Global Journal of Management and Business Research, A: Administration and Management, Volume 23 Issue 10

Finally, to identify which capabilities should be treated with priority by Real DIPAT ( A ), the proximity index is calculated using the equation: = ∗ - ∗ (17) where ∗ represents the value of the ideal solution from Reference DIPAT ( ) and ∗ represents the ideal solution of Real DIPAT ( A ). The maturity index determines how close to Real DIPAT it is to Reference DIPAT concerning its process management capabilities. Base on this, the methodology proposes improvements to the process to reach a higher maturity level, which makes the model prescriptive. c) Model Prescription To determine the model prescription, it is analyzed the characteristics of each capacity and factors of the BPM-CF model, presented by Rosemann et al. (2006), the PEMM model described by Hammer (2007), and the characteristics of the maturity levels, according to Rosemann et al. (2006). For more details on the prescriptiveness of the proposed model, one can see Appendix 2 (prescriptions for reaching levels 2 and 3) and section 5.2 (prescription for reaching levels 4 and 5). IV. A pplication Determining the Weight of the Criteria To assign weights to the model's capacities, questionnaires were sent by email for peer comparison to apply the AHP method to the heads of the 12 campus campus divisions of a federal public university, with a total of 11 questionnaires answered. After returning the questionnaires, the results were transferred to a matrix, based on the Saaty scale, and the property vector of each decision maker was calculated. Then, the consistencies of the results were verified through equation (2), which resulted in the inconsistency of one of the questionnaires of a decision maker, since the value of the consistency rate was greater than 0, as shown in Table V. This decision-maker's questionnaire was disregarded for the calculation of weights. Table V: Consistency of AHP Results Decisors Consistency Rate Result Decisionmaker 1 0,02980 Consistent Decisionmaker 2 0,06825 Consistent Decisionmaker 3 0,08296 Consistent Decisionmaker 4 0,09780 Consistent Decisionmaker 5 0,05995 Consistent Decisionmaker 6 0,07847 Consistent Decisionmaker 7 0,08344 Consistent Decisionmaker 8 0,08183 Consistent Decisionmaker 9 0,09472 Consistent Decisionmaker 10 0 Consistent Decisionmaker 11 0,50702 inconsistent This was followed by the application of the entropy method by filling in the decision matrix with the priority vectors of each decision maker, resulting in Table VI. Table VI: AHP Decisionmatrix StrategicAlignment Governance Methods IT People Culture Decisionmaker1 0,153618968 0,17818011 0,1946722 0,1946722 0,22785679 0,05099973 Decisionmaker2 0,081897082 0,49901384 0,06111282 0,11213032 0,12292297 0,12292297 Decisionmaker3 0,22024 0,23683 0,18233 0,04453 0,23229 0,08378 Decisionmaker4 0,040505325 0,28220836 0,13075129 0,16244427 0,35509583 0,02899493 Decisionmaker5 0,184814832 0,21711734 0,1569437 0,24941985 0,10291097 0,0887933 Decisionmaker6 0,154532553 0,26187292 0,04423887 0,28042254 0,10630691 0,15262621 Decisionmaker7 0,062808121 0,05382998 0,20030112 0,21747771 0,4096101 0,05597297 Decisionmaker8 0,093849867 0,10615536 0,33528473 0,27381347 0,12011584 0,07078074 Decisionmaker9 0,397100587 0,16655904 0,15180675 0,1086708 0,103581 0,07228183 Decisionmaker10 0,192307692 0,19230769 0,19230769 0,19230769 0,19230769 0,03846154 Subsequently, the decision matrix was normalized using equations (3) and (4), obtaining the results shown in Table VII. Innovative Multicriteria Approach to Business Process Management Maturity in the Public Sector Global Journal of Management and Business Research ( A ) XXIII Issue X Version I Year 2023 60 © 2023 Global Journals a)