and Knowledge Capabilities of the Organization
OPERATIONALIZATION OF VARIABLES
The purpose of this section is to describe the data collection method used, the various techniques used to test the research hypotheses as presented before, and it also It discusses reliability statistics of the sample, the descriptive statistics and the results of the confirmatory model analysis.
Sample
The sample used in this study consists of 179 respondents representing various organizations in both private and public sector in Syria. The sample was non random (purposeful), selected among many organizations which have fulfill two criteria, firstly it should have working infor-mation system; secondly it should have it own IT department. Given these condition a unique dataset were produced and were used to test the research model of this study.
The secondary Data were generated for this study from the empirical investigation through a survey method designed to test the validity of the model and research hypotheses. In addition the primary data were obtained from the literature written about the constructs of the proposed model, statistics and cases, and tracking and analyzing the existing organizations.
Measures
Dependent construct in this study are business intelligence systems, organizational knowledge and IT governance, and all of these constructs representing a latent factor, which has a number of attributes. On the other hand, IT governance, organizational knowledge are considered inde-pendent constructs.
Descriptive, relational, associational statistics were used to satisfy the research objectives and hypothesis testing, the analysis of the measurement models and results in this research went through two phases: the first phase was the descriptive analysis using SPSS 17 software, and the test conducted were factor analysis which were used to validate and measures the internal consistency of a constructs. Different methods used to measure the degree to which the distributions of the sample data to be in line with the normal distribution theory; such as Standard deviation, Skewness and Kurtosis. The second phase was examining the hypotheses by applying the partial least squares
171 Enhancing BI Systems Application
method using (EQS 6.1) to analyze the collected data. In this method the interaction between each set of indicators and their underlying construct were found and analyzed. Accordingly the results all hypothesizes testing were accepted.
Measurement Model Validity
To validate measurement model in the proposed model, three types of validity were achieved: first;
content validity, second; convergent validity, and finally discriminate validity. Content validity was used to insure the consistency between the
mea-surement items and the relevant literature. This was done through pilot-testing the instrument.
Secondly convergent validity was obtained by testing composite reliability and average variance extracted from the measures (Hair et.al., 1998).
Finally the researcher confirmed the discriminate validity of instrument by checking the square root of the average variance extracted as recommended by (Fornell & Larcker, 1981).
Table 1. Study Hypothesis
Hypothesis 1:
There is a significant relationship between IT Governance and its components (Business Goals Alignment, Business Value Delivery, Resource Allocation, Risk Management and Performance Measurement).
H1a: Strong Alignment between IT and Business Goals is positively associated with IT Governance application.
H1b: Greater presence of Business Value Delivery through IT is positively associ-ated with IT Governance application.
H1c: Greater presence of good Resource Allocation of IT is positively associated with IT Governance application.
H1d: Greater presence of good IT Risk Management is positively associated with IT Governance application.
H1e: Greater presence of good IT Performance Measurement is positively associ-ated with IT Governance application.
Hypothesis 2:
There is a significant relationship between Organizational knowledge and its components (Intention to share Knowledge, Knowledge Management Plan Development, Organizational Culture, Organizational Structure)
H2a: Greater presence of Intention to share Knowledge is positively associated with knowledge management application rate.
H2b: Greater presence of Management Plan Development is positively associated with knowledge management application rate.
H2c: Focusing on Organizational Collaboration Culture is positively associated with knowledge application rate.
H2d: Convenient organization’s Structure is positively associated with knowledge application rate.
Hypothesis 3:
There is a significant relationship between Busi-ness Intelligence System and its components (System Flexibility, System Reliability, System Performance, System Alignment, System Analysis and IT workers
Understanding of Business Goals)
H3a: High Flexibility is positively associated with Business Intelligence System application.
H3b: High Reliability is positively associated with Business Intelligence System application.
H3c: High Performance is positively associated with Business Intelligence System application.
H3d: Greater System Alignment with organizational goals is positively associated with Business Intelligence System application.
H3e: Greater Understanding by IT workers of Business Goals is positively associ-ated with Business Intelligence System application.
Hypothesis 4: There is a significant relationship between IT Governance and Business Intelligence System within the organiza-tion.
Hypothesis 5: There is a significant relationship between Organizational Knowledge Management Business Intelligence Systems within the organization.
