End-Users Acceptance of Enterprise Resource Planning Systems: An Investigation of Antecedents

Transcription

1 End-Users Acceptance of Enterprise Resource Planning Systems: An Investigation of Antecedents Lisa Seymour, Wadzanai Makanya, Simon Berrangé University of Cape Town, South Africa Abstract Businesses that are implementing Enterprise Resource Planning (ERP) systems are becoming more concerned with, and realising the importance of, end-user acceptance, a key success factor of ERP implementations (Tchokogué, Bareil & Duguay, 2005, Calisir & Calisir, 2004). Previous research concerning ERP acceptance has been based on the popular Technology Acceptance Model first proposed by Davis (1989). Criticism against applying the Technology Acceptance Model (TAM) to examine ERP acceptance is that the use of an ERP is mandatory while an implicit assumption of TAM is that users of the information systems have some level of choice with regard to the extent that they use the technology. This research paper aims to investigate the factors that affect end-user acceptance of ERP systems and is primarily based on the Unified Theory of Acceptance and Use of Technology (UTAUT) first proposed by Venkatesh, Morris, Davis and Davis (2003). UTAUT is considered as an improvement on the TAM models when evaluating end-user acceptance of ERP systems because it purports to consider the mandatory nature of ERP systems. This paper evaluates the factors that make up UTAUT and leads to the proposal of a research model that is an adjustment of UTAUT. The model contains the dependant variable symbolic adoption that has been shown to better indicate end-user acceptance of mandatory technologies by Nah, Tan and Teh (2004). The research model is validated through a 2006 survey of users of the PeopleSoft Student Administration System at the University of Cape Town and subsequent quantitative analysis. The PeopleSoft system is mainly used for the maintenance of access to student related data and for student processes such as registration and graduation. The factors, performance expectancy; effort expectancy; project communication; training and shared belief were all found to be antecedents to symbolic adoption and age was found to have a moderating influence on the relationships between: Effort expectancy and symbolic adoption. Training and symbolic adoption. Shared belief and symbolic adoption. Project communication and symbolic adoption. These findings should be of relevance to future researchers and to organisations that intend installing ERP systems. It is hoped that practitioners pay attention to these factors that can influence end-user acceptance of a new ERP system.

2 Pg 26-2 Keywords: enterprise system; ERP; performance expectancy; effort expectancy; project communication; training; shared belief; social influence; symbolic adoption; user acceptance. Introduction Enterprise Resource Planning (ERP) systems are made up of a suite of integrated software applications that are designed to support a business core functions (Aldwani, 2001; Amaoko-Gyampah, 1999). ERP systems help organisations to reduce operating costs and improve business process management through integration of business functions and information (Aldwani, 2001). Despite the advantages associated with ERP systems, their adoption is often problematic (Amaoko-Gyampah, 1999). Approximately 50% of all ERP implementations fail to meet the adopting organisations expectations (Jasperson, Carter & Zmud, 2005; Adam & O Doherty, 2000). In an article entitled How to Overcome the Most Dangerous Issues Facing Corporations Today, the DA Consulting Group states that one of the major reasons why SAP implementations fail to achieve the business goals intended is because many companies fail to address the user side of the equation (DA Consulting Group, 2006). Aldwani, (2001) identifies end-user resistance as one of the main contributing factors towards the failure of ERP adoption. The implementation of the ERP system may have been successful but end-users often only make use of a subset of the available features (Boudreau, 2003; Ross & Weill, 2002; Jasperson et al., 2005; Yi, Wu & Tung, 2006). Wang and Liu (2005) identify end-users attitude towards an information system, their acceptance of the system and the level of intended usage of the system as factors that directly impact its successful adoption. Examining these factors is therefore important in order for organisations to function effectively (Yi et al., 2006). This research aimed to identify the factors that affect and are related to end-user acceptance of ERP Systems. The findings and conclusions of the research are relevant to organisations that intend to or are installing ERP systems and to future researchers. This research paper reviews the most recent models used to measure end-users acceptance of mandatory information systems such as ERP systems. A research model is proposed using factors identified in the literature review. A discussion of the research methodology, quantitative data analysis and results obtained then follows. The paper concludes with a validated model and proposes recommendations for future research pertaining to end-user acceptance of ERP systems. End-User s acceptance of ERP systems Many models to measure end-users acceptance of a new technology have been suggested and from these, the Technology Acceptance Model (TAM) is the most predominantly cited and used model (Wang & Liu, 2005). TAM, represented in Figure 1, was introduced by Davis (1989) and identifies the level of usage of an information system, (Actual System Use), as a superior indicator of the level of acceptance of the technology by endusers (Amaoko-Gyampah & Salam, 2004). Research has been performed using TAM to

