The Influence of the Provider’s Service Fairness on the Customer’s
Service Recovery Satisfaction and on Positive Behavioral Intentions
in Cloud Computing
Montri Lawkobkit
1
and Roland Blomer
2
1
Faculty of Business Administration, Sripatum University, Bangkok, Thailand
2
Institute of Biomedical Informatics, UMIT, University for Health Scien
ces,
Medical Informatics and Technology, Hall in Tirol, Austria
Keywords: Service Fairness, Service Recovery Satisfaction, Behavioral Intentions, Continual Improvement, Structural
Equation Modelling.
Abstract: The study shows a statistically significant positive effect between the provider’s perceived structural service
fairness and the customer’s service recovery satisfaction and, in turn, also shows statistically positive
regression weights between the customer’s service recovery satisfaction and the intension to react positively
in three directions: (1) to continue with the software, (2) to propagate a positive word-of-mouth (WOM), (3)
to give honest feedback. The influence of the provider’s perceived social service fairness on the customer’s
service recovery satisfaction does not appear to be significant but indicates a positive correlation. The study
is based on data collected via a structured questionnaire from qualified users who have subscribed to
Business-to-Business customer relationship management software and who use it as Software-as-a-Service
in the cloud. Structural Equation Modelling was applied for the data analysis in order to confirm the chosen
dependency model. The findings may help service providers to better understand their customers and to
stimulate constructive actions to their continual improvement process.
1 INTRODUCTION
Cloud computing has developed to be one of the
fastest growing markets with an expected value of
approximately US$68 billion by 2018 wherein
customer relationship management (CRM)
applications used in software-as-a-service mode
(SaaS) will capture a market share of about 25%
with a compound annual growth rate (CAGR) of
about 12% (Buyya et al. 2009; Dhar 2012; Pang
2014). More than 500 major vendors and service
providers compete in this arena in which the
customer ultimately decides on the provider’s
business success or failure. Customer satisfaction
(CS) plays the role as the main key performance
indicator in service management and represents an
important control in the continual improvement
process of every service provider.
A particular challenge in service management is
presented when expected and agreed service levels
are – for whatever reason- not met i.e. in the case of
service failure. Disappointed customers will not only
complain and possibly switch provider, but will also
disseminate their bad experiences. Negative word-
of-mouth (WOM) may reach up to 20 other
(potential-)customers and may thus harm the
provider’s business significantly (Zemke 1999).
A service provider should be well-equipped for
service failure recovery so that he can retain
customers and maybe regain CS. This should also
occur in cases of painful service failure (Johnston
1995). Some authors claim that after effective
service recovery, customers might feel higher levels
of satisfaction when compared with previous levels
(known as “service recovery paradox”)
(McCollough & Bhardwaj 1992). Effective service
recovery, however, must be part of any service
provision concept in order to survive and grow in a
highly competitive market.
The main objective of this paper is to design and
to test a model which shows the dependencies
between the perceived internal structures and
processes of a service provider and the service
recovery satisfaction (SRS) of the customer and
268
Lawkobkit M. and Blomer R..
The Influence of the Provider’s Service Fairness on the Customer’s Service Recovery Satisfaction and on Positive Behavioral Intentions in Cloud
Computing.
DOI: 10.5220/0005438902680275
In Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER-2015), pages 268-275
ISBN: 978-989-758-104-5
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
how, in turn, CS stimulates customer behavioral
outcomes in favor of the current and future business
of the service provider.
Service recovery, moreover, has been an
interesting area for practitioners and marketing
scholars for years (Kau & Loh 2006; Zhou et al.
2013).
This study examines the focal determinants of
fairness based on Greenberg’s (1993) taxonomy of
organizational fairness and their influence on SRS.
The two distinct fairness dimensions are structural
and social fairness. Figure 1 presents the conceptual
model and hypothesized relationships in this study.
The service fairness (structural and social) of the
provider would then positively impact the SRS of
the customer which, in turn, favorably influences the
customer behavior intensions in three directions: (1)
to continue with the software, (2) to propagate
positive word-of-mouth (WOM), (3) to give honest
feedback to the provider and external agencies, such
as consumer protection organizations. In a previous
paper, a similar chain of effects was evident in cases
where the service was performed correctly
(Lawkobkit & Larpsiri 2014).
