=Paper=
{{Paper
|id=Vol-2889/PAPER_19
|storemode=property
|title=A sustainable Customer Satisfaction Model based on new dimensions of SERVQUAL & SERVPERF in today’s Telecom world: An empirical approach
|pdfUrl=https://ceur-ws.org/Vol-2889/PAPER_19.pdf
|volume=Vol-2889
|authors=Saumya Choudhury
}}
==A sustainable Customer Satisfaction Model based on new dimensions of SERVQUAL & SERVPERF in today’s Telecom world: An empirical approach==
https://ceur-ws.org/Vol-2889/PAPER_19.pdf
A sustainable Customer Satisfaction Model based on new
dimensions of SERVQUAL & SERVPERF in today’s Telecom world:
An empirical approach
Saumya Choudhury
Principal Consultant: Ericsson Global Consulting
Lovely Professional University, Phagwara, Punjab, India
Abstract
SERVQUAL & SERVPERF are the two most popular service quality evaluation methodologies
predominantly used for modeling the impact of service quality on customer satisfaction. These
models are functioning since 1985. The purpose of the study is to investigate thoroughly the tried
& tested multi-dimensional research instrument tool such as SERVQUAL & SERVPERF, as the
means to capture the service perceptions in customer minds with respect to five dimensions to
assess the end game “Customer Experience”. Moreover, the question is, why do we need another
framework which delve into yet another construct with the realm of completely different set of
considerations. The researchers from industry as well as from academia working in the area are
very much aware of the lacuna exists in the area of customer expectation, service quality standards,
service performance and service delivery so as the validity of the models in today’s technology
scenario, as well as to debate on the very inner structure and redefining those to make those
contemporary to the exact ask for the recent telecom world. The objective is to establish the
hypothesis of how the improving service quality & performance could lead to greater customer
satisfaction based on the new scale. As a part of the study, a customer survey result will be analyzed
to understand the direct link, if any, in between SERVQUAL & SERVPERF and customer
satisfaction.
Keywords 1
SERVQUAL & SERVPERF, Service quality dimensions, service performance dimensions,
Cronbach Alpha, Multiple Linear Regression Model.
1. Introduction
The new service mantra is what value can be delivered to the customer; how the services can be offered
which can be customizable according to the need to get the customer positive approval on the services
offered; as a result of that customer would consume the service more often and perhaps even purchase more
than they usually do in other situations. For any organization this means enhanced competitiveness.
Services can also be offer as hybrid for example a typical car services company where with the services are
done along with the changes of the spare parts if need be.
Customer approval for a product or service is essentially the reaction to the liking or disliking based on
the perception of that offering’s performance (or outcome) to that of expectation. Customers, within the
WCNC-2021: Workshop on Computer Networks & Communications, May 01, 2021, Chennai, India.
EMAIL: e08saumya@iima.ac.in (Saumya Choudhury)
ORCID: 0000-0001-5711-834X (Saumya Choudhury)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
174
bounds of their resources, want to buy a product or service from which, they believe they would get the
highest return-on-investment. In a way customer satisfaction is tightly coupled with the product and/or
service quality & performance. These are constantly tested in customer mind at each of the service
encounter. If customer is not satisfied with the kind of answer that they are getting during their encounters
with the service provider or the performances are very poor, customers will have the tendency to leave the
service provider in search for a better one. Service Quality & Performance is nothing but an assessment of
the delivered service that conforms to the customer expectations.
In telecommunication, a communication service provider’s (CSP) job is essentially enabling the
consumers to avail basic and advanced communication services like telephony, internet and content
consumption against a fixed (wired and wireless) and/or mobile. Service quality & performance in telecom
world broadly discuss about level of satisfaction of the rendered services by the CSPs. This is broadly
dependent upon the infrastructure (network and IT) of the CSP (whether they own it or on rent or revenue
share model) and on their business model. Often the Service performance is measured by the term of QoS
(Quality of Service). The standard framework like SERVPERF and SERVQUAL can be used for measuring
service quality.
