Directory UMM :Data Elmu:jurnal:J-a:Journal Of Business Research:Vol48.Issue3.2000:

The Conceptual Domain of Service Quality
for Inpatient Nursing Services
Melissa M. Koerner
HK & ASSOCIATES

This exploratory study examines the conceptual domain of service quality
for inpatient nursing services. The findings suggest that the prevailing
conceptual definition of service quality, as articulated by Parasuraman,
Zeithaml, and Berry (1985, 1988), does not accurately describe service quality for the customers of inpatient nursing services. A definition of inpatient
nursing service quality is provided, and an instrument based on that definition, the Inpatient Nursing Service Quality Scale, is presented. The instrument’s construct validity, nomological validity, and reliability are examined,
and the findings are favorable. The results of the study reveal that service
quality perceptions for hospital inpatients consist of five dimensions: compassion, uncertainty reduction, reliability, close relationships, and individualized care. The dimensions are significant predictors of several outcomes,
including global perceptions of service quality, willingness to recommend,
and repurchase intentions. It is argued that industry-specific qualitative
research should be conducted before using generic service quality measurement tools or instruments developed in other industries. J BUSN RES 2000.
48.267–283.  2000 Elsevier Science Inc. All rights reserved.

T

he purpose of this exploratory study is to broaden and
deepen the conceptual domain of service quality for

one service industry, inpatient nursing, by focusing
on elements of service quality that have received insufficient
attention in previous research. A conceptual definition of inpatient nursing service quality is provided, and an instrument
based on that conceptualization is presented. The instrument’s
construct validity, nomological validity and reliability are examined, and its relationship to SERVQUAL (Parasuraman,
Zeithaml and Berry, 1985, 1988), an instrument that operationalizes the prevailing conceptual definition of service quality and has been used extensively in service quality research,
is also investigated. The assumption underlying this study is
not that the prevailing definition of service quality is incorrect,
but rather, that is incomplete. Thus, the general intent of the

Address correspondence to: Dr. M. M. Koerner, HK & Associates, 2299 Wyoming St., Salt Lake City, UT 84109, USA.
Journal of Business Research 48, 267–283 (2000)
 2000 Elsevier Science Inc. All rights reserved.
655 Avenue of the Americas, New York, NY 10010

study is to identify service quality dimensions that supplement
those that have already been described.
What criteria do customers use to evaluate an organization’s
service quality? Parasuraman, Zeithaml, and Berry (1985, 1988)
have developed a conceptual definition of service quality they

believe answers this question for all types of services. Their
definition is well-known, widely accepted, and viewed by
both scholars and practitioners as an important contribution
to the field of service quality. However, the definition lacks
several elements that may be integral to the service quality
construct in many service settings. These elements include
the quality of interpersonal relationships between service providers and those they serve (Berry, 1983; Brown and Swartz,
1989; Crosby, Evans, and Cowles, 1990; Gummesson, 1987),
service provider effort (Mohr and Bitner, 1995a, 1995b), emotion and affect (Fineman, 1993; Gummesson, 1991; Hochschild, 1983), social support (Adelman, Ahuvia, and Goodwin,
1994; Adelman and Ahuvia, 1995), and individualized service
(Berry, 1995; Sasser and Fulmer, 1990).
This study examines the conceptual domain of service quality for one type of service: inpatient nursing. Inpatient nursing
was chosen, because it was believed that the emotional intensity of the inpatient experience and the strong interpersonal
elements of the nurse–patient relationship would highlight service quality themes that might otherwise go unnoticed (O’Guinn and Faber, 1989; Schouten, 1991). In addition, satisfaction
with inpatient nursing services has been shown to contribute
more significantly than any other service to over-all perceptions of a hospital’s service quality (Carman, 1990; Woodside,
Frey, and Daly, 1989).

The Prevailing Conceptualization
of Service Quality

Parasuraman, Zeithaml, and Berry (1985, 1988) (hereafter
referred to as PZB) have undertaken a broad research agenda
intended to define and measure the construct of service quality. In their initial research, they developed a series of propositions that were then integrated into a model, part of which
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is called “perceived quality,” which represents the conceptual
domain of service quality. To measure the “perceived quality”
portion of the model, PZB developed the SERVQUAL scale,
and they have assessed its reliability and validity repeatedly.
Initially, 10 categories of service quality were identified. After
subsequent testing, these categories were reduced to the following five dimensions.
Reliability: the degree to which a promised service is performed dependably and accurately
Assurance: the extent to which service providers are knowledgeable and courteous, and able to inspire trust and confidence
Tangibles: the degree to which physical facilities, equipment,

and appearance of personnel are adequate
Responsiveness: the degree to which service providers are
willing to help customers and provide prompt service
Empathy: the extent to which customers are given caring,
individualized attention
In their research PZB consistently have found that customers rate reliability as the most important service quality dimension, regardless of the service studied. They also have found
reliability to be the dimension with the greatest discrepancy
between customers’ expectations and the service firm’s performance. They conclude that “The number one concern of customers today, regardless of type of service, is reliability” (Zeithaml, Parasuraman, and Berry, 1990, p. 28).
Despite SERVQUAL’s widespread acceptance and use, a
number of criticisms have been brought against it. First, some
researchers are skeptical that the dimensions apply to all service operations (Babakus and Boller, 1992; Cronin and Taylor,
1992; Dabholkar, Thorpe, and Renz, 1996). Otto and Ritchie
(1995) found several common elements in consumers’ service
experiences in five different service sectors, but they also
observed significant differences in the nature and magnitude
of the experiences across industries, with particularly striking
differences between high- and low-involvement industries.
Thus, it may not be possible for any instrument to measure
service quality accurately in all industries.
A second set of criticisms relate to the restricted view of