2 LITERATURE REVIEW AND
HYPOTHESES
2.1 Service Recovery Satisfaction
Levesque and McDougall defined satisfaction as the
“overall customer attitude towards a service
provider” (Levesque & McDougall 1996, p.14). It
means the customer’s overall judgment on the
service provider (McDougall & Levesque 2000) that
a product or service itself, or the product or service
feature, is providing a level of under or over
fulfilment (Tronvoll 2011). A service failure occurs
whenever the service provider fails to deliver his
services as expected by the consumer (Kelly &
Davis 1994). A service failure is basically a flawed
outcome that might indicate a breakdown in
reliability (Berry & Parasuraman 1991).
In the computing area, customer SRS can be
defined as the end-user’s perception when
interacting with a specific application, including
perception, toward service failures and CS or
dissatisfaction with the organization’s approach to
service recovery (Kwok et al. 2009).
Service failures and recoveries and their
determinants have been studied in different contexts
such as public and private service delivery (Zhou et
al. 2013) and can enhance service quality and avoid
negligence (Kuo et al. 2011).
Previous research studied many factors
influencing SRS such as recovery and order (time)
(Boshoff 1997), redress and responsiveness (Hocutt
et al. 2006), distribution, procedural and
interactional justice (Choi & Choi 2014). Past
research has used the term ‘justice’ and ‘fairness’
interchangeably. Here, the term ‘fairness’ is used for
the purpose of consistency.
Previous research shows that service recovery
justice for customers affects their level of
satisfaction (Kuenzel & Katsaris 2009). SRS can
bring several benefits such as positive WOM and re-
purchase intention (Tax & Brown 1998).
The literature suggests that fairness could play a
significant role in service failure and recovery
(Lawkobkit & Larpsiri 2014; Yang & Peng 2009). In
service management, perceptions of fairness are
important antecedents of recovery satisfaction and
lead to recovery satisfaction (Lawkobkit &
Kohsuwan 2012).
The level of SRS results from many factors
although these are all grounded in the customer’s
experience of the application, of the services taken
and the interaction with their service providers.
Therefore, improving the level of CS would be a
very important goal to the service provider.
2.2 The Focal Determinants of Service
Fairness and Service Recovery
Satisfaction
Organizational fairness is one of the important
factors that has been widely studied also in the field
of organizational behavior (Colquitt et al. 2001).
Organizational fairness has also received attention in
the context of employee perceptions of fairness in
the workplace with regard to matters such as job
satisfaction, complaint handling, and human
research management (Folger & Greenberg 1985).
Organizational fairness may be defined as the
perception of fairness by an individual in the
working environment (Byrne & Cropanzano 2001;
Greenberg 1990). Greenberg’s (1993) rudimentary
taxonomy highlights the distinction between the
structural and social determinants of fairness. A
taxonomy is formed with two independent
dimensions: fairness (procedural and distributive),
and focal determinants (structural and social).
One of the major research areas in organizational
psychology has been focused on the concept of focal
determinants (Cropanzano 1993). Some prior
research has discussed focal determinants in the area
TheInfluenceoftheProvider'sServiceFairnessontheCustomer'sServiceRecoverySatisfactionandonPositive
BehavioralIntentionsinCloudComputing
269
of strategic decision making in leadership and ethics
(Tatum & Eberlin 2007).
In addition, prior studies have revealed a
relationship between social fairness and both
managerial performance (Tatum et al. 2002) as well
as employee behaviors (Masterson et al. 2000).
Social fairness has become one of the important
components of outcome fairness. In a
transformational leadership study, social fairness
had more impact than structural fairness because the
leader cares about the needs and well-being of the
followers and wants to be open and responsive
(Eberlin & Tatum 2005).
Greenberg’s (1993) taxonomy positions the focal
determinants of fairness as the immediate focus of a
just action relative to existing categories of fairness.
The two specific determinants of service fairness can
be briefly characterised by the following:
1) Structural Fairness: This type of fairness
refers to the structural elements of the organization
and focuses on the environmental context within
which interaction occurs (Greenberg 1993).
In cloud service, structural fairness refers to the
structural elements of the service provider that allow
the involvement of their customers in decision-
making and provide a fair distribution of outcomes.
The customer is convinced that he and the supplier
follow the same agenda. When customers perceive
high structural fairness, they will believe that an
unfair outcome is merely an accident and will expect
that structural fairness will still hold.
