The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
IMDS
114,2
A firm’s post-adoption behavior:
loyalty or switching costs?
Jun-Gi Park
258
IT Policy & Strategy Research Institute, Yonsei University, Seoul,
Republic of Korea, and
Kijun Park and Jungwoo Lee
Received 13 June 2013
Revised 20 August 2013
Accepted 31 August 2013
Graduate School of Information, Yonsei University, Seoul,
Republic of Korea
Abstract
Purpose – This study aims to investigate the influences of loyalty and switching costs toward a
firm’s overall post-adoption behavior in using information system.
Design/methodology/approach – A research model is developed around two constructs found in
the literature – loyalty and switching costs – that are most critical in firms’ decisions on continued use
of the same IS service providing company. It is empirically tested using a survey of IT decision makers
in total 102 companies in South Korea. Partial least squares method is used to assess the relationships
specified in research model.
Findings – The findings suggest that both loyalty and switching costs have positive influences on
the continuous intention to use and the inattentiveness of alternatives.
Research limitations/implications – Findings are based on a single point cross-sectional survey.
To further investigate the continuance of specific IT service firms, triangulation will be necessary with
longitudinal and qualitative data concerning the process of decision-making, including political and
contractual situation.
Originality/value – The study fills the research gap in studying post-adoption behavior at the firm
level by empirically testing the duality of loyalty and switching costs.
Keywords Switching costs, Continued use, Information system service, Information system usage,
Post-adoption behavior
Paper type Research paper
1. Introduction
Organizations have been making continuous investments in building and maintaining
their information system (IS). As the IS have been utilized more than decades, research
on post-adoption issues are now giving into more focus. According to Jasperson et al.
(2005), the post-adoption behavior is:
[. . .] myriad of feature adoption decisions, feature use behaviors, and feature extension
behaviors made by an individual user after an IS application has been installed, made
accessible to the user, and applied by the user in accomplishing his/her work activities.
Industrial Management & Data
Systems
Vol. 114 No. 2, 2014
pp. 258-275
q Emerald Group Publishing Limited
0263-5577
DOI 10.1108/IMDS-06-2013-0259
Individual user’s post-adoption behavior is different from the behavior in pre-adoption
stages since user’s perceptions on the IS are formulated from direct experience and
interaction with the adopted IS and its provider. Such differentiation is illuminated
from the innovation diffusion theory (Cooper and Zmud, 1990).
On user’s behavior after the IS adoption, most of prior research have largely focused
on the factors that impact individual user’s cognitive intention to use the IS continually
(Vatanasombut et al., 2008; Chiu and Wang, 2008) since the continued use is a
fundamental condition to judge whether the IS adoption is successful or not. Hence,
these studies on post-adoption behavior largely focused on the interaction between
individual users and system. Thus, their concerns were concentrated on the IS
characteristics which may influence on user’s continued use. However, post-adoption
behavior is also influenced by users’ accumulated experience. Some researchers
emphasized the changes of users’ post-adoption behavior from their accumulated
experience in investigating the factors which determine users’ post-adoption behavior
(Saeed and Abdinnour, 2013; Chang and Chou, 2011).
Meanwhile, most of prior studies on post-adoption behavior were focused on the
individual cognition level. It may be because the actual users of the information
systems are individuals although a firm decides to adopt the enterprise system.
However, the continued use of IS in the firm does not only depend on the individual
usage. Ultimately, it is a firm that decides whether to use current IS continually or
switch to another IS service. Thus, it is very important to study post-adoption behavior
in focusing on the firm’s level of analysis but prior studies overlooked this.
According to theory of relationship maintenance, certain features have repeatedly
been found to be important in building quality relationships (Bendapudi and Berry,
1997). Specifically, loyalty and switching costs on the IS service are central factors in
motivating to participate or engage in successful and mutually beneficial exchange
relationships (Kim and Son, 2009). Kim and Son (2009) consider that loyalty and
switching costs can play a (important) role in determining IS user’s post-adoption
behavior. These authors propose that switching costs will determine the stability of the
relationship, whereas loyalty determines the quality of the relationship. In order to
study post-adoption behavior, a number of previous research have been focused on
loyalty and switching costs (Li et al., 2007; Kim and Son, 2009). Yet, the mutual
mechanism between these two constructs in IS service was not fully examined because
most of studies are focused particularly on online services context. Besides, previous
studies are focused on the individual level of analysis. Therefore, the present study will
mainly focus on these two constructs to see the impacts on the firm’s post-adoption
behavior by analyzing the relationship between a firm and an IS service provider.
This study has three objectives: to investigate how loyalty and switching costs
influence a firm’s overall post-adoption behavior in using IS service; to examine the
primary factors which influence on loyalty and switching costs; and to explore the
outcomes of firm’s post-adoption behavior in using IS service, and how loyalty and
switching costs will influence on these outcomes.
2. Literature review
2.1 Post-adoption behavior: user’s loyalty and switching costs
Most literature on post-adoption behavior of IS users have focused on continued use
after their initial adoption of a specific IS (Bhattacherjee, 2001; Hong et al., 2006;
Thong et al., 2006; Vatanasombut et al., 2008). The adopted IS should be continually
used in order to receive its sufficient benefits. Since an enormous investment is needed
in adopting a new IS, the discontinued use means a big loss to the firm. Many
researches have studied the continued use of IS in post-adoption stage but most
Firm’s
post-adoption
behavior
259
IMDS
114,2
260
of them mainly focused on individual’s cognitive aspects, such as perceived ease of use,
perceived usefulness, and so on (Bhattacherjee, 2001).
However, post-adoption usage is independent of initial adoption since users’
experience with the system can generate new inputs to re-evaluate the value of a
specific system (Kim et al., 2005). After the adoption of the IS, it becomes an integral
part of the organization’s work system and user’s experience will also be accumulated
with the continued use. Besides, user’s loyalty to the system tends to be generated.