service reflected in SERVQUAL. For example, Gummesson
(1991) suggests that emotional components of service quality—compassion, sense of humor, and love—have received
fairly superficial treatment in service quality research, and that
emotion should be measured in all service quality instruments,
particularly those used in the helping professions. There is
increasing evidence that emotional reactions to service encounters are common and, in fact, represent “the essence of
the service experience” (Otto and Ritchie, 1995, p. 54). In
the PZB model, empathy is defined as “caring, individualized
service given to customers”; however, its operational definition, as reflected in SERVQUAL, emphasizes the “personal
attention” rather than the “caring” aspect of the dimension.
It seems that SERVQUAL addresses relatively superficial aspects of emotion.

M. M. Koerner

Similarly, individualized service—identifying and responding
to the customer’s true needs in a way that acknowledges his
or her individuality—is viewed by several researchers as critical
to service quality (Berry, 1995; Sasser and Fulmer, 1990, Surprenant and Solomon, 1987; Treacy and Wiersema, 1995), but
it is not addressed fully in SERVQUAL. Individualized service
is one aspect of PZB’s empathy dimension, but rather than

focusing on delivering service consistent with an individual’s
unique circumstances, the emphasis is on “individual attention”
and “personal attention.” Again, it seems that SERVQUAL measures a relatively mild form of individualized service.
Adelman, Ahuvia, and Goodwin (1994) suggest that social
support, which consists of reducing uncertainty, improving
self-esteem, and enhancing one’s social connection to others,
is an important aspect of service quality that has been overlooked. Social support has been found to increase customers’
willingness to recommend a service to others and to enhance
over-all perceptions of service quality (Adelman and Ahuvia,
1995). Research in the nursing field suggests that one form of
social support, regular and open information sharing between
nurses and patients, is viewed positively by patients (Fosbinder, 1994; Ludwig-Beymer, Ryan, Johnson, Hennessy, Gottuso, and Epsom, 1993; Morse, Bottorff, Anderson, O’Brien,
and Solbert, 1992). Social support is not addressed directly
or indirectly in SERVQUAL.
Service provider effort also may have an important impact
on customers’ evaluations of service (Mohr and Bitner, 1995a,
1995b). Service provider competence and courtesy are addressed in SERVQUAL’s assurance dimension, and service provider promptness and helpfulness are assessed in the responsiveness dimension, but in rating these dimensions, the service
provider’s capability and effort are being evaluated simultaneously. Mohr and Bitner (1985) believe that effort may make
a unique contribution to service quality evaluations; they
found that effort had a positive influence on customers’ service

evaluations, even when the service outcome was statistically
controlled. In particular, service failures may be viewed quite
differently, depending upon whether customers attribute the
problem to factors within the service provider’s control (i.e.,
effort), or outside their control (Folkes, 1987; Weiner, 1986).
Finally, the quality of the relationship between service providers and their customers is likely to be important in determining service quality perceptions (Dwyer, Schurr, and
Oh, 1987), particularly in professional services (Berry, 1995;
Grönroos, 1995). Bowen and Jones (1986) suggest that close,
collaborative customer relationships are especially important
when the service is characterized by high performance ambiguity and high buyer/seller goal congruity—conditions that
apply to many health-care service encounters. None of the
items on the SERVQUAL scale addresses customer/provider
relationships.
In addition to concerns with SERVQUAL’s conceptual
framework, several researchers have been critical of SERVQUAL’s measurement properties, including its unstable factor
structure, its use of “difference scores” in the analysis of results,

Service Quality for Inpatient Nursing Services

its use of self-reported importance weights, the validity, reliability and methodology of the scale, and ambiguity surrounding the “expectations” section of the instrument (Babakus and Boller, 1992; Brown, Churchill, and Peter, 1993;

Carman, 1990; Cronin and Taylor, 1992, 1994; Taylor, 1995;
Teas, 1993, 1994). However, despite these criticisms, SERVQUAL and its derivatives (e.g., SERVPERF, Taylor, 1995) are
still used extensively in service quality research and theory
development (e.g., Crompton and MacKay, 1989; Cronin and
Taylor, 1994; Dabholkar, Thorpe, and Rentz, 1996; Dunlap,
Dotson, and Chambers, 1988; Finn and Lamb, 1991; Reidenbach and Sandifer-Smallwood, 1990; Scardina, 1994; Spreng
and Singh, 1993; Taylor, 1995; Woodside, Frey, and Daly,
1989). Interestingly, even the most ardent of these critics (e.g.,
Cronin and Taylor, 1992, p. 58) have indicated that the model
seems to “define the domain of service quality adequately,”
and they have continued to conduct research based on that
conceptual domain. Although measurement issues are not
the primary focus of this study, the growing concern about
SERVQUAL’s measurement properties provides further impetus to explore alternative ways to define and measure service
quality in different industries.

The Need for Accurate
Measures of Service Quality
Organizations need accurate measures of service quality to
assure their continued survival and success. Research has

shown repeatedly that service quality influences many important organizational outcomes. For example, service quality
and customer satisfaction both have been found to be related
to repurchase intentions (Bearden and Teel, 1983; Bolton and
Drew, 1992; Cronin and Taylor, 1992; Oliver and Swan,
1989; Woodside, Frey, and Daly, 1989), and customers who
rate service as “excellent” and particularly likely to intend to
repurchase (Gale, 1994). Other behavioral intentions, such
as word of mouth referrals and defections, also may be influenced by service quality (Zeithaml, Berry, and Parasuraman,
1996). Service encounter satisfaction has been linked to actual
purchase behavior (LaBarbera and Mazursky, 1983), and service quality perceptions are related to willingness to recommend a company’s service to others (Boulding, Kalra, Staelin,
and Zeithaml, 1993; Parasuraman, Berry, and Zeithaml, 1991;
Parasuraman, Zeithaml, and Berry, 1988). Gale (1994) has
written extensively on the need for organizations to achieve
“market-perceived quality versus competitors.” He argues that
customer attraction and loyalty cannot be attained simply by
providing superior service quality; customers must believe an
organization’s offerings are a superior value in relation to
competitors’ offering. Any instrument that purports to measure service quality should be capable of predicting the organizational outcomes described above and should offer explicit
guidance on which aspects of service must be addressed to
achieve the outcomes.

In addition, accurate measures of service quality are needed

J Busn Res
2000:48:267–283

269

to improve the service experience for both customers and
service providers. This goal is furthered when service quality
tools take both customer and service provider perspectives
into account and are used to pinpoint the similarities and
differences between customers and employees in their definitions of what constitutes high-quality service. Many researchers imply that the service provider’s opinion is irrelevant when
developing service measures, some even suggesting that “the
only criteria that count in evaluating service quality are defined
by customers” (Zeithaml, Parasuraman, and Berry, 1990,
p. 16). However, when services are high in credence properties
(as many medical services are), customers may not have the
expertise to judge their quality at all (Darby and Karni, 1973).
Health-care providers’ quality judgements are not irrelevant
when it comes to the health or survival of the patient. In fact,

research has shown that front-line employees have a unique
and valuable “back stage” perspective of service (Mangold
and Babakus, 1991) and that the behaviors, perceptions, and
attitudes of service providers often are strikingly similar to
those of their customers (Parkington and Schneider, 1979;
Schlesinger and Zornitsky, 1991; Schneider and Bowen, 1985;
Tornow and Wiley, 1991). Thus, accurate measures of service
quality have the potential to generate data that improve service
to customers, while also increasing the quality of work life
for service providers.

Method
The study was conducted in two general phases. In phase
one, qualitative research methods were used to explore the
conceptual domain of service quality and clarify the service
quality dimensions used by patients to evaluate an inpatient
nursing experience. The qualitative findings of the study have
been reported in detail previously (Koerner, 1996) and, therefore, are summarized only briefly here. In phase two of the
study, scale development and testing, quantitative methods
were used to develop an instrument to measure the dimensions
identified in phase one and to assess the instrument’s validity
and reliability. The development and testing of this instrument, called the Inpatient Nursing Service Quality (INSQ)
Scale, is the primary focus of this paper. Following a brief
description of the qualitative research phase, the four stages
in developing and testing the instrument are described.
Both the qualitative and quantitative portions of the study
were conducted through the hospitals of a major health-care
system located in a large western community. Recent inpatients of the hospitals and nurses providing inpatient nursing
services were the research subjects.

Phase One: Qualitative Research to
Conceptualize Inpatient Nursing Service Quality
The qualitative research that preceded the development of the
INSQ Scale consisted of three steps. First, an interdisciplinary
literature review was conducted that focused on service quality
in nursing and in other industries and service quality measure-

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M. M. Koerner

Table 1. Conceptual Definitions of Inpatient Nursing Service Quality Dimensions
Compassion
Positive experiences involved nurses who were perceived to demonstrate genuine compassion, defined as a sympathetic consciousness of
each individual patient’s vulnerability along with a desire to lessen it. Generally, more compassion was related to more positive experiences,
and less compassion or indifference was related to more negative experiences.
Preserving dignity
Positive experiences involved nurses who treated patients respectfully and were concerned about helping them avoid embarrassment and
preserve their sense of dignity. Generally, more concern for maintaining patient dignity was associated with more positive experiences.
Close relationships
Positive experiences involved nurses who were viewed as having ongoing, interpersonally close relationships with patients, including familylike relationships and friendships characterized by trust, liking, or love. In general, closer relationships occurred in more positive
experiences, and more distant, professional, or businesslike relationships occured in more negative experiences.
Individualized care
Positive experiences involved nurses who were percieved to be sensitive to each individual patient’s unique situation and needs, flexible
and adaptable in their delivery of care, inclined to offer options to patients, able to anticipate patient needs, and able to make the best
use of the patient’s individual capabilities. Generally, more positive experiences were associated with more individualized service, and
more negative experiences were associated with more standardized service.
Uncertainty Reduction
Positive experiences involved nurses who helped reduce patients’ uncertainty by teaching and explaining, reporting on the patient’s progress
and status, interpreting information from doctors and providing additional support. Generally, more uncertainty reduction was related
to more positive experiences and less uncertainty reduction was related to more negative experiences.
Extra effort
Positive experiences involved nurses who were perceived as psychologically engaged in their interactions with patients and trying hard,
which included working with extra intensity, putting in extra time, taking unusual risks, performing extra-role activities, and displaying
extra responsiveness. In general, high effort was related to more positive experiences and average or low effort was related to negative
experiences.

ment. On the basis of those findings, an interview protocol
was developed and used to conduct depth interviews with
nurses and patients.
To recruit informants for the qualitative phase of the study,
an announcement memo was sent from the hospital administrator to the heads of all nursing departments. Managers were
urged to discuss the study with their nurses and request
volunteers. Two or three nurses were recruited from each
nursing unit, including intensive care/critical care, intermediate care, pediatrics, obstetrics–gynecology, orthopedics/urology, and medical/surgical. Those who volunteered for the
study participated in a brief telephone interview with the
researcher to determine their suitability for the study. In this
interview, each nurse was asked to identify a patient with
whom he or she had a recent positive experience, and this
patient was also contacted for an interview. In all, 15 nurses
and 14 of their patients were interviewed. The nurses ranged
in age from 25 to 55, and all were Caucasian. Two were males
and 13 were females, and their nursing experience ranged
from 2 to 35 years. The patients ranged in age from 16 to
74, and all were Caucasian. Nine were female and 5 were
male. The patients had been hospitalized for a wide variety
of reasons, including childbirth, knee surgery, cancer, heart
attack, meningitis, head trauma, and back injury.
The interviews were conducted within one hospital owned
and operated by the health-care system sponsoring the research. Using McCracken’s (1988) depth interviewing proce-

dures, the nurses and patients were asked to describe in detail
one or two of their most positive patient care experiences and
one or two of their most negative patient care experiences.
The interviews were audiotaped and transcribed and then
were analyzed using Strauss and Corbin’s (1990) constant
comparative method. According to this method’s procedures,
interview transcripts were first examined closely and open
coding was used to name and categorize phenomena. At the
conclusion of this step, the major themes that emerged in
the interviews were identified (e.g., “receiving compassionate
care” and “being embarrassed”), and examples of each theme,
taken from the interviews, were compiled. Next, the transcripts were re-read and axial coding was used to examine the
context and conditions under which the phenomena occurred
and the causes and consequences of the phenomena. This
step served to clarify how the themes related to each other
and to very positive and very negative service experiences.
For example, positive experiences were associated with individualized care and close personal relationships with nurses;
whereas, negative experiences were associated with standardized care and businesslike or distant relationships with nurses.
Finally, selective coding was used to assemble the findings into
an emergent model, which included conceptual definitions
for the six service quality dimensions that were identified in
the analysis. The themes that emerged in nurses’ and patients’
interviews were very similar, so they were integrated into one
model. The service quality dimensions identified in phase

Service Quality for Inpatient Nursing Services

Table 2. Phase Two: INSQ Scale Development and Testing
Procedures
Stage 1: Domain specification
Refine conceptual definitions of dimensions
Generate scale items
Review and pretest items
Stage 2: Data collection
Design survey administration procedures
Select sample
Administer survey
Stage 3: Development of measurement model
Step 1:
Perform exploratory factor analysis (SERVQUAL, New Items)
Determine dimensions (INSQ)
Modify instrument
Step 2:
Perform confirmatory factor analysis
Assess convergent and discriminant validity and reliability
Refine instrument
Stage 4: Assessment of nomological validity
Examine relationships among dimensions and service quality
constructs

one of the research are compassion, preserving dignity, close
relationships, individualized care, uncertainty reduction, and extra
effort. The conceptual definitions for these dimensions are
shown in Table 1.

Phase Two: Scale Development and Testing
As Briggs and Cheek (1986, p. 142) and others have argued,
“thorough conceptual analysis should precede data collection.”
The purpose of phase one, therefore, was to identify and
conceptualize the new service quality dimensions; phase two
was intended to create and test a tool to measure those dimensions. INSQ Scale development and testing occurred in four
stages, which are summarized in Table 2.
The objective of this stage
was to specify the definition for each new service quality
dimension further. The researcher accomplished this by writing survey items to operationalize each dimension, being careful to follow closely the conceptual definitions. During this
process, care was taken to use simplified language, and whenever possible, words and phrases used by the patients themselves during the interviews were included in the questions
(e.g., “The nurses genuinely cared about me” and “The nurses
really understood my personal situation”).
A pool of between 5 and 15 survey items was developed
for each dimension, with the goal of having 4 to 8 items per
dimension in the final version of the instrument (Bagozzi,
1994). The items were reviewed by two individuals with survey development experience, four experienced professors of
marketing and management, and the sponsoring organization’s research department staff. Survey items were modified
based on their feedback.
In addition to the items generated for this study, the 22
items from the perceptions section of SERVQUAL were inSTAGE 1: DOMAIN SPECIFICATION.

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271

cluded in the instrument (e.g., “The nurses were consistently
courteous with me” and “The nurses gave me prompt care”).
The decision to use only the perceptions section of SERVQUAL
(rather than both the expectations and perceptions sections)
was made because of the growing concern among researchers
about SERVQUAL’s use of difference scores and explicit expectations ratings, and in light of compelling evidence that the
perceptions section alone is a superior predictor of service
quality (Taylor, 1995).
Five items were included on the instrument to measure
dependent variables commonly used in service quality research: global perception of nursing service quality (two
items), perception of service quality relative to competitors
(one item), intentions to repurchase the service (one item),
and willingness to recommend the service to others (one item).
The instrument was pretested with five former patients
(nonacademic associates of the researcher who had experienced a hospitalization) to measure completion times, determine areas of confusion, and assess affective responses to the
survey. Several minor modifications were made based on the
pretesting.
The sample frame for data collection consisted of patients who had been discharged from
the sponsoring organization’s inpatient medical facilities during the month before the survey was administered. To select
participants from the sample frame the following procedure
was used. The sponsoring organization conducted a brief telephone survey with all former inpatients within 10 days of
their discharge from its hospitals. Over a 2-week period, at
the end of the telephone survey, respondents were asked if
they would be willing to complete a written survey focusing
on the nursing care they received during their hospital stay.
Four-hundred-eighteen volunteers were identified through
this process and were sent the survey the following week.
Approximately 4 weeks after volunteers were solicited for the
study, 249 questionnaires had been returned. This represents
a response rate of 60% of those who received the questionnaire, and approximately 21% of those who were asked during
the telephone survey to participate in the mail survey.
The demographic characteristics of respondents are as follows. Women were over-represented in the study: 69% of the
respondents were female, and 31% were male. Participants
were fairly evenly distributed across age categories: 17% were
between the ages of 18 and 25; 36% were between 26 and
45; 20% were between 46 and 64, and 25% were over 64.
Respondents had been hospitalized at six different hospitals
in one state, ranging from small, community-based hospitals
to large tertiary care hospitals.
STAGE 2: DATA COLLECTION.

STAGE 3: DEVELOPMENT OF MEASUREMENT MODEL

Exploratory Factor Analysis. The first step in developing
the measurement model was to examine several potentially
meaningful structures in the data collected from former patients. Exploratory factor analysis (EFA) is commonly viewed
as the best analytical tool for this purpose (Briggs and Cheek,

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M. M. Koerner

Table 3. Factor Matrix for SERVQUAL Items, After EFA
2

Factor
3

4

0.82
0.80
0.76
0.74
0.72
0.71
0.66
0.66
0.60
0.59
0.55

0.20
0.32
0.36
0.16
0.37
0.37
0.25
0.46
0.32
0.33
0.23

0.26
0.24
0.09
0.35
0.29
0.11
0.47
0.19
0.36
0.27
0.43

0.14
0.14
0.21
0.28
0.07
0.21
0.07
0.22
0.09
0.08
0.23

0.06
0.17
0.17
0.03
0.25
0.20
0.10
0.08
0.29
0.33
0.18

Factor 1
The nurses had my best interests at heart. (empathy)
The nurses gave me personal attention. (empathy)
When I had a problem, the nurses showed a sincere interest in solving it. (reliability)
The nurses understood my spacific needs. (empathy)
The nurses were always willing to help me. (responsiveness)
The nurses gave me individual attention. (empathy)
The behavior of the nurses instilled confidence. (assurance)
The nurses were never too busy to respond to my requests. (responsiveness)
The nurses appeared neat. (tangibles)
The nurses were consistantly courteous with me. (assurance)
The nurses had the knowledge to answer my questions. (assurance)

0.31
0.37
0.43

0.89
0.80
0.79

0.13
0.26
0.28

0.14
0.11
0.09

0.11
0.14
0.10

Factor 2
When the nurses said they would do something by a certain time, they did it. (reliability)
The nurses provided care at the time they said they would. (reliability)
The nurses gave me prompt care. (responsiveness)

0.20
0.15
0.51
0.51

0.26
0.10
0.16
0.21

0.80
0.78
0.63
0.60

0.17
0.19
0.18
0.12

0.12
0.20
20.01
0.13

0.14

0.09

0.21

0.88

0.15

0.45

0.22

0.28

0.67

20.05

Factor 4
The written information I received from the nurses about my medical condition was
visually appealing. (tangibles)
The nurses told me exactly when procedures would be performed. (reliability)

0.29

0.19

0.26

0.15

0.84

Factor 5
The hospital had visiting hours convenient to all its patients and their families. (empathy)

12.44
59
59

1.41
7
66

0.98
5
71

0.91
4
75

0.68
3
78

1

5

Item

Factor 3
The hospitial had modern-looking equipment. (tangibles)
The hospitial’s facilities were visually appealing. (tangibles)
I felt safe in the environment at the hospital. (empathy)
The nurses performed treatment and care right the first time. (reliability)

Eigenvalue
Percentage of variance explained
Cumulative percentage of variance explained

1986); therefore, a series of EFAs were performed. For each
EFA, principal components analysis and orthogonal rotation
were used because the objectives of the analysis were: (1) to
summarize the data into a minimum number of factors; (2)
to identify relatively discrete factors; and (3) to use the factors
for prediction purposes (Hair, 1995). In addition, for each
EFA, several criteria were used in determining the number of
factors to extract (e.g., eigenvalues and scree tests), rather
than using the “eigenvalue-one” procedure, which can produce
distorted results if used in an arbitrary manner (Comrey, 1978).
Because the items developed for this study were intended
to supplement, rather than replace, the SERVQUAL scale, EFA
was first performed on the SERVQUAL items to determine
that scale’s dimensionality. In light of the extensive previous
testing of SERVQUAL’s five-factor structure, it was decided
to extract five factors. Table 3 shows the SERVQUAL items
and their factor loadings, after EFA. The SERVQUAL items
did not load with their assigned dimensions. Of the five factors,

one factor contained items from each of the five SERVQUAL
dimensions, another factor contained a combination of reliability and responsiveness items, and a third factor contained items
from the tangibles dimension. The two remaining factors contained two items and one item, respectively, but were not
interpretable. As shown in Table 3, the eigenvalue for the
first factor was 12.44, representing 59% of the variance. The
second, third, fourth, and fifth factors’ eigenvalues were 1.41,
0.98, 0.91, and 0.68, respectively. The five factors represented
a total of 78% of the variance in the SERVQUAL variables.
Second, EFA was performed on the service quality items
developed for this study (excluding the SERVQUAL items) in
an iterative fashion to determine their dimensionality. Based
on the qualitative research phase of the study, a “first stage
conception” of the factor structure (Comrey, 1978) was formulated: six factors were expected to be produced. However,
after examining a variety of solutions, the most meaningful
produced four distinct factors, three of which were consistent

