Abstract
The evaluation of government data sustainability is a multicriteria decision making problem. The analytic network process (ANP) is among the most popular methods in determining the weights of criteria, and its limitation is the un-convergence problem. This paper proposes a hybrid ANP (H-ANP) method, which aims to improve the ANP by combining the weights obtained from the analytic hierarchy process (AHP). The proposed method is proved to be convergent since the network of the H-ANP is strongly connected. According to the simulation experiments, H-ANP is more robust than ANP under different settings of parameters. It also shows a higher Kendall cor-relationship and lower MSE with respect to AHP, compared with the existing method (e.g., the averagely connected ANP method). An empirical example is also provided, which uses H-ANP to evaluate the government data sustainability of a city.
Highlights
Hybrid analytic network process (ANP) Method for EvaluationGovernment agencies generate, acquire, and preserve a large amount of data in fulfilling their administrative duties every day
To demonstrate the effectiveness of the H-ANP, simulations are conducted on three sets of randomly generated instances as follows
To address the non-convergence problem of ANP in weighting the indexes of data sustainability, this paper proposes a hybrid method, denoted as H-ANP, to combine the weights information obtained from Analytic Hierarchy Process (AHP)
Summary
Hybrid ANP Method for EvaluationGovernment agencies generate, acquire, and preserve a large amount of data in fulfilling their administrative duties every day. The evaluation of government data sustainability is deemed as an effective way to increase the performance of the public sector [2]. The evaluation of government data sustainability is a multicriteria decision making (MCDM) problem. The AHP [3,4,5,6,7] and the ANP [8,9,10,11] have been widely used to determine the weights of the criteria [12,13,14]. The AHP organizes the criteria in a multilevel hierarchy and uses ratio scales to derive relative priorities for elements at the same level by making pairwise comparisons [3]. As a generalization of the AHP, the ANP replaces the hierarchies with networks and makes it possible to network decisions that involve functional dependencies. By taking into consideration both inner (among elements in a cluster) and outer (among elements in different clusters) dependency to prioritize alternatives, the ANP provides more information and reduces the error from expert judgement, ensuring the accuracy of estimation [11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
