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How Important Is Customer Satisfaction? Quantitative Evidence from Mobile Telecommunication Market

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Abstract

To investigates the importance of customer satisfaction in Pakistani mobile telecommunication market. This study explores whether customer satisfaction affects the relationship between customer loyalty and service quality, and also between customer loyalty and perceived value. The study found the coefficient of determination (R2) for the overall model to be considerable. The role of customer satisfaction was significant in assessing the contribution of exogenous constructs to the R2 value of endogenous constructs (f2> 0.35). All exogenous constructs in the model had good predictive relevance for endogenous constructs, as Q2 value was above the threshold (0.156 for customer satisfaction, and 0.467 for customer loyalty). The q2 effect size of customer satisfaction on customer loyalty is large (q2= 0.448). VAF accounted for more than 80% of both indirect effects, indicating the importance of customer satisfaction on the relationship between service quality and customer loyalty, and between perceived value and customer loyalty.
International Journal of Business and Management; Vol. 11, No. 6; 2016
ISSN 1833-3850 E-ISSN 1833-8119
Published by Canadian Center of Science and Education
57
How Important Is Customer Satisfaction? Quantitative Evidence from
Mobile Telecommunication Market
Irfan Muhammad1, Mohammad Farid Shamsudin1 & Noor Ul Hadi1
1 Universiti Kuala Lumpur Business School (UNIKL BIS), Bangunan Yayasan Selangor, Malaysia
Correspondence: Noor Ul Hadi, Universiti Kuala Lumpur Business School (UNIKL BIS), Bangunan Yayasan
Selangor, Jalan Raja Muda Abdul Aziz, Kg Baru, 50300 Kuala Lumpur, Malaysia. Tel: 601-6974-4518. E-mail:
n_hadi1@yahoo.com; noor.ul@s.unikl.edu.my
Received: March 31, 2016 Accepted: April 27, 2016 Online Published: May 22, 2016
doi:10.5539/ijbm.v11n6p57 URL: http://dx.doi.org/10.5539/ijbm.v11n6p57
Abstract
To investigates the importance of customer satisfaction in Pakistani mobile telecommunication market. This
study explores whether customer satisfaction affects the relationship between customer loyalty and service
quality, and also between customer loyalty and perceived value. The study found the coefficient of determination
(R2) for the overall model to be considerable. The role of customer satisfaction was significant in assessing the
contribution of exogenous constructs to the R2 value of endogenous constructs (f2> 0.35). All exogenous
constructs in the model had good predictive relevance for endogenous constructs, as Q2 value was above the
threshold (0.156 for customer satisfaction, and 0.467 for customer loyalty). The q2 effect size of customer
satisfaction on customer loyalty is large (q2= 0.448). VAF accounted for more than 80% of both indirect effects,
indicating the importance of customer satisfaction on the relationship between service quality and customer
loyalty, and between perceived value and customer loyalty.
Keywords: customer loyalty, customer satisfaction, service quality, perceived value, Pakistan, PLS-SEM
1. Introduction and Background
Mobile phone services are one of the most promising growth areas in the telecommunication industry, with more
than 1.7 billion subscribers worldwide, and about 80% of the world population as the potential target (Farid,
2010). Mobile phone service providers thus operate in a competitive environment. Effective strategies to meet
the demands of competition need to consider the factors that affect their performance (Lim, Widdows, & Park,
2006). Customer satisfaction is a major factor contributing to the success of any organisation. It influences
customer loyalty, which, in turn, affects business performance (Gerpott et al., 2001). Studies on mobile phone
service in the United States have shown that the level of customer satisfaction is much lower for cellular services
compared to other service sectors (Customer Report, 2005; McKinsey Quarterly, 2004).
