Impact of Internet Service Quality on Customer Satisfaction Special Reference to Internet Service Providers During COVID – 19 Period

Abstract

Customers may switch to a different service provider if they are displeased with the standard, hence tracking service quality is crucial for a firm. The influence of Internet Service Quality (ISQ) on Customer Satisfaction will be studied in detail by the researcher. The relevance of this study is highlighted by the current COVID-19 scenario in Sri Lanka, where government laws and restrictions have been implemented to promote work from home, online learning, and online entertainment. Previous research in other countries have looked at the impact of ISQ on customer satisfaction; however, to our knowledge, no such study has been conducted in Sri Lanka. Furthermore, earlier study looked at a variety of scenarios, including the discovery of research gaps; however, in the context of COVID-19, the research gaps were identified. Past research identifies the following factors which impact ISQ: Tangibility, Assurance, Empathy, Reliability, Responsiveness and Price. This study was carried out as a deductive study and a quantitative method was employed. The convenience sample approach was used to collect 505 responses by distributing a questionnaire survey to consumers in Sri Lanka's Western region. Statistical Package for Social Science (SPSS) version 23 was used to analyze the data. The studies indicated that Internet Service Quality and Customer Satisfaction had a favorable association. The study was also able to provide insights for ISP management by emphasizing areas of ISQ that can satisfy their customer base, as well as actions that might be implemented in response to the observed practice gaps.

Full text

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ABSTRACT
Customers may switch to a different service provider if they are displeased with the standard,
hence tracking service quality is crucial for a firm. The influence of Internet Service Quality
(ISQ) on Customer Satisfaction will be studied in detail by the researcher. The relevance of
this study is highlighted by the current COVID-19 scenario in Sri Lanka, where government
laws and restrictions have been implemented to promote work from home, online learning,
and online entertainment. Previous research in other countries have looked at the impact of
ISQ on customer satisfaction; however, to our knowledge, no such study has been conducted
in Sri Lanka. Furthermore, earlier study looked at a variety of scenarios, including the
discovery of research gaps; however, in the context of COVID-19, the research gaps were
identified. Past research identifies the following factors which impact ISQ: Tangibility,
Assurance, Empathy, Reliability, Responsiveness and Price. This study was carried out as a
deductive study and a quantitative method was employed. The convenience sample approach
was used to collect 505 responses by distributing a questionnaire survey to consumers in Sri
Lanka's Western region. Statistical Package for Social Science (SPSS) version 23 was used to
analyze the data. The studies indicated that Internet Service Quality and Customer Satisfaction
H. M. U. S. Hendeniya
Department of Marketing Management, Rajarata University of Sri Lanka, Sri Lanka
udarahendeniya@mgt.rjt.ac.lk
A. L. Fernando
Department of Marketing Management, University of Kelaniya, Sri Lanka
lfern201@kln.ac.lk
DOI: http://doi.org/10.4038/sljmuok.v8i0.95
Impact of Internet Service Quality on Customer
Satisfaction Special Reference to Internet
Service Providers During COVID – 19 Period

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had a favorable association. The study was also able to provide insights for ISP management
by emphasizing areas of ISQ that can satisfy their customer base, as well as actions that might
be implemented in response to the observed practice gaps.
Keywords: Customer Satisfaction, Internet Service Quality, Mobile Telecommunication
Industry (MTI), Service Quality
Copyright: This is an open access article distributed under the Creative Commons Attribution
License 4.0, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
1. INTRODUCTION
Once after the COVID-19 pandemic erupted in early 2020, it confronted the
foundations of society and economic standards around the world. Countries started
banning one by one public safety agreements and social distancing measures bringing
daily life to Remote work, online education, video calling and digital banking (2021).
Pandemic situation has done a big impact on the society with the result of social
distancing. According to 2021 statistics Sri Lankan population is around 21.46
million, mobile connections over 30.41 million which is 141.7% from the total
population, internet users are 10.90 million. 2021 statics of internet users shows
considerable increment compared to 2020 year that is 7.9% (Digital 2021:Sri Lanka,
2021). Since study focuses on internet service providers, the statistics show that there
is a significant shift in internet usage Digital 2021:Sri Lanka (2021) which results
high competition among the Internet service providers. Competition makes busy
players and sometimes it keeps ignoring the actual customer needs which leads the
customers for dissatisfaction (Oliver, 1997). The important thing is to make any
company a huge part of its success with customers. Anderson & Srinivasan (2003)
believe that customer satisfaction is a positive reaction of customers to their buying
experience. Satisfaction as a concept has been extensively studied in the literature.
Reichheld, Teal, & Smith (1996) emphasized the role and role of contentment.
According to Oliver (1997) satisfaction is "the consumer's answer to fulfillment".
Gilbert & Veloutsou (2007) defines the term expectation as a preconceived view or

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belief about the provision of a service, which serves as a reference or standard point
for judging product performance. The process of measuring customer satisfaction is
complicated. Customer satisfaction is subjective in nature and varies from customer
to customer, so it is difficult to create a standardized dimension toolkit for this
phenomenon (Oliver, 1997). Previous researchers believes that quality is becoming
more and more important, it is the main function of gaining a competitive benefit, it
is an important success factor for any modern service company (Anderson &
Srinivasan, 2003). Further this difficulty has been minimized with the development
of service model introduced by Parasuraman , Zeithaml, & Berry (1988) which was
known as SERVQUAL model. Researcher has developed the service quality model
with the contribution of Joudeh & Dandis (2018) by adding price dimension to the
SERVQUAL model.
● Tangibles: physical facilities, appearance of personnel, equipment.
● Reliability: capacity to provide the assured service reliably, accurately and
dependably.
● Responsiveness: will serve customers and deliver prompt service
● Assurance: employees' knowledge and politeness and their capacity to inspire
trust and confidence.
● Empathy: ability of the company to provide thoughtful and customized
attention to its customers.
● Price: ability and willingness of consumers to pay for a particular product or
service
2. RESEARCH PROBLEM
Due to the prevailing Covid 19 situation in Sri Lanka, people need better Internet
services as they are working online. When considering the studies which have been
done in Sri Lanka, there are a lot of studies done on service quality for different
service sectors like Hotels, Banking, Education institutes; (Jayawardhena, 2016)
except the study done by Dharmadasa & Gunawardhana (2017) related to the
communication industry and those studies have helped the relevant sectors for their

