# Nazli Sadat Safavi, Nor Hidayati Zakaria and Mahyar Amini. Department of Information System, Faculty of Computing, Universiti Teknologi Malaysia

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5 Table 1: Reliability and Validity Statistics for the Questionnaire Items Average Variance Extracted (AVE) Composite Reliability Cronbachs Alpha Adequate System Selection Business Process Re-Engineering Cost Reduction ERP Project Success Reliability and Validity: Validity is displayed when each Hypotheses Test: For preceding the study, according measurement item related strongly to its assumed to the evaluation and prediction of the structural model, theoretical construct. These two validities capture some some data about the path coefficients ( ), T-values (T), of the aspects of the goodness of fit model, i.e., how 2 P-values (P) and squared R (R ) are identified in details. well the measurement items relate to constructs. When factorial validity is acceptable, it means each Path coefficients ( ): Path coefficients ( ) show how measurement item correlates strongly with the one strong and significant the associations between construct it is related to, while correlating weakly or not dependent and independent variables are [42]. It means significantly with all other constructs. Smart PLS also that, a path coefficient reveals the immediate influence shows the validity. Establishing discriminant validity of a variable (considered as cause) that is supposed to requires an appropriate Average Variance Extracted result in a different variable (considered as effect). (AVE) analysis. We examined to check whether the square Since a Path coefficient can be identified based on the root of every AVE (there is one for every latent construct) correlation, it is standardized while a path regression is much larger than any correlation among any pair of coefficient cannot be considered standardized. latent constructed. As a rule of thumb, the square root of each construct should be much larger than the correlation T-Value: According to Reddy and Chin (1998), for of the specific construct with any of the other constructs conducting the hypothesis testing the path significance in the model and should be at least 0.5 [39] (Table 1). can be determined via t-tests values by using the Internal consistencies of all variables are considered bootstrapping procedure. Commonly, the acceptable Fornell and Larcker [39] acceptable since they exceed value for T-values larger than two (T-value >1.96) means 0.70, signifying tolerable reliability. Smart PLS also significant level [43]. shows the composite reliability. Table 1 illustrates the results of the reliability and validity analysis of different P-value: The P-value can be considered as a quantitative constructs of the questionnaire. measure of the numerical importance of testing a The acceptable values for composite reliability would hypothesis. Furthermore, regarding the studies conducted be the same as the researcher sets for Cronbach's alpha. formerly, P-value < 0.05 implies the significance of the The composite reliability should be greater than 0.60 for related hypothesis (e.g., [44]). exploratory purposes [40]. The composite reliability of this study is greater than 0.80 for all measurements. The 2 2 Squared R (R ): The R shows the expected effect of internal consistency was measured using Cronbach s the model of dependent variables through estimating alpha ( ) [41]. Alpha should be greater or equal to 0.80 the percentage of a construct s variance in the model for a good scale, 0.70 for an acceptable scale and 0.60 for [44]. a scale for exploratory purposes. In this study, all The results of the study confirmed that the measurements have a value greater than According organizational identification positively impacted on to, establishing discriminant validity requires an perceived usefulness ( = 0.536, p < 0.001). Therefore, appropriate AVE analysis that must be at least 0.50 [39]. hypothesis H1 was supported (see table 2). This result In this study, all measurements had values much higher also supports the second hypotheses of this study. than 0.50 (Table 1). We found adequate internal The results for the effect of business process consistency between items in the questionnaire; re-engineering on cost reduction in ERP implementation adequate system selection, adequate business process showed positively impacted on satisfaction ( = , re-engineering, cost reduction and ERP project success p < 0.001). Therefore, a hypothesis H2 was supported. (Figure 1). This result also support third hypothesis of this study. 1673

8 32. Tsai, W.H., et al., A comprehensive study of 39. Fornell, C. and D.F. Larcker, Evaluating the relationship between enterprise resource planning structural equation models with unobservable selection criteria and enterprise resource planning variables and measurement error. Journal of marketing system success. Information & Management, 49(1): research, pp: Chin, A., The morphologic structure of 33. Peslak, A.R., Enterprise resource planning step pools in mountain streams. Geomorphology, success: An exploratory study of the financial 27(3): executive perspective. Industrial Management & Data 41. Cronbach, L.J., Coefficient alpha and Systems, 106(9): the internal structure of tests. Psychometrika, 34. Tsai, W.H., et al Examining the implementation 16(3): risks affecting different aspects of Enterprise 42. Ko, D.G., L.J. Kirsch and W.R. King, Resource Planning project success. in Computers and Antecedents of knowledge transfer from consultants Industrial Engineering (CIE), th International to clients in enterprise system implementations. MIS Conference on: IEEE. Quarterly, pp: Boehm, B.W., Software risk management: 43. Eckblad, J.W., How many samples should be principles and practices. Software, IEEE, 8(1): taken? BioScience, 41(5): Somers, T.M. and K.G. Nelson, A taxonomy of 44. Ifinedo, P., Examining the influences of external players and activities across the ERP project life expertise and in-house computer/it knowledge on cycle. Information & Management, 41(3): ERP system success. Journal of Systems and 37. Shehab, E., et al., Enterprise resource planning: Software, 84(12): An integrative review. Business Process 45. Tsai, W.H., et al., A comprehensive study of Management Journal, 10(4): the relationship between enterprise resource planning 38. Schreiber, J.B., et al., Reporting structural selection criteria and enterprise resource planning equation modeling and confirmatory factor analysis system success. ELSEVIER. results: A review. The Journal of Educational Research, 99(6):

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