RELATION BETWEEN ZONE OF TOLERANCE AND ...2013/07/10 · The zone of tolerance (ZOT) is defined as...
Transcript of RELATION BETWEEN ZONE OF TOLERANCE AND ...2013/07/10 · The zone of tolerance (ZOT) is defined as...
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RELATION BETWEEN ZONE OF TOLERANCE AND IMPORTANCE OF
SERVICE QUALITY ATTRIBUTES: AN EMPIRICAL STUDY
COMMERCIAL BANKS
HAVINAL VEERABHADRAPPA*; DR. SIRIGERI JAYANNA**;
UDAY KOKATNUR***
*PROFESSOR,
INDUSTRIAL AND PRODUCTION ENGINEERING DEPARTMENT,
R Y M ENGG COLLEGE, BELLARY, KARNATAKA, INDIA,
** PROFESSOR,
DEPT. OF MANAGEMENT STUDIES,
VSK UNIVERSITY, BELLARY, KARNATAKA, INDIA,
*** ASSOCIATE PROFESSOR,
KLE COLLEGE OF ENGINEERING, BELGAUM, KARNATAKA,
_____________________________________________________________________________________
ABSTRACT
In ever increasing intense competition, providing service quality is vital for banks to compete.
The of this study is to identify the service quality dimensions, establish the zone of tolerance
based on the minimum and desired service and to explore the relation between the height, width
of tolerance zone and the importance of service quality attributes. The responses from five
hundred customers of the commercial banks on perception of service quality, importance,
minimum and desired level of service of twenty two attributes of service quality are used to
identify the five dimensions of service quality. The height and width of tolerance zone are then
established. The study reveals that the perceived service with respect to all the service quality
attributes is within the tolerance zone. It is also observed from the study that there is positive
correlation between importance of service quality attributes and the height of tolerance zone,
negative correlation between importance of service quality attributes and the width of tolerance
zone
KEYWORD: Service quality, zone of tolerance, desired service, minimum service. _____________________________________________________________________________________
Introduction
Today‟s customers of banking services are more aware of quality than ever. The customers are
becoming more sophisticated in their needs and are increasingly demanding higher standard of
service. Service quality is defined as the conformance of services to the customer‟s specifications
and expectations. The customer lays the level of quality of services he expects and anything
below this is not acceptable. In simple words, he establishes a range of acceptable service
quality. This range is known as zone of tolerance.
The zone of tolerance (ZOT) is defined as the range of customer perceptions of a service
between desired as acceptable standards (Zathaml et al., 1993). It is the range of service
performance that the customer considers satisfactory. Performance above this zone is seen as
delighting and below this zone as dissatisfying. Customer may accept variations within this range
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of performance. Any decrease or increase within this area will have marginal effect on
perceptions. When performance moves outside this range, it will have real effect on perceived
service quality.
If a customer‟s zone of tolerance is wide, then the customer may be less sensitive to the service
experience, thus increasing the likelihood of a satisfactory or acceptable outcome. Conversely, if
a customer‟s zone is narrow, then he or she may be highly sensitive to the service experience,
with a greater likelihood of dissatisfying or delighting outcomes. That is to say, the width of the
zone of tolerance may vary from customer to customer.
The tolerance zone can be established by customer‟s expectations for a set of service attributes.
