Information & Management 44 (2007) 104–113
www.elsevier.com/locate/im
A comparison of Magal’s service quality instrument
with SERVPERF
Hollis Landrum a,1,2, Victor R. Prybutok b,3, Xiaoni Zhang c,*
a
U.S. Army Engineer Research and Development Center, CEWES-IM-R, 3909 Halls Ferry Road, Vicksburg, MS 39180, United States
Information Technology and Decision Sciences Department, College of Business Administration, Denton, TX 76203-5249, United States
c
Department of Business Informatics, College of Informatics, Northern Kentucky University, Highland Heights, KY 41099, United States
b
Received 26 May 2005; received in revised form 8 April 2006; accepted 3 November 2006
Available online 11 December 2006
Abstract
The role of service quality has become critical to the success of organizations. Therefore, it is important to use a reliable
instrument to measure information service quality. SERVQUAL is a popular instrument for doing this, but, though widely used, it
has been criticized for its reliability and validity. Use of the performance measures from SERVQUAL to form SERVPERF has
addressed some of these issues. However, Magal’s instrument on information center success was found effective within a service
context because of its service orientation.
In our study, we therefore compared Magal’s instrument with the SERVPERF instrument in predicting satisfaction and
usefulness; we found that Magal’s instrument had predictive advantages in determining future usefulness and satisfaction. An
important result was to show that our results supported the use of Magal’s instrument as an alternative to SERVQUAL for
researchers and managers interested in service quality assessment.
# 2006 Elsevier B.V. All rights reserved.
Keywords: Service quality; Usefulness; User self-efficacy; Information quality; SERVQUAL; SERVPERF
1. Introduction
Service quality matters in every industry: all
companies recognize its importance because it affects
customer loyalty and satisfaction [24]. Thus, it is
necessary to use a reliable instrument to measure
information service quality. SERVQUAL has been
applied to various settings and different users but it has
* Corresponding author. Tel.: +1 859 572 6408;
fax: +1 859 572 6627.
E-mail addresses: hlandrum@att.net (H. Landrum),
prybutok@unt.edu (V.R. Prybutok), zhangx@nku.edu (X. Zhang).
1
Tel.: +1 601 634 3561.
2
Retired.
3
Tel.: +1 940 565 4767; fax: +1 940 565 4935.
0378-7206/$ – see front matter # 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.im.2006.11.002
been criticized for its reliability and validity [23,34,44].
Modified versions of SERVQUAL have only used its
performance measures or SERVPERF. Magal’s [27]
instrument also addressed service quality. It could be
applied to the information service industry and its
validity has been proven. Also, while Magal’s instrument uses 16 items, SERVPERF needs 21 items. We
therefore decided to compare these instruments in an
information service context to provide insight into the
effectiveness of the two instruments.
Many researchers have suggested that service quality
should be included as a measure of IS success [36,46]
but despite numerous studies, few have performed
empirical tests on the relationship between service
quality and IS success factors. One notable exception
was that of Landrum and Prybutok [25].
H. Landrum et al. / Information & Management 44 (2007) 104–113
In this work we relied on portions of Landrum and
Prybutok’s instrument and built upon this prior work to
further our understanding of service quality measures,
we:
(1) validated Magal’s and SERVPERF’s dimensions,
(2) compared the predictive validity of Magal and the
SERVPERF instrument using satisfaction and
usefulness as the dependent variables.
2. Literature review
2.1. Service quality
The most influential instrument in measuring service
quality has been SERVQUAL, developed in 1988 by
Parasuraman et al. [33]; it contains 22 items and has
been widely used for measuring service quality in
marketing. It features five service dimensions—
tangibles, reliability, responsiveness, assurance, and
empathy. SERVQUAL was grounded in the Gaps Model
[34] which stated that expectations are subjective and
consist of user wishes or beliefs that a service provider
should exhibit certain characteristics. Customers form
their judgment of service performance through interaction with the providers. The gap or difference
between customer expectations and perceptions about
the provided service results in the customers’ perceptions of service quality.
Many studies have validated the use of SERVQUAL
in several applications; for example, Van Dyke et al.
[45] suggest the use of an IS-context-modified version
of the instrument to assess the quality of services
supplied by an ISP. However, there are different beliefs
on the appropriateness of using SERVQUAL in an IS
context and there is some debate on the appropriateness
of the instrument itself: prior studies have questioned its
dimensionality; for example, Nitecki [29] used a three
dimensional model rather than the five proposed by
Zeithaml et al. [48]. Recently, Jiang et al. [22] surveyed
IT professionals and concluded that four dimensions
were more appropriate than five. Furthermore, Wright
and Martin [47] investigation of service quality in the
higher education sector using importance–performance
technique determined that reliability was not significant
but that contact, tangibles, and response were significant
components of online library services.
