Because of the COVID-19 outbreak, the supply of both essential and non-essential goods and services has been unprecedentedly disrupted. In the absence of any playbook, the need for innovative technologies to aid recovery from the Supply Chain (SC) disruptions quickly and effectively becomes persistent. The study aims to develop an innovative decision-making technology to handle the supplier selection problems arising from the frequent impreciseness and incompleteness in the nowadays' SC reports within the framework of the gresilient supply chain management. Through the integration of green and resilience aspects of the SCs, the supply chain 'gresilience' has been conceptualized. In light of this construct and based on the data collected from a manufacturing firm, the study deploys a two-fold decomposition of the core algorithm of the Ordinal Priority Approach (OPA), one for attributes and other for alternatives. It extends the OPA to the Fuzzy OPA (OPA-F) for solving the supplier selection problems. The study illustrates how green and resilience aspects of the SC can be integrated to better understand the gresilient suppliers in the wake of the SC disruptions. A novel construct of SC Gresilience is also furnished along with a novel definition of SC Gresilience. It also provides an innovative SC decision-making technology to help procurement managers to evaluate their suppliers. The resultant framework can help them better prepare for the COVID-19 like disruptions in the future.

Purpose: The purpose of this paper is to assess the influence of patients’ expectations from healthcare service quality on their satisfaction with nursing in public and private hospitals of Pakistan. Design/methodology/approach: Data (n=456) were collected from three public sector hospitals and three private sector hospitals of Lahore, the capital of Pakistan’s most populous province. Male and female patients who have experience of both sectors were surveyed using a self-administered questionnaire developed using the original SERVQUAL approach. Data were analyzed using the statistical techniques and the Laplace criterion. Findings: This paper attempts to explain degree of influences of five service quality constructs (empathy, responsiveness, tangibility, reliability and assurance) on Pakistani patients’ expectations from the private and public sector hospitals and thus patient satisfaction. Further, this work can offer several intuitions into the effect of five constructs of service quality on patients’ expectations of healthcare service quality and patient satisfaction with the service providers/nursing. The results reveal that the patient satisfaction is most strongly related to empathy in public sector and to responsiveness in private sector. Research limitations/implications: In light of the previous studies and the current research findings, the study anticipates no apparently significant improvement in healthcare sector of Pakistan in near future considering various factors discussed in the study. The study will also help the service providers and the policy makers in understanding the deteriorating situation of the Pakistani healthcare sector and will guide them in identifying the areas by improving which not only the healthcare service quality in the country can be improved but also the image of healthcare sector among the masses and competitiveness of the healthcare sector can be enhanced. Originality/value: The value of the study rests in its critical analysis of the current status of the healthcare sector of Pakistan with a view to suggest the areas that need to be worked on by the service providers and policy makers. Also, the study tries to settle a controversy within Pakistani healthcare literature concerning the question that who is producing more satisfied patients: private hospitals or their public counterparts?

The Distinguishing Coefficient (ξ) is an important parameter of Grey Relational Analysis (GRA), a flagship multi-criteria decision making (MCDM) model of Grey System Theory, an intelligent and multifaceted field developed by Chinese scientists in 1980s. However, the scholars widely assume ξ = 0.5. The current study questions this practice. Also, some scholars have argued that the variation in ξ doesn’t influence the ranking of the factors through GRA. On contrary, the study demonstrates, the variation in ξ can influence the ranking. This has been shown through a case involving primary data concerning the perceived relative importance of Project Management Knowledge Areas (PMKAs). This study is significant for the analysts of uncertain systems, represented by grey or fuzzy systems, who intend to use GRA for intelligent multi-criteria decision making. It encourages ξ – driven sensitivity analysis of GRA model before interpreting the results. The study reveals, by tailoring the value of ξ a point can be achieved where the ranking obtained through GRA can be made most comparable to the other MCDM methods. For comparative analysis of the GRA based results the study deployed three other MCDM techniques; Analytic Hierarchy Process, Best Worst Method and Simple Additive Weighting.