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Table 4. Factor Matrix for INSQ Scale, After EFA
2

Factor
3

4

5

0.76
0.72
0.71
0.64
0.62

0.29
0.28
0.33
0.18
0.29

0.18
0.39
0.15
0.30
0.26

0.12
0.23
0.20
0.23
0.32

0.20
0.13
0.23
0.23
0.24

0.29
0.26
0.45

0.79
0.77
0.66

0.21
0.16
0.13

0.13
0.20
0.16

0.14
0.16
0.11

0.21
0.11

0.62
0.60

0.25
0.12

0.23
0.28

0.36
0.47

Uncertainty Reduction
The nurses gave me and my family members frequent updates on my condition.
The nurses helped me understand information given to me by my doctor.
The nurses regularly explained what was or would be happening to me during my
hospital stay.
The nurses regularly checked with me to see if I had any special concerns or questions.
The nurses sometimes knew what I wanted or needed even before I asked for it.

0.19
0.30
0.23
0.45

0.16
0.21
0.18
0.26

0.89
0.84
0.83
0.50

0.18
0.14
0.19
0.20

0.14
0.21
0.20
0.29

Reliability
When the nurses said they would do something by a certain time, they did it.
The nurses gave me prompt care.
The nurses provided care at the time they said they would.
The nurses were never too busy to respond to my requests.

0.12
0.20
0.33
0.36

0.16
0.18
0.26
0.29

0.18
0.14
0.14
0.25

0.83
0.78
0.61
0.60

0.09
0.15
0.41
0.37

Close Relationships
The nurses and I talked about things in our lives other than my medical concerns.
The nurses and I sometimes kidded, laughed, or joked with each other.
The nurses and I enjoyed each other’s company.
The nurses and I liked each other.

0.41
0.51
0.31
0.35
0.09

0.25
0.10
0.37
0.43
0.37

0.21
0.28
0.34
0.21
0.32

0.28
0.05
0.31
0.25
0.35

0.65
0.62
0.57
0.50
0.57

Individualized Care
The nurses took my unique situation into account in caring for me.
The nurses were willing to do things a little differently for me if my situation required it.
The nurses really understood my personal situation.
The nurses knew my individual preferences and needs.
The nurses made sure I understood any instructions I was given.

12.71
55
55

1.53
7
62

1.27
6
67

1.00
4
72

0.74
3
75

1

Item
Compassion
The nurses treated me with respect.
I had the nurses’ full attention when they were with me.
The nurses genuinely cared about me.
The nurses helped me keep my sense of dignity during my hospital stay.
The nurses were kind to me.

Eigenvalue
Percentage of variance explained
Cumulative percentage of variance explained

with hypothesized dimensions: close relationships, uncertainty
reduction, and individualized care. Items from the dimensions
of compassion and dignity consistently loaded together and
were combined to form a single dimension. Items from the
effort dimension produced dominant loadings on all four dimensions, so effort was disregarded as a distinct dimension.
The eigenvalues for the four factors were 10.83, 1.26, 1.05,
and 0.75, and explained 57, 7, 6, and 4% of the variance,
respectively. In total, the four factors represented 73% of the
variance in the new items developed for this scale.
Third, the service quality items developed for this study
were combined with SERVQUAL to form the Inpatient Nursing Service Quality (INSQ) Scale, and EFA was performed on
these items. The original intent was to include all of the
SERVQUAL dimensions in this procedure. However, after
inspecting the SERVQUAL factor matrix, it was decided to
incorporate only items from SERVQUAL’s second factor into

the INSQ Scale, because this was the only dimension that was
clearly interpretable and not already represented by the other
INSQ dimensions. All of the items in this factor were reliability/
responsiveness items. The factor matrix for the INSQ Scale,
after the EFA, is shown in Table 4. The eigenvalues for the
INSQ Scale factors were 12.71, 1.53, 1.27, 1.00, and 0.74,
and represent 55, 7, 6, 4, and 3% of the variance, respectively.
In all, 75% of the variance in INSQ items is explained by the
five factors.
After completing the EFA for the INSQ Scale, the remaining
service quality items produced primary loadings on a single
factor and secondary loadings differing by 0.10 or more. Five
distinct factors emerged: compassion, close relationships, uncertainty reduction, individualized care, and reliability.
Confirmatory Factor Analysis. Once a clear 5-factor structure
had been identified for the INSQ, the solution was subjected
to CFA using Lisrel 7 (Jøreskog and Sorbom, 1989). Lisrel

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Table 5. Goodness-of-Fit Indices for SERVQUAL and INSQ Scale
Model
INSQ Scale
Null model
4-Factor model
SERVQUAL
Null model
5-Factor model

n

x2

df

GFI

AGFI

RMR

202
202

1843.66*
114.40*

91
67

0.222
0.917

0.103
0.870

0.483
0.037

181
184

3639.10*
767.24*

210
179

0.131
0.709

0.044
0.625

0.550
0.066

GFI 5 goodness-of-fit index; AGFI 5 adjusted goodness-of-fit index; RMR 5 root mean squared residual.
* p , 0.001.

was used because of its ability to provide detailed diagnostic
information about a measure’s reliability and validity, including the degree of model fit, data regarding convergent and
discriminant validity, and information about method and error
variance (Bagozzi, Yi, and Phillips, 1991).
During the CFA, several items were deleted from the INSQ
Scale as indicated by the modification indices. In the analysis,
items were permitted to load only on their assigned factors,
with cross-loadings set to zero, and the intercorrelations among
the factors were freely estimated. The covariance matrix for
the items was used in the analysis, and parameter estimates
were made under the maximum-likelihood method. For comparison purposes, the analysis also included a null model (no
constructs were recognized among the observed variables).
The results of this analysis are shown in Table 5.
The fit tests that are relatively less dependent on sample
size suggest a good fit for the 5-factor model (GFI 5 0.917,
AGFI 5 0.870), although the chi-square tests for both the
null and 5-factor models of the combined scale are significant,
suggesting an unsatisfactory fit (x2 5 1843, 91 df for the
null model, and x2 5 114, 67 df for the 5-factor model).
Nevertheless, the 5-factor model produced a chi-square statistic of less than twice the degrees of freedom, which is a commonly accepted method of assessing fit (Podsakoff and MacKenzie, 1994). In addition, the difference between the chi-square
statistic in the 5-factor model does show significant improvement over the null model (xd2 1729, 24 df; GFI D 0.70).
The complete SERVQUAL scale also was subjected to CFA,
with items assigned to their hypothesized dimensions. Again,
items in the 5-factor SERVQUAL scale were permitted to load
only on their assigned factors, with cross loadings set to zero,
and the intercorrelations among the factors were freely estimated. The covariance matrix for the items, again, was used
in the analysis, and parameter estimates were made under the
maximum-likelihood method. For the SERVQUAL scale, the
analysis indicated a poor fit for the null model (x2 5 3639,
210 df; GFI 5 0.131, AGFI 5 0.044) as well as for the 5-factor
model (x2 5 767, 179 df; GFI 5 0.79, AGFI 5 0.63). For
the 5-factor model the x2 is more than twice its degrees of
freedom, again, suggesting a poor fit, although there is improvement in the 5-factor model over the null model (x2 5
2872, 31 df; GFI D 0.58).

Convergent Validity. Convergent validity of the INSQ Scale
was assessed in five ways. First, the goodness-of-fit indices
are above or approaching 0.90, which in intself is an indication
of convergent validity (Bagozzi and Yi, 1988). Second, all of
the individual items have a statistically significant factor loading on their assigned dimensions as indicated by the factor
loadings and t-values listed in Table 6 (Anderson and Gerbing,
1988). Third, as was seen in Table 4, the secondary loadings
on factors to which items are not assigned are not large. Fourth,
Table 6 indicates that the average proportion of variance explained by each dimension is 0.59 or higher, which exceeds
Bagozzi and Yi’s (1988) criterion of 0.5 or higher. Therefore,
the dimensions specified in the INSQ Scale measurement
model seem to account for a substantial proportion of variance
in the items used to measure them. These findings suggest
that convergent validity is established for the INSQ Scale.
Discriminant Validity. Discriminant validity was assessed in
three ways. First, the correlation between each pair of dimensions was examined to determine if it is significantly different
than 1.0 (Schmitt and Stults, 1986). Although this test is not
a rigorous one, Table 7 shows that the dimensions are not
highly correlated. A similar procedure for assessing discriminant validity is to determine if the covariance plus two standard errors for each pair of dimensions add to less than 1.0
(Dabholkar, Thorpe, and Rentz, 1996). This procedure produced values ranging from 0.36 to 0.63 for each pair of
dimensions, suggesting that the INSQ dimensions are distinct
even when measurement error is taken into consideration.
A third, more stringent method of assessing discriminant
validity has been outlined by Fornell and Larcker (1981).
They contend that the average variance accounted for by the
construct among the individual items included in the construct should be greater than the amount of variance the
construct shares with any other construct. Satisfying this criterion shows that the measures within the dimension have more
in common with each other than the dimension has with other
dimensions. Table 6 shows the average variance extracted for
each dimension, and Table 7 shows the variance shared by
each pair of dimensions. Again, in every case, the square of
the construct intercorrelations is less than the average variance
extracted for items in each dimension. These findings provide

Service Quality for Inpatient Nursing Services

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275

Table 6. Pattern Coefficients, Reliabilities and t-values for INSQ Scale Items
Pattern
Coefficient*

Individual
Item
Reliability

Average
Variance
Extracted

0.81 (0.050)
0.81 (0.050)
0.73 (0.058)

0.65
0.66
0.54

0.75 (0.055)

0.56

11.23

0.73 (0.057)

0.53

10.86

0.81 (0.048)

0.66

12.62

0.77 (0.052)

0.59

11.74

0.94 (0.047)
0.87 (0.047)

0.88
0.75

0.80 (0.047)

0.64

12.53

0.85 (0.041)
0.78 (0.050)

0.73
0.60

13.74
11.96

0.70 (0.065)

0.49

10.03

0.86 (0.062)

0.74

12.79

t-value

Item Description

12.40
12.57
10.87

Compassion
I had the nurses’ full attention when they were with me.
The nurses genuinely cared about me.
The nurses were kind to me.

0.60

0.59

Uncertainty Reduction
The nurses helped me understand information given to
me by my doctor.
The nurses regularly explained what was or would be
happening to me during my hospital stay.
The nurses regularly checked with me to see if I had any
special concerns or questions.
The nurse sometimes knew what I wanted or needed even
before I asked for it.

0.82

Reliability
The nurses gave me prompt care.
The nurses provided care at the time they said they would.

15.38
13.67
0.66

Individualized Care
The nurses took my unique situation into account in caring
for me.
The nurses really understood my personal situation.
The nurses knew my individual preferences and needs.

0.62

Close Relationships
The nurses and I sometimes kidded, laughed or joked with
each other.
The nurses and I enjoyed each other’s company.

Standard errors are listed in parentheses following the factor loadings.
* Pattern coefficients represent the relationship between the observed indicators (items) and the latent constructs (dimensions).

evidence for the discriminant validity of dimension in the
INSQ Scale.
As a final assessment of discriminant validity, the phi matrix
for each pair of dimensions was fixed at 1.0, and then freed,
and chi-square difference tests were performed to determine
whether the values for the unconstrained models were significantly lower than those of the constrained models (Anderson
and Gerbing, 1988). This procedure was performed for one

pair of dimensions at a time. For each pair of dimensions,
this procedure resulted in significantly lower chi-square values
for the unconstrained models, with differences that exceeded
the critical chi-square value at p , 0.01 in every case. Again,
this provides substantial evidence for the discriminant validity
of the dimensions.
Reliability. Individual item reliabilities are listed in Table 6.
All but one are above 0.5, which exceeds Bagozzi and Yi’s

Table 7. Means, Standard Deviations, Alphas and Intercorrelations for INSQ Scale Dimensions
r
Dimension
1.
2.
3.
4.
5.

Compassion
Individualized care
Reliability
Relationships
Uncertainty reduction

Nursing service quality

M

SD

a

1

6.12
5.39
5.79
5.56
5.32

0.86
1.17
1.18
1.15
1.10

0.83
0.86
0.91
0.74
0.86

1.0
0.69
0.63
0.60
0.70

6.12

1.20

0.94

0.75

(0.48)
(0.40)
(0.36)
(0.49)

2

3

4

5

1.0
0.66 (0.44)
0.69 (0.48)
0.75 (0.56)

1.0
0.48 (0.23)
0.62 (0.38)

1.0
0.60 (0.36)

1.0

0.72

0.70

0.63

0.77

M 5 mean; SD 5 standard deviation; a 5 coefficient alpha. The square of the construct correlation (variance shared) is listed in parentheses after correlation coefficients.

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Table 8. Summary of Initial Nomological Validity Assessment
Dimension
Compassion
Individualized care
Reliability
Close relationships
Uncertainty reduction

Nursing Service
Quality

Willingness To
Recommend

Intent To
Repurchase

Service Quality
Relative to Competitors

0.75*
0.72*
0.70*
0.63*
0.77*

0.71*
0.62*
0.62*
0.52*
0.63*

0.63*
0.55*
0.55*
0.43*
0.55*

0.62*
0.61*
0.51*
0.47*
0.57*

Note: For all dimensions, Pearson correlations are reported.
* Significant ,0.01.

(1988) criterion. Additionally, INSQ scale reliability was assessed by calculating the internal consistency reliability (Cronbach, 1951) of the items included in each of the dimensions.
As shown in Table 7, Cronbach’s alphas ranged from 0.74 to
0.91, suggesting highly reliable scales.
To examine the nomological validity of the INSQ Scale, several analyses
were conducted to investigate the nature of the instrument
and its relationship to several other service quality constructs.
Of primary interest were four dependent variables commonly
used in service quality research: over-all perceptions of nursing
quality, perceptions of hospital service quality relative to competitors, willingness to recommend the hospital to others, and
intention to repurchase the hospital’s service.

STAGE 4: ASSESSMENT OF NOMOLOGICAL VALIDITY.

Relationships Among INSQ Scale
Dimensions and Dependent Variables
As an initial assessment of the relationships among the INSQ
dimensions and each of the dependent variables, correlation
analysis was performed. Table 8 shows that each of the dimensions is positively and significantly related to the dependent
variables. This outcome was expected in light of previous
research suggesting that favorable perceptions of important
aspects of service quality lead to willingness to recommend
and intent to repurchase the service. The finding provides
evidence of nomological validity of the INSQ Scale.
Another preliminary assessment involved examining the
correlation between the INSQ Scale’s 14 items and SERVQUAL’s 21 items, with all items equally weighted and averaged. (It should be noted that the INSQ Scale contains two
items that are also used in SERVQUAL). The correlation between the two scales is high (0.90) and demonstrates substantial nomological validity of the INSQ.
Next, a series of regression analyses were conducted to
determine the extent to which INSQ dimensions predict valued organizational outcomes. For each of these analyses, all
five INSQ dimensions were entered into the equation, using
SPSS PC. The detailed results of these analyses are presented
in Table 9, and summarized in Table 10.
First, the over-all nursing quality scale was regressed on the
INSQ dimensions. The equation is significant; 70% of the

variance in over-all quality of nursing is explained by the
INSQ dimensions. The t-values for all of the dimensions except
individualized care are significant (p , 0.01). Uncertainty reduction, reliability, and compassion are the best predictors in the
equation, followed by close relationships. This finding contradicts PZB’s assertion that reliability is the most significant
contributor to over-all service quality perceptions. The finding
also shows that the INSQ dimensions do contribute to global
service quality perceptions, which is another indication of the
scale’s nomological validity.
Second, service quality relative to competitors was regressed
on the INSQ dimensions. Although the equation is significant
(43% of the variance in service quality is accounted for by
the dimensions), only compassion and individualized care are
si