Once loyalty is arising, users will not only continue to use the system (Chiu and Wang,
2008) but also refer it to others (Bowen and Shoemaker, 1998).
Meanwhile, as the usage has been increased, another type of behavior is also
appeared. That is switching behavior influenced by switching costs. Switching costs
defined as “the onetime costs that customers associate with the process of switching
from one provider to another” (Burnham et al., 2003). Switching costs refer to customers’
costs from switching and can be called as switching barriers. In addition, switching
costs can be considered as a benefit from constraints (Kim and Son, 2009). Customers do
not need to pay these costs when they do not switch their service provider. Higher
switching costs are likely to make customers to stay with current service provider since
they feel more burdens in switching the provider. Since switching costs are inextricably
linked with extant investment, the service-specific investments are considered as a
major source of these constraints (Palmatier et al., 2007).
2.2 Perceived benefits
Perceived benefits in IS service refer to the user’s beliefs about the positive outcomes
from using IS. Users will use IS when they expect any benefits from using them. Thus,
the benefits from IS appear to be prerequisite for IS use. Many research studied on the
system usages resulted in this cognitive fit (Goodhue and Thompson, 1995).
When a firm decides to adopt a new system, a decision maker’s perceived benefits
on the IS will affect its adoption. Many studies have pointed out that post-adoption
behavior is mainly driven by the perceived fit between information system usage and
the user’s perceived benefits (Bhattacherjee, 2001; Lin et al., 2005). In order to
understand user’s perceived benefits, diverse aspects were examined. Some studies
focused on the quality. For example, DeLone and McLean (2003) regarded net benefit
as perceived benefits and considered service quality on the used systems. Some studies
focused on the perceived value (Yang and Peterson, 2004) which was conceptualized by
Zeithaml (1988) as the consumer’s evaluation of the utility of perceived benefits and
perceived sacrifices. In addition, some studies focused on perceived usefulness or user
satisfaction (Kuo and Lee, 2011).
2.3 Service-specific investments
Service specific investment refers to extra investment to fit with the current services.
After the IS adoption, users need to receive suitable IS services continuously for
performing their tasks. However, rational task-technology models have some
difficulties to explain the post-adoption behavior. If users would not receive
supportive IS services after their adoption, they could not continue to use the adopted
IS even though they thought their tasks were fit with the technology when adopting
the system.
When service specific investments occur, switching costs are obviously increased.
As Burnham et al. (2003) proposes, there are three higher-order switching costs types.
First, procedural switching costs are the costs primarily related to the expenditure of
time and effort, which consist of economic risk, evaluation, learning, and set up costs.
They are additional costs after the IS adoption, which may arise in the course of using
IS services smoothly. Especially, when introducing a new system, learning is necessary
for employees to use the IS. Previous research has mentioned learning as a cost in this
reason (Thong et al., 2006).
Second, financial switching costs are the cost related to the loss of financially
quantifiable resources, which consist of benefits-loss and financial-loss costs
(Burnham et al., 2003). Additional costs may be necessarily required after the IS
adoption. Especially if user feels that certain IS are not suitable for doing their tasks,
their usage of such systems tend to be reduced. Thus, IS should be updated and often
customized to fit with user’s tasks. Therefore, customization is regarded as a primary
financial cost.
Third, relational switching costs are the cost related to psychological or emotional
discomfort due to the loss of identity and the breaking of bonds. Relational investments
occur in the relationship between the user and the service provider (Chiu et al., 2005)
formed after the IS adoption. The increase of user’s participation strengthens this
relationship, which supports in facilitating the information usages (Wang et al., 2011).
The literature on IS development had interests in user participation and investigated it
particularly in the system implementation stage (Saleem, 1996; McKeen and
Guimaraes, 1997). These relational switching costs incurred while switching from
one service provider to another, which are related to personal- and brand-relationship
lost costs (Burnham et al., 2003).
Firm’s
post-adoption
behavior
261
3. Research model and hypotheses
Based on the notion that perceived benefits and service-specific investments are
essential factors for deciding firm’s post-adoption behavior, a set of hypotheses are
developed to predict a firm’s post-adoption behavior (Figure 1). The model reflects
the characteristics of IS services, conceptualizes a firm user’s post-adoption behavior
Perceived Benefits
Information system
Quality
H1
Information system
Service Value
Outcomes
H2
H6a
Loyalty
H6b
Service Specific
Investment
Participation
Continuous
Intention to use
H8
H3
H4
H7a
Switching Cost
H7b
Inattentiveness
to alternatives
Learning
H5
Customization
Figure 1.
Research model
IMDS
114,2
262
from the impact of these IS services’ elements, and shows the outcomes of firm’s
post-adoption behavior.
3.1 User’s perceived benefits
Prior research showed that perceived benefits serve as the foundation for creating
loyalty (Kim and Son, 2009). According to Oliver’s attitude-based framework, the causal
relationship between perceived benefits and loyalty can also be explained (Oliver, 1999).
Therefore, user’s perceived benefits are important factors to increase the loyalty and to
encourage the attention for the continued use of IS. When a user perceives more benefits,
the user’s loyalty on the IS will be increased and thus the user is likely to use the IS
continually. The perceived benefits to the IS service can be examined from both system
quality and service value that the user perceives (Kettinger et al., 2009).
System quality is one of fundamental determinants of benefits in using the
information system (Park et al., 2012). DeLone and McLean (2003) suggest net benefits
which are greatly influenced by system quality, information quality, and service
quality. Some studies suggest that these net benefits determine user’s loyalty (Lam et al.,
2004; Yaya et al., 2011). When total benefits from the IS are bigger than total costs,
users will have good perceptions on the IS and accordingly users’ loyalty on it will be
increased. Chen and Hu (2012) found that service quality had positive impacts on
relational benefit and customer loyalty in the airline industry. If a system is configured
in a way that enables a user to reach the required functionalities, the user’s loyalty on
the system will be increased.
