Reconciling internal and external satisfaction through probabilistic graphical models

Abstract

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.

Leadership to promote patient safety culture in public hospitals managed by social health organizations

“…In this research, considering the literature regarding internal climate in public hospitals (Steinke, 2008;Guglielmetti Mugion et al, 2020;ALFadhalah and Elamir, 2021;Musella et al, 2017) and that safety culture arises from management practices (Churruca et al, 2021;ALFadhalah and Elamir, 2021), we assume that a good internal climate is necessary to create a patients safety culture in a public hospital. More specifically, we hypothesize that both the internal climate and the patient safety culture are multidimensional constructs and that the dimensions of the internal climate are associated with at least one of the dimensions that make up patient safety culture.…”

The “service excellence chain”: an empirical investigation in the healthcare field

“…Improvements in nurses' work environments in hospitals can simultaneously reduce high levels of nurses' job burnout and risk of turnover and increase patient satisfaction with their care (Bates, 2004;Baumruk, 2004;Harter et al, 2002;Richman, 2006). Furthermore, in the healthcare industry, there is a positive relationship between the level of satisfaction of medical staff and their patients (DeVoe et al, 2007;Haas et al, 2000;Linn et al, 1985;Musella et al, 2017;Raharjo et al, 2015;Scheepers et al, 2015;Vahey et al, 2004).…”

“…Although there are many articles showing the value of connecting employee and patient satisfaction, however, there is little evidence of quantitative models and indexes able to jointly assess these two perspectives. To fill this gap, this paper proposes a synthetic and original index as a unique numeric measure of patient and employee satisfaction: the IEGSI (see also Musella et al, 2017).…”

“…Based on the literature, for the employees, this paper further investigates the "leadership" construct, which is a crucial factor of the SEC model, as well as in performance excellence models and employee engagement theories (Flynn and Saladin, 2001;Karimi et al, 2014;He et al, 2011;Moon et al, 2011;Sun, 2011;Wilson and Collier, 2000;Zhang and Bartol, 2010). The IEGSI is calculated as the linear combination of attribute evaluations, with the application of some weighting (Musella et al, 2017). The index provides a numeric measure of patient satisfaction, accounting for employee satisfaction, where: X 5 patient satisfaction by unit/ward; Y 5 employee satisfaction by unit/ward; w 5 unit/ward relevance for managers (uniform in this case); and j 5 1, 2 unit/wards.…”

“…Therefore, the network becomes a tool for carrying out what-if analyses, that is for evaluating different scenarios, such as the impact of possible strategic actions that can be suitably designed to improve the performance of the target variable. With easy-to-read pictorial representations and the ability to evaluate scenarios, BNs are increasingly used as a managerial decision support system (Musella et al, 2017). Latent factors' scores were obtained from SEM results for all constructs (Dougherty et al, 1995;Liu et al, 2002) and were discretized, allowing BN learning.…”