3 Pg 26-3 examine end-user acceptance of ERP systems such as that done by Brown, Massey, Montoya-Weiss and Burkman (2002) and by Venkatesh and Davis (2000). Figure 1: Technology Acceptance Model (TAM) (Davis, 1993) One of the assumptions of research performed using TAM is that usage of the technology is voluntary. This assumption makes TAM unsuitable for evaluating ERP acceptance because the usage of ERP systems is mandatory (Amaoko-Gyampah & Salam, 2004; Davis, 2004; Nah et al., 2004). Brown et al. (2002) found that using TAM to evaluate ERP acceptance provides limited explanations of end-users behaviour, attitudes and perceptions towards the system and that the results that were collected provided misleading recommendations for organisations. Venkatesh et al. (2003) proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) represented in Figure 2 as an alternative to TAM. UTAUT is a combination of eight user acceptance models (including TAM) and is seen as an improvement to TAM. In contrast to TAM, UTAUT takes into account the fact that some systems are mandatory and others voluntary and results obtained using this model explained end-users acceptance of a mandatory account management and accounting system more clearly than the results obtained using TAM (Venkatesh et al., 2003). For this reason the UTAUT model was used as the basis of our study.

4 Pg 26-4 Figure 2: Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) Performance Expectancy and Perceived Usefulness The performance expectancy factor identified in UTAUT was derived from the perceived usefulness factor identified in TAM (Venkatesh et al., 2003). Venkatesh et al. (2003) established that performance expectancy strongly influenced usage of ERP systems and perceived usefulness was found to be the best predictor of ERP end-user satisfaction by Calisir and Calisir (2004). Venkatesh et al. (2003, p447) describe performance expectancy as the degree to which an individual believes that using the system will help him or her to attain gains in job performance. This definition is confirmed by Amaoko- Gyampah (2004) who found that end-users are more concerned with whether an ERP system can help them perform their daily job functions than with its capabilities to integrate data. This is supported by Nah, Tan & Beethe (2005) who proposed relative advantage as being more accurate than perceived usefulness. Effort Expectancy Effort expectancy is defined as the degree of ease associated with the use of the system (Venkatesh et al., 2003, p450). Effort expectancy was derived from the perceived ease of use factor identified in TAM (Venkatesh et al., 2003). Venkatesh et al. (2003) found that effort expectancy determined end-users intention to use an information system. In agreement, Amaoko-Gyampah and Salam (2004) found in an ERP study, that perceived ease of use influenced behavioural intention to use the ERP system through influencing perceived usefulness.