Service value is considered as an important factor for the user’s perceived benefits.
Prior research found that perceived value had a positive effect on loyalty. Evoked from
a survey of online service users, Yang and Peterson (2004) examined the relationship
between customer loyalty and perceived value. Fernández-Sabiote and Román (2012)
also explored how overall customer loyalty was built as a consequence of the value
provided by offline and online channels. Users are not likely to use IS services if they
found no value in using them. However, a user who perceives more values in IS
services will use the services more and more, and the user’s loyalty to the services will
be increased as well. Thus, we propose the following hypotheses:
H1. IS quality has a positive relationship with the user’s loyalty to the system.
H2. IS service value has a positive relationship with the user’s loyalty to the
system.
3.2 Service specific investments: participation, learning, and customization
Switching costs make users difficult to change providers (Jones et al., 2007). Switching
costs include not just financial damages but also procedural and relational costs
(Burnham et al., 2003). Thus, certain efforts for service specific investment represent
switching costs that may prevent users from switching the service. Based on the types
of switching costs shown by Burnham et al. (2003), service specific investments can
also be categorized into three types:
(1) relational investment;
(2) procedural investment; and
(3) financial investment.
In the context of this study, we regard user participation as the relational investment
during the IS implementation, learning as the user’s procedural investment for primary
use, and user customization as the financial investment for user’s convenient use.
User’s participation in implementing IS will improve the relationships between user
and IS service provider (Wang et al., 2011). User’s participation has positive effects on
joint decision-making by improved communication which increases information,
knowledge, creativity, and understanding (Wagner et al., 1998). User’s participation
can create a good relationship between the related parties through enhanced
communication. This relationship cannot be transferred to the other relationship.
Thus, the strengthened relationship between user and service provider can be
considered as a type of switching costs and user’s participation in using the service will
be positively related to switching cost.
Learning refers to the investment of time and effort to earn certain experience. The
investment incurred in learning to use the incumbent system is associated with
learning cost (Chen and Hitt, 2002). It is a type of investment after adopting the IS.
Indeed, considerable time and effort is required to use a new system. When a new
system is introduced to the organization, users need to invest their additional time and
efforts to learn the basic usages of the systems (Lindner and Wald, 2011). In addition,
skills with the adopted systems are not automatically transferred to a new system (Kim
and Son, 2009). Hence, the more the skills are needed to learn a new IS, the higher the
procedural switching costs are increased.
Customization may refer to the degree to which user preferences are accommodated
(Teng, 2010). The greater product customization will generally be associated with the
higher switching costs (Burnham et al., 2003). Thus, if the current system is highly
customized, it will be much more suitable to offer adapted services for customer’s usages.
Whereas the switching costs will be increased because users need to spend lots of time
and money for interacting with service provider to modify the systems. Furthermore,
such customization cannot be transferred to the other services so it leads to increase the
switching costs (Bharadwaj et al., 1993). We propose the following hypothesis:
H3. The IS user’s participation in system implementation has a positive effect on
switching cost.
H4. Learning has a positive effect on switching cost.
H5. Customization has a positive effect on switching cost.
3.3 Continuous intention to use and inattentiveness to alternatives
Repeated usage is considered as a dedication-based behavioral outcome (Bendapudi
and Berry, 1997). A user who has loyalty to the IS is willing to use the systems
repeatedly and, by using the IS service repeatedly, user tends to raise the service
specific investment. Some studies suggested that loyalty is actually an important
predictor of usage intention (Kim and Son, 2009; Jones et al., 2007; Moore et al., 2012). A
user who uses the IS repeatedly is not likely to consider any substitute of the systems.
Thus, we propose the following hypotheses:
H6a. The IS user’s loyalty to the service has a positive relationship to user’s
continuous intention to use.
Firm’s
post-adoption
behavior
263
IMDS
114,2
264
H6b. The IS user’s loyalty to the service has a negative relationship to and interest
in alternative systems.
Switching costs will be a strong incentive for users to stay in current services. Lee and
Neale (2012) suggest that high switching costs relate to customer retention. Chang and
Chou (2011) also propose that the users’ perceived switching costs are positively
associated with their continuance intention. Based on this, a user who has higher
switching costs on the IS service is willing to use the systems continuously and is not
likely to consider any substitute of the systems. Thus, we propose the following
hypotheses:
H7a. Switching costs have a positive relationship to user’s continuous intention to
use.
H7b. Switching costs have a negative relationship to and interest in alternative
systems.
3.4 Loyalty and switching costs
Researchers in IS argue that switching costs will positively influence customer
loyalty (Thatcher and George, 2004). High switching costs may develop a “lock-in”
phenomenon which makes users difficult to switch to another alternative. In this case,
users have to remain in and use the current service but this lock-in effect is not
necessarily to guarantee the increase of user’s loyalty to the services. Hence, we cannot
expect positive relationship between switching costs and loyalty just from lock-in
phenomenon. However, the increase of switching costs by expanding voluntary
service-specific investment seems to reflect user’s intention for utilizing current IS
services more actively. Thus, as the service specific investment has increased, it does
not only increase switching costs on the current service but also strengthen user’s
loyalty to use current systems. Thatcher and George (2004) found that switching
costs ultimately have an additive influence on the relation to loyalty. Accordingly, we
expect that switching costs will have a positive impact on IS user’s loyalty to the
service:
H8. Switching costs have a positive relationship to the IS user’s loyalty to the
service.
4. Research method and results
This study has an interest in the relationships among nine constructs as proposed in
the research model. In order to examine firm’s post-adoption behavior in IS services
environment, the data were collected from 102 companies in South Korea. A self-report
survey was conducted for supervisors who were at least responsible to make decisions
on implementation of enterprise systems, such as ERP, CRM, and SCM in their
companies.
