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EJIM
21,3 Service innovation and customer
satisfaction: the role of customer
value creation
402 Mahmoud Abdulai Mahmoud
Department of Marketing and Entrepreneurship, University of Ghana,
Received 7 September 2017
Revised 20 November 2017 Accra, Ghana
Accepted 20 November 2017 Robert E. Hinson
Department of Marketing and Entrepreneurship, University of Ghana,
Accra, Ghana and
University of the Free State Business School, Bloemfontein, South Africa, and
Patrick Amfo Anim
Department of Marketing and Entrepreneurship, University of Ghana,
Accra, Ghana
Abstract
Purpose – The purpose of this paper is to explore the relationships between service innovation, customer value
creation (CVC) and customer satisfaction (CS) with specific emphasis to Ghanaian telecommunication operators.
Design/methodology/approach – Assuming a positivist philosophical approach with a quantitative data
analysis technique, the study samples 510 registered adult customers of at least one telecommunication network
in Ghana. An exploratory factor analysis, confirmatory factor analysis and structural equation modeling were
used to assess and confirm the proposed scales validity and the relationships of the research model.
Findings – The study unveiled that a service firm’s ability to achieve CS is dependent on how
telecommunication operators harness and deploy their service innovation activities. In addition, the study
showed that CVC mediates the relationship between service innovation and CS. Thus, service innovation
must create value for customers in order to enhance CS.
Practical implications – By relating the study findings to firms’ innovation strategies, managers can
improve the strength of their service offerings to achieve CS by spending more on consumer research, market
research and increased customer interactions.
Originality/value – Considering the uniqueness of this study in a Ghanaian context, the research draws on
two influential theories, which are signaling theory and expectation disconfirmation theory to examine the
differential role played by service innovation in enabling telecommunication operators in Ghana, to create
customer value in order to achieve CS amidst the constraints in the business environment.
Keywords Innovation, Ghana, Customer satisfaction, Telecommunication industry, Service innovation,
Customer value creation
Paper type Research paper
1. Introduction
In a global competitive market place where consumers seek value in their daily purchases,
innovation has become contemporarily essential in both theory and practice. Innovation is
all about offering new or adapted solutions to customer needs or problems in such a way
that adds value as defined and used by customers (Vargo and Lusch, 2004; Michel et al.,
2008). In Sub-Saharan African countries, for which Ghana is not an exception, the
continuous growth in technology and its sophistication, coupled with the proliferation of
mobile phones, especially in the telecommunication industry (Aker and Mbiti, 2010), have
European Journal of Innovation
Management pushed many mobile network operators to build robust innovative service products in order
Vol. 21 No. 3, 2018
pp. 402-422 to gain consumer satisfaction.
© Emerald Publishing Limited Evidently, it can be seen that operators within the Ghanaian telecom sector have
1460-1060
DOI 10.1108/EJIM-09-2017-0117 introduced several innovative services in order to offer customers with variety of choices,
aside simply receiving and making phone calls (Mahmoud and Hinson, 2012). The purpose Service
of this move is to increase the satisfaction of customers toward their service offerings. innovation
The strength of achieving customer satisfaction (CS) is therefore acknowledged as a and customer
significant indicator of a service innovation success. Despite the tremendous benefits firms
acquire from satisfied customers, and the heavy investments made toward innovation to satisfaction
achieve these benefits, Wilke and Sorvillo (2005) explain that a vast majority of innovations
fail within the first three years of their introduction into the market place. In recognition of 403
this challenge, one key issue championed by marketing scholars, academics and
practitioners is acknowledging that the success of service innovation to achieve CS greatly
depends on customer value creation (CVC).
In the past, there have been some erroneous perceptions that innovation was a preserve of
manufacturing businesses, reflecting the fact that innovation theory has its roots in a time
where manufacturing was the major economic activity (Drejer, 2004). While services are seen
to be generating a lot more to support nations’ GDP and have outdistanced manufacturing
from an employment perspective (Menor et al., 2002), manufacturing has continued to
dominate innovation studies ( Jaw et al., 2010; McDermott and Prajogo, 2012). Quite
profoundly, an earlier taxonomical review of literature on innovation studies revealed a
paucity of research in the area of services, with little clear coverage of the subject area
(Küpper, 2001; Hauser et al., 2006; Jaw et al., 2010). Scholars such as Ettlie and Rosenthal (2012)
and Ostrom et al. (2010) have therefore championed a call for further extensively research into
the area of innovation in services. In an attempt to catch up with the pace of manufacturing
innovation, scholars have looked at different aspects of service innovation subject to the view
of bridging this gap (Flint et al., 2005; Oke, 2007; Yusif, 2012; Carlborg et al., 2014). In spite of
these attempts, a major area of service innovation literature that has not yet received massive
attention is the service innovation measurement, particularly from the customers perspective
( Janssen, 2011), as the above-mentioned studies mainly focused on management’s perception.
With respect to CVC, despite its centrality to marketing thought, research on the concept is
still nascent and in the early stages of conceptual development (Zhao et al., 2015; Cheng and
Chen, 2009; Brodie et al., 2009; Palmatier, 2008; Woodruff and Flint, 2006). Many companies
fail to meet customer expectations with respect to creating customer value (Cheng and Chen,
2009; Klingmann, 2007; Van Riel and Lievens, 2004), probably because managers are not
completely sure of what brings value to the customer, or how it is created. The apparent
confusion of how value is defined by different scholars lend to the fact that there is a real
importance for a study in value creation. This study makes an attempt to fill this gap by
investigating into the value creation process and consumption of value by the customer.
Contextually, the study is conducted in an emerging economy. The study sees a contextual
gap in the literature as empirical tests of potential business outcomes of innovation in service
firms are heavily biased to data originating in developed nations’ settings (e.g. Sundbo, 1997;
Grawe et al., 2009; Carlborg et al., 2014), meaning the benefits or costs of investing in
innovation in less developed societies are unknown. The study sees the scanty research into
innovation in service firms in emerging market contexts as telling, since the literature
indicates that the beneficial effects of a firm’s strategic orientation such as an innovation may
be context specific as opposed to being universally applicable (e.g. Li and Zhou, 2010).
