Abstract This paper provides an empirical assessment of the impact of Information Technology (IT) implementation, as a learning process, on the people who use computers or the products of computers in the performance of their daily activities. In particular, this study examines the changes in those people's perception of structural dimensions (the level of centralization and formalization), their perception of IT attributes (belief compatibility, work compatibility, relative advantage, complexity, and observability), and their attitudes toward IT (attitude toward change, and computer related anxiety). This study examines both direct changes produced by these constructs and their indirect changes through IT usage, user satisfaction, and user performance as mediating variables. The results show that small changes occurred between the paired constructs of centralization, formalization, belief and attitude. Meanwhile, the path coefficients of observability, complexity, and anxiety indicate that they have experienced changes of medium size; and the path coefficients of compatibility and relative advantage indicate that large changes occurred. Most of the relationships recorded are due to the direct effects of the initial measures while the indirect effects through usage, satisfaction, and performance show only small influences.

Abstract Introduction Research Framework and Research Model Measures Data Collection Data Analysis Conclusion References

 

Introduction

In the present era of globalisation, innovations in information technology (IT), centered in telecommunications and informatics, have had very substantial effects on communities and businesses. The availability of ever cheaper and more powerful personal computers, combined with the capability of telecommunication infrastructures have put increasing power into the hands of a greater number of people in organizations (Kraemer & Dedrick, 1997; Rischard, 1996; Willcocks, 1994). However, two powerful and contrary images are widely linked with the use of IT in organizations. In one view, this technology is the great problem solver, producing important gains in the efficiency and effectiveness of people in their work. In the contrasting view, the technology is a problem generator, an expensive and disruptive technology that has often failed to match its promise in many of the actual tasks to which it has been applied, has generated many negative effects on end users, namely, people who use computers or their products in the performance of their daily activities, and sometimes seems uncontrollable by these end users (Danzinger & Kraemer, 1986).  IT usage and user satisfaction are considered to be two major factors that impact on the success of an IT implementation (Kim, Suh, & Lee, 1998). They have been noted as indicators of IT acceptance by a number of studies (Baroudi, Olson, & Ives, 1986; Gelderman, 1998; Mahmood, 1995; Taylor & Todd, 1995; Thompson, Higgins, & Howell, 1991). Furthermore, IT utilization and user attitude toward technology have an impact on performance (Woodroof & Kasper, 1998; Goodhue & Thompson, 1995). Therefore, IT implementation is looked at from three angles: IT usage, user satisfaction, and user performance. This paper provides an empirical assessment of these issues, analyzing the impact of the IT implementation processes on end users. In particular, this study examines the changes in end users' perceptions of structural dimensions (the level of centralization and formalization), the changes in end users' perceptions on IT attributes (belief compatibility, work compatibility, relative advantage, complexity, and observability), and the changes in end users' attitudes toward IT (attitude toward change, and computer related anxiety). This study examines both direct changes produced by these constructs and their indirect changes through IT usage, user satisfaction, and user performance as mediating variables.