When service rendered effectively occurs, the customer evaluates the level of services received
or perceived, through his or her own expectations. Zeithaml and Bitner (2003), reported that
zone of tolerance is narrow for those attributes that the customers find more important. That is,
the more important the attributes are, the closer the limits of desired service (DS) and minimum
service (MS) while zone of tolerance is wider for those attributes which the customers find less
important as in figure 1. The tolerance zone height (Alt Z) is the average between the desired
service (DS) and minimum service (MS) for each attribute. The width of tolerance zone (Larg Z)
of an attribute corresponds to the difference between the level of desired service (DS) and the
minimum service (MS). Algebraically, for a sample size „n‟, the average values of Alt Z and
Larg Z can be computed as
(1)
(2)
Tolerance zone
Zone Height
Zone Height
Tolerance zone
Attributes More important Less Important
Minimum service
Desired service
Zone width
Minimum Service
service
Desired service
Zone width
Figure 1 Relation between Importance and Expectations
Source: Adopted from Zeithaml and Bittner (2003)
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Review of literature
SERVQUAL and SERVPERF Models
Measuring quality of services might be difficult because of intangible nature of services. The
most widely used models in service quality in the banking sector are SERVQUAL and
SERVPERF models. According SERVQUAL model, developed by Parasuraman (1985, 1988),
service quality can be measured as a difference between customer‟s expectations of the service to
be rendered and their perceptions of the actual performance of the service. SERVQUAL is based
on five dimensions namely Tangible, Reliability, Assurance, Responsiveness and Empathy. Each
dimension is measured by four to five attributes. Mathematically, Service quality is measured as
(3)
Where
SQi = Perceived service quality of individual „i‟
k = number of attributes / items
P = Perception of individual „i‟ with respect to performance of a service firm attribute „j‟
E = Service quality expectation for attribute „j‟ that is relevant norm for individual „i‟.
Several issues have been raised with regard to use of ( P – E ) scores. Most studies have found a
poor fit between service quality measured through Parasuraman, Zeithaml and Berry (1988)
scale and the overall service quality measured through a single-item scale (Babacus and Boller
1992, Babakus and Mangold 1989; Finn and Lamb 1991 and Spreng and Singh 1993). Though
the „gap scores‟ is conceptually sensible, the ability of these scores to provide additional
information beyond that already contained in the perception component of the service quality
scale is under doubt (Babacus and Boller 1992). Teas ( 1993, 1994) observed that a ( P – E ) gap
of „-1‟ can be produced in six ways: P=1, E=2; P=2, E=3; P=3, E=4; P=4, E=5; P=5, E=6; P=6,
E=7 and these tied gaps cannot be trusted as implying equal perceived service quality shortfalls.
Brown, Peter and Churchil (1993) found different scores being beset with psychometric
problems and, therefore, cautioned the use of (P-E) score. Cronin and Taylor (1992, 1994)
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challenged the gap score approach and developed the SERVPERF scale which directly captures
customer‟s performance perceptions in comparison to their expectations of the service encounter
which can be expressed mathematically as follows.
(4)
Where, P = Perception of individual „i‟ with respect to performance of a service firm attribute
„j‟.
Babacus and Boller (1992), Brady at al. (2002), also proved that SERVPERF is better alternative
than SERVQUAL. Jain and Guptha (2004), reported that SERVPERF was more strongly
correlated with overall service quality than SERVQUAL. Beerli et al. (2004), Wang et al.
(2003), Lee and Hwan (2005), Zahoor (2011), Mensah (2010) and Suleman (2011) reported that
SERVPERF is better alternative than SERVQUAL as it reduces the amount of data needed to
measure service quality. Since Service quality attributes are not expected to be equally important
across service industries, it has been suggested to include importance weights in the service
quality measurement scales. Hence the service quality is measured as:
(5)
Where Wij is the weighting factor, i.e., importance of attribute „j‟ to an individual „i‟
Though, on theoretical grounds, addition of weights makes sense (Bolton and drew, 1991), not
much improvement in the measurement potency of either scale has been reported after inclusion
of weights. Hence, the present study uses the un-weighted SERVPERF scale to measure the
service quality of banking services based on the perceptions of service.
Zone of tolerance
The zone of tolerance (ZOT) is defined as “the difference between desired service and the level
of service considered adequate” (Zeithaml et al., 1993). The ZOT model recognises that
consumer enter service encounters with different expectation levels, desired service is “the level
of service the customer hopes to receive and the adequate service is “the level the customer will
accept (Zeithaml et al., 1993). The difference between these two expectation standards is the
zone of tolerance. The customer satisfaction will result as long as customer perception of service
performance, fall in this zone of tolerance. Zeithaml et al., (1993) reported that ZOT varies
across customers and can expand /contract with the same customer. They also propose that
adequate service expectations are subject to change, while desired service expectations are
relatively enduring.