Research shows that each dissatisfied customer communicates their experience to an average of 5 to 15 people,
of whom 13% continue the chain of negative promotion by disseminating contrary feedback to 10 more potential
customers (Harari, 1992). Dissatisfied customers may not worry about losing their numbers, as they can request
for a churn. For telecommunications companies, the average annual churn rates lie between 10 to 67% (Hughes,
2007). According to Aydin and Ozer (2005), telecommunication companies lose 2 to 4% of their customers every
month, and these “disloyal” customers amount to a loss of millions. According to a study conducted in Malaysia
by the Malaysian Communication and Multimedia Commission (2007), 28.1% of users switched to an
alternative service provider in three months. Introduction of new value-added services and reduction of tariff
may be some of the strategies used to uphold customer loyalty.
Retaining loyal customers is an important factor for the sustainable success of the telecommunication industry,
because the paradigm in marketing has shifted from acquiring new customers to maintaining and keeping current
customers. According to Reichheld and Sasser (1990), acquiring new customers is far pricier than their
maintenance. Goodman et al. (2000) explained that the cost of acquiring new customers is two to 20 times more
expensive than keeping them. Brown (2004) also pointed out that mobile operators spend USD 300 to obtain
new customers. Since it is dearer to attract new customers than to maintain existing ones, mobile phone service
providers need to pay particular attention to customer loyalty. However, before embarking on any strategy to
maintain existing customers, it is important to gauge the determinants of customer loyalty.
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58
1.1 Motivation for the Study
Compared to other industries, customer loyalty in the telecommunication industry is more crucial, since customers
can change service providers easily, given the high level of competition. In Pakistan, for instance, Mobilink owns
29 percent market shares in 2013, in June 2014 their market shares were decreased to 28 percent, the survey further
projected that the market shares will be decreased to 27 percent by the mid of 2016P. In contrast, the market shares
of Telenor has increased from 25 percent in 2013 to 29 percent in 2016P. Despite the historical lead of Mobilink,
the extend of competition is still on the boundary (Note 1). Therefore, this study made an attempt to investigate the
importance of customer satisfaction with a focus on Mobilink customers.
2. Review of Literature
2.1 Factors Affecting Customer Satisfaction and Customer Loyalty
A lot of research has been conducted to identify the factors crucial in influencing various industries, such as
airlines, financial services, tourism, etc. Factors such as commitment, service fairness, switching barrier,
communication, conflict handling, price fairness, and relational benefit are some of the determinants of customer
satisfaction and customer loyalty. The determinants vary depending on the scope of the particular industry.
Commitment, service fairness and conflict handling, for instance, have been largely used as determinants of
customer loyalty in the financial services industry; whereas relational benefit and switching barrier are important
in the airline industry. Price fairness is a crucial determinant of customer satisfaction and customer loyalty in
service industries such as auto repairs and maintenance.
Factors used in the study of customer satisfaction and customer loyalty related to telecommunication include
perceived value, trust, switching cost, customer satisfaction, corporate image, and service quality (Shamsundin,
2010). Aydin and Ozer (2005) exclude corporate image from the list, as they did not find a correlation between
corporate image and customer loyalty. Studying the perception of Chinese customers, Han et al. (2008) found
that commitment, trust, service quality, and customer satisfaction were the key determinants of customer loyalty.
Similarly, Chang and Chen (2007) collected data from Taiwanese airline passengers and identified that relational
benefits had some effect on customer loyalty. Akbar et al. (2010) reveal that service quality and customer loyalty
had a positive and significant correlation. A similar correlation was also found by Hoq and Amin (2009) between
customer satisfaction and customer loyalty. Trust as a key influencing factor of customer loyalty was identified
by Omar et al. (2009). Additionally, Alam et al. (2016) cited the same concern.
Overall, findings from research on customer satisfaction and customer loyalty in several industrial sectors from
2001 to 2016 suggest that service quality, perceived value, and customer satisfaction are the crucial factors
influencing customer loyalty. In this study, these four common constructs will be used. To identify the
importance of customer satisfaction in the context of Pakistani Mobilink telecommunication market its dual
aspects (exogenous and endogenous) was considered. We further take into account perceived value and service
quality as exogenous constructs whilst customer loyalty as endogenous construct see Figure 1.
