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PurposeThe linkage between internal and external satisfaction is an understudied topic in the service field. This study aims to address this gap by proposing an original research model, the service excellence chain (SEC), that connects the internal and external perspectives by conjoining performance-excellence models and the service-profit-chain approach. Theoretical assumptions and quantitative measures are proposed by using advanced statistical techniques.Design/methodology/approachThe SEC is investigated through an empirical study in the healthcare sector, focusing on an Italian hospital and involving two of its core units. Qualitative and quantitative approaches were used. First, internal and external customer satisfaction were separately tested through structural equation modeling. The linkage between internal and external satisfaction is then proposed by mathematically defining a synthetic index, the internal and external customer satisfaction index (IEGSI), modeled through Bayesian networks (BNs) and object-oriented BNs to provide an overall measure able to predict organizational improvement.FindingsThe distinct measured models show good internal validity and adequate fit both for patients' and employees' perspectives. The IEGSI allows rigorously connecting internal and external satisfaction by developing conjoint scenarios for organizational improvement.Originality/valueThis study proposes the SEC model as an innovative way to connect internal and external satisfaction. The findings can be useful both for private and public organizations and may provide several useful insights for healthcare managers as well as for policy-makers in relation to developing strategies for improving service quality.

PurposeThe purpose of this paper is to propose the use of probabilistic expert systems (PES), as a tool for managing complex multivariate and highly structured information. The aim of this paper is to verify PES' effective potentiality in quality management.Design/methodology/approachIn the social surveys context, the questionnaire is a well‐known tool for gathering data. Statistical techniques characterised by different levels of complexity can be used to extract from the collected data differently detailed information to support decisions. The paper focuses on PES, belonging to the family of multivariate statistical models, that are presented by an application to a public administration survey.FindingsThe results of an application of PES to a citizens' satisfaction survey are presented. These results will show that probabilistic expert systems are a promising tool for service improvement analysis, based on customer perceptions. The key factors that have an impact on overall satisfaction, suggesting potential improvement areas in processes are identified in detail.Practical implicationsPES can integrate subject‐matter knowledge and statistical information obtained from the questionnaire producing a knowledge instrument. Having this kind of knowledge helps one to make managerial decisions and plan improvement actions.Originality/valuePES can be considered as an innovative and valid way to orient strategic decisions. In particular, using the information enclosed in PES and the know‐how concerning the organization, the decision‐maker can take decisions supported by a scientific and objective tool.

Purpose This paper aims to holistically reconcile internal and external customer satisfaction using probabilistic graphical models. The models are useful not only in the identification of the most sensitive factors for the creation of both internal and external customer satisfaction but also in the generation of improvement scenarios in a probabilistic way. Design/methodology/approach Standard Bayesian networks and object-oriented Bayesian networks are used to build probabilistic graphical models for internal and external customers. For each ward, the model is used to evaluate satisfaction drivers by category, and scenarios for the improvement of overall satisfaction variables are developed. A global model that is based on an object-oriented network is modularly built to provide a holistic view of internal and external satisfaction. The linkage is created by building a global index of internal and external satisfaction based on a linear combination. The model parameters are derived from survey data from an Italian hospital. Findings The results that were achieved with the Bayesian networks are consistent with the results of previous research, and they were obtained by using a partial least squares path modelling tool. The variable ‘Experience’ is the most relevant internal factor for the improvement of overall patient satisfaction. To improve overall employee satisfaction, the variable ‘Product/service results’ is the most important. Finally, for a given target of overall internal and external satisfaction, external satisfaction is more sensitive to improvement than internal satisfaction. Originality/value The novelty of the paper lies in the efforts to link internal and external satisfaction based on a probabilistic expert system that can generate improvement scenarios. From an academic viewpoint, this study moves the service profit chain theory (Heskett et al., 1994) forward by delivering operational guidelines for jointly managing the factors that affect internal and external customer satisfaction in service organizations using a holistic approach.