Acceptance models of enterprise resource planning systems

advertisement
Acceptance models of enterprise resource planning systems
Levi Shaul1, Information Systems Research Department at the Bar-Ilan University (Corresponding author).
Doron Tauber2, Information Systems Research Department at the Bar-Ilan University.
1
Levi Shaul
Bar Ilan University
Ramat Gan 52900
Israel
Phone: 972-52-9283676
Fax: 972-531-8899
E-mail: [email protected]
2
Dr. Doron Tauber
Bar Ilan University
Ramat Gan 52900
Israel
Phone: 972-54-2340731
Fax: 972-54-7005809
E-mail: [email protected]
Acceptance models of enterprise resource planning systems
Abstract
Organizations perceive Enterprise Resource Planning (ERP) as a vital tool for organizational competition as it integrates
dispersed organizational systems, and enables flawless transactions and production. The importance of ERP systems has
been increasingly recognized by organizations of all kinds. Nevertheless the implementation of such systems has proved
to be difficult, in that it demands considerable resources for long periods of time. This study has several goals: (1) review
the literature on information systems acceptance models in terms of prospective individual adoption, (2) empirically
compare eight prominent models and their extensions to ERP systems, (3) examine the relationships among fundamental
constructors, (4) examine the effect of moderators on these relationships including age, gender and experience and (5)
formulate a model that integrates elements across these eight models and best describes the acceptance of ERP systems.
Keywords: Enterprise Resource Planning, ERP, Acceptance Models, Moderating variables, Information systems.
1. Introduction
Organizations consider ERP to be its backbone and a vital tool for organizational excellence because it integrates
varied organizational systems, and enables flawless transactions and production (Al-Mashari et al. 2003, Koh et al. 2008,
Parthasarathy et al. 2007).An ERP system can reduce costs, and thus lead to greater effectiveness and a better
competitive edge in terms of improved strategic initiatives and responsiveness to customers (O'Leary 2000, Sandoe et al.
2001, Rashid et al. 2002, Bharadwaj et al. 2007, Ge & VoB 2009). Enterprise system software constitutes a multi-billion
dollar industry that produces components to support a variety of business functions (Chellappa & Saraf 2010). IT
investments have grown to be the largest category of capital expenditures in United States-based businesses over the past
decade (Ranganathan & Brown 2006). Implementing an ERP system is different from implementing a traditional
software development system since it is not “built to order” but rather bought “as is”, and is transaction driven rather than
process-centric in its focus, with different levels of adaptability (Basu & Kumar 2002). Although ERP has been depicted
as a panacea in both the literature and in practice, there are many reports of difficulties in implementing ERP systems
(Ram et al. 2013). Chang (2004) reported that (a) 90% of ERP implementations are delivered late or are over -budget,
(b) enterprise initiatives show a 67% fail rate in achieving corporate goals and outcomes are considered negative or
unsuccessful, (c) more than 40% of all large-scale projects fail. Furthermore, ERP projects also fail because of errors in
managing leadership (42%), organizational and cultural (27%), human and people (23%), technology and other
dimensions (8%) (Waters 2006).
This study has several goals: (1) review the literature on information systems acceptance models in terms of
prospective individual adoption, (2) empirically compare eight prominent models and their extensions in the field of ERP
systems (Table 1), (3) examine the relationships among fundamental constructors, (4) examine the effect of moderators
on these relationships including age, gender and experience and (5) formulate a model that integrates elements across
these eight models that best captures the steps toward acceptance of ERP systems.
#
1
2
3
4
5
6
Model
TAM - Technology acceptance model
TAM2 - a revised model of TAM
UTAUT - Unified theory of acceptance and use of technology
TTF - Task technology fit model
TAM+TTF – a combined model
DOI - Diffusion of Innovation model
Source
Davis, 1989
Venkatesh & Davis, 2000
Venkatesh et al., 2003
Goodhue & Thompson, 1995
Dishaw & Strong, 1999
Moore & Benbasat, 1991
7
CSE - Computer self- efficacy model
8
D&M - Delone and McLean IS success model
Table 1 – eight prominent technology acceptance models
Compeau & Higgins 1995
Delone & McLean, 2003
2. Literature review
Along with increasing investments in new technologies, their acceptance has become a frequently studied topic in the
field of information systems. In the last two decades acceptance models have been proposed, tested, refined, extended
and unified. Previous studies have presented a variety of theoretical models to support successful ERP adoption and
implementation (Calisir & Calisir, 2004). Studies on acceptance in the field of information systems reflect two
mainstreams of research (Venkatesh et al. 2003). Each of these which has made an important and unique contribution to
the literature, although as noted by Lin et al. (2007) most empirical studies of technology acceptance models have been
limited to the technology acceptance-related issues of individual users.
One stream examines the individual psychological characteristics that influence technology acceptance, and use
intention or usage as a dependent variable (Compeau & Higgins 1995b; Davis et al. 1989). This type of approach is valid
for almost any technology. Although developed within the IS field, it nevertheless does not consider the specific
characteristics of software and makes no distinction between software, hardware and services of the IT departments
(Delone & McLean 2003). Thus although the individual perceptions are differentiated the technology is blackboxed and no specific features, tools and mechanisms are included (Bhattacherjee & Sanford 2006). The second
stream examines implementation success through the fit of the technology either overall in terms of its technological
characteristics or at the organizational level (Goodhue & Thompson 1995, Autry et al. 2010). This stream explicitly
considers the attributes of information and systems which produce information such as data quality, ability to retrieve
and consolidate required data and reliability (Moore & Benbasat 1991, Delone & McLean 1992, 2003).
Among the theoretical models within the first stream, the technology acceptance model (TAM) developed by Davis
(1989) appears to be the most widely used by technology researchers and managers because of its empirical support (Lee
et al. 2009). The TAM model draws on the theory of reasoned action (TRA) developed by Fishbein and Ajzen (1975)
and is based on the hypothesis that technology acceptance and use can be explained in terms of the individual's internal
and perceived beliefs of technology usefulness, ease of use and intentions (Davis 1989). The TAM model can be applied
to predict future technology use by examining data from the time that the technology was introduced. The TAM has
given rise to two subsequent models. TAM2, developed by Venkatesh & Davis (2000) preserves the core philosophy of
the model but incorporates additional theoretical constructs spanning social influence processes to reflect the impact on
an individual deciding to adopt or reject a new system. The UTAUT model refines how the determinants of intention and
behavior evolve over time and emphasizes that most of the key relationships in the model are moderated (e.g. age,
gender, experience) to respond to the interest in workplace environments to create equitable settings for women and men
of all ages (Venkatesh et al. 2003).