2.1.1 Customer Loyalty
Edvardsson et al. (2000) define customer loyalty as the desire or propensity of customers to buy on a continual
basis from the same firm. According to Caruana (2004) and Keropyan and Gil-Lafuente (2012), customer loyalty
is a deep commitment to repurchase the preferred product despite environmental volatility. Jones and
Mothersbaugh (2002) also define it as an attachment with the same organisation for a long period, with the
purpose of repeat purchase. For this study, customer loyalty is defined as the reappearance of the customers with
the same organisation for longer periods. As the level of competition increases, so does the need for customer
loyalty, since there is a wide range of choice, fast, creative, and innovative services (Bodet, 2008; Kim et al.,
2016; Kumar et al., 2013; Karjaluoto et al., 2012; Aktepe et al., 2015; Rasheed & Abadi, 2014; Stevens, 2000;
Chang, 2015).
2.1.2 Service Quality
Service quality is regarded as a key source of competitive advantage, as it helps retain and attract customers.
According to Shin and Kim (2008), Tsoukatos and Rand (2006), Cronin and Taylor (1992) and Kim et al. (2015a)
service quality is associated with loyalty and customer satisfaction. This association has been confirmed, and
research has proven the positive role of service quality on customer satisfaction, which eventually leads to
customer loyalty (Santouridis & Trivellas, 2010; Deng et al., 2009; Turel & Serenko, 2006; Kim et al., 2004;
Rashed & Abadi, 2014). As such, service quality is included as an independent variable to customer loyalty.
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2.1.3 Perceived Value
Perceived value is the comparison that customers make between the advantages or disadvantages of one or more
service providers (Sanchezet et al., 2005). It has a marked association with customer loyalty (Park et al., 2006;
Kuoet al., 2009; Rasheed & Abadi, 2014; Chang, 2015). Sirdeshmukh et al. (2002), Yang and Peterson (2004),
and Wathneet al. (2001) also substantiate the fact with their findings. Atalik and Arslan (2009) found that
perceived value positively affected Turkish airline passengers. Similarly, in the Chinese phone industry, Lai et al.
(2009) pointed out how closely the two are related. Lin and Wang (2006), in their study of Taiwanese mobile
phone consumers, reiterated its significance. The importance of perceived value was also identified by other
researchers, such as Roiget et al. (2006), Anderson and Srinivasan (2003), Chen and Dubinsky (2003), Cronin et
al. (2000), Hellier et al. (2003), and Parasuraman and Grewal (2000). Overall, findings from research on
customer loyalty in telecommunication industries from 2001 to 2010 suggest that perceived value is one of the
most common key determinants of customer loyalty. Thus, we hypothesise that when consumers receive more
value from what they paid, they will decrease their search and will remain loyal to the firm.
2.1.4 Customer Satisfaction
Customer expectation is important in global competition, according to Parasuraman et al. (1991). In marketing
literature, customer satisfaction has been considered as a crucial factor influencing customer loyalty (Gerpott et al.,
2001; Kumar et al., 2013; Kim et al., 2015a; Kim et al., 2016). Omachonu et al. (2008) suggest that it is a
psychological state where there is a consistency between the emerging emotion and expectation. Gerpott et al.
(2001) state that satisfied customers tend to retain their pattern of purchases. Grönholdt et al. (2000) point out that
customer loyalty is a function of customer satisfaction, and that loyal customers affect a company’s financial
performance. Wong and Zhou (2006), Aktepe et al. (2015) and Chang, (2015) specify that satisfaction is one of the
key factors affecting customer loyalty. Analytical studies by Maxham and Netemeyer (2002) and Blodgett et al.
(1997) recognise the fact that satisfied customers publicise the firm and are more likely to remain loyal. Therefore,
it is crucial that customer satisfaction is selected as a factor determining customer loyalty in this study.
2.1.5 Satisfaction as a Mediating Variable
Several empirical studies reveal that customer satisfaction mediates the relationship between various factors and
customer loyalty. Some of these researches were conducted by Caruana (2002), Wang et al. (2006), Turel and
Serenko (2006), Akbar et al. (2010), Santouridis and Trivellas (2010), Deng et al. (2009), Lim et al. (2006),
Picon et al. (2014) Lee (2005) Chang (2015) and Kim et al. (2016). Therefore, satisfaction is included as a
mediating variable in this study.