In addition, others doubted the predictive power of
using gap theory and preferred SERVPERF, the
performance-only approach based simply on customers’ perceptions of the service provider performance.
They contended that performance alone provides better
105
predictive power than SERVQUAL, which uses the gapbased scale: indeed many studies have provided
evidence that performance scores exhibit better
reliability and validity than do difference scores
[3,5]. We therefore used only performance scores in
examining SERVPERF for its dimensionality, convergent, and reliability measures.
2.2. Magal’s instrument
Two instruments, one by Essex and Magal [13] for
measuring information center success and another by
Seddon and Kiew [40] for measuring IS success, were
adapted in our study. Both were based on the well
known instrument of Ives et al. [21] based on work by
Bailey and Pearson [4] for measuring IS satisfaction.
Magal made a number of modifications to the
instrument to reflect aspects believed important to
information center success and also changed the scale
from a semantic differential to a seven-point Likert
scale ranging from low to high for both importance and
performance of each item.
Magal’s model was based on three dimensions:
quality of information center staff service, quality of
user-developed applications, and user self-sufficiency.
Substituting information quality for quality of userdeveloped applications, the dimensions for success of
libraries became: quality of library staff service, quality
of information received, and user self-sufficiency.
Because of the nature of information, it is difficult to
separate the tangible from the intangible process of
delivery [17]. However, since information was the
product or outcome, it could be considered separate
from, and not a subset of service. In a study of a library
reference service, Murfin and Gugelchuk [28] found
that library customers appeared to distinguish between
satisfaction with service provided and satisfaction with
the information received. In our work, service therefore
referred to the delivery process, and the variables
associated with service were related to how the service
was delivered by the staff. Variables associated with
information, on the other hand, referred to the outcome
or nature of the information product, such as its
comprehensiveness or accuracy.
2.3. Service quality and satisfaction
Customer satisfaction is the result of a product or
service exceeding what the customers expected [31,32].
Hernon and Whitman defined satisfaction as a sense of
contentment that arose from an actual experience [18].
Several researchers, such as Pitt et al. [37], proposed
106
H. Landrum et al. / Information & Management 44 (2007) 104–113
that service quality was a missing factor in the DeLone
and McLean’s 1992 model and, in 2003, they modified
it to include service quality as a success measure [11]. In
fact, if service quality is high users are satisfied.
2.4. Usefulness and satisfaction
Usefulness is experienced when the user believes that
the system has enhanced his or her job performance
[20]. Satisfaction occurs when a system enhances a
user’s performance [15]. The theoretical relationship
between these originated in DeLone and McLean’s [10]
model that posited satisfaction and use as outcome
measures of IS success. Seddon and Kiew [40] replaced
use with usefulness and showed usefulness and
satisfaction as two outcomes of IS. They also
hypothesized that perceived usefulness leads to user
satisfaction. The theoretical relationship between
usefulness and satisfaction was then validated; for
example, Rai et al. [38] confirmed this relationship and
in an ERP context, usefulness was shown to be
positively related to satisfaction [49]. Hsu and Chiu
examined post-adoption cognitive beliefs and factors
influencing e-service continuance and their results
suggested that satisfaction was determined by perceived
usefulness [19]. We therefore investigated the relationship between Magal’s instrument and SERVPERF for
both usefulness and satisfaction because both instruments are potentially justified as success outcomes that
relate to service quality. Fig. 1 shows the structural
model using SERVQUAL dimensions to predict
Fig. 2. Model 2 Magal’s instrument.
usefulness and satisfaction and Fig. 2 shows the
structural model using Magal’s dimensions to predict
usefulness and satisfaction.
3. Methodology
3.1. SERVQUAL instrument and its constructs
We used the SERVPERF items from the SERVQUAL instrument to measure service quality with
minimum wording changes; the instrument had five
service quality dimensions—tangibles, reliability,
responsiveness, assurance, and empathy. The 1988
version of SERVQUAL consisted of 22 questions on
expectations of service and 22 identical questions on
performance of service. Five were used to measure
tangibles, five to measure reliability, five to measure
responsiveness, four to measure assurance, and three to
measure empathy. Three changes were made to the
1994 items [35]. First, the item about the maintenance
of error free records was deleted. Second, the item about
keeping customers informed when services would be
performed was reassigned from the responsiveness to
the reliability dimension. Third, the item about
convenience in the hours of operation was moved from
the empathy to the tangibles dimension. In our study, we
therefore used the performance items (SERVPERF)
from the 1994 version of PZB’s SERVQUAL with only
21 items and minimal adaptation to accommodate the IS
studied.
3.2. Magal’s constructs
Fig. 1. Model 1 SERVQUAL satisfaction.