4.1 Instrument development
This study generates potential items for measuring the constructs in the proposed
model from existing scales which used or adapted to enhance validity. All constructs
are measured through seven-point Likert scales anchored from “strongly disagree”
through “strongly agree”. Items measuring the level of IS service value are developed
based on the four items used by Kettinger et al. (2009). To measure IS quality
constructs, we adopted six items covering information and system quality from
DeLone and McLean (1992). The measurements for the participation are modified
forms of five items from Kearns (2006). Learning and customization are measured
using items adapted from Kim and Son (2009). Loyalty and switching costs are also
measured using items adapted from Kim and Son (2009). Continuous intention to use is
measured by three items adapted from Bhattacherjee (2002) and inattentiveness to
alternatives is measured by two items developed and validated by Kim and Son (2009).
Firm’s
post-adoption
behavior
265
4.2 Sampling and data collection
An initial version of the questionnaire was developed with the subjects of questionnaire
which asked to think about firm’s IS service provider and then to answer questions
about this provider. Then three domain experts reviewed the questionnaire. Later, this
study used 38 items to conduct a pilot test of the modified version of the questionnaire.
Finally, a field study using modified final 34 items was conducted to collect the data
necessary for testing the causal model and the hypotheses. Three items of IS quality
such as information richness, timeliness, and reliability were dropped after
measurement test. An online survey was conducted to collect the data and email
invitations were sent to total 105 companies which were using the information systems
provided by major IT service companies in South Korea. The survey ran for two weeks
and another follow-up invitation was sent during this period. This resulted in a total of
102 usable responses, yielding an effective response rate of 97.1 percent. Three of the
responses were deleted because more than half of the questions were unanswered.
Descriptive profile of respondents is provided in Table I.
4.3 Data analysis and results
This study used partial least squares (PLS) to evaluate the proposed model and its
hypotheses for the following reasons. First, it is suitable for assessing theories of
Characteristics
Job position
CEO
COO, CIO, CSO
IT team manager/leader
Company type
Products/food and beverage
Manufacturing
Telecommunication
Shipping/transportation
Software
Technology/network
Banking/insurance
Revenues (2012)
Less than 10 billion won
10-30 billion won
30-50 billion won
More than 50 billion won
Total
n
Percentage
40
49
13
39.2
48.0
12.8
4
8
30
2
11
44
3
3.9
7.8
29.4
2.0
10.8
43.1
2.9
25
50
15
12
102
24.5
49.0
14.7
11.8
100
Table I.
Summary of sample
profile
IMDS
114,2
266
development stage. Second, it requires minimal sample size as opposed to other SEM
techniques. Due to the large-scale survey, the size of the sample for analysis was
acceptable at a modest level, making the PLS appropriate for testing our model. With
PLS, the psychometric properties of the scales used to measure the variables are tested
and the strengths and directions of the pre-specified relationships are analyzed
(Barclay and Osei-Bryson, 2009).
4.3.1 Measurement model. The assessment of the measurement model is determined
by examining several tests of convergent and discriminant validity (Hair et al., 1995).
To assess convergent validity:
.
individual item reliability; and
.
construct reliability are assessed.
Internal consistency is assessed by examining the loadings of the measures with their
respective constructs. Generally, item loadings of 0.7 or higher are considered as
acceptable. It suggests that there is more shared variance than error variance between
the construct and its measures (Gefen et al., 2000). The descriptive statistics, weights,
and loadings can be found in Table II.
Construct reliability is assessed utilizing two internal consistency indicators:
composite reliability and average variance extracted (AVE) scores. AVE is similar to
Cronbach’s a. All relevant composite reliability measures in this survey are higher
than 0.859 (Table III), providing strong evidence of reliability (Gefen et al., 2000). With
respect to the AVE scores, a value of 0.5 is required to provide evidence of satisfactory
construct reliability (Fornell and Larcker, 1981). All of our scores meet this standard.
The reliability of measures (items and scales) is adequate for analysis.
To evaluate discriminant validity, AVE can be used. There are two procedures for
assessing discriminant validity. First, AVE values must be examined to see if they are
consistently greater than the off diagonal correlations. Table III shows the correlations
among the constructs and the values in the diagonal are the square roots of the AVE.
Hence, it can be concluded that the measurement model demonstrated adequate
discriminant validity (Fornell and Larcker, 1981). Second, each within-construct item
loading must be high on the measured construct and cross-loadings should be lower
than the within-construct item loadings. All constructs meet these requirements. When
assessing discriminant validity, an item that is not loading highly on their own
constructs but loading on other constructs should be deleted. No item is deleted since
all of constructs meet the suggested requirements.
4.3.2 Structural model. Measurement model analysis verified the reliability and
validity of the measurement items for this study. In the next stage, Assessment of the
structural model involves estimating the path coefficients and the R 2-value using PLS.
Path coefficients explain strengths of the relationships between the independent and
dependent variables, whereas the R 2 value is a measure of the predictive power of a
model for the dependent variables.
To assess the statistical significance of the model’s path estimates, the
bootstrapping method (with 500 re-samples) was used (Chin, 1998). The target t-test
value was 1.960 (for p , 0.05, using two-tailed tests). Results of PLS analysis are
shown in Figure 2. Path coefficients are the standardized b coefficients from the PLS
analysis.