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Normality
The main purpose of Normality is to measure the degree to which the distributions of the sample data match up the normal distribution, which looks similar to a bell shape. Normal distribu-tion is the most popular method used to explain symmetrical, bell-shaped curve, which has the greatest frequency of scores in the middle, with smaller frequencies towards the extreme. In ad-dition Standard deviation can be used to measure the normality of the variable’s data, when standard deviation is less than one it indicates normality.
Moreover skewness and kurtosis values are very important indicators for normality. Skewness is a measure to indicate the symmetry. Kurtosis is a measure to test if the data are peaked or flat in accordance to a normal distribution. From the results illustrated in table 2, it can concluded that the sample meets the normality conditions
Structural Equation Modeling
Structural Equation Modeling (SEM) is the sec-ond generation of data analysis methods that is used for testing the statistical conclusion validity i.e. “testing the degree to which researches meet recognized standards for high quality statistical analysis” (Gefen et al., 2000). SEM is more prefer-able over the first generation statistical methods such as regression, another thing SEM facilitates analyzing the measurement errors of the observed variables as part of the model, and also combining the factor analysis with the hypotheses testing in the same analysis. The outcome is a more accu-rate analysis of the proposed research model and, most of the time, makes a better methodological assessment means. SEM methods offer better information about the degree to which the data support the research model than in regression methods (Gefen et al., 2000).
Table 2. Multi-variant normality test
Descriptive Statistics
N Mean Std. Deviation Skewness Kurtosis
Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Intention 179 3.28 1.044 -.432- .182 -.544- .361
Plan 179 3.00 1.049 -.089- .182 -.559- .361
Culture 179 3.13 .918 -.214- .182 -.524- .361
Structure 179 3.14 1.004 -.419- .182 -.791- .361
Flexibility 179 3.61 1.191 -.393- .182 -.994- .361
Reliability 179 3.87 .985 -1.010- .182 .908 .361
Performance 179 3.53 1.029 -.646- .182 -.187- .361
Alignment 179 3.37 1.054 -.478- .182 -.359- .361
Analysis 179 3.23 1.032 -.224- .182 -.539- .361
Understanding 179 3.51 .926 -.427- .182 -.049- .361
ITBA 179 3.4637 .76037 -.431- .182 -.090- .361
ITVD 179 3.3575 .78653 -.260- .182 -.334- .361
ITResM 179 3.3948 .76963 -.399- .182 -.164- .361
ITRiskM 179 3.2402 .83540 -.400- .182 -.346- .361
ITPerM 179 3.4581 .74929 -.391- .182 -.337- .361
Valid N (listwise) 179
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The Model Components
This proposed model comprises of three constructs as described below:
• IT Governance was modeled as a first order construct comprised of the five first-order dimensions: (1) IT/Business Alignment, (2) Business Value Delivery, (3), Risk Management (4), Resource Allocation and (5) Performance Measurement.
• Organizational Knowledge was modeled as a second order construct comprised of four first-order dimensions: (1) Intention to share Knowledge, (2) Plan (3) Culture and (4) Structure.
• Business Intelligence Systems was mod-eled as a third order construct comprised of the five first-order dimensions: (1) Flexibility, (2) Reliability, (3) Performance, (4) Alignment, (5) Analysis and (6) IT workers Understanding of Business Goals.
Fit Statistics
Goodness-of-fit measures the degree to which the actual or observed input matrix is predicted by the proposed model. Goodness-of-fit measures can be classified into three types as follows:
1. Absolute fit measures (AFM): assess the overall model fit; these measures include:
◦ Chi-square (X2) accompanied by the model’s degree of freedom and its probability, Chi-square compares the proposed model to a saturated model;
the model does fit the data when the probability (p) is greater than or equal 0.5 (Alkhaldi F. M., 2007)
◦ Goodness-of-fit index (GFI):
Compares the proposed model to no model, it ranges from 0 - 1.0, and
when its value is above 0.90 this means good fit.
◦ The Root Mean Squared Error of Approximation (RMSEA) which es-timates of discrepancy per degree of freedom in the model. The values are recommended to be less than 0.08 (Alkhaldi F. M., 2007).
2. Incremental fit measures (IFM): allow the comparison between the proposed model and the competing models and it used to assess the incremental fit of the model compared to the null model; The IFM measures include:
◦ Tucker-Lewis Index (TLI).
◦ Comparative Fit Index (CFI).
