Implementation of Information System Higher Education in Achieving Good University Governance (GUG)

Transcription

1 204 International Conference on Information, Communication Technology and System Implementation of Information System Higher Education in Achieving Good University Governance (GUG) Muhammad Tajuddin Department Management of Informatic STMIK Bumi Gora Mataram Mataram, Indonesia. judin6@yahoo.com Lalu Hamdani Husnan Department of Management Mataram University Mataram, Indonesia. husnan@yahoo.com, Endang Siti Astuti Department Business Adminstration Brawijaya University Malang Indonesia. Nenet Nata Sudian Jaya Department Management Mahasaraswati University Mataram Mataram, Indonesia. natasudianjaya@yahoo.com Abstract Application of Information Technology (IT) is a major requirement for private universities (PTS) in improving governance to achieve the Good University Governance (GUG). ICT grant has been awarded up to,072 PTSs from 2006 until 2008 which it is followed by a Program Development Grant of Private Higher Education (PHP-PTS). The number of ICT grant is so great, it is necessary to examine to what extent of ICT successfull and failure in achieving the GUG. The sample that is used in this research is 6 PTS under Kopertis VII of East Java Region including the University, the Institute, the Polytechnic and the Academy of ICT. The characteristics of this sample is a grant recipient during 3 years, each PTS consist of 4 respondents, namely ICT Leadership Management Academic Affairs, Finance, Website and Library, so that the the number of respondents is 244 people. The data were collected through questionnairies, interviews and depth interviews, 2 respondens used for analayzing data in this study. Quantitative data were analyzed by Path Analyses using Structural Equation Modeling (SEM) that can analyze the six variables, namely the quality system, quality information and quality services to user satisfaction and individual impact to achieve the GUG. The results showed no significant effect of the ICT success on impact individual, as well as user satisfaction is not significant to GUG, There fore, it is necessary for improvement, especially in human resources to handle ICT Key words: Application, System, Information, and the Good University Governance I. Introduction A higher education institution is an academic organization that uses information technology to assist various business processes in them (Prastowo, 2005). This form of organization has its own character that are typical to form the necessary information systems also must have its own character (Kertahadi, 998). But so far, there is no specific model of the basic framework for building information systems in higher education, especially in Indonesia ( Mutyarini and Jake, 2007). The development of information technology (IT) has provided the means for management in managing and decision-making. IT-supported information system can provide added value to the organization if it is designed to be an effective information system (Nandika and Soekartawi, 2007), the information system that indicates that the system is successful, however, the measurement or assessment of the quality of an effective information system difficult to directly such as measurement of costs and benefits (Laudon and Laudon, 2000). Difficulty ratings success and effectiveness of the information systems are directly encouraged many researchers to develop a model for assessing the success of information systems (McLeod, 996). Research in the source of this information system has been carried out to identify the factors that led to the success of information systems. One study conducted by DeLone & McLean, (992, 2002, 2003), this model quickly got a response. One reason is that the model is a simple model but is considered quite valid. Model of information system success model that is both complete but simple. This model is called the model of parsimony. Based on the theories and the results of previous studies that have examined DeLone & McLean, (992, 2002, 2003, 2004) then develop a model of parsimony, which they call information systems success model name DeLone and McLean (D & M Information System Success Model). College characteristics different from the manufacturing business entities, as well as other service /4/$ IEEE ICTS 204, Surabaya, Indonesia 77