Research Framework and Research Model

Research findings in adoption and diffusion of new technology indicate various sets of variables that affect the successful implementation of IT. Researchers have defined these variables in various ways or they have grouped them differently. Nevertheless, all of these approaches in one form or another consider the environmental, human, organizational, and the technological factors to be potential factors that affect the successful adoption and implementation of IT. This study focuses on end users' perceptions of organizational structure, end users' perceptions of the attributes of IT, and end users' attitudes toward IT and how they change after the IT implementation. For the purpose of this study, a conceptual model for the change of structural, perception and attitudinal dimensions in information technology implementation processes, as shown in Figure 1, was built. Figure 1. Conceptual model   Structural Dimensions The major dimensions studied under the category of structural dimensions have been centralization, formalization, complexity, and organizational size. Two of them used in this study are: centralization and formalization. According to Lai and Guynes (1997, p.148), centralization is "the degree to which power and control are concentrated in the hands of relatively few individuals". There have been mixed views about the effects of centralization; higher degrees of this dimension have negative effects on the adoption of innovation due to severe constraints on autonomy and authority (Lai & Guynes, 1997), whereas positive effects have also been observed because, under such circumstances, it may be easier to impose the adoption and implementation of innovations (Rogers & Shoemaker, 1971; Zatlman, Duncan & Holbeck, 1973). However, many studies (Moch & Morse, 1977; Goslar, 1993, cited in Lai & Guynes, 1997) have suggested that a centralized organization can be expected to correlate negatively with the decision to adopt those innovations that are more compatible with the interests of lower-level personnel. Formalization is another attribute of organizations believed to explain significant differences in the adoption of technologies. Formalization is the degree to which an organization emphasizes rules and procedures in the role of performance of its members. It is believed that formalization has a negative effect on the adoption of innovation (Bingham, 1976; Lai & Guynes, 1997).   Technology Attribute Dimensions Despite the importance of this domain, particularly for technological innovations, past research has been plagued with a number of conceptual and methodological problems as articulated by Tornatzky and Klein (1982). Over 25 attributes (e.g. compatibility, complexity, costs, risk, trialability, relative advantage, profitability) have been studied. One of the most comprehensive treatments of this subject area was conducted by Rogers (1983). His summary of research in a variety of disciplines indicated the five most important attributes of innovations: (a) relative advantage, (b) compatibility with existing operational practices and values, (c) complexity, (d) trialability, and (e) observability (Rogers, 1983). Relative advantage, as the label implies, depicts the degree to which an innovation is perceived as being better than the existing situation it supersedes or superior to other competing alternatives, and the extent to which it can provide more benefits (Rogers, 1983). This has been evaluated on many dimensions such as profitability, productivity, time saved, and hazards removed. Compatibility refers to the degree of fit the innovation has with the adopting organizational unit and has been conceptualized to encompass two aspects: (a) fit or match with current technical and operational practices, and (b) fit with or conformance to the prevailing beliefs, attitudes, needs of receivers and value system (culture) (Rogers & Shoemaker, 1971). A greater degree of compatibility on both the dimensions has generally been observed to generate more favourable attitudes and behaviour towards adoption (Ettlie & Vellenga, 1979). However, some negative findings have also been noted (Fliegel & Kivlin, 1966). The complexity of an innovation is "the degree to which an innovation is perceived as relatively difficult to understand and use" (Tornatzky & Klein 1982, p. 35). Most of the past research has demonstrated a negative effect of complexity on adoption and implementation of innovation (Fliegel & Kivlin, 1966). Trialability is "the degree to which an innovation may be experimented with on a limited basis" (Tornatzky & Klein 1982, p. 38). Theoretically, innovation that can be tried on the instalment plan is adopted and implemented more often and more quickly than less trialable innovations. Observability is the "degree to which the results of an innovation are visible to others" (Tornatzky & Klein 1982, p. 38). The more visible the results of an innovation, the more likely the innovation is quickly adopted and implemented.   Attitudinal Dimensions This category draws links from the personality theory of organizational behaviour with an underlying premise that the characteristics of individuals can be used to predict adoption behaviour. At the individual level, the most important attitudinal factors are fear of change (Mohr, 1969; Peterson & Peterson, 1988) and the feeling of anxiety (Peterson & Peterson, 1988; Anderson, 1996). Fear of change is expressed through the concern of people about safety, security or self-esteem. It is manifested primarily through worrying about loss of skill or possible replacement by more efficient equipment. Loss of power and absence of an obvious personal benefit may also be a sufficient ground for rejection. The second attitudinal factor, anxiety, is a natural feeling of uneasiness when exploring and facing unfamiliar terrain.   IT Implementation Most researchers have argued that IT usage is one of the primary variables that affect an individual's performance. IT usage is frequently used as a surrogate for evaluating IT success and has occupied a central role in IT implementation research. IT usage has been noted as an indicator of IT acceptance (Gelderman, 1998). It reflects the interaction of IT with the users. Most studies have argued that IT usage is one of the primary variables which affects an individual's performance (DeLone & McLean, 1992; Goodhue & Thompson, 1995). Another dimension which is regarded to be a major factor in measuring implementation success is user satisfaction with the technology performance. User satisfaction reflects the interaction of IT with users. A number of researchers have found that user satisfaction has a positive association with IT usage (Baroudi et al., 1986; Cheney et al., 1986; Doll & Torkzadeh, 1991; Thompson et al, 1991; Goodhue & Thompson, 1995; Gelderman, 1998). Because the impacts of IT on organizations are so pervasive in postindustrial society, it is useful to define the domains of IT impacts that are the focus of this study. There are impacts of IT on collectivities, such as the work group, the department, the organization, or even the society, and impacts on individuals. This study focuses on the impacts of IT utilization on individuals who work in governmental agencies in terms of efficiency, effectiveness, and appropriateness (Kahen, 1995; Sharp, 1996; Sharp, 1998). DeLone and McLean (1992) in their study showed that user satisfaction affects user performance. Their findings were also supported by Gelderman (1998), who found that the relationship between user satisfaction and user performance was significant. From these theories, a research model was developed for the change of structural, perception and attitudinal dimensions in information technology implementation processes, as shown in Figure 2. Figure 2. Research model

