Improvements of Decision Support Systems for Public Administrations via a Mechanism of Co-creation of Value

Tindara Abbate, Clara Bassano, Giuseppe D’Aniello, Sergio Miranda, Mirko Perano, Paolo Piciocchi, Luigi Rarità


This paper focuses on a possible improvement of knowledge-based decision support systems for human resource management within Public Administrations, using a co-creation of value’s mechanism, according to the Service-Dominant Logic (SDL) paradigm. In particular, it applies ontology-driven data entry procedures to trigger the cooperation between the Public Administration itself and its employees. Advantages in such sense are evident: constraining the data entry process by means of the term definition ontology improves the quality of gathered data, thus reducing potential mismatching problems and allowing a suitable skill gap analysis among real and ideal workers competence profiles. The procedure foresees the following steps: analyzing organograms and job descriptions; modelling Knowledge, Skills and Attitudes (KSA) for job descriptions; transforming KSAs of job descriptions into a standard-based model with integrations of other characteristics; extracting information from Curricula Vitae according to the selected model; comparing profiles and roles played by the employees.

The 'a priori' ontology-driven approach adequately supports the operations that involve both the Public Administration and employees, as for the data storage of job descriptions and curricula vitae. The comparison step is useful to understand if employees perform roles that are coherent with their own professional profiles.

The proposed approach has been experimented on a small test case and the results show that its objective evaluation represents an improvement for a decision support system for the re-organization of Italian Public Administrations where, unfortunately often, people are engaged in activities that are not so close to their competences.


Decision support systems, service quality, SDL, co-creation of value, ontology-driven data entry, skill gap analysis.

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DOI: 10.7250/csimq.2015-2.02


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