenges

through Management Innovation

Edited by: Demetris Vrontis,

Yaakov Weber,

Evangelos Tsoukatos

Published by: EuroMed Press

6th EuroMed Conference of the

EuroMed Academy of Business

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September 23rd-24th, 2013

Estoril, Cascais, Portugal

Confronting Contemporary Business Challenges

through Management Innovation

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ISBN: 978-9963-711-16-1 Published by: EuroMed Press

6th Annual EuroMed Conference of the EuroMed Academy of Business 1028

Confronting Contemporary Business ISBN: 978-9963-711-16-1

Challenges Through Management Innovation

SERVICE FAIRNESS, SERVICE QUALITY AND RELATIONSHIP

QUALITY EVALUATION EFFECTS ON CUSTOMER LOYALTY

Giovanis, Apostolos1; Athanasopoulou, Pinelopi2; Tsoukatos, Evangelos3

1Dept. of Business Administration, TEI of Athens, Greece, [email protected]

2Dept. of Sport Management, Univ. of Peloponnese, Greece, [email protected] 3Dept. of Finance and Insurance, TEI of Crete, Greece, [email protected]

ABSTRACT

The purpose of this study is to establish and empirically validate an integrated model

identifying the causal relationships among service fairness, service quality and

relationship quality and their joint effects on customer loyalty. The findings of the

empirical study, based on data collected from the automobile after-sales service

industry, largely support the hypothesized relationships proposed in the theoretical

model. More specifically, they show that relationship quality measured as a higher-

order construct reflecting supplier-customer relationship level in terms of

satisfaction, trust and commitment is the main determinant of customer loyalty.

Furthermore, it partially and fully mediates the relationships between service fairness

and service quality with customer loyalty respectively. Thus, service quality and

service fairness influence loyalty indirectly by strengthening the supplier-customer

relationship quality, which was proved to be a better predictor of customers’ attitude

and behavior. Managerial implications of the findings are discussed and directions

for further research are provided.

Keywords: Relationship marketing, Service fairness, Service quality, Relationship

quality, Customer loyalty, Higher-order constructs, PLS-PM, Automobile after-sales

services

INTRODUCTION

A great deal of research exists on the interactions among the service evaluation constructs as

well as their correlations with relationship quality and customer loyalty. However, an

integrative model of these concepts has not been tested before. This study draws on three

theoretical streams: the service fairness theory, the service quality theory and the relationship

quality theory to predict customers’ loyalty in services. Fairness theory explains how

customers’ fair treatment is linked to service quality and various attitudinal outcomes, such




as relationship quality, while the literature concerning relationship quality explains how the

service delivery process works. Service fairness and service quality are considered as the

foundations of establishing healthy bonds between customers and suppliers, which in lead to

long-term customer relationships. Finally, relationship quality theory relates its dimensions

with customer loyalty. The purpose of this study is to establish and empirically validate an

integrated model that identifies the causal relationships among service fairness, service

quality and relationship quality and their joint effects on customer loyalty. The paper is

structured as follows. The next section reviews the literature and develops hypotheses. It is

followed by sections devoted to methodology analysis, discussion of findings, implications

and, last but not least, limitations and suggestions for further research.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

In this section the concepts of service fairness, service quality, relationship quality, and

customer loyalty are analyzed and the interrelations among them are discussed.

Service Fairness

The concept of fairness is rooted in the theory of justice, which is in turn is adapted from

equity theory (Adams, 1965), suggesting that over-rewarded and under-rewarded

relationship outcomes cause distress that people strive to reduce. Individuals are seeking a

fair input/output balance and become satisfied whenever they feel their inputs are fairly

rewarded. Perception of unfairness can lead to distress and dissatisfaction. In contrast,

perception of fairness results in positive emotions and satisfaction (Patterson et al., 1997;

Szymanski and Henard, 2001).

Service fairness (SF) or justice is understood across three dimensions: distributive, procedural

and interactional fairness (Friman et al., 2002; Meng and Elliot, 2008; Clark et al., 2009; Ha and

Jang, 2009; Yen et al., 2010). Distributive fairness (DF) involves price in relation to the

outcomes of the service delivered and reflects customers’ perceptions of the benefits and costs

balance among parties. Procedural fairness (PF) is about exchange relationships and is

associated with the elements of impartiality, refutability, explanation and familiarity of

service personnel. Last but not least, interactional fairness (IF) deals with the fairness of

interpersonal treatment and refers to: a) courtesy, respect and consideration shown by service

representatives during the transaction and b) the extent and quality of mutual

communication.




Several recent studies consider the relationship between customers and service providers on

the grounds of the service fairness notion (Han et al., 2008; Ting, 2011; Chen et al., 2012; Zhu

and Chen, 2012). Most, investigate individually the effects of each fairness dimension on

other constructs such as satisfaction, service quality etc. or consider service fairness as a

reflective higher-order construct having the three aspects of fairness as its sub-dimensions.

Lately, Chiu et al. (2010) proved that service fairness is better represented as a higher-order

construct having the three dimensions of fairness as first-order formative constructs. Given

that the latter do not necessarily correlate, the elimination of any fairness dimension may alter

the conceptual domain of the overall construct (MacKenzie et al., 2005). The current study,

drawing upon Chiu et al. (2010), considers service fairness as a higher-order formative

construct that is assessed by the summative effect of its distributional, procedural and

interactional elements.

Service Quality

Service quality (SQ) is a fundamental concept of services marketing representing along with

perceived service value the factors that are used by customers to evaluate their service

providers (Cronin et al., 2000; Brady et al., 2005). Consensus has been reached on that

perceived SQ is an attitude or global judgment about the superiority or inferiority of a service

(Grönroos, 1984; Parasuraman et al., 1998; Cronin and Taylor, 1992; Teas, 1993). Berry et al.

(1988) maintain that SQ is a great differentiator and the most powerful competitive weapon of

service organizations.

Grönroos (1984, p. 38) defines SQ as a perceived judgment, resulting from an evaluation

process where customers compare their expectations with the service they perceive to have

received. Later, Parasuraman et al. (1988, p. 17), defined SQ as “the degree of discrepancy

between customers’ normative expectations for the service and their perceptions of the service

performance”. They also argue that perceived SQ is interpreted across five dimensions:

reliability, assurance, tangibility, empathy and responsiveness.

Many previous studies have identified a relationship between SF and SQ (Carr, 2007; Aurier

and Siadou-Martin, 2007; Han et al., 2008; Kwortnik and Han, 2011). Carr (2007) argues that

customers are considering the level of SF, when they evaluate the SQ of their service

providers, in their effort to achieve a balanced level of service which in turn will increase

perceived levels of SQ. As a result the following hypothesis is formulated:

H1: Service fairness positively affects perceived service quality




Relationship Quality

Relationship quality (RQ) refers to customers’ perceptions of how well a relationship fulfils

customers’ expectations, predictions, goals and desires (Jarvelin and Lehtinen, 1996). Hennig-

Thurau and Klee (1997, p. 751) postulate that RQ is “the degree of appropriateness of the

relationship to fulfil the needs of the customer associated with the relationship”. Although, there is no

clear consensus regarding the dimensions comprising the construct (Hennig-Thurau et al.,

2002; Athanasopoulou, 2009), most researchers converge on the idea that relationship quality

is a higher-order construct made of several distinct, though related dimensions (Dwyer and

Oh, 1987; Crosby et al., 1990; Kumar et al., 1995; Gummesson, 2002; Roberts et al., 2003; Woo

and Ennew, 2004). The three most common such dimensions are customer satisfaction (CS),

trust (TR) and commitment (COM) (Athanasopoulou, 2009). In services, Crosby et al. (1990)

first conceptualised customer-salesperson RQ through TR and CS. Later attempts of research

on RQ conceptualization added COM to the bundle of the concepts dimensions (Friman et al.,

2002; Hennig-Thurau et al., 2002; Kim and Cha, 2002; Bennett and Barkensjo 2005; Farrelly

and Quester, 2005; Huntley 2006). In the retail market, various studies seem also to converge

on the notion that the dimensions of retail RQ include CS, TR, and COM (De Wulf et al., 2001;

Moliner et al., 2007; De Cannière et al., 2009; Qin et al., 2009). However, other researchers split

COM in its affective and calculative facets (Vesel and Zabkar, 2010). Affective commitment

(AC) refers to a customers’ free will to maintain the relationship with the firm that they do

business with (Vesel and Zabkar, 2010), while calculative commitment (CC) is the state of

attachment to a partner in recognition of the sacrificed benefits and losses incurred if the

relationship ends (Geyskens et al., 1996), suggesting a rational, in financial terms weighing up

(Pritchard et al., 1999). Following the studies of Vesel and Zabkar (2010) and Hennig-Thurau

et al., 2002, the current study views RQ as a reflective higher-order construct having CS, TR,

AC and CC as its first-order dimensions.

Research has shown that the perception of relationship fairness enhance RQ (Kumar et al.,

1995). Nguyen and Mutum (2012, p. 408) argues that “It is, in particular, important to develop

processes and procedures which the other member of the relationship judges as being fair, in order to

sustain the relationship”. Based on that, it is posited that:

H2: Service fairness positively affects relationship quality

RQ is also strongly linked to SQ in various studies (Wong and Sohal, 2002; Roberts et al.,

2003; Venetis and Ghauri, 2004; Carr, 2006, 2007; Shabbir et al., 2007; Liu et al., 2011; Ou et al.,

2011). SQ is a recognised antecedent of CS (Herington and Weaven, 2009; Hu et al., 2009;




Jamal and Anastasiadou, 2009), which is one of the main dimensions of RQ. Caceres and

Paparoidamis (2007) and Hsieh and Hiang (2004) indicated that improved customers’

perceptions of SQ will influence aspects of RQ such as CS and TR, while Fullerton, (2005)

validated the link between SQ and both dimensions of commitment. As a result, the following

hypothesis can be postulated:

H3: Service quality positively affects relationship quality

Customer Loyalty

Customer loyalty (CL) is the most important goal of relationship marketing given that results

in increased profits, better acquisition rates from positive WOM, and reduced acquisition and

retention costs (Zeithaml et al., 1996). Oliver (1997, p.392) defined CL as a “deeply held

commitment to rebuy or repatronize a preferred product or service consistently in the future, thereby

causing repetitive same-brand or same brand-set purchasing, despite situational influences and

marketing efforts having the potential to cause switching behavior”. According to Chu (2009), CL is

a positive attitude and behavior related to the level of re-purchasing commitment to a brand

in the future and as such a composite approach to loyalty is most appropriate. This study

treats CL as a second-order reflective bi-dimensional construct having its attitudinal and

behavioral aspects as sub-dimensions (Kwortnik and Han, 2011; Karjaluoto et al., 2012)

A number of studies have investigated the linkages between SF and CL (Carr, 2007; Ha and

Jang 2009) and SQ and CL (Roberts et al., 2003; Fullerton, 2005; Carr, 2007; Chao, 2008). Carr

(2007) argues that when a customer feels that service provider’s operational strategy is to

provide unbiased services then he/she will feel greater levels of loyalty and repatronage

intentions. Based on that the following hypothesis is outlined below:

H4: Service fairness positively affects customer loyalty

Building relationships with customers is a good way towards retaining customers loyal in the

long-run (Sheaves and Barnes, 1996). To further reinforce this posture, a study conducted by

Barnes (1997) suggested that it is unlikely for customers to be retained for long unless a

genuine provider-customer relationship is present. Except of those studies which validated

the direct links between RQ’s dimensions and CL separately (Bettencourt, 1997; Garbarino

and Johnson, 1999; Fullerton, 2005; Han et al., 2008; Deng et al., 2009; Sanchez-Franko et al.,

2009). Hennig-Thurau and Klee (1997), Roberts et al. (2003) and Keating et al. (2011) have

empirically validated the link between RQ represented as a higher-order construct composed

by CS, TR, and COM and CL. Based on these findings the following hypothesis is posited:




H5: Relationship quality positively affects customer loyalty

The literature reports that SQ also has a well-established direct or indirect influence on CL

(Zeithaml et al., 1996; Roberts et al., 2003; Fullerton, 2005; Carr, 2007; Chao, 2008). Fullerton

(2005) found that both AC and CC partially mediates the relationship between SQ and CL in

the retailing services setting, Keating et al. (2011) validated a direct link between SQ and CL

in internet banking services except of the indirect one through RQ. Accordingly, the following

hypothesis is advanced

H6: Service quality positively affects customer loyalty

Based on the review of the aforementioned studies, a model describing the proposed

relationships among the nine constructs is shown in Figure 1.

Figure 1: Proposed Model

RESEARCH METHODOLOGY

The model was tested on evidence from the auto repair and maintenance industry. The

particular service is selected for the following reasons. First, these services are crucial for

manufacturers’ future growth since brand repurchasing rates largely depend on customers’

after sales experiences (Little, 2009). Second, due to the recession that many European

countries are currently experiencing, high revenues from new sales cannot be easily achieved.




According to a report prepared by Little (2008), the after sales industry, which has been long

neglected by automotive manufacturers in their business strategies, achieves a significantly

higher ROI than new car sales. In Germany, for example, the after sales business generates

more than half of profits while accounting for only 23% of revenues. Third, currently

potential new private car buyers postpone or cancel buying a new car due to recession. This

leads to an increasing average fleet age, where older cars demand more service, thus, creating

a profitable market for after-sales service offerings.

Sample Profile and Data Collection

In this study, prospective respondents were drawn from the customer base of a major

European automotive distributor in Greece owning three service facilities in different areas of

the Attica basin. A random sample of 1,000 active customers was drawn with no

discrimination requirements. The fieldwork was conducted in September 2012 by four trained

senior students over the phone. Prospective respondents were told that the survey was

conducted on behalf of their after-sales service supplier. Contacts were made at different

times of the day and days of the week in order for date and time related bias to be reduced.

The procedure resulted in 420 filled questionnaires. After eliminating those with unanswered

items, 408 usable questionnaires were coded for data analysis, yielding a net response rate of

about 41%. Among the 408 respondents, 61% were males and 65% married. In relation to age,

12% of respondents were in the 18-24 age-group; 22% were in the 25-34 age-group; 26% were

in the 35-44 age-group; 25% were in the 45-54 age-group, and 15% were older than 55. In

terms of monthly income, 40% of respondents had a net income of less than € 1,000; 41%

between € 1,000 and € 1,500; 15% between € 1,500, and € 2,000, and 4% more than € 2,000.

Also, 40% of respondents had a university degree; 14% had some college education; 11% were

university students, and 35% had a secondary school education. The sample included

customers from different areas of the Attica region, 27% living in the southern part, 27% in

the western part, 18% in the northern part and 9% in the eastern part. Finally, the median age

of the respondents’ cars was 5 years old.

Measures and Survey Instrument Design

The questionnaire was designed with measures of the relevant constructs based on scales

published in the literature. With the exception of the SQ items (measured with semantic

differential scales), items were measured on 7-point Likert scales anchored at 1 (strongly

disagree) and 7 (strongly agree). Four items were used to measure AL based on the work of




Harris and Good (2004) reflecting the cognitive; affective, and conative aspects of Oliver’s

(1997, 1999) four-step loyalty framework. To measure BL, the four-item scale developed by

De Wulf et al. (2001); Oliver (1997), and Zeithaml et al. (1996) was utilized. The 5-item scales,

included into Han et al.’s (2008) study, were used to measure AC and CC. Items measuring

CS and TR were derived from the studies undertaken by Fornell (1992) and McKnight et al.

(1998) respectively. The seven items used to measure SQ were based on the study of

Parasuraman et al. (1988). Finally, the three subdimensions of SF were measured using the

scales provided by Han et al. (2008).

EMPIRICAL RESULTS

Measurement Model Assessment – First Order Constructs

The method of partial least squares path methodology (PLS-PM), an implementation of

structural equation modeling (SEM) with Smart PLS 2.0 M3 (Ringle et al., 2005), was

employed to examine the model and test the proposed hypotheses. The selection of PLS-PM

was based on criteria as outlined in the work of Hair et al. (2011) such as the complexity of the

proposed model and the inclusion of reflective and especially formative higher-order

constructs.

The initial analysis is concerned with the measurement model adequacy assessment. The test

of the measurement model involves the estimation of convergent validity, internal

consistency reliability, and discriminant validity of the study’s first-order reflective

constructs, which indicate the strength of measures used to test the proposed model (Hair et

al., 2011). Convergent validity is suggested if loadings are greater than 0.7 on its respective

factor, which implies more shared variance between the construct and its measures than the

error variance (Carmines and Zeller, 1979) and . In the initial run of PLS-PM, where all

available measured items were included, five items (2 items for the PF and DF scales, 1 item

from the IF scale and 1 item from the SQ scale) presented factor loadings less than 0.6 and for

that reason were eliminated. The factor loadings of the non-eliminated items, as shown in

Table 1, exceed 0.725 providing strong convergent validity. Then the reliability of all latent

constructs was examined using the measures of Cronbach’s Alpha (CA) and Composite

Reliability (CR) (Fornell and Larcker, 1981). Hair et al. (1998) suggest that a value of 0.7

provide adequate evidence for internal consistency.




LV Variable Mean Std. Dev. Loadings t-stat CA CR AVE

IF

IF1 4.924 0.888 0.905 49.055

0.927 0.948 0.820 IF2 4.813 0.927 0.920 54.198

IF3 4.823 0.958 0.916 54.635

IF4 4.976 0.985 0.881 34.219

PF

PF1 4.704 1.112 0.892 41.975

0.890 0.932 0.819 PF2 4.736 0.986 0.916 53.988

PF3 4.736 1.021 0.907 52.484

DF

DF1 4.837 1.011 0.909 41.416

0.872 0.922 0.797 DF2 4.883 1.027 0.927 54.307

DF3 4.622 1.194 0.839 20.752

SQ

SQ1 5.174 1.025 0.887 45.494

0.929 0.945 0.741

SQ2 5.198 1.020 0.884 33.992

SQ3 5.356 1.043 0.903 43.124

SQ4 5.321 1.027 0.725 8.729

SQ5 5.315 0.997 0.867 25.012

SQ6 5.313 1.007 0.885 34.374

CS.TR

TR1 4.620 1.041 0.886 34.348

0.955 0.963 0.786

TR2 4.715 1.031 0.898 38.704

TR3 4.641 1.041 0.880 30.165

TR4 4.633 1.083 0.915 51.153

CS1 4.736 1.070 0.881 33.553

CS2 4.609 1.060 0.854 24.587

CS3 4.753 1.106 0.890 37.450

AC

AC1 4.454 1.042 0.928 48.638

0.965 0.973 0.877

AC2 4.402 1.094 0.941 73.757

AC3 4.432 1.111 0.942 71.514

AC4 4.565 1.061 0.930 49.367

AC5 4.530 1.076 0.943 83.594

CC

CC1 4.516 0.909 0.882 29.297

0.908 0.932 0.732

CC2 4.459 1.013 0.860 23.423

CC3 4.720 0.973 0.881 27.532

CC4 4.663 0.978 0.884 36.956

CC5 4.582 1.095 0.766 12.354

AL

AT1 4.742 0.987 0.888 37.589

0.903 0.932 0.775 AT2 4.649 0.997 0.900 41.993

AT3 4.440 1.203 0.819 17.214

AT4 4.668 0.972 0.911 52.431

BL

BL1 4.761 1.077 0.864 23.706

0.903 0.932 0.774 BL2 4.764 1.082 0.920 60.421

BL3 4.658 1.080 0.871 29.283

BL4 4.872 1.044 0.864 16.076

Table 1: Descriptive statistics and psychographic properties of the first-order constructs




LV Mean Std. Dev. IF PF DF SQ CS.TR AC CC AL BL

IF 4.883 0.852 0.906

PF 4.726 0.941 0.765 0.905

DF 4.785 0.958 0.731 0.759 0.893

SQ 5.277 0.883 0.671 0.697 0.749 0.861

CS.TR 4.673 0.943 0.681 0.750 0.773 0.788 0.887

AC 4.477 1.010 0.495 0.609 0.651 0.677 0.796 0.937

CC 4.586 0.848 0.558 0.624 0.648 0.686 0.766 0.729 0.856

AL 4.632 0.910 0.628 0.707 0.696 0.716 0.798 0.760 0.764 0.880

BL 4.761 0.946 0.607 0.638 0.656 0.680 0.778 0.645 0.719 0.740 0.880

Table 2: Correlations of latent constructs

As shown in Table 1, CA and CR of all reflective measures included in the study exceed 0.7,

suggesting that all items are good indicators of their respective components.

Discriminant validity was assessed in two ways. First, the Average Variance Extracted (AVE),

which indicates the amount of variance that is captured by the construct in relation to the

variance due to measurement error, is examined. Second, the square root of AVE extracted

from each construct, is compared with the correlations among constructs. The initial run of

PLS revealed that, similar to Vesel and Zackbar (2010) and De Cannière et al.’s (2009) study,

CS and TR are not distinct from one another and are thus combined to form a

satisfaction/trust factor (CS.TR). The findings of the new run of PLS provided strong evidence

of discriminant validity among first order constructs because, as shown in Table 1, all AVE

values are greater than 0.73 which in turn exceeds the recommended cutoff value of 0.5

(Barclay et al., 1995), and as shown in Table 2, the square root of AVE for all first-order

constructs is higher than their shared variances.

Measurement Model Assessment – Second-Order Constructs

Second-order factors can be approximated using two approaches. One commonly-used

approach is the repeated indicator approach (Lohmöller, 1989) where the second-order factor

is directly measured by using items of all its lower-order factors. The procedure performs

better when the lower-order constructs have about equal number of items. As stated

previously, RQ and CL are modeled as second-order reflective constructs. In Table 3, the CR

and AVE measures of both higher-order constructs are provided. CR and AVE for RQ equals

to 0.972 and 0.676 respectively and for CL equals to 0.942 and 0.673 respectively, which are

well above the recommended thresholds of 0.7 and 0.5 respectively, providing evidence of

reliable second-order constructs (Wetzels et al., 2009). Finally all loadings of the second-order

constructs on the first-order constructs exceed 0.885 and are significant at p=0.01. All of the




above support the proposed conceptualization of RQ and CL that they are reflecting

customers’ perception of their pre-specified sub-dimensions, namely SA.TR, AC, CC and AL,

and BL respectively.

A. RELATIONSHIP QUALITY (CR =0.972 AVE = 0.676)

1st-Order

Construct Loadings t-stat

95% Confidence

Interval R2

CS.TR 0.950 57.931 [0.918, 0.973] 0.902

AC 0.914 42.979 [0.881, 0.959] 0.835

CC 0.885 36.298 [0.796, 0.941] 0.783

B. CUSTOMER LOYALTY (CR = 0.942 AVE = 0.673)

1st-Order

Construct Loadings t-stat

95% Confidence

Interval R2

AL 0.935 50.484 [0.899, 0.967] 0.874

BL 0.930 48.227 [0.873, 0.961] 0.864

Table 3: Second-order reflective constructs assessment

Furthermore, the SF construct is modeled as a formative second-order construct. According to

Tenenhaus et al. (2005), PLS-PM, as in the case of reflective higher-order constructs, can also

be used for hierarchical models with formative constructs, by reversing the direction of the

relationships between the higher and the lower-order constructs. However, this will result in

a R2 value for the higher order construct of unity. The measurement quality of the formative

second-order factors was tested following the suggestions by Chin (1998) and

Diamantopoulos and Winklhofer (2001). First, the correlations among the first-order

constructs were examined. As shown in Table 4, the absolute correlations among the three

first-order SF related dimensions vary from 0.731 to 0.765 with an average of 0.75. Though the

correlations between first-order constructs of SF are relatively high, this result suggests that

SF is better represented as a formative second-order construct, instead of reflective, since

reflective second-order constructs would show extremely high correlations (≥ 0.8) among its

first-order factors (Pavlou and El Sawy, 2006).

The strength of the relationship between second-order constructs and their first-order

dimensions is subsequently assessed. As shown in Table 5, all first-order fairness-related

components were found to have significant path coefficients in forming the customer

perception about SF. Results suggest that among the factors forming fairness perception, IF is

the most important followed by PF and DF. This result is similar with those of Chiu et al.

(2010) in the online auctions service setting and can be attributed to the credence nature of the

service under investigation. The variance inflation factors (VIF) were then computed for these




first-order justice factors to assess multicollinearity (see Table 4 for VIF values). VIF values

above 10 would suggest the existence of excessive multicollinearity and raise doubts about

the validity of the formative measurement (Diamantopoulos and Winklhofer, 2001). The VIF

values for the first-order justice-related factors varied from 2.709 to 3.028. Therefore,

multicollinearity is not a concern for the Service Fairness construct.

SERVICE FAIRNESS

1st-Order

Construct

Coefficient

Value t-stat

95% Confidence

Interval R2 VIF

IF 0.427 1,533.148 [0.386, 0.459]

1.000

2.771

PF 0.337 1,156.184 [0.309, 0.373] 3.028

DF 0.331 1,200.919 [0.306, 0.366] 2.709

Table 4: Second-order formative construct assessment

Assessment of the structural model and hypotheses testing

The PLS-PM method was also used to confirm the hypothesized relations between constructs

in the proposed model. The significance of the paths included into the proposed model was

tested using a bootstrap resample procedure with 500 runs. In assessing the PLS model, the

squared multiple correlations (R2) for each endogenous latent variable were initially

examined and the significance of the structural paths was evaluated. The proposed

relationships are considered to be supported if the corresponding path coefficients had the

proposed sign and were significant. The PLS analysis results are given in Table 5.

DV IV Coefficient

Value t-stat R2

Hypotheses

Test

SQ SF 0.769 22.983 0.591 H1 confirmed

RQ SF 0.403 8.810

0.688 H2 confirmed

SQ 0.479 10.470 H3 confirmed

CL

SF 0.199 4.743

0.784

H4 confirmed

SQ 0.077 1.764 H5 rejected

RQ 0.658 15.079 H6 confirmed

Table 5: Estimated results of the structural model and hypotheses tests

SF positively affects SQ as indicated by the value of the relevant coefficient (β = 0.769; t =

22.983) explaining 59.1% of the variance in SQ. Therefore, H1 is confirmed since it was found

to be statistically significant and the relevant path coefficients have the hypothesised signs.

As far as the determinants of RQ, both SQ (β = 0.479; t = 10.470) and SF (β = 0.403; t = 8.810)




almost equally affect RQ formation explaining 68.8% of its variance. These findings support

the validity of H2 and H3, meaning that if customers perceive that the service provided is of

high quality and provided fairly, the quality of their relationship with the provider increases.

Finally, the determinants of CL are RQ and SF, explaining 78.4% of the variance in CL.

However, RQ present a stronger influence on CL than SF, as indicated by model’s coefficients

of β = 0.658 (t = 15.079) for RQ and β = 0.199 (t = 4.743) for SF. Therefore, hypotheses H5 and

H6 are confirmed. Although it was expected that both SQ will directly affect CL, the findings

revealed that the relevant path coefficients (β = 0.077, t = 1.764) is not statistically significant.

Thus, H4 was not confirmed, meaning that the relationship between SQ and CL is fully

mediated by RQ. Roberts et al. (2003) arrived also at the same result suggesting that “the

Relationship Quality scale completely subsumes the effect of the Service Quality scale” (p.

189). However, SQ indirectly affects CL through RQ since the relevant coefficient (β = 0.315),

as shown in Table 7, is statistically significant. Moreover, the indirect effect of SF on CL is

statistically significant (β = 0.566) and about 3 times higher than the relevant direct effect,

highlighting the importance of RQ in customers’ attitudes and behaviors.

CONCLUSIONS AND IMPLICATIONS

The results of this study present many implications for service managers. First, the fact that

interactional fairness is the most important for customers shows that service companies

should manage the interaction with customers effectively at every point of contact. For

example, in car service providers, there should be enough trained employees at the phone

service of the company knowing how to deal with customers and engineers should be trained

to deal with customer inquiries and complaints appropriately. Also, all customers should be

equally treated because preferential treatment of certain customers can destroy the

relationship developed. Second, the previous implication becomes even more important if we

consider that the effect of service fairness to perceived SQ is very high. The results also

showed that SQ is heavily affected by SF. So, if service providers are successful in providing a

fair service, they will also probably be considered as high quality service providers. Thirdly,

although there is a direct effect of service fairness to customer loyalty, customer loyalty is

mainly affected through relationship quality. As customers stay with the same provider for

longer periods, trust and commitment increase and therefore these customers exhibit an

intention to stay with the same provider, and a tendency to refer the provider to others. So, if

car service providers want to reap the benefits of loyal customers, the key rests in developing

strong long-term relationships with customers.




Finally, SQ only affects CL through RQ. This means, that offering a high quality service is not

enough to create loyal customers. Only when SQ is coupled by long-term quality

relationships do we see signs of CL. This is particularly important in today’s competitive

environment where many providers offer the same type of service. However, the competitive

advantage comes from managing relationships with customers effectively.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

This study, despite the significance of its findings, has a number of limitations. A first

limitation is the industry-specific sample, which may affect our findings’ generalizability. It is

necessary to implement the proposed model in different relevant service settings before the

interrelationships among service evaluation, relationship quality and customer loyalty are

fully clarified. A second limitation pertains to that the findings and implications of this

research were obtained using a cross-sectional study. This reduces the ability of the study to

reflect the temporal changes in the research constructs. A longitudinal study on the subject is

necessary in order to clarify the effects of temporal changes.

As far as future research recommendations are concerned, certain variables could be

incorporated into the proposed framework to enhance its predictive performance and to

provide better understanding of the customer’s decision-making process. For example, a

future research effort could consider the role of different type of switching barriers in the

relation between relationship quality and customer loyalty. Moreover, future research can

replicate this study in different types of services; and across different countries that are

characterized by a different culture.

REFERENCES

Adams, J.S. (1965), “Inequity in social exchange. In L. Berkowitz (Ed.), Advances in experimental social

psychology, New York: Academic Press, Vol. 2, pp. 267-299

Athanasopoulou, P. (2009), “Relationship quality: a critical literature review and research agenda”,

European Journal of Marketing, Vol. 43, No. 5/6, pp. 583-610.

Aurier, P. and Siadou-Martin, B. (2007), "Perceived justice and consumption experience evaluations: A

qualitative and experimental investigation", International Journal of Service Industry Management, Vol. 18,

No. 5, pp. 450 – 471.

Barclay, D., Higgins, C. and Thompson, R. (1995), “The partial least squares (PLS) approach to causal

modelling: personal computer adoption and use as an illustration”, Technology Studies, Vol. 2, No. 2, pp.

285-309.

Barnes, J.G. (1997), “Closeness, strength, and satisfaction: examining the nature of relationships between

providers of financial services and their retail customers”, Psychology and Marketing, Vol. 14, No. 8, pp.

765-790.




Bennett, R. and Barkensjo, A. (2005), “Relationship quality, relationship marketing, and client

perceptions of the levels of service quality of charitable organisations”, International Journal of Service

Industry Management, Vol. 16, No. 1, pp. 81-106.

Berry, L. L., Parasuraman, A. and Zeithaml, V. A. (1988), “The service quality puzzle”, Business Horizon,

Vol. 31, No. 5, pp. 35-43.

Bettencourt, L. (1997), “Customer voluntary performance: customer as partners in service delivery”,

Journal of Retailing, Vol. 73, No. 3, pp. 383-406.

Brady, M.K., Knight, G.A., Cronin, J.J., Hult, G.T.M. and Keillor, B.D. (2005), “Removing the contextual

lens: A multinational, multi-setting comparison of service evaluation models”, Journal of Retailing, Vol.

81, No. 3, pp. 215-230.

Caceres, R. C. and Paparoidamis, N. G. (2007), “Service quality, relationship satisfaction, trust,

commitment and business-to-business loyalty”, European Journal of Marketing, Vol. 41, No. 7, pp. 836–

867.

Carmines, E. G. and Zeller, R. A. (1979), Reliability and Validity Assessment, Sage Publications, Newbury

Park, CA.

Carr, C. L. (2006), “Reciprocity: the golden rule of IS-User Service Relationship Quality and

Cooperation. Seeking a mutually beneficial relationship between departments and users”,

Communication of the ACM, Vol. 49, No. 6, pp. 77-83.

Carr, C. L. (2007), “The FAIRSERV model: consumer reactions to services based on a multidimensional

evaluation of service fairness”, Decision Sciences, Vol. 38, No. 1, pp. 107–130.

Chao, P. (2008), “Exploring the nature of the relationships between service quality and customer loyalty:

an attribute-level analysis”, The Service Industries Journal, Vol. 28, No.1, pp. 95-116.

Chen, H-G., Liu, J. Y-C., Sheu, T. S. and Yang, M-H. (2012), "The impact of financial services quality and

fairness on customer satisfaction", Managing Service Quality, Vol. 22 No. 4, pp. 399 – 421.

Chin, W. W. (1998), “The partial least squares approach to structural equation modeling”, in

Marcoulides, G. A. (Ed.), Modern Methods for Business Research, Lawrence Erlbaum Associates, Mahwah,

NJ, pp. 295-336.

Chin, W. W., Marcolin, B. L. and Newsted, P. N. (2003), “A partial least squares latent variable modeling

approach for measuring interaction effects: results from a Monte-Carlo simulation study and an

electronic-mail emotion/adoption study”, Information Systems Research, Vol. 14, No. 2, pp. 189–217.

Chiu, C. M., Huang, H. Y. and Yen, C. H. (2010), “Antecedents of trust in online auctions”, Electronic

Commerce Research and Applications, Vol. 9, No. 2, pp. 148-159.

Chu, K-M. (2009), “The construction model of customer trust, perceived value and customer loyalty”,

The Journal of American Academy of Business, Vol.14, No.2, pp. 98-103.

Clark, M. N., Adjeib, M. T. and Yanceya, D. N. (2009), “The impact of service fairness perceptions on

relationship quality”, Services Marketing Quarterly, Vol. 30, No. 3, pp. 287-302.

Cronin, J. J. and Taylor, S. A. (1992), “Measuring service quality: A re-examination and extension”,

Journal of Marketing, Vol. 56, No. 3, pp. 55-68.

Cronin, J. J., Brady, M. K., and Hult, G. T. M. (2000), “Assessing the effects of quality, value, and

customer satisfaction on consumer behavioral intentions in service environments”, Journal of Retailing,

Vol. 76, No. 2, pp. 193-218.

Crosby, L. A., Evans, K. R. and Cowles, D. (1990), “Relationship quality in services selling: an

interpersonal influence perspective”, Journal of Marketing, Vol. 54, No. 2, pp. 68-81.

De Cannière, M. H., De Pelsmacker, P. and Geuens, M. (2009), “Relationship quality and the theory of

planned behavior models of behavioral intentions and purchase behavior”, Journal of Business Research,

Vol. 62, No. 1, pp. 82-92.

De Wulf, K., Oderkerken- Schröder, G. and Iacobucci, D. (2001), “Investment in consumer relationships:

A cross-country and cross-industry exploration”, Journal of Marketing, Vol. 65, No. 4, pp. 33–50.




Deng, Z., Lu, Y., Wei, K. K. and Zhang, J. (2009), “Understanding customer satisfaction and loyalty: An

empirical study of mobile instant messages in China”, International Journal of Information Management,

Vol. 30, No. 4, pp. 289-300.

Diamantopoulos, A. and Winklhofer, H., (2001), “Index construction with formative indicators: an

alternative to scale development”, Journal of Marketing Research, Vol. 38, No. 2, pp. 269–277.

Dwyer, F. R. and Oh, S. (1987), “Output sector munificence effects on the internal political economy of

marketing channels”, Journal of Marketing Research, Vol. 24, No. 4, pp. 347-358.

Farrelly, F. J. and Quester, P. G. (2005), “Examining important relationship quality constructs of the focal

sponsorship exchange”, Industrial Marketing Management, Vol. 34, No. 3, pp. 211-219.

Fornell, C. (1992), “A National customer satisfaction barometer: the Swedish experience”, Journal of

Marketing, Vol. 56, No. 1, pp. 6-21.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables

and measurement error”, Journal of Marketing Research, Vol. 18, No. 1, pp. 39-50.

Friman, M., Gaerling, T., Millett, B., Mattsson, J. and Johnston, R. (2002), “An analysis of international

business-to-business relationships based on the commitment-trust theory”, Industrial Marketing

Management, Vol. 31, No. 5, pp. 403–409.

Fullerton, G. (2005), “How commitment both enables and undermines marketing relationships”,

European Journal of Marketing, Vol. 39, No. 11/12, pp. 1372–1388.

Garbarino, E. and Johnson, M.S. (1999), “The different roles of satisfaction, trust, and commitment in

customer relationships”, Journal of Marketing, Vol. 63, No. 2, pp. 70-87.

Geyskens, I., Steenkamp, J-B., Scheer, L. K. and Kumar N. (1996), "The effects of trust and

interdependence on relationship commitment: A Trans-Atlantic study", International Journal of Research

in Marketing, Vol. 13, No. 4, pp. 303-317.

Grönroos, C. (1984), “Service quality model and its marketing implications”, European Journal of

Marketing, Vol. 18, No. 4, pp. 36 – 44.

Gummesson, E. (2002), Total Relationship Marketing, 2nd ed., Oxford: Butterworth-Heinemann.

Ha, J. and Jang, S. (2009), “Perceived justice in service recovery and behavioral intentions: The role of

relationship quality”, International Journal of Hospitality Management, Vol. 28, No. 3, pp. 319–327.

Hair, J. F., Anderson, R. E., Tatham, R. L. and Black, W. C. (1998), Multivariate Data Analysis, 5th ed.,

Prentice Hall, Englewood Cliffs, NJ.

Hair, J. F., Ringle, C. M. and Sarstedt, M. (2011). "PLS-SEM: Indeed a Silver Bullet." Journal of Marketing

Theory and Practice, Vol. 18 No.2, pp. 139-152.

Han, X., Kwortnik, R. J. and Wang, C. (2008),“Service loyalty”, Journal of Service Research, Vol. 11, No. 1,

pp. 22-42.

Harris, L. C. and Good, M. H (2004), “The four stages of loyalty and the pivotal role of trust: A study of

online service dynamics”, Journal of Retailing, Vol. 80, No. 2, pp. 139–158.

Hennig-Thurau, T., Gwinner, K. P., and Gremier, D. D. (2002). “Understanding relationship marketing

outcomes”, Journal of Service Research, Vol. 4 No. 3, pp. 230–247.

Hennig-Thurau, T. and Klee, A. (1997), “The impact of customer satisfaction and relationship quality on

customer retention - a critical reassessment and model development”, Psychology & Marketing, Vol. 14,

No. 8, pp. 737-765.

Herington, C. and Weaven, S. (2009), “E-retailing by banks: e-service quality and its importance to

customer satisfaction”, European Journal of Marketing Vol. 43, No. 9/10, pp. 1220-1231

Hsieh, Y.-C. and Hiang, S.-T. (2004), “A study of the impacts of service quality on relationship quality in

search-experience-credence services”, Total Quality Management, Vol. 15, No. 1, pp. 43–58.




Hu, H. H. S., Kandampully, J. and Juwaheer, T.D. (2009), “Relationships and impacts of service quality,

perceived value, customer satisfaction, and image: An empirical study”, The Service Industries Journal,

Vol. 29, No. 2, pp. 111-125.

Huntley, J. K. (2006), “Conceptualization and measurement of relationship quality: Linking relationship

quality to actual sales and recommendation intention”, Industrial Marketing Management, Vol. 35, No. 6,

pp. 703-714.

Jamal, A. and Anastasiadou, K. (2009), “Investigating the effects of service quality dimensions and

expertise on loyalty”, European Journal of Marketing, Vol. 43, No. 3/4, pp. 398-420.

Jarvelin, A. and Lehtinen, U. (1996), “Relationship quality in business-to-business service context”, in

Edvardsson, B.B., Johnston, S.W. and Scheuing, E.E. (Eds.), QUIS 5 Advancing Service Quality: A Global

Perspective, Warwick Printing Company Ltd., UK, pp. 243-254.

Karjaluoto, H., Jayawardhena, C., Leppäniemi, M. and Pihlström, M. (2012), “How value and trust

influence loyalty in wireless telecommunications industry”, Telecommunications Policy, Vol. 36, No. 8, pp.

636–649.

Keating, B. W., Alpert, F., Kriz, A. and Quazi, A. (2011), “Mediating role of relationship quality in online

services”, Journal of Computer Information Systems, Vol. 52, No. 2, pp. 33 -41.

Kim, W.G. and Cha, Y. (2002), “Antecedents and consequences of relationship quality in hotel industry”,

International Journal of Hospitality Management, Vol. 21, No. 4, pp. 321-338.

Kumar, N., Scheer, L.K. and Steenkamp, J.E. (1995), “The effects of supplier fairness on vulnerable

resellers”, Journal of Marketing Research, Vol. 32, No. 1, pp. 5-65.

Kwortnik, R.J. and Han, X. (2011), “The Influence of guest perceptions of service fairness on lodging

loyalty in China”, Cornell Hospitality Quarterly, Nol. 52, No. 3, pp. 321-332.

Little, A. D. (2008), “Automotive after sales 2015”, available at:

http://www.adlittle.com/downloads/tx_adlreports/AMG_Automotive_after_sales_2015_01.pd

f, (accessed March 30, 2013).

Little, A. D. (2009), “Driving after sales excellence”, available at:

http://www.adlittle.com/downloads/tx_adlreports/ADL_AMG_Drivinf_after__sales_excellen

ce.pdf, (accessed March 30, 2013).

Liu, C-T., Guo, Y.M. and Lee, C-H. (2011), “The effects of relationship quality and switching barriers on

customer loyalty”, International Journal of Information Management, Vol. 31, No. 1, pp. 71-79.

Lohmöller, J.-B. (1989), Latent Variable Path Modeling with Partial Least Squares, Physica-Verlag,

Heidelberg.

MacKenzie, S. B., Podsakoff, P. M., and Jarvis, C. B. (2005), "The problem of measurement model

misspecification in behavioral and organizational research and some recommended solutions", Journal of

Applied Psychology, Vol. 90, No.4, pp. 710-730.

McKnight, D. H., Cummings, L. L. and Chervany, N. L. (1998), “Initial trust formation in new

organizational relationships”, The Academy of Management Review, Vol. 23, No.3, pp. 473-490.