Poiesz and Bloomer (1991) proposed that the zone of tolerance can be used as unifying construct
between expectations, performance and outcomes. Johnston (1995) theoretically examined the
relationship between involvement and ZOT, as well as the impact of various service
performances out comes upon ZOT. He reported that “width of ZOT is inversely proportional to
the degree of involvement”. That is to say high involvement generates narrower ZOT, while low
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involvement generates a wider ZOT. Zeithaml et al., (1993) and Johnston (1995) propose that (i)
performance below the customers adequate expectations level (below ZOT) will generate
dissatisfaction, (ii) Performance within ZOT may go unnoticed by the customer, and (iii)
performance above the customers adequate expectations level (above the ZOT)will generate
“delight”, or what Zeithaml termed as a “Customer franchise”, It is important to note that
disagreement exists in whether perceived service quality should be measured using a
performance based frame work or with a standards (or norms) based framework. Teas and
Decarlo (2004) reported that performance based frame works specify perceived performance
without any comparative referents, while standard based frameworks specify a relative or
comparative performance conceptualization of perceived quality. Parasuraman et al., (1994)
from his study of 3069 customers, urged that norms-based (such as ZOT) would be more useful
because of the measurement of more precise information about customer perceptions across the
multiple expectation levels. Teas and Teas and Decarlo (2004) in their study of student‟s
perception of a university found that performance based models offered more explanatory power
than ZOT regarding the relationship between perceived quality and purchase intensions. They
also found that ZOT offered superior performance when evaluating the linkage between
perceived quality and satisfaction. They provided support for the ZOT as a useful managerial and
research tool for better understanding how customer perceptions of quality impact satisfaction
with service?
Walker and Baker (2000), proposed that the width of ZOT varies for essential versus less
essential service quality components, They also reported that the width of ZOT would be
influenced by the degree of customer experience with the service, as well as the number of
competitive alternatives the customers perceived. They found that ZOT is narrower for
experienced customers „because they have increasingly demanding expectations‟. In addition
Walker and Baker (2000) found no support for Zeithaml et al., (1993) proposition that the
presence of readily available alternatives would narrow the ZOT. Walker and Baker (2000)
attributed this finding to the stability of the desired expectations standard. Sachdev and Verma
(2004) conducted study on banking, insurance, fast food and beauty-saloon services. They found
that in case of banking, the perceived service performance is below „would be „level of
performance in four of five service quality dimensions. That is, the banking services even do not
perform at the adequate level in respect of reliability, responsiveness, assurance and empathy.
Banks seem to have performed better in case of „tangibles‟ dimension.
Objectives of the Study
The objectives of the study are summarized below.
1. To identify the service quality factors / dimensions of selected commercial banks
using SERVPERF scale.
2. To Establish zone of tolerance using minimum and desired service of service quality
attributes.
3. To explorer the relations between the importance of service quality attributes, height
and width of tolerance zone
Methodology
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Research methodology is a blue print of the way in which the research is going to be conducted.
It enlightens the methods to be followed in research activities. It includes research design, locale
of research, sampling framework, collection of data, framework for analysis and limitations.
Research Design
The present study describes the perception of service, minimum and desired service of each
attribute / item of service quality dimensions of the selected commercial banks. The mean of
desired and minimum service are used to determine the tolerance zone for each service quality
attributes.
Locale of Research
The present study is conducted in Bellary city including the suburban areas of the city. As no
exclusive study on service quality in commercial banks is carried out in this area and the
customer belonging to this area may reveal their opinions on service quality which are versatile
in nature, this area is selected. More over the researcher is familiar with the culture, language,
local dialect and infrastructure facilities available in this area, which is highly essential for the
response on the questionnaire.
Sample and Data Collection
The relevant literature and survey developed by the past studies provided the basis for
development of self administered questionnaire. After review of the literature twenty two
variables / attributes (table 8) of service quality (Parasuraman et al., 1885, 1988, 1994; Cronin
and Tailor, 1994) were used to develop questionnaire. Methodologically the SERVPERF scale
represents marked improvement over the SERVQUAL scale. Not only is the scale more efficient
in reducing the number of items to be measured by 50 percent, it has also been empirically found
superior to the SERVQUAL scale for being able to explain greater variance in the overall service
quality measured through the use of single-item scale (Sanjay and Gupta, 2004). Also when
applied in conjunction with SERVQUAL scale, the SERVEPERF measure has outperformed the
SERVQUAL scale (Babakus and Boller, 1992; Cronin and Taylor, 1994; Dhabolkar et al., 2000).