Measures

Centralization Centralization refers to where the locus of decision making is located near the top of the management level of the organization and the consequent lack of freedom at the various levels of the organizational hierarchy in making important organization-related decisions (Hage & Aiken, 1967, Lay & Guyness, 1997). This study used four items to capture the locus of decision making responsibility.   Formalization Formalization is the amount of written documentation that directs, guides, and controls employees (Lay & Guyness, 1997). Three items were used to capture the formalization level of the organization.   Relative Advantage The technology has to offer clear benefits to the organizational members in order to be adopted. IT has to have a comparative advantage over previous practice used. Some measurements for relative advantage have been developed by various researchers (Danziger & Kraemer, 1986; Down & Mohr, 1976, 1979; Iacovau et.al, 1995; Moore and Benbasat, 1991; Panizzolo, 1998; Rogers, 1983; Rogers & Shoemaker, 1971; Tornatzky & Klein, 1982). Danziger and Kraemer (1986) summarized the benefits that might be anticipated from the use of computing in organizations. They classified the benefits into three categories, such as information benefits, efficiency benefits, and effectiveness in serving the public. The information benefits were measured by gathering the assessments of end users regarding the extent to which IT had improved four aspect of their information environment: (a) the speed with which information can be obtained; (b) the ease of access to information; (c) the availability of new information; and (d) the timeliness of the information. The efficiency benefits were measured by assessing the extent to which IT had reduced departmental staff, had reduced the cost of departmental operations, and had enabled the department to increase its work volume without corresponding increases in cost. Lastly, effectiveness was measured by including end user evaluation of whether IT had improved the department's effectiveness in serving the public.   Compatibility IT is more likely to be used if compatible with organizational members' existing values and beliefs, needs and previous experiences regarding computerized technology. Three dimensions of compatibility that are used in this study are: (a) personal values and belief compatibility, (b) workstyle compatibility, and (c) previous experience compatibility. The use of four items for the measurement of workstyle compatibility has been proposed by Moore and Benbasat (1991). The same items were used in this study. In addition, one item on previous experience and four items on values and beliefs are employed to capture the two other dimensions of compatibility.   Complexity It is quite obvious that new technologies or even ideas that are simpler to understand are more readily and rapidly accepted than those that require the adopting organization to develop new skills and understanding (Rogers, 1983). The complexity is associated with difficulty to understand and to use the technology. Two items dealing with the difficulty experienced in understanding and using the technology were employed to operationalize technological complexity.   Trialability Innovation that can be tried on an instalment plan is adopted more quickly (Rogers & Shoemaker, 1971). The trialability was operationalized by using two items.   Observability Organizational members who have been exposed to IT, hypothetically, are more likely to adopt it. The observability is defined as the opportunity to try out or view the technology (Rogers & Shoemaker, 1971). Two items were used to operationalized observability.   The Attitude Toward Change The items that were used to measure user attitude toward change are based on the Kirton Adaptor-Innovator Inventory (Kirton, 1984). The KA-I Inventory consists of 32 questions, using a five-point scale, measuring individual creativity in terms of the form or style of creativity behaviour.   Computer Related Anxiety Computer related anxiety has been found to be a detrimental factor for engagement in using computerized technology. A feeling of apprehensiveness may result in rejection of the source of uneasiness. Anderson (1996) developed a ten-item measure of computer related anxiety. Five items out of the ten items were adopted for this study.   Utilization The IT usage scale is taken from Thompson et. al. (1991). Three dimensions are suggested for IT usage: (a) intensity of use, (b) frequency of use, and (c) diversity of software packages used. The first two dimensions are also supported by Geldermen (1998). For the purpose of this study, four different measures were used to assist in capturing IT usage, namely: (a) frequency of use, (b) time of use, (c) number of tasks for which the system is used by employees, and (d) number of computer applications used by employees.   User Satisfaction Starting with the user satisfaction measure originally developed by Bailey and Pearson (1983), Ives et al. (1983) produced a shorter form by excluding 26 items from the original 39-item instrument. Raymond (1985) also adapted the instrument and developed a 20-item questionnaire. Doll and Torkzadeh (1988, 1991, 1994) developed another instrument. They promoted a 12-item scale to measure user satisfaction. The scale is a measure of overall user satisfaction that includes a measure of the satisfaction of the extent to which computer applications meet the end-user's needs with regards to five factors, namely (a) content, (b) accuracy, (c) format, (d) ease of use, and (e) timeliness. The use of these five factors with the 12-item instrument developed by Doll and Torkzadeh (1988, 1991, 1994) as a general measure of user computing satisfaction has been supported by Harrison and Rainer (1996). In addition to these five factors, two more factors were adopted from Palvia's (1996) measurement of small business user satisfaction. The two additional factors were hardware and software adequacies.   User Performance This study only concentrates on evaluating the impacts of IT utilization and user satisfaction on user performance. User performance is measured in terms of efficiency, effectiveness, and appropriateness (Kahen 1995; Sharp 1996; Sharp 1998). The importance of appropriateness has also been considered by Kahen (1995). He argued that the problems and complexity of information technology transfer to developing countries are affected by the existing local and national characteristics. Therefore a successful IT transfer should involve appropriateness criteria. Some items that have been developed by Moore and Benbasat (1991) were modified for this study.