2 providers companies (Tajuddin et al, 203) The main difference lies in the delivery of services that deal directly with customers. Leader Higher Education oversees the dean, head of the study program and all of whom are faculty colleagues and peer group, Because it is a power - oriented leadership style will be less effective than the leadership style oriented expertise in Achieving good university governance ( GUG ) although still a concept for college (Tajuddin et al., 20). needs to be understood is the emergence of two concepts of good governance and good corporate Governance (GCG), motivated by the realization that the management of an institution can not be equated with the organization of a corporation (Tajuddin et al, 202). This is because of differences in the nature and objectives of the two institutions was the basis of the formation, where the management of an institution intended to meet the needs of the public while a corporation formed to make a profit. Based on the description in the background, the authors wanted to do an empirical study designed to determine the effect of variable quality system, quality of information, quality of service, the user satisfaction, individual impact, and good university governance (GUG). II. Research Method 2.. This type of research Type of research is survey research, namely by taking a sample of the population using a questionnaire as a data collection tool that fits (Singarimbun, 989). Research survey conducted for the purpose of research behavior in the implementation of the information system of private colleges (Astuti, 2005) which provides an explanation of the relationship between variables through research and pengujuian previously formulated Research Sites The location of this study is on Private Higher Education Institution (PTS) which has received grant funding of ICT in East Java province with some of the criteria of the following considerations : ) the PTS has received a grant from the Higher Education ICT; 2) PTS has run grants, especially in the field of ICT Academic, Finance, Library and Website as an information media ; 3) the PTS has been running the college information system in the form of online or offline (Tajuddin et al, 202) Population and Sample The sample is the same as the population called the saturated sample, respondents were level leaders of the management information system in private universities as many as 244 were obtained from each of the 4 leaders PTS managing information systems, Academic, Financial Management, Library Managers and Website so that 4 x 6 PTS = 244 samples. Some considerations that the PTS has received grant funding from the ICT Directorate General of Higher Education from 2006 to 2008 in which the number of private universities in East Java that received ICT funds are as many as 6 PTSs. The detailed description of PTS of Kopertis VII received the ICT in East Java are as follows: 3 academies; 2 polytechnics; 28 colleges; 4 institutes; and 23 universities (Tajuddin et al, 202) Data Collection Techniques. In this study data collection is done by: Questionnaire, Interview; Documentation; Observations (Tajuddin et al, 202) Operational Definition of Research Variables System Quality Variable System quality means the quality of the combination of hardware and software in information systems. The focus is on the performance of a system that refers to how well the capabilities of the hardware, software, policies, procedures, information systems can provide for the needs of information users (DeLone and McLean, 992). The indicator used is 5 (five) indicators used by (Wixon and Todd, 2005), namely: a) System reliability. b) System flexibility, c) Integration of systems, d) Accessibility, and e) Timeliness Information Quality Variable Information quality refers to the output of the system information, concerning the value, benefits, relevance, and urgency of the information produced (Pitt and Watson, 997). These variables describe the quality of information that is perceived by users as measured by four (4) indicators used (Wixon and Todd, 2005), namely: a) Completeness, b) Accuracy, c) Presentation of information (Format), d) Currency Service Quality Variable Variable of service quality of information systems concerning the value of the quality of the resulting system is in accordance with the wishes of the user or not and to what extent the system can assist users in generating jobs. This variable is measured using indicators degan (Delon and McLean, 2004): a) Speed of response (quick responsiveness), b) Guarantees ( ansurance), c) Empathy ( empathy), d) Services thereafter (following up), e) Effectiveness of support online (online effectiity) User Satisfaction Variable Users Kepusasan system ( user satisfaction) is the response and feedback that appear after the user wears the information system. User attitudes toward information systems are subjective criteria like the user on how to use the system. This variable was measured with four indicators (Seddon and Kiew, 2003; McGill and Klobas, 2005) consists of: a) Satisfaction software (Software satisfaction). b) Efficiency (Efficiency), c) Effectiveness (Efectiveness), d) Satisfaction Individual Impact Variable Individual impact is the effect of information on user behavior is closely linked to performance, which improves the performance of individual users of the system. (Mason, 978) describes the impact sequence starting from receiving the 78