The present study adopted SERVPERF scale to measure the service quality.
The questionnaire consists of two sections: Demographic profile of the customers and twenty
two items/attributes (listed in table 7) about five dimensions service quality for assessing
perception of customers, minimum service and the desired service of each service quality
attribute. For each statement, the respondent indicates his or her opinion on a five point Likert-
scale ranging from “Strongly Disagree” (1) and “Strongly Agree” (5) for perception of service
quality. For each item customers are also asked to give the rating for minimum and desired
service on a five point Likert-scale.
Pretesting of the questionnaire was conducted on a random sample of 50 bank customers. The
pretest was conducted to obtain feedback to improve the content of questions, instructions,
clarity and layout of the questions. Furthermore, pretesting of the questionnaire also assessed the
reliability and the likely response rate. Some minor wording modifications to the questionnaire
were made as a result of this process.
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The non-probability sampling strategy called „Convenient sampling‟ was adapted to collect the
data. A convenience sampling is available to the researcher by virtue of its accessibility (Bryman
and Bell, 2003). Data from five hundred customers of five public sector banks namely State bank
of India, State bank of Mysore, State bank of Hyderabad, Canara bank and Syndicate bank, and
five private sector banks namely Axis bank, ICICI, SUCO bank, Ing. Vysya and Karnataka bank,
arriving to the banks from 10-30 A.M to 5.00 P. M, from Monday to Friday and from 10-30 A.M
to 1.00 P. M on Saturday are collected.
Data Analysis and Findings
It is very important to categorize the respondent‟s demographic profile because it gives a broad
picture and helps bankers to analyze the demographic factors which influence and are associated
with satisfaction level. 60.8 percent of the respondents are males and 39.2 percent of the
respondents are females.23 percent of the respondents are below 35 years age, 47, 30 percent of
the respondents are 35 to 55 year and above 55 years respectively. 70.4 percent of the
respondents are from urban and 29.6percent from rural.19.8 percent of the respondents have
studied up to 7th
standard, 63.2 percent of the respondents have education between 7th
standard to
PUC, 36.8 percent of the respondents are graduates and above. 23.6 percent of the respondents
have annual income of less than 1 lakh, 64.2 percent of the respondents have annual income
between 1 to 5 lakh and 35.8 percent of the respondents have annual income of more than 5 lakh.
15.2 percent of the respondents are employees, 50.6 percent of the respondents are businessman
and others accounts for 34.2 percent.
Table1 Demographic Profile of Respondents
S. No Characteristics Numbers Percentage
1 Gender Male
Female
Total
304
196
500
60.80
39.20
100
2 Age (years) Below 35
35 to 55
Above 56
Total
115
235
150
500
23.00
47.00
30.00
100
3 Nativity Urban
Rural
Total
352
148
500
70.40
29.60
100
4 Education Below 7th
standard
7th
std to PUC
Degree and above
Total
99
217
184
500
19.8
63,2
36.8
100
5 Annual
income (Rs.)
Below 1 lakh
1 to 5 Lakh
Above 5 Lakh
Total
118
203
179
500
23.60
40.60
35.80
100
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6 Occupation Employee
Business
Others
Total
76
253
171
500
15.20
50.60
34.20
100
Source: Primary data
The important service quality factors are identified using twenty two variables/attributes of
service quality with factor analysis. Before conducting factor analysis, validity of data for factor
analysis is examined with the help of Kaiser-Meyer-Olkin measure of sampling adequacy and
Bartletts test of sphericity. The KMO measure of sampling adequacy (0.649) reported in table 1
is greater than the minimum required (Nunnally, 1978). The Chi-square is significant even at
zero level. These two confirm the validity of data for factor analysis.