Data Collection

The total number of agencies that participated in this study were 153 government agencies across all regions of Bali, Indonesia. Those 153 agencies employed a total of 10,034 employees. Of these, 1,427, or approximately 14 per cent, used information technology in their daily duties. They may be considered end users. A total of 1,187 questionnaire forms were distributed during the beginning of August 1999. By January 2000, a total of 957 completed questionnaire responses had been returned (81% return rate). From the total of 957 respondents, 61.5 per cent of them were male. In terms of age, the largest group of respondents was those between the ages of 31-40 years (40.2%). The second and the third largest group in the study were employees in the age group 41-50 years (25.6%) and the age group 21-30 years (23.7%) respectively. In total, 89.5 per cent of the respondents were in the age group range 21-50 years. Almost two-thirds of the government employees who participated in this survey (66.2%) had at least a tertiary diploma or a university degree. About 30 per cent had only completed their high school education. In terms of computer training and computer experience, almost one-third of them (32.8%) had never completed any training. Most of them had attended some sort of software training (64.9%), a small number of them (4.8%) had experience in attending hardware training, and only 2.5 per cent had experience in both forms of training. Among the respondents, most of them (80.75%) were operators. Only 3.9 per cent and 1.4 per cent had the experience of being a programmer and system analyst respectively. More than half of the questionnaire responses were completed by staff level members (51.2%), while most of the remaining (43.5%) were completed by mid/low managers, and only 4.8 per cent were completed by the top managers in the organizations.