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development. But we were unable to find that commitment towards the Internet
service quality of Telecommunication industry, the reason is a lot of service providers
are struggling in their financial perspective even though the industry is developing.
As a result of that, Internet service providers try to merge with other service providers,
for example Etisalat and Hutchison (Dharmadasa & Gunawardhana, 2017).
Researchers propose this scenario happens because service providers lack knowledge
in retaining the customers. Therefore, researchers intend to fill the practice gap
through this study which will support the Telecommunication services develop their
service commitments towards financial performance and lack of literature pays the
way to add new knowledge for the future researchers for their future studies.
Through the above justification researcher intends to find answers for the below main
research objective,
• To identify the impact of Internet Service quality on Customer Satisfaction
Sub research objectives,
● To find the impact of Tangibility in Service quality on Customer satisfaction.
● To find the impact of Reliability in Service quality on Customer satisfaction.
● To find the impact of Responsiveness in Service quality on Customer
satisfaction.
● To find the impact of Assurance in Service quality on Customer satisfaction.
● To find the impact of Empathy in Service quality on Customer satisfaction.
● To find the impact of Prices in Service quality on Customer satisfaction.
Therefore, researcher intends to investigate the impact of Internet Service quality on
Customer Satisfaction in Telecommunication industry in Sri Lanka.

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3. LITERATURE REVIEW
3.1. Customer Satisfaction
Oliver R. L. (1980) studied the concept of satisfaction in the service industry and built
the most popular model based on the “expectancy uncertainty model”. The model
argues that actual performance is measured according to the customer's initial
expectations to assess satisfaction Chiou & Spreng (1996) defines “uncertainty” as
the calculation of “difference scores” (specifically, the difference between expected
performance evaluations and perceived performance evaluations). Anderson &
Srinivasan, (2003) also ensures that it is not asserted as "a difference between post-
purchase evaluation and post-use evaluation of product or service performance and
pre-purchase expectations". Confirmation occurs when the performance matches up
to the initial expectations; no more or less (Erevelles & Leavitt, 1992). On the other
hand, once the actual performance is deemed to be worse than the initial expectations
of the service, there will be a 'negative disconfirmation. Here, customers are not
satisfied, and the trend of re-purchasing products or services in the future is rare
(Zammit, 2000). When actual performance is viewed as exceeding the customer's
initial expectations, consumer is very satisfied or even 'happy'. This positive
experience reinforces consumers' inclination towards the brand. Above studies show
the importance of considering customer satisfaction for sustaining the customer base
within the company.
3.2. Service Quality
Concept of Service Quality According to Abdullah & Afshar (2019), quality is a
nebulous and elusive idea. It is critical to distinguish between products and services
due to their distinct qualities. The former is more concrete, in the shape of a thing;
the latter is intangible, in the form of real performance (Abdullah & Rahman, 2015).
It is a process, not a product, which is one of the most important and unique aspects
of services. As a result, service businesses don't sell anything, but they do have
engaging processes. Because services are intangible, it is difficult for suppliers to

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describe them and for consumers to assess them (Ali et al. 2021). Parasuraman ,
Zeithaml, & Berry (1988) proposed a better approach for measuring service quality
termed the SERQUAL model.
3.3. The SERVQUAL Model
SERVQUAL model is a broadly used model for determining service levels across
industries and requires similar responses to different business conditions. Asubonteng
, McCleary , & Swan (1996) notes that in view of the fierce competition and the
strong focus on environmental factors, service standards have become more
important. If service quality is to become the basis of marketing policy, it should be
able to be calculated and made feasible by companies; SERVQUAL has become a
very common method. It has been widely exposed to marketing literature and industry
for an almost reliable study of service efficiency (Jayawardhena, 2016). Parasuraman
, Zeithaml , & Berry (1985) originated a metric called service quality, since there are
many models (metrics) used to calculate levels of service and customer satisfaction,
which are usually too general or temporary to be implemented in the hotel industry.
By the way, the SERVQUAL model, which requires efficiency, responsiveness,
integrity, reputation, access, courtesy, connectivity, assurance, empathy, and
tangibles, has 10 factors of service quality during continuous study in the grounds of
service quality. The follow-up study by Parasuraman , Zeithaml , & Berry (1985)
changed the determinants of service quality and explicitly derived five service
efficiency criteria, such as tangibility, reliability, responsiveness, Assurance, and
empathy.
3.4. Validity of Using SERVQUAL Model
At the Anderson Cancer Center, Macaulay & Cook (1994) used the SEVQUAL
model to assess service levels. This tool is suitable for patients with multiple diseases,
including comparison of preferences and experience. Patients consider waiting time
and billing accuracy to be important. It strongly demonstrates that consumer
preferences may have a significant impact on measuring the quality of a company's

