The rapid expansion of new technologies and services significantly affects society’s development and initiates significant changes within public administration. Many have decided to implement citizen-centric, data-driven, and performance-focused governance and prepare to transform the existing e-government system into a smart government. Along the way, they have encountered problems such as flaws in existing legislation and in the integration of heterogeneous infrastructure from technical, financial, and privacy perspectives. We propose a new approach to information system modeling that introduces an integration layer for existing databases and services and suggests the application of several innovative technologies to achieve better problem-solving, optimal utilization of resources, and policy innovation. To test the effectiveness of the proposed solution, we have used corresponding weighted digraph models to confirm that the proposed solution achieves the desired effects. We have used the time required to collect documents to measure similarity. The obtained results prove the efficiency of the proposed model and indicate that the same model could be used elsewhere in public administration.
In recent decades, many researchers and practitioners have believed that reaching a high level of business excellence leads to the continuous realization of a set of business goals. In the literature, a vast number of models for business excellence evaluation that contain different criteria depending on the cultural, technological, organizational, and socio-economic factors can be found. The aims of the proposed fuzzy two-stage model are to address some of the main shortcomings of the EFQM2020 model and to adapt it to the needs of process manufacturing. The relative importance of quality criteria and their values are presented by pre-defined linguistic expressions modeled by the triangular fuzzy numbers. The determination of the weight vector of criteria is stated as a fuzzy group decision-making problem and determined by using the fuzzy best-worst method. The proposed fuzzy multi-objective optimization by ratio analysis is implemented for determining the rank of enterprises. The management initiatives that should lead to the improvement of business excellence should be based on the business practices of enterprises that are highly placed in the rank. Testing and verification of the proposed model are performed on real data originating from enterprises operating in the same economic sector.
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