Data Analysis

The data collected were analyzed using SPSS ver.10 (SPSS, 1986) and AMOS ver. 4.01 (Arbuckle & Wothke, 1999). SPSS was mainly used to do univariate and bivariate analyses. While AMOS was used to model the change and to explain the structure or pattern among a set of latent (unobserved or theoretical) variables, each was measured by one or more manifest (observed or empirical) variables.   Validity Construct validity testifies as to how well the results obtained from the use of the measure fits the theories around which the test is designed. The construct validity is usually verified through factor analytic techniques examining the items representing a particular construct that have high factor loadings on one construct and low loadings on all other constructs (Stevens, 1996). All the items representing one or more of the research constructs belonging to each domain were subjected to factor analysis.   Reliability The reliability of measurement indicates the stability and consistency with which the instrument is measuring the concept (Sekaran, 1992). In this study, the internal consistency reliability of the scales is measured by the Cronbach alpha coefficient. The factors extracted from the exploratory factor analysis were subjected to reliability checks for further simplification. From the results of these analyses, the research model was then modified accordingly.   Paired Sample t-test In order to understand the change of each item used, a paired sample t-test was employed. Each item used a scale that ranged from not at all (0) to a very large extent (5). Most of the items changed significantly (with |t-value| > 2) except for items CEN4 and DIUS. According to Cohen (1992), the effect size (ES) for the difference between independent means (d) is expressed in units of the within population standard deviation; and the lower boundarys for the small, medium, and large ESs are d=0.20, 0.50, and 0.80. The changes of the mean values are summarized in Table 1. Table 1. The Difference between paired item means No Constructs Change Description 1 Centralization small increase for all items 2 Formalization small increase for all items 3 Belief small increase for four items and medium increase for one item 4 Relative Advantage medium increase for all items 5 Compatibility medium increase for two items and large increase for one item 6 Complexity small increase for all items 7 Observability medium increase for all items 8 Attitude Toward Change small increase for all items 9 Computer Related Anxiety small decrease for all items   Trial on Direct and Indirect Changes A series of further analyses to explore the nature of change were undertaken using AMOS ver.4.01. First, a one to one direct change model was built as can be seen in Figure 3. In order to reduce the number of variables in the model, the 32-item measure for attitudes toward change were grouped into five factors (in concordance with the factor analysis result). A principal component score for each of these five factors was, then, extracted. The maximum likelihood estimations (MLE) for unstandardized and standardized values of the path coefficients and the corresponding critical ratios were estimated. The next step was to assess the goodness of fit of the model. Chi-square divided by the number of degrees of freedom was used as the goodness of fit indicator. A value of the ratio of a chi-square to the number of degrees of freedom which is less then 5 can be considered adequate for a large model (Al-Gahtani & King, 1999). Figure 3. Direct change model Other criteria are the goodness of fit index (GFI) and the adjusted goodness of fit index (AGFI), which approach unity the better the model fits the data. A third criterion is the root mean square residual (RMSR). This is a measure of the average of the residual variances and covariances, and values close to zero indicate a good model fit. Using these test criteria, the value of c 2/DF ratio, GFI, AGFI, and RMSEA of 7.8, 0.612, 0.589, and 0.084 respectively indicate that this model does not fit the data very well. This is reasonably acceptable because this model only tries to impose one-to-one relationships by ignoring other possible paths. From the measurement model it can be seen that none of the indicators has a factor loading less than 0.4 which means that all of the indicators have a reasonably high loadings on each latent variable. From the structural model, it can be inferred that a higher path coefficient indicates that less change occurred between the before and the after IT implementation constructs. The path coefficients of formal_b- formal_a, central_b-central_a, belief_b-belief_a, and attitude_b-attitude_a are 0.92, 0.75, 0.84, 0.88 respectively. These relatively large coefficients indicate that only small changes occurred between those paired constructs. The path coefficients of observ_b- observ_a, anxiety_b- anxiety_a, and complex_b-complex_a are 0.48, 0.47, 0.37 respectively. These indicate that medium changes occurred. The path coefficients of compa_b-compa_a and relad_b-relad_a are 0.27 and 0.15 respectively. These relatively small coefficients indicate large changes occurred. The variances explained (R2) for the exogenous variables are as follows: central_a (0.57), formal_a (0.84), observ_a (0.23), complex_a (0.13), compa_a (0.08), belief_a (0.71), relad_a (0.02), attitude_a (0.77) and anxiety_a (0.23). Second, a model of indirect changes through IT usage, user satisfaction, and user performance as mediating variables was build as can be seen in Figure 4. Using the same test criterion, the value of c 2/DF ratio, GFI, AGFI, and RMSEA of 5.699, 0.601, 0.582, and 0.070 respectively indicate that this model also