5 Pg 26-5 Social Influence Social influence is defined as the degree to which an individual perceives that others believe he or she should use the new system (Venkatesh et al., 2003). There is strong evidence that the attitudes and behaviours of other individuals in a user s social and work circles significantly impacts the user s actions regarding technology use (Fulk, Schmitz & Steinfield 1990; Kraut, Rice, Cool & Fish, 1998; Rice, Grant, Schmitz & Torobin, 1990; Schmitz & Fulk, 1991; Yuan, Fulk & Shumate, 2005). When new technologies are implemented within an organisation, institutional and social influence may arise because individuals experience ambiguity and uncertainty about the value of a new information technology for their work (Weick, 1990). Therefore, they strive to interpret and to assign meanings to the technology and its capabilities (Prasad, 1993). During this sense making process, individuals draw upon the prevailing institutions and social circles, in addition to exercising their own knowledge in measuring a new technology s value to themselves and the organisation (Jasperson, Sambamurthy & Zmud, 1999; Venkatesh et al., 2003). Venkatesh et al. (2003) define three constructs related to social influence: Subjective norm, social factors and image. Empirical research conducted by Venkatesh et al. (2003) found that none of these three social constructs is significant when the systems usage is voluntary. However, when system usage is mandatory these three constructs become significant in contributing to end-user acceptance (Venkatesh & Davis, 2000). Venkatesh and Davis (2000) suggested that such effects could be attributed to compliance in mandatory contexts that causes social influences to have a direct effect on intention; in contrast social influence in voluntary contexts operates by influencing perceptions about the technology (Venkatesh et al., 2003). Social influence was only found to be significant in the early stages of use (Venkatesh & Davis, 2000; Venkatesh et al., 2003). Experience was found to exert a strong moderating effect on social influence, with social influence becoming less significant as direct-use experience increased (Venkatesh et al., 2003). Facilitating Conditions: Training, Shared Belief and Project Communication Facilitating conditions are described as the degree to which an individual believes that an organisational and technical infrastructure exists to support use of the system (Venkatesh et al., 2003, p453). There are three factors that are believed to be part of facilitating conditions, namely training and support, belief in the system and project communication. Training has been identified as one of the important factors for end-user acceptance of ERP systems (Amaoko-Gyampah, 2004; Amaoko-Gyampah & Salam, 2004; Venkatesh, 1999). Amoako-Gyampah (2004) indicates how important it is for organisations installing ERP systems to get the length, timing and thoroughness of end-user training right. Aldwani (2001), Amaoko-Gyampah and Salam, (2004), Galleta, Ahuja, Hartman, Teo and Peace (1995) and Venkatesh, (1999) observed how important it is to train end-users during ERP implementations because of the proven positive influence on end-user acceptance of the system. Training gives end-users time to adjust to the change that will

6 Pg 26-6 occur with the implementation of an ERP system and allows them to gain first hand experience and explore the usefulness of the system (Amoako-Gyampah & Salam, 2004). Aldwani (2001) adds that through training, end-users can appreciate the benefits offered by an ERP system. Brown et al. (2002) also noted how training increased the selfefficacy of end-users of ERP systems because they understood better how the system could improve their job functions. Amoako-Gyampah (2004) defines shared belief as the end-users belief that the ERP system will have an overall positive effect on the organisation. Many end-users from different functional areas of a business make use of ERP systems because such systems are designed to integrate different business functions (Amoako-Gyampah, 2004). All endusers must therefore believe that the ERP system will benefit the organisation for it to be accepted (Amoako-Gyampah & Salam, 2004). Wixom and Todd (2005) consider the belief in a system to greatly influence the end-users attitude toward the information system and the usage of the system. Amoako-Gyampah (2004) states that if all end-users have a shared belief and an understanding of why the ERP system is being implemented, including how it would benefit the organisation and improve their work environment, the system will be more readily accepted. Communicating the implementation of an ERP system to end-users has been identified as an important factor in increasing end-user acceptance of the system (Ward, Hemingway & Daniel, 2000; Besson & Rowe, 2001; Nah, Lau & Kuang, 2001). Organisations need to justify the ERP system to end-users as communication of the system benefits can lead to a shared belief in the benefits of the system and curb resistance (Amoako-Gyampah, 2004; Al-Mashiri & Zairi, 2000; Oliver, Whymark & Romm, 2005). This justification must be communicated to end-users when the decision to implement the ERP system is made (Amoako-Gyampah, 2004; Aldwani, 2001). End-users who feel they are included in the decision to adopt the system from the beginning are more likely to readily accept the system because communication provides information about the new system and allows users to give feedback about any issues they may have (Amoako-Gyampah & Salam, 2004; Ward et al., 2000). In this way resistance to the new system is detected early on and measures can be taken to increase end-user acceptance (Amoako-Gyampah, 2004). Gender Venkatesh and Morris (2000) and Yi et al. (2006) acknowledged that gender influences the use of any information system in both mandatory and voluntary settings. Venkatesh and Morris (2000) and Venkatesh et al. (2003) observed how male end-users tended to be more comfortable with new systems and this contributed more to their usage of the system. Female end-users were observed to have higher levels of computer anxiety and their perceived ease of use tended to be lower than that of their male counterparts; women also weighted ease of use as a much stronger determinant of behavioural intention than men (Venkatesh & Morris, 2000). On the other hand Yi et al. (2006) found that the female end-users found the system to be more useful than their male counterparts.