Parasuraman, Zeithaml and Berry (PZB’s1988) led to the development of a multi-dimensional research
instrument, called SERVQUAL, designed to capture consumer expectations and perceptions of a service
along the five dimensions Based on this service quality model researchers identified five determinants of
service quality, like Reliability, Responsiveness, Assurance, Empathy, Tangibles. However, SERVQUAL
framework couldn’t become flawless and there came the other framework called SERVPERF that measure
the same quality as an attitude instead of satisfaction. It is a modification of SERVQUAL and used the
same five determinants of service quality.
In telecom, research shows [4] that service outcomes are influenced by many environmental factors such
as pricing, Inconvenience of services, Communication failures, alert failures, tough competition, ethical
problems, involuntary transfer of plans that cause customers to switch services etc. Uninterrupted and
convenient service delivery is the ideal scenario for any service organization. Marketing research further
revealed a comprehensive set of guidelines to improving service quality. continuous feedback and
improvement, basic service, proper service design and recovery constitute the aforesaid guidelines and
stress upon managing customer expectations and incorporating Self-service technologies.
Table 1: Idea in Brief: The Problem Statement, The Argument, The Probable Solution
The Problem The Argument The Probable Solution
SERVQUAL and SERVPERF The telecommunication sector is As the new technologies being
are running for ages for service going through a major change implemented, the modification is
organization and the two most with the advent of newest getting faster than what was
prominent scales forming the technologies, virtualization of expected. Automation makes
origins of service quality communication network and human interactions redundant
assessments in different service services, arising complexities with no error in regular
sectors but are they able to with related data and delivery operational work, zero
accurately asses the QoS in models. Newer technologies downtime, 24/7 available
telecom sector? Can those five coined the terms like workforce etc., which make the
dimensions be able enough to cloudification, smart network, modification even less popular
justify any customer satisfaction IoT, zero-touch-provisioning within the employees. With this
/dissatisfaction? etc., are ready to change the new condition, the re
telecom service world by leaps dimensioning is the need of the
and bounds. hour to make those suitable with
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the telecom scenario. Based on
the new customer parameters
given in TM Forum [4], we have
identified six dimensions for
SERVQUAL which Awareness,
Trust, Personalization,
Fulfillment, Assurance and
Remodeling are and for
SERVPERF five dimensions are
there which are Trust,
Personalization, Fulfillment,
Assurance and Remodeling. The
items under the dimensions are
designed accordingly to capture
the customer sentiment.
2. Research Methodology
The main purpose of the study is to validate the practicality, fitness, and validity of the SERVQUAL &
SERVPERF methodologies while modeling the service quality impact on customer satisfaction and loyalty
in case of telecom service company, the innate structure of those methodologies and their appropriateness
in terms of influencing factors.
A specific questionnaire has been designed to measure the Service level from different telecom operators
mainly from India in the frame of the above-mentioned dimensions for both SERVQUAL & SERVPERF.
The questionnaire has been distributed amongst the users to capture the survey results. Expectations are
assessed by using Likert scale only. This questionnaire-based survey is distributed among 300 persons out
of which all 300 responses are collected.
The data collected in two different forms one for the demographics and another for the customer’s
answers against each of the items under the dimensions.
The demographic data is shown below:
Table 2: Demographic data as part of Survey
Variable
Attribute Name Attribute Description Values
Name
Vodafone-Idea
Name of the
Service Provider , Bharti-Airtel
Communication Service D1
(SP) , Reliance Jio
Provider
, BSNL
Kind of Service used by Mobile, Fixed Line, Broadband,
Service Used D2
consumer Other
Mobile/Broadband
Radio Access Type D3 2G, 3G, 4G, 5G
Service Type
Service Class Mode of service D4 Pre-paid, Postpaid
Name of the country of
Country Name D5 Free Text
the consumer
Name of the city name of
City Name D6 Free Text
the consumer
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< 18
, >= 18 and < 25
, >= 25 and < 35
Age Group The different age buckets D7
, >=35 and < 45
, >= 45 and < 55
, >= 55
Gender Gender of consumer D8 Female, Male, “Prefer not to say”
Marital Status of the Married, Unmarried, Divorced,
Marital Status D9
consumer Widow
Student, Professional, Business,
The profession of the
Engagement D10 Teacher, Retired, Housewife, Not
consumer
working
The dimensions are shown below:
Table 3: Service Quality dimensions
SERVQUAL (ATPFAR)
1. Awareness
2. Trust
3. Personalization
4. Fulfilment
5. Assurance
6. Re-modelling
Table 4: Service Performance dimensions
SERVPERF (TPFAR)
1. Trust
2. Personalization
3. Fulfilment
4. Assurance
5. Re-modelling
As mentioned above the survey data mainly used Likert Scale (1-5) for non-demographic dimensions.