does not fit the data very well. This is also reasonably acceptable because the model only tries to impose the indirect changes by ignoring other possible paths. From the measurement model, it can be seen that even though the structure of the loadings change slightly, none of the indicators has a factor loading less than 0.4 which means that all of them have a reasonably high loading on each latent variable. Central_b, formal_b, belief_b, and attitude_b have a positive influence on satisfaction with the unstandardized path coefficients of 0.06 (CR=2.28), 0.144 (CR=6.62), 0.08 (CR=2.59), 0.22 (CR=6.89) respectively, while relad_b has a negative influence on satisfaction with an unstandardized path coefficient of -0.09 (CR=-5.21). This negative influence indicates that the higher the initial perception of relative advantage of IT the less satisfied is the user. In other words, the higher the expectation the lower is the satisfaction. In turn, user satisfaction has positive effects on central_a, formal_a, observ_a, compa_a, belief_a, attitude_a with the unstandardized path coefficients of 0.25 (CR=4.16), 0.64 (CR=9.96), 0.42 (CR=7.59), 0.17 (CR=3.47), 0.20 (CR=4.28), 0.24 (CR=5.41), 0.32 (CR=7.39), and a negative effect on anxiety_a with the coefficient of -0.17 (CR=-2.75) which means the more the users are satisfied the less they feel anxious about computer usage. The variances explained (R2) for exogenous variables are as follows: usage (0.21), satisfaction (0.17), performance (0.20), central_a (0.04), formal_a (0.28), observ_a (0.22), complex_a (0.01), compa_a (0.13), belief_a (0.11), relad_a (0.14), attitude_a (0.24) and anxiety_a (0.03). From these two separate analyses, it was fund that the models do not fit the data well. In addition, it was also difficult to combine the two into one model and to trim the more complex model. In order to get a more detailed model that fits the data, three sub-models namely: (a) structural dimensions change model, (b) IT perception dimensions change model, and (c) attitudinal dimensions change model were built. Each separate analysis is discussed in the following sections. Figure 4. Indirect change model    Structural Dimensions Change Model A diagrammatic representation of the final structural change model can be seen in Figure 5. Since the paired error terms represent the unique term of the same indicator on two different measurement occasions, it may indeed be reasonable to assume that they are correlated over time. By adding these correlation along with the correlation between central_b and formal_b and the four other correlations (e1-e2, e3-e4, e5-e6, e7-e8), the values of c 2/DF ratio, GFI, AGFI, and RMSEA of 5.625, 0.939, 0.903, and 0.070 respectively indicate this model fits the data reasonably well. Figure 5. Structural Dimensions Change Model It can be seen in Figure 5, that central_a and formal_a are mainly influenced by central_b and formal_b with the standardized path coefficients of 0.72 and 0.86 respectively. The influences of the mediating variables are very small. Central_a is influenced by satisfaction, and formal_a is influenced by satisfaction and performance with the path coefficients of 0.09, 0.09, 0.06 respectively. The variances explained (R2) for the endogenous variables are as follows: usage (0.01), satisfaction (0.20), performance (0.40), central_a (0.51), and formal_a (0.80).   IT Perception Dimensions Change Model A diagrammatic representation of the final IT perception change model is presented in Figure 6. By allowing the paired error terms to be correlated , the model fits the data better. The value of c 2/DF ratio, GFI, AGFI, and RMSEA of 4.028, 0.871, 0.843, and 0.056 respectively indicate that this model fits the data reasonably well. Figure 6. IT Perception dimension model It can be seen in the model that compatibility after implementation (compa_a) is influenced directly by compa_b, complex_b and relad_b with the path coefficients of 0.15, 0.22, and -0.09 respectively and indirectly though performance, satisfaction and usage with the path coefficients of 0.22, 0.11, and 0.10 respectively. Observability after implementation (observ_a) is influenced directly by observ_b, relad_b, belief_b, and compa_a with the path coefficients of 0.43, -0.36, 0.13, and 0.26 respectively and indirectly through satisfaction, performance, and usage with the path coefficients of 0.17, 0.14, and -0.11 respectively. Relative advantage after implementation (relad_a) is influenced directly by relad_b, observ_b, observ_a, compa_a with the path coefficients of 0.50, -0.30, 0.69, 0.12 and indirectly through satisfaction and performance with the path coefficients of 0.90, 0.97. Complexity after implementation (complex_a) is influenced directly by complex_b and relad_a with the path coefficients of 0.71 and 0.14 respectively. There are no indirect effects through mediating variables. Belief after implementation (belief_a) is influenced directly by belief_b, relad_b, compa_b, compa_a, relad_a, complex_a with the path coefficients of 0.80, -0.38, -0.16, 0.20, 0.25, 0.11 respectively. The variances explained (R2) for the endogenous variables were as follows: usage (0.05), satisfaction (0.12), performance (0.41), observ_a (0.53), complex_a (0.55), compa_a (0.23), belief_a (0.85), relad_a (0.77).   