7 Pg 26-7 Venkatesh et al. (2003) identified gender as a moderating variable for the following relationships: Performance expectancy and system use. Effort expectancy and system use. Social influence and system use. Age Venkatesh et al. (2003) identified age as a moderating variable for the relationship between: Performance expectancy and system use. Effort expectancy and system use. Social influence and system use. Facilitating conditions and system use. Burton-Jones and Hubona (2005) determined that age is a significant moderating factor between effort expectancy and usage of the system but that age was not a significant moderating factor between performance expectancy and system use. Older end-users tend to find new information systems more difficult to users but do not seem to find them any less useful when performing their job functions (Burton-Jones & Hubona, 2005). Morris and Venkatesh (2000) observed how older end-users have difficulties when retrieving information from systems such as ERP systems because they struggle to adjust to the new environment necessitated by the ERP system. This leads to lower performance expectancy among older workers because they do not believe that the system can help them perform their job functions. Experience Increased direct-use experience has been found to influence end-user beliefs in information systems such as ERP systems (King & Xia, 1997; Rivard & Huff, 1988; Venkatesh & Davis, 2000; Venkatesh et al., 2003) and enhance the end-user s confidence in their ability to understand and use the information systems in performing their tasks (DeLone 1988; Kraemer, Danziger, Dunkle & King, 1993; Venkatesh et al., 2003). As end-users direct-use experience with information systems increases over time, their perceptions and adoption intentions change substantially (Venkatesh & Davis, 2000; Xia & Lee, 2000). Venkatesh et al. (2003) defined experience in their empirical study as experience with the technology. They gathered their results at different stages of the training program run in conjunction with their study and found in their research that experience had a moderating influence on the relationship between: Effort expectancy and system use. Social influence and system use. Facilitating conditions and system use

8 Pg 26-8 Behavioural Intention, Use Behaviour and Symbolic Adoption According to DeLone & McLean (1992), without a well-defined dependent variable, much Information Systems research is purely speculative. The choice of dependent variable lies at the crux of the problem of ERP acceptance studies. The UTAUT model uses use behaviour (actual use of the system) as the main factor indicating acceptance of a technology (Venkatesh et al., 2003). The UTAUT model suggests that behavioural intention strongly influences use behaviour. Behavioural intention refers to the intention of an end-user to make use of the new technology. In support, (Amaoko-Gyampah & Salam, 2004) believe it appropriate to examine behavioural intention to use a technology even when usage might be mandatory. They argue that use of an ERP system includes both mandatory and voluntary usage. While, mandatory use represents the level of use needed to perform minimal job functions; usage beyond that might become voluntary. Venkatesh et al. (2003) believe that all independent variables except for facilitating conditions influence use behaviour indirectly through behavioural intention and in testing their model, only the direct effects on intention were modelled. However the research performed by Rawstorne, Jayasuriya and Caputi (1998) and by Brown et al. (2002) concluded that the behavioural intention construct may not be suitable for measuring acceptance in a mandatory environment because the results obtained provided were contradicting and limited in terms of the explanations of endusers behaviours. The questions that define behavioural intention in the research instrument used by Venkatesh et al. (2003), shown below, indicate the inappropriateness of using behavioural intention as a dependant variable since use of ERP systems is mandatory. BI1; I intend to use the system in the next <n> months. BI2: I predict I would use the system in the next <n> months. BI3: I plan to use the system in the next <n> months. Nah et al. (2004) proposed that models that use behavioural intention as a measure of acceptance of mandatory technologies such as ERP systems, be revised. Because use behaviour cannot be measured without taking into consideration behavioural intention, this factor is also deemed unsuitable as a measure of ERP acceptance Symbolic adoption has been proposed as a more superior dependant variable when measuring end-users acceptance of ERP systems by Karahanna (1999), Nah et al. (2004) and Rawstorne et al. (1998). Symbolic adoption was first suggested as a means of measuring acceptance of new technologies by Klonglan and Coward (1970) and is described as an end-user s mental acceptance of a new system (Nah et al., 2004). Rawstorne et al. (1998) believe that end-users in a mandatory setting undergo symbolic adoption before actual system acceptance takes place and that end-users in a mandatory environment will demonstrate differences in symbolic adoption and these differences can then be used to investigate and evaluate end-users adoption of ERP systems.