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operations. In their research, Pariseau & McDaniel (1997) used service quality to
determine consistency and meaning for each dimension: assurance, durability,
empathy, responsiveness, and tangibility to assess consensus among participants'
opinions. To improve service efficiency, service quality can be used to measure
results and it has been greatly developed. Galloway analyzed the efficacy of
SERVQUAL in assessing the level of service in the education service (Galloway,
1988). The effectiveness of SERQUAL tested by Bojanic & Rosen (1998) through
their extensive research on the catering industry as a method for assessing service
quality has been shown to be effective in defining consumer expectations of service
quality in a restaurant model tool. Li , Tan, & Xie (2003) noted that a firm's ability to
obtain service quality consistency depends on defining service characteristics and
required standards, as well as prioritizing service characteristics. Service quality
assessment tools, such as service quality, established a linear and symmetric
connection among differences in service superiority and general service quality.
Douglas & Connor (2003) studied the balance between consumers' quality
preferences and managers' and employees' perceptions of customer expectations.
Because in the dynamic hotel market, customers control the company's secrets of
sustainable development and profitability, so service standards are an important tool
for gaining a competitive advantage, it is important to calculate quality to determine
if the industry is providing the service that customers want. Therefore, the above
empirical evidence shows the significance of using SERVQUAL model for this study.
SERVQUAL model presented by Parasuraman et al. (1985), mainly investigates
identifying the gap among expectation and experience. (Iwaarden & Wiele, n.d.) also
described this model emphasizes on five generic dimensions (RATER Metric) and
listed as follows,
3.4.1. Reliability
Service providers need to ensure that the information provided to customers is
accurate and correct, and to share relevant information with customers in a timely
manner within the given deadline as promised (Joudeh & Dandis, 2018).

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3.4.2. Assurance
Knowledge and courtesy of the employees and their ability to inspire trust and
confidence of the customers in terms of meeting their expectations and address
concerns competently (Jayawardhena, 2016).
3.4.3. Tangibles
Tangibility entails physical evidence of the service where the concept refers to the
physical facilities and appearance that of stores, personnel, equipment and tools
includes in a service facility in which attracts the customers in repurchase the product
or service (Parasuraman , Zeithaml, & Berry , 1988).
3.4.4. Empathy
Caring and individualized attention that the firm provides to its customers, the
customized solutions available with organization and acknowledging specific
concerns (Joudeh & Dandis, 2018).
3.4.5. Responsiveness
Willingness to help customers and provide prompt service through multiple service
channels (email, phone, social media) and acknowledge complaints immediately
(Joudeh & Dandis, 2018).
Besides the SERVQUAL model, researcher has used model developed by Joudeh &
Dandis (2018) in his study. Therefore, price dimension has been added to with
SERVQUAL model.
4. CONCEPTUALIZATION AND OPERATIONALIZATION
4.1. Conceptualization
Conceptual framework consists of two main variables, independent variable (Internet
Service Quality) and dependent variable (Customer Satisfaction). Independent

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variable has sub variables those are, Tangible, Reliability, Responsiveness,
Assurance, Empathy and Price. The price component was not in the SERVQUAL
model but it was included in the framework to exploit accurate information from the
respondents (Uchenna & Yew , 2008; Safi & Alagha, 2020).
Figure 1: Conceptual Framework
Source: Uchenna & Yew (2008); Safi & Alagha (2020)
4.2. Hypothesis Development
H1: There is a positive relationship between Internet Service Quality and Customer
Satisfaction.
H1a: There is a positive relationship between Tangibles and Customer Satisfaction
Tangibles are frequently utilized by service providers to reinforce their reputation,
give congruity, and sign quality to client, most organizations unite together tangibles
with other in order to establish a service quality technique for the company which
results to give a first impression on the service that they are going to receive (Anwar
& Balcioglu, 2016). This impression directly impacts on the satisfaction of the
customers (Anwar k. , 2017)
H1b: There is a positive relationship between Reliability and Customer Satisfaction
Reliability depicts whether a service supplier follows assured promises and how
precious it is in the actions. The significant importance lies in fulfilling promptly the
customer’s requests (Hameed & Anwar, 2018). Reliability “reflects the service

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provider’s ability to perform service dependably and accurately”. It includes “doing
it right the first time” and as for the customer it is one of the most significant
dimensions as cited in (Anwar & Ghafoor, 2017). When companies can deliver their
promise while performing their service dependably and accurately then it will direct
the customers for satisfaction (Hameed & Anwar, 2018).
H1c: There is a positive relationship between Responsiveness and Customer
Satisfaction
“Being willing to help” - refers to the organization's readiness to settle happened
issues and availability to provide fast service. It is important to respond to all
customer requests, otherwise the request can turn into a complaint Service suppliers'
capability to ensure that they are providing with a service on time is a basic part of
service quality for major customers. This dimension underscores mindfulness and
immediacy in managing customers’ appeals, questions, complaints and other issues
(Ali, Gardi, Othman, & Ahmed, 2021).
H1d: There is a positive relationship between Assurance and Customer Satisfaction
Trust refers to the expertise, skills, politeness and willingness of employees to instill
trust in consumers (Parasuraman , Zeithaml , & Berry , 1985). The consumer should
feel safe when he or she consumes different services from a hotel and would like to
feel secure during his stay (Anwar & Louis, 2017). Also based on the study of
consumers should feel safe in all financial transactions; therefore, employees should
be trustworthy (Hameed & Anwar, 2018). Researchers have proven that
trustworthiness holds much power on making a considerable change in the customer
satisfaction.
H1e: There is a positive relationship between Empathy and Customer Satisfaction
Caring and individualized attention that the firm provides to its customers, the
customized solutions available with organization and acknowledging specific
concerns. This is the area that the most of the company’s neglect. Ali, Gardi, Othman,
& Ahmed (2021) identified that even if any product or service holds the expected

