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The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

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.

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.

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.

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.

The 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.

The 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.

The 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.

This 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.

The 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.

In 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.

The 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.

PES 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.

PES 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.