3. Methodology
We used convenience sampling method to collect data from 99 university students (Note 2) who are Mobilink
mobile users. 92 valid responses (Note 3) were analysed via EFA using SPSS (version 20) to identify the
underlying structure of items that make all scales, keeping in view cultural differences and the research setting
(Hadi et al., 2016a). The constructs were further verified via Partial Least Squares second generation Structural
Equation Modelling (PLS-SEM) technique using SmartPLS3.
4. Analyses and Results
4.1 Descriptive Statistics
The male students in this study consist 75% (n=69) of the sample, and female students involved 25% (n=23). 66
respondents were enrolled in a Master’s degree, while 26 were in a bachelor programme. 34% of the respondents
were research students, and the outstanding were mainly engaged in coursework. 81 of the respondents were
single, whilst 11 were married.
4.2 Unidimensionality
4.2.1 Factor Analyses for Constructs Used in Study
Ten items of service quality were analysed using SPSS. The data are suitable for factor analysis, as the
Kaiser-Meyer-Olkin value is 0.837, which exceeds the recommended minimum value of 0.5 (Kaiser, 1970; 1974).
Bartlett’s Test of Sphericity (BTS) for service quality is significant indicating a strong correlation.
Based on eigenvalues, two factors were retained for further analysis. The two factors of service quality explained
a total of 65.7% variance. The eigenvalue for the first factor was 4.55, and explained 45.5% of the variance in
the original data. The eigenvalue for the second factor was 2.02, and explained 20.2% of the variance. Oblimin
rotation method from oblique rotation technique was performed in detail (Kim & Mueller, 1994; Schmitt, 2011;
Hadi et al., 2016a; Fabrigar et al., 1999). Each method in oblique rotation generated a similar result.
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Table 1. Pattern and structure matrix for service quality construct
Items
Pattern matrix Structure matrix
Factor Factor
1 2 1 2
Customer services are always courteous
Customer services staff provide me with prompt service
Customer services staff have the knowledge to answer customers
Customer services staff always respond to customer request promptly
The service provider tells me exactly when services will be performed
The service provider provides its services at the time it promises to do so
The service provider always performs the service at the first opportunity
When a service provider promises to do something by a certain time, they do it
The service provider is dependable
When I have a problem, my service provider shows sincere interest in solving it
0.895
0.882
0.845
0.745
0.729
0.888
0.794
0.763
0.752
0.700
0.862
0.873
0.856
0.813
0.726
0.459
0.850
0.811
0.744
0.769
0.747
Source: Own survey results.
The data are suitable for factor analysis, as the Kaiser-Meyer-Olkin value for perceived value was 0.832, which
exceeds the recommended minimum value of 0.5. Bartlett’s Test of Sphericity for perceived value was
significant, indicating a strong correlation. One factor was retained based on eigenvalue for further analysis. The
factor explained a total of 60% variance. The eigenvalue for this factor is 3.0.
The seven items of customer satisfaction are suitable for factor analysis, as the Kaiser-Meyer-Olkin value was
0.805. Bartlett’s Test of Sphericity for customer satisfaction was significant, indicating a strong correlation.
Based on eigenvalue, one factor was retained for further analysis. The factor explained a total of 40.4% variance,
and its eigenvalue is 2.8.
The exploration of six items of customer loyalty shows that the data is suitable for factor analysis (KMO= 0.827,
Bartlett’s Test of Sphericity = 0.000). Based on eigenvalue, one factor was retained for further analysis. The
factor explained a total of 69% variance, and its eigenvalue is 3.5.
4.3 Measurement Models Evaluation
4.3.1 Convergent Validity
To assess construct validity in confirmatory factor analysis (CFA), we examined both convergent and
discriminant validity (Hadi et al., 2016b). Convergent validity was confirmed according to the average variance
extracted (AVE) and item loadings. All the items load above the threshold of 0.5. Similarly, all constructs explain
more than half of the variance, as the value for all constructs is above 0.5 (Fornell & Larcker, 1981). As the AVE
for the first order constructs (perceived value, reliability, responsiveness, customer satisfaction and customer
loyalty) are 0.6, 0.61, 0.68, 0.51, and 0.592, the measures of five reflective first order constructs have a higher
level of convergent validity. Customer satisfaction explains 0.49 of the error variance. Therefore, we propose a
limited number of items in future research.
