Towards
Achieving Web Customer Loyalty: An Innovative Research Model
Anas A.
Mohammad Salameh,
Assistant Professor,
Department of Management Information
Systems,
College of Business Administration,
Prince Sattam bin Abdulaziz
University,
Saudi Arabia.
Email: a.salameh@psau.edu.sa
Ahmad.M.A.Zamil,
Associate Professor,
Department of Marketing,
College of Business Administration,
Prince Sattam bin Abdulaziz University,
Saudi Arabia.
Email: am.zamil@psau.edu.sa
M.M.Sulphey,
Professor,
Department of Human Resource
Management,
College of Business Administration,
Prince Sattam bin Abdulaziz
University,
Saudi Arabia.
Email: s.manakkattil@psau.edu.sa
Abstract
The aim of this research is to highlight the significance of an
integrated view for evaluating web service performance, thereby achieving
e-customer loyalty. The study examined the effect of transaction cost
performance analysis with analysis forecasting factors (time/schedule analysis and
forecasting, cost
analysis and forecasting, and quality cost analysis forecasting), as well as,
their relationship among them with satisfaction analysis forecasting and
finally the relationship with e-loyalty. Since the contributions of technology appear in all trends and aspects
nowadays, this study developed a conceptual model. The model proposes that the
transaction cost performance has influence on forecasting factors. Moreover, there is relationship between
satisfaction analysis forecasting (SAF) factors and the final effects on
e-loyalty
Keywords: Web Loyalty,
E-Loyalty, Web Service Quality, Web Service Time, Web Service Cost
Introduction
With the turn
of the century, e-business and commerce has grown rapidly with its inherently
competitive characteristics promising multiple avenues for wealth creation
(Amit and Zott, 2001).Now it can be considered that e-commerce has attained a
certain level of maturity. With further
advancements in information technology (IT), multitude of services that were
hitherto offered in the physical marketplace has been shifted to online business.
Though multiple offline functions have been substituted by online techniques,
there exists many limitations which makes customers reluctant to use online
channels (Cho and Park, 2002).However, with fierce competition raging in e-business, customer
loyalty has become a key driver towards long-term profitability. Loyalty is
mostly based on the characteristics of website (Arya and Srivastava, 2015). Loyalty
can be considered to imply a higher level of satisfaction. However, satisfaction by itself need not necessarily
result in loyalty. This has led to researchers,
for instance Waddell (1995), Oliver (1999), to opine that loyalty and
satisfaction have a form of asymmetric relationship between them. This relationship is all the more importance
in online marketing, as customers have multitude of choices in e-marketplaces. Online
business is totally different from traditional service which dominated the
market for many decades. Traditional business was totally based on interactive
information flow between buyers and sellers. If e-businesses need to have the
required service quality, the operational efficiency and profitability need to
be enhanced. E-service is now attaining strategic importance and it has become
critical for organizations to attract and retain customers. A high sense of loyalty derived from a higher
level of service offered by the organizations have been identified as the main
cause of brining online customers back to the websites.
Highly loyal customers have been found to continue to stay so if it
is possible to create a positive attitude towards the brand (Baldinger and
Rubinson, 1996; Mallika andSulphey,2018). It has also been found that it is indeed
possible to convert a probable switching customer to a loyal buyer, in the
event of there being a favorable attitude with respect to the brand (Gommans, et
al., 2001).Loyal online customers, just like offline ones tend to spend more,
refer more people and show more willingness to expand their purchasing into new
categories. Thus, creating a loyal customer base is one of the most reliable
success strategies for e-business. Now web loyalty is becoming a focusing
factor and is a central theme for research in the academic community. E-loyalty
has all the required ingredients to positively influence the long-term
profitability; and it is vital for online service providers (Arya and
Srivastava, 2015).
Multiple
studies have attempted to construct models that identify various factors that
have effects on e-service quality. They have also predicted the extent to which
e-service quality could affect customer loyaltyand the resultant organizational sustainability (Durmus, et al., 2013, Sulphey and
Alkahtani, 2017; Sulphey, 2019).E-business has the immense potential to keep
customers loyal, anticipate their future needs, respond to customer concerns
and provide top-quality based customer service. A number of views that
facilitate value creation strategies have been proposed. They include resource based view (Barney,
1997), market based view with service marketing approach (Zeithaml, Parasuraman,1988),the
Schumpeterian innovation viewpoint (Schumpeter, 1942), strategic network view
(Gulati, et al., 2000), transaction cost theory (Hagel and Singer, 1999), value
chain analysis (Porter, 1985), and e-performance management view (Striteska and
Spickova, 2012 as cited in Ungerer and Schutte, 2015).