Construct
IS quality
ISQ1
ISQ2
ISQ3
ISQ4
Items
The IS provides reliable information
The IS provides sufficient information
The IS provides valuable information
The IS design makes it easy for us to collect
information that we need
ISQ5
The IS design interface is easy to use
ISQ6
The IS design is easy to navigate
IS service value
ISV1
Compared to what we had to give up, the overall
ability of IS to satisfy our wants and needs is
ISV2
Compared to other IS services, the value of the
computing services is
ISV3
The value of the IS in functional terms is
ISV4
Overall, the value of the IS to us is
Learning
LEN1
Learning to use the features offered by the IS took a
lot of time and effort
LEN2
There was a lot involved for us to understand the IS
well
LEN3
We spent a lot of time and effort to learn how the
“system works”
Customization
CUS1
System is modified in some way
CUS2
We “set up” the IS to use it the way we want to
CUS3
We have put effort into adapting the IS to meet our
needs
CUS4
We have chosen features offered by the IS to suit our
style of IS use
User’s participation
PAR1
Users are actively involved in the process of IS
implementation
PAR2
The level of participation in IS implementation is
high
PAR3
IS involves an evaluation of future information needs
of us
PAR4
Users participate in setting IS actives
PAR5
Users are involved in the selection of major IS
functions
Loyalty
LOY1
We consider ourselves to be highly loyal to the IS
LOY2
We feel loyal towards the IS
LOY3
It means a lot to us to continue to use the IS
Switching costs
SWC1
Switching to a new IS would involve some hassle
SWC2
Some problems may occur when we switch to
another IS
SWC3
It is complex for us to change IS
SWC4
If we stop using the IS, we will waste a lot of the
effort that we have already made in this IS
Mean
SD
Factor
3.343
3.127
2.873
0.798
0.871
0.763
0.713
0.709
0.725
3.176
2.951
3.137
0.821
0.809
0.841
0.804
0.819
0.813
3.333
0.796
0.821
3.833
3.225
3.392
0.755
0.791
0.854
0.720
0.764
0.898
3.422
0.857
0.868
3.529
0.915
0.921
3.578
0.857
0.905
3.029
3.167
0.664
0.687
0.755
0.878
3.186
0.668
0.771
3.127
0.605
0.882
3.510
0.764
0.825
3.373
0.791
0.891
3.480
3.461
0.801
0.813
0.914
0.924
3.392
0.769
0.905
3.392
3.539
3.461
0.716
0.893
0.836
0.868
0.875
0.843
3.461
0.882
0.886
3.402
3.216
0.899
0.893
0.948
0.880
3.373
0.928
0.953
(continued)
Firm’s
post-adoption
behavior
267
Table II.
Results of confirmatory
factor analysis
IMDS
114,2
268
Table II.
Construct
Items
Inattentiveness to alternatives
ATL1
We will try the services offered by the other ISa
ATL2
We will try occasionally other ISa
Continuous intention to use
CNT1
We intend to continue using IS rather than
discontinue using it
CNT2
Our intentions are to continue using IS than use any
alternative means
CNT3
If we could, we would like to continue using IS as
much as possible
Mean
SD
Factor
2.676
2.422
0.730
0.845
0.810
0.744
3.706
0.762
0.956
3.716
0.797
0.956
3.686
0.767
0.967
a
Note: Reversed item
As expected, the user’s loyalty is significantly associated with IS quality (b ¼ 0.232,
p , 0.01), IS service value (b ¼ 0.279, p , 0.01), and switching costs (b ¼ 0.304
p , 0.01) which together explain 41.5 percent of the dependent variable’s variance.
Three paths are in the hypothesized direction and, therefore, H1, H2, and H8 are
supported. As also hypothesized, switching costs are significantly associated with
user’s participation (b ¼ 0.241, p , 0.01), learning (b ¼ 0.380 p , 0.001), and
customization (b ¼ 0.246, p , 0.01), which together explain 40.7 percent of the
dependent variable’s variance. Three paths are in the hypothesized direction and H3,
H4, and H5 are also supported. As shown in Figure 2, loyalty (b ¼ 0.347, p , 0.01) and
switching costs (b ¼ 0.292, p , 0.01) significantly influence on continuous intention to
use, accounting for 31.0 percent of the variance and providing support for H6a and
H7a. And loyalty (b ¼ 2 0.334, p , 0.05) and switching costs (b ¼ 2 0.301, p , 0.01)
were also found to significantly affect inattentiveness to alternatives which together
explain 30.6 percent of the dependent variable’s variance, and H6b and H7b are
supported.
Finally, to assess model goodness fit in PLS, we performed the Stone-Geisser test of
predictive relevance (Tenenhaus et al., 2005). Q-square is a measure of how well the
observed values are reproduced by the model and its parameter estimates. The model
has predictive relevance if Q-square is greater than 0. In this model, Q-square is 0.18 for
inattentiveness to alternatives, 0.20 for continuous intention to use, 0.12 for loyalty and
0.13 for switching cost.
5. Discussion and conclusion
This study considers a firm as a user of information systems and empirically analyzes
how loyalty and switching costs significantly influence on firm’s post-adoption
behavior in using IS service. All of our hypotheses about firm’s post-adoption behavior
are supported by the analysis of collected data. The results show that loyalty is
influenced by user’s perceived benefits while switching costs are influenced by
additional service specific investments to use IS, and both loyalty and switching costs
have influence on determining users’ post-adoption behavior. Furthermore, the results
show that both loyalty and switching costs have positive relationships to firm’s
continuous intention to use the current system and negative relationships to firm’s
attention to an alternative system. If a firm has loyalty to the current IS service, it
0.586
0.646
0.807
0.678
0.797
0.743
0.841
0.684
0.921
Information systems quality
IS service value
Learning
Customization
User’s participation
Loyalty
Switching costs
Inattentiveness to alternatives
Continuous intention to use
0.894
0.879
0.926
0.894
0.951
0.896
0.955
0.866
0.972
CR
0.858
0.814
0.881
0.84
0.937
0.827
0.937
0.764
0.957
CA
0.765
0.479
20.004
0.497
0.446
0.475
0.360
20.312
0.436
ISQ
0.804
0.222
0.472
0.346
0.530
0.460
20.412
0.366
ISV
0.898
0.178
0.265
0.326
0.488
2 0.203
0.211
LEN
CUS
0.824
0.537
0.570
0.442
20.411
0.501
Notes: AVE – average variance extracted; CR – composite reliability; CA – Cronbach’s a
AVE
Constructs
0.893
0.479
0.473
2 0.403
0.489
PAR
0.862
0.516
20.490
0.497
LOY
0.917
2 0.473
0.471
SWC
CNT
0.960
ALT
0.827
20.422
Firm’s
post-adoption
behavior
269
Table III.