Table 2 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .649
Bartlett's Test of
Sphericity
Approx. Chi-Square 10651.056
Degree of freedom 231.000
Significance. .000
Table 3 Mean importance, mean of minimum, desired service, perception,
height (Alt Z) and width (Larg Z) of tolerance zone
Item
Mean
importance
Mean
Minimum
Service
(MS)
Mean
Desired
service
(DS)
Mean
Perception
Alt Z
Larg Z
E1 3.360 2.86 4.34 4.72 3.5999 1.4812
E2 3.334 2.87 4.37 4.30 3.6200 1.5022
E3 3.341 2.87 4.37 4.63 3.6221 1.5011
E4 3.332 2.86 4.37 4.34 3.6150 1.5131
E5 3.321 2.86 4.38 4.18 3.6212 1.5232
E6 3.313 2.85 4.37 3.50 3.6111 1.5215
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E7 3.300 2.85 4.37 3.43 3.6100 1.5223
E8 3.273 2.85 4.38 3.29 3.6153 1.5311
E9 3.262 2.86 4.39 3.21 3.6250 1.5324
E10 3.271 2.87 4.38 3.36 3.6252 1.5119
E11 3.212 2.86 4.37 3.67 3.6152 1.5128
E12 3.222 2.87 4.41 3.07 3.6431 1.5443
E13 3.196 2.87 4.42 3.46 3.6455 1.5499
E14 3.202 2.87 4.40 3.40 3.6350 1.5300
E15 3.223 2.87 4.42 4.36 3.6457 1.5502
E16 3.194 2.88 4.42 4.26 3.6511 1.3289
E17 3.183 2.87 4.41 3.46 3.6429 1.5431
E18 3.186 2.82 4.37 3.38 3.5949 1.5500
E19 3.198 2.82 4.36 3.23 3.5928 1.5451
E20 3.275 2.86 4.42 3.14 3.6412 1.5618
E21 3.224 2.87 4.43 3.30 3.6535 1.5624
E22 3.203 2.87 4.42 3.66 3.6453 1.5549
Table 4 Ranking items
in descending order
of Alt Z
Table 5 Ranking
items in ascending
order of Larg Z
Table 6 Ranking items
in the descending order
of important
Item Alt Z Rank Item
Larg Z Rank Item Mean
Importance
Rank
E21 3.6535 1 E16 1.3289 1 E1 3.360 1
E16 3.6511 2 E1 1.4812 2 E3 3.341 2
E15 3.6457 3 E3 1.5011 3 E2 3.334 3
E13 3.6455 4 E2 1.5022 4 E4 3.332 4
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E22 3.6453 5 E10 1.5119 5 E5 3.321 5
E12 3.6431 6 E11 1.5128 6 E6 3.313 6
E17 3.6429 7 E4 1.5131 7 E7 3.300 7
E20 3.6412 8 E6 1.5215 8 E20 3.275 8
E14 3.6350 9 E7 1.5223 9 E8 3.273 9
E10 3.6252 10 E5 1.5232 10 E10 3.271 10
E9 3.6250 11 E14 1.5300 11 E9 3.262 11
E3 3.6221 12 E8 1.5311 12 E21 3.224 12
E5 3.6212 13 E9 1.5324 13 E15 3.223 13
E2 3.6200 14 E17 1.5431 14 E12 3.222 14
E8 3.6153 15 E12 1.5443 15 E11 3.212 15
E11 3.6152 16 E19 1.5451 16 E22 3.203 16
E4 3.6150 17 E13 1.5499 17 E14 3.202 17
E6 3.6111 18 E18 1.5500 18 E19 3.198 18
E7 3.6100 19 E15 1.5502 19 E13 3.196 19
E1 3.5999 20 E22 1.5549 20 E16 3.194 20
E18 3.5949 21 E20 1.5618 21 E18 3.186 21
E19 3.5928 22 E21 1.5624 22 E17 3.183 22
Source: Primary data
Table 7 Ranking of items based on three measurement scales
Item Item Description Ranking
based on
Importance
Ranking
based on
Alt Z
Ranking
based on
Larg Z
E1 Prompt service 1 20 2
E2 Communicate what is to be served 3 14 4
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E3 Always willing to help customer 2 12 3
E4 Respond to customer request 4 17 7
E5 Never being too busy to respond to
customer‟s request
5 13 10
E6 Clarity of facilities 6 18 8
E7 Decoration of facilities 7 19 9
E8 Efficacious work environment 9 15 12
E9 Visually appealing equipments 11 11 13
E10 Complementary equipments 10 10 5
E11 Feeling of security 15 16 6
E12 Knowledgeable employees 14 6 15
E13 Friendliness of employees 19 4 17
E14 Consistently courteous with
customers
17 9 11
E15 Individual attention, understand
customer specific needs
13 3 19
E16 Customers best interest in heart 20 2 1
E17 Services as per the promise 22 7 14
E18 Provide right service at first time 21 21 16
E19 Precision in filing system 18 22 16
E20 Absence of error in service delivery 8 8 21
E21 Sincere in solving problems 12 1 22
E22 Precision in account statements 16 5 20
Source: Primary data
The factor analysis results in five factors known as service quality dimensions. These are termed
as Tangibles (E6 to E10), Reliability (E17 to E22), Responsiveness (E1 to E5), Assurance (E11 to
E14) and Empathy (E15 to E16). These five factors account for 72.6% of variance. The reliability