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quality and features, it requires to stress the concern on how the product or service
will be their solution for their purpose. Anwar k. (2017) has proven that the
understanding of customers has a direct impact on customer satisfaction in hotel
industry and Joudeh & Dandis (2018) in Internet Service providers.
H1f: There is a positive relationship between Prices and customer Satisfaction
Price is one part of the marketing mix, which is a value of certain goods attached to
goods or services that are being traded in the market (Uchenna & Yew , 2008). The
compatibility of good or bad prices can be seen from the consumer's response to the
price offered, accepted, or rejected. In telecommunications services, prices are the top
priority of consumers in choosing service providers in addition to service quality. The
price increase offered by cellular operators can affect a consumer. The research of
Manilall, Chengedzai, & Tshepiso (2014) that prices show a significant positive
effect on customer satisfaction. In other words, prices affect customer satisfaction.
Research Sujuan, Qiying, & Weiqi (2017) also shows that the market has a high
sensitivity to prices, changes in price increases are very sensitive to the level of
customer satisfaction, the prices offered are accordingly the greater the effect on
customer satisfaction. The result is that appropriate pricing can affect the increase in
customer satisfaction and can attract new customers.
5. METHODOLOGY AND DATA ANALYSIS
5.1. Methodology
The core purpose of this study is to understand the level of Impact of Internet Service
excellence on Customer satisfaction during Covid 19 period in Western province of
Sri Lanka. This research aims to examine the influence of an independent variable on
its dependent variable. Therefore, this research will be carried out as a correlation
study (Sekaran, 2003). A quantifiable study will be approved out under this research
via a survey method to collect information from the sample who consume Internet
services of Internet service providers. This study was also conducted with a deductive
approach because it discussed the influence of Internet Service quality on Customer

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satisfaction, a theory that already exists. Based on this, theoretical basic research
hypotheses were developed; Research is being carried out to test those hypotheses.
According to Sunders (2009) for this study the "survey" method was chosen as the
research strategy. Thus, the time limit of the current study can be defined as a single
snapshot / cross section meaning that the data were only collected once (Sahay, 2016).
The study population of the study is the people live in the western province of Sri
Lanka and by considering previous literature articles, the researchers have taken their
sample between 500 and 900 therefore the researcher has decided to select a sample
of over 500 respondents which are represented by both male and female individuals
from Sri Lanka (Joudeh & Dandis, 2018; Eze, Ismail, Sin, & Siang, 2008). In here
the researcher intends use the sampling technique as non-probability convenience
sampling and data will be collected through a distribution of online questionnaire
among the respondents. Dimensions of the study will be measured on the five-point
Likert scale. Which is ranging from 1 to 5 where 1=Strongly Disagree,5= Strongly
Agree.
5.2. Data Analysis
5.2.1. General Characteristics
Distribution of Gender
The total number of respondents that were involved in the research accounts to 505.
Based on the categorization according to gender of the respondents involved in the
study, it was evident that most of the respondents were Female accounting to 63.8%
and Male respondents accounting to 36.2%.
Table 4.1 Distribution of Age
Frequency
Percent
Valid
Percent
Cumulative
Percent
16-20 years
46
9.1
9.1
9.1
21-25 years
200
39.60
67.3
76.4
26-30 years
140
27.72
20.0
96.4
31-40 years
101
20.0
3.2
99.6

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(Source: SPSS 23 Analyzed Data)
According to the above table 39.60% answers were given by the people who are in
the age 21 – 25 years. And the second highest value is 27.72%, they were aged
between 26 – 30 years. Most of the time, in this age category people are studying in
universities or doing higher education.
Table 4.2 Type of Connection
Frequency
Percent
Valid
Percent
Cumulative
Percent
Fiber Optic
68
13.5
13.5
13.5
Wireless Router
295
58.4
58.4
71.9
Hotspot
3
.6
.6
72.5
ADSL Cable
connection
75
14.9
14.9
87.3
Mobile data
26
5.1
5.1
92.5
Dongle
35
6.9
6.9
99.4
4G Wired Router
3
.6
.6
100.0
Total
505
100.0
100.0
(Source: SPSS 23 Analyzed Data)
According to the above table shows the type of connections used by respondents. 295
respondents are using wireless router connections, as a percentage 58.4%. Although,
14.9% of respondents are using ADSL cable connection as their internet connection.
That shows that most of the respondents are likely to get a faster internet connection
for their work.
Table 4.3 Distribution of Internet Service Providers
Frequency
Percent
Valid
Percent
Cumulative
Percent
SLT/Mobitel
248
49.1
49.1
49.1
Dialog
217
43.0
43.0
92.1
Bell 4G
12
2.4
2.4
94.5
Hutch
17
3.4
3.4
97.8
Airtel
11
2.2
2.2
100.0
Total
505
100.0
100.0
(Source: SPSS 23 Analyzed Data)
above 40 years
18
3.5
.4
100.0
Total
505
100.0
100.0

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Table shows most of the respondents are using SLT/ Mobitel. As a percentage 49.1%
and 43% of respondents are Dialog users.
Table 4.4 Distribution of Package Use
Frequency
Percent
Valid
Percent
Cumulative
Percent
Prepaid
157
31.1
31.1
31.1
Post paid
348
68.9
68.9
100.0
Total
505
100.0
100.0
(Source: SPSS 23 Analyzed Data)
According to the above details 68.9% respondents are postpaid package users. As a
number 348 respondents.
Table 4.5 Size of Package
Frequency
Percent
Valid
Percent
Cumulative
Percent
5 to 20 GB
67
13.3
13.3
13.3
20 to 40 GB
146
28.9
28.9
42.2
40 to 60 GB
104
20.6
20.6
62.8
60 to 80 GB
95
18.8
18.8
81.6
100+ GB
93
18.4
18.4
100.0
Total
505
100.0
100.0
(Source: SPSS 23 Analyzed Data)
Based on the categorization according to the size of the package of the respondents
involved in the study, it was evident that a majority of the respondents were 20 to 40
GB package users accounting to 28.9 % and 40 to 60 GB package users’ respondents
accounting to 20.6%.
Table 4.6 Price of Package
Frequency
Percent
Valid
Percent
Cumulative
Percent
Less than 500
35
6.9
6.9
6.9
500 – 1000
79
15.6
15.6
22.6
1000 – 2000
227
45.0
45.0
67.5
2000 – 3000
95
18.8
18.8
86.3
3000 – 4000
33
6.5
6.5
92.9
4000 – 5000
18
3.6
3.6
96.4
5000 above
18
3.6
3.6
100.0
Total
505
100.0
100.0