Compeau & Higgins (1995b) extended one of the most influential theories of human behavior, Social Cognitive
Theory (SCT) to the context of technology utilization. SCT, developed by Bandura (1986) defines human behavior as an
interaction of personal factors, behavior and the environment. SCT posits that learning will most likely occur if there is a
close identification between the observer and the model (i.e. the individual who is imitated) and if the observer also has a
good deal of self-efficacy. Bandura (1986) argued that an individual's self-efficacy beliefs affect behavior and function as
an important set of proximal determinants of human motivation and action which operate on action through affective
intervening processes. These include motivational process (people are more likely to expend more effort and persist
longer in a task) and cognitive process (people are more likely to take a wider picture of a task and be encouraged by
obstacles to greater effort when performing the task).
Several models draw on constructs from both streams of research. Diffusion of Innovation (DOI) theory views
innovation as communicated through certain channels over time and within a particular social system (Rogers, 1995).
The rate of adoption of innovations is influenced by five factors: relative advantage (i.e. usefulness), complexity (i.e. ease
of use), compatibility, trainability and observability (Rogers, 1995). Moore &Benbasat (1991), working in an IS context,
expanded on the Rogers' factors to generate eight factors: voluntariness, relative advantage, compatibility, image, ease of
use, result demonstrability, visibility and trialability which all impact the adoption of IT. Since the early applications of
DOI to IS research, the theory has been applied and adapted in numerous ways. However, research has consistently
found that technical compatibility, technical complexity, and relative advantage are important antecedents to the adoption
of innovations (Bradford & Florin, 2003; Crum et. al., 1996) all of which have led to a generalized and simpler model.
Dishaw & Strong (1998) adapted key models of information technology (IT) utilization behavior from the MIS
literature (TAM and TTF models) to suggest a combined model that delivers more explanatory power than either model
alone. The result is an extension of TAM to include a Task-technology fit (TTF) construct. Models that integrate
constructs from both streams of research have greater explanatory power. They argued that research using the integrated
models should lead to a better understanding of choices concerning the use of IT. Each of these combined models
provides a much needed theoretical basis for exploring the factors that explain software utilization and its links with user
performance.
Delone & McLean (1992) defined four antecedents of user acceptance and organizational benefits: system quality,
information quality, user satisfaction and user intention to use the technology. DeLone & McLean (2003) suggested that
use and intention to use are alternatives in their model, and that intention to use may be worthwhile in the context of
mandatory usage such as ERP systems. Most researchers agree with DeLone & McLean’s (2003) argument that service
quality, when properly measured, should be added to system quality and information quality as predictors of user
satisfaction and user intention to use the technology (Wang & Liao 2006).
These models have contributed to our understanding of user technology acceptance factors and their relationships.
The acceptance models in the field of information systems are based on different (and partially overlapping) sets of
dependent and independent constructs. Nevertheless they also present two limitations: their relatively low explanatory
power and inconsistent influences of the factors across studies (Sun & Zhang 2006).
3. Hypotheses
Beyond the empirical comparison of these known acceptance models as described above, this research also aims to
explore the effect of key individual user differences on the main relationships among core constructs. Agarwal & Prasad
(1999) explored the effect of individual user differences on technology acceptance. They found that each of these
moderators was fully mediated by core constructs, implying that simpler models could be constructed that exclude
individual differences. However, different studies have shown that core constructs do not fully mediate the effects of key
individual user differences (Burton-Jones & Hubona 2006; Venkatesh et al. 2003; Morris & Venkatesh 2000; Karahanna
et al. 1999; Taylor & Todd 1995a). Burton-Jones & Hubona (2006) found consistent proof of relationships between
users’ characteristics and IT in the literature. They argued that there are several justifications for key individual user
differences including the fact that older users tend to resist change and may be less able to appreciate or understand it.
They therefore perceive new IT as less useful, and find it more difficult to learn and use unfamiliar technology even if
they are willing to adopt a new IT. In addition, these authors' view most user behavior as non-cognitive and claim that
core constructs cannot fully mediate individual differences associated with user habits.
Several key individual user differences have been found to be significant in acceptance models in the context of
information systems. This study incorporates: age, gender and experience, the three best documented individual user
differences to examine the key relationships among fundamental constructors system in both mandatory and voluntary
settings (Yi et al. 2006, Burton-Jones & Hubona 2005, Morris & Venkatesh 2000). It deliberately neglects other
individual user differences because of either irrelevance to the field of ERP systems (i.e. voluntariness, since ERP is
perceived to be associated with mandatory usage) or inconsistent findings the field of information systems (i.e. level of
education).
Most of the models investigated in this study aim to measure potential user's attitudes toward adopting an information
technology (Moore & Benbasat 1991, Davis 1989, Venkatesh & Davis 2000, Dishaw & Strong 1998, Venkatesh et al.
2003). Therefore, intention to use an information technology is a prominent dependent variable in most models.
However, the CSE model developed by Compeau & Higgins (1995b), used actual usage as a dependent variable. Here
we examine the predictive validity of all models in the context of intention to enable a comparison of the models.
However, the intention construct in many technology acceptance studies has been measured via voluntary oriented
statements of usage such as "I intend", "I plan'' or "I predict". Nah et al. (2004) claimed that these measures are
inappropriate to assess acceptance of mandatory technologies such as ERP systems. Chang et al. (2008) argue that
although the use of ERP systems may not be voluntary, the understanding of system adoption from the user’s perspective
is useful in helping the organizations prepare their employees to face new challenges and learn how to make good use of
the technology. Seymour et al. (2007) suggested that this dependent variable should be redubbed the 'symbolic adoption'
variable, to describe potential adopters' mental acceptance of mandatory information technology in a better way. Based
on these the models and literature review, a number of hypotheses were formulated to identify antecedents of symbolic
adoption (Table 2). These hypotheses are refined to include the moderating variables that have been acknowledged as
having an effect on the relationships between the independent variables and symbolic adoption.