Satisfied customers will be less likely to
terminate their relationship with their service
providers. Moreover, the level of satisfaction will
increase if their service providers use technological
support to track and monitor their services with on-
line and off-line customers. Several results from
previous studies support the concept of perceived
structural fairness that has impacted directly on
outcomes (Tatum & Eberlin 2007). This
consideration leads to the following hypothesis:
H
1
: Perceptions of structural service fairness are
positively associated with SRS.
2) Social Fairness: This type of fairness is
recognized also as one of the significant sources of
fairness perception in Greenberg’s study (1993),
who proposed a distinguishable fairness in the
taxonomy. Social fairness focuses on information
exchange on an individual level by “showing
concern for individuals regarding the distributive
outcomes they receive” (Greenberg 1993, p.85), and
“may be sought by providing knowledge about
procedures that demonstrate a regard for people’s
concerns” (Greenberg 1993, p.84).
In cloud service, social service fairness indicates
to customers that the service provider cares about
their well-being and keeps customers informed
before and during changes to the service process.
Information about services is given to customers
who have been involved. The CS resp. SRS level
will increase when they feel the service provider has
treated them with respect, politeness, sincerity and
fairness throughout the service process. Once the
service providers are truthful in all communication
and tailor their explanations to match customer
needs, the level of information fairness will always
be high. The customers perceive a fair information
exchange before, during and after the service
process from the perspective of social fairness, and a
positive customer outcome can occur. From this, the
following hypothesis is developed:
H
2
: Perceptions of social service fairness are
positively associated with SRS.
These two service fairness factors should have an
impact on SRS, and H
1
& H
2
address the question of
whether an individual’s perception of structural and
social fairness is strong enough to influence
satisfaction, thus indirectly contributing to continued
usage and behavioral intention.
2.3 Service Recovery Satisfaction and
IS Continuance Intention
SRS is one of the key factors for IS service scholars
(Kassim et al. 2012; Sun et al. 2014; Wu 2013).
Several IS researchers have also found that
satisfaction is a strong predictor of system usage, IS
success, service recovery and continuance behavior
(Kim et al. 2012).
Satisfaction is an influential factor in the re-
consumption intention of customers. In accord with
the study of Bhattacherjee (2001), the post-
acceptance model of IS continuance (PAM) views
relationship satisfaction as a basis for the continued
intention to use IS; satisfaction with prior use has a
strong positive impact on customer intentions to
continue using the system. The more an individual
customer is satisfied with prior usage experience, the
greater the chance that the customer will continue to
use the system.
Continuance behavior may be defined as
explaining user intentions to continue or discontinue
using an IS, where a continuance decision follows an
initial acceptance decision. Therefore, satisfaction is
a main determinant influencing continuance
intention as revealed in various research (Zhou
2013) in previous continuance study contexts such
as shopping (Chen & Chou 2012), e-learning (Cheng
CLOSER2015-5thInternationalConferenceonCloudComputingandServicesScience
270
2014).
This research employs the concept of IS
continuance intention and applies the measurement
approach from Bhattacherjee (2001). This dimension
has three scale items to measure the continued usage
of the SaaS application rather than discontinuing its
use or using an alternative. Thus, the relationship
between satisfaction and continuance intention can
be hypothesized as:
H
3
: Service recovery satisfaction with IS usage is
positively associated with IS continuance intention.
2.4 Service Recovery Satisfaction and
Behavioral Intentions
Fishbein and Martin (1975) and Ajzen and Fishbein
(1980) developed the Theory of Reasoned Action,
which is a model to predict behavioral intention.
Behavioral intention measures a person's relative
strength of intention to perform a behavior. In this
regard, two customer behaviors are WOM and
feedback to the service provider, both of which are
related to customer retention and the customer’s
long-term relationship with their providers.
WOM refers to “informal communication
between private parties concerning evaluations of
goods and services” (Anderson 1998, p.6), which is
about valence (positive, negative or neutral). A key
motivation for this behavior is a customer’s
experience with the service. This service experience
produces “a tension which is not eased by the use of
the product alone, but must be channeled by ways of
talk, recommendation, and enthusiasm to restore the
balance” (Dichter 1966, p.148). Additionally, WOM
reflects a sense of loyalty (Zhang et al. 2010).
WOM behavior is defined in this study to refer to
the customer’s intention to share favorable
information about the service provider and its
service among peers. We believe that any positive
WOM activity contributes to the viability of a
technology with support services (CRM-SaaS)
because it influences service fairness and can be
exploited by the service provider.
Several previous studies discussed the
relationship between recovery satisfaction and
WOM (Seawright et al. 2008). Many scholars have
revealed the positive relationship between recovery
satisfaction and WOM (Wen & Geng‐qing Chi
2013); therefore, this study proposes the following
hypothesis:
H
4
: Service recovery satisfaction related to
positive word-of-mouth is positive and strong.
Customer feedback with regard to the second
behavior indicates that positive feedback is always
driven by satisfaction (Saha & Theingi 2009). A
very interesting finding from Söderlund (1998) was
that negative feedback is more likely to be provided
by dissatisfied customers because of the
compensation involved. However, customers always
provide positive feedback without expecting a
reward. In the digitized era, customers can provide
their feedback in various forms of online feedback
mechanism based on the specific category (Liu &
Zhang 2010).
In this study of cloud service, feedback refers to
the communication from customers as service
receivers to their service providers and external
agencies (e.g., consumer protection organizations).
Customers might use satisfaction as a proxy for the
level of service fairness that they should receive.
Previous research revealed a positive relationship
between feedback and satisfaction (Saha & Theingi
2009; Söderlund 1998). On the basis of the above
discussion, the following hypothesis is therefore
proposed:
H
5
: Service recovery satisfaction related to
positive feedback is positive and strong.
This study applies a conceptual model in which
the perceptions of the focal determinants of service
fairness and satisfaction result from the use of a
technology with support services. This then leads to
continuance intention and customer behavioral
intention including WOM and feedback to their
service provider.
3 METHODS, SAMPLE AND
DATA COLLECTION
A quantitative study was conducted to assess the
relationships between two dimensions of service
fairness and SRS and their further propagation on IS
continuance intention, WOM and feedback to the
service provider.
Previously developed methods have been chosen
as guides in this study for their merit and overall
utility. However, they have been modified in order
to reflect the specific cloud service context, as well
as the targeted users. The service fairness items were
adapted from a number of works but generally
follow (Bies & Moag 1986; Leventhal 1980;
Maxham & Netemeyer 2003; Shapiro et al. 1994).
Other items were adopted from Maxham &
Netemeyer (2002) for SRS. Bhattacherjee (2001) for
IS continuance intention, and finally Zeithaml, Berry
& Parasuranman (1996) for WOM and feedback.
All items were reworded to relate specifically to
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271
CRM-SaaS. A 7-point Likert-scale was employed
for each survey item, ranging from 1 = “strongly
disagree” to 7 = “strongly agree”.
In order to acquire and develop the most
appropriate pilot version for the questionnaire, an
expert panel reviewed the initial draft. These are
professionals from both sides of service
management: the academics and the industry. The
pilot test (n = 60) showed good results for all
variables on the service fairness concepts,
satisfaction, IS continued usage, WOM, and
feedback. After the various changes were
incorporated and considered, the final version of the
survey was then carried out.
SaaS providers in cloud service providing a
service together with an application is the context of
this study. Individuals from small and medium-sized
enterprises (SMEs) were tapped. Those who use
business-to-business (B2B) CRM-SaaS formed the
population of the study. The pilot and main study
focused on respondents who were B2B SRM SaaS-
users.
Company databases of full-time employees
working in organizations provided the source for
prospective panel members. In all, 30,899
recruitment emails were sent. The first response rate
was 11.62% (3,589). Four stringent screening
questions constraints reduced them to 475
questionnaires, which gives a response rate of
1.54%.
There were 475 sample respondents, and among
them, sixty percent were male while the other forty
were female. The majority of the respondents were
within the age range from thirty to fifty years old,
and nearly ninety percent (88.84%) had over five
years working experience. As shown in the data, the
most common positions were operating staff
(17.24%), supervisors (17.05%) and sales
representatives (14.54%). Half of the respondents
(52.20%) were from organizations employing
between fifty and five hundred employees. The
business service industry covered the highest
percentage of respondents (58.52%).
The sample thus exhibited the following
significant characteristics: they are from an
experienced working-age group, have responsibility
at their present company requiring frequent use of
CRM-SaaS software, and interact with the software
service provider.
4 RESULTS
The analysis results of the descriptive statistics for
internal reliability of the measures ranged from .961
(structural fairness) to .993 (Social fairness) for the
two service fairness dimensions. The other four
measures are .909 for satisfaction, .896 for
continuance intention, .914 for WOM and .751 for
feedback. All the measures included in the
questionnaire showed adequate levels of initial
internal reliability (> .70) (Hair et al. 2009).
Figure 1 and Table 1 present the standardized
estimates and standardized regression weights, with
all five hypotheses supported. The structural model
was accepted and the chi-square was significant
(chi-square = 1532.601; df = 399, p = .000, relative
chi-square = 3.841; NFI = .888; GFI = .808; CFI =
.907; TLI = .907; RMSEA = .077). The path
coefficients for the structural model are shown in
Table 1. The relative effect (standardized regression
weights) between independent and dependent
variables shows a statistical significance for all
hypothesized relationships.
A summary of standardized path coefficients and
the square multiple correlations (R
2
), of the best-fit
measurement model are shown in Table 1. The
significance of four of five path coefficients to the
model is amplified, even though they are positive
and statistically significant at p > 0.05. Moreover,
most of the R
2
values of the observed variables were
greater than 0.50, indicating the reasonably good
convergent validity of the model.
Figure 1: Result of Structural Equation Modelling (SEM).
Table 1: Results of standardized coefficients.
Outcome (R
2
)
Determinant
(Hypothesis)
Coefficients
(P-value)
SRS (.950)
Structural fairness
(H
1
)
0.805 (***)
Social fairness (H
2
) 0.178 (.049)
Contin. .682) SRS (H
3
) 0.826 (***)
WOM (.682) SRS (H
4
) 0.826 (***)
Feedback (.688) SRS (H
5
) 0.829 (***)
Coefficients - Standardized regression weights (*** P-Value < .001)
The analysis of path coefficients indicates that
four hypotheses are supported. The influence of
CLOSER2015-5thInternationalConferenceonCloudComputingandServicesScience
272
structural fairness (coefficient = 0.805) on SRS was
significant. Unfortunately, social fairness
(coefficient = 0.178) on SRS was only nearly
significant (p = 0.49). Moreover, the influence of
SRS on IS continuance intention was significant
(coefficient = 0.826). Similarly the influences of
SRS on WOM (coefficient = 0.826) and on feedback
(coefficient = 0.829) were significant (see Table 1).
The impact of the endogenous variables is indicated
by the R
2
values. The highest R
2
appeared in
satisfaction (95%) and the next R
2
was shown in
feedback (68.8%), and continuance intention and
WOM that had the same values (68.2%). (See Table
1) The results of the research model (H1 – H5) show
that all five hypotheses are supported, so the model
does work well in this context.
5 CONCLUSIONS
One of the key success factors for service
management is related to successful service recovery
when there has been service failure. The service
providers’ actions during service failure can
influence their customer perceptions and the
providers can have lessons to learn in order to be
able to manage more effectively in success and
failure areas in the future (La & Kandampully 2004).
The analytical results of this study showed that
SRS is significantly influenced by the provider’s
structural service fairness. In other words, CS can be
regained by fair and equal treatment of customers.
This SRS in turn furthers the customer’s intention to
continue the service under consideration, to
disseminate favourable information about this
service (WOM), and to enter into a feedback process
with the provider. Other factors that could influences
the co-operation between customer and provider
after a service failure is trust in the service provider
and the commitment of the provider to resolve the
failure.
The findings are consistent with previous
research which placed greater importance on the
information and contact for service recovery in a
Korean context (e.g., Park & Kim 2011) and a
positive relationship between satisfaction and
feedback (e.g., Saha & Theingi 2009).
This study contributes to both academia and
practice. In academia, the study builds on previous
research on the relationships of service recovery
attributes and CS enhancing continuance as well as
behavioral intentions. For practitioners, especially
for managers, the study provides an insight into the
usefulness of service recovery measures to enhance
effectively CS, continued usage, WOM and
feedback to the respective service providers.
In summary, this paper suggests that cloud
service fairness promises to be a fruitful arena for
additional research into the area of customer
satisfaction, continued usage and behavioral
intentions. Practitioners in the service support area
would find additional practices to improve the level
of CS during service recovery after a failure. Service
support management should consider and must
account for these areas.
In regard to the research background, CRM-SaaS
was studied. It is suggested to expand the study to
other cloud service applications in order to
generalize the study by understanding the
characteristics of cloud computing and possible
deviations from the results of this study. Greater
diversity in service recovery would be suggested for
further research.
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