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(Source: SPSS 23 Analyzed Data)
According to the above table shows the price of packages used by respondents. 225
respondents are using 1000 - 2000, as a percentage 45%. Although, 18.8% of
respondents are using 2000 – 3000 as their internet connection.
Table 4.7 Distribution of Average Speed
Frequency
Percent
Valid
Percent
Cumulative
Percent
Less than 1mb
86
17.0
17.0
17.0
1 – 4mb
200
39.6
39.6
56.6
4 – 8mb
130
25.7
25.7
82.4
8 – 12mb
63
12.5
12.5
94.9
Above 12mb
26
5.1
5.1
100.0
Total
505
100.0
100.0
(Source: SPSS 23 Analyzed Data)
According to the above table describe the Average speed of internet service providers.
Most of the respondents have between 1 – 4mb speeds. It shows as a percentage
39.6% and 2nd highest value is 25.7% percent. There is an average speed between 4 –
8mb.
Table 4.8 How long you use internet service provider
Frequency
Percent
Valid
Percent
Cumulative
Percent
One Year
117
23.2
23.2
23.2
Two Years
83
16.4
16.4
39.6
Three Years
105
20.8
20.8
60.4
Four Years
68
13.5
13.5
73.9
More than Four Years
132
26.1
26.1
100.0
Total
505
100.0
100.0
(Source: SPSS 23 Analyzed Data)
Table shows the use time of service providers that can be considered as respondents'
loyalty. According to those 132 respondents, they get the service from the same
service provider for more than four years and it as a percentage 26.1%. And also,
23.2% percent of respondents are getting their service since last year. Maybe with the
pandemic situation.
Table 4.9 Reason for Shifting

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Frequency
Percent
Valid
Percent
Cumulative
Percent
Lower competitive
Price
47
9.3
19.0
19.0
Speed of Service
144
28.5
58.1
77.0
Promotion offers
32
6.3
12.9
89.9
Customer Service
12
2.4
4.8
94.8
Coverage
8
1.6
3.2
98.0
Data Consumption
1
.2
.4
98.4
Separate Internet
Connection for Work
and Education
4
.8
1.6
100.0
Total
248
49.1
100.0
(Source: SPSS 23 Analyzed Data)
According to the above table 58.1% of respondents changed their service provider
because of the speed of the service. Respondents are considering the speed more than
the price. 19% of respondents are considering the price and have changed their service
provider. Therefore, respondents are considering speed more than the prices of the
service provider.
5.2.2. Descriptive Statistics
Table 4.10: Descriptive Statistics of All Variables
Tangibility
Reliability
Responsiven
ess
Assurance
Empathy
Price
Satisfaction
Mean
3.2770
3.0360
2.9505
3.2094
3.0371
3.1363
3.0198
Std.
Error of
Mean
.05029
.05378
.05304
.05291
.05342
.05272
.05552
Median
3.5000
3.0360
3.0000
3.2500
3.0371
3.1363
3.0198
Mode
4.00
4.00
2.00
4.00
4.00
3.00
1.00
Std.
Deviatio
n
1.1301
3
1.2085
1
1.1920
2
1.1889
5
1.2004
0
1.1846
7
1.2476
1
Variance
1.277
1.461
1.421
1.414
1.441
1.403
1.557
Skewnes
s
-.470
-.116
.001
-.371
-.144
-.235
-.146
Std.
Error of
Skewnes
s
.109
.109
.109
.109
.109
.109
.109

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Kurtosis
-.596
-.995
-.993
-.827
-.961
-.859
-1.005
Std.
Error of
Kurtosis
.217
.217
.217
.217
.217
.217
.217
Range
4.00
4.00
4.00
4.00
4.00
4.00
4.00
Minimu
m
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Maximu
m
5.00
5.00
5.00
5.00
5.00
5.00
5.00
Tangibility denotes the highest mean value the 3.27 representing those users among
respondents agree with the Tangibility factor of Internet service providers that have
a significant impact on their decision making referring with service provider,
responses have dispersed from the mean value by 1.13 amount of standard deviation.
All the independent variables ranged among 2.95 to 3.27 of mean value. And
customer Satisfaction stated a mean value of 3.01 which has dispersed from the mean
value amounting 1.24 of standard deviation and lies at the acceptable range.
Reliability
Table 4.11 Total reliability
Reliability Statistics
Cronbach's Alpha
N of Items
0.980
30
(Source: SPSS 23 Analyzed Data)
Total Number of 30 items were disclosed 0.980 Cronbach’s α value and it was greater
than 0.7. Therefore, it proved the reliability of this research questionnaire. Therefore,
there is a higher level of internal consistency of the measure.
Table 4.12 Reliability Statistics as per the Variables
Cronbach's Alpha
N of Items
Tangibles
0.923
4
Reliability
0.950
5
Responsiveness
0.927
4
Assurance
0.932
4
Empathy
0.952
5
Prices
0.906
4
Customer Satisfaction
0.948
4
(Source: SPSS 23 Analyzed Data)