9 Pg 26-9 Research Model Proposed Figure 3 represents the proposed research model that was used for this research. The model is derived from UTAUT proposed by Venkatesh et al. (2003) (Figure 2) with a few adjustments. The behavioural intention factor and the Use Behaviour factor have been replaced with the symbolic adoption factor since it has been viewed as a more suitable measure of end-users acceptance of ERP systems. The voluntariness moderating variable has been removed from the model because end-users of ERP systems are mandated to use the system and therefore it has been deemed redundant to measure voluntariness. Three constructs: training, project communications and shared belief replaced the facilitating conditions construct. Although experience was included in the model it was excluded from this research because as an exploratory study, this research was done at a point in time and not longitudinally. Figure 3: Proposed Research Model When writing this paper, the research performed by Venkatesh et al. (2003) using UTAUT had not been validated for a large ERP context. Symbolic adoption had been hypothesized by Rawstorne et al. (1998) as an improved dependant variable and correlation between symbolic adoption and perceived ease of use; perceived compatibility and attitude toward system use had been found by Nah et al. (2004). This confirmed the need for validating further relationships and hence further research. Research Methodology and Hypotheses This research was conducted using quantitative means as it aimed to validate the proposed relationships between factors affecting ERP end-user acceptance and ERP end-

10 Pg user acceptance. Studies performed to measure end-users acceptance of new systems are usually conducted using a longitudinal study of a single organisation or group. The research instrument used is usually a questionnaire distributed to the end-users at different intervals starting immediately after implementation up to a 12 month period (Brown et al., 2002; Davis, Bagozzi & Warshaw, 1989; Venkatesh et al., 2003). From the model proposed the hypotheses listed in Table 1 were constructed. Hypotheses were proposed to identify antecedents of symbolic adoption. The second part of each of the hypotheses then took into consideration the moderating variables that were acknowledged as having an effect on the relationships between the independent variables and symbolic adoption. Table 1: Hypotheses Instrument Development and Data Collection The research was based on the University of Cape Town s (UCT) 2006 implementation of the PeopleSoft ERP. The project to implement a new student administration system was named the Integrated Student Information System (ISIS) and aimed to investigate the viability of implementing a new student administration system (UCT ISIS Project History, 2005). A contract with PeopleSoft was signed in 2003 and the new system went live in January Questionnaires were distributed in July 2006 to staff members in the different student administration functional areas within UCT such as the Fees office, Admissions office and the International Academic Programme Office (IAPO) as well as departmental and faculty office staff in the six academic faculties. The faculty office staff members were using the system to register or deregister students to particular courses and

11 Pg degrees while departmental staff members were using the system to maintain class lists and capture student marks. The different student administration functional areas were making use of the system for a range of purposes such as maintaining student fee accounts, capturing data pertaining to student admissions and performing pre-registration of international students. The instrument used was adapted from instruments validated in previous quantitative studies of a similar nature (Amoako-Gyampah & Salam, 2004; Calisir & Calisir, 2004; Davis, 1989; Davis et al., 1989; Nah et al., 2004; Venkatesh and Morris, 2000; Venkatesh et al., 2003; Zviran, Pliskin & Levin, 2005). The questions were answered according to a seven point Likert scale which ranged from strongly disagree to strongly agree. Sixty of the 120 questionnaires distributed were returned. One questionnaire was omitted due to the inappropriate nature of the responses, which resulted in a sample of 59. Figure 4 shows the various faculties and offices that respondents belong. From the figure it can be seen that questionnaires were distributed to a wide range of PeopleSoft end-users across UCT. It is believed that the users selected as a sample were making use of most of the functions identified for student administration. This ensured that the sample was varied and that end-users that were performing different functions gave their input. Respondents Faculties & Offices International Academic Program Office 17% UCT Fees Office 5% Health Sciences 7% Commerce 8% Admissions Office 12% Science 22% Law 13% Engineering and Built Environment 3% Humanities 13% Figure 4: Respondents Faculties & Offices.

