ABSTRACT
PurposeThe present situation of COVID-19 pandemic has put the health-care systems under tremendous stress and stringent tests for their ability to offer expected quality of health