We adapted Magal’s 16 item set to our study with
minor modifications to the wording of the items so that
they referred to a library rather than an information
center. The instrument consisted of seven items to
H. Landrum et al. / Information & Management 44 (2007) 104–113
measure staff service quality, four to measure information quality, and five to measure user self-sufficiency.
3.3. Usefulness
The measures for usefulness were adopted from
Davis’s study [9]: six were used.
3.4. Satisfaction
Measurements of satisfaction vary; some studies
have used only one item [42], while others have
multiple items [12,26]. In general studies have
examined performances and expectations [7]. In our
study we used an outcome-based definition of satisfaction and thus our measures included four seven-point
overall service satisfaction scales dealing with value,
effectiveness, efficiency, and overall satisfaction.
4. Data analysis
4.1. Data collection
The survey (see Appendix A) was given to randomly
selected customers of two Army Corps of Engineers
research centers. These centers have similar missions
and similar types of knowledge-worker customers, and
all of them pay for the services and information they
receive. Technically speaking, these facilities are
research libraries; they function as information service
centers and research support systems for engineers and
scientists. There is physical access to the facilities but a
great deal of electronic access occurs through an online
public access catalog that enables users to locate
information and make requests to the help desk for
technical and topical support (and determine service
quality). The main difference between the two sites is
size. Customers were chosen randomly from user lists at
each site. Questionnaires were distributed and returned
Table 1
Descriptive statistics
Information quality
User self-sufficient
Staff service quality
Tangible
Reliability
Responsive
Assurance
Empathy
Satisfaction
Usefulness
Mean
Standard deviation
Reliability
6.08
5.27
6.02
5.03
5.93
6.18
5.96
5.95
5.67
5.38
0.89
1.14
0.92
1.04
0.95
0.94
0.93
1.00
1.12
1.19
0.93
0.89
0.91
0.84
0.91
0.89
0.87
0.92
0.93
0.96
107
anonymously in sealed envelopes. Respondents rated
the perceived performance of each SERVPERF item on
a Likert seven-point scale ranging from low to high.
Because we are interested in determining the users’
opinion of the quality of the information service, it was
necessary to survey the users. During the pilot study, we
randomly selected half of the users at the small library
site. For the full scale survey we mailed out surveys to
the users at the small site that had not participated in the
pilot and to the entire population of users at the large
site. In addition we took care to ensure that the
respondent sample was consistent with the populations
at each facility.
Table 2
Convergent validity of the constructs in model 1
Constructs and indicators
Standardized loadings
Tangibility (TAN)
Tan1
Tan2
Tan3
Tan4
Tan5
0.74
0.79
0.82
0.82
0.55
Reliability (REL)
Rel1
Rel2
Rel3
Rel4
Rel5
0.89
0.89
0.94
0.95
0.74
Responsiveness (RES)
Res1
Res2
Res3
0.81
0.95
0.94
Assurance (ASU)
As1
As2
As3
As4
0.85
0.83
0.89
0.85
Empathy (EMP)
Emp1
Emp2
Emp3
Emp4
0.85
0.91
0.89
0.92
Usefulness
U1
U2
U3
U4
U5
U6
0.91
0.92
0.95
0.95
0.92
0.87
Satisfaction
Sat1
Sat2
Sat3
Sat4
0.92
0.97
0.97
0.89
108
H. Landrum et al. / Information & Management 44 (2007) 104–113
Table 3
Discriminant validity of the constructs in model 1
Construct pair
Tangible
Tangible
Tangible
Tangible
Reliability
Reliability
Reliability
Responsive
Responsive
Assurance
Useful
Useful
Useful
Useful
Useful
Useful
Satisfaction
Satisfaction
Satisfaction
Satisfaction
Satisfaction
*
Reliability
Responsive
Assurance
Empathy
Responsive
Assurance
Empathy
Assurance
Empathy
Empathy
Tangible
Reliability
Responsive
Assurance
Empathy
Satisfaction
Tangible
Reliability
Responsive
Assurance
Empathy
DChi-square
DDegrees of freedom
Discriminant validity
280
360
276
304
68
196
402
84
108
19
497
1337
452
632
834
864
338
872
388
506
789
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Significant at 1%.
A total of 385 usable responses were received
representing a response rate of 37%. Over 70% of the
respondents were men and 68% engineers or scientists.
These percentages closely reflect the demographics of
the study sites. More than 75% had used the research
center more than six times in the past year, and at least
77% indicated they relied on center staff frequently when
seeking information. This suggested that the respondents’ experience qualified them to evaluate the variables
meaningfully.