Cronbach’s Alpha test has been applied on the survey data to check the reliability or internal consistency
of the 38 variables for SERVQUAL and 32 variables for SERVPERF. This is co-efficient of reliability.
Both R Studio and SPSS has been used on the actual and standardized data to obtain the reliability scores.
The expected value of Alpha should lie between 0.80 and 0.90 and we got result which is very much within
the desired range.
Table 5: Reliability Statistics
SERVPERF
Cronbach's Alpha
Cronbach's
Based on N of Items
Alpha
Standardized Items
0.887 0.872 32
SERVQUAL
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Cronbach's Alpha
Cronbach's
Based on N of Items
Alpha
Standardized Items
0.898 0.889 38
Converting regular customer into loyal customer to increase the NPS (net promoter score) is essential
in Marketing, to make them as brand ambassador and can act as a genuine influencer. According to Pareto
Principal or “80/20” rule, 20% of these loyal customers represent 80% of the sales. Based upon the literature
study in the area of customer experience, several white papers, journals and Webinars from HBR,
McKinsey and CX Research Gate have been read and the need of few prudent categories comprising of the
available non-demographic variables has been understood to form some pre-conceived models before the
analysis as per the customer value matures over the period. These models give the flexibility to assimilate
the parameters from these identified determinants of service quality. The segregation of subscribers helps
to get the insight of fitment of parameters congruent to each segment and justifies the framed survey
questions. The models are representative of the length of time one subscriber spends on the network starting
just from a rookie to become an engaged customer to fully advocating the services to other prospects to the
state of customer delight. Hence, basis on the activity four models have been chosen such as
“Consumption”, “Engaged”, “Advocacy” and “Satisfaction”. The “Advocacy” and “Satisfaction” are the
most desired states for any service providers as these states would have significant contribution to the
revenues. It can be depicted as below:
Figure 1: Proposed Model
Some worth noting demographics data of the survey are the following:
93% of the respondents are from Vodafone, Airtel and Reliance.
98 % of the respondents are using mobile services.
95% of the respondents are using 4G.
67% of the respondents are using postpaid connection
59.7% of the respondents are from age group between 35 and 45.
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78.7% respondents are male.
80.7% respondents are married.
66.3% respondents are working professionals.
Following hypothesis can be formed based on the data collected:
On Age Group:
H0: There is no difference in the effect of overall customer satisfaction amongst the different Age Groups
H1: There is the significant difference in the effect of overall customer satisfaction amongst the different
Age Groups
On the Relation between Dimensions & Overall Customer Satisfaction:
H0: There is no significant relation between overall customer satisfaction and the five dimensions of the
SERVPERF and/or the six dimensions of the SERVQUAL.
H1: There is significant relation between overall customer satisfaction and the five dimensions of the
SERVPERF and/or the six dimensions of the SERVQUAL.
Multiple Linear Regression (MLR) has been used to understand the effect of the dimensions used against
the different assumed models namely Consumption, Engaged, Advocacy and Satisfaction respectively.
Here, the specific combinations of independent variables have been used to create the weighted average
scores for each of the models to use them as dependent variables to do the regression with respect to the
standard weighted average scores. R Studio has been used to produce the regression results. Below are
the results for SERVPERF and SERVQUAL.
Table 6: Multiple Linear Regressions for the proposed models using perception score and five components
for assessing service quality (SERVPERF)
Model 1 Model 2 Model 3 Model 4
Consumption Engaged Advocacy Satisfaction
Standardized Co- Standardized Co- Standardized Co- Standardized
efficient efficient efficient Co-efficient
Beta p Beta p Beta p Beta p
0.15501 0.10836 0.025122 26651 2.39e-10 0.27159
Trust
0.000302 *** * *** 3.84e-09 ***
Personali 0.24267 3.32e-09 0.17189 0.000172 0.23320 2.93e-09 0.33581
zation *** *** *** 2.86e-14 ***
Fulfilmen 0.14879 0.002507 0.01010
0.05401 0.330737 0.05322 0.25551
t ** 0.844388
Assuranc 0.20529 7.37e- 0.27094 1.50e-12 0.08860 0.00436 0.25342
e 10 *** *** ** 1.02e-12 ***
Re- -0.06924 0.29353 1.24e-13 0.29647 < 2e-16 0.13477
Modelling 0.037576 * *** *** 0.000142 ***
Multiple
0.6972 0.6633 0.6879 0.7504
R2=
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170.2 on 5 and
Anova F 130.3 on 5 and 283 111.5 on 5 and 283 124.8 on 5 and 283
283 DF, p-
test. DF, p-value: < DF, p-value: < 2.2e- DF, p-value: <
value: < 2.2e-
P<0.01 2.2e-16 16 2.2e-16
16
The calculated regression results of SERVPERF depicting the following:
All 5 (six) dimensions are significantly contributing to overall satisfaction score when Consume
model is used
SERVPERF perception gap score explains 69.72% on the variation of customer consumption,
66.33% on the variation of customer engaged, 68.79% on the variation of customer advocacy,
75.04% on the variation of overall satisfaction
Re-modelling dimensions have no significant contribution to overall satisfaction score in case of
customer Consumption model
Fulfillment dimension has no significant contribution to the overall satisfaction score when using
customer Engaged, customer Advocacy and overall Satisfaction models
For all models the ANOVA results seem to be satisfactory (p-values are well below 0.01 at 99%
CI) and Multiple values imply that more than 66% variations of the overall satisfaction scores are
explained by these 5 (five) dimensions.
Table 7: Multiple Linear Regressions for the proposed models using perception expectation gap score and
six components for assessing service quality (SERVQUAL)
Model 1 Model 2 Model 3 Model 4
Consumption Engaged Advocacy Satisfaction
Standardized Co- Standardized Co- Standardized Co- Standardized
efficient efficient efficient Co-efficient
Beta p Beta p Beta p Beta p
Awarenes 0.18018 0.325300 0.09607 0.000754 0.20739
s 6.417 5.85e-10 *** < 2e-16 *** *** 9.70e-12 ***
0.10000 0.009042 0.82403 0.23718 0.20827
Trust
0.014268 * 1.58e-08 *** 1.33e-06 ***
0.131326 0.00052
Personali 0.22020 0.22122 0.30994
***
zation 1.06e-08 *** 1.05e-08 *** 3.56e-14 ***
0.05864
Fulfilmen 0.15895 0.072347 0.11501 0.02179
0.202359
t 0.000582 *** 0.646540
0.06996
Assuranc 0.17034 0.207838 0.21319
0.023667 *
e 6.14e-08 *** 7.18e-11 *** 1.13e-10 ***
0.12057
Re- -0.08158 0.271255 0.28989
0.000228 ***
Modelling 0.009126 ** 2.55e-16 *** 2e-16 ***
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Multiple
0.7358 0.7716 0.7003 0.7884
R2=
F-statistic:
F-statistic: 158.8 on F-statistic: 109.8 on
Anova F F-statistic: 130.9 175.1 on 6 and
6 and 282 DF, p- 6 and 282 DF, p-
test. on 6 and 282 DF, 282 DF, p-
value: < 2.2e-16 value: < 2.2e-16
P<0.01 p-value: < 2.2e-16 value: < 2.2e-
16
The calculated regression results of SERVQUAL depicting the following:
All 6 (six) dimensions are significantly contributing to overall satisfaction score when Consume
model is used.
SERVQUAL perception-expectation gap score explains 73.58% on the variation of customer
consumption, 77.16% on the variation of customer engaged, 70.03% on the variation of customer
advocacy, 78.84% on the variation of overall satisfaction.
Trust and Fulfillment dimensions have no significant contribution to overall satisfaction score in
case of customer Engaged model.
Fulfillment dimension has no significant contribution to the overall satisfaction score when using
customer Advocacy and overall Satisfaction models.
For all models the ANOVA results seem to be satisfactory (p-values are well below 0.01 at 99%
CI) and Multiple values imply that more than 70% variations of the overall satisfaction scores are
explained by these 6 (six) dimensions.
Hypothesis Testing:
On the age group:
H0: There is no significant difference in service satisfaction score among the age groups.
The Sig value is less than 0.05 at 5% level of significance. So, we reject the null hypothesis and conclude
that there is a significant difference in service satisfaction among the age groups of the customer responded
for SERVPERF.
Table 8: Anova Result (SERVPERF)
ANOVA
SERVPERF
Score
Mean
Sum of Squares df F Sig.
Square
Between
9.749 4 2.437 7.029 0.000
Groups
Within
102.288 295 0.347
Groups
Total 112.037 299
ANOVA test has been carried out for the overall satisfaction score with respect to the different age
groups. The p-value of the F-statistic is very much less than 0.05 at 95% CI and therefore, the Null
hypothesis has been rejected. There is a significant difference in the effect of overall customer satisfaction
amongst the different Age Groups. Below table shows the ANOVA results
181
Table 9: Anova result (SERVQUAL)
ANOVA
SERVQUAL
Score
Mean
Sum of Squares df F Sig.
Square
Between
7.006 4 1.752 5.891 0.000
Groups
Within
87.714 295 0.297
Groups
Total 94.720 299
On the Relation between Dimensions & Overall Customer Satisfaction:
H0: There is no significant relation between overall customer satisfaction and the five dimensions of the
SERVPERF and/or the six dimensions of the SERVQUAL.
Correlation analysis were performed to find relationship between five dimensions of the scale and the
overall customer satisfaction. The Results are summarized below in tabular format.
Table 10: Correlation between Five dimensions and Customer satisfaction (SERVPERF)
Correlation Overall Satisfaction
Trust Pearson Correlation .716**
Sig. (2-tailed) 0.000
N 300
Personalization Pearson Correlation .556**
Sig. (2-tailed) 0.000
N 300
Fulfilment Pearson Correlation .787**
Sig. (2-tailed) 0.000
N 300
Assurance Pearson Correlation .837**
Sig. (2-tailed) 0.000
N 300
Re-Modelling Pearson Correlation .307**
Sig. (2-tailed) 0.000
N 300
Overall
Pearson Correlation 1
Satisfaction
Sig. (2-tailed)
N 300
182
All the five dimensions are statistically significant at 1% level of significance. So, it proves that all the
five dimensions are correlated with the overall customer satisfaction.
Table 11: Correlation between Six dimensions and Customer satisfaction (SERVQUAL)
Correlation Overall Satisfaction
Awareness Pearson Correlation .579**
Sig. (2-tailed) 0.000
N 300
Trust Pearson Correlation .666**
Sig. (2-tailed) 0.000
N 300
Personalization Pearson Correlation .572**
Sig. (2-tailed) 0.000
N 300
Fulfilment Pearson Correlation .748**
Sig. (2-tailed) 0.000
N 300
Assurance Pearson Correlation .799**
Sig. (2-tailed) 0.000
N 300
Re-Modelling Pearson Correlation .367**
Sig. (2-tailed) 0.000
N 300
Overall
Pearson Correlation 1
Satisfaction
Sig. (2-tailed)
N 300
All the six dimensions are statistically significant at 1% level of significance. So, it proves that all the
five dimensions are correlated with the overall customer satisfaction.
3. Conclusion
Based on the data analysis it can be concluded as follows:
The overall dimensions of SERVQUAL & SERVPERF have significant impact on customer
satisfaction.
Individually all the dimensions, Awareness, Trust, Personalization, Fulfilment, Assurance, Re-
Modelling irrespective of SERVPERF or SERVQUAL model have strong links to customer
satisfaction.
Out of five dimensions Trust and Assurance is has the higher impacts compared to Fulfilment and
Re-Modelling in SERVPERF.
183
Out of five dimensions Awareness and Assurance is has the higher impacts compared to Fulfilment
and Re-Modelling in SERVQUAL.
Regression results shows that feedback capturing process is affecting the satisfaction score in an
adverse way in the personalization dimension.
4. References
[1] J. Joseph Cronin, Jr. & Steven A. Taylor. SERVPERF Versus SERVQUAL: Reconciling
Performance-Based and Perceptions-Minus-Expectations Measurement of Service Quality. Journal
58(1):125-131 · January 1994
[2] Alex Rawson, Ewan Duncan and Conor Jones. The Truth About Customer Experience the
September 2013 Issue.
[3] Dr. Tareq N. Hashem, Farah I. Hamdan. Measuring service quality level in the JORDANIAN
TELECOMMUNICATION SECTOR from its customers’ perspective using the SERVPERF scale.
European Journal of Business and Social Sciences, Vol. 5, No. 12, March 2017. P.P. 15 - 27 URL:
http://www.ejbss.com/recent.aspx-/ ISSN: 2235 -767X
[4] Fogarty, G., Catts, R., & Forlin, C. (2000). Identifying shortcomings in the measurement of service
quality. Journal of Outcome Measurement, 4(1), 425-447.
[5] Andreani F*, Wijayanty D - The Influence of Service Performance on Customer Satisfaction of
Bank Central Asia in Surabaya- ISSN: 2278-3369.
[6] Abd-Elrahman Hassanein Abd-Elrahman1 - A Review of Telecommunications Service Quality
Dimensions - Sch J Appl Sci Res 2018 Vol .1
[7] Dr. Vijay Kumar, Nagaraju. Kolla, Dr.ADINARAYANA - SERVICE QUALITY GAPS IN
CELLULAR TELECOMMUNICATION SERVICE PROVIDERS - IOSR Journal of Economics
and Finance (IOSR-JEF) e-ISSN: 2321-5933, p-ISSN: 2321-5925 PP 01-08 www.iosrjournals.org
International
[8] Dyah R. Rasyida, M. Mujiya Ulkhaq, Priska R. Setiowati and Nadia A. Setyorini.- Assessing
Service Quality: A Combination of SERVPERF and Importance Performance Analysis - MATEC
Web of Conferences 66 ICIEA 2016.
[9] AlperÖzeraMehpare TokayArganbMetinArgan.- The Effect of Mobile Service Quality Dimensions
on Customer Satisfaction - MATEC Nov 2013
[10] Philip Kotler, Kevin Lane Keller : Marketing Management, 2017.
[11] TMForum.org
[12] http://innovationinfo.org/articles/SJASR/SJASR-1-103.pdf, A Review of Telecommunications
Service Quality Dimensions, Abd-Elrahman Hassanein Abd-Elrahman1, 2018.
[13] https://www.researchgate.net/publication/274509597_The_Influence_of_Service_Performance_o
n_Customer_Satisfaction_of_Bank_Central_Asia_in_Surabaya- The Influence of Service
Performance on Customer Satisfaction of Bank Central Asia in Surabaya- Andreani F*, Wijayanty
D, Dec 2014
[14] Forrestar.com
[15] Gartner.com
[16] How the Telecom Industry Can Set the Customer Experience Bar Higher by Phil Britt
https://www.cmswire.com/customer-experience/how-the-telecom-industry-can-set-the-customer-
experience-bar-higher/
[17] https://www.emerald.com/insight/content/doi/10.1108/07363760610712939/full/html
[18] Lasting customer loyalty: a total customer experience approach Oswald by
A. Mascarenhas, Ram Kesavan, Michael Bernacchi
[19] Journal of Consumer Marketing ISSN: 0736-3761 Publication date: 1 December 2006 Customer
experience, organisational culture and the employer brand Journal of Brand Management
November 2007, Volume 15, Issue 2, pp 123–134 by Richard Mosley
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