Attitudinal Dimensions Change Model The final attitudinal dimensions change model is given in Figure 7. The values of c 2/DF ratio, GFI, AGFI, and RMSEA of 3.212, 0.942, 0.923, and 0.048 respectively and they indicate that this model fits the data very well. Figure 7. Attitudinal dimensions change model Attitude_a and anxiety_a are mainly influenced by attitude_b and anxiety_b with the standardized path coefficients of 0.59 and 0.46 respectively. The influences of the mediating variables are relatively small. Attitude_a is influenced by satisfaction, and performance with the path coefficients of 0.15 and 0.17 respectively. Anxiety is influenced negatively by usage and attitude_a with the path coefficients of -0.15 and -0.18 respectively. This means that the higher the usage and the higher the attitude toward change the less anxious the users feel. The variances explained (R2) for endogenous variables are as follows: usage (0.01), satisfaction (0.15), performance (0.41), attitude_a (0.54) and anxiety_a (0.26).   Combined Model In order to obtain a complete picture, in which the components of the whole model interact with each other, these three sub models were then combined into one model. Moreover, to simplify the model, a principal component score was extracted for each set of indicators. By allowing the exogenous variables to be correlated with each other, the final result is slightly different as can be seen in Figure 8. Using the same test criterion, the value of c 2/DF ratio, GFI, AGFI, and RMSEA of 3.353, 0.960, 0.925, and 0.050 respectively indicate that this model fits the data very well. Figure 8. Combined model For the structural dimensions, as can be seen in the model, central_a is only influenced directly by central_b with the path coefficient of 0.61. Formal_a is influenced directly by formal_b, central_b, and relad_b with the path coefficients of 0.77, -0.11, -0.14 respectively and indirectly through satisfaction and performance with the path coefficients of 0.11 and 0.06 respectively. For the IT perception dimensions, compa_a is influenced directly by compa_b, complex_b, relad_a , and belief_a with the path coefficient of 0.11, 0.10, 0.17 and 0.23 respectively and indirectly though performance and usage with the path coefficients of 0.16 and 0.10 respectively. Observ_a is influenced directly by observ_b, relad_b, formal_a and belief_a with the path coefficients of 0.31, -0.14, 0.37 and 0.25 respectively and indirectly through performance with the path coefficient of 0.18. Relad_a is influenced directly by relad_b, observ_b, observ_a, and belief_a with the path coefficients of 0.38, -0.24, 0.53, 0.16 and indirectly though satisfaction and performance with the path coefficients of 0.80 and 0.13 respectively. Complex_a is influenced directly by complex_b relad_b, and belief_a with the path coefficients of 0.15, 0.17, and 0.20 respectively. There are no indirect effects through mediating variables. Belief_a is influenced directly by belief_b and relad_b with the path coefficients of 0.69 and -0.26 respectively and indirectly through satisfaction and performance with the path coefficients of 0.12 and 0.06 respectively. For the attitudinal dimensions, attitude_a is influenced directly by attitude_b, formal_b, formal_a, and observ_a with the standardized path coefficients of 0.64, -0.32, 0.41, and 0.09 respectively and indirectly though satisfaction and performance with the path coefficients of 0.07 and 0.09 respectively. Anxiety_a is influenced directly by anxiety_b, complex_b, relad_b, and complex_a with the path coefficients of 0.41, -0.12, 0.13, and 0.23 respectively and indirectly through satisfaction and usage with the path coefficients of -0.12, -0.13. The variances explained (R2) for the endogenous variables are as follows: usage (0.07), satisfaction (0.20), performance (0.41), central_a (0.38), formal_a (0.73), observ_a (0.54), complex_a (0.10), compa_a (0.27), belief_a (0.50), relad_a (0.43), attitude_a (0.63) and anxiety_a (0.28).   Higher Level Model In order to simplify the model even further, a higher level model was build as can be seen in Figure 9. Initially, an attempt to combine two structural dimensions (centralization and formalization), five IT perception dimensions (belief, relative advantage, compatibility, complexity, and observability), and two attitudinal dimensions (attitude toward change and computer related anxiety) into a higher level construct called structure, perception, and attitude was made. However, the structural dimensions and attitudinal dimensions could not be combined due to their small correlations. Finally only the perception dimension could be combined into the perception construct. The value of c 2/DF ratio, GFI, AGFI, and RMSEA of 5.165, 0.933, 0.895, and 0.066 respectively indicate this model fit the data reasonably well. Figure 9. Higher level model In this model, central_a is only influenced directly by central_b with the path coefficient of 0.61. Formal_a is influenced directly by formal_b and central_b with the path coefficients of 0.79 and -0.14 respectively and indirectly through usage, satisfaction and performance with the path coefficients of -0.05, 0.12 and 0.08 respectively. Attitude_a is influenced directly by attitude_b, formal_b, and formal_a with the standardized path coefficient of 0.63, -0.38, and 0.61 respectively and indirectly though satisfaction and performance with the path coefficients of 0.06 and 0.10 respectively. Anxiety_a is influenced directly by anxiety_b with the path coefficient of 0.40 and indirectly through satisfaction and usage with the path coefficients of -0.12 and -0.12. Perception_b is influenced directly by perception_b, formal_a, and atitude_a with the path coefficients of 0.32, 0.32, and 0.16 respectively and indirectly through satisfaction and performance with the path coefficients of 0.13 and 0.18 respectively. The variances explained (R2) for the endogenous variables are as follows: usage (0.08), satisfaction (0.19), performance (0.40), central_a (0.38), formal_a (0.71), attitude_a (0.68), anxiety_a (0.20), and perception_a (0.55).