4. Research methodology
4.1. Data Collection
The authors developed eight structured questionnaires, one for each model. The instruments were adapted from
measures developed throughout the model development and from instruments validated in previous quantitative studies
of a similar nature as listed in Table 3.
#
1
2
3
4
5
6
7
8
Model
TAM
TAM2
UTAUT
TTF
TAM+TTF
DOI
CSE
D&M
Source for validated instruments
Davis 1989; Davis et al. 1989
Venkatesh & Davis 2000
Venkatesh et al. 2003
Goodhue & Thompson 1995
Dishaw & Strong 1999, Goodhue & Thompson 1995
Moore & Benbasat 1991
Compeau & Higgins 1995b, Compeau et al. 1999
Delone & McLean 2003, Iivari 2005, Ifinedo & Nahar 2007
Table 3 - Source for validated instruments
Each questionnaire consisted of two components. The first component was demographic questions about the
respondents and the extent to which they used the ERP system. This questionnaire was administered at a certain point in
time and therefore a question on prior experience in ERP systems was added to enable an analysis of the impact of
experience on adoption. The second component consisted of the items measuring the core constructs that were defined in
the models. A five point Likert-type scale was used where 1=strongly disagree to 5=strongly agree. The full
questionnaires are not shown due to space constraints. Each questionnaire was referred by approximately 100
respondents. The questionnaires were mailed, from September 2010 to December 2011 and returned by approximately
800 respondents (eight questionnaires in overall - one for each model) in the Mediterranean region working in SMEs in
which an ERP system was implemented.
Several constructs are common across models. For example, previous studies have indicated that performance
expectancy (defined in the UTAUT and CSE model) and relative advantage (defined in the DOI model) constructs are
similar (Compeau & Higgins 1995b, Davis et al. 1989, Moore & Benbasat 1991, Plouffe et al. 2001, Venkatesh et al.
2003). Therefore, to enhance the explanatory power of the following analyses, constructs that were common across
models were measured in the same manner to enlarge the data sample. Thus, for example, the analysis of the TAM model
that was returned by approximately 100 respondents could be measured on a sample size of approximately 500
respondents because the 'perceived usefulness' and 'perceive ease of use' constructs are common across five models
(TAM, TAM2, UTAUT, DOI and TAM+TTF).
4.2. Reliability analysis
A reliability analysis determines the extent to which the measurements resulting from an analysis are the result of
characteristics of the features being measured. A reliability analysis also evaluates the internal consistency of the
measurement items grouped under the core constructs in the models. In most cases and in this research as well, the
available variables were only the observed variables and therefore this method is purely theoretical. As a result, we used
an internal consistency method that is closely associated with reliability analysis and enables an empirical analysis of
measurement reliability.
Internal consistency was measured by Cronbach’s Alpha. High communality values for all sub factors indicate that
the total amount of variance that an original factor shares with all other factors is high. Hair et al. (1995) indicated that
the lowest acceptable value ranges between 0.60 and 0.70 whereas Nunnally (1978) and Fornell & Larcker (1981)
recommended a Cronbach's Alpha limit of 0.70 for reasonably high reliability.
The measurement model estimations for the models, based on the internal consistency reliability (ICR) analysis,
showed similar internal consistency values, means and standard deviations for both the entire questionnaire and the set of
reduced measurement items. In addition, the square roots of the shared variance between the constructs and their
measurement items were higher than the correlations across constructs, supporting convergent and discriminant validity.
The results of the measurement model estimations for both cases are not shown here due to space considerations.
4.3. Multicollinearity analysis
Unlike reflective measurement items where multicollinearity between construct items is desirable as illustrated by a
high Cronbach’s alpha or internal consistency scores, excessive multicollinearity in formative constructs can destabilize
the model. If measures are highly correlated, it may suggest that multiple indicators are tapping into the same aspect of
the construct (Diamantopoulos & Siguaw 2006). Therefore, to ensure that multicollinearity was not present,
multicollinearity analysis was performed using the variance inflation factor statistic (VIF). Although general statistics
theory posits that multicollinearity occurs if the VIF value is higher than 10, the authors tested multicollinearity for a
strict VIF threshold of 3.3 out of model destabilization considerations (Diamantopoulos & Siguaw 2006).
4.4. Hierarchical regression
Cronbach (1987) suggests that interaction effects should be evaluated by stepwise hierarchical regression. Prior to the
hierarchical regression an additive transformation on the predictor variables should be performed. The transformation for
a given predictor involves subtracting the mean of the predictor variable from each individual's raw score on that
predictor, thus forming deviation scores. To eliminate the effect of multicollinearity of variables, the interaction term
was formed by multiplying the two centered variables together (Aiken & West 1991). Thus, such a transformation will
yield low correlations between the product term and the component parts of the term. This is desirable, because it
decreases the probability of computational errors (Jaccard et al. 1990). In the first step, we entered the independent
variables into the regression model to verify the main effects of the independent variables. Then, in a separate step, the
product of the independent variables, which represents the moderator effect, was entered. This stepwise hierarchical
approach provides an unambiguous test of moderator effects (Aiken & West 1991). Furthermore, to determine the nature
of this interaction, we performed a simple slopes analysis (Aiken & West 1991). Past studies have used this technique for
determining the influence of potential moderator variables (Stone & Hollenbeck 1989).
5. Results
The variance explained by the models, without the inclusion of the moderating variables, was relatively modest, as
presented in Table 12. In addition, the variance explained by the models after the inclusion of the moderating variables
increased across all models. However, the variance explained by the models, in the field of ERP systems, in an absolute
manner, even after the inclusion of moderating variables, increased only slightly and at best only accounts for 41% of the
variance. The models show a 29% increase in explained variance (on average) whereas the CSE model shows the
highest percentage of increase in explained variance after including the moderating variables (45%) but nevertheless
shows the least explained variance in both cases (before and after the inclusion of moderating variables- 15% and 21%
respectively). The D&M model does not include the influence of any moderating variables and therefore was analyzed
for the influence of core constructs alone.
