To reconceptualize the role and profile of public managers, a survey has been designed and performed in collaboration with the National Association of the Municipalities of Italy (Veneto region). Public managers and in particular municipal directors were asked to rate the importance of various requirements of skills and training for their job. The aim of the paper is to understand how municipal directors rank requirements of skills and training from the least to the most important one. Preference ratings are combined using composite indicators. The outcome of a composite indicator depends on normalization, aggregation and weighting of partial indicators. Therefore the robustness of results is under question. To address this issue, an uncertainty analysis on the ranking of skill and training requirements has been performed. Three uncertainty factors have been considered: normalization, aggregation and weighting. It is shown that the uncertainty analysis allows a better understanding of the problem.

Public Manager Requirements of Skills and Training: Importance Rank Analysis Using Composite Indicators

MAROZZI, Marco;
2014-01-01

Abstract

To reconceptualize the role and profile of public managers, a survey has been designed and performed in collaboration with the National Association of the Municipalities of Italy (Veneto region). Public managers and in particular municipal directors were asked to rate the importance of various requirements of skills and training for their job. The aim of the paper is to understand how municipal directors rank requirements of skills and training from the least to the most important one. Preference ratings are combined using composite indicators. The outcome of a composite indicator depends on normalization, aggregation and weighting of partial indicators. Therefore the robustness of results is under question. To address this issue, an uncertainty analysis on the ranking of skill and training requirements has been performed. Three uncertainty factors have been considered: normalization, aggregation and weighting. It is shown that the uncertainty analysis allows a better understanding of the problem.
2014
26
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3672599
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