12 Pg Data Analysis The data analysis started with testing the questions to ensure that they are suitable and reliable to be grouped and averaged, ultimately forming one individual variable that can be used to test the model. The item reliability test performed indicates whether the set of questions intended to test a single construct are reliable for testing that construct without external influence from other factors. Factor analysis was then used to test whether a group of questions is testing a single factor and not related to multiple factors or constructs (Keller & Warrack, 2000). After grouping and averaging the selected questions, t-tests were used to test whether the majority of responses differed from the neutral point (Keller & Warrack, 2000). Bivariant Spearman correlation tests (non-parametric) were performed to test the main hypotheses and then multiple and stepwise regression was performed to identify the equation that best describes the significant variables of the research model. Reliability and Item Validity Testing Table 2 acts as key to the construct codes and question summary used in the analysis and other areas of the research paper. Table 3 summarises the various Cronbach Alpha s for all initial questions and for the final set of accepted questions for each construct. Constructs with Cronbach Alpha s above 0.7, with a maximum value of 1.0, are considered reliable (Nunnaly, 1978). All constructs were considered reliable except for social influence which had a Cronbach Alpha of A Cronbach Alpha > 0.6 is often acceptable for exploratory studies and hence the construct was retained. Table 2: Key to Construct Codes.

13 Pg Table 3: Reliability Analysis Summary When conducting the factor analysis (summarised in Table 4), varimax normalisation, a factor analysis standard (Keller & Warrack, 2000), was used. Factor loadings above 0.7, with a maximum value of 1.0, are considered acceptable. Two constructs loaded in one column (the independent variable shared belief with the dependent variable symbolic adoption), while other columns had single factors. Table 4: Factor Analysis Summary Two project communication questions (PC3 and PC4) were removed as they loaded higher with training than with communication. An investigation of the questions showed

14 Pg their unsuitability. Question T4 ( The training was of adequate length and detail ) was omitted because it was neither a negative nor positive question which meant end-users that believed the training was too long may have similar responses to those who believed it was too short. Question PE7 ( I often make use of the reports and outputs provided by the system ) was omitted because many of the end-users who responded to the questionnaire had no access to the report functionality at the time of the study. A further four questions (EE1, EE3, EE5, SI3) were removed as they loaded below 0.7 and their removal resulted in an improved Cronbach alpha. Three questions which loaded below 0.7 (0.66, 0.66 and 0.69) were retained as their removal did not improve the Cronbach alpha. After the removal of the relevant questions, the question responses were averaged and t-tests were performed against Neutral (4), with results shown in Table 5. Age and Gender Table 5: t-test Summary. A limitation in the data gathered was the small number of male respondents (3 males versus 56 females). For this reason gender could not be analysed. The number of respondents from various age groups is shown in Figure 5, a good spread was obtained. To analyse the moderating effect of Age, responses were separated into two groups, those younger than 35 years (21 respondents) and those equal to or older than 35 years (38 respondents). Respondent Age Groups Below to to to to & Above

15 Pg Figure 5: Respondent Age Groups. Correlation Testing Spearman correlation tests were performed to test correlation between the variables. The results are shown in Table 6. When analysing all responses, all variables were found to be correlated at a 95% level of significance other than social influence with project communication and symbolic adoption. As the social influence construct had failed the reliability test, it was omitted from further analysis. Table 6: Spearman bi-variant correlation results. Significant correlations are in bold font. The correlations for the younger and older age groups showed different results indicating that age appears to have a moderating affect on the relationships between the variables. The only correlation that was stronger for younger respondents was the relationship between effort expectancy and symbolic adoption. Table 7: Regression Summary for Dependent Variable SA; Significant variables in the regression equation are in bold font.

