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. 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