1
2
3
4
Model
TAM
TAM2
UTAUT
DOI
Before
0.24
0.25
0.29
0.32
After
0.31
0.35
0.37
0.41
% change
+28%
+37%
+27%
+29%
5
6
7
Model
TTF
CSE
TAM+TTF
Before
0.20
0.15
0.31
After
0.23
0.21
0.39
% change
+13%
+45%
+26%
Table 4 - Variance explained by the models before and after including moderating variables
With regard to TAM model and its extensions (i.e. TAM2 and UTAUT) the findings indicate that newer versions
increased the amount of explained variance of the previous model both before including the moderating variables (i.e.
TAM explains 24%, TAM2: 25% and UTAUT: 29%) and after (i.e. TAM explains 31%, TAM2: 35% and UTAUT:
37%). In addition, three models - DOI, the combined model (TAM+TTF) and UTAUT model - showed the highest
explained variance in both cases. These three models, in contrast to the other models, are not focused solely on the
individual perspective but include organizational and management dimensions in addition to the individual dimensions.
Brown et al. (2002) found that using TAM to evaluate ERP acceptance provided a limited explanation of end-users’
behavior, attitudes and perceptions towards the system, and thus delivers misleading recommendations for organizations.
In addition, UTAUT is considered an improvement over the TAM extension models when evaluating end-user
acceptance of ERP systems because it makes it possible to consider the mandatory nature of ERP systems. An implicit
assumption of earlier technology acceptance models (i.e. TAM, TAM2) is that users of the information systems have
some level of choice with regard to the extent that they use the technology (Amaoko-Gyampah & Salam, 2004, Nah et al.
2004). Furthermore, the UTAUT model incorporates a facilitating conditions construct which is defined as the objective
factors, such as the provision of support for users, in the environment that makes an application easy to use. The DOI
model is based on a diffusion process developed by Rogers (1962) which is defined as a communicative process rather
than an individually focused process. Thus, the DOI model introduces variables related to the organizational aspects such
as result demonstrability, trialability and visibility within the organization. In this sense, the DOI model is considered an
improvement over previous models when evaluating end-user acceptance of ERP systems.
It is important to emphasize that most of the key relationships in the models were moderated. Gender, which has
received more attention in the literature, was found to be a key moderating influence. User prior experience in complex
IT settings, such as ERP systems, was the second key moderating variable. According to Venkatesh et al. (2003) another
moderating variable, age, has received little attention in the technology acceptance research literature. Our findings
indicate that in the context of complex IT settings, age emerges as an important moderator of key relationships in the
models.
Hypothesis
Result
H1a
Medium Support (4 of 7 positive)
H1b
Week Support (1 of 7 positive)
H1c
Strong Support (7 of 7 positive)
H2a
Strong Support (5 of 5 positive)
H2b
Strong Support (5 of 5 positive)
H3a
Strong Support (3 of 4 positive)
H3b
Strong Support (4 of 4 positive)
Table 13- Hypotheses results
Hypothesis
H4a
H4b
H5a
H5b
H6
H7
H8
Result
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Hypothesis
H9
H10
H11
H12
H13
Result
Supported
Supported
Not Supported
Supported
Supported
The perceived usefulness, performance expectancy, relative advantage and task-technology-fit constructs were
acknowledged by previous studies as similar (Calisir et al. 2009, Venkatesh et al. 2003). These constructs, in this study,
were not found to be significant within all models. This finding corroborates a few studies in the field of ERP (Seymour
et al. 2007) but is inconsistent with most general information systems acceptance research. This result is nevertheless is
very significant in that it shows that in a complex technology implementation environment such as ERP implementation,
unlike less complex environments, the perceived usefulness of the technology is perhaps less important than its ease of
use. Many organizations are committed to a “vanilla” implementation to avoid ERP software modifications and business
process re-engineering in particular to align best business standards for a successful ERP implementation (Al-Mudimigh
2007, Finney & Corbett 2007, El-Sawah et al. 2008). Consequently, potential adopters are less troubled by how to
execute old processes in the new system because of the obligation to run new business processes based on best practice
that are already well implemented in the ERP system with minimal changes needed. Thus, managerial attempts that have
focused on enhancing the perceived usefulness of the ERP system will be less worthwhile than the managerial attempts
focused on enhancing the perceived ease of use. In addition, in cases where these similar constructs were found to be
significant, they were not found to be the strongest predictor of user symbolic adoption by contrast to several studies.
These results perhaps suggest that perceived usefulness has lower explanatory power in comparison to other constructs in
the context of complex IT settings.
Contrary to predictions and in contrast to previous studies, the results indicate, that the influence of usefulness
constructs on symbolic adoption was not moderated by age or gender. Venkatesh et al. (2003) posited that since men tend
to be highly task-oriented, performance expectancy centered on task accomplishment is likely to be especially important
to men because of socialization processes. In addition, they argued that research on age differences indicates that younger
users may place more importance on extrinsic rewards. However, in the case of ERP systems the latter may be perceived
as rich in functionality and beyond the needs of the reasonable user (Yi et al. 2006). Therefore, users' main concern may
be the extent to which the ERP system is easy to use rather than the extent to which the system is useful. Thus, the
present study reveals that age and gender differences do not play a role in ERPs contexts with regard to the perceived
usefulness construct.
Another frequent hypothesis concerns the potential moderating effect of experience. According to Castaneda et al.
(2007) user beliefs are the key perceptions driving IT usage and may change with time as users gain experience. It was
found that the effect of perceived usefulness on user symbolic adoption increases with increasing experience. One
explanation may be related to training programs. Users' training is important not only for acquiring skills but also enables
adjustment to changes created by the implementation of an ERP system and allows potential adopters to get firsthand
experience and explore the ERP system (Amoako-Gyampah & Salam, 2004, Aldwani 2001, Brown et al. 2002).
Experienced users evaluate a system in a more in-depth way and hence may consider perceived usefulness to a greater
extent than inexperienced ones (Jasperson et al. 2005).
In this study, and consistent with most previous studies, perceived ease of use, as formulated by different constructs
(e.g. effort expectancy), was found to be a significant predictor of user symbolic adoption. Furthermore, in the context of
moderating factors, and consistent with previous research (e.g., Agarwal & Prasad 1997, 1998; Davis et al. 1989;
Thompson et al. 1991, 1994, Morris & Venkatesh 2000), less experienced younger woman ascribed more importance to
ease of use aspects than men, as they tend to gain efficacy over time. Age differences have been associated with growing
difficulty in processing complex stimuli and allocating attention to information on the job (Venkatesh et al .2003). Scott
& Walczak (2009) suggested that ERP users in organizations with diverse ages often find ERP training challenging,
despite their work experience. In addition, it was found that women may place more importance on ease of use aspects
than men because of individual perceptions related to gender roles. Thus, age, gender and experience differences exist in
the context of ERPs.