This study is opportune, as there is increasing call by scholars for an extensive study
that focuses on innovation in services, which has arguable received scanty research focus
and its impact on CS within the African context (e.g. Li and Zhou, 2010). This paper,
therefore, sought to find answers to the following questions:
RQ1. What is the effect of service innovation in terms of new service concept (NSC), new
service process (NSP) and new technological systems (NTSs) on CS in Ghana’s
telecommunication industry?
EJIM RQ2. What is the relationship between service innovations, customer value creating and
21,3 CS in the telecommunication industry in Ghana?
RQ3. Does CVC have a negative or positive effect on CS in the telecommunication
industry in Ghana?
404 2. Theoretical foundation and hypotheses development
The theoretical foundation of this study is based on two influential theories. They are
as follows:
(1) signaling theory (ST); and
(2) expectation disconfirmation theory (EDT).
2.1 ST
ST and EDT have since become dominant contemporary approaches to the analysis of
innovation and satisfaction research (Pappu and Quester, 2016; Henard and Dacin, 2010;
Boulding and Kirmani, 1993; Kirmani, 1990; Erdem and Swait, 1998; Giese and Cote, 2000).
Far back from the time of Spence (1974), who was the first to formally model the signaling
equilibria, the theory as of today, has become the basis of many models, ideas, other theories
and hypotheses. Importantly, the ST provides a parsimonious clarification amongst
frameworks used in extant literature to explain the innovativeness-satisfaction relationship
(Henard and Dacin, 2010). This is so because of the complex nature of other theories, such as
the exchange theory, cues utilization theory and many others (Eisingerich and Rubera, 2010;
Kunz et al., 2011). The basic premise of the ST, as suggested by prominent scholars like
Spence (1974, 2002) and Stigler (1961), is that the market place is characterized by
information asymmetry or imperfect information. Thus, unlike the firm, consumers do not
have the full information required for judging the quality or value of an innovation in
services newly introduced into the market (Stiglitz, 2000). Firms therefore attempt to convey
information about the quality and value of their innovations to the market using signals.
Consumers therefore rely on variables such as advertising (Kirmani, 1990), brand name
(Erdem and Swait, 1998; Rao et al., 1999), price (Dawar and Sarvary, 1997) and warranty
(Boulding and Kirmani, 1993), as signals to infer value of these communicated innovations,
to determine their satisfaction or dissatisfaction levels.
2.2 EDT
EDT further provides explanation to how customers become satisfied with an
introduction of a new product or service offering. Expectancy disconfirmation theory is
built on the base of cognitive dissonance theory definition, which was developed by Leon
Festinger in 1957, to explain how dissonance between an individual’s cognition and reality
influence his/her subsequent cognition and/or behavior (Bhattacherjee and Premkumar,
2004). EDT measures CS from the difference between customer’s expectation and
experience in perceived products or services (Oliver, 1980; Patterson et al., 1996).
EDT has been used severally to examine various field of study, such as marketing
(Oliver, 1977, 1980; Diehl and Poynor, 2010; Santos and Boote, 2003), tourism
(Fallon and Schofield, 2003), psychology (Gotlieb et al., 1994), information technology
(Bhattacherjee and Premkumar, 2004; Bhattacherjee, 2001a, b; Hsu et al., 2006; Khalifa and
Liu, 2002), repurchase behavior and retention (Bhattacherjee and Premkumar, 2004;
Hsu et al., 2006; Patterson et al., 1996), and the airline industry (Chen, 2008) for a better
understanding of customer’s expectations and requirements for attracting their
satisfaction. The basic premise of the EDT model is that customers form expectation
before the purchase or use of an offering. These expectations are used as a frame of Service
reference in the evaluation/judgment of the actual performance perception. CS therefore innovation
emerges after the comparison between the perceived performance and pre-purchase/use and customer
expectations. Three possible outcomes occur at this stage: the customer becomes satisfied,
neutral or dissatisfied toward the new product or service offering. satisfaction
2.3 Service innovation and CS 405
The key to remaining competitive and surviving in the market is the firm’s ability to provide
products tailored to meet the needs of its customers. In a chain relationship, scholars have
suggested that the key to remaining competitive through meeting customer needs is
innovation (Darroch and McNaughton, 2002), as trends, customer needs and perceptions
keep evolving with the passage of time. In this respect, firms in attempt to produce superior
value at all times have to adopt the practice and culture of innovation. This is to say that
innovation explicably increases the chances of the firm producing to meet the very need of
customers, consequently offering opportunity for the firm to satisfy its customers.
2.4 Service innovation and CVC
In today’s competitive global market, companies should focus on their customers’ needs to
gain an understanding of the buyer’s entire value chain (holistic needs), referring to not only
as it is today (the current needs), but also as it changes/evolves over time (anticipated needs)
(Slater and Narver, 1994). Companies should then use the understanding of the buyer’s value
chain to mix and match their various products and/or services (service package/bundle),
refer to adaptations of existing services or newly launched services, and adjust or evolve
the processes to deliver and maintain these services (like improved service delivery rates), to
meet the customer’s needs with the goal to influence the customers perceived value of
the offering. Service innovations can be regarded as a value creating activity (Slater and
Narver, 1995).
For service providers, innovating services in such a way that enables them to serve their
customer’s present and future needs which adds to their perceived value is what establishes
a firm’s competitive advantage (Kandampully and Duddy, 1999). Besides, innovation on its
own is of lesser significance, as it is the value of the innovation as perceived by the customer
that provides the advantage of the offering (Chapman et al., 2002). According to Tether et al.
(2001), service innovations typically transform the state of the customer’s perceptions
(De Jong et al., 2003). This influence will add to the customer’s perception of the value of the
service, as has also been suggested in other studies (Flint et al., 1997; Kandampully and
Duddy, 1999). The view of service innovation as a value creating activity or one that
influences/adds to the perceived value of the offering is important, as it suggests and
emphasizes the interrelation between service innovation and perceived customer value.
2.5 Service innovation, CVC and CS
Within this study, CS has been defined as a response to an evaluation of perceived product
or service performance, based on the customer’s judgments of the value that has been
created for them (Flint et al., 1997). While perceived customer value has been defined as the
customer’s assessment of the value that has been created for them by a supplier, given the
trade-offs between all relevant benefits and sacrifices in a specific use situation (Flint et al.,
2002). When looking at these definitions, one can easily confuse one with the other.
Although they show similarities, they are different.
Based on the comparison of the definitions, theoretically, perceived customer value
antecedes CS, as the value judgment (perceived value) comes before the evaluation response
(satisfaction). The theoretical relation between perceived customer value and CS has been
empirically confirmed as several service marketing literature have shown the CVC concept
EJIM to be an important factor in gaining CS (Zeithaml, 1988; McDougall and Levesque, 2000;
21,3 Cronin et al., 2000; Wang and Ahmed, 2004; Turel and Serenko, 2006; Hume and
Sullivan Mort, 2008; Kuo et al., 2009). The argument for creating customer value as a
significant driver of CS is that when customers perceive higher levels of value in an offering
they are likely to feel positive about their consumption experience and purchase decision
(Oh, 2000; Zeithaml, 1988).
406 From the ensuing discussion, the researchers propose a conceptual model presented in
Figure 1, which theorizes that firm’s innovation in services acts as an antecedent to CVC to
enable firms gain CS. It can, therefore, be hypothesized that:
H1. A NSC innovation has a positive and significant relationship on CS.
H2. A NSP has a positive and significant relationship on CS.
H3. A NTS innovation has a positive and significant relationship on CS.
H4. The greater the fit between a service firm’s CVC and service innovation, the greater
the CS.
H5. There is a positive and significant relationship between CVC and CS.
3. Methodology
This paper adopts a positivist methodological paradigm in formulating research questions,
hypothesis and empirically testing constructs under careful controlled circumstances
(Boateng, 2016). In this quantitative study, a survey design was used with a semi-structured
questionnaire as the primary data collection instrument. The survey strategy is deemed
appropriate due to the fact that it is usually associated with a deductive approach and
explanatory research of this nature. Adopting a convenience sampling technique, the study
sampled 510 registered adult customers of at least one telecommunication network in the
Greater Accra metropolitan area out of a target population of all registered adult subscribers
of mobile telecommunication network in Greater Accra.
Customer value
creation
functional value
New service emotional value
concept social value H5
innovation
H4
Customer
satisfaction
New service cumulative view
process innovation H1-H3 outcome view
New technological
system innovation
Figure 1.
Conceptual framework
Source: Researchers’ own construct (2017)
Statistical Package for Social Sciences (SPSS V.20) was used for the preliminary coding and Service
inputting of the raw data as well as for data cleaning and exploratory factor analysis (EFA). innovation
Drawing on the view of Pallant (2011), three main steps were followed in carrying out the and customer
EFA: “(1) Assessment of the suitability of the data for factor analysis, (2) Factor extraction,
and (3) Factor rotation and interpretation.” After EFA, a confirmatory factor analysis (CFA) satisfaction
was performed before onward transferal to AMOS 22 for further analysis. It must be
mentioned that there were two different sets of data responses used for the EFA and CFA; 200 407
responses were used for the EFA while 310 were used for the CFA. Thus, two major phases
were undergone in carrying out the analysis. The proposed model was, therefore, assessed for
model fitness using the structural equation modeling (SEM) via Amos 22. Assessing model fit
involves the interpretation of how well the conceptualized model fits the empirical research.
The process is comparative in nature because it involves choosing between numerous fit
indices that subjectively indicate whether the data fit the theoretically postulated model
(Bagozzi and Yi, 2012; Hair et al., 2010; Schumacker and Lomax, 2004). Scholars have
proposed a number of fit indices. However, there are at least two main conventions for the
assessment of model fit that are apparent in literature; thus, the assessment of the absolute fit
of the model and the assessment of the comparative fit (Tanaka, 1993).
Model fit criteria commonly used in absolute fit are χ2, goodness-of-fit index (GFI),
adjusted goodness-of-fit index, root mean square residual and root mean square error of
approximation (RMSEA). These criteria are based on differences between the observed and
model-implied correlation or covariance matrix (Hair et al., 2014). Comparative fit deals with
whether the model being considered is better than a competing model in accounting for
observed data. Comparative fit assessment is based on the examination of a “baseline”
model in comparison with theoretically derived models (Kelloway, 1998). Some criteria in
this category include normed fit index (NFI), comparative fit index (CFI) and the relative
non-centrality index. The following fit indexes were used to evaluate how well the
measurement model fit the data collected, with each one having conventionally acceptable
values: RMSEA⩽ 0.08, GFI⩾ 0.90, NFI⩾ 0.90 and CFI⩾ 0.90 (Hair et al., 2014; Bagozzi and
Yi, 2012; Hu and Bentler, 1999). The sufficiency of the theorized model’s creation of a
covariance matrix is evaluated by the χ2 goodness-of-fit value; it also estimates coefficients
compared with the observed covariance matrix. However, since the value of χ2 is affected by
the sample size, a large number of participants can cause χ2 to be inflated when assessing
model fit (Hu and Bentler, 1999). Many researchers have applied the method that divides the
value of χ2 by degrees of freedom instead of relying only on the overall χ2and its associated
test of significance. It is typically suggested that a normed χ2 of less than 3 is favorable for a
large sample. These fit indices were employed to assess the strength and acceptability of the
construct measurements. The selection of these fit indices was based on the classification
proposed by Kline (2015) and Byrne (2013) as being the most commonly accepted criteria in
social sciences.
To ensure reliability of the current study, two main criteria were adopted which include
Cronbach’s α and composite reliability (CR), which are widely and mostly used in SEM
(Hair et al., 2016). To ensure validity in this study, construct validity measures such as
convergent validity (Rezaei and Ghodsi, 2014; Rezaei, 2015) and discriminant validity
(Rezaei, 2015; Rezaei and Ghodsi, 2014) were employed. Discriminant validity used construct
correlations and cross-loading criterion while convergent validity employed average
variance extracted (AVE) and factor loadings (Kim et al., 2016; Rezaei, 2015).
4. Results
Before performing the actual analysis of the main data, preliminary data analysis (PDA)
was done. During the PDA, data sets and variables were cleaned and cleansed to eliminate
unengaged responses, outliers and replace missing data. Data analysis was then conducted
EJIM in different distinct phases. First the paper shows the descriptive statistics of measurement
21,3 items; depicts the exploratory and CFA; tests for hypothesis; examines the mediating effect
of CVC on service innovation and CS; and finally discusses the findings.
4.1 Descriptive statistics of measurement items
Table I shows the means and standard deviations of the various variables used. The highest
408 mean was 4.033 (my mobile service has automated service options) while the lowest was
2.868 (using my mobile service gives me pleasure). This gives an indication that
telecommunication network subscribers are satisfied with their network providers
automated service options that give them adequate and frequent information through ATM
alerts, SMS alerts and many others. However, subscribers indicated that they gain very little
pleasure with their mobile service. The 35 variables displayed in Table I represented the
components of the five main constructs depicted in the conceptual framework for the study:
NSC, NSP, NTS, CVC and CS.
4.2 EFA
The 35 items used for the scales on the conceptual constructs were factor analyzed and
subjected to principal component analysis (PCA) using SPSS version 22. Prior to performing
PCA, the suitability of data for factor analysis was assessed. The Kaiser-Meyer-Olkin value
was 0.878, exceeding the recommended value of 0.6 (Kaiser, 1970) and Bartlett’s (1954) test
of sphericity reached statistical significance (approx.: χ2¼ 5,469.993, df 595, sig. 0.000). This
confirms that there was a significant correlation among the variables; thus, factor analysis
was appropriate. The PCA revealed the presence of seven components with eigenvalues
exceeding 1. The seven-component solution altogether explained 63.088 percent of the
variance, with the highest component contributing 10.842 percent and the lowest component
contributing 7.923 percent.
To assist in the interpretation of these seven components, varimax method of rotation
was performed on the 35 variables to examine the number of strong loadings and ascertain
the specific variables which loaded substantially onto the various components. The variable
loadings for EFA are considered high if they are all 0.7 or greater to be retained for analysis,
although some scholars suggest a considerable loading value of 0.5 to be adequately strong
(Hair et al., 2014). Out of 35 variables rotated, 33 loaded perfectly onto 7 components with a
threshold of 0.6. Table II presents a comparison of the principal component extraction of the
various rotation methods as well as the internal consistency measures on the final retained
variables of the various constructs.
Having established an internal consistency in the final structures of the construct scales
through the EFA, the second phase of the study was conducted. The surviving items were
designed into a new questionnaire in order to test the relationships among the constructs of
the study. A 33-item questionnaire was therefore re-designed to collect data from 310 adult
registered subscribers of at least one telecommunication network in Greater Accra,
exclusive of those used for the exploratory design. The subsequent section presents the
results obtained from the CFA analysis for the purpose of establishing the best possible
underlying structure for the conceptual model of the study.
4.3 CFA
The measurement models of the five constructs (NSC, NTS, NSP, CVC and CS) were
assessed through a CFA. It must be noted that the variable codes stipulated in the EFA were
maintained in the confirmatory stage. As a result, the codes represent the scale statements
indicated earlier in this paper. Consequently, the original measurement model was then
subjected to modification according to the sizes of factor loadings, cross-loadings,
Service
Variable SE
Scale items codes Mean mean SD innovation
and customer
My mobile service has creative service packages (voice, SMS and NSC1 3.454 0.069 1.207
internet combinations) satisfaction
My mobile service has flexible service package options (client NSC2 3.391 0.067 1.169
customization)
My mobile service is noticeably different in concept and design, compared NSC3 3.266 0.068 1.179 409
to preceding services
My mobile service is a totally different service experience compared to NSC4 3.194 0.064 1.122
preceding services
My mobile service is noticeably different in concept and design, compared NSC5 3.197 0.070 1.216
to competing services
My mobile service has online service options (procedures, support and NSP1 4.003 0.055 0.956
usage history)
My mobile service has automated service options NSP2 4.033 0.046 0.804
My mobile service provider uses modern interaction media (creative NSP3 3.934 0.050 0.869
website, social media)
My mobile service provider has mobile shops at special occasions or events NSP4 3.655 0.054 0.93
My mobile service provider offers quick and easy call center support NSP5 3.599 0.060 1.048
Mymobile service hasmany innovative features (SMS to e-mail, calling circles) NTS1 3.484 0.058 1.015
My mobile service provider offers the latest user equipment NTS2 3.112 0.063 1.096
My mobile service provider is always the first on the market with the NTS3 3.589 0.054 0.947
latest technology
My mobile service is based on the latest technology applications NTS4 3.635 0.054 0.934
My mobile service provider shows its efforts for service NTS5 3.474 0.059 1.024
quality improvement
My mobile service usage makes me feel good CVCEM1 3.375 0.063 1.092
I find my mobile network service engaging CVCEM2 3.030 0.063 1.097
Using my mobile service gives me pleasure CVCEM3 2.868 0.063 1.103
Using my mobile service makes me feel relaxed CVCEM4 3.247 0.062 1.079
Using my mobile service is an enjoyment CVCEM5 3.135 0.063 1.092
My mobile service is worth the price I pay CVCFU1 3.763 0.061 1.064
My mobile service is worth the technical quality CVCFU2 3.530 0.062 1.087
My mobile service is worth the customer service CVCFU3 3.424 0.061 1.069
My mobile service offers consistent quality of service CVCFU4 3.595 0.059 1.026
Using my mobile service is not a financial burden or stress CVCFU5 3.434 0.060 1.054
My mobile service usage makes a good impression in my social group CVCSO1 3.530 0.056 0.974
My mobile service usage gives me a sense of belonging CVCSO2 3.480 0.057 0.992
My mobile service usage helps me to feel accepted by others CVCSO3 3.151 0.061 1.064
Using my mobile service gives me social approval and recognition CVCSO4 3.526 0.058 1.008
My mobile service usage improves the way I am perceived by other people CVCSO5 3.319 0.059 1.025
What is your overall satisfaction level regarding the mobile call CSO1 3.500 0.059 1.037
service (voice)?
What is your overall satisfaction level regarding the mobile internet CSO2 3.454 0.060 1.043
service?
What is your overall satisfaction level regarding the additional mobile CSO3 3.434 0.058 1.013
services (SMS, voice mail)?
What is your overall satisfaction level regarding the customer service? CSO4 3.217 0.061 1.056 Table I.
What is your overall satisfaction level regarding the total mobile CSO5 3.171 0.063 1.104 Descriptive statistics
service offering? of variables
measurement errors and correlation between measurement errors. In the case of this
investigation, the AMOS software output suggested modification of some items via
stage-by-stage re-specifications of some weak variables. As a result, scale items were rather
dropped systematically to ensure that the deletion of each item was necessary. During the
modification of the original unfitted model, six items (CVCF1, CVCFU3, CVCEM3, CVCEM5,
EJIM
21,3 Principal component loadings and internal consistenciesVariance Item-total α if item is
Items Variable codes Varimax loadings explained Cronbach’s α KMO correlation deleted
Factor 1 NSC1 0.810 74.066 0.912 0.846 0.756 0.896
NSC2 0.840 0.796 0.888
NSC3 0.860 0.810 0.885
410 NSC4 0.843 0.785 0.891
NSC5 0.809 0.736 0.901
Factor 2 CVCFU1 0.757 61.994 0.846 0.836 0.661 0.812
CVCFU2 0.788 0.748 0.788
CVCFU3 0.707 0.580 0.834
CVCFU4 0.669 0.642 0.818
CVCFU5 0.696 0.638 0.818
Factor 3 CVCEM1 0.638 63.674 0.856 0.842 0.630 0.836
CVCEM2 0.800 0.732 0.810
CVCEM3 0.693 0.570 0.852
CVCEM4 0.790 0.716 0.814
CVCEM5 0.780 0.706 0.817
Factor 4 NSP1 0.670 59.149 0.822 0.827 0.578 0.798
NSP2 0.801 0.681 0.773
NSP3 0.789 0.649 0.778
NSP4 0.694 0.578 0.798
NSP5 0.685 0.618 0.789
Factor 5 CS1 0.711 64.170 0.812 0.764 0.617 0.770
CS2 0.778 0.680 0.739
CS3 0.808 0.691 0.735
CS4 0.734 0.537 0.807
Factor 6 CVCSO2 0.715 63.511 0.807 0.757 0.661 0.739
CVCSO3 0.625 0.585 0.776
CVCSO4 0.707 0.687 0.726
CVCSO5 0.687 0.561 0.786
Factor 7 NTS1 0.705 54.503 0.790 0.782 0.563 0.752
Table II. NTS2 0.687 0.572 0.751
Rotated component NTS3 0.636 0.581 0.747
matrix and internal NTS4 0.638 0.544 0.759
consistencies NTS5 0.685 0.587 0.745
CVCSO3 and CVCSO5) were deleted from CVC, two items (NSP1, NSP4) from NSP, another
two items (NSC4, NSC5) from the NSC and one item (CS4) from CS. Thus, 11 items were
eliminated after the CFA which left the new purified constructs with 22 items which
provided the best fit indices.
4.4 Reliability and validity test
Research scholars such as Bagozzi and Yi (2012) have specifically suggested that “testing of
a structural model may be meaningless unless it is established that the measurement model
holds; if the chosen indicators for a construct do not measure that construct, the specified
theory must be modified before it can be tested.” Table III shows the robustness test result
of the study constructs for the model to check for model fit.
Table III depicts the PCA which revealed the presence of seven components, internal
consistencies of items, reliability test and validity test. Data reliability was tested via
Cronbach’s α and CR. For CR, the values recorded a range between 0.70 and 0.90. Evidently,
all CR values were above 0.6 meeting the acceptable recommended limits stipulated by
extant renowned research scholars (Hair et al., 2016). Again, the Cronbach’s α coefficients
Service
Constructs Items Standardized loadings CR AVE Cronbach’s α innovation
New technological system (NEWTS) NTS1 0.750 0.902 0.649 0.913 and customer
NTS2 0.800
NTS3 0.900 satisfaction
NTS4 0.810
NTS5 0.760
Emotional value creation (EMVC) CVCEM1 0.770 0.810 0.588 0.805 411
CVCEM2 0.830
CVCEM4 0.690
Functional value creation (FUNVC) CVCFU2 0.750 0.834 0.626 0.832
CVCFU4 0.830
CVCFU5 0.800
New service process (NEWSP) NSP2 0.730 0.769 0.525 0.759
NSP3 0.730
NSP5 0.720
Customer satisfaction (CUSTSA) CS1 0.750 0.812 0.591 0.810
CS2 0.850
CS3 0.710
Social value creation (SOCVC) CVCS02 0.750 0.707 0.547 0.705
CVCSO4 0.730 Table III.
New service concept (NEWSC) NSC1 0.790 0.796 0.566 0.794 Validity and reliability
NSC2 0.710 results for CFA final
NSC3 0.760 measurement model
ranged from 0.70 to 0.91. The result indicates appreciable values above the accepted
0.5 desirable level (Coakes et al., 2008; Nunnally, 1978). The researchers ascertained the
internal consistency to draw validity conclusions for the data using AVE and factor
loadings. The AVE values fell between 0.50 and 0.65, exceeding the acceptable stipulated
limits of 0.5 (Fornell and Larcker, 1981; Hair et al., 2016). The factor loadings ranged from
0.69 to 0.90, which exceeded the acceptable limit of 0.6, showing a strong reliability status of
the items (Hair et al., 2016).
4.5 Measurement model fit indices
In evaluating the appropriateness of the measurement model fit in relation to the data
collected, conventionally acceptable values including RMSEA⩽ 0.08, GFI⩾ 0.90, NFI⩾ 0.90
and CFI⩾ 0.90 (Hair et al., 2016; Bagozzi and Yi, 2012; Hu and Bentler, 1999) were used. The
sufficiency of most theorized model’s creation of a covariance matrix is evaluated by the χ2
goodness-of-fit value; it also estimates coefficients compared with the observed covariance
matrix. The selection of these fit indices was based on the classifications proposed by Kline
(2015) and Byrne (2013) as being the most commonly and widely accepted criteria in social
science research. The model fit indices revealed a good fit to the data (RMSEA¼ 0.04,
NFI¼ 0.92, RFI¼ 0.90, IFI¼ 0.97 and CFI¼ 0.97). The χ2-statistic was 1.45 representing the
normed χ2 value. This value fell within the acceptable limit between 0 and 3. Figure 2 shows
the final measurement model.
4.6 Second-order construct measurements model
This study also modeled one second-order constructs, namely, service innovation, and
assessed its measurement model. The outer loadings for NTS innovation (0.799), NSP
innovation (0.878) and NSC innovation (0.853), AVE (0.559), Cronbach’s α (0.898) and CR
(0.868) were evaluated for the second-order reflective constructs. Composite reliabilities
EJIM e1 NTS3 0.90
21,3 e2 NTS4 0.810.79
0.34 e3 NTS2 NEWTS0.70
0.54 e4 NTS5 0.75
e5 NTS1
0.25
e6 CVCEM2 0.83
412 0.77e7 CVCEM1 0.69 EMVC 0.23
e10 CVCEM4
0.52 0.34
e11 CVCFU2 0.75
0.82
e12 CVCFU4 0.350.80 FUNVC 0.22
e13 CVCFU5
0.36
0.37
0.25
0.32
e16 NSP2 0.73
0.72 0.34
e17 NSP3 0.22NEWSP
0.72
e19 NSP5
0.36 0.39 0.69
e21 CS3 0.710.84
e22 CS2 0.75 CUSTSA 0.37 0.53
e23 CS1
0.31
0.40
e26 CVCS02 0.75
0.72 SOCVC
e27 CVCS04 0.41
0.46
Figure 2.
e31 NSC1 0.79
Final 0.71
measurement model e32 NSC2 0.70 NEWSC
e35 NSC3
exceeded the 0.6 benchmark and Cronbach’s α exceeds 0.70 (Fornell and Larcker, 1981;
Vandenbosch, 1996).
The researchers also assessed the fit indices of the second-order construct modeled. The
model fit indices revealed a good fit to the data (RMSEA¼ 0.042, NFI¼ 0.966, RFI¼ 0.952,
IFI¼ 0.988 and CFI ¼ 0.988). The χ2-statistic was 1.545 representing the normed χ2 value.
This value fell within the acceptable limit between 0 and 3.
4.7 The structural model
The structural model conducted in this study was intended to test some hypothetical
propositions based on the conceptual framework for this research. For this study, the R2
value for service innovation, NSC, NSP and NTS was 0.221 for CS. The R2 values show
that a service firm’s innovation in NSC, NSP and NTS could have a moderate predictive
capacity in determining CS. The path coefficients were assessed based on signs and
magnitude. The path coefficient and t-value for significant (α) level of 0.05 is 1.96 and
α level of 0.01 is 2.575. Specifically, a service firms NSC innovation related significantly to CS Service
( β¼ 0.31, po0.01) with a t-value W2.575 (3.932). This indicates that an innovation in a firm’s innovation
service concept significantly influences CS. Customers are satisfied with their mobile network and customer
provider’s new ideas or solutions to a problem. Therefore, H1 was supported. Also, a service
firm’s NSP innovation related significantly to CS ( β¼ 0.22, po0.01) with a t-value W2.575 satisfaction
(2.797). This indicates that an innovation in a firm’s service process significantly influences
CS. Customers are satisfied with the extent to which their mobile service providers alter their 413
service systems to enhance value delivery. Therefore, H2 was also supported. However,
a service firm’s NTS innovation failed to relate significantly to CS ( β¼ 0.05, pW0.05) with a
t-valueo2.575 (0.700). This indicates that an innovation in a service firm’s technological
systems has no significant influence on CS. Customers are not satisfied with their mobile
networks latest technological options. Therefore, H3 was not supported. The extent of
variance of the endogenous variables accounted for by the hypothesized influences was as
follows: R2 for CS¼ 0.22. This is also displayed in Table IV.
4.8 Test for mediation
In testing for the mediation, the second-order construct modeled above was used as it
combined all the three individual construct, which are NSC, NSP and NTSs to comprise of
service innovation as operationalized earlier for this study. To establish meditation effects,
all significant parameters were tested using guidelines from Baron and Kenny (1986) for
partial and full mediation conditions. A number of regression equations were estimated. In
this study, first, CVC (mediator) was regressed on service innovation (independent variable)
and it showed a significant effect (service innovation → CVC, β¼ 0.60, p¼ 0.000). Second,
CS (dependent variable) was regressed on service innovation (independent variable) and
this showed a significant effect (service innovation → CS, β¼ 0.59, p¼ 0.000). Third, CS
(dependent variable) was regressed on the service innovation (independent variable) and
CVC (mediator) and the effect was significant (service innovation → CVC → CS, β¼ 0.60,
p¼ 0.000 was recorded for the first path, β¼ 0.24, p¼ 0.000 was recorded for the second
path). The results of the regressions are presented in Table V.
The assumption was that if all the three relationships are significant, then mediation
testing would be possible. From Table V, all three relationships tested were significant.
Hypothesis Unstandardized regression path β estimate t-value p-value Hypothesis results
H1 New service concept→ customer satisfaction 0.31 3.93 *** Supported
H2 New service process→ customer satisfaction 0.22 2.80 0.01 Supported
H3 New technological system → customer 0.05 0.70 0.48 Not supported
satisfaction
Notes: RMSEA¼ 0.039, NFI¼ 0.953, RFI¼ 0.939, CFI¼ 0.984, χ2/df¼ 1.472. p-values of ***Represent 0.000 Table IV.
significance level Hypothesis testing
Model Unstandardized regression path β estimate t-value p-value
1 Service innovation → customer value creation 0.60 8.31 ***
2 Service innovation → customer satisfaction 0.59 8.16 ***
3 Service innovation → customer satisfaction 0.59 8.16 0.02 Table V.
4 Customer value creation → customer satisfaction 0.24 4.21 *** Mediation
Notes: p-values of ***Represent 0.000 significance level possibility test
EJIM Now, determining the type of relationship, if service innovation on CS is less in the third
21,3 model than in the second model, then the mediation is said to be partial. Full mediation holds
if the service innovation has no effect when the CVC is controlled. Even though the t-values
in the third and second models were the same (8.16), the significant level for model
two ( p-value¼ 0.00) was stronger than that of model 3 ( p-value¼ 0.02). This indicates a
partial mediation.
414 After determining that a customer value created by a service firm partially mediates the
relationship between their service innovation and CS, a systematic analysis was applied to
the structural model to provide a comprehensive representation of H4 and H5. First,
the path coefficients for the relationships service innovation and the CS was examined.
The effect of service innovation on CS was positive and statistically significant ( β¼ 0.59,
p-value¼ 0.02) with a t-value W2.575 (8.16). Second was the inclusion of the mediator.
The second and third objectives of this study were to identify the mediating role of CVC and
the relationship between CVC and CS.
The results indicate that CVC partially mediated the relationship between service
innovation and CS as both the direct effect with mediator (service innovation→ CVC→ CS,
path 1: β¼ 0.48, p¼ 0.00; path 2: β¼ 0.24, p¼ 0.02) and the indirect effect with mediator
(service innovation→ CS, β¼ 0.59, p¼ 0.00) were significant. The results imply that service
innovation influences CS even without creating customer value. However, CVC improves the
relationship between service innovation and CS since it has a significant positive
relationship with CS ( β¼ 0.24, p¼ 0.00). This provides support for H4 and H5. Table VI
presents a summary of the mediation test.
5. Discussions
The key objective of this study is primarily to explore the utilization of service firms’
innovation to achieve CS on service firms’ CVC efforts. The fundamental question
underlying the current study is thus articulated as: How do service firms realize
CS through the deployment of their service innovations amidst the constraints of their
environment? In an attempt to address this question, the study sought to evaluate three
key questions which cumulated into both theoretical and empirical investigations in the
previous chapters.
5.1 Influence of service innovation on CS
The first objective of this study sought to examine the influence of Ghanaian
telecommunication operators’ innovation, that is, in a NSC (H1), or a NSP (H2), or a NTS
Unstandardized regression path β estimate t-value p-value Hypothesis results
(Direct effect) without Service innovation → customer 0.59 8.16 *** Supported
mediator satisfaction
H4 (direct effect) with Service innovation → customer 0.59 8.16 0.02 Supported
mediator satisfaction partial mediation as
Customer value creation → 0.24 4.21 *** both direct and
customer satisfaction indirect paths are
H5 (indirect effect) Service innovation → customer 0.59 8.16 *** significant
with mediator satisfaction
Customer value creation → 0.24 4.21 *** Supported
Table VI. customer satisfaction
Summary of Notes: RMSEA¼ 0.043, NFI¼ 0.95, GFI¼ 0.98, TLI¼ 0.96, CFI¼ 0.99, χ2/df¼ 1.43. ***p-value: 0.001
mediation test (two-tailed)
(H3) on CS. Supporting H1, the findings of the study showed that Ghanaian Service
telecommunication operators’ innovation in a NSC has a positive and significant effect on innovation
CS ( po0.01). Thus, as firms continuously innovate and strive for the creation of new ideas, and customer
products and services that have the potential of solving customer problems satisfactorily,
customers’ consideration to repurchase the service/product is assured. This finding is satisfaction
congruent to that of some scholars (Edvardsson et al., 1995, 2005), who posit that
for a service firm to increase CS, a service concept innovation that has a higher degree 415
of solving customer problems, not only providing information on the service system, must
be implemented.
Supporting the proposed H2, the results revealed that Ghanaian telecommunication
operators’ outstanding service process innovations have a positive and significant effect on
CS ( po0.05). The results could be interpreted as “a service firm’s deployment of an
enhanced way of customer interaction and outstanding service delivery process to meet
customer needs, guarantees customer satisfaction.” This finding confirms Iacobucci et al.’s
(1994) assertion that during interactions and service delivery that take place between a
customer and a firm, customers form perceptions about their service encounter, and
subsequently evaluate whether the service experience met their expectations by providing
value for them before they consider repurchase intentions.
Disconfirming the proposedH3, the findings indicated that Ghanaian telecommunication
operator’s NTS innovation does not significantly relate to CS ( pW0.05). The possible
translation for this outcome would be that just the mere introduction of a new technology
application does not guarantee CS. In order to enhance CS in a more significant manner,
innovation in technological systems must be able to meet customer needs satisfactorily, as
well as provide outstanding value to customers. Confirming this finding, Joseph and Stone
(2003) postulate that an organization’s technological innovations will engender CS if only
they are responsive to advances in the technological environment and meet the expectations
and needs of customers.