3 information, understanding information, the application of the information is a little market specific issues and to change the behavior of the decision, with the result of changes in organizational performance. Impact can also have a significant contribution to the user, a better understanding of the decision making is done, increase the productivity of decision-making, change or alter the activities of the users of the user 's perception of the importance or usefulness of the information system. As for the impact of individual variables (Goodu, 995) are as follows: a) Effectiveness and Productivity, b) Important and Valuable Good University Governance (GUG) Variable Good university governance itself is not a standard concept in its application, except in the case of basic managerial principles. The application can vary according to the conditions and ideologies that embraced by a nation or society. For example, good university governance in the United States are usually applied to provide full autonomy, both in terms of academic and managerial and financing, the higher education institutions as long as it can be justified. Consequently, the effect is relatively weak government and vice versa, the authority of the executive managers and board of a university to be strong (Tajuddin et al, 20). The importance of autonomy in its efforts in achieving academic excellence (i.e. in terms of teaching and research) for higher education, but the same would not apply in the case of managerial and financing. The difference this view is usually associated with important functions for the community college and the high cost of higher education. The current trend, the high cost of higher education is usually considered to burden the state and society, so that universities are considered better seek independent funding sources. In GUG measurements at the college of adopting good corporate governance (GCG) includes four indicators (Ramaswamy et al, 2008): a) Fairness, b) responsibility, c) Transparency, d) Accountability 2.6. Analysis Method Analysis is using path analysis method (Path Analysis) (Ghozali, 2007) and Structural Equation Modeling (SEM) (Hair et al, 998) Conceptual Framework Based on the theoretical study underlying this study, the research hypothesis can be formulated as shown below: Figure. Model hypothesis III. Results and Analysis 3.. Description of Research Variables This study was conducted by distributing questionnaires to the respondents who participate in the management of information and communication technology (ICT) in the private colleges in the province of East Java. The questionnaire is not worth analyzing questionnaires from respondents' answers back yet incomplete, inconsistent, not eligible sample. The results showed that only 30 private universities with the number of respondents who returned a total of 20 respondents, of these 20 after verification of data that can be used only 2 respondents. Grouping results of the 2 respondents based on the form of higher education institutions grouped into five parts, those are 3 universities with 47 respondents, institute with 4 respondents, 3 colleges with 49 respondents, 2 polytechnics with 8 respondents and academy with 4 respondents. Based on sex, is dominated by men for 64 people and 48 women. Based on the age classified into 4 intervals of = < 25 years as many as 5 people, as many as 24 people years old, years old for 5 people and a fourth group > 45 years as many as 32 people. Based on education, is grouped into five degrees of education for 3 people of Diploma Degree, Bachelor Degree is 50 people, 5 people of Master Degree, 8 people of Doctoral Degree, and no for Other. Based on work experience, is long enough that is under 5 years only 3 people, 27 people have 5-0 years, -5 years work experience are 27 people, and over than 5 years are 45 people. 3.. Measurement Model Test Construct Validity and Reliability Validity of the observed variables actually represent unobservable construct (unobserved variables). Testing the construct validity required in a behavioral study to ensure that the operational variables in a study to measure the true desired construct (Hair, et al., 998) Validity measured in this study is construct validity. Construct validity is measured through convergent validity and discriminant validity. Convergent validity is demonstrated confirming the validity of the measurement of a construct through specific measurement method. While the discriminant validity is a measurement indicates that a construct different from the other construct (Hair, et al., 998). Convergent and discriminant validity can be measured through the use of factor analysis. Convergent and discriminant validity of the measurement of the authors do refer to measures proposed by (Hair, et al., 998), measuring the convergent validity through variance extracted, whereas discriminant validity was measured by comparing the average of the square of the correlation (variance correlation) between the construct variance extracted by each construct. Alpha value of each variable variance extracted greater close to or greater than 0.5. The variance extracted values indicate that all the variables analyzed have adequate converegent validity. 79