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Considering the independent and dependent variables, Tangibles, Reliability,
Responsiveness, Assurance, Empathy, Prices and Customer satisfaction respectively
state 0.923, 0.950, 0.927, 0.932, 0.952, 0.906 and 0.948 of Cronbach's Alpha values
and all of those values are greater than 0.7. Therefore, the extent to which the
questionnaire of this study remains the same, and the questionnaire or the
measurement continue to be stable over time and the similarity of measurements
within a given time period is ensured.
Validity Test
Table 4.13 KMO and Bartlett's Test of all items
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.972
Bartlett's Test of Sphericity
Approx. Chi-Square
17135.954
Df
435
Sig.
.000
(Source: SPSS 23 Analyzed Data)
In order to test the validity of the measures KMO and Bartlett’s test were conducted.
sample is deemed to be adequate if the value of KMO is greater than 0.5, in Bartlett's
test Taking a 95% level of Significance p-value (Sig.) of .000 < 0.05 considered the
valid range. Therefore, in this study, total validity is way beyond 0.5 and it validates
the outcomes of the study.
Table 4.13 KMO and Bartlett's Test variable wise
Variable
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy
Bartlett’s Test
(Sig)
Tangibles
0.831
0.000
Reliability
0.908
0.000
Responsiveness
0.845
0.000
Assurance
0.862
0.000
Empathy
0.901
0.000
Prices
0.839
0.000
Satisfaction
0.861
0.000
(Source: SPSS 23 Analyzed Data)
Considering the dependent and independent variables, Customer satisfaction,
Tangibles, Reliability, Responsiveness, Assurance, Empathy and Prices respectively
state 0.861, 0.831, 0.908, 0.845, 0.862, 0.901 and 0.839 of Kaiser-Meyer-Olkin

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(KMO) values and all and all of those values are greater than 0.5, Further all the
variables stated 0.000 of Bartlett’s Test (sig) values which are on the acceptable
range.
Multicollinearity Analysis
Table 4.14 Multicollinearity Analysis
Model
Collinearity Statistics
Tolerance
VIF
1
(Constant)
Final Tangibility
.364
2.751
Final Reliability
.267
3.749
Final Responsiveness
.304
3.286
Final Assurance
.242
4.127
Final Empathy
.292
3.430
Final Price
.389
2.573
(Source: SPSS 23 Analyzed Data)
Basically, the multicollinearity can be measured using fault-tolerant linear and VIF
values. Linear tolerance measurement if the tolerance exceeds 1 it can be considered
as a polyline. Likewise, there is no formal way to measure the VIF value for
determining the incidence of multi-linear relationship. It is recognized that VIF values
exceeding 10 which are multi-linear. When illustrating the above table, it depicts
independent variables Tangibility, Reliability, Responsiveness, Assurance, Empathy,
Price respectively states 0.364, 0.267, 0.304, 0.242, 0.292, 0.389 collinearity
Tolerances which are lesser than the value 1, similarly independent variables state
2.751, 3.749, 3.286, 4.127, 3.430, 2.573 of VIF values which are lesser than the 10,
Thus, it can conclude there is no any multicollinearity situation in the independent
variables of the study.

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Multiple Regression Analysis
Table 4.15 Model Summary
Model
R
R Square
Adjusted R
Square
Std. Error of the
Estimate
1
.849a
.721
.718
.66241
a. Predictors: (Constant), Final Price, Final Tangibility, Final Responsiveness, Final
Empathy, Final Reliability, Final Assurance
Source: (SPSS 23, Analyzed Data)
R Square represents the Coefficient of Determination which measures the
proportion of variation in one variable that is explained by the other. R square of
the model is obtained as 0.721. Hence it can be identified that 27.9% of
unexplained variations are involved in the model. Therefore, 72.1% of the
dependent variable can be explained from the independent variables Tangibility,
Reliability, Responsiveness, Assurance, Empathy and Price.
Table 4.16 ANOVA
Model
Sum of
Squares
Df
Mean
Square
F
Sig.
1
Regression
565.974
6
94.329
214.979
.000b
Residual
218.514
498
.439
Total
784.488
504
a. Dependent Variable: Final Satisfaction
b. Predictors: (Constant), Final Price, Final Tangibility, Final Responsiveness, Final
Empathy, Final Reliability, Final Assurance
Source: (SPSS 23, Analyzed Data)
The ANOVA table revealed that the F value is 214.979 therefore it is clear that
the model is fitted since the calculated F value is greater than the F statistic value.
The P value is 0.000, which is less than 0.05. It illustrates that the overall model
applied can be statistically significant and predict the dependent variable. Further
table revealed that out of 784.488 of the sums of squares, 565.974 of variation
can be explained by regression where 218.514 of variation of dependent variable
Customer Satisfaction is explained by the residual. Thus, it can be concluded that

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the model is fitted because a relatively considerable portion is explained by
regression.
Table 4.17 Coefficients of Multiple Regression
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std.
Error
Beta
1
(Constant)
-.052
.096
-.537
.592
Final Tangibility
-.003
.043
-.002
-.064
.949
Final Reliability
.192
.047
.186
4.068
.000
Final
Responsiveness
.093
.045
.089
2.070
.039
Final Assurance
.155
.050
.148
3.075
.002
Final Empathy
.275
.046
.265
6.047
.000
Final Price
.283
.040
.269
7.096
.000
a. Dependent Variable: Final Satisfaction
Source: (SPSS 23, Analyzed Data)
Multiple regression model can be construct as follows,
Y = Dependent variable = Customer Satisfaction
X1 = Independent Variable 1 = Tangibility
X2
= Independent Variable 2 = Reliability
X3
= Independent Variable 3 = Responsiveness
X4
= Independent Variable 4 = Assurance
X5
= Independent Variable 5 = Empathy
X6
= Independent Variable 6 = Price
a
= constant value
Є = Error β1, β2, β3, β4, β5, β6, β7, β8,
Y = -.052 -0.003X1 +0.192X2 +0.093X3 +0.155X4 - 0.275X5 + 0.283X6 + Є