Consistent with most previous studies in mandatory settings, the results showed for all models that the social
influence construct is a significant predictor of symbolic adoption. In addition and in line with previous research, the
social influence effect on symbolic adoption of ERP system was moderated by: 1) age because affiliation requirements
increase with age, 2) gender because women tend to be more sensitive to others’ opinions and 3) experience, in
mandatory settings, because in the early stages of individual experience social issues impact the technology and its roles
but eroding over time and eventually become non-significant with sustained usage (Venkatesh & Davis 2000, Morris &
Venkatesh 2000, Venkatesh et al. 2003). Thus, these moderating variables simultaneously influence the social influenceintention relationship not only in a simple technology environment but in a complex technology environment as well.
The facilitating conditions construct, in the context of information systems, is associated with the provision of IT
support. Venkatesh (2000) argued that effort expectancy fully mediates the effect of facilitating conditions on intention
because facilitating condition issues (e.g. support) are largely captured within the effort expectancy construct which taps
the ease with which that tool can be applied. Thus in the context of complex IT settings, such as an ERP system, these
constructs may not share similar themes since the support given to users may not be good enough to satisfy users and
deliver an ease of use experience. The current results show that in complex IT settings such as an ERP system, this
construct is not fully mediated by effort expectancy and influences symbolic adoption considerably. In addition and
consistent with previous studies, this study shows that the effect of facilitating conditions on symbolic adoption increases
with experience in that users gradually find multiple avenues for help and support. Age also has an effect since older
users attach more importance to receiving help and assistance on the job which is more strongly emphasized in the
context of a complex IT because of the increasing cognitive and physical limitations associated with age (Morris &
Venkatesh 2000, Venkatesh et al. 2003).
Self efficacy and anxiety constructs emerged as significant direct determinants of intention. McIlroy et al. (2001)
found that the male- female gap in computer anxiety, which initially showed women to be more anxious, is slightly
declining but still persists in the USA. In addition, although affect was found to be a significant determinant of user
symbolic adoption, previous research has shown that affect, associated with intention to use, is fully mediated by
performance and effort expectancy (Venkatesh et al. 2003).
Rogers (1995) related compatibility with existing values, belief, past experiences and the needs of potential adopters.
Since the early applications of DOI to IS research, this theory has been applied and adapted in numerous ways. Several
studies defined compatibility as the extent to which the innovation is perceived to be consistent with the potential
adopters' existing values, previous experience and needs. Other studies defined it in terms of technical compatibility with
regard solely to hardware and software issues (Bradford & Florin 2003). Nevertheless, studies have consistently found
that technical compatibility is an important antecedent to the adoption of innovations (Bradford & Florin, 2003).
However, in terms of ERP packages, compatibility, from a standards perspective, may be broader.
Iivari (2005) found that system quality emerged as more significant than information quality, presumably because of
the mandatory nature of analyzing the system for acceptance. The present study is consistent with Iivari's (2005) study.
Since an ERP system is used on a daily basis in organizations, it is natural that the information output is timely.
However, Zhang et al. (2004) argued that the variables of information quality and system quality from the D&M model
should be modified to take the specific conditions of a large mature off- the- shelf ERP package into account. First, in the
environment of an ERP system, the integrity of raw input data affects others users who operates the different modules.
Second, ERP system packages have been developed for many years and used in many sites, which enables the packages
to be very mature and reliable. In addition, this study showed that service quality is a significant predictor of symbolic
adoption.
5.1. Enterprise resource planning acceptance model
A major paradigm in psychology and marketing argues that affect (defined as an umbrella for a set of more specific
mental processes including emotions, moods, and attitudes) and cognition (referring to more specific mental processes
are separate and partially independent systems (Zajonc, 1984). Most models or theories in IS focus on the cognitive and
behavioral aspects of human decision-making processes and on individual reactions to using technologies in
organizations (Sun & Zhang 2006).
The basic idea in the model proposed below is that a user's symbolic adoption of an information system in complex
IT settings is influenced by cognitive reactions and technical features that are considered separate and partially
independent systems. The hypothesis is that these two components together determine the user's final symbolic adoption.
We drew on the analysis above to identify several key constructs and key moderators to make up the main dimensions
of the model (see Figure 9). The model is based on the incorporation of the main constructs defined in previous research
in the field of information systems that are thought to be significant in the field of ERP systems, as described in Table 13.
With regard to ERP systems we assumed that the facilitating condition construct is very similar to the service quality
construct in terms of the extent to which an individual believes that an organizational and technical infrastructure exists
to support use of the system. In addition, task-technology-fit and compatibility are very similar constructs. The
compatibility construct incorporates items that tap the fit between all aspects of an individual’s work and the use of the
system in the organization (Venkatesh et al. 2003). These aspects are covered by three constructs in the new model: 1)
perceived usefulness, defined by the degree to which a person believes that using an IS system will enhance his job
performance, 2) level of integration, which influences job performance beyond users' initial perception and 3) offset from
standard, which can increase job performance, and its counterpart, hazard system quality.
In this study, as in previous work, the CSE model was analyzed for the effect of these constructs on users' willingness
to use the system (dropping the ease of use construct). According to Venkatesh et al. (2003) self-efficacy and anxiety are
theorized not to be direct determinants of intention. Previous research has shown that self-efficacy and anxiety are
conceptually and empirically distinct from perceived ease of use and yet are fully mediated by perceived ease of use in
explaining intention to use and thus were modeled as indirect determinants of user symbolic adoption. Therefore, the
suggested model ignores the self-efficacy and anxiety construct although they were found significant.