We performed a z test on the difference in proportions
of gender and occupations between respondents and nonrespondents and the mean difference between the
respondents and the non-respondents. The p values for
the z test were all above 0.05, which supported the
contention that there was no significant difference
between the respondents and non-respondents for these
demographic variables. In addition, non-response bias
was evaluated by comparing responses from early and
late respondents [2]. t-Test results on age, gender, and
occupations between late and early responses showed no
significant differences at 1% level.
Table 4
Structural equations for model 1
Convergent validity was examined by using the t-tests
for the factor loadings. Anderson and Gerbing [1] stated
that convergent validity was established if all factor
loadings for the indicators measuring the same construct
were statistically significant. Our results showed that all
t-tests were significant for all the indicators in Table 2.
Discriminant validity was evaluated by fixing the
correlation between various constructs at 1.0 and
reassessing the modified model [16,41]. Significant
differences in the Chi-square statistics for the con-
R2
Equations
**
**
**
Useful = 0.24TAN + 0.27REL + 0.49RES
+ 0.14AS + 0.75EMP***
Sat = 0.31TAN*** + 0.11REL* + 0.058RES
+ 0.19AS* + 0.26EMP** + 0.43Useful***
*
**
***
Significant at 0.05 level.
Significant at 0.01 level.
Significant at 0.001 level.
0.50
0.72
4.2. Analytical methods
The focus of our research was to compare Magal’s
instrument and SERVPERF in predicting usefulness. To
perform the analytical computations we used LISREL
because all the constructs were well established and its
confirmatory factor analysis technique was suitable
[6,8]. Table 1 shows the descriptive statistics for each of
the research constructs. The reliability for all the
constructs ranged from 0.84 to 0.96, exceeding the
suggested value of 0.7 [30].
4.3. Validation of model 1
H. Landrum et al. / Information & Management 44 (2007) 104–113
Table 5
Convergent validity of the constructs in model 2
Constructs and indicators
Standardized loadings
Staff service quality
SSQ1
SSQ2
SSQ3
SSQ4
SSQ5
SSQ6
SSQ7
0.88
0.91
0.88
0.89
0.84
0.69
0.75
Information quality
IQ1
IQ2
IQ3
IQ4
0.93
0.97
0.97
0.84
User self-sufficiency
USS1
USS2
USS3
USS4
0.84
0.88
0.85
0.88
Satisfaction
Sat1
Sat2
Sat3
Sat4
0.93
0.98
0.92
0.91
Usefulness
U1
U2
U3
U4
U5
U6
0.89
0.92
0.94
0.93
0.93
0.90
109
Table 4 shows the structural equations for model 1.
The fit indices for model 1 were NFI = 0.93,
NNFI = 0.95, GFI = 0.90, and AGFI = 0.85. Gefen
et al. stated that GFI and AGFI above 0.8 were
acceptable fit indices [14].
4.4. Validity of model 2
In addition to the loadings shown in Table 5 which
supported the convergent validity of Magal’s dimension, there were methodologies used to assess discriminant validity [39,43]. Table 6 supports the
discriminant validity among the three dimensions
within Magal’s instrument. Table 7 shows the structural
equations for model 2. The fit indices for model 2 using
Magal’s dimensions were NFI = 0.91, NNFI = 0.92,
GFI = 0.85, AGFI = 0.82.
5. Discussion
strained and unconstrained models indicated high
discriminant validity among constructs. Table 3 shows
the results of construct pair tests among the seven
constructs, providing support for their discriminant
validity.
The results of the analysis for model 1 supported the
convergent and discriminant validity of the five dimensions of SERVPERF. They also showed that the
SERVPERF dimensions explained 50% of the variance
in usefulness. When predicting usefulness, four dimensions: tangible, reliability, responsive, and empathy of
SERVPERF were significant and that the assurance
dimension was not significant. The coefficients were
0.24, 0.27, 0.49, and 0.75, respectively. The magnitude of
these coefficients indicated the relative importance in
predicting usefulness. Empathy played the most important role and the next most important dimension was
responsiveness. Our LISREL results for model 1 also
showed that the SERVPERF dimensions explained 72%
of variance in satisfaction. When predicting it, four
dimensions: tangible, reliability, assurance, and empathy
of SERVPERF were significant and the responsive
Table 6
Discriminant validity of the constructs in model 2
Construct pair
Staff service quality
Staff service quality
Information quality
Usefulness
Usefulness
Usefulness
Usefulness
Satisfaction
Satisfaction
Satisfaction
*
Significant at 1%.