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5.2.3. Hypothesis Testing
To test the hypothesis of the current study, linear regression and multiple regression
analyses have been adopted. Hypothesis can be tested by using the R, R Square P -
value (sig level) and the Coefficient (B) therefore R and R square results taken from
simple linear regression tables and coefficient values have taken from the multiple
linear regression tables. The below table depicts the summary results of regression
analyses.
Table 4.18 Hypothesis Testing
Hypothesis
Relationship
Status
Justification
H1a
Not significant
Rejected
R= 0.653
R square= 0.427
P-value= 0.949
Coefficient= -0.003
H1b
Positive significant
accepted
R= 0.745
R square= 0.555
P-value= 0.000
Coefficient= 0.192
H1c
Positive significant
accepted
R= 0.720
R square= 0.518
P-value= 0.039
Coefficient= 0.093
H1d
Positive significant
accepted
R= 0.763
R square= 0.582
P-value= 0.002
Coefficient= 0.155
H1e
Negative significant
accepted
R= 0.776
R square= 0.603
P-value= 0.000
Coefficient= -0.275
H1f
Positive significant
accepted
R= 0.751
R square= 0.563
P-value= 0.000
Coefficient= 0.283
H1
Positive significant
accepted
R= 0.839
R square= 0.704
P-value= 0.000
Coefficient= 1.009
Source: (SPSS 23, Analysed Data)

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H1a: There is a positive relationship between Tangibility and Customer Satisfaction.
According to the findings in the table, it indicates that R= 0.653, R square= 0.427, P-
value= 0.949, Coefficient= -0.003. There is no significant relationship between
Tangibility and Customer Satisfaction. Therefore, null hypothesis is accepted and
alternatively H1a is rejected. As a conclusion study found that Tangibility has no
impact on Internet Service Quality on Customer Satisfaction special reference to
ISPs.
H1b: There is a positive relationship between Reliability and Customer Satisfaction.
According to the findings in the table, it indicates that R= 0.745, R square= 0.555, P-
value= 0.000, Coefficient= 0.192. There is a positive significant relationship between
Reliability and Customer Satisfaction. Therefore, the null hypothesis is rejected and
alternatively H1b is accepted. As a conclusion study found that Reliability has a
significant positive impact of Internet Service Quality on Customer Satisfaction
special reference to ISPs.
H1c: There is a positive relationship between Responsiveness and Customer
Satisfaction.
According to the findings in the table, it indicates that R= 0.720, R square= 0.518, P-
value= 0.039, Coefficient= 0.093. There is a positive significant relationship between
Responsiveness and Customer Satisfaction. Therefore, null hypothesis is rejected and
alternatively H1c is accepted. As a conclusion study found that Responsiveness has
a significant positive impact of Internet Service Quality on Customer Satisfaction
special reference to ISPs.
H1d: There is a positive relationship between Assurance and Customer Satisfaction.
According to the findings in the table, it indicates that R= 0.763, R square= 0.582, P-
value= 0.002, Coefficient= 0.155. There is a positive significant relationship between
Assurance and Customer Satisfaction. Therefore, null hypothesis is rejected and
alternatively H1d is accepted. As a conclusion study found that Assurance has a

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significant positive impact of Internet Service Quality on Customer Satisfaction
special reference to ISPs.
H1e: There is a positive relationship between Empathy and Customer Satisfaction.
According to the findings in the table, it indicates that R= 0.776, R square= 0.603, P-
value= 0.002, Coefficient= 0.155. There is a positive significant relationship between
Empathy and Customer Satisfaction. Therefore, the null hypothesis is rejected and
alternatively H1e is accepted. As a conclusion study found that Empathy has a
significant positive impact of Internet Service Quality on Customer Satisfaction
special reference to ISPs.
H1f: There is a positive relationship between Price and Customer Satisfaction.
According to the findings in the table, it indicates that R= 0.751, R square= 0.563, P-
value= 0.000, Coefficient= 0.283. There is a positive significant relationship between
Price and Customer Satisfaction. Therefore, the null hypothesis is rejected and
alternatively H1f is accepted. As a conclusion study found that Price has a significant
positive impact of Internet Service Quality on Customer Satisfaction special reference
to ISPs.
H1: There is a positive relationship between Internet Service Quality and Customer
Satisfaction.
According to the findings in the table, it indicates that R= 0.839, R square= 0.704, P-
value= 0.000, Coefficient= 1.009. There is a positive significant relationship between
Internet Service Quality and Customer Satisfaction. Therefore, null hypothesis is
rejected and alternatively H1e is accepted. As a conclusion study found that Internet
Service Quality has a significant positive impact of Internet Service Quality on
Customer Satisfaction special reference to ISPs.