Table 2. Examination of measurement models
LV S, loadings C, alpha CR AVE
Perceived Value
PV1
PV2
PV3
PV4
PV5
,73
,80
,80
,73
,78
,83
,88
,6
Responsiveness
RS1
RS2
RS3
RS4
RS5
,72
,86
,85
,83
,84
,88
,91
,68
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Reliability
Rl1
Rl2
Rl3
RL4
RL5
,72
,77
,76
,83
,82
,84
,89
,61
Service quality (HCM) ,88 ,89 ,645
Customer satisfaction
TS1
TS2
TS3
TS4
TS5
TS6
TS7
,74
,73
,66
,79
,58
,71
,77
,75
,82
,51
Customer Loyalty
CL1
CL2
CL3
CL4
CL5
CL6
,67
,75
,57
,84
,82
,89
,85
,89
,59
Source: Own survey.
4.3.2 Discriminant Validity
Discriminant validity is achieved if all square roots of the AVE (diagonal values) surpass the inter-construct
correlation. Table 3shows that for each individual construct, the square root of the AVE is greater than its
correlations with other constructs. It also shows that discriminant validity is ensured for this research, because
the square roots of AVE for perceived value, customer satisfaction, and customer loyalty are higher than
corresponding latent variable correlations (LVC).
Table 3. Discriminant validity results
Customer
loyalty
Customer
satisfaction
Perceived
value
Service
quality
Customer loyalty
Customer satisfaction
Perceived value
Service quality
0.770
0.735
0.609
0.565
---
0.635
0.620
0.581
---
0.775
0.696
---
0.675
Source: Own survey results.
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epted
epted
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4.4.4 Predictive Relevance (Q2)
To know whether the indicators of endogenous construct (reflective measurement model) can be predicted
accurately, we used predictive relevance (Q2). Blindfolding algorithm is used for predictive relevance (Hair et al.,
2014). It should be noted that blindfolding algorithm is selected only for the endogenous variables (customer
loyalty and customer satisfaction) in our case. Results indicate that the model is highly predictive, as the value of
predictive relevance is above the threshold (Note 5) of zero (Chin, 1988).
4.4.5 Effect Size (f2)
The effect size (Note 6) of customer satisfaction on customer loyalty (R2 value) is large and above the threshold
of 0.35. The effect size of service quality and perceived value is low. Thus, by omitting customer satisfaction
from the model, the change in R2becomes large.
4.4.6. Q2 Effect Size
The impact of predictive relevance of customer satisfaction on the endogenous latent variable (customer loyalty)
is large (Note 7) and above the threshold (Note 8) (0.448).
5. Discussion and Conclusion
In order to identify the importance of customer satisfaction in Pakistani telecommunication market, this study
hypothesised customer satisfaction as a mediator between service quality and customer loyalty, and between
perceived value and customer loyalty, this study analysed 92 valid responses. We first ensured that the model fits
the data; all regressors in the model explain 81% of the variance in customer loyalty. In the first step, results
found the direct effect without mediation to be significant, as the t-value for service quality and perceived value
was above the threshold at 5% (2.0 and 3.3 respectively). In the second step, we found that customer satisfaction
mediated the relationship, as the preceding relationship was no more significant. The strength of mediation was
assessed via VAF, which accounted for more than 80% of the variance, indicating customer satisfaction as a
mediator, fully mediates the relationships between service quality and customer loyalty, as well as between
perceived value and customer loyalty. The effect of customer satisfaction on customer loyalty was large, as the f2
value is above 0.35. The predictive relevance of endogenous construct was also assessed, and all exogenous
constructs in the model were found to have good predictive relevance for endogenous constructs, as Q2 is above
the threshold (0.156 for customer satisfaction; 0.467 for customer loyalty). The predictive relevance effect size
(q2) of customer satisfaction was large.