Information quality
User self-sufficiency
User self-sufficiency
Staff service quality
Information quality
User self-sufficiency
Satisfaction
Staff service quality
Information quality
User self-sufficiency
DChi-square
DDegrees of freedom
Discriminant validity
692
589
692
767
711
602
549
488
754
493
1
1
1
1
1
1
1
1
1
1
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
110
H. Landrum et al. / Information & Management 44 (2007) 104–113
Table 7
Structural equations for model 2
Equations
R2
Useful = 0.45SSQ*** + 0.088IQ + 0.33USS***
Sat = 0.24SSQ*** + 0.087IQ + 0.16USS** + 0.5Useful***
Useful = 0.73SSQ***
Sat = 0.4SSQ*** + 0.54Useful ***
0.61
0.79
0.54
0.76
*
Significant at 0.05 level.
Significant at 0.01 level.
***
Significant at 0.001 level.
**
dimension was not significant. The coefficients for the
predictors: tangible, assurance, empathy, and usefulness
were 0.31, 0.11, 0.19, 0.26, and 0.43, respectively.
Model 2 showed the relationships of the three
components of Magal’s instrument with usefulness and
satisfaction. The LISREL analysis confirmed the
convergent and discriminant validity of Magal’s
instrument, which explained 61% of the variance in
usefulness. Staff service quality and user self-sufficient
components were significant predictors while information quality was not significant. The coefficients for
staff service quality and user self-sufficiency were 0.45
and 0.33, respectively. Magal’s instrument also
explained 79% of the variance in satisfaction. These
results showed that staff service quality, user selfsufficiency, and usefulness were significant predicators.
The coefficients for staff service quality, user selfsufficiency, and usefulness were 0.24, 0.16, and 0.5,
respectively. In this model information quality was not a
significant predictor for either usefulness or satisfaction. When staff service quality was used alone to
predict usefulness and satisfaction, all the fit indices
were comparable to model 2 but the R2 squares were 54
and 76% for usefulness and satisfaction. Comparisons
of the models in Table 7 showed that including user selfsufficiency in the model only improved R2 7% for
usefulness and 3% for satisfaction.
Comparing models 1 and 2 showed that the five
dimensions of SERVPERF explained 50% of variance
in usefulness whereas staff service quality explained
54% of variance in usefulness. In addition, the five
dimensions of SERVPERF explained 72% of the
variance in satisfaction whereas staff service quality
explained 76% of the variance in satisfaction. These
results also showed that in both instances staff service
quality was superior to SERVPERF in predicting
usefulness and satisfaction. In addition, these findings
showed that staff service quality explained the majority
of the variance in usefulness. In Magal’s instrument,
there were only seven items to measure staff service
quality. If we were interested in service quality
comparison where staff was a relevant measure, the
seven item staff service quality provided a more
parsimonious instrument than the complete set of 16
items Magal’s instrument and 21 items SERVPERF.
Our results showed that Magal’s instrument
explained more variance than SERVPERF when
predicting usefulness. The higher R2 derived from
Magal’s instrument was potentially attributed to its
three distinct components: staff service quality,
information quality, and user self-sufficiency. The three
constructs in Magal’s instrument represented perceptions about the service provider, system itself, and user
characteristics. In essence, Magal’s instrument captured
a complete service provider, system, and users’
interaction, and, as a result, provided a higher predictive
power than SERVPERF. On the other hand, although
SERVPERF has five dimensions, these measured the
same underlying construct—service quality. Therefore,
when SERVPERF was used to predict usefulness, there
was only one construct that predicted usefulness
whereas Magal’s instrument contained three independent constructs related to usefulness. One explanation
for our finding was that, because Magal’s instrument
provided a greater number of non-redundant statistically significant independent variables for analysis, it
provided higher predictive validity.
6. Conclusion
Using the right instrument to determine service
quality helps management evaluate service performance and provides the ability to use the analysis to
design better service operations and delivery. In our
study, we compared SERVPERF and Magal’s instrument.
First, our analysis showed that both Magal’s
instrument and SERVPERF are valid instruments.
However, Magal’s provided higher predictive validity
for usefulness and satisfaction as a measure of success.
We also found that though only one component of
Magal’s instrument, staff service quality, was used to
predict usefulness and satisfaction, staff service quality
still produced higher predictive validity for usefulness
and satisfaction.
In addition to instrument validation, we compared
the predictive validity of the two instruments. Because
Magal’s instrument contained only seven items to
measure staff service quality whereas SERVPERF
contained 21, the parsimonious nature of Magal’s
instrument with its higher predictive validity makes it a
better choice in evaluating service quality.
H. Landrum et al. / Information & Management 44 (2007) 104–113
Our results also provided strong support for using
the seven-item measure of staff service quality from
Magal’s instrument, because it provided a parsimonious instrument with a relatively high predictive
capability for both usefulness and satisfaction.
111
However, it is only appropriate to assess overall
service quality when staffing is relevant. Because of
fewer items in evaluating service quality, management
may find this small instrument appealing for data
collection.