5.2. Service Quality
The Service Quality construct is defined as the overall support delivered by the service provider, and applies
regardless of whether this support is delivered by the IS department, a new organizational unit, or outsourced (Delone
and McLean 2003). Support of users by the service provider is often measured by the assurance, responsiveness,
reliability, and empathy of the support organization (Petter & McLean 2009). The inclusion of service quality in the
updated DeLone & McLean (2003) model reflects IS functions or IS organizations rather than IS applications, to reflect
the importance of service and support in successful information system (Iivari 2005, Wu &Wang 2006). It was added
because the changing nature of IS called for a measure to assess service quality when evaluating IS acceptance (Petter &
McLean 2009). Lin et al. (2006) argued that system quality and information quality may be the most important quality
dimensions whereas service quality may be the most important factor for measuring the overall success of the IS
department. Therefore, service quality was not considered in their study, because their focus was to measure the success
of ERP systems rather than the IS department. However, researchers believe that service quality is an important element
in information system success (Landrum & Prybutok 2004, Bienstock et al. 2008). Although a claim could be made that
service quality is merely a subset of the system quality, the changes in the role of IS over the last decade argue for a
separate variable (Delone & McLean 2003). Chien & Tsaur (2007) argued that service quality needs to be included to
measure service-level aspects since system quality focuses more on technology-level measures. Bienstock et al. (2008)
found empirical evidence for a significant causal relationship between service quality and constructs related to users'
satisfaction and intention to use.
5.3. Level of Integration
Organizations perceive ERP as a vital tool for organizational competition as it integrates dispersed organizational
systems and enables flawless transactions and production (Koh et al. 2008). ERP vendors traditionally offered a single
ERP system (Huang et al. 2003). ERP systems suffered from limitations in coping with integration challenges dealing
with changing requirements. However, companies preferred to implement an ERP suite from one vendor that
incorporated stand-alone point solutions (that once filled functionality gaps in older ERP releases) to achieve higher
levels of integration and improve customer relationships and the supply chain's overall efficiency (Huang et al. 2003,
Tchokogue et al. 2005). However, although most companies still follow the single source approach, a significant number
of firms employ a strategy of “best of breed” ERP to maintain or create a competitive advantage (Shaul & Tauber, 2013).
ERP vendors begun to acquired products or develop their own functionality that was either comparable or better than
many of the "best of breed" applications, and hence enabled companies to maintain or create a competitive advantage
based on unique business processes, rather than adopting the same business processes which would leave no firm with an
advantage (Bradley 2008). In recent years, integration has prompted leading investments due to the functionality gap and
the need to extend and integrate the ERP system to other enterprises or "best of breed" applications (Jacobson et al.
2007). Integration was ranked as one of the leading investments for 2003, and well over 80% of U.S. companies
budgeted for some type of integration in 2002 and roughly one-third of U.S. companies defined application integration
as one of their top three IT investments in 2003 (Caruso 2003). ERP license revenue remained steady as companies
continued their efforts to broadly deploy core applications and then added complementary functionalities in later phases.
Today a greater effort is being made to integrate more mobile devices with the ERP system. ERP vendors are working to
extend ERP to these devices along with users’ other business applications. The technical stakes of the ERP concern
integration: this has involved hardware, applications, networking, supply chains and has covered more functions and
roles including decision making, stakeholders' relationships, standardization, transparency, globalization, etc.
(Akkermans et al. 2003, Lim et al. 2005, Botta-Genoulaz et al. 2005).
5.4. Offset from standard
An ERP system is radically different from traditional systems development (Dezdar & Sulaiman, 2009). ERP
systems are based on industry best practices, and are intended to be deployed as is, thus offering organizations
configuration options that allow them to incorporate their own business rules. However, there are often functionality gaps
remaining even after the configuration is complete between the best practices processes implemented within the ERP
system and the organization's pre-implementation business processes, and organizations often suffer from poor fit
between the ERP system and the organization. Organizations can avoid major misfits by applying two different strategies
to better match the delivered ERP functionality: technical customization such as rewriting part of the delivered
functionality within the ERP system, or interfacing to an external system, which is the most invasive, or finally business
process reengineering (Fryling 2010).
Customization potentially leads to more software process customization, more cycles of re-implementation and an
increase in testing activities, complexity, resources and a longer project schedule which can slow down the project and
generating risky bugs in both present and in future maintenance. ERP vendors provide upgrades to guarantee support for
the system o 'fix' outstanding ‘bugs’, current best practices or design weaknesses (Agerfalk et al. 2009, Shaul & Tauber
2011). To avoid ERP software modifications and its consequences many organizations are committed to a “vanilla”
implementation (Al-Mudimigh 2007, Finney & Corbett 2007). However, ERP vendors have a rather different view of
customization than the adopting organizations, in that most vendors consider customization to be an evolving process
(Luo & Strong 2004).
6. Limitations
Regardless of the significance of the relationships between factors in the regression model, these relationships may
not apply to large enterprises since the respondents' experience relates to SMEs operating in the local market. SMEs,
unlike LEs, face much greater constraints in terms of the resources that can be committed to all stages of information
gathering, although the complexity and amount of IT functionality and integration requirements are often similar (Chan
et al. 2012, Shaul & Tauber 2011). As a result, SMEs are forced to make implementation compromises according to
resource constraints, which increase the risks inherent to the implementation process (Sun et al. 2005). In addition
differences in the scope of implementation in general as well as organizational, technological and environmental factors
make it difficult to present a generalized perspective on implementation (Koh & Saad 2006). Finally this study was
conducted with limited samples across different models and therefore, for practical analytical reasons, the authors
operationalized each of the core constructs in the models by using the highest-loading items from each of the respective
scales as recommended by Nunnally & Bernstein (1994).
7. Conclusion
The primary purpose of this paper was to synthesize the current state of the art with respect to users' symbolic
adoption of information technologies in complex IT settings such as ERPs. It reviewed the literature on the main
information system acceptance models and their extensions, and empirically compared them as regards ERP systems.
Each of these models makes important and unique contributions to the literature on user acceptance of IT. It also
examined the effect of key moderators on these relationships (i.e. age, gender and experience) were also examined.