Correlation between
constructs
IMDS
114,2
Perceived Benefits
Information system
Quality
Information system
Service Value
0.232**
Outcomes
0.279**
Loyalty
(R2 = 0.415)
270
Service Specific
Investment
Participation
0.304**
0.347**
–0.334*
Continuous Intention
to use (R2 = 0.310)
0.292**
0.241**
0.380***
Switching Cost
(R2 = 0.407)
–0.301**
Inattentiveness to
alternatives (R2 = 0.306)
Learning
0.246**
Customization
Figure 2.
Results of model
Notes: *t > 1.96, **t > 2.58 and ***t > 3.29; n.s. – insignificant at the 1.96 level
would continue to use the current IS service and would not consider to other alternative
systems. Similarly, if a firm has high switching cost to the current IS service, it would
continue to use the current IS service and would not have attentive to other alternative
systems.
As shown in the results, the loyalty and switching costs are very important in
maintaining the relationship between IS users and IS service providers. For example, if
a service provider would furnish more benefits for its customers in post-adoption stage,
it would retain its customers much more easily since the user’s loyalty is strengthened
and switching costs are increased. Therefore, this study contributed to analyze firm’s
post-adoption behavior with a new perspective focusing the user-provider relationship
at the firm level and has the following theoretical implications.
First, this study expands the research scope of the post-adoption behavior in IS
service to the firm level of analysis and advances its understanding. Most of prior
studies on post-adoption behavior were mainly focused on the relationship between
individual user and system. This relationship is certainly important but the continued
use of the system is ultimately dependent on the firm’s decision. This study considers a
firm as a IS user and examines the user’s post-adoption behavior in the relationship
between user and service provider. Thus, this study contributes to deepen our
understanding of post-adoption behavior in the firm.
Second, this study presents the elements of service specific investments according
to the types of switching costs defined by Burnham et al. (2003). Linking three
constructs of participation, learning, and customization with three types of switching
costs, the study confirms that three types of switching costs are concurrently
increasing the switching costs in IS services and constrain users from switching
systems. Previous research has regarded switching costs as only constraint condition.
However, a firm may have benefits from constraints, and increase switching costs
voluntarily with the additional service specific investment. The present study confirms
that the increase of switching costs has a positive relationship to the firm’s loyalty to
the IS service, which makes the firm continue to use the adopted system. This finding
contributes to expand the understanding of the relationship between switching costs
and user’s loyalty to the systems in the post-adoption stage.
Third, this study examines how user’s loyalty and switching costs toward the IS
services influence on two major post-adoption behaviors such as continuous use or
switching to the alternatives. Traditionally, IS research focused on loyalty and
continued use, and relational marketing research focused on the relationship between
switching costs and inattentiveness to alternatives but the results of both research
were separated. This study empirically shows that loyalty may lessen the switching to
the alternatives and, in turns, switching cost may affect the continued use in IS service.
This finding has significance because we were able to integrate and bridge findings
from separate research. Furthermore, we confirmed this integration at the firm level of
analysis.
From the practical view, this study has two major implications for IS user and
service provider. In the user’s aspect, the results of this study suggest a guideline
which a firm should consider in adopting and using IS services. If a firm is planning to
adopt a new IS, it should contemplate the results of this study for using the adopted IS
continuously. That is to say, a firm should consider system quality in the first place
because this will be fundamental to increase the firm’s loyalty to the adopted systems.
But, a firm should also consider the services provided by a service providing company
after adopting the systems. Thus, it is important that a firm needs to confirm the
services in the post-adoption stages such as training or customization, etc.
Furthermore, a firm should make sure whether to receive continuous services from
the service provider when the adopted systems have certain troubles.
In the service provider’s aspect, the customer retention is one of the most important
for a service providing company. It is regardless to say that the customer retention is
much more important than a new customer acquisition. This importance is also
significant in IS service environment. Now that IS service providers should regularly
offer certain services to update or improve the systems, the relationship with customer
is very important. When users decided to switch IS, switching loss is not only existed
for the users but also for the service providers. According to this study’s findings, a
firm will make service-specific investment after IS adoption which will increase its
switching costs and have positive influence on its loyalty. It suggests the strategy how
a service provider should offer the related services to its customers in the post-adoption
stage.
Although this study has found the significant influences of loyalty and switching
costs on post-adoption behavior in IS service, there are a few limitations that should be
considered in the future research. First, the data used in this study were collected from
the decision makers of the firms which employed ERP, SCM, or CRM for supporting
their own tasks. Thus, it might have possibility for a bias from survey respondents
depending on the specific system. Second, this study focused on the loyalty and the
switching costs as major factors which can influence on the firm’s post-adoption
behavior. Future research needs to find more factors which may have influence on
post-adoption behavior at the firm level. For instance, it may be interesting to examine
a firm’s post-adoption behavior related to the importance level of the system in the
firm. A firm’s post-adoption behavior to certain system can be influenced by the
importance of the system. Third, the external validity of this study may be limited
because our findings are based on a single point cross-sectional survey. To further
Firm’s
post-adoption
behavior
271
IMDS
114,2
272
investigate the continuance of specific IT service firms, triangulation will be necessary
with longitudinal and qualitative data on the process of decision-making, including
political and contractual situation.