4 Calculation of discriminant validity of each variable. In appendix 3 shows the average value of the variance of correlation between variables, and the results compared with the average variance ex tracted obtained. All the analyzed construct variance extracted is greater than the average variance correlation. So it can be said that the entire analyzed constructs have good discriminant validity Structural Model Test Assumptions Test Analysis through structural equation modeling was performed using a two-stage approach (Hair, et al., 998), some of the advantages of a two-stage approach compared to the approach of the stage, of which it is possible to test all the patterns of the relationship coefficients in the model, allowing for understand whether any structural model would give acceptable conformity, minimize interpretational confounding by prior measurement model estimation Data Normalization Test Normality of data can be tested by observing the value of skewness and curtosis which is usually presented in descriptive statistics. Statistical values are used to test for normality is the z-value. When the z value is greater than the critical value, it can be assumed that the data distribution is not normal. This critical value can be determined based on the determined level of significance. At the 0.0 significance level, if the value of z is greater than ± 2:58, data normality assumption is rejected Outliers Test of available or unavailable outliers, can be seen with Mahalanobis Distance (Md.). Mahalanobis distance is a distance measure remote data centers nearby point "average" with each point of observation. In this case the point of observation is the number of questionnaire respondents. Examination of the multivariate oultiers performed using Mahalanobis criteria at the level of p < Mahalanobis distance is evaluated using the number of degrees of freedom parameter in the model used is = 4 which is obtained from statistical tables = 66.4 decision rule, if Md from the point of observation > 66.4 then it is said that the observation point is an outlier, whereas if Md from the point of observation < 66.4 then it is said that the observation point is not an outlier Model Fit Analysis Measurement model of the measured value of loading factor (standardize coefficient) for each indicator to the latent variables. Factor loading value indicates the weight of each indicator as a measure of each variable. Indicator with a large factor loading indicates that the indicator variable as a measure of the strongest (dominant). Results of confirmatory factor analysis to the indicators of the first four variables in the model can be seen in the following table..20 e6 X.00 e7 X Kualitas Sistem e8 X (X).46 e9 X4 e0 X5 e X e2 X22.7 Kualitas Inf..20 (X2) e3 X e4 X24 e5 X e6 X Kualitas Lay. e7 X (X3).85 e8 X34.32 e9 X f Y Y2.8 f5 f2.8 Y f3 Y3.7 Kepuasan Peng (Y).6 Dampak Indv (Y2) Y22.8 f6.59 Y23.28 f7 f4 Y Y24.7 Pigure 2. SEM Result Structural Model.92 f8 u u2.0.3 Y Y32 GUG.7 (Y3).07Y33.0 Y34 u3 Goodness of Fit Model Chi Square =3702 Sig Prob=.00 Chi Square DF=.272 RMSEA=.050 GFI=.8 AGFI=.772 TLI=.925 CFI=.933 Based on the Figure 2. the results of structural model testing is presented as follows: Based on the analysis, the coefficient of relationship between the Quality System with User Satisfaction amounted to with p-value of Since the p-value of <0.05 indicates that the hypothesis that "the relationship between the User Satisfaction Quality System accepted. Because the relationship is positive coefficient indicates a direct relationship. This means that the higher the quality score system, the higher the value of User Satisfaction. The results of the analysis, the coefficient of relationship between Information Quality with User Satisfaction is equal to with a p-value of 0.0. Since the p-value of <0.05 indicates that the hypothesis that "the relationship between Information Quality with User Satisfaction" is received. Because the relationship is positive coefficient indicates a direct relationship. This means that the higher the value of information quality, the higher the value of User Satisfaction. Analysis, the coefficient of relationship between Service Quality with User Satisfaction amounted to with p-value of Since the p-value of <0.05 indicates that the hypothesis that "the relationship between Quality of Service with User Satisfaction" is received. Because the relationship is positive coefficient indicates a direct relationship. This means that the higher the value of service quality, the higher the value of User Satisfaction. The coefficient of relationship between the Quality System with Individual Impact is at 0.28 with p-value of 04. Because p-value > 0:05 indicates that the hypothesis that "the relationship between Quality System with Individual Impact" was rejected. This means that regardless of the value of Quality Systems, will have no effect on the high and low value of individual impact. Indirect effect coefficient is positive indicating a direct relationship. This means that the higher the quality score system, the higher the impact of individual values, if the value of User Satisfaction also high. From the above explanation shows that the influence of the quality system (X2) on the impact of the individual (Y2) user satisfaction through an intermediary variable (Y). Thus the user satisfaction (Y) is an intervening variable that influences the f f0.2 f.20 f2 80