According to
Al-Kasabeh, Dasguptaand Al-Faouri (2011) user satisfaction is an important
predictor of online consumer behavior and the success of a web-based
system. Web-customers and their
behavioral patterns differ drastically from traditional customers. Now business
organizations need to go beyond web site performance to enhance customer
satisfaction. The e-business model works beyond e-commerce models as it
facilitates organizations to collaborate and integrate their data processing
systems with other business partners to provide efficient and effective products
and services. Thus e-business models go beyond the customer expectations,
firm’s business model and cost reduction (AluriandSlevitch, 2011). In This
research, the researchers will examine what
Aim
of this research
The aim of this
research paper is to examine the significance of an integrated view for
evaluating web service performance, thereby achieving e-customer loyalty. By
studying the effect of transaction cost performance analysis with analysis
forecasting factors (time/schedule analysis and forecasting, cost analysis and
forecasting, and quality cost analysis forecasting), as well as, their
relationship among them with satisfaction analysis forecasting and finally the
relationship with e-loyalty. The
work has been undertaken primarily based on review of related literature. The present work has focused on the value creation potential of e-business
integration based on the available theoretical perspectives, rather than
adopting a single strategic management theory; as proposed by Amit and Zott
(2001).This allow creation of a framework which is comprehensive in outlook and
more realistic in nature.
Literature review
Web
services
Web service is
a software interface that describes a collection of operations that can be
accessed over the network through standardized messaging. For effective
performance and quality measures, web service should include both technical and
business aspects and need to consider web services as business services delivered
through multiple channels (Nathand Singh, 2010). Latest developments in the Internet and web
technologies have changed the way organizations do business. Now-a-days,
businesses are willing to put their core business processes on the internet as
a collection of web services (Mathew, et al., 2015, Parvathi, et al., 2014).
With the development of internet, distributed systems, high-performance, high
reliability, high sensitivity, scalability and system transparency make the
application of distributed systems much wider. In recent years, these
applications include e-business applications, collaborative deal of work etc.
Top companies such as Microsoft, IBM and SUN have launched supports for
technologies related to web services (Zhai-wei, et al., 2010). Many studies have empirically proved that the
web site performance plays a central role in the enhancement of customer
satisfaction (Bai, et al, 2008, Lin, 2007, Kim andNiehm, 2009, Mallika, et al.,
2014; McKinney, et al., 2002). This is
also a predecessor of e-loyalty.
E-Loyalty
E-loyalty is
defined as perceived loyalty of customer towards websites and their intention
to visit the same web site on their second purchase (Winnie, et al., 2014). According to this definition, e-loyalty is
applicable to consumers who are likely to buy from the same web site, rather
than switch to others. E-loyalty is
considered to include components like e-word-of-mouth (EWOM), complaining
behavior and future purchase intention. In e-business the seller, the computer
systems, the end user, etc. forms the major components. System constituting users and computers are
able to perform transactional tasks though application domains. The influences
that systems are able to effect are determined by the nature and configuration of
the user and computer. The performance effectiveness
of the system is measured in terms of accomplishment with respect to quality, timeliness
and cost (Dowell and Long, 1988; Vasista and Kumar, 2016). This brings up the need for having a fair
discussion about e-service quality.
E-service quality is defined as “the extent to which a web site
facilitates efficient and effective shopping, purchasing and delivery of
products and services” (Al-kasabeh, et al., 2011).
The quality of
e-services and e-relationship have been found to be antecedents of loyalty. SprengandMackoy
(1996) proposed a model of service performance that helps in assessing service
quality, and measure customer satisfaction.
This model is such that it can be customized to forecast and measure web
service quality cost (AlSudairi, 2005). The model is depicted as shown in
figure 1.
Fig.
1.Modified satisfaction and service quality model (SprengandMackoy,
1996)
Spreng and Mackoy
(1996) proposed this model by structurally validated the Oliver’s model (1993)
of service quality, and the results confirmed that the service quality is
antecedent to customer satisfaction. Further the modified model presented the
link between the service performance and overall satisfaction. The relationship
between the satisfaction, frequency of use and loyalty has been subsequently
validated by Drosos and Tsotsolas (2014). That efficiency and effectiveness can be
achieved by factors like quality, cost, schedule, performance and
supportability has been proposed by Al-Fawaeer (2014). In general,
acceptability and functionality are considered as the three characteristics
that could lead to effectiveness (Dowell and Long, 1988). This qualitative subjective content also matches
with the propositions of Vasistaand Kumar (2016), from the perspective of
strategic cost management.
Gaoand Lai
(2015) proposes two dimensions of customer satisfaction – transaction specific
satisfaction and overall satisfaction. The first one refers to satisfaction with
individual service transactions. The overall
satisfaction would be the outcome of a series of transactions occurring during
the entire service process. Thus, the overall satisfaction is a unified form of
integrated satisfaction. This integrated satisfaction has been found to play a significant
mediating and moderating role between transaction-specific satisfaction and
customer loyalty (Gao and Lai, 2015). Website loyalty also varies according to
the motives (Gupta and Kabadayi, 2010).
The present
study attempts to reflect this model, and draws heavily from the fact that
transaction-specific satisfaction influences repurchase intentions through the
mediator of overall satisfaction (Gao and Lai, 2015). There are yet another section of customer who
are act as pivots in enhancing customer satisfaction – the internal
customers. While internal customers and
their satisfaction levels have received attention form management experts and
researchers, internal customers are yet to receive the due attention. Internal customers are anyone in an
organization who is supplied with products or services by others within the organization.
In recent years, service organizations have begun to place emphasis on
satisfying the needs of internal customers as well as external customers
(Grimler, et al, 1994).With advancement in technology, many aspects of
face-to-face interpersonal dynamics in service encounters between providers and
customers have been replaced with technology-based web interfaces. Due to this
any model that deals with customer satisfaction need to consider the variables
that can influence customer satisfaction in technology mediate service
encounters. An empirical examination by
Lee and Joshi (2007) about customer satisfaction in technology mediated service
encounters in the context of web-based shopping identified a number of factors that
are of paramount importance. They
include delivery performance, time saved, web site functional properties,
internet familiarity and price saved. It is also found that e-satisfaction strongly impacts attitudinal
loyalty (Wang et al., 2018).
A number of
studies, for instance Park and Chapin (1992), Zheng et al., (2002) proposed
that effective planning and estimation of conducting online business in terms
of time and cost is of paramount importance for a customer service-based
organization. This is true both from
product and service selling perspective.
There is a definite need to establish an optimum time-cost equilibrium
for any organization to be competitive (Park and Chapin, 1992; Zheng et al.,
2002). Towards there need to be
subjective selection from a potential solution pool (Soorentino, 2013).
Proposed Model of Web-customer Loyalty
This study has
adopted a “Genetic Algorithm’ (GA) technique as a tool for planning and
controlling the organizational activities.
GAs is search and optimization tools that assists decision makers in
identifying optimal or near-optimal solutions for problems with considerably
large search space (i.e. for solutions to be derived from higher abstraction
level search space). GAs employs a random but yet directed search for locating
the global optimal solutions. The GA
approach provides better solutions in terms of solving the business total cost
of ownership problems. This is because the GA uses objective function rather
than derivatives or another auxiliary knowledge. In addition, GA utilizes
probabilistic transition rules as compared to other deterministic models. All
these are capable of contributing to the robustness, and hence result in a more
accurate TCO model over other heuristic or mathematical programming techniques
(Park and Chapin, 1992; Zheng et al., 2002).
Further, as
cited in Sorrentino (2013), the concept of the Pareto optimum is the commonly
accepted tool for comparing two solutions in multi-objective optimization that
have no unified criterion with respect to optima. Such solutions do necessitate
improvement in any objective function without sacrificing at least one of the
other objective functions. The region defined by Pareto optimal solutions is
called the Pareto front. The objective of multi-objective optimization is to
establish the entire front for the problem instead of a single best solution
(Zheng et al., 2004).The proposed model also closely follows the Oliver model
(1993). In the model the disconfirmation
approach states that there is no difference between perceptions and
expectations of performance. Applying this on web performance it is assumed
that the performance is associated with parameters such as time, cost and quality.
Further inputs towards this has been gathered from Sorrentino (2013), who
highlighted the importance of a key performance indicator approach of making
decisions related to time, cost and quality trade-off.
The success of
online business success is based on the performance of a web application, which
in turn is measured based on the how fast it responds to URL requests. However,
a broader evaluation of performance should also include the effects of
simultaneous requests, latency in responding to requests, salability of a
solution to handle growth in demand, and levels of operational degradation due
to increases in transaction loads. Latency (late in web service processing
time) as a measurable parameter to support a given number of users, a given
number of simultaneous requests, or transactions to be completed within given
periods of time. It is further proposed
that a transaction cost performance analysis is done by customers as the
preliminary step towards web customer loyalty.
|
Time/ ScheduleAnalysis
andForecasting |
|
Transaction Cost
Performance Analysis |
|
H3 |
|
E-Loyalty |
|
Fig. 2.Proposed Theoretical Framework for Online
Business Success |
|
Cost Analysis and
Forecasting |
|
Quality Cost Analysis
Forecasting |
|
H1a |
|
Satisfaction
Analysis Forecasting |
|
H1b |
|
H1c |
|
H2a |
|
H2b |
|
H2c |
The success of
online business success is based on the performance of a web application, which
in turn is measured based on the how fast it responds to URL requests. However,
a broader evaluation of performance should also include the effects of simultaneous
requests, latency in responding to requests, salability of a solution to handle
growth in demand, and levels of operational degradation due to increases in
transaction loads. Latency (late in web service processing time) as a
measurable parameter to support a given number of users, a given number of
simultaneous requests, or transactions to be completed within given periods of
time. It is further proposed that a
transaction cost performance analysis is done by customers as the preliminary
step towards web customer loyalty.
It is
considered that customer do not consider just the optimization problem of time
and cost alone, but also the quality and their trade-off. Any business organization considers a trade-off
between these three parameters. It is thus
proposed that the performance analysis of transaction costs is directly related
to three aspects – time, cost and quality.
Any customer would consider these three aspects before being satisfied
with any service and this forms the basis for any though pattern that could
lead to satisfaction and the resultant loyalty. Based on these it is
hypothesized that transaction cost performance analysis (TCPA) is positively related to TSAF, CAF, and QSAF. Therefore, the
researchers propose the following hypothesis:
H1a: There a
strong positive relationship between transaction
cost performance analysis (TCPA) toward time/schedule
analysis and forecasting (TSAF).
H1b: There a
strong positive relationship between
transaction cost performance analysis (TCPA)toward cost analysis and forecasting (CAF).
H1c: There a
strong positive relationship between transaction
cost performance analysis (TCPA) toward quality
cost analysis forecasting (QCAF).
It is now
pertinent to discuss these three aspects, which are briefly discussed:
1)
Web Services Time processing (Time based Service quality issue)
Response time
is an important factor to online business success. Many studies have identified the efficacy of
response time. For instance, ZiloraandKetha
(2008) observed that the response time (in milli-seconds) for client languages
and return type combinations on Sun Server is better on Java among Java, C# and
PHP. The response time (in milli-seconds) for return data type and message size
combinations for the Java client would be increasing with number contacts as
well as the response is significantly high for 1-d arrays as compared to 2-d
arrays. Better response time is observed when processing on Sun Server as
compared to Microsoft IIS Server. Therefore, it is possible to have a profound
impact on performance using tools that are routinely and dependably available
now. While XML hardware acceleration and SOAP compression schemes can improve
the overall response, ZiloraandKetha (2008) found that appropriate selection of
client software, server software and data structures can have a substantial
impact on the web services performance. Depending on the previous literature in
terms of time/schedule analysis and forecasting. Thus, the researchers
hypothesize the following:
H2a: There a
strong positive relationship between time/schedule analysis and forecasting(TSAF) toward satisfaction
analysis forecasting (SAF).
2)
Web Service Quality processing (Web services quality issues)
With widespread
proliferation of web services, quality of service (QoS) will become a
significant factor in distinguishing the success of service providers (Udo, et
al., 2008). The influence of perceived online QoS has been a matter of extensive
empirical examination (Huang, et al., 2019; Zavareh, et al., 2012).QoS
determines the service usability and utility, both of which influence the
popularity of the service. Delivering QoS on the Internet is a critical and
significant challenge because of its dynamic and unpredictable nature.
Unresolved QoS issues cause critical transactional applications to suffer from
unacceptable levels of performance degradation (Mani and Nagarajan, 2002).
Electronic QoS is now an important factor that determine the success of
e-commerce applications (Huang et al., 2019).Web services technology offers a
novel computing model in which infrastructures and application system are
presented by service providers and made attainable to service consumers via web
services such that the total welfares of both the service providers and the
service consumers are optimized to the QoS requirements of service requests.
The quality of web service is calculated based on the functionality of the web
service. The customer constraint is the leading aspect for confirming the
functionalities (Shaet al., 2016). Based on the literature in terms of cost performance
analysis (TCPA), the following hypothesis
is formulated:
H2b: There a
strong positive relationship between cost analysis and forecasting (CAF) toward satisfaction
analysis forecasting (SAF).
3)
Web Services Cost processing (Cost based Service quality issue)
The growth of
e-commerce has brought in strong growth of computer applications which are
priced based on algorithms (Chen et al., 2016).
Evidences show that customers expect that the price of web service would
be significantly lower than normal elementary services (Günther, et al,
2007). As such investments in innovative
technologies to improve quality of service effects in given prime importance by
organizations. There are multiple
challenges that organizations need to face towards this direction. Many experts have introduced certain costing
model that scientifically evaluates various services taking into consideration
various techniques (2018).
While service
providers seek expectedness in revenues, consumers and users of the web
services look for flexibility in costing by not being charged for services that
are not used and services that are not used and service features that are not
delivered. QoS thus becomes a crucial element of pricing in web services. The
cost of the web service is intended only for the accessible quality. The goal
is to pay the service only for the functionality achieved (Sha, et al., 2016). According to Günther, et al. (2007) if a
viable business model is envisaged to provide the required web service quality,
then certain amount of non-standard pricing mechanisms needs to be adopted.
Depending on the previous literature in terms of quality cost analysis
forecasting (QCAF). Thus, the researchers hypothesize the following:
H2c: There a
strong positive relationship between quality cost analysis forecasting (QCAF) toward satisfaction
analysis forecasting (SAF).
Relationship between satisfaction, frequency of use and loyalty
E-satisfaction
has been identified to be the antecedent of e-loyalty (Chiou, 2004; Cyr, et al.
2008). Multiple studies have empirically
analyzed the relationship between e-satisfaction and e-loyalty (Dharmestive Nugroho,
2013; Drosos and Tsotsolas, 2014; Ltifi and Gharbi, 2012). Positive relationships between e-satisfaction
and e-loyalty were found by ChristodoulidesveMichaelidou (2010) and Dharmestive
Nugroho (2013). The relationship between
the satisfaction, frequency of use and loyalty based were assessed by Drosos and
Tsotsolas (2014) and Ltifi and Gharbi (2012).
The study by Drosos and Tsotsolas (2014) using correlation analysis examined
the willingness to continue the use of online services, customers-word-of-mouth
and willingness to continue the use of online services in case of price rise. The
relationship of e-satisfaction on e-loyalty was also assessed by LtifiandGharbi
(2012). They found that emotional state
during e-commerce has a significant and positive relationship with
e-satisfaction. Another study by BüyükdağandKitapci (2017) found the level of
the internet experience to be having a moderating effect on web-satisfaction
and e-loyalty. Though adequate empirical evidences exist to show the
relationship between the variables (DrososandTsotsolas, 2014; LtifiandGharbi,
2012), the results are inconclusive. The present study proposes a model to link
the variables of web-satisfaction and loyalty. Based on these literatures, it
can be seen that the relationship is growing steadily once the satisfaction
rises up that will affect e-loyalty to be increased as well. The following hypothesis
is thus framed:
H3: There a
strong positive relationship between satisfaction
analysis forecasting (SAF) toward E-loyalty.
Conclusion and implications
This conceptual piece of work attempted to develop and propose a model for e-loyalty. This is of high relevance to the current
scenario as customers have multitude of choices on the web, and the customer
loyalty gets naturally divided and eventually dissipate. This conceptual paper the aspects of time, quality
and price in the framework. These are
the factors that customers value most while doing e-commerce. The model also sheds light on the
satisfaction level of the customers.
Developing on the framework further would help the practitioners and
researchers of consumer behavior to arrive at strategies to maintain the
e-loyalty of customers. However, as a
matter of caution, it needs to be considered that the present framework has
been constructed based on review of related literature. The framework needs to be tested further by
practitioners and social scientists for application and practical
utilization. It is earnestly expected
that the present work will act as a trigger for further works in this area
which is futuristic and fecund.
Acknowledgment
“This
Publication was supported by the Deanship of Scientific Research at Prince
Sattam bin Abdulaziz University"
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