The findings are consistent with previous research in less complex IT settings, with regard to the interaction between
key moderators and core construct in complex IT settings such as ERPs. For instance, in implementing enterprise
systems such as ERP systems, PEOU was found to be a significant predictor of user symbolic adoption within each
model and less experienced users place more importance on ease of use r than experienced users as they tend to gain
efficacy over time. However, the findings also show that complex IT settings are unique in a certain sense. Contrary to
initial hypotheses, and in contrast to previous studies, the influence of the perceived usefulness (defined in TAM, TAM2
and TAM+TTF models), performance expectancy (defined in the UTAUT and CSE model) and relative advantage
(defined in the DOI model) on user symbolic adoption of an ERP system is not moderated by age and gender but rather
by experience. In addition, these constructs were found to be unstable across the different studies, thus implying that
further examination is needed. Complex IT settings such as ERP systems are rich in functionalities beyond the needs of
the average user. Therefore, users' main concern may be the extent to which the ERP system is easy to use rather than the
extent to which the system is useful.
8. Future research
The acceptance of complex information technology such as ERPs is still affected by intangibles; hence future work
on adoption is critical. As shown in the review of the literature, recent efforts to develop technology acceptance models
have mostly focused on two dimensions: enriching or extending the model from theoretical perspectives and empirically
further validating the performance of the models with various innovations in different environments.
Although studies have made great progress and the variance explained by several models are respectable in terms of
behavioral research, further work should attempt to identify and test additional boundary conditions of the model to
provide an even richer understanding of technology adoption and usage behavior. In particular more attention should be
paid to investigating the influence of broad organizational, managerial, technological, operational and environmental
variables. The influence of other moderating variables such as organization size, education level, orientation (e.g.
technological, business), level of management, private vs. public sector and developing countries vs. developed countries
also deserve work. A closer examination of the role moderating variables and their psychological and organizational
basis could also shed light on their moderating role.
References
Agarwal, R., Prasad, J, 1991. Are individual differences germane to the acceptance of new information technologies?.
Decision Sciences, 30(2), 361–391.
Agerfalk, P.J., Fitzgerald, B., Slaughter, S, 2009. Flexible and distributed information systems development: state of
the art and research challenges. Information Systems Research, 20(3), 317–328.
Aiken, L.S., West, S.G, 1991. Multiple regression: testing and interpreting interactions. Newbury Park, CA: Sage.
Akkermans, H.A., Bogerd, P., Yucesanc, E., Van-Wassenhove, L.N, 2003. The impact of ERP on supply chain
management: exploratory findings from a European Delphi study. European Journal of Operational Research,
146(2), 284–301.
Al-Mudimigh, A.S, 2007. The role and impact of business process management in enterprise systems implementation.
Business Process Management Journal, 13(6), 866-874
Amoako-Gyampah, K., Salam, A.F, 2004. An extension of the Technology Acceptance Model in an ERP
Implementation Environment. Information & Management, 41(6), 731–745.
Bienstock, C.-C, Royne, M.B., Sherrell, D., Stafford, T.F., 2008. An expanded model of logistics service quality:
Incorporating logistics information technology. International Journal of Production Economics, 113(1), 205-222.
Bhattacherjee, A., Sanford, C, 2006. Influence processes for information technology acceptance: An elaboration
likelihood model. MIS Quarterly, 30(4), 805-825
Botta-Genoulaz, V., Millet, P.A., Grabot, B, 2005. A survey on the recent research literature on ERP systems.
Computers in Industry, 56(6), 510–522.
Bradley, J, 2008. Management based critical success factors in the implementation of Enterprise Resource Planning
systems. International Journal of Accounting Information Systems, 9(3), 175-200.
Brown, S.A., Massey, A.P., Montoya-Weiss, M.M., Burkman, J.R, 2002. Do I Really Have To? User Acceptance of
Mandated Technology. European Journal of Information Systems, 11(4), 283–295.
Burton-Jones, A., Hubona, G.S, 2005. Individual Differences and Usage Behaviour: Revisiting a Technology
Acceptance Model Assumption. The DATA BASE for Advances in Information Systems, 36(2), 58–77.
Calisir, F., Calisir, F, 2004. The relation of interface usability characteristics, perceived usefulness, and perceived ease
of use to end-user satisfaction with enterprise resource planning (ERP) systems. Computers in Human Behaviour,
20(4), 505–515.
Caruso, D, 2003. The World Class Challenge: Six Critical Issues Midmarket Manufacturers Must Address. AMR
Research, Boston, MA
Castaneda, J.A., Munoz-Leiva, F., Luque, T, 2007. Web Acceptance Model (WAM): Moderating effects of user
experience. Information & Management, 44(4), 384–396.
Chan, F.T.S, Chong, A.Y.L., Zhou, L., 2012. An empirical investigation of factors affecting e-collaboration diffusion in
SMEs. International Journal of Production Economics, 138(2), 329-344.
Chang, M.K., Cheung, W., Cheng, C.H., Yeung, J.H.Y., 2008. Understanding ERP system adoption from the user’s
perspective. International Journal of Production Economics, 113(2), 928-942.
Chien, S.W., Tsaur, S.M., 2007. Investigating the success of ERP systems: Case studies in three Taiwanese high-tech
industries. Computers in Industry, 58 (8-9), 783–793 .
Compeau, D.R., Higgins, C.A, 1995a. Application of Social Cognitive Theory to Training for Computer Skills.
Information Systems Research, 6(2),118-143.
Compeau, D.R., Higgins, C.A, 1995b. Computer Self-Efficacy: Development of a Measure and Initial Test. MIS
Quarterly, 19(2), 189-211.
Compeau, D.R., Higgins, C.A., Huff, S, 1999. Social Cognitive Theory and Individual Reactions to Computing
Technology: A Longitudinal Study. MIS Quarterly, 23(2), 145-158.
Cronbach, L., 1987. Statistical tests for moderator variables: Flaws in analysis recently proposed. Psychological
Bulletin, 102, 414-417.
Davis, F.D, 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS
Quarterly, 13(3), 319–339.
DeLone, W.H., McLean, E.R., 1992. Information systems success: the quest for the dependable variable. Information
Systems Research, 3(1), 60–95.
DeLone, W.H., McLean, E.R, 2003. The DeLone and McLean model of information systems success: a ten-year
update. Journal of Management Information Systems, 19(4), 9–30.
Dezdar, S., Suliman, A., 2009. Successful enterprise resource planning implementation: taxonomy of critical factors.
Industrial Management & Data Systems, 109(8), 1037-1052.
El-Sawah, S., Tharwat, A.A.E.F., Rasmy, M.H., 2008. A quantitative model to predict the Egyptian ERP
implementation success index. Business Process Management Journal, 14(3), 288–306.