◦ Incremental Fit Index (IFI).
The value of these three measurements should be greater than 0.9 to indicate good fit (Alkhaldi F. M., 2007).
3. Parsimonious Fit Measures (PFM): “adjust”
the measures of fit to compare between models with different numbers of esti-mated coefficients so that the amount of fit achieved by each estimated coefficient can be determined”.
These measures include the normed fit index X2/df (the adjusted Chi-square by the degree of freedom), (Alkhaldi F. M., 2007) said that:
• If value is > 5 then Model does not fit data
• If value is between 2 – 5 then model may
• fitIf value is < 2 fair fit of model to data
The proposed model was analyzed using SEM.
The confirmatory modeling approach was carried out to examine the significant of the research model using EQS 6.1 Software. The results were as shown in Table 3.
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Table 3 also shows the values derived from the research model. As shown in table 7.2, Chi-square value is significant at 0.05 significance level, X2 (.05 = 83.156, P = 0.06), and all other fit measures point to that the revised model is accepted as GFI = .945, RMSEA = .040, IFI = .987, CFI = .986, and X2/df = 1.28. Therefore, the model was accepted and adopted for testing the hypothesis of this study.
Structural Model Testing
Several techniques were used to assess the hy-potheses of the model. The first method is the overall coefficient of determination (R square value) which is a measure of the entire structural equation; second the standardized estimation coef-ficients (beta). This beta can closely approximate the magnitude of the effect, when the value of beta closes to zero, it means that the relationship is weak, but when the value of beta increased, this means the relationship is strong.
Table 4 shows the results of the evaluation test for the data used in building research model.
BI Systems Sub model Measurement Analysis
Measurement BI Systems sub model stands for the first question in the research which discusses the existence of a significant relation between BI systems and its pillars (Flexibility, Reliability, Performance, Alignment, Analysis and Experts).
To test direct significant relationships between the six pillars and BI systems, Standardized Beta was used as indicator for this relationship. Referring to table 4, it is obvious that a positive significant relationship between BI and each pillar does ex-ist. The value of t-test is examined in order to test hypotheses and analyzing the systems structural model. It is noticed from table 3 that t-values be-tween BI and its pillars are significant at .05, so this indicate that all of them are part of BI systems.
KM Sub Model Measurement Analysis
Measurement KM sub model stand for the second question in the research which discusses the ex-Table 3. Shows benchmarks and values of the model fit indicators
ABSOLUTE FIT MEASUREMENT
Index names abbreviation Accepted level Model
Calculated Values
CHI Square X2 - 83.156
Degree of freedom df - 65
X2/df X2/df ≤ 2 (fair fit) 1.28
Probability P P ≥ 0.05 .06404
Bentler-Bonett Normed Fit Index NFI ≥ 0.9 .942
Bentler-Bonett Non-Normed Fit Index NNFI ≥ 0.9 .978
Comparative Fit Index CFI ≥ 0.9 .986
Bollen’s Fit Index IFI 0 to1 .987
Goodness of fit index GFI ≥ 0.9 .945
Adjusted Goodness of fit index AGFI ≥ 0.9 .898
Root Mean-Square Residual RMR Close to 0 .041
Standardized RMR SRMR ≤ 0.05 .045
Root Mean-Square Error Of Approximation RMSEA ≤ 0.1 .040
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istence of a significant relation between KM and its pillars (Intention, Plan, Culture, and Structure).
To test direct significant relationships between the three pillars and KM, Standardized Beta was used as indicator for this relationship. Referring to table 5, it is obvious that a positive significant relationship between KM and each pillar does ex-ist. The value of t-test is examined in order to test hypotheses and analyzing the structural model. It is noticed from table 5 that t-value between KM relatedness and are significant @ .05, so this indicate that all of them are part of KM.
ITGOV Sub Model Measurement Analysis
Measurement IT Gov sub model stand for the third question in the research which discusses the existence of a significant relation between
IT Governance and its pillars (IT/Business Alignment, Business Value Delivery, Resource Management, Risk Management and Perfor-mance Measurement). To test direct significant relationships between the five pillars and ITGOV, Standardized Beta was used as indicator for this relationship. Referring to table 5, it is obvious that a positive significant relationship between ITGOV and each pillar does exist. The value of t-test is examined in order to test hypotheses and analyzing the structural model. It is noticed from table 5 that t-value between ITGOV and its pillars are significant at .05, so this indicate that all of them are part of ITGOV.
