Flaminia Musella

Reconciling internal and external performance in a holistic approach

The paper innovatively proposes the use of BNs in higher education sector.A holistic approach is adopted to jointly monitor internal and external indicators.Most relevant variables affecting KPI were identified by mutual information analysis.Results point out the critical issues of the analyzed Master degree. University education is crucial for cultural and economic growth. Thus, the academic mission recognizes the achievement of both institutional and social objectives, and research provides the basis for the systematic creation of knowledge and the development of human capital. Universities attempt to manage a global system with a holistic vision based on data and facts and oriented to the continuous improvement of its effectiveness and efficiency. The goal is achieved by implementing a monitoring system based both on internal and external performances. As a consequence, it is necessary to consider both students perspective regarding needs, expectations, level of satisfaction and loyalty and internal key performance indicators.This paper proposes the use of Bayesian networks for jointly monitoring internal and external performance of a Masters programme of an Italian University in a holistic approach. A Bayesian network is estimated using a learning algorithm able to analyze the association structure among mixed ordinal and nominal variables. Various scenarios are evaluated thanks to efficient computational algorithms of Bayesian networks.

Reconciling internal and external satisfaction through probabilistic graphical models: An empirical study

Purpose: Nowadays, service organizations have become increasingly aware of the fact that satisfied employees can positively affect customer satisfaction. This paper is a first stage of a wider research aiming at holistically reconciling internal customer (employees) and external customers satisfaction by using a statistical tool for multivariate data analysis i.e. Bayesian networks and their Object-Oriented version. Methodology: This study is based on survey data collected in an Italian hospital. For each ward a model has been estimated to evaluate the satisfaction drivers by category and some scenarios for the improvement of the overall variables are developed. A global model based Object-Oriented network is modularly built in order to provide aholistic view of internal and external satisfaction. The linkage has been reached by building a global index of internal and external satisfaction based on a linear combination. Findings: First, results achieved with Bayesian networks are consistent with the results of previous research obtained by using PLS-SEM tool. Moreover, probabilistic models allow to evaluate the impact of some scenarios for pursuing continuous improvement focusing on some variables of interest. Then, with the proposed global model, we aim at implementing some simulations in order to identify the strategic factors for achieving standards of global satisfaction. This is facilitated with an index useful for jointly managing internal and external satisfaction. Originality/value of paper: The novelty of the paper lies in the efforts to link internal and external satisfaction based on a probabilistic expert system which is able to generate scenarios of improvement. From an academic viewpoint in the service operations, the study is pioneer since it proposes a holistic approach to jointly manage factors affecting internal and external satisfaction.