Purpose The purpose of this paper is to introduce an integrated approach using failure modes and effects analysis (FMEA), multiple-criteria decision making (MCDM), mathematical modeling and quality function deployment (QFD) techniques, for risk assessment and service quality enhancement in coronary artery bypass grafting (CABG) as a treatment for cardiovascular diseases (CVDs). Design/methodology/approach First, the disruptions in the CABG process are identified and prioritized following FMEA instructions, using two MCDM techniques, called analytic hierarchy process (AHP) and TOPSIS. Consequently, several corrective activities are identified and weighted on the basis of QFD. Finally, a mathematical model is established to determine the most cost-effective activities for implementation. The approach is developed in a fuzzy environment to reflect the uncertainty and ambiguity of human reasoning. Findings Regarding the CABG process disruption, a total of 30 failure modes in four main categories were identified and prioritized. Moreover, eight corrective activities were devised and ranked according to their impact on the failure modes. Finally, considering a limited amount of budget, a sensitivity analysis on the mathematical model’s objective function indicated that using 30 percent of the total budget, required to implement all corrective activities, was enough to cover more than 70 percent of the effects of corrective activities on the failure modes. Originality/value This paper contributes to the quality risk assessment knowledge by introducing an integrated approach to evaluate and improve healthcare services quality. Also, the case study conducted on the CABG process has not been done by other related studies in the literature.

The outbreak of the COVID-19 pandemic has led to significant alterations in people’s social and economic behaviour. This paper aims to study the pandemic’s influence on online shopping and travel behaviour and discover how these phenomena are related. To this end, eight variables were identified that describe socio-demographic status, COVID-19 variables, online shopping variables, and travel behaviour. The structural equation modelling (SEM) approach was adopted to analyse the relationships between these variables. A conceptual model was formed by devising hypothetical relationships, and then the validity and reliability of the model were evaluated using SEM tools. Among the 19 theoretical relationships, 17 were verified. It was found that socio-demographic status directly affects the COVID-19 variables, influencing online shopping variables. As a result, it was inferred that during the pandemic, people’s daily travel habits had been affected by their inclinations toward online shopping, and the more people are aware of COVID-19 and feel responsible about the pandemic, the more they are persuaded to shop online rather than in-person shopping. Policymakers can use the findings of this study to change the public’s travel and shopping behaviour to tackle the pandemic.