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6. CONCLUSION AND RECOMMENDATIONS
6.1. Theoretical Implications
This study was done to analyze the Impact of Internet Service Quality of Internet
Service Providers on Customer Satisfaction in Western province of Sri Lanka. The
Basis for the study is increasing demand for Internet services due to the Covid impact
and when considering the studies which have been done in Sri Lanka, there are a lot
of studies done on service quality for different service sectors like Hotels, Banking,
Education institutes etc. But we were unable to find that commitment towards the
Telecommunication industry. A lot of service providers are struggling in their
financial perspective even though the industry is developing. As a result of that,
Internet service providers try to merge with other service providers, for example
Etisalat and Hutchison (Dharmadasa & Gunawardhana, 2017). Researcher propose
this scenario happens because service providers lack knowledge in retaining the
customers.
Therefore, as a solution for this scenario, researcher intended to find a better solution
for retaining and attracting customers. According to Oliver, Emotional expression in
the satisfaction response. Satisfaction: A behavioral perspective on the consumer
(1997) Customer Satisfaction is a concept which can support the businesses to grow
and retain customers while satisfying. Satisfaction depends on Customer expectations
and Gilbert & Veloutsou (2007) defines the term expectation as a preconceived view
or belief about the provision of a service, which serves as a reference or standard
point for judging product performance. Even though Customer Satisfaction is an old
concept still it is practical and fundamental when it comes to Consumer Behavior
(Joudeh & Dandis, 2018).Outcomes of the study has proven that the Customer
Satisfaction concept is an overwhelming practical and suitable concept for the study
which enables us to determine solutions for the context.
Customer Satisfaction as an isolated concept cannot solve the issue or create the
atmosphere for Satisfaction, therefore it does need a concept or variable which can

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impact significantly. There were much researches researcher could find on different
independent variables which drives Customer Satisfaction. Brand experiences on
Customer Satisfaction by Almohaimmeed (2020), Digital marketing on Customer
satisfaction by Oladipupo (2021) etc. are few examples. But with the empirical
evidence and proper justifications (Eze, Ismail, Sin, & Siang, 2008; Joudeh & Dandis,
2018), researcher decided to establish the model with Internet plus Service Quality
because research context is Internet Service providers. In here Internet plus Service
quality means, SERVQUAL model of Service quality introduced by Parasuraman,
Zeithmal, & Berry (1988) was modified by adding Price dimension. Service quality
is the base for this Internet Service quality model which has five dimensions
Tangibility, Reliability, Responsiveness, Assurance and Empathy. As a modification
and to gain additional value to the study researcher added Price dimension to the
SERVQUAL model with the support of study done by Joudeh & Dandis (2018).
Model was successfully analyzed through Multiple regression analysis with the
support of article Harpe (2015) which highlights that Likert scale data can be
considered as Ordinal data when they are in the form of Items but it is not the same
when the Items are computed to create a variable, it can be considered as an Interval
data. Therefore, the study was done using Mean and standard deviation values.
According to the study, except the Tangibility dimension all the other dimensions
depict a significantly positive relationship towards dependent variable Customer
Satisfaction. Researcher was unable to find any empirical evidence on rejecting
Tangibility therefore researcher has concluded that customers are not concerned
about the Tangibility aspect as the study has done on Internet Service quality. But
researcher propose that this area should be further researched in a Qualitative way to
grasp the implicit meaning. Modification of the Service quality model was successful
as the Price dimension depicted a significant positive relationship towards Customer
satisfaction, therefore this explanatory variable can articulate to the future studies as
well.

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6.2. Managerial Implication
The study of Impact of Internet Service Quality of Internet Service Providers on
Customer Satisfaction in Western province of Sri Lanka is providing information for
managerial decision making. According to the findings of the study, there is a
significant relationship between Internet Service Quality of ISP on Customer
Satisfaction. And also, findings have proven that there is a significant positive
relationship between independent variables (Reliability, Assurance, Responsiveness,
Empathy and Price) except Tangibility variable and dependent variable (Customer
Satisfaction). Therefore, managers can focus on each independent variable to get an
idea about the behavior of those variables towards the predicted variable Customer
Satisfaction which will help to determine remedies for the issue of switching behavior
of customers by attracting and retaining (Dharmadasa & Gunawardhana, 2017).
Furthermore, with the prevailing COVID 19 situation in Sri Lanka, most of the people
are working through the online platform to adhere to the social distance criteria
imposed by the government. Hence, the need for Internet facilities became a must for
the students who are engaging in different levels of education and for employees as
well. As mentioned in the above paragraph, the tangibility variable was rejected from
the analysis, and this would be helpful to make strategies in future. Because it depicts
that people do not give much attention to the Tangibility aspect of the Internet Service
provider. Tangibility aspect generally includes the physical evidence of the Service
provider whether they are using modern looking equipment, Interiors, Exteriors and
their online presence like ISP website, Apps etc.
Through this study Service providers can understand the current level of Service they
are delivering to their customer base and at the same time ISP can determine whether
they are continuing the right path with what kind of perception in the consumer's
mind. Using that information, ISPs can select their future strategy or what should be
changed in the current strategy to compete with the industrial competitors. On the
other hand, Service providers should focus on Reliability, Responsiveness,

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Assurance, Empathy and Price. Because these are the dimensions that affect their
customers.
First limitation of the study is responses were collected through an online
questionnaire because it is convenient during the COVID 19 period but there may be
errors on decoding the questions by the respondents therefore it should be done
through an offline questionnaire or virtual face to face interview while describing the
meaning of the research and questions included in it. Second one is, this study has
been carried out as a cross sectional study which means one-time study, but it would
be more precise through a longitudinal study. Furthermore, the study on identifying
the impact of Internet Service Quality on Customer Satisfaction has a much
weightage due to lack of empirical evidence on the model of Internet Service Quality
which was modified using SERVQUAL model introduced by (Parasuraman,
Zeithmal, & Berry, 1988). Hence, more studies should be done in this arena to fill the
empirical gap. For innovative researchers, this study can be further improved in
different directions as examples,
Researchers can suggest different dependent variables such as brand loyalty, brand
evangelism etc. or more dependent variables for the prevailing issue in ISP sector.
Studies can be further developed within the context of ISP rather than
concentrating on Customer Satisfaction and Internet Service quality because
Telecommunication sector is a sector which has not been studied thoroughly
(Dharmadasa & Gunawardhana, 2017).
In the study Internet Service quality has successfully shown the significant impact to
the study, this concept can be used with different contexts, independent and
dependent variable for the studies of future researchers.

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