5 quality of the bridge system ( X2) on the impact of the individual (Y2). Based on the analysis, the coefficient of relationship between the quality of the information is for 0062 Impact Individuals with a p -value of Because p-value > 0:05 indicates that the hypothesis that "the relationship between the quality of the Impact of Individual Information" is rejected. This means that regardless of the value of information quality, the higher the value of individual impact. The results of the analysis, the coefficient of relationship between Quality of Service with Individual Impact is at 039 with a p -value of Because p-value > 0:05 indicates that the hypothesis that "the relationship between Quality of Service with Individual Impact is rejected. This means that regardless of the value of quality of service, will have no effect on the high and low value of individual impact. Analysis, the coefficient of relationship between the user satisfaction with individual impact is equal to with a p -value of Since the p-value of <0.05 indicates that the hypothesis that "the relationship between satisfaction with the impact of individual user" is acceptable. This means that the higher the value of user satisfaction, the higher the value of individual impact. The relationship between user satisfactions with the Good University Governance is at with p-value of Since the p-value of <0.05 indicates that the hypothesis that "the relationship between user satisfaction with the Good University Governance" is acceptable. This means that the higher the value the user satisfaction, the higher the value of Good University Governance. The coefficient of relationship between Impact Individuals with Good University Governance is equal to 0.48 with a p-value of 0.0. Since the p-value of <0.05 indicates that the hypothesis that "the relationship between Impact Individuals with Good University Governance" is acceptable. Because the relationship is positive coefficient indicates the influence of both unidirectional. This means that the higher the individual impact value, the higher the value of Good University Governance. 3. Conclusion There is a significant and positive effect of System Quality on User Satisfaction, of Information Quality on User Satisfaction, of Service Quality on User Satisfaction. Of User Satisfaction on Individual Impact, of User satisfaction on Good University Governance. This means that regardless of the value of User Satisfaction, will not affect the value of Good University Governance. However, there is a significant indirect effect between User Satisfaction of the Good University Governance through intermediaries of Individual Impact. This means that the higher the value of User Satisfaction, the higher Individual Impact on Good University Governance. There is no significant effect of System Quality on Individual Impact. This means that regardless of the value of System Quality, will have no effect on the high and low value of Individual Impact. However, there are significant indirect effect of System Quality on Individual Impact through User Satisfaction intermediaries. This means that the higher the quality score system, it will affect the higher the value of Individual Impact, if User Satisfaction is also higher. There is no significant effect of Information Quality on the Individual Impact. This means that regardless of the value of quality information, it will not affect the high and low value of Individual Impact. However, there are significant indirect effect of Information Quality on Individual Impact through User Satisfaction intermediaries. This means that the higher the value of Information Quality, it will impact the higher value of Individual Impact, if User Satisfaction is also higher. There is no significant influence of Service Quality on Individual Impact. This means that regardless of the value of Service Quality, will have no effect on the value of Individual Impact. However, there are significant indirect effect of Service Quality on Individual Impacts through User Satisfaction intermediaries. This means that the higher the value of Service Quality, it will impact the higher value of Individual Impact, if user satisfaction is also higher. Acknowledgment Directorate of Research and Community Services, Directorate General of Higher Education that has provided the National Strategic Research Grant 204. References [] Prastowo, Nurcahyo Prastowo Arah Strategis TI Universitas Gadjah Mada. Sarbanes-Oxley Compliance : Managing Techonology Controls. Februari [2] Kertahadi,998. 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