Appendix A. Survey instrument
We want to survey your impressions about how the library facility performs. For each of the questions below, please
rate your perception of the facility’s performance by circling your assessment on the 1–7 scale, with 1 as low and 7 as
high. If you do not know how to rate the performance you should circle 0 for ‘‘unknown’’. Please do not omit any
questions.
Magal’s instrument by dimension
Staff service quality
1. The staff at this facility maintain good relations with the users
2. The staff at this facility maintain good communication with the users
3. The staff are technically competent at this facility
4. The staff at this facility have a cooperative attitude
5. The staff at this facility provide fast response/turnaround time
6. The facility allows convenient access
7. There is relevance of the facility’s support to needs
Low-to-high
Unknown
1234567
1234567
1234567
1234567
1234567
1234567
1234567
0
0
0
0
0
0
0
Information quality
8. The information received is accurate
9. The information received is complete
10. The information received is relevant
11. The information received is current
1234567
1234567
1234567
1234567
0
0
0
0
User self-sufficiency
12.
13.
14.
15.
16.
1234567
1234567
1234567
1234567
1234567
0
0
0
0
0
1234567
1234567
1234567
1234567
1234567
0
0
0
0
0
SERVPERF by dimension
Tangibles Items
1.
2.
3.
4.
5.
I feel like a participant with the facility
I have a clear understanding of how to use the facility
Appropriate training on the use of the facility’s resources is provided
I am able to use the facility independently
I feel in control when using the facility
This facility has modern equipment
This facility looks appealing
The staff at this facility look neat and professional
The documentation, such as signs, handouts, and brochures are appealing
This facility provides convenient hours of operation
Reliable items
6. This facility provides service as promised
7. I can depend on the staff at this facility to handle user service problems
8. The staff at this facility perform the right service the first time
9. The staff at this facility provide service at the promised time
10. The staff at this facility keep users informed about when services will be performed
1234567
1234567
1234567
1234567
1234567
0
0
0
0
0
Responsiveness items
11. The staff at this facility provide prompt service to users
12. The staff at this facility are willing to help users
13. The staff at this facility are ready to respond to users’ requests
1234567
1234567
1234567
0
0
0
Assurance items
14.
15.
16.
17.
The
The
The
The
staff
staff
staff
staff
at
at
at
at
this
this
this
this
facility
facility
facility
facility
are courteous
instill confidence in users
make users feel secure in their transactions
have the knowledge to answer users’ questions
1234567
1234567
1234567
1234567
0
0
0
0
Empathy items
18.
19.
20.
21.
The
The
The
The
staff
staff
staff
staff
at
at
at
at
this
this
this
this
facility
facility
facility
facility
provide individual attention to users
have the users’ best interests at heart
deal with users in a caring fashion
understand the needs of users
1234567
1234567
1234567
1234567
0
0
0
0
1234567
1234567
0
0
Satisfaction
1. The service at this facility provides value
2. The service at this facility is effective
112
H. Landrum et al. / Information & Management 44 (2007) 104–113
Appendix A (Continued )
Usefulness
Low-to-high
Unknown
3. The service at this facility is efficient
4. I am satisfied with service at this facility
1234567
1234567
0
0
1.
2.
3.
4.
5.
6.
1234567
1234567
1234567
1234567
1234567
1234567
0
0
0
0
0
0
This
This
This
This
This
This
facility
facility
facility
facility
facility
facility
allows me to accomplish my tasks faster
improves my ability to do research
enhances my effectiveness
enables me to be more productive
makes it easier to do research
is useful
References
[1] J.C. Anderson, D.W. Gerbing, Structural equation modeling in
practice: a review and recommended two step approach, Psychological Bulletin 103 (3), 1988, pp. 411–423.
[2] J.S. Armstrong, T.S. Overton, Estimating nonresponse bias in
mail surveys, Journal of Marketing Research 14 (3), 1977, pp.
396–402.
[3] E. Babakus, G.W. Boller, An empirical assessment of the
SERVQUAL scale, Journal of Business Research 24 (3),
1992, pp. 253–268.
[4] J.E. Bailey, S.W. Pearson, Development of a tool for measuring
and analyzing computer user satisfaction, Management Science
29 (5), 1983, pp. 530–545.
[5] M.K. Brady Jr., J. Cronin Jr., R.R. Brand, Performance-only
measurement of service quality: a replication and extension,
Journal of Business Research 55 (1), 2002, pp. 27–31.
[6] D.T. Campbell, D.W. Fiske, Convergent and discriminant validation by the multitrait-multimethod matrix, Psychological
Bulletin 56 (1), 1959, pp. 81–105.
[7] A. Caruana, Service loyalty: the effects of service quality and the
mediating role of customer satisfaction, European Journal of
Marketing 36 (7/8), 2002, pp. 811–828.