We conclude that the mobile industry in Pakistan (especially Mobilink) needs to build strategies to satisfy their
customers, as the loyalty of customers strongly depends on customer satisfaction.
5.1 Limitation and Future Studies
Our study is only limited to Pakistan and especially, KPK Province, therefore, comparative study for future
research is suggested to generalize the findings of this study. Other interactive variable could be included in
future research to test it combine effects. We used two components of service quality other may be included in
future research. Finally, the sampling method and calculated sample size which was based on G*Power analysis
could be of consideration in future research.
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Notes
Note 1. http://www.techjuice.pk/pakistan-mobile-network-industry/
Note 2. Sample size was calculated via G*Power software, to obtain the desired effect size (medium, 0.15),
power at 0.90, alpha at 0.05, and the number of predictors = 3, the power analysis calculated the required sample
size to be 99 (appendix A).
Note 3. Out of 99 questionnaires, 96 questionnaires were received. 92 valid observation was analysed for further
investigation. 3 observation was dropped from study as the missing value on questionnaires were above the
threshold of 15% (Hair et al., 2014), we found one observation suspicious by means of straight lining which was
also taken out.
Note 4. Total effect=indirect effect direct effect; VAF=indirect effect/total effect *100; T-value for indirect
path=indirect effect/Std dev.
Note 5. Q2 for customer satisfaction is 0.156, and for customer loyalty is 0.467.
Note 6. f2 =R2included-R2
excluded/1-R2
included.
Note 7. q2 =Q2
included -Q2
excluded/1 - Q2
included.
Note 8. Guideline for f2 and Q2 effect size = 0.02, 0.15, and 0.35, representing small, medium, and large.
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Appendix A
Power analysis for minimum sample size.
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Total sample size
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... Researchers have examined 319 that Information Quality, Usability, and Trust are the three important factors for examining the rural citizen satisfaction on using e-governance services (Bhuvana & Vasantha, 2020b, c). Many academicians have stated that service quality and customer satisfaction seem to be an associated construct even they are distinct (Irfan et al., 2016;Parasuraman et al., 1994;Oliver & Gregg, 2017). Various factors of service quality are considered for analyzing the satisfaction of the customers based on the performance of the institution or an organization (Connolly & Bannister, 2007). ...
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... The classifiers used were Support Vector Machine (SVM), Naïve Bayes, and Deep Learning. Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input [16][17]. Those Machine Learning models will be applied using RapidMiner. ...
... Findings suggest that all the values are greater than 0.70 (Nunnally and Bernstein, 1978). As far as construct validity is concerned, all of the understudied items converged on their underlying factors, ensuring construct validity (Muhammad, Shamsudin, & Hadi, 2016). The Andrew F. Hayes (2012) process was applied to undertake parallel mediation analysis to ascertain the role of the mediators between the independent and dependent variables. ...
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... Findings suggest that all the values are greater than 0.70 (Nunnally and Bernstein, 1978). As far as construct validity is concerned, all of the understudied items converged on their underlying factors, ensuring construct validity (Muhammad, Shamsudin, & Hadi, 2016). The Andrew F. Hayes (2012) process was applied to undertake parallel mediation analysis to ascertain the role of the mediators between the independent and dependent variables. ...
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The literature to date on the topic is inconsistent and lacks maturity. Thus, to promote the discipline by establishing mechanisms, the current study shed light on how employer can attract and retain employees through CSR by affecting their affective commitment and organizational citizenship behavior, which to our understanding leads to hotel service innovation performance. Drawing on Resource-based view and Social Exchange Theory this study analyzed 155 hotel managers' valid responses via PROCESS macro. Findings of the study revealed that CSR do affect service innovation performance. However, this complex relationship is parallel mediated by Strategic Human Resources, as Human Resources in its true sense are rare, valuable, inimitable and non-substitutable. The study advance knowledge in the area of service innovation in hospitality industry. The study concluded that CSR is promising area that generates innovation within the framework of SHRM.
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