Analysis of Structural Model Structural model consists of three segments.
Table 4. Test statistics
Test statistics - Measurement Models
Regression path Standardized Beta (β) t - test R² Significance @ .05
BI Systems Sub model
Flexibility BI .426 9.161 .182 √
Reliability BI .659 8.214 .435 √
Performance BI .780 7.216 .609 √
Alignment BI .781 6.965 .610 √
Analysis BI .698 7.432 .488 √
Understanding BI .576 8.772 .331 √
KM Sub model
Intention KM .618 8.159 .381 √
Plan KM .735 6.870 .541 √
Culture KM .631 8.038 .398 √
Structure KM .680 7.583 .462 √
IT Gov Sub model
ITBA ITGOV .778 7.266 .605 √
ITVD ITGOV .770 7.566 .593 √
ITResM ITGOV .796 6.891 .633 √
ITRiskM ITGOV .749 7.878 .560 √
ITPerfM ITGOV .781 6.841 .609 √
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First segment: which stand for the fourth ques-tion of the research which discusses the significant relation between BI systems and ITGOV. To test direct significant relationships between the two constructs, Standardized Beta was used as indica-tor for this relationship. Referring to table 5, it is obvious that a significant relationship between BI and ITGOV does exist.
Second segment: stand for the fifth question in the research which discusses the significant rela-tion between KM and BI systems, standardized Beta was used as indicator for these relationships.
Referring to table 5, it is obvious that a significant relationship between KM and BI systems.
The structural model fit was accepted, as Chi-square value is not significant at 0.05 significance level, (CHI-square = 83.156 based on 65 degrees of freedom, and the probability value for the chi-square statistic is 0.667
Each hypothesis was tested, analyzed and the overall results of the empirical investigation have supported the general framework that was presented in the research model.
CONCLUSION
The main purpose of this study is to explore the importance of enhancing the IT Governance in business environment. The outcomes of the statistical analyses are used in order to situate a practical suggestion that companies can carry out to enhance business intelligence systems imple-mentation. Each hypothesis was tested, analyzed and the overall results of the empirical
investiga-tion have supported the general framework that was presented in the research model. Based on the findings of this research, number of recom-mendations and results are presented that aim at developing the awareness about the importance of business intelligence systems, IT governance and organizational knowledge.
The study aimed to suggest based on the vari-ous relationship findings illustrated in statistical results of the proposed model a means to enhance understanding of the concept of Business intel-ligence systems and its importance by enhancing the combination of IT governance in the firm and the knowledge capabilities (KM) and their role in business environment. Additionally this research describes the pillars of IT governance, particularly IT business Alignment, IT value delivery, IT resource management, IT risk management and IT performance management used by COBIT as a framework. This research clearly highlight the imperative needs to build the right culture that keeps looking at Information technology as a tool and not as a goal, also to adopt specialized frameworks to assess the contribution of infor-mation technology in the whole business. This study finding were based on coherent model that integrates a number of models into one model that describes the importance of KM pillars with business intelligence systems and its pillars and also why it should be one of the important issues in business environment culture.
This study provides comprehensive statistical discussion about the methods and techniques that can be used to have right and suitable imple-mentation of business intelligence systems, and Table 5. Structure statistics
Test statistics - Structure Model
Hypo. No. Path Standardized Beta (β) Significance @ .05
H4 ITGOV BI .760 √
H5 KM BI .070 √
BI = 0.76 * ITGOV + 0.07 * KM
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increase the awareness of the importance of these systems. Finally, there was no previously detailed research available on the topic of IT Governance combination with knowledge capabilities of the organization to enhance the business intelligence systems. This research discusses all these factors in details.
In summary, this research investigates the re-lationship between business intelligence systems, Organizational Knowledge and IT Governance was explained. The results indicated that IT Governance and Organizational Knowledge can enhance the business intelligence systems of the organizations but with different ratios. IT gover-nance can strongly enhance business intelligence systems but the organizational knowledge supports it weakly, the reason might simply refer to the fact that knowledge concept is still new studied
environment or not applied in the correct way in the sampled organizations. Model was introduced to help understanding the areas where the sampled organizations need to focus on and try to enhance the mechanism of their work in order to achieve the goals of this research and also urge these organizations to apply correctly the discipline of knowledge management.