[8] W. Chin, Issues and opinion on structural equation modeling,
MIS Quarterly 22 (1), 1998, pp. 7–16.
[9] F. Davis, Perceived usefulness, perceived ease of use, and user
acceptance of information technology, MIS Quarterly 13 (3),
1989, pp. 318–340.
[10] W.H. DeLone, E.R. McLean, Information systems success: the
quest for the dependent variable, Information Systems Research
3 (1), 1992, pp. 60–95.
[11] W.H. DeLone, E.R. McLean, The DeLone and McLean model
of information systems success: a ten-year update, Journal
of Management Information Systems 19 (4), 2003, pp. 9–
30.
[12] W.J. Doll, W. Xia, G. Torkzadeh, A confirmatory factor analysis
of the end-user computing satisfaction index, MIS Quarterly 18
(4), 1994, pp. 453–461.
[13] P.A. Essex, S.R. Magal, Determinants of information center
success, Journal of Management Information Systems 15 (2),
1998, pp. 95–117.
[14] D. Gefen, E. Karhanna, D.W. Straub, Trust and TAM in online
shopping: an integrated model, MIS Quarterly 27 (1), 2003, pp.
51–90.
[15] M. Gelderman, The relation between user satisfaction, usage of
information systems and performance, Information & Management 34 (1), 1998, pp. 1–11.
[16] E.E. Ghiselli, J.P. Campbell, S. Zedick, Measurement Theory for
the Behavior Sciences, Freeman, San Francisco, CA, 1981.
[17] J. Goldhar, STI dissemination: issues and opportunities, in:
W.R. King, G. Zaltman (Eds.), Marketing Scientific and
Technical Information, Westview Press, Colorado, 1979 , pp.
23–27.
[18] P. Hernon, J. Whitman, Delivering Satisfaction and Service
Quality: A Customer-based Approach for Libraries, American
Library Association, Chicago, 2001.
[19] M.-H. Hsu, C.-M. Chiu, Predicting electronic service continuance with a decomposed theory of planned behavior, Behavior &
Information Technology 23 (5), 2004, pp. 359–373.
[20] M. Igbaria, N. Zinateli, P. Cregg, A. Cavaye, Personal computing: acceptance factors in small firms: a structural equation
model, MIS Quarterly 21 (3), 1997, pp. 279–305.
[21] B. Ives, M.H. Olson, J. Baroudi, The measurement of user
information satisfaction, Communications of the ACM 26
(10), 1983, pp. 785–793.
[22] J.J. Jiang, G. Klein, C.L. Carr, Measuring information system
service quality: SERVQUAL from the other side, MIS Quarterly
26 (2), 2002, pp. 145–166.
[23] W.J. Kettinger, C.C. Lee, Pragmatic perspectives on the measurement of information systems, MIS Quarterly 21 (2), 1997,
pp. 223–240.
[24] J. Kim, J. Lee, K. Han, M. Lee, Businesses as buildings: metrics
for the architectural quality of Internet businesses, Information
Systems Research 13 (3), 2002, pp. 239–254.
[25] H. Landrum, V.R. Prybutok, A service quality and success model
for the information service industry, European Journal of Operational Research 156 (3), 2004, pp. 628–642.
[26] H.-H. Lin, Y.-S. Wang, An examination of the determinants of
customer loyalty in mobile commerce contexts, Information &
Management 43 (3), 2006, pp. 271–282.
[27] S.R. Magal, A model for evaluating information center success,
MIS Quarterly 8 (1), 1991, pp. 91–106.
[28] E.M. Murfin, G.M. Gugelchuk, Development and testing of a
reference transaction assessment instrument, College &
Research Libraries 48 (4), 1987, pp. 314–336.
[29] D. Nitecki, Changing the concept and measure of service quality
in academic libraries, Journal of Academic Librarianship 22 (3),
1996, pp. 181–190.
[30] J.C. Nunnally, Psychometric Theory, first ed., McGraw-Hill,
New York, 1967.
[31] R.L. Oliver, A cognitive model of the antecedents and consequences of satisfaction decisions, Journal of Marketing Research
17 (4), 1980, pp. 460–469.
[32] R. Oliver, Satisfaction: A Behavioral Perspective on the
Consumer, New York, McGraw-Hill, 1997.
[33] A. Parasuraman, L.L. Berry, V.A. Zeithaml, SERVQUAL: a
multiple-item scale for measuring consumer perceptions of
service, Journal of Retailing 64 (1), 1988, pp. 12–40.
H. Landrum et al. / Information & Management 44 (2007) 104–113
[34] A. Parasuraman, L.L. Berry, V.A. Zeithaml, Refinement and
reassessment of the SERVQUAL scale, Journal of Retailing 67
(4), 1990, pp. 420–450.
[35] A. Parasuraman, L.L. Berry, V.A. Zeithaml, Alternative scales
for measuring service quality: a comparative assessment based
on psycholometric and diagnostic criteria, Journal of Retailing
70 (3), 1994, pp. 201–230.
[36] L.F. Pitt, R.T. Watson, C.B. Kavan, Measuring information
systems service quality: concerns for a completer canvas, MIS
Quarterly 21 (2), 1997, pp. 209–221.
[37] L. Pitt, R.T. Richard, C.B. Bruce, Service quality: a measure of
information systems effectiveness, MIS Quarterly 19 (2), 1995,
pp. 173–187.
[38] A. Rai, S.S. Lang, R.B. Welker, Assessing the validity of IS a
success model: an empirical test and theoretical analysis, Information Systems Research 13 (1), 2002, pp. 50–69.
[39] A.L. Sanjay, S. Devaraj, An empirical comparison of statistical
construct validation approaches, IEEE Transactions on Engineering Management 48 (3), 2001, pp. 319–329.
[40] P. Seddon, M. Kiew, A partial test and development of the
DeLone and McLean model of IS success, in: J.I. DeGross,
S.L. Huff, M.C. Munro (Eds.), in: Proceedings of the 15th
International Conference on Information Systems, Vancouver,
British Columbia, Canada, 1994, pp. 99–110.
[41] A.H. Segars, V. Grover, Re-examining perceived ease of use and
usefulness: a confirmatory factor analysis, MIS Quarterly 17 (4),
1993, pp. 517–525.
[42] F. Selness, H. Hansen, The potential hazard of self-service in
developing customer loyalty, Journal of Service Research 4 (2),
2001, pp. 79–90.
[43] D.W. Straub, Validating instruments in MIS research, MIS
Quarterly 13 (2), 1989, pp. 147–165.
[44] T.P. Van Dyke, L.A. Kappelman, V.R. Prybutok, Measuring
information systems service quality: concerns on the use of
the SERVQUAL questionnaire, MIS Quarterly 21 (2), 1997,
pp. 195–208.
[45] T.P. Van Dyke, V.R. Prybutok, L. Kappelman, Cautions on the
use of the SERVQUAL measure to assess the quality of information systems services, Decision Sciences 30 (3), 1999, pp.
877–892.
[46] R.T. Watson, L.F. Pitt, C.B. Kavan, Measuring information
systems service quality: lessons from two longitudinal case
studies, MIS Quarterly 22 (1), 1998, pp. 61–79.
[47] C. Wright, O.N. Martin, Service quality evaluation in the higher
education sector: an empirical investigation of students’ perceptions, Higher Education Research & Development 21 (1), 2002,
pp. 23–39.
[48] V.A. Zeithaml, A. Parasuraman, L.L. Berry, Delivering Quality
Service: Balancing Customer Perceptions and Expectations,
Macmillan, New York, NY, 1990.
113
[49] M. Zrivan, N. Pliskin, R. Levin, Measuring user satisfaction and
perceived usefulness in the ERP context, Journal of Computer
Information Systems 45 (3), 2005, pp. 43–52.
Hollis Landrum retired as an information
systems management specialist with the US
Army Corps of Engineers at the Engineer
Research and Development Center. He
received a PhD in information science from
the University of North Texas in 1999. He
has published numerous articles in information and library science. In addition, he
has published articles in national music
journals and has had several plays produced. He has won numerous awards for his work with the Army
Corps of Engineers, including the Commander’s Award for Civilian
Service, one of the highest civilian awards in the Army.
Victor R. Prybutok is a regents professor
of decision sciences in the Information
Technology and Decision Sciences Department and Director of the Center for quality
and productivity in the College of Business
Administration at the University of North
Texas. He received a PhD in from Drexel
University in 1984. He is a senior member
of the American Society for Quality (ASQ)
and is an ASQ certified quality engineer,
certified quality auditor, and certified quality manager. Journals where
his published articles have appeared include The American Statistician, Communications of the ACM, Communications in Statistics,
Data Base, Decision Sciences, European Journal of Operational
Research, IEEE Transactions on Engineering Management, MIS
Quarterly, OMEGA: The International Journal of Management
Science, and Operations Research. In addition, he serves on the
editorial board of the Quality Management Journal
Xiaoni Zhang is an assistant professor of
business informatics at the Northern Kentucky University. She received her PhD in
business computer information systems
from the University of North Texas in
2001. Her research interests include software volatility, Web usability, e-commerce
systems, enterprise systems and mobile
technology. She is a member of Decision
Science Institute and Association for Information System. Her publications appear in IEEE Transactions on
Engineering Management, Communication of the ACM, International
Conference of Information Systems, and other journals.