Conclusion

The results from this analysis provide trial models for both direct and indirect effects, three sub models, a combined model, a higher level model for predicting the change in structural dimensions, IT perceptions dimensions, and attitudinal perception both directly and indirectly though usage, satisfaction, and performance as mediating variables. The goodness of fit of these models is presented in Table 2. The partial models for direct and indirect effects do not fit the data very well. However, the three sub models, structural dimensions, IT perceptions dimensions, and attitudinal dimensions models, and the combined model fit the data very well. Although the higher level model gives simpler explanations and fit the data reasonably well, the combined model fit the data slightly better. Table 2. Model Comparison No Model c 2/DF GFI AGFI RMSEA 1 Direct Effects Model 7.367 0.693 0.613 0.082 2 Indirect Effects Model 5.699 0.601 0.582 0.070 3 Structural Dimensions Change Model 5.625 0.939 0.903 0.070 4 IT Perception Dimensions Change Model 4.028 0.871 0.843 0.056 5 Attitudinal Dimensions Change Model 3.212 0.942 0.923 0.048 6 Combined Model 3.353 0.960 0.925 0.050 7 Higher Level Model 5.165 0.933 0.895 0.066 The results show that formalization, centralization, belief and attitude have large coefficients in all models indicating that a small change occurred between those paired constructs. The path coefficients of observability, complexity, and anxiety indicate that they have experienced changes of medium size. The path coefficients of compatibility and relative advantage are small. These indicate that large changes occurred. Although the values are slightly different, all models provide largely consistent results. Most of the relationships recorded are due to the direct effects of the initial measures while the indirect effects through usage, satisfaction, and performance show only small influences. In addition to this pattern of changes, the results of this study also contribute to identifying the facilitators and inhibitors for IT implementation in local government agencies of Bali. The policy experiences of the developing countries show that the government, as the leading sector, is the trend setter. Therefore, the way information technology is used in government agencies is a significant factor in national development. (Sanwal, 1991). The higher level model shows that centralization makes no contribution to the IT implementation processes. This is partly due to the fact that most of the governmental agencies in Bali are experiencing a highly similar level of centralization. Formalization has a negative effect on IT usage. This result supports the previous findings (Bingham, 1976; Lay & Guynes, 1997). In addition, it is also found in this study that the higher the degree of formalization the higher the satisfaction and performance felt by the users. Although attitudes toward change have no effect on IT usage, it is shown that attitudes have a positive relationship with user satisfaction and user performance. However, these three constructs have experienced only small changes. On the other hand, large to medium changes occurred between the paired constructs of anxiety and perception. Anxiety has a negative effect on IT usage. However, once the users use the technology and feel satisfied with the technology, they will feel less anxious. Perception has a positive effect on IT usage, and IT usage has a positive effect on user satisfaction and user performance. User satisfaction and user performance, in turn, have positive effects on perception after the implementation process. By realizing these factors, Bali's government agencies are expected to be able to formulate better strategies in adopting and implementing IT in order to increase their service quality and productivity.

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Darmawan, I.G.N. (2000) The change of structural, perception and attitudinal dimensions in information technology implementation processes.  International Education Journal, 1 (3), 181-200. [Online] http://iej.cjb.net