No abstract

In recent times, the increasing healthcare spending due to the rising health awareness signifies the importance of identifying the appropriate factors that influence patient satisfaction, weight assignment to these factors, and measurement of patient satisfaction becomes important. However, devising a robust objective weighting method for weight assignment of the factors and an analytical method for determining patient satisfaction scores has not been paid enough attention. Motivated by these issues, this work focuses on devising a robust objective weighting method for weight assignment of the factors that influence patient satisfaction, an analytical method for determining patient satisfaction, and real‐life implementation. We first propose a joint weighting methodology to allocate the weights to the factors by integrating the weights determined by different objective weighting methods, namely, mean weight, SD, entropy, criteria importance through intercriteria correlation, and preference selection index‐based approaches. Next, using the weights of these factors, we design a modified weighted aggregated sum product assessment method to determine a single patient satisfaction score by integrating the scores obtained from the weighted sum model and the weighted product model. The proposed methodology is applied to a real‐world dataset provided by a large healthcare provider and diagnostic clinic in Kolkata, India, to exhibit the real‐life implementation. The theoretical insights obtained through non‐parametric tests highlight the significant difference between joint weighting‐based and single weighting‐based methods. The context‐specific insights signify that the patients suffering from arthritis and hyperlipidaemia exhibit lower satisfaction. Also, the patients suffering from dengue express lower satisfaction than the patients suffering from malaria. Additionally, the healthcare provider should focus on improving the service quality of the departments such as ophthalmology, ENT, and dietician.

No abstract

PurposeThis article investigates the impact of the COVID-19 pandemic on the consumer preference for the attributes of online food delivery (OFD) services in India. It also shows how the order size influences the consumer's willingness to pay (WTP) for the attributes of OFD services.Design/methodology/approachThis work incorporates a conjoint analysis-based approach to determine the consumer preference for the attributes of OFDs such as price, delivery time, restaurant rating and packing quality during the COVID-19 pandemic. The fractional factorial design is applied for the data collection. The relative importance of the attributes and the part-worth utility of the attributes' levels have been determined. Further, the utility associated with the attributes' levels is used to find the consumer's WTP for different attributes.FindingsThe COVID-19 pandemic has changed consumer preference from price to food and packing quality in India. When the order is small, consumers exhibit a higher preference to the delivery time than packing quality. In contrast, consumers show a higher preference to packing quality than delivery time with the increase in order size. The consumer's WTP attains the highest level in case of food quality, followed by convenience and packing quality. The WTP for the attributes rises with the increase in order size.Practical implicationsThe insights highlight the need for the online food delivery industry to redesign the business framework in the post-pandemic era. The hygiene and safety measures maintained by the consumers during the pandemic have significantly changed their purchasing behaviour, raising their preference for service quality (food and packing quality) of the OFD services apart from price.Originality/valueThis work determines the consumers' utility for each attribute level of OFDs, along with their relative importance. Moreover, this study contributes to the existing literature by exhibiting the impact of the COVID-19 pandemic on the consumer preference and order size on consumer's WTP for the attributes.