5.2 Mediating role of CVC on service innovation and CS
The second objective of the study sought to examine the mediating role of CVC on the
relationship between their service innovation and CS. To achieve this objective, the
researcher first modeled a second-order construct named service innovation, which
combined all three construct, that is, NSC, NSP and NTS, to examine its effect as a whole
on CS. It was not surprising that service innovation positively and significantly influenced
CS ( po0.01). This empirical result therefore can be interpreted that the more Ghanaian
telecommunication operators engage in robust service innovation practices that meet
customer needs, the higher the level of their CS toward their service offering. This
conclusion is consistent with previous studies (Georghious and Keenan, 2006; Georgantzas
and Acar, 1995; Auluck, 2002). Confirming the findings of earlier studies, Byerlee et al. (2002)
suggest that through service innovation, companies can create highly satisfied customers
who are loyal to an organization.
For the mediation effect, the researcher followed the suggestion of Baron and Kenny
(1986). From the study, CVC was found to have a mediating effect on the relationship
between service innovation and CS. Concerning the type of mediation, the results of the
study showed that CVC partially and significantly mediates the relationship between
service innovation and CS ( po0.01). This indicates that telecommunication operators’
innovations alone can influence CS even without creating customer value. However,
a service innovation that creates value for customers can better improve and increase
CS toward the service operator’s offerings. Confirming this result, the findings of
Chapman et al. (2002) indicate that innovation on its own is of lesser significance, as it is the
value created by the innovation as perceived by the customer that provides the advantage of
EJIM the offering. According to Tether et al. (2001), service innovations typically transform the
21,3 state of the customer’s perceptions (De Jong et al., 2003). This influence will add to the
customer’s perception of the value of the service, as has also been suggested in other studies
(Flint et al., 1997; Kandampully and Duddy, 1999). Additionally, Kandampully and Duddy
(1999) severally suggest that a service firms’ innovation that create, serve and add value to
their customers’ present and future needs is what establishes the firms’ competitive
416 advantage through increased CS.
5.3 The influence of CVC on CS
The third objective of the study sought to examine the influence of CVC on CS. The findings
of the study suggested that creating value for customers through a service innovation could
positively and significantly influence CS ( po0.00). This result could be interpreted that
when customers perceive to gain high social value, emotional value and functional value
from a telecommunication operator’s innovation, they are more likely to feel positive about
their consumption experience, purchase decision and satisfaction toward the offering.
This finding is similar to the findings of numerous scholars in different studies within
marketing literature (Zeithaml, 1988; Rust and Oliver, 1993; McDougall and Levesque, 2000;
Cronin et al., 2000) who found out that CVC is a significant driver of CS. This finding also
confirms earlier suggestions that when customers recognize higher levels of value in an
offering, they are likely to feel positive about their consumption experience and purchase
decision (Oh, 2000; Zeithaml, 1988).
6. Managerial implications
From the research objectives and results, it was found that firms are better positioned if they
have consumers’ top of the mind consideration to continuously repurchase their products
and services. This infers that service firms must invest wisely and co-ordinate their service
innovation activities well, so as to deliver outstanding innovative services that provide
satisfaction to customers. This will enable companies to gain consumers’ top of the mind
consideration to build and enhance loyalty.
Furthermore, this research also identified three interrelated constructs which
characterize the strategic process through which service firms can harness and deploy
their service innovations to achieve CS. The conceptual framework for this study suggests
that NSP, NSC and NTSs should be given astute consideration by managers if they want to
increase CS toward their service offerings. In this regard, service providers should take into
account the multi-dimensional nature of service innovation, as the different dimensions
encompass very different service innovation aspects, offering varying possibilities for them
to innovate on.
Service companies should also be aware that the service innovation dimensions are
interrelated. Hence, changes within one service innovation dimension usually coincide or
require relating or fitting changes in one or more of the other dimensions. In light of the
significant results pertaining to service innovation and CVC influencing CS, managers
should keep up the constant process of service innovation enhancement in order to create
value for customers.
Practically, it is logical to realize that customers accepting a service product and their
intentions to repurchase are essential to gaining higher market share. “Intentions to
repurchase” is a viable indicator in estimating the number of re-acquisitions of the customer.
It is therefore essential for managers to need to balance the two very well in order to
maintain their market share through repeat patronage. Managers can therefore create very
smart marketing strategies with outstanding service innovations to capture the market
while leveraging on CVC to maintain their market share.
7. Conclusion and further research directions Service
The first objective of the study provided evidence for the justification that service innovation
innovation aid in achieving some level of CS. Thus, service innovation in a NSC and a NSP and customer
positively and significantly influences CS. However, service firm’s NTS innovation had a
feeble influence on CS. Results from the second objective saw a very strong and significant satisfaction
relationship between service innovation, CVC and CS. The third objective revealed a
positive and significant relationship between CVC and CS. 417
From the various analysis and discussions, the study established that service innovation
in creating customer value has a strong and positive influence on CS. This is essentially
rational, as a firm’s ability to put together innovative services that meet customers’
functional, social and emotional value expectations will lead to CS. The study further
established three major dimensions of service innovation (NSC, NSP and NTS) that are very
effective in influencing CS.
The results from this current study are largely in accord with theoretical expectations.
However, as with any scientific research, there are a number of study limitations and future
direction which need to be pointed out. This research primarily used a specific service type,
namely, mobile telecommunication service. This forms a certain limitation, as it may not yet
be enough to derive conclusions for different types of services within the sector. However,
this does form a basis for further research, in which other service types, such as hospitality,
insurance, banking and many others can be assessed, offering data for cross-comparisons
within the services sector.
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Further reading
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, 5th ed.,
Prentice Hall International, New York, NY.
Corresponding author
Mahmoud Abdulai Mahmoud can be contacted at: mamahmoud@ug.edu.gh
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