16 Pg Multiple and then forward stepwise regression was performed to look at the simultaneous effect of the independent variables on symbolic adoption. The multiple regression results are shown in Table 7. The stepwise and multiple regression tests showed similar results. Because of high correlation between variables, regression equations (shown in Table 8) were generated from the stepwise regression. Table 8: Regression Equations The coefficient of determination (R 2 ) indicates what percentage of variation in the dependent variable can be explained by variation in the independent variables (Keller & Warrack, 2000). The independent variables accounted for 79% of the total variation in the dependant variable, symbolic adoption, for older respondents. This percentage went down to 56% for the total group and further down to 39% for younger respondents. Discussion of Results The results of the t-tests were shown in Table 5. Effort expectancy, project communication, shared belief and symbolic adoption showed no significant deviation from the neutral response point 4. The low levels of symbolic adoption, 6 months after implementation is disappointing but not unusual. The project put considerable emphasis on communication so a neutral response is disappointing. Training is seen as being positive which should be pleasing to the project. However, performance expectancy was seen to be low. The legacy system at UCT had been in use for over 20 years and the staff who 6 months prior had started using the PeopleSoft ERP were extremely comfortable, proficient and knowledgeable with regard to the procedures and workings of the legacy system, this could account for the low performance expectancy. Validation of the Model In support of the literature, positive linear correlation was found between performance expectancy; effort expectancy; project communication; training and shared belief and the dependent variable symbolic adoption. The relationships with their hypotheses numbers and Spearman correlation values are included in Figure 6. Together these relationships accounted for 56% of the variation in symbolic adoption. While this is a good result, it does show that there are other variables not tested in this study which impact symbolic adoption. No correlation was found between social influence and symbolic adoption. According to Venkatesh & Morris (2000), subjective norm (which forms part of social influence) reduces over time and for their study it was already found to be non-significant three months after implementation. The social influence construct did fail the item reliability test and was included for exploratory reasons only and therefore this finding is tentative.

17 Pg For this reason, Appendix 1, which contains the validated constructs, has social influence omitted. While support was found for the following relationship: H2b. The relationship between effort expectancy and symbolic adoption is influenced by age, such that the effect will be stronger for younger respondents. No support for the following hypothesis could be found as age appeared to have the opposite affect on the relationship: H1b - The relationship between performance expectancy and symbolic adoption is influenced by age, such that the effect will be stronger for younger respondents. These findings are supported by the finding of Burton-Jones and Hubona (2005) who determined that age is a significant moderating factor between effort expectancy and usage of the system but that age was not a significant moderating factor between performance expectancy and system use. Support was also found for the following three moderating relationships: H5b. The relationship between shared belief and symbolic adoption is influenced by age, such that the effect will be greater for older workers. H3b The relationship between project communication and symbolic adoption is influenced by age, such that the effect will be greater for older workers H4b. The relationship between training and symbolic adoption is influenced by age, such that the effect will be greater for older workers. Hypothesis 3b, 4b and 5b were proposed as Venkatesh et al. (2003) identified age as a moderating variable for the relationship between facilitating conditions and system use. We had split the facilitating conditions construct into three separate constructs, so this result is pleasing. Figure 6: Significant Relationships

18 Pg This study was not able to test the affect of two variables in the model. Firstly, the study was performed at a certain point in time and therefore the results obtained only indicated the level of acceptance at that point and the experience variable, which requires a longitudinal study, could not be effectively measured. Secondly, the sample selected and in fact the entire sample population did not contain enough male end-users to be able to analyse gender as a moderating variable. This limited the study. Conclusion One of the main reasons ERP implementations fail to achieve their predicted benefits is because the system is not completely accepted by end-users. The ERP implementation might have been declared successful but end-users are often only using a subset of the available features. This research aimed to identify the factors that affect and are related to end-user acceptance of ERP Systems. A model was proposed that was primarily based on the Unified Theory of Acceptance and Use of Technology (UTAUT) first proposed by Venkatesh et al. (2003). The model contained the dependant variable symbolic adoption that has been shown to better indicate end-user acceptance of mandatory technologies by Nah et al. (2004). The model was validated through a survey followed by quantitative analysis. Performance expectancy; effort expectancy; project communication; training; and shared belief were all found to be antecedents to symbolic adoption and age was found to have a moderating influence on the relationships between: Effort expectancy and symbolic adoption. Training and symbolic adoption. Shared belief and symbolic adoption. Project communication and symbolic adoption. This study points to required further research. The social influence construct requires further work and it is suggested that this is addressed in future studies and that studies are carried out using the longitudinal approach and with sample populations that have sufficient of each gender. The findings in this paper should be of relevance to future researchers and to organisations that intend installing ERP systems. It is hoped that practitioners pay attention to these factors that can influence end-user acceptance of a new ERP system. Getting end-users to use a new ERP system correctly is much harder than the actual ERP implementation and more important. The success of an implementation should, and often is, measured by how well the system is used by endusers.

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22 Pg Appendix 1: Validated construct questions