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Customer satisfaction and qualitiesin public service: an intermediarycustomer perspectiveJune-Young Rha aa Department of Business Administration , The Catholic Universityof Korea , 43-1 Yeokgok 2-dong, Wonmi-gu, Bucheon-si , Republicof KoreaPublished online: 23 May 2011.

To cite this article: June-Young Rha (2012) Customer satisfaction and qualities in public service:an intermediary customer perspective, The Service Industries Journal, 32:12, 1883-1900, DOI:10.1080/02642069.2011.574274

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Customer satisfaction and qualities in public service: an intermediarycustomer perspective

June-Young Rha∗

Department of Business Administration, The Catholic University of Korea, 43-1 Yeokgok 2-dong,Wonmi-gu, Bucheon-si, Republic of Korea

(Received 13 April 2010; final version received 3 March 2011)

From an intermediary customer perspective, this study empirically investigates thecausal relationships between service quality, relationship quality, design quality, andcustomer satisfaction in the public service value chain. Cross-sectional survey datawere collected from social work, childcare, and healthcare services and wereanalyzed using structural equation modeling. It is found that all the qualitydimensions are antecedents to customer satisfaction and relations between qualitydimensions show distinctive patterns. The results show that relationship quality doesnot directly affect customer satisfaction, but can indirectly influence satisfactionthrough the mediation of both service and design qualities. The effect of therelationship and design qualities on customer satisfaction is stronger than that ofservice quality.

Keywords: service quality; relationship quality; design quality; customer satisfaction

Introduction

The topics of service quality and customer satisfaction in public service have received con-

siderable research attention during the past decade (e.g. Donnelly, Kerr, Rimmer, & Shiu,

2006; Wisniewski, 2001). Existing studies on the quality of public service are mostly

empirical research on the verification of SERVQUAL models applied to civil affairs

administration (e.g. Bigne, Moliner, & Sanchez, 2003; Brysland & Curry, 2001). The

SERVQUAL model was originally developed as a quality model for measuring process

quality in private service, where human encounters are crucial to deliver quality service

to customers (Dabholkar & Overby, 2005; Parasuraman, Zeithaml, & Berry, 1988;

Powpaka, 1996). However, the sources of quality in public service are not confined to

service encounters, but extended to policy development and service design where the

service contents are determined, and the inter-organizational relationships in the delivery

of public service (Rhee & Rha, 2009).

The service value chain model in Figure 1, which flows from government to agencies

to citizens, represents the generic structure of public service delivery (Larason-Schneider,

1999). Rather than perform services directly, governments tend to delegate them to non-

governmental agencies, whether profit or non-profit. However, governments remain

accountable for the supply of the public services even though service agencies play a

role of directly delivering public services to final customers. Thus, governments must

pay much attention to the quality issues which customers regard as importance.

To improve the quality of public service and enhance customer satisfaction, governments

ISSN 0264-2069 print/ISSN 1743-9507 online

# 2012 Taylor & Francis

http://dx.doi.org/10.1080/02642069.2011.574274

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∗Email: jyrha@catholic.ac.kr

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must distinguish final customers from intermediary customers in the service value chain

(Rhee & Rha, 2009).

Intermediary customers are employees of the agency and are responsible for the front

lines of the public service delivery. They could be considered as ‘internal customers’ of a

service profit chain presented by Heskett, Jones, Lovenman, Sasser, and Schlesinger

(1994). Nonetheless, as they are not actual members of the government but of the

service agencies, it is appropriate to conceptualize these as intermediary customers.

They have motivation and incentive to engage in public service provision that differ

structurally from those of public servants. Hence, the quality attributes which intermediary

customers emphasize might be different from those of internal and final customers of

public services.

Rhee and Rha (2009) explored new quality dimensions and attributes in the value

chain of public services using critical incidents technique (CIT) and found that intermedi-

ary customers place more focus on design quality and relationship quality than on service

quality (Rhee & Rha, 2009). Design quality as perceived by intermediary customers is

about how well public policies and services are established at the stages of policy devel-

opment and service design. On the other hand, relationship quality is about the depth and

climate of the relationship between suppliers that participate in the public service delivery

(Johnson, 1999).

Design, service, and relationship qualities that are valued by intermediary customers

are the result of a search for various sources of quality that do exist on the service

value chain. The service value chain is a series of value creation processes through

which the service value provided to customers is planned, designed, and delivered. This

process is also a causal chain of customers running from external and internal suppliers

to internal customer and to external customer (Krajewski & Ritzman, 2005). Therefore,

individual quality dimensions are highly likely to follow the causal relationship that

exists on that service value chain.

This study analyzes the causal relationship between design quality, service quality,

and relationship quality and investigates how these quality dimensions affect the satisfac-

tion of intermediary customers involved in public service. Such research is significant

in that it will shed light on the causal order of service-marketing constructs as well as

the priority of management programs needed to enhance the performance of the service.

Those in charge of quality management in the public service sector can establish key

performance objectives of quality management in accordance with proven quality

dimensions and the causal model of customer satisfaction and attain a clear understanding

of the priority of related activities and resource allocation. In addition, the results can be

applied as a base model of quality measurement systems for public inter-organizational

services.

Figure 1. Service value chain and the types of customers in public service (Rhee & Rha, 2009).

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Theoretical background

Service quality

Previous studies on service quality attributes can be largely divided into two perspectives,

namely, the Nordic perspective and the American perspective (Brady & Cronin, 2001).

The Nordic perspective (Gronroos, 1982, 2000) investigates service quality by further

subdividing and classifying it into technical quality and functional quality. Technical

quality is concerned with ‘what the customer is left with’ after a service process ends

and is generally known also as outcome quality. Functional quality is concerned with

‘how the service outcomes are delivered’ in the human encounter of a service process,

and is known also as process quality. The representative service quality model of the

American perspective is SERVQUAL (Parasuraman, Zeithaml, & Berry, 1985, 1988).

The five quality attributes of SERVQUAL were originally developed from the analyses

of four industries where the moment of truth in the human encounter was emphasized.

As such, the relevant research on SERVQUAL has mostly focused on the process

quality of the human encounter (Dabholkar & Overby, 2005; Kang, 2006; Powpaka,

1996; Richard & Allway, 1993).

Some researchers point out that the reliability of SERVQUAL is close to outcome

quality (Brady & Cronin, 2001; Hui, Zhao, Fan, & Au, 2004), while some measurement

items for the responsiveness of SERVQUAL are conceptually indistinguishable

between the processes or outcome quality (Llosa, Chandon, & Orsingher, 1998).

However, service quality for intermediary customers, which is the main focus of this

study, is the quality provided by individual public servants during human encounters

between the government and service agencies. Therefore, in this study, we have estab-

lished a service quality model for intermediary customers based on the attributes and

items of SERVQUAL. Rha and Rhee (2007) have determined that the service quality attri-

butes for intermediary customers are reliability, responsiveness, behavioral assurance,

intellectual assurance, and empathy, based on their survey and statistical test on intermedi-

ary customers of public services. Of the five quality attributes of SERVQUAL, tangibles

were excluded, while assurance was divided into intellectual assurance, which emphasizes

expertise and knowledge of public servants, and behavioral assurance, represented by

courteousness, kindness, and confidence.

Relationship quality

In most studies on relationship quality, affective variables such as trust, commitment, and

satisfaction are used as constructs that form relationship quality (e.g. Athanasopoulou,

2009; Bennet & Barkensjo, 2005; Shemwell, Yavas, & Bilgin, 1998). These constructs

were mainly developed in the context of business-to-consumer (B2C) transaction of

which relationship attributes are relatively simple. Most empirical studies on the causal

relationship between relationship quality and customer satisfaction suggest that customer

satisfaction is an antecedent to relationship quality (e.g. Hennig-Thurau & Klee, 1997;

Moliner, 2009). In other words, their research suggests that relationship quality emerges

as trust and commitment by customers as they experience satisfaction repetitively in

their service encounters. However, trust and commitment are affective variables

(Shemwell et al., 1998; Storbacka, Starandvik, & Gronroos, 1994), which are perceived

ex-post as customers experience the excellent services delivered by service providers

trying to improve the relationship. Therefore, trust and commitment cannot be quality

variables that will offer direct information on improvement activities made by service

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providers like SERVQUAL, which acts as a cognitive variable (Rhee & Rha, 2009).

Rather, it is more appropriate to regard trust and commitment as customer performance

variables similar to the customer satisfaction concept. Rhee and Rha (2009) previously

indeed classified affective relationship quality variables, such as trust and commitment,

as ‘ex-post relationship quality’ based on the precedence of customer satisfaction.

This study pays particular attention to the inter-organizational relationship between

government and service agencies. The characteristics of inter-organizational relationships

are fundamentally different from consumer relationships in terms of complexity and the

uncertainty of the relationship. While consumer relationship mainly deals with communi-

cations and transactions between consumers and service providers, inter-organizational

relationships include a variety of exchanges that can involve information, resources, auth-

orities, functions, and capabilities (Rhee & Rha, 2009). Inter-organizational relationships

also have a variety of relational mechanisms, including communication, collaboration, and

coordination. This relational complexity necessarily brings about a high uncertainty in

the exchange process between organizations. Therefore, specific dimensions of inter-

organizational relationship quality must be constructed in a different manner from those

for consumer relationship quality. However, most existing research on inter-organization

relationships in business-to-business (B2B) context also use B2C-based terms, such as

trust and commitment as relationship quality constructs (Caceres & Paparoidamis,

2009; Hennig-Thurau, Gwinner, & Gremler, 2002, Hewett, Money, & Sharma, 2002;

Leuthesser, 1997; Smith, 1998b).

Inter-organizational relationship quality is defined as the depth and climate of the

relationship between organizations that participate in the service delivery (Johnson,

1999). In the context of public service delivery, Rha and Rhee (2007) reorganize relation-

ship quality by using such institutional variables as cooperation, coordination, devolution,

and atmosphere, all of which have been used as relationship management variables in the

field of marketing and supply chain management, rather than applying affective variables,

such as trust and commitment. Cooperation and coordination represent behavioral aspects

of an organizational relationship, while devolution and atmosphere represent structural

and attitudinal aspects of such a relationship, respectively. In particular, devolution and

coordination reflect the hierarchical and multilateral relation characteristics of government

service delivery and thus serve as unique relationship quality variables that cannot easily

be found among relationship management variables of private services. In addition, these

variables can be called as ‘ex-ante relationship quality’ variables, in the meaning that

relationship quality is antecedent to customer satisfaction (Rhee & Rha, 2009). This

quality attributes can be directly controlled by service providers because these attributes

are not affective but cognitive variables (Rhee & Rha, 2009).

Design quality

Other than Gummesson’s (1993) remarks, design quality has never been mentioned in any

study on quality of services. The existing studies discuss nothing but the procedure for

developing service content and standards (Zeithaml, Bitner, & Gremler, 2006). It is

only natural when we consider the characteristics of service industries where there is a

less clear distinction between design and production processes than for manufacturing

industries (Slack, Chambers, & Roberts, 2004). Above all, because major attributes of

design quality are too service-specific and technical, it is difficult to generalize them at

all (Rhee & Rha, 2009). In the service fields, however, the customers are more directly

involved in the production of services. Thus, service customers have more opportunities

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to appreciate design quality compared to customers who receive products (Zeithaml et al.,

2006). Rha and Rhee (2007) came up with newly defined design quality and empirically

developed its attributes and measurement items, which had not been dealt with in service-

marketing research. More specifically, they constructed specific dimensions of design

quality with four attributes: of policy legitimacy and strategic alignment representing

the aspect of policy contents; substantial policy measures representing the aspect of

policy tools; and operational effectiveness representing the aspect of policy

implementation.

Service quality and customer satisfaction

The question of precedence between service quality and customer satisfaction has long

been debated in the marketing field (e.g. Brady & Robertson, 2001; Lee, Lee, & Yoo,

2000; Roberts, Varki, & Brodie, 2003; Shemwell et al., 1998). While customer satisfaction

generally includes affective, emotional, and cognitive responses (Oliver, 1997), most

empirical studies generally address satisfaction only as an affective and emotional con-

struct (Dabholkar & Overby, 2005). In addition, while customer satisfaction is a compre-

hensive evaluation of entire service, service quality is different in that it is perceived

through an evaluation of a variety of individual attributes (Oliver, 1997). When it is

said that service quality precedes customer satisfaction, the meaning is that the emotion

of satisfaction or dissatisfaction can be formed only after customers experience a

service then evaluate and perceive the excellence of that service. This understanding is

because customer satisfaction is an affective variable that is formed by the influence of

preceding factors, while service quality is a cognitive yardstick that evaluates the excel-

lence of individual service attributes (Oliver, 1997).

On the contrary, when it is said that customer satisfaction is a prerequisite for service

quality, the meaning is that service quality is a variable that represents overall judgment

and attributes of service excellence (Parasuraman et al., 1988), and as such, the perception

of service quality is formed over an extended period of time during which customers

experience a service repetitively rather than just once. In other words, if customer satisfac-

tion toward individual services is not repetitive and accumulated, it is not easy to judge the

excellence of a given service. In the end, this concept views customer satisfaction as a

transactional variable that can be formed in the short-term at the service encounter.

However, almost all studies aimed at analyzing the causal relationship between

service quality and customer satisfaction are cross-sectional. Therefore, when customer

satisfaction for past service experiences in general is measured rather than for individual

service transactions, it is difficult to conclude that customer satisfaction is a measurement

of short-term perception. Rather, it is appropriate to see satisfaction as a comprehensive

mental state in which emotions accumulated over a long period of time from past

transactional experiences are combined with current dissatisfaction with services in a

more complex manner (Oliver, 1981). Crosby, Evans, and Cowles (1990) divide

customer satisfaction into transactional customer satisfaction and cumulative customer

satisfaction. Despite such disputes, however, the results of a number of recent empirical

studies indicate that customer satisfaction is a more extensive concept than is service

quality, which emphasizes individual attributes of service; service quality is thus an

antecedent to customer satisfaction (Zeithaml et al., 2006). Today, there is a general

consensus within the service-marketing field that service quality precedes customer

satisfaction.

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Relationship quality, service quality, and customer satisfaction

Few studies have analyzed the causal relationship of ex-ante relationship quality with

service quality occurring in the social exchange process between different organizations

(Woo & Ennew, 2004). However, some studies have analyzed the causal relationship

between ex-post relationship quality and relationship management variables which is

similar to ex-ante relationship qualities (e.g. Bennet & Barkensjo, 2005; Leuthesser,

1997; Ouwersloot, Lemmink, & de Ruyter, 2004; Roberts et al., 2003). Smith (1998a)

first defined cooperation, relational investment, communication, conflict resolution, and

relationalism as relationship management variables and then empirically analyzed how

these affect ex-post relationship quality as represented by trust, commitment, and satisfac-

tion. Ouwersloot et al. (2004) defines support, communication, cooperation, and harmoni-

zation as relationship attributes to be analyzed to see how they can affect trust and

commitment.

A study by Woo and Ennew (2004) was the first attempt to launch a comprehensive

analysis of the causal relationship between ex-ante relationship quality, service quality,

customer satisfaction and behavioral intention in B2B services. They ruled out consu-

mer-based relationship quality variables, such as trust and commitment, and reorganized

relationship quality in three dimensions from the perspective of business customers:

cooperation, adaptation, and atmosphere. Although these are part of relationship

quality, trust and commitment have stronger properties related to outcome compared

with variables such as cooperation, adaptation, coordination, and communication. They

are affective and emotional variables on the customer’s side, which are established as a

result of repeatedly experiencing suppliers’ excellent services and their efforts to

improve the relationship. As such, existing studies that analyze the causal relationship

by using trust and commitment as relationship quality variables are forced to conclude

that service quality and customer satisfaction are antecedents to relationship quality.

On the other hand, relationship quality attributes, such as cooperation, adaptation,

coordination, and devolution are variables from the viewpoint of suppliers. Suppliers

can enhance customer value by controlling and managing these variables in accordance

with customer expectation. Woo and Ennew (2004) use relationship quality variables

that can be easily controlled by service providers, such as cooperation, adaptation, and

atmosphere, instead of using trust and commitment to represent the causal relationship

of relationship quality � service quality � customer satisfaction � behavioral inten-

tion. In other words, unlike the existing research, the attitude of service providers

toward customers changes through improvement of relationship quality, and, as such, cus-

tomer service quality can be improved in the service encounter. In this way, it is possible to

discuss relationship quality as a premise to guarantee service quality and customer

satisfaction in the long run while yet maintaining the causal order of service quality �customer satisfaction � behavioral intention, which has seen a broad consensus in

academia of late. That is, it is now possible to logically explain the value of relationship

marketing that targets corporate clients for the purpose of improving ex-ante relationship

quality. However, Woo and Ennew (2004) have a weakness in that they oversimplify

service quality attributes and measure them as just a single indicator of overall service

quality.

Research model

A causal model of intermediary customers is organized as presented in Figure 2. First,

ex-ante variables that can be controlled directly by the government, such as cooperation,

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coordination, devolution, and atmosphere, comprise relationship quality. Such relation-

ship quality is an antecedent to satisfaction of intermediary customers and affects both

service quality and design quality. Design quality of public services is a direct factor

that leads to satisfaction of intermediary customers. Also, design quality can have a

direct effect on service quality. This relationship is only natural when we consider that

the content of a service as well as its delivery process is determined at the planning and

design stage of a service.

Our study is the first research to analyze these causal relationships of design quality in

service-marketing field. However, in this study, all design quality items are not concerned

with the designing service process, but rather are designing policies used to determine

service contents. Hence, the causal relationship between design quality and service

quality is excluded from our causal model. In the future, when construct and measurement

items directly related to designing service process are developed, this causal relationship

can be additionally analyzed.

Relationship quality and service quality

Service quality in the service encounter has the strong characteristics of a short-term trans-

action, whereas relationship quality has the strong characteristics of a long-term social

exchange (Rhee & Rha, 2009). For example, responsiveness and empathy of service

quality are mostly an immediate response to short-term changes in customer demands

and service situations and are related to personal attitude and the resolve of public ser-

vants. On the other hand, the atmosphere of relationship quality is associated with the fun-

damental attitude of governments toward service agencies and is cultural and institutional

attributes. Relationship quality attributes, such as cooperation, coordination, and devolu-

tion have all been institutionalized as policy, regulations, practices, procedures, and oper-

ational standards in the government. Such changes involving relationship quality represent

changes in culture and institutions regarding the society of public servants, and these

changes bring about changes in public servants’ attitudes toward services. As a result,

service quality attributes, such as empathy and responsiveness during individual service

encounters, can naturally be improved.

Figure 2. Causal relationships between the quality dimensions and satisfaction of intermediarycustomers.

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This argument is consistent with the findings of prior studies that maintain that it is

common to have high service quality when relationship quality at a certain point of

time is positive (Gummesson, 1987; Woo & Ennew, 2004). Yet high service quality in

the human encounter does not necessarily indicate good relationship quality (Gummesson,

1987; Woo & Ennew, 2004). The relationship quality attributes developed in this study are

different from affective variables, such as trust and commitment, which is formed inside of

a customer’s mind following repeated experience of services. Cooperation, coordination,

devolution, and atmosphere are attributional and cognitive quality variables that can be

controlled and improved directly by service providers (Rhee & Rha, 2009). Of course,

they have some similarities to SERVQUAL in terms of measurement and operation.

Also, SERVQUAL partly represents relational attributes as well as transactional attributes

(Jamal & Naser, 2002; Roberts et al., 2003). Assurance represents confidence in employ-

ees, which is a similar item to that of traditional relationship quality, and empathy partially

shares the same meaning as trust in good will. In the end, when considering a causal

relationship of necessary and sufficient conditions, long-term relationship quality with

institutional and cultural characteristics is more likely to be an antecedent to service

quality, which is a relatively short-term transactional attribute. Thus, the following

hypothesis is offered:

H1: Relationship quality perceived by intermediary customers positively affects their percep-tion of service quality.

Relationship quality and design quality

Design quality, as investigated in this study, is a perceived quality of intermediary custo-

mers that pertains to policy development and the implementation process that determines

the contents of public services. Judgments on design quality are formed based on inter-

mediary customer evaluation of individual attributes that comprise design quality.

Design quality attributes are related to legitimacy, alignment, measures, and implemen-

tation of policies. Intermediary customers judge each of these attributes based on their

own opinions, professional interests, interests of their organizations, and related infor-

mation. In other words, intermediary customers determine policy design quality by cogni-

tively comparing measurement items of each attribute to their own opinions and

expectations. For example, when communication and joint decision making practices

take root between government and service agencies, opinions from the front lines of

public services and stakeholder needs can be better reflected in the processes of policy

development and budget compilation. The quality of policy content and means can be

objectively improved as a result.

Next, joint problem-solving and implementation practices engage intermediary custo-

mers in actual implementation of policies, thereby ensuring procedural fairness, transpar-

ency, and accountability. The cooperation of such relationship quality, above all, resolves

the information asymmetry concerning policies between government and service agencies

in the process of policy development and implementation, thereby enabling intermediary

customers to evaluate the government’s policies more accurately. Given the CIT results in

Rhee and Rha (2009), which contained a great number of dissatisfying events regarding

design quality, an objective evaluation through the resolution of information asymmetry

will naturally lead to an enhanced quality perception of overall policy processes.

Meanwhile, governments’ ability to coordinate and link the relationships among

service agencies reduces the possibility of inter-agency conflicts for intermediary

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customers and eliminates waste in the public service delivery system caused by redundant

functions. The perception of intermediary customers regarding operational effectiveness

of government policies can be greatly improved. Also, the government’s active devolution

leads to the strengthened authority of service agencies and the acquisition of additional

resources, thus helping intermediary customers have more favorable views toward govern-

ment policies. Moreover, when the overall atmosphere surrounding governmental

relations becomes favorable and friendly, the expert-like cynicism or skepticism of inter-

mediary customers can be dispelled. Then, intermediary customers will evaluate govern-

ment performance regarding design quality outcomes in a more positive manner. Like in

this scenario, relationship quality is a preceding variable of design quality and can have a

direct impact on design quality. Thus the following hypothesis is presented.

H2: Relationship quality perceived by intermediary customers positively affects their percep-tions of design quality.

Relationship quality, service quality, design quality, and customer satisfaction

Quality dimensions that concern intermediary customers, such as relationship quality,

service quality, and design quality, are all perceived quality aspects that are based on a

cognitive evaluation of the individual attributes of quality dimensions. These three cogni-

tive dimensions can only be antecedents to customer satisfaction, which is an affective and

emotional response regarding overall services. These dimensions are also the result of the

CIT and discovered as direct drivers leading to customer satisfaction and also are consist-

ent with findings of a great many empirical studies that conclude that perceived quality

precedes customer satisfaction (Zeithaml et al., 2006). However, CIT outcomes do indi-

cate that critical incidents related to service quality by intermediary customers account

for only 15.5% of overall critical incidents (Rhee & Rha, 2009). In other words, service

quality is not very important in terms of intermediary customer satisfaction. As such, it

can be predicted that service quality has a relatively smaller effect on customer satisfaction

than does design quality and relationship quality. Therefore, the following hypotheses are

offered.

H3a: Relationship quality perceived by intermediary customers positively affects theirsatisfaction.

H3b: Service quality perceived by intermediary customers positively affects their satisfaction.

H3c: Design quality perceived by intermediary customers positively affects their satisfaction.

H3d: Design quality and relationship quality perceived by intermediary customers have astronger effect on their satisfaction than does service quality.

Methodology and results

Data collection and the development of measurement items

To explore the quality attributes in public service, we conducted CIT research on social

welfare service, which is typically representative of public services (e.g. Bitner, Booms,

& Tetreault, 1990; Flanagan, 1954). We found that quality dimensions, such as design

quality, relationship quality, and service quality, and developed their attributes and

measurement items (Rhee & Rha, 2009). Next, we complemented the missing items of

CIT results, using SERVQUAL’s 22 items, completing a total of 59 measurement items

and corresponding survey questions. An expert in each service sector was consulted,

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and the survey text was modified accordingly to improve the implementation, responsive-

ness, and reliability of the developed survey.

The questionnaire adopted a five-point Likert scale, following the SERVPERF

measurement method (Cronin & Taylor, 1992). To achieve external validity, the survey

was distributed to three service sectors: Social work, childcare, and healthcare services.

The unit of analysis is the intermediary customer. He or she is an employee of the

service agencies responsible for actual delivery of the service in the front line. The

social workers in social work centers, the baby sitters in childcare centers, and

the nurses in healthcare centers are surveyed. We surveyed all the organizations in all

the local wards of the Republic of Korea with a paid reply envelope distributed for

responses. All the organizations are public institutions. We collected 606 questionnaires

from 124 social work centers (of 373 centers), 716 questionnaires from 382 childcare

centers (of 1320 centers), and 1439 questionnaires from 166 healthcare centers (of 256

centers). Through application of the Komogorov-Smirinov test, we found that there

were no variations between the questionnaires that arrived earlier and those that arrived

later. Multi-collinearity was also found to be non-existent.

To avoid any distortion from possible variation in sample size by service sectors, we

randomly selected 606 questionnaires from the childcare and healthcare sectors and com-

pleted a total sample size of 1808. We conducted the Komogorov–Smirinov test between

the randomly sampled set and the original full set. The results showed that there were no

variations between the two sets. We conducted all the statistical tests exactly used in our

paper, the causal model analyses without random sampling and merging different data

sets. However, the results showed almost the same statistics as the results displayed in

our paper. There was no difference in result patterns across sectors.

An exploratory factor analysis and a confirmatory factor analysis were then both

applied in sequence to the survey data to develop a quality measurement model for inter-

mediary customers. Results of the analysis showed a high level of reliability and validity

while allowing for development of a measurement system with an identical factor structure

and measurement items for the three service sectors (Table 1). This is a hierarchical

measurement model that uses quality dimensions as the second-order variables and

quality attributes as first-order variables. We directly used the construct and measurement

items developed through this process as the latent variables and measurement items of

our causal model. The results are presented in Table 2. Additionally, we measured the

satisfaction of intermediary customers in terms of their satisfaction regarding overall

public service and inter-organizational service was directly measured. Internal reliability

(Cronbach’s a) was 0.856.

Measurement model

To overcome the attenuation of correlation, we organized the measurement model as a

second-order hierarchical structure (Cho, 1999; Tanriverdi & Venkatraman, 2005). A con-

firmatory factor analysis with LISREL 8.51 was used to validate the structural relationship

of first-order factors, which were discovered through the exploratory factor analysis.

Maximum-likelihood method was used for estimating factor loadings. First, as shown in

Table 2, all the first-order factors (quality attributes) presented the desirable level of

composite reliability (r) and average variance extracted (AVE); 0.5 and 0.7 are the

recommended threshold values, respectively and demonstrated the internal consistency

of the measures (Fornell & Larcker, 1981). Next, we analyzed the content validity of

first-order factors. The loadings of measurement items on their respective factors were

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all highly significant (p , 0.001), providing support for convergent validity (Anderson &

Gerbing, 1988). Also, correlations between the first-order factors were all highly signifi-

cant (p , 0.001), and their correlation coefficients were less than the recommended

upper limit of 0.9, demonstrating their discriminative validity. Therefore, the entire

first-order construct achieved theoretical distinctiveness.

Lastly, we confirmed whether design quality, service quality, and relationship quality

were second-order constructs, consisting of first-order factors of quality attributes. The

Table 1. Definition and measurement items of quality dimensions and attributes.

Quality dimension andattribute Definition

Number ofitems

Service quality The quality that customers have experienced or perceivedin relation to the service process or human encounter

15

SERVQUALReliability Delivering on promises: ability to perform the promised

service dependably and accurately (Zeithaml et al.,2006)

2

Responsiveness Being willing to help: willingness to help customers andprovide prompt service (Zeithaml et al., 2006)

4

Intellectual assurance Inspiring trust and confidence through professionalism:Provision of expert knowledge and information

3

Behavioral assurance Inspiring trust and confidence through action:courteousness, kindness, and confidence

3

Empathy Treating customers as individuals: caring, individualizedattention provided customers (Zeithaml et al., 2006)

3

Relationship quality The quality that employers of organizations participatingin the delivery process of a public service experiencedor perceived in relation to their relationship with thegovernment

11

Cooperation Collaboration with partners: communication, jointdecision making, and joint activities

4

Coordination Coordination and linking of relationships among partneragencies: identifying distinctive roles and functions,integrating services, and mediating conflict betweenagencies

3

Devolution Transfer of authority to service agency: transfer ofauthority, function and resources, and autonomy

2

Atmosphere Overall atmosphere surrounding relationships: closenessand friendliness

2

Design quality The quality that customers have experienced or perceivedin relation to policy-making and service designing

11

Policy legitimacy Objective appropriateness of policy objective: political,social and legal legitimacy of policy, and feasibility ofpolicy objective

2

Strategic alignment Strategic consistency of policy: alignment with higher-level strategy, past policy, and policy in other areas

2

Substantial policymeasures

Substantiveness of policy measures: concreteness andprocedural justice of policy means, and overlap withother policy measures

3

Operationaleffectiveness

Effectiveness of policy operation: consistency in budgetand administration directives, adequacy of resources,and effectiveness of resource allocation

4

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target coefficient developed by Marsh and Hocever (1985) was used as the criteria to

evaluate and determine the existence of any second-order factors. The target coefficient

(x21st order/x

22nd order) is a ratio that indicates the extent to which the second-order

factors explain the correlations among the first-order factors: The higher the explanatory

power, the higher the validity of second-order factors is. Results of the analysis on the

entire second-order factor model showed a high factor loading and fitness indicator

across the entire model, and target coefficients were relatively high at 0.75 (social

work), 0.83 (childcare) and 0.90 (healthcare). Ultimately, we established a second-order

measurement model of intermediary customers, made up of 3 quality dimensions, 13

quality attributes, and 37 measurement items. The overall fit indices of measurement

model is presented in Table 3.

Structural model

As the measurement model follows second-order hierarchical structure, we also conducted

a second-order structural equation modeling analysis (Gerbing, Hamilton, & Freeman,

1994; Tanriverdi & Venkatraman, 2005). We used LISREL 8.51 to identify the model

and estimate its factor loadings, the path coefficients, and error variances (Joreskog &

Sorbom, 1993). The estimating method was maximum-likelihood estimation.

The causal model finally did identify from the entire sample is shown in Figure 3.

All three service sectors showed the same pattern of results. Therefore, we explain the

identified causal model for the entire services sample only. All estimated parameters

Table 2. Composite reliability and average variance extracted.

First-order factors

Social work Childcare Healthcare

r AVE r AVE r AVE

Reliability 0.72 0.56 0.82 0.70 0.76 0.62Responsiveness 0.83 0.55 0.87 0.62 0.84 0.57Intellectual assurance 0.84 0.64 0.87 0.68 0.85 0.65Behavioral assurance 0.88 0.72 0.90 0.74 0.85 0.65Empathy 0.85 0.66 0.89 0.74 0.87 0.69Cooperation 0.84 0.57 0.88 0.65 0.85 0.59Coordination 0.80 0.57 0.81 0.60 0.81 0.59Devolution 0.67 0.51 0.79 0.65 0.67 0.51Atmosphere 0.71 0.55 0.83 0.71 0.81 0.68Policy legitimacy 0.73 0.58 0.77 0.63 0.79 0.65Strategic alignment 0.79 0.66 0.82 0.69 0.77 0.63Substantial measures 0.81 0.59 0.87 0.69 0.81 0.59Operational effectiveness 0.83 0.56 0.87 0.63 0.80 0.51

Table 3. Overall fit indices of measurement model.

Types of services df

Absolute-fit index Incremental-fit indexParsimonious-

fit index

x2 RMSEA GFI CFI IFI NNFI PGFI PNFI

Social work 613 3506.08 0.057 0.89 0.91 0.91 0.91 0.77 0.83Childcare 613 4514.46 0.062 0.87 0.91 0.91 0.91 0.76 0.83Healthcare 613 3388.94 0.052 0.90 0.92 0.92 0.91 0.78 0.83

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were standardized. Except for the direct path from relationship quality to customer satis-

faction, factor loadings across the measurement model and path coefficients of the struc-

tural model were all highly significant (p , 0.001). Overall fitness indicators also showed

favorable results, as shown in Table 4. H1, H2, H3b, and H3c of the causal model were all

supported; only H3a was rejected.

All results for the estimated path coefficients between quality dimensions and satisfac-

tion of intermediary customers are presented in Table 5. Excluding the path of the rejected

hypothesis, in the finally identified model as shown in Figure 3, the path coefficients from

service quality to satisfaction and the path coefficient from design quality to customer sat-

isfaction showed a difference of 0.02 and 0.01, respectively, but these coefficients are suf-

ficiently minor enough to be ignored for the analysis. We found that relationship quality

perceived by the intermediary customer positively affected service quality (0.85) and

design quality (0.71) and service quality (0.28), and design quality (0.58) directly affected

the satisfaction of intermediary customers. Also, the path coefficient from design quality to

satisfaction (0.58) was shown to be substantially larger than the path coefficient from

service quality to satisfaction (0.28). To determine whether or not this difference was stat-

istically significant, we established a causal model that restricted the coefficients of both

paths to be equal and test the difference in the value of x2. The result (x2d(1) ¼ 84.40, p ,

Figure 3. Final causal model.

Table 4. Overall fit indices of causal model.

df

Absolute-fit index Incremental-fit indexParsimonious-fit

index

x2 RMSEA GFI CFI IFI NNFI PGFI PNFI

684 2570.01 0.041 0.93 0.97 0.97 0.96 0.81 0.87

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0.001) indicated a high significance. Therefore, it can be construed that design quality has

a larger impact on the satisfaction of intermediary customer than does service quality.

Generally, intermediary customers who are experts in the corresponding service

sector have their own opinions and interest in the policy development process because

policy content, operation principles, and resource allocation are determined by those

processes.

On the other hand, relationship quality did not have a direct effect on the satisfaction of

intermediary customers, but only an indirect effect through the mediation of service

quality and design quality (0.65 ¼ 0.85 × 0.28 + 0.71 × 0.58). This result shows that

relationship quality as an institutional and cultural variable has the characteristics of an

ex-ante condition factor that affects the perception of service quality and design quality.

This also coincides with the results of the research by Woo and Ennew (2004), who ver-

ified that relationship quality does not directly affect customer satisfaction, but only

indirectly through overall service quality. In addition, the indirect effect of relationship

quality on customer satisfaction emerged vastly larger (0.65) than the direct effect

caused by service quality (0.28), and was similar to the direct effect caused by design

quality (0.58). This result is in line with the CIT conclusion that relationship quality

and design quality, not service quality, are the major factors for satisfaction of intermedi-

ary customers (Rhee & Rha, 2009).

Conclusions and discussion

This study analyzes the causal model of service quality dimensions and satisfaction of

intermediary customers in public service. It further analyzes simultaneously the causal

relationship between three quality dimensions and their relationship with satisfaction,

which had been unclear in the existing and past research. In order to fully reflect the

structure of quality dimensions and their measurement items, we established a hierarchical

second-order measurement model and conducted a causal analysis on that structural model

between the second-order factors. Second-order structural equation modeling is a method-

ology that is known to overcome the attenuation of correlation between constructs and

directly reflect the actual correlation of constructs (Cho, 1999).

The results confirm that relationship quality is not a factor directly affecting customer

satisfaction, but instead an indirect factor that uses service quality and design quality as its

mediators. This finding reaffirms that relationship quality is a variable with institutional

and cultural characteristics. The result shows a causal relationship similar to the service

profit chain presented by Heskett et al. (1994). That is, relationship quality management

is the starting point to use to enhance satisfaction of intermediary customer. The finding

here implies that a systematic and organized approach to fundamentally revamp the

Table 5. Path coefficients and hypothesis testing.

Path Path coefficient t-Value R2 Hypotheses

Relationship quality �Service quality 0.85 21.75∗ 0.73 Support H1Relationship quality �Design quality 0.71 21.64∗ 0.50 Support H2Relationship quality � Customer satisfaction 0.03 0.52 0.61 Reject H3aService quality � Customer satisfaction 0.26 5.08∗ Support H3bDesign quality � Customer satisfaction 0.57 16.05∗ Support H3c

∗p , 0.001.

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relationship between the government and service agencies is required rather than

improving service quality at the level of human encounter.

Next, this study confirmed that service quality and design quality are both factors that

directly affect customer satisfaction and that design quality has a stronger effect on cus-

tomer satisfaction than does service quality. This finding implies that to obtain the satis-

faction of the intermediary customer, design quality is more important than service

quality. In other words, quality management policies for service agencies in the field

must be fundamentally differentiated from civil affair services that emphasize the

service quality of the human encounter. The intermediary customer who is an expert

responsible for the front lines in the field has a particular concern and interest in the

policies that determine the contents of public service and their delivery. Therefore, the

government needs to directly improve the quality of the policy content and execution

that is perceived by the intermediary customer. Transparency in policy development

and service design thus can reduce the prejudice of intermediary customers and turn

their perception of design quality into a favorable one.

However, since relationship quality is an ex-ante factor to design quality and service

quality, the primary focus should be on improvement of relationship quality. Effective

strategy and a systematic program for relationship management should be developed

and carried out to enhance the levels of cooperation, coordination, devolution, and atmos-

phere perceived by intermediary customers. Only then will it be possible to not only

elevate design quality and service quality, which are direct factors for intermediary custo-

mer satisfaction, but also to maximize the effect on satisfaction improvement of those

direct factors. It should in fact be possible to use the quality model and measurement cri-

teria proposed as a result of this research as tools to measure and evaluate service quality

for the intermediary customer from the viewpoint of the service profit chain.

These research results may extend beyond a discussion on public service quality and

into the field of general service marketing, further contributing to expanding the breadth of

theoretical research on quality measurement and devising a causal model. Especially in the

case of franchise businesses with a service delivery structure similar to government (fran-

chiser headquarter) � service agencies (franchisees) � citizen (customers), the causal

model of intermediary customers may be applied as is. Only the design quality variable

would need to be reorganized from a viewpoint of service design rather than policy

design. In that case, the causal relationship between design quality and service quality

may need to be additionally analyzed.

From the position of intermediary customers, another important factor together with

government service quality is internal service quality within an agency. No matter how

high the level of government service quality, the service quality within the agency may

be low. These two facets of service quality are completely separate issues, however. In

the case of public service institutions, a principal-agency problem may arise. If the

service value chain is further refined to reflect such issues, we would be able to predict

not only the causal relationship of government service quality � intermediary customer

satisfaction but also that of government service quality � internal service quality of

service agency � intermediary customer satisfaction. As such, future research should

perform additional investigation and analysis into the internal service quality of the

service agency that affects the intermediary customer.

This causal model research is a cross-sectional analysis that uses data obtained in a

single point in time. It mainly verified the predecessor and successor relationship

between individual quality dimensions and customer satisfaction. However, cross-

sectional data cannot verify the causal chain between constructs in a time series. Actual

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precedence tells us clearly that relationship quality is an ex-ante factor to service quality,

but over time, these are correlated factors that will affect each other. To take relationship

quality and service quality as an example, as improvements in empathy and responsive-

ness accumulate, the intermediary customer’s perception, such as atmosphere, may

improve as well. Conversely, if cooperation, coordination, and such relationship quality

improve, the perception of intermediary customers of service quality, such as assurance,

can become favorable. Such an interactional relationship over a time series cannot be

tracked by the causal model produced in this research. Empirical research is additionally

required to explore such interactions further through the application of new time series

research methodology and associated data measurement.

Acknowledgement

This study was supported by the Research Fund, 2009 of the Catholic University of Korea.

References

Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review andrecommended two-step approach. Psychological Bulletin, 103(3), 411–423.

Athanasopoulou, P. (2009). Relationship quality: A critical literature review and research agenda.European Journal of Marketing, 43(5/6), 583–610.

Bennet, R., & Barkensjo, A. (2005). Relationship quality, relationship marketing, and client percep-tions of the levels of service quality of charitable organizations. International Journal ofService Industry Management, 16(1), 81–106.

Bigne, E., Moliner, M.A., & Sanchez, J. (2003). Perceived quality and satisfaction in multiserviceorganizations: The case of Spanish public services. The Journal of Service Marketing,17(4), 420–442.

Bitner, M.J., Booms, B.H., & Tetreault, M.S. (1990). The service encounter: Diagnosing favorableand unfavorable incidents. Journal of Marketing, 54(1), 71–84.

Brady, M.K., & Cronin, J., Jr (2001). Some new thoughts on conceptualizing perceived servicequality: A hierarchical approach. Journal of Marketing, 65(3), 34–49.

Brady, M.K., & Robertson, C.J. (2001). Searching for a consensus on the antecedent role of servicequality and satisfaction: An explorative cross-national study. Journal of Business Research,51(1), 53–60.

Brysland, A., & Curry, A. (2001). Service improvements in public service using SERVQUAL.Managing Service Quality, 11(6), 389–401.

Caceres, R.C., & Paparoidamis, N.G. (2007). Service quality, relationship satisfaction, trust, com-mitment, and business-to-business loyalty. European Journal of Marketing, 41(7/8), 836–867.

Cho, H. (1999). Structural equation modeling. Seoul: Sukjung.Cronin, J.J., & Taylor, S.A. (1992). Measuring service quality: A reexamination and extension.

Journal of Marketing, 56(3), 55–68.Crosby, L.A., Evans, K.R., & Cowles, D. (1990). Relationship quality in services selling: An inter-

personal influence perspective. Journal of Marketing, 54(3), 68–81.Dabholkar, P.A., & Overby, J.W. (2005). Linking process and outcome to service quality and cus-

tomer satisfaction evaluations: An investigation of real estate agent service. InternationalJournal of Service Industry Management, 16(1), 10–27.

Donnelly, M., Kerr, N.J., Rimmer, R., & Shiu, E.M. (2006). Assessing the quality of police servicesusing SERVQUAL. International Journal of Police Strategies and Management, 29(1),92–105.

Flanagan, J.C. (1954). The critical incident technique. Psychological Bulletin, 51(2), 215–250.Fornell, C.D., & Larcker, F. (1981). Evaluating structural equation models with unobservable and

measurement errors. Journal of Marketing Research, 18(1), 39–50.Gerbing, D.W., Hamilton, J.G., & Freeman, E.B. (1994). A large-scale second-order structural

equation model of the influence of management participation on organizational planningbenefits. Journal of Management, 20(4), 859–885.

1898 J.-Y. Rha

Dow

nloa

ded

by [

UQ

Lib

rary

] at

09:

20 0

4 N

ovem

ber

2014

Gronroos, C. (1982). Strategic management and marketing in the service sector. Helsingfors:Swedish School of Economics and Business Administration.

Gronroos, C. (2000). Service management and marketing: A customer relationship managementapproach (2nd ed.). Chichester: John Wiley and Sons.

Gummesson, E. (1987). The new marketing: Developing long-term interactive relationships. LongRange Planning, 24(4), 10–20.

Gummesson, E. (1993). Quality management in service organization. New York: IQSA.Hennig-Thurau, T., Gwinner, K.P., & Gremler, D.D. (2002). Understanding relationship marketing

outcomes: An integration of relational benefits and relationship quality. Journal of ServiceResearch, 4(3), 230–247.

Hennig-Thurau, T., & Klee, A. (1997). The impact of customers satisfaction and relationship qualityon customer retention: A critical reassessment and model development. Psychology andMarketing, 14(8), 737–765.

Heskett, J.L., Jones, T.O., Lovenman, G.W., Sasser, W.E., Jr, & Schlesinger, L. (1994). Putting theservice profit chain to work. Harvard Business Review, 72(2), 164–174.

Hewett, K.R., Money, B., & Sharma, S. (2002). An exploration of the moderating role of buyer cor-porate culture in industrial buyer-seller relationships. Journal of Academy of MarketingScience, 30(3), 229–239.

Hui, K.H., Zhao, X., Fan, X., & Au, K. (2004). When does the service process matter? A test of twocompeting theories. Journal of Consumer Research, 31(2), 465–475.

Jamal, A., & Naser, K. (2002). Customer satisfaction and retail banking: An assessment of some ofthe key antecendnts of customer satisfaction in retail banking. The International Journal ofBank Retailing, 20(4/5), 146–160.

Johnson, J.L. (1999). Strategic integration in industrial distribution channels: Managing the interfirmrelationship as a strategic asset. Journal of Academy of Marketing Science, 27(1), 4–18.

Joreskog, K.G., & Sorbom, D. (1993). Structural equation modeling with the SIMPLIS commandlanguage. Chicago, IL: Scientific Software International Inc.

Kang, G. (2006). The hierarchical structure of service quality: Integration of technical and functionalquality. Managing Service Quality, 16(1), 37–50.

Krajewski, L.J., & Ritzman, L.P. (2005). Operations management: Process and value chains (7thed.). New York, NY: Prentice-Hall.

Larason-Schneider, A. (1999). Policy partnering between the public and the not-for-profit privatesectors. American Behavioral Scientist, 43(1), 177–191.

Lee, H., Lee, Y., & Yoo, D. (2000). The determinants of perceived service quality and its relation-ship with satisfaction. The Journal of Service Marketing, 14(3), 217–231.

Leuthesser, L. (1997). Supplier relational behavior: An empirical assessment. Industrial MarketingManagement, 26(3), 27–46.

Llosa, S., Chandon, J.L., & Orsingher, C. (1998). An empirical study of SERVQUAL dimensional-ity. The Service Industries Journal, 18(2), 16–44.

Marsh, H.W., & Hocever, D. (1985). The application of confirmatory factor analysis to the study ofself-concept: First and higher order factor structures and their invariance across age groups.Psychological Bulletin, 97(3), 562–582.

Moliner, M.A. (2009). Loyalty, perceived value and relationship quality in healthcare service.Journal of Service Management, 20(1), 76–87.

Oliver, R.L. (1981). Measurement and evaluation model of satisfaction process in retail setting.Journal of Retailing, 57(3), 25–48.

Oliver, R.L. (1997). Satisfaction: A behavioral perspective on the consumer. New York: McGrawHill.

Ouwersloot, H., Lemmink, J., & de Ruyter, K. (2004). Moving beyond institution-managingallocation decisions in relationship marketing in business to business markets. IndustrialMarketing Management, 22(8), 701–710.

Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1985). A conceptual model of service quality andits implications for future research. Journal of Marketing, 49(4), 41–50.

Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1988). SERVQUAL: A multiple item scale formeasuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

Powpaka, S. (1996). The role of outcome quality as a determinant of overall service quality in differ-ent categories of services industries: An empirical investigation. The Journal of ServicesMarketing, 10(2), 5–25.

The Service Industries Journal 1899

Dow

nloa

ded

by [

UQ

Lib

rary

] at

09:

20 0

4 N

ovem

ber

2014

Rha, J., & Rhee, S. (2007). Developing the measurement model of service quality in the publicsector. IE Interfaces, 20(3), 339–352.

Rhee, S., & Rha, J. (2009). Public service quality and customer satisfaction: Exploring theattributes of service quality in the public sector. The Service Industries Journal, 28(8),1491–1512.

Richard, M.D., & Allway, A.W. (1993). Service quality attributes and choice behavior. The Journalof Service Marketing, 7(1), 59–68.

Roberts, K., Varki, S., & Brodie, R. (2003). Measuring the quality of relationships in consumerservices: An empirical study. European Journal of Marketing, 38(1/2), 169–196.

Shemwell, D., Yavas, U., & Bilgin, Z. (1998). Customer–service provider relationships: An empiri-cal test of a model of service quality, satisfaction and relationship-oriented outcomes.International Journal of Service Industry Management, 9(2), 155–168.

Slack, N., Chambers, S., & Roberts, J. (2004). Operations management (4th ed.). Harlow: PearsonEducation.

Smith, J.B. (1998a). Buyer-seller relationships: Bonds, relationship management and sex-type.Canadian Journal of Administrative Sciences, 18(1), 79–92.

Smith, J.B. (1998b). Buyer-seller relationships: Similarity, relationship management, and quality.Psychology and Marketing, 15(1), 3–21.

Storbacka, K., Starandvik, T., & Gronroos, C. (1994). Managing customer relationships for profit:The dynamics of relationship quality. International Journal of Service IndustryManagement, 5(5), 21–38.

Tanriverdi, H., & Venkatraman, N. (2005). Knowledge relatedness and the performance of multi-business firms. Strategic Management Journal, 26(2), 97–119.

Wisniewski, M. (2001). Using SERVQUAL to assess customer satisfaction with public sectorservices. Managing Service Quality, 11(6), 380–388.

Woo, K., & Ennew, C.T. (2004). Business to business relationship quality: An IMP interaction-basedconceptualization and measurement. European Journal of Marketing, 38(9), 1252–1271.

Zeithaml, V.A., Bitner, M.J., & Gremler, D.D. (2006). Service marketing: Integrating customerfocus across the firm (4th ed.). New York, NY: McGraw-Hill.

1900 J.-Y. Rha

Dow

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ded

by [

UQ

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rary

] at

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