Meehl, P. E. (1990), “Why summaries of research on psychological theories are often uninterpretable”,

Psychological Reports, Vol. 66, No. 1, pp. 195-244.

Meng, J. G. and Elliot, K. M. (2008), “Predictors of relationship quality for luxury restaurants”, Journal of

Retailing and Consumer Services, Vol. 15, No. 6, pp. 509-515.

Moliner, M. A., Sanchez, J., Rodriguez, R.M., and Callarisa, L. (2007), “Perceived relationship quality

and post-purchase perceived value: an integrative framework”, European Journal of Marketing, Vol. 41,

No. 11/12, pp. 1392-1422.

Oliver, R. L. (1997), Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill., NY.

Nguyen, B. and Mutum, D. S. (2012) "A review of customer relationship management: successes,

advances, pitfalls and futures", Business Process Management Journal, Vol. 18 No. 3, pp.400 - 419




Oliver, R.L. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63 (Special issue), pp. 33-44.

Ou, W. M., Shih, C-M., Shan, K. and Wang, K.C. (2011), “Relationships among customer loyalty

programs, service quality, relationship quality and loyalty: An empirical study”, Chinese Management

Studies, Vol. 5, No. 2, pp. 194-206.

Parasuraman, A., Zeithaml, V.A. and Berry, L. L. (1988). “SERVQUAL: a multi-item scale for measuring

customer perceptions of service quality”, Journal of Retailing, Vol. 64, No. 1, pp. 12-40.

Patterson, P. G., Johnson, L. W. and Spreng, R. A. (1997), “Modeling the determinants of customer

satisfaction for business-to-business professional services”, Journal of the Academy of Marketing Science,

Vol. 25, No. 1, pp. 4 – 17.

Pavlou, P.A. and El Sawy, O.A. (2006), “From IT leveraging competence to competitive advantage in

turbulent environments: The case of new product development”, Information Systems Research, Vol. 17,

No. 3, pp. 198-227.

Pritchard, P. M., Havitz, E. M. and Howard, R. D. (1999), “Analyzing the commitment-loyalty link in

service context”, Journal of the Academy of Marketing Science, Vol. 27, No. 3, pp. 333-48.

Qin, S., Zhao, L. and Yi, X. (2009), “Impacts of customer services on relationship quality: an empirical

study in China”, Managing Service Quality, Vol. 19, No. 4, pp. 391-409.

Quester, P. and Lim, A. L. (2003), “Product involvement/ brand loyalty: is there a link?”, Journal of

Product & Brand Management, Vol. 12, No. 1, pp. 22-38.

Ringle, C. M., Wende, S. and Will, A. (2005), SmartPLS 2.0 (M3) Beta. Hamburg, available at:

http://www.smartpls.de (accessed on 5 May 2010).

Roberts, K., Varki, S. and Brodie, R. (2003), “Measuring the quality of relationships in consumer

services: an empirical study”, European Journal of Marketing, Vol. 13, No. 1/2, pp. 169–196.

Sanchez-Franco, M. J., Ramos, A, F, V, Velicia, F. A. M. (2009), “The moderating effect of gender on

relationship quality and loyalty toward Internet service providers,” Information & Management, Vol. 46

No. 3, pp. 196-202

Shabbir, H., Palihawadana, D., and Thwaites, D. (2007). “Determining the antecedents and

consequences of donor-perceived relationship quality - a dimensional qualitative research approach”,

Psychology & Marketing, Vol. 24, No. 3, pp. 271-293.

Sheaves, D.E. and Barnes, J. G. (1996), “The fundamentals of relationships: an exploration of the concept

to guide marketing implementation”, Advances in Services Marketing and Management, Vol. 5, pp. 215-245.

Szymanski, D. M. and Henard, D. H. (2001), “Customer satisfaction: A meta-analysis of the empirical

evidence”, Journal of the Academy of Marketing Science, Vol. 29, No. 1, pp. 16-35.

Teas, R. K. (1993), “Expectations, performance evaluation, and consumer perceptions of quality'', Journal

of Marketing, Vol. 57, No. 4, pp. 18-34.

Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y., and Lauro, C. (2005), “PLS path modeling”,

Computational Statistics and Data Analysis, Vol. 48, No. 1, pp. 159–205.

Ting, S-C. (2011), “The role of justice in determining relationship quality”, Journal of Relationship

Marketing, Vol. 10, No. 2, pp. 57-75.

Venetis, K.A. and Ghauri, P.N. (2004), “Service quality and customer retention: building long-term

relationships”, European Journal of Marketing, Vol. 38, No. 11/12, pp. 1577-1598.

Vesel, P. and Zabkar, V. (2010), "Comprehension of relationship quality in the retail environment",

Managing Service Quality, Vol. 20, No. 3, pp. 213 – 235.

Wetzels, M., Odekerken-Schröder, G. and Van Oppen, C. (2009), “Using PLS path modeling for

assessing hierarchical construct models: Guidelines and empirical illustration”, MIS Quarterly, Vol. 33.

No. 1, pp. 177-195.

Wong, A. and Sohal, A. (2002), “Customers’ perspectives on service quality and relationship quality in

retail encounters”, Managing Service Quality, Vol. 12, No. 6, pp. 424-433.




Woo, K. and Ennew, C.T. (2004), “Business-to-business relationship quality. An IMP interaction-based

conceptualisation and measurement”, European Journal of Marketing, Vol. 38, No. 9/10, pp. 1252-1271.

Yen, T. F., Wu, C.Y. and Wu, C. C. (2010), “Effects of justice on relationship quality: empirical study of

cross strait resorts”, International Journal of Asian Management. Vol. 1 No. 1, pp. 90-97.

Zeithaml, V., Berry, L. and Parasuraman, A. (1996). “The behavioral consequences of service quality”,

Journal of Marketing, Vol. 60, No. 2, pp. 31-47.

Zhu, Y-Q. and Chen, H-G. (2012), "Service fairness and customer satisfaction in internet banking:

Exploring the mediating effects of trust and customer value", Internet Research, Vol. 22, No. 4, pp. 482-

498.

SERVICE FAIRNESS, SERVICE QUALITY AND RELATIONSHIP QUALITY EVALUATION EFFECTS ON CUSTOMER LOYALTY

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