Finney, S., Corbett, M., 2007. ERP implementation: a compilation and analysis of critical success factors. Business
Process Management Journal, 13(3), 329-347
Fryling, M, 2010. Estimating the impact of enterprise resource planning project management decisions on postimplementation maintenance costs: a case study using simulation modeling. Enterprise Information Systems, 4(4),
391-421.
Ge, L., VoB, S., 2009. ERP Application in China: An Overview. International Journal of Production
Economics, 122(1), 501-507.
Huang, A., Yen, D.C., Chou, D.C., Xu., Y., 2003. Corporate applications integration: Challenges, opportunities and
implementation strategies. Journal of Business and Management, 9(2), 137-150.
Iivari, J., 2005. An empirical test of the DeLone-McLean model of information system success. ACM SIGMIS
Database, 36(2), 8-27.
Jacobson, S., Shepherd, J., D’aquila, M., Carter, K., 2007. The ERP Market Sizing Report, 2006–2011. AMR Research,
Boston, MA.
Jasperson, J., Cater, P.E., Zmud, R.W., 2005. A comprehensive conceptualization of postadoptive behavior associated
with information technology enabled work systems. MIS Quarterly, 29 (3), 525–557.
Karahanna, E., Straub, D.W., Chervany, N.L., 1999. Information technology adoption across time: a cross-sectional
comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23 (2), 183–213.
Koh, S.C.L., Saad, S.M., 2006. Managing uncertainty in ERP-controlled manufacturing environments in SMEs.
International Journal of Production Economics, 101(1), 109-127.
Koh, S.C.L., Gunasekaran, A., Rajkumar, D., 2008. ERP II: The involvement, benefits and impediments of
collaborative information sharing. International Journal of Production Economics, 113(1), 245-268.
Landrum, H., Prybutok, V.R., 2004. A service quality and success model for the information service industry. European
Journal of Operational Research, 156(3), 628-642.
Lee, D.H., Lee, S.M., Olson, D.L., Chung, S.H., 2010. The effect of organizational support on ERP implementation.
Industrial Management & Data Systems, 110(2), 269-283.
Lim, E.T.K., Pan, S.L, Tan, C.W., 2005. Managing user acceptance towards enterprise resource planning (ERP)
systems: understanding the dissonance between user expectations and managerial policies. European Journal of
Information Systems, 14(2), 135–149.
Lin, H.Y., Hsu. P.Y., Ting, P.H., 2006. ERP Systems Success: An Integration of IS Success Model and Balanced
Scorecard. Journal of Research and Practice in Information Technology, 38(3), 215-228.
Luo, W., Strong, D.M., 2004. A Framework for Evaluating ERP Implementation Choices. IEEE Transactions on
Engineering Management, 51(3), 322-333.
Morris, M.G., Venkatesh. V., 2000. Age Differences in Technology Adoption Decisions: Implications for a Changing
Work Force. Personnel Psychology, 53(2), 375–403.
Nah, F.F., Tan, X., Teh, S.H., 2004. An Empirical Investigation on End-Users’ Acceptance of Enterprise Systems.
Information Resource Management Journal, 17(3), 32–53.
Nunnaly, J., 1978. Psychometric Theory. New York: McGrawHill.
Petter, S., McLean, E.R., 2009. A meta-analytic assessment of the DeLone and McLean IS success model: An
examination of IS success at the individual level. Information & Management, 46(3), 159-166.
Ram, J., Corkindale, D., Wu, M.L., 2013. Implementation critical success factors (CSFs) for ERP: Do they contribute
to implementation success and post-implementation performance?. International Journal of Production Economics,
144(1), 157-174.
Rogers, E.M., 1962. Diffusion of Innovations. New York: Free Press.
Shaul, L., Tauber, D., 2011. CSFs along ERP life-cycle in SMEs: a field study. Industrial Management & Data
Systems, 112(3), 360-384.
Shaul, L., Tauber, D., 2013. Critical Success Factors in Enterprise Resource Planning Systems: Review of the Last
Decade. ACM Computing Surveys, 45(4), 481-516.
Stone, E.F., Hollenbeck, J.R., 1989. Clarifying some controversial issues surrounding statistical procedures for
detecting moderator variables: empirical evidence and related evidence. Journal of Applied Psychology, 74(1), 310.
Sun, H., Zhang, P., 2006. The role of affect in IS research: A critical survey and a research model. In: Zhang, P.,
Galletta, D. (Eds). Series of Advances in Management Information Systems, M.E. Sharpe Publisher, 295-329.
Sun, A.Y.T, Yazdani, A., Overend, J.D., 2005. Achievement assessment for enterprise resource planning (ERP) system
implementations based on critical success factors (CSFs). International Journal of Production Economics, 98(2),
189-203.
Tchokogue, A., Bareil, C., Duguay, C.R., 2005. Key lessons from the implementation of an ERP at Pratt & Whitney
Canada. International Journal of Production Economics, 95(2), 151-163.
Venkatesh, V., Davis, F.D., 1996. A model of the antecedents of perceived ease of use: development and test, Decision
Sciences, 27(3), 451–481.
Venkatesh, V., Davis, F.D., 2000. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal
Field Studies. Management Science, 45(2), 186-204.
Venkatesh. V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User Acceptance of Information Technology: Towards a
Unified View. MIS Quarterly, 27(3), 425–478.
Wu, J.H., Wang, Y.M., 2006. Measuring KMS success: A respecification of the DeLone and McLean’s model.
Information & Management, 43(6), 728–739.
Yi, Y., Wu, Z., Tung, L.L., 2006. How Individual Differences Influence Technology Usage Behaviour? Towards an
Integrated Framework. Journal of Computer Information Systems, 46(2), 52–63.
Zajonc, R.B. 1984. On the primacy of affect. American Psychologist, 39(2), 117-123.
Zhang, Z., Lee, M.K.O., Huang, P., Zhang, L., Huang, X., 2005. A framework of ERP systems implementation success
in China: An empirical study. International Journal of Production Economics, 98(1), 56-80.
Zviran, M., Pliskin, N., Levin, R., 2005. Measuring User Satisfaction and Perceived Usefulness in the ERP context.
Journal of Computer Information Systems, 45(3), 43–52.
Download