J. Service Science & Management, 2010, 3, 227-234
doi:10.4236/jssm.2010.32027 Published Online June 2010 (http://www.SciRP.org/journal/jssm)
Copyright © 2010 SciRes. JSSM
An Importance-Performance Analysis of Primary
Health Care Services: Managers vs. Patients
Francisco J. Miranda, Antonio Chamorro, Luis R. Murillo, Juan Vega
Economics and Business Management Faculty. University of Extremadura, Badajoz, Spain.
Email: fmiranda@unex.es
Received February 10th, 2010; revised March 11th, 2010; accepted April 15th, 2010.
Using importance-performance analysis (IPA), this paper examines the perceptions of patients and managers of health
centres of several health care quality services attributes. IPA is an approach to the measurement of customer/user sat-
isfaction which allows for a simple and functional identification of both the strong and the weak aspects, or improve-
ment areas, of a given service. Taking both the importance assigned by users to all relevant aspects of a given service
and the perceived performance of the establishment in providing the service, the result is an IPA grid with four quad-
rants. To the best of our knowledge, this is the first time this methodology has been used to compare the perceptions of
health centre patients and managers. The results showed patients and managers to have very different perceptions of all
the quality service attributes. Implications for researchers and health centre managers are discussed. The study illus-
trates the usefulness of the IPA model as a managerial tool in identifying areas to which marketing resources should be
allocated in order to improve and enhance the quality of the health centre services provided.
Keywords: Health Services, Importance-Performance Analysis, Patient Perception, Satisfaction, Service Quality
1. Introduction
Health care providers are increasingly using higher levels
of service quality to satisfy patients. Indeed, satisfaction
surveys have been used widely as a management tool to
address the problems of access and performance. They
have also been instrumental in helping government agen-
cies identify target groups, clarify objectives, define mea-
sures of performance, and develop performance informa-
tion systems. In addition, the emerging health care lit-
erature suggests that patient satisfaction is a dominant
concern that is intertwined with strategic decisions in the
health services [1].
The present work attempts to formulate a strategic vi-
sion to enable health care centres and the overall health
care system to deliver higher levels of patient satisfaction.
Although numerous studies have examined patients’ as-
sessments, many questions still remain unanswered. Pa-
tients’ evaluations of quality remain unclear because, in
the absence of medical training, they are less qualified
than their providers to determine technical competence.
Additionally, the number of distinct concepts upon which
patients base their evaluations is questionable.
The relationship between the established variables and
the models that deal with satisfaction and quality pro-
vides a unique research opportunity to enhance manage-
rial understanding. This paper exploits this opportunity
by identifying both the importance and the performance
of service quality attributes in Spain's health care system
using the importance-performance analysis (IPA) model.
In particular, the perceptions of health centre patients and
managers are compared in terms of the importance and
performance of service quality attributes.
To the best of our knowledge, this is the first time that
the IPA methodological approach has been used to com-
pare the perceptions of health centre users and managers.
Our research extends the existing literature in two direc-
tions. Firstly, unlike prior studies with similar objectives,
we consider a wide range of attributes to reflect the most
relevant dimensions in primary health service, and sec-
ondly, the method allows a direct comparison to be made
between users’ and managers’ perceptions.
The rest of the paper is structured as follows. First, we
analyze the IPA technique’s advantages. Next, we de-
scribe the method used to measure the gap between the
perceptions of users and managers of health care centres.
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions
Then, we analyze the main results of our study, and fin-
ish with the conclusions and final reflections.
2. Methods
Importance-performance analysis conceptually underlies
the multi-attribute models that date back to the late 1970s.
Martilla and James [2] were the first to apply the IPA
technique to analyze the performance of a car dealer’s
service department. They declared IPA to be a low-cost,
easily understood technique for exploring different aspe-
cts of the marketing mix, and enabling managers to real-
locate resources according to the four areas identified.
Originally devised with marketing uses in mind, the
applications of IPA now extend to a wide range of fields,
including health service provision [3-9].
The key objective of IPA is diagnostic in nature. It
aims to facilitate identification of attributes for which,
given their importance, the product or service underper-
forms or overperforms. To this end, the interpretation of
the IPA is graphically presented on a grid divided into
four quadrants. Figure 1 illustrates the IPA grid. The Y-
axis reports the customers’ perceived importance of se-
lected attributes, and the X-axis shows the product’s (or
service’s) performance in relation to these attributes. The
four identifiable quadrants are: Concentrate Here, Keep
up the Good Work, Low Priority and Possible Overkill.
Therefore, IPA provides a useful and easily under-
standable guide to identifying the most crucial product or
service attributes in terms of their need for managerial
action, and hence to developing successful marketing
programs to achieve competitive advantage.
Attribute importance is generally regarded as a per-
son’s general assessment of the significance of an attrib-
ute for a product. Many studies have attempted to ana-
lyse customer satisfaction in terms of both expectations
Hig h
Quadrant I
Low Performance
Quadrant II
Keep upthe GoodWork
High Importance
Low Priority
Low Importance
Low Performance
Low Importance
Figure 1. Importance-Performance analysis grid
that relate to certain important attributes and judgements
of the performance of those attributes [10,11]. However,
there appears to have been some diversity in the conclu-
sions drawn about how one should relate attribute im-
portance and performance.
There exists a variety of approaches to defining meas-
ures of importance. In particular, two quite different kinds
of measure are common in IPA applications: 1) direct
measures based on Likert scale, k-point scale, or metric
ratings obtained in the same way as for performance, and
2) indirect measures obtained from the performance
scores, either by multivariate regression of an overall
product or service rating on the ratings given to the indi-
vidual attributes [4,12-15] or by means of conjoint
analysis techniques [12,16,17].
A recent review of these methods [18] supports earlier
studies [19] in finding that direct measures capture the
importance of attributes better than indirect measures.
We therefore used a Likert scale to measure importance.
3. Data and Results
The first step in implementing the IPA analysis was to
define a suitable questionnaire. The questionnaire for this
study included two main sections. The first section con-
sisted of 25 health care centre attributes, for which pa-
tients were asked to indicate the perceived importance of
each attribute and their perceptions of actual health care
centre. These 25 attributes were identified based on a
review of the relevant literature [1,5,9]. The question-
naire was structured so that each health care centre at-
tribute was scored on a 7-point Likert scale, ranging from
1 (least important) to 7 (most important) in the Impor-
tance part, and from 1 (strongly disagree) to 7 (strongly
agree) in the Performance part.
The second section was designed to elicit socio-dem-
ographic information about the respondents. Prior to the
main survey, a pilot study was conducted comprising 10
patients, 10 health professionals, and 10 health manage-
ment experts. This led to several items being re-worded
to improve their comprehensibility and the overall clarity
of the instrument. In particular, this pre-test revealed that
respondents perceived some of the items included in the
scale to be redundant. Because this redundancy led to
frustration and low response rates, the researchers agreed
to further reduce the number of items.
The final scale consisted of 25 perception items repre-
senting all five dimensions of service quality (see Ap-
pendix A for the list of items). The preliminary test also
indicated that the mixture of negatively and positively
worded statements created confusion and frustration on
the part of respondents. For this particular population, it
was believed that the confusion and inaccurate responses
resulting from the use of negatively worded statements
would adversely affect the quantity and the quality of the
data. Therefore, the negatively worded statements con-
Copyright © 2010 SciRes. JSSM
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions229
tained in the research instrument were converted to posi-
tive connotations.
In September 2008, questionnaires were mailed to
20000 patients who had used the health care services of
Extremadura (a Region in southwest Spain) within the
previous month. Due to its extensive area (41634 km²)
and low population density (26.18 inhab/km²), Extre-
madura has structured its health care system around two
territorial administrative levels of aggregation: Health
Areas and Basic Health Zones. There are 8 Health Areas,
each consisting of a number of Basic Health Zones. The
total population covered is 1081845 inhabitants, and in
2008 the number of operating Basic Health Zones was
105, each organized around a Health Care Centre as the
main provider of primary health care services in the zone.
There were 2556 returns, for a 12.78% response rate.
Questionnaires were also mailed to the 105 Extremadura
health centre managers. There were 88 returns, yielding a
4.2 sample error. The study’s technical record is presen-
ted in Table 1.
Comparison of the respondents’ gender and age dis-
tributions with those of the target population showed no
significant differences between the two groups.
The demographic profile of the respondents is present-
ed in Table 2. The largest group of respondents (60.15%)
was aged > 65 years. The next largest group (28.2%) was
aged 30-45 years. Female respondents represented a little
more than 60% of the survey population.
Table 1. The study’s technical sheet
Users of Extremadura
Health Services
Managers of Extre-
madura Health Services
AREA Extremadura (Spain)
DIMENSION 2566 questionnaires 88 questionnaires
ERROR 1.9% 4.2%
LEVEL 95% z = 1.96 p = q = 0.5
Stratified random sampling
(in proportion to the users
of each health care centre)
Entire population
10 September 2008 to 15 January 2009
Table 2. Profile of surveyed users
Gender Male: 39.85% Female: 60.15%
Age < 30 years: 9.6% 30-45 years: 28.23%
45-64 years: 24.98% > 65 years: 60.15%
Descriptive statistics including simple frequencies and
mean scores were computed for the respondents’ demo-
graphics and for the 25 attributes. IPA was then used to
compare the patients’ and managers’ perceptions of these
attributes. Each attribute was plotted according to the
mean score of its perceived importance and performance,
with the importance of attributes on the vertical axis from
low (bottom) to high (top), and the performance of at-
tributes on the horizontal axis from low (left) to high
(right). The four quadrants are constructed with cross-
hairs set at the average scores of the Importance and Per-
formance scores [2,3,8]. For patients (Figure 2), these
averages for the pooled data were: importance 4.94, and
performance 5.77. For health centre managers (Figure 3),
they were: importance 6.26, and performance 5.45.
These figures show that patients and managers have
different perceptions of the 25 factors. The following pa-
ragraphs describe some of the meaningful insights gath-
ered from this “quadrant” presentation.
Table 3 lists the aggregate importance and perform-
ance values of each attribute together with the difference
between the two for patients and managers. That all the
importance scores are higher than the performance scores
implies that there is room for improvement in all the ar-
eas. To decide, however, which attributes most merit im-
provement; one can analyze the discrepancies between
the performance and importance scores, so that attributes
with greater differences will be given higher priority [20].
In this regard, in order to maintain as far as possible the
original structure of the IPA, information from the IPA
grid was combined with the differences between the per-
formance and importance scores (see Table 3).
The first interesting conclusion to be drawn from Fig-
ure 2 is that the data show a clear trend of the most im-
portant attributes for the patient also being scored as the
best performing, showing that the Region’s health care
system appears to have clearly identified the user’s needs
and concentrated its effort on the most relevant variables.
The Concentrate Here quadrant captured a single at-
tribute for patients: health centre’s timetable (Efic7).
This attribute also presents a major discrepancy between
importance and performance (see Table 3), so that it
calls for especial attention. In Figure 3, the managers
include four attributes in this quadrant: cleanliness of
facilities (Fac1), equipment at the health centre (Fac2),
level of bureaucracy (Efic2), and time to focus on each
patient (Efic6).
Patients identified 13 attributes in The Keep up the
Good Work quadrant which thus could be considered
satisfactory in meeting their needs. In view of the infor-
mation in Table 3, managers should focus on improving
the “equipment at the health centre” (Fac2), “health staff
understands patients’ problems” (HS9), and “health staff’s
interest in solving the patients’ problems” (HS8). From
the managers’ point of view, 14 attributes are included in
Copyright © 2010 SciRes. JSSM
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions
Copyright © 2010 SciRes. JSSM
Efic 4
Efic6 No HS3
Efic 7
3,50 4,00 4,50 5,00 5,50 6,00 6,50
Fac 2
Fac 1
Concentrate here
Keep upthe Good Work
Low priority
Possible Overkill
Figure 2. IPA grid of primary health care service (patients)
Efic1 Efic3
Efic2 Efic6
3,00 3,50 4,00 4,50 5,00 5,50 6,00 6,50 7,00 7,50
Concentrate hereKeepuptheGoodWork
Low priorityPossible Overkill
Figure 3. IPA grid of primary health care service (managers)
this quadrant. According to Table 3, the administration
should focus on improving the “non-health staff interest
in solving the patients’ problems” (NoHS5) as it presents
the greatest potential for improvement (0.79). This sends
a meaningful message to health centre managers in that
they should concentrate on these aspects from their pa-
tients’ point of views. Resources should be directed to
improving and maintaining the quality of equipment and
the health staff’s motivation to understand and solve pa-
tients’ problems.
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions231
The Low Priority quadrant identifies those items whe-
re health centres are performing adequately but patients
perceive them as less important when compared with
other attributes. Nine attributes are perceived as of low
importance by the patients, but some of them present the
greatest improvement potential (see Table 3). This is the
case for some of the efficiency attributes (ease of making
an appointment, bureaucracy, waiting times in the health
centre before entering the consulting room, speed of
complementary tests, and time to focus on each patient).
While these attributes are perceived as of lower impor-
tance than others, their great improvement potential must
be taken into account. Thus, those patients did not per-
ceive these attributes as being important does not mean
that managers should reduce their effort to improve these
services. On the contrary, these service categories are
often considered to be the basic attributes for patients
who might simply be regarding them as necessary ser-
vice provisions without being aware of their importance.
The managers include 4 attributes in this quadrant, but
some of them have the highest improvement potential.
This is the case for two efficiency attributesease of
making an appointment (1.28) and speed of complemen-
tary tests (0.81)and for the location for accessibility of
the health centre (0.95). While their importance is less
than that of other attributes, again their great improve-
ment potential must be taken into account in defining
policies to improve health centre service quality.
Finally in the Possible Overkill quadrant, our analysis
identifies only two attributes (trust in health staff, HS4,
and non-health staff professionalism, NoHS2) by patients
and three by managers (complaints resolution, Efic5; he-
alth centre’s timetable, Efic7; and health staff’s prestige,
HS10) as being of low importance with relatively high
performance. In all of them the improvement potential is
also low, so that they should be given only low priority.
Table 3. Aggregate performance and importance scores of each attribute (patients and managers)
Managers Patients
Importance Performance Difference Importance Performance Difference
Fac1 6.30 5.06 1.24 5.90 5.40 0.50
Fac2 6.48 4.35 2.12 5.94 5.20 0.75
Fac3 5.70 4.74 0.95 5.96 5.16 0.80
HS1 6.34 6.18 0.16 6.27 5.01 1.26
HS2 6.69 6.08 0.61 6.22 5.43 0.79
HS3 6.45 5.85 0.60 6.02 5.35 0.67
HS4 6.35 5.89 0.46 5.72 5.16 0.56
HS5 6.36 5.97 0.39 5.85 5.23 0.62
HS6 6.37 5.84 0.53 5.9 5.4 0.50
HS7 6.47 5.72 0.75 5.94 5.2 0.74
HS8 6.50 5.97 0.53 5.96 5.16 0.80
HS9 6.35 5.52 0.83 5.89 5.01 0.88
HS10 6.14 5.69 0.45 5.88 5.15 0.73
NoHS1 6.33 6.03 0.29 5.96 5.72 0.24
NoHS2 6.43 5.74 0.69 5.75 5.01 0.74
NoHS3 6.47 5.76 0.70 5.71 4.87 0.84
NoHS4 6.38 5.73 0.65 5.49 4.6 0.89
NoHS5 6.42 5.63 0.79 5.46 4.58 0.88
Efic1 5.98 4.70 1.28 5.29 3.53 1.76
Efic2 6.44 3.28 3.16 5.45 4.31 1.14
Efic3 5.95 5.15 0.81 5.17 3.72 1.45
Efic4 5.15 4.93 0.21 5.49 3.99 1.50
Efic5 5.81 5.92 –0.11 5.35 4.19 1.16
Efic6 6.40 4.71 1.69 5.65 4.73 0.92
Efic7 6.16 5.78 0.39 5.84 4.78 1.06
Copyright © 2010 SciRes. JSSM
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions
In order to analyze the possible discrepancies between
the perceptions of the health centre patients and managers
about service quality, we performed a t-test with the fol-
lowing hypotheses:
H0: µPatients = µManagers
Ha: µPatients µManagers
Table 4 presents the results for the difference between
the patients’ and the managers’ perceptions. One ob-
serves that for 23 of the 25 items the null hypothesis of
equal means can be rejected at a 95% confidence level.
Most of the gaps between the patients’ and the manag-
ers’ perceptions are negative and statistically significant,
indicating that the managers are too optimistic about the
service that they provide. The differences are particularly
important for efficiency attributes, in particular, the ease
of making an appointment (Efic1), waiting times in the
health centre before entering the consulting room (Efic3),
and complaints resolution (Efic5), for which the patients
have a markedly lower perception of quality.
Table 4. Health centres’ perceived service quality (patients
vs. managers)
Users Managers Gap
Efic5 4.19 5.92 –1.73**
Efic3 3.72 5.15 –1.43**
Efic1 3.53 4.70 –1.17**
NoHS4 4.6 5.73 –1.13**
NoHS5 4.58 5.63 –1.05**
Efic7 4.78 5.78 –1.00**
Efic4 3.99 4.93 –0.94**
NoHS3 4.87 5.76 –0.89**
HS8 5.16 5.97 –0.81**
HS5 5.23 5.97 –0.74**
NoHS2 5.01 5.74 –0.73**
HS4 5.16 5.89 –0.73**
HS2 5.43 6.08 –0.65**
HS10 5.15 5.69 –0.54**
HS7 5.2 5.72 –0.52**
HS9 5.01 5.52 –0.51*
HS3 5.35 5.85 –0.50**
HS6 5.4 5.84 –0.44*
NoHS1 5.72 6.03 –0.31*
HS1 6.02 6.18 –0.16
Efic6 4.73 4.71 0.02
Fac2 5.00 4.35 0.65**
Fac1 5.73 5.06 0.67**
Fac3 5.52 4.74 0.78**
Efic2 4.31 3.28 1.03**
** 99% significance
* 95% significance
There are also significant differences in several attrib-
utes related to attributes of the non-health staff: kind-
ness and politeness (NoHS3), attention to patients’ pro-
blems (NoHS4), and interest in solving patients’ prob-
lems (NoHS5). The case is similar for some of the health
staff attributespersonalized service (HS5) and interest
in solving the patients’ problems (HS8)where again the
managers are overestimating the patients’ perceived qua-
lity of these attributes.
In contrast, the managers undervalue attributes relating
to the facilities: cleanliness (+0.65), equipment (+0.68),
and location for accessibility (+0.78). They also under-
value one efficiency attribute: the level of bureaucracy
In general therefore, one can say that the managers’
perception of the service provided in their health centres
is quite distant from the views of patients.
4. Conclusions
Using IPA, this study has compared the importance and
performance of 25 service quality attributes as perceived
by health centre patients and managers. They were found
to have quite different perceptions of the quality of those
The measurement of patient perceptions provides a
valuable dimension of insight into the process by which
the quality of health care service is evaluated. In order to
identify and correct service quality problems quickly,
managers need to understand patients’ perceptions of the
quality of service actually delivered. The present results
have shown, however, that managers have a quite differ-
ent perception of the service provided in their health cen-
tres from that of the patients. In particular, they are over-
estimating the perceived quality of almost all the service
quality attributes that we studied.
The findings have implications for managing primary
health care centres. In particular, the perceived quality of
a health care centre depends mainly on dimensions that
are closely linked to the health personnel who are in
touch with the patient, as well as to certain measures of
efficiencythe ease of making an appointment, level of
bureaucracy, waiting times before entering the consulting
room, speed of complementary tests, complaints resolu-
tion, time to focus on each patient, and the timetable of
the health centre.
In practical terms, the IPA technique objectively cate-
gorized the health centre quality attributes into four iden-
tifiable quadrants, which will enable health centre man-
agers better understand how patients perceive their ser-
vices. There are two clear advantages for health centre
managers in adding IPA to their tool-kit of management
techniques. First, IPA is relatively inexpensive and easily
understood. Using a straightforward two-dimensional pre-
sentation, the results can be plotted on a simple grid that
explicitly displays the strengths and weaknesses of the
Copyright © 2010 SciRes. JSSM
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions233
quality attributes being studied. Second, using the results
provided by IPA, managers can tailor their marketing
strategies to the patients’ perception of importance and
performance revealed in each quadrant. This is a useful
and effective way to identify problems and the reasons
behind them.
In determining patients’ needs and expectations, health
centre managers will be better able to prioritize tasks,
allocate resources, and match their marketing strategies
to their target segments. Once the patients’ requirements
have been clearly identified and understood, a manager
will likely be in a better position to anticipate and cater to
their desires and needs rather than merely react to their
dissatisfaction [21]. Evaluating a health centre’s perform-
ance from the patient’s point of view would improve the
manager’s understanding of customer satisfaction. Pa-
tients who are satisfied with their health centre’s service
are more likely to spread favourable word-of-mouth pub-
licity [22]. Knowing how patients perceive the quality of
services and facilities is the means by which a health
centre can achieve a competitive advantage, differentiate
itself from competitors, foster customer loyalty, enhance
its corporate image, increase business performance, and
retain existing customers and attract new ones.
In an academic context, the use of IPA to investigate
the differences between how patients perceive the impor-
tance of health centre attributes and the centre’s actual
performance in relation to those attributes could contrib-
ute to broadening the scope of research studies in the area
of consumer decision-process theory. In particular, the
potential applications of IPA in several areas need to be
addressed, including the analysis of the perception of
quality in terms of different segments which would help
health centre managers formulate and develop marketing
strategies to meet the needs of each of those segments.
[1] F. W. Gilbert, J. R. Lumpkin and R. P. Dant, “Adaptation
and Customer Expectation of Health Care Options,” Jour-
nal of Health Care Marketing, Vol. 12, No. 3, 1992, pp.
[2] J. A. Martilla and J. C. James, “Importance-Performance
Analysis,” Journal of Marketing, Vol. 41, No. 1, 1977, pp.
[3] J. Abalo, J. Varela and V. Manzano, “Importance Values
for Importance-Performance Analysis: A Formula for
Spreading out Values Derived from Preference Rankings,”
Journal of Business Research, Vol. 60, No. 2, 2007, pp.
[4] A. L. Dolinsky, “Considering the Competition in Strategy
Development: An Extension of Importance-Performance
Analysis,” Journal of Health Care Marketing, Vol. 11,
No. 1, 1991, pp. 31-36.
[5] A. L. Dolinsky and R. K. Caputo, “Adding a Competitive
Dimension to Importance-Performance Analysis: An Ap-
plication to Rraditional Health Care Systems,” Health
Care Marketing Quarterly, Vol. 8, No. 3, 1991, pp. 61-79.
[6] J. M. Hawes, G. E. Kiser and C. P. Rao, “Analysing the
Market for Planned Retirement Communities in the
Southwest,” Baylor Business Studies, Vol. 13, 1982, pp.
[7] J. M. Hawes and C. P. Rao, “Using Importance-Per-
formance Analysis to Develop Health Care Marketing
Strategies,” Journal of Health Care Marketing, Vol. 5, No.
4, 1985, pp. 19-25.
[8] M. Hemmasi, K. C. Strong and S. A. Taylor, “Measuring
Service Quality for Strategies Planning and Analysis in
Service,” Journal of Applied Business Research, Vol. 10,
No. 4, 1994, pp. 24-34.
[9] U. Yavas and D. J. Shemwell, “Modified Importance-
Performance Analysis: An Application to Hospitals,” In-
ternational Journal of Health Care Quality Assurance,
Vol. 14, No. 3, 2001, pp. 104-110.
[10] J. Myers and M. Alpers, “Determining Attributes: Mean-
ing and Measurement,” Journal of Marketing, Vol. 32, No.
4, 1968, pp. 1-4.
[11] J. Swan and I. L. Coombs, “Product Performance and
Consumer Satisfaction: A New Concept,” Journal of
Marketing, Vol. 40, No. 2, 1976, pp. 25-33.
[12] P. J. Danaher and J. Mattsson, “Customer Satisfaction
during the Service Delivery Process,” European Journal
of Marketing, Vol. 28, No. 5, 1994, pp. 5-16.
[13] S. A. Neslin, “Linking Product Features to Perceptions:
Self-Stated Versus Statistically Revealed Importance Wei-
ghts,” Journal of Marketing Research, Vol. 18, No. 1,
1981, pp. 80-86.
[14] S. A. Taylor, “Assessing Regression-Based Importance
Weights for Quality Perceptions and Satisfaction Judg-
ments in the Presence of Higher Order and/or Interaction
Effects,” Journal of Retailing, Vol. 73, No. 1, 1997, pp.
[15] D. R. Wittink and L. R. Bayer, “The Measurement Im-
perative,” Marketing Research, Vol. 6, No. 4, 1994, pp.
[16] P. J. Danaher, “Using Conjoint Analysis to Determine de
Relative Importance of Service Attributes Measured in
Customer Satisfaction Surveys,” Journal of Retailing, Vol.
73, No. 2, 1997, pp. 235-260.
[17] A. Ostrom and D. Iacobucci, “Consumer Trade-Offs and
the Evaluation of Services,” Journal of Marketing, Vol.
59, No. 1, 1995, pp. 17-28.
[18] D. R. Bacon, “A Comparison of Approaches to Impor-
tance-Performance Analysis,” International Journal of
Marketing Research, Vol. 45, No. 1, 2003, pp. 55-71.
[19] R. M. Heeler, C. Okechuku and S. Reid, “Attribute Im-
portance: Contrasting Measurements,” Journal of Mar-
keting Research, Vol. 16, No. 1, 1979, pp. 60-63.
[20] U. Oberoi and C. Hales, “Assessing the Quality of the
Conference Hotel Service Product: Towards an Empiri-
cally Based Model,” The Service Industries Journal, Vol.
10, No. 4, 1990, pp. 700-721.
[21] C. Fornell, “A National Customer Satisfaction Barometer:
The Swedish Experience,” Journal of Marketing, Vol. 56,
No. 1, 1992, pp. 6-21.
[22] E. H. Watson, M. A. McKenna and G. M. McLean,
“TQM and Services: Implementing Change in the NHS,”
International Journal of Contemporary Hospitality Man-
agement, Vol. 4, No. 2, 1992, pp. 17-20.
Copyright © 2010 SciRes. JSSM
An Importance-Performance Analysis of Primary Health Care Services: Managers vs. Patients Perceptions
Appendix A
Fac1: Cleanliness of facilities
Fac2: Equipment at the health centre
Fac3: Location for accessibility of the health centre
HS1: Health staff cleanliness
HS2: Health staff professionalism
HS3: Health staff kindness and politeness
HS4: Trust in health staff
HS5: Personalized service
HS6: Communication with health staff
HS7: Health staff’s attention to patients’ problems
HS8: Health staff’s interest in solving the patients’ problems
HS9: Health staff understand patients’ problems
HS10: Medical staff’s prestige
NoHS1: Non-health staff cleanliness
NoHS2: Non-health staff professionalism
NoHS3: Non-health staff kindness and politeness
NoHS4: Non-health staff attention to patients’ problems
NoHS5: Non-health staff interest in solving the patients’ problems
Efic1: Ease of making an appointment
Efic2: Level of bureaucracy
Efic3: Waiting times in the health centre before entering the consulting room
Efic4: Speed of complementary tests
Efic5: Resolution of complaints
Efic6: Time to focus on each patient
Efic7: Health centre’s timetable
Copyright © 2010 SciRes. JSSM