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 ShaulBar Ilan UniversityRamat Gan 52900IsraelPhone: 972-52-9283676Fax: 972-531-8899E-mail: Levi.Shaul@Live.biu.ac.il

2 Dr. Doron TauberBar Ilan UniversityRamat Gan 52900IsraelPhone: 972-54-2340731Fax: 972-54-7005809E-mail: Doron.Tauber@gmail.com

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.

# Model Source1 TAM - Technology acceptance model Davis, 19892 TAM2 - a revised model of TAM Venkatesh & Davis, 20003 UTAUT - Unified theory of acceptance and use of technology Venkatesh et al., 20034 TTF - Task technology fit model Goodhue & Thompson, 19955 TAM+TTF – a combined model Dishaw & Strong, 19996 DOI - Diffusion of Innovation model Moore & Benbasat, 1991

# Model Source7 CSE - Computer self- efficacy model Compeau & Higgins 19958 D&M - Delone and McLean IS success model Delone & McLean, 2003

Table 1 – eight prominent technology acceptance models

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  black-

boxed  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.

# Model Source for validated instruments 1 TAM Davis 1989; Davis et al. 19892 TAM2 Venkatesh & Davis 20003 UTAUT Venkatesh et al. 20034 TTF Goodhue & Thompson 19955 TAM+TTF Dishaw & Strong 1999, Goodhue & Thompson 19956 DOI Moore & Benbasat 19917 CSE Compeau & Higgins 1995b, Compeau et al. 19998 D&M 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.

Model Before After % change Model Before After % change1 TAM 0.24 0.31 +28% 5 TTF 0.20 0.23 +13%2 TAM2 0.25 0.35 +37% 6 CSE 0.15 0.21 +45%3 UTAUT 0.29 0.37 +27% 7 TAM+TTF 0.31 0.39 +26%4 DOI 0.32 0.41 +29%

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 Hypothesis Result Hypothesis ResultH1a Medium Support (4 of 7 positive) H4a Supported H9 SupportedH1b Week Support (1 of 7 positive) H4b Supported H10 SupportedH1c Strong Support (7 of 7 positive) H5a Supported H11 Not Supported H2a Strong Support (5 of 5 positive) H5b Supported H12 SupportedH2b Strong Support (5 of 5 positive) H6 Supported H13 SupportedH3a Strong Support (3 of 4 positive) H7 Supported

H3b Strong Support (4 of 4 positive) H8 Supported

Table 13- Hypotheses results

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 influence-

intention 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.

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