The findings suggest the views of what a firm ultimately needs to consider in
adopting and using the IS. Considering IS service provided in the post-adoption stage
will enhance the benefits of investment on the IS. Particularly, as the size of system is
bigger, switching to alternative systems is a big challenge once the systems are
implemented. Thus, when managers in the firm decide the adoption of new systems, it
is very important that they should deliberate on the provided IS service which enables
the company to maximize the benefit from the adopted IS utilization.
References
Barclay, C. and Osei-Bryson, K.-M. (2009), “Toward a more practical approach to evaluating
programs: the multi-objective realization approach”, Project Management Journal, Vol. 40
No. 4, pp. 74-93.
Bendapudi, N. and Berry, L.L. (1997), “Customers’ motivations for maintaining relationships with
service providers”, Journal of Retailing, Vol. 73 No. 1, pp. 15-37.
Bharadwaj, S.G., Varadarajan, P.R. and Fahy, J. (1993), “Sustainable competitive advantage in
service industries: a conceptual model and research propositions”, The Journal of
Marketing, Vol. 57, October, pp. 83-99.
Bhattacherjee, A. (2001), “Understanding information systems continuance: an expectationconfirmation model”, MIS Quarterly, Vol. 25 No. 3.
Bhattacherjee, A. (2002), “Individual trust in online firms: scale development and initial test”,
Journal of Management Information Systems, Vol. 19 No. 1, pp. 211-242.
Burnham, T., Frels, J. and Mahajan, V. (2003), “Consumer switching costs: a typology,
antecedents, and consequences”, Journal of the Academy of Marketing Science, Vol. 31
No. 2, pp. 109-126.
Chang, S.-C. and Chou, C.-M. (2011), “Factors affecting user’s online shopping behavior:
integrating the constraint-based and dedication-based relationship perspectives”, African
Journal of Business Management, Vol. 5 No. 2, pp. 370-382.
Chen, P.-T. and Hu, H.-H.S. (2012), “The mediating role of relational benefit between service
quality and customer loyalty in airline industry”, Total Quality Management & Business
Excellence, Vol. 24 Nos 9/10, pp. 1084-1095.
Chen, P.Y.S. and Hitt, L.M. (2002), “Measuring switching costs and the determinants of customer
retention in internet-enabled businesses: a study of the online brokerage industry”,
Information Systems Research, Vol. 13 No. 3, pp. 255-274.
Chin, W.W. (1998), “The partial least squares approach for structural equation modeling”,
in Marcoulides, G.A. (Ed.), Methodology for Business and Management,
Lawrence Erlbaum, Mahwah, NJ, pp. 295-336.
Chiu, C.M. and Wang, E.T.G. (2008), “Understanding web-based learning continuance
intention: the role of subjective task value”, Information & Management, Vol. 45 No. 3,
pp. 194-201.
Chiu, H.-C., Hsieh, Y.-C., Li, Y.-C. and Lee, M. (2005), “Relationship marketing and consumer
switching behavior”, Journal of Business Research, Vol. 58 No. 12, pp. 1681-1689.
Cooper, R.B. and Zmud, R.W. (1990), “Information technology implementation research:
a technological diffusion approach”, Management Science, Vol. 36 No. 2, pp. 123-139.
DeLone, W.H. and McLean, E.R. (1992), “Information systems success: the quest for the
dependent variable”, Information Systems Research, Vol. 3, pp. 60-95.
DeLone, W.H. and McLean, E.R. (2003), “The DeLone and McLean model of information systems
success: a ten-year update”, Journal of Management Information Systems, Vol. 19 No. 4,
pp. 9-30.
Fernández-Sabiote, E. and Román, S. (2012), “Adding clicks to bricks: a study of the
consequences on customer loyalty in a service context”, Electronic Commerce Research
and Applications, Vol. 11 No. 1, pp. 36-48.
Fornell, C. and Larcker, D.F. (1981), “Structural equation models with unobservable variables
and measurement error: algebra and statistics”, Journal of Marketing Research, Vol. 18
No. 3, pp. 382-388.
Gefen, D., Straub, D.W. and Boudreau, M.-C. (2000), “Structural equation modeling and
regression: guidelines for research practice”, Communications of AIS, Vol. 4 No. 7, pp. 1-79.
Goodhue, D.L. and Thompson, R.L. (1995), “Task-technology fit and individual performance”,
MIS Quarterly, Vol. 19 No. 2, pp. 213-236.
Hair, J. Jr, Anderson, R., Tatham, R. and Black, W. (1995), Multivariate Data Analysis: With
Readings, Prentice-Hall, Upper Saddle River, NJ.
Hong, S., Thong, J.Y. and Tam, K.Y. (2006), “Understanding continued information technology
usage behavior: a comparison of three models in the context of mobile internet”, Decision
Support Systems, Vol. 42 No. 3, pp. 1819-1834.
Jasperson, J., Carter, P.E. and Zmud, R.W. (2005), “A comprehensive conceptualization of
post-adoptive behaviors associated with information technology enabled work systems”,
MIS Quarterly, Vol. 29 No. 3, pp. 525-557.
Jones, M.A., Reynolds, K.E., Mothersbaugh, D.L. and Beatty, S.E. (2007), “The positive and
negative effects of switching costs on relational outcomes”, Journal of Service Research,
Vol. 9 No. 4, pp. 335-355.
Kearns, G.S. (2006), “The effect of top management support of SISP on strategic IS
management: insights from the US electric power industry”, Omega, Vol. 34 No. 3,
pp. 236-253.
Kettinger, W.J., Park, S.-H.S. and Smith, J. (2009), “Understanding the consequences of
information systems service quality on IS service reuse”, Information & Management,
Vol. 46 No. 6, pp. 335-341.