is tested using coefficient alpha (Cronbach, 1951). On analysis, the alpha values are found
greater than (0.7), the cut off recommended by Nunnally (1978). Hence the reliability analysis
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predicted the trustworthiness of the data obtained from the questionnaire and the data was used
for further analysis.
The mean of importance, minimum and the desired service along with the standard deviations
are listed in table 3. The tolerance zone height (Alt Z) and width of tolerance zone (Larg Z) are
computed using equations 1 and 2 as reported in table 3. The mean importance varies from 3.183
(for „Services as per the promise‟) to 3.360 (for Prompt service). The minimum service varies
from 3.82 to 3.87 and the desired service varies from 4.34 to 4.43.
The service quality attributes are ranked using the three measurement scales namely Alt Z, Larg
Z and importance. The results are shown in table 4, 5 and 6. Table 7 gives the ranking of each
service quality attribute using the three measurement scale. Table 3 reveals that the customers
are getting the service much above the minimum level of service with respect to all the service
quality attributes as their perception are within the desired and minimum service levels. The
perception of item number „E11‟ i.e., „feeling of security‟ is above the desired level of service.
Hence the customers are delighted with respect to this service quality attribute.
The degree of correlation between the measurements used can be compared by the ranking made
by the three measurement scales. A coefficient „1‟ indicates a degree of 100% alignment, „-1‟
indicates complete miss-alignment. the correlation between the ranking using the three
measurement scales is carried as given in table 8.The spearman‟s test reveals positive correlation
coefficient (0.40) among the ranking the by importance and the ranking by height of tolerance
zone. That is to say, the height of tolerance zone is proportional to the importance of the service
quality attributes. Negative correlation is observed (-0.355) between the width of tolerance zone
and the importance of the service quality attributes. That means the width of tolerance zone has
inverse behaviour with the importance of the service quality attributes. The test also reveals that
the height and width of tolerance zone are inversely related as correlation coefficient is negative
(-0.241).
Table 8 Spearman coefficient of correlation
Ranking Importance Alt Z Larg Z
Importance 1.000 0.400 -0.355
Alt Z 0.400 1.000 -0.241
Larg Z -0.355 -0,241 1.000
Conclusions
The aim of this study was to assess the service quality based on the zone of tolerance. The study
aims at exploring the service quality with respect to which the customers are dissatisfied,
satisfied and delighted. Five dimensions of service quality namely Tangibles, Reliability,
Responsiveness, assurance and Empathy are derived from twenty two attributes/items of service
quality using factor analysis.
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It is observed from the study that the mean importance of the service quality attributes varies
from 3.183 to 3.360, the height of tolerance zone varies from 3.5928 to 3.6535 and the width of
tolerance zone varies from 1.3289 to 1.5624.
The study reveals that the perceived service with respect to all the service quality attributes is
within the tolerance zone. Hence the service provided by the banks is acceptable to customers.
The customers are delighted with respect to the service quality attribute „Feeling of security‟ as
its perceived service is above the desired level of service. It is also observed from the study that
the height of tolerance zone is proportional to the importance of the service quality attributes and
width of tolerance zone has inverse behaviour with the importance of the service quality
attributes.
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