Kim, S.S. and Son, J.-Y. (2009), “Out of dedication or constraint? A dual model of post-adoption
phenomena and its empirical test in the context of online services”, MIS Quarterly, Vol. 33
No. 1, pp. 49-70.
Kim, S.S., Malhotra, N.K. and Narasimhan, S. (2005), “Research note – two competing
perspectives on automatic use: a theoretical and empirical comparison”, Information
Systems Research, Vol. 16 No. 4, pp. 418-432.
Kuo, R.-Z. and Lee, G.-G. (2011), “Knowledge management system adoption: exploring the effects
of empowering leadership, task-technology fit and compatibility”, Behaviour
& Information Technology, Vol. 30 No. 1, pp. 113-129.
Lam, S.Y., Shankar, V., Erramilli, M.K. and Murthy, B. (2004), “Customer value, satisfaction,
loyalty, and switching costs: an illustration from a business-to-business service context”,
Journal of the Academy of Marketing Science, Vol. 32 No. 3, pp. 293-311.
Lee, R. and Neale, L. (2012), “Teractions and consequences of inertia and switching costs”,
Journal of Services Marketing, Vol. 26 No. 5, pp. 365-374.
Firm’s
post-adoption
behavior
273
IMDS
114,2
274
Li, D., Browne, G.J. and Wetherbe, J.C. (2007), “Online consumers’ switching behavior:
a buyer-seller relationship perspective”, Journal of Electronic Commerce in Organizations
( JECO), Vol. 5 No. 1, pp. 30-42.
Lin, C.H., Sher, P.J. and Shih, H.Y. (2005), “Past progress and future directions in conceptualizing
customer perceived value”, International Journal of Service Industry Management, Vol. 16
No. 4, pp. 318-336.
Lindner, F. and Wald, A. (2011), “Success factors of knowledge management in temporary
organizations”, International Journal of Project Management, Vol. 29 No. 7, pp. 877-888.
McKeen, J.D. and Guimaraes, T. (1997), “Successful strategies for user participation in systems
development”, Journal of Management Information Systems, Vol. 14 No. 2, pp. 133-150.
Moore, M.L., Ratneshwar, S. and Moore, R.S. (2012), “Understanding loyalty bonds and their
impact on relationship strength: a service firm perspective”, Journal of Services Marketing,
Vol. 26 No. 4, pp. 253-264.
Oliver, R.L. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63, pp. 33-44.
Palmatier, R.W., Dant, R.P. and Grewal, D. (2007), “A comparative longitudinal analysis of
theoretical perspectives of interorganizational relationship performance”, Journal of
Marketing, Vol. 71 No. 4, pp. 172-194.
Park, J., Lee, J., Lee, H. and Truex, D. (2012), “Exploring the impact of communication
effectiveness on service quality, trust and relationship commitment in IT services”,
International Journal of Information Management, Vol. 32 No. 5, pp. 459-468.
Saeed, K.A. and Abdinnour, S. (2013), “Understanding post-adoption IS usage stages: an
empirical assessment of self-service information systems”, Information Systems Journal,
Vol. 23 No. 3, pp. 219-244.
Saleem, N. (1996), “An empirical test of the contingency approach to user participation in
information systems development”, Journal of Management Information Systems, Vol. 13
No. 1, pp. 145-166.
Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M. and Lauro, C. (2005), “PLS path modeling”,
Computational Statistics & Data Analysis, Vol. 48 No. 1, pp. 159-205.
Teng, C.-I. (2010), “Customization, immersion satisfaction, and online gamer loyalty”, Computers
in Human Behavior, Vol. 26 No. 6, pp. 1547-1554.
Thatcher, J.B. and George, J.F. (2004), “Commitment, trust, and social involvement: an
exploratory study of antecedents to web shopper loyalty”, Journal of Organizational
Computing and Electronic Commerce, Vol. 14 No. 4, pp. 243-268.
Thong, J.Y.L., Hong, S.-J. and Tam, K.Y. (2006), “The effects of post-adoption beliefs on the
expectation-confirmation model for information technology continuance”, International
Journal of Human-Computer Studies, Vol. 64 No. 9, pp. 799-810.
Vatanasombut, B., Igbaria, M., Stylianou, A.C. and Rodgers, W. (2008), “Information systems
continuance intention of web-based applications customers: the case of online banking”,
Information & Management, Vol. 45 No. 7, pp. 419-428.
Wagner, J.A. III, Leana, C.R., Locke, E.A. and Schweiger, D.M. (1998), “Cognitive and
motivational frameworks in US research on participation: a meta-analysis of primary
effects”, Journal of Organizational Behavior, Vol. 18 No. 1, pp. 49-65.
Wang, E.T.G., Chang, J.Y.T., Jiang, J.-Y.J. and Klein, G. (2011), “User advocacy and information
system project performance”, International Journal of Project Management, Vol. 29 No. 2,
pp. 146-154.
Yang, Z. and Peterson, R.T. (2004), “Customer perceived value, satisfaction, and loyalty: the role
of switching costs”, Psychology & Marketing, Vol. 21 No. 10, pp. 799-822.
Yaya, L.H.P., Marimon, F. and Casadesus, M. (2011), “Customer’s loyalty and perception of ISO
9001 in online banking”, Industrial Management & Data Systems, Vol. 111 No. 8,
pp. 1194-1213.
Zeithaml, V.A. (1988), “Consumer perceptions of price, quality, and value: a means-end model
and synthesis of evidence”, The Journal of Marketing, Vol. 52 No. 3, pp. 2-22.
Firm’s
post-adoption
behavior
Further reading
Rampersad, G., Troshani, I. and Plewa, C. (2012), “IOS adoption in innovation networks: a case
study”, Industrial Management & Data Systems, Vol. 112 No. 9, pp. 1366-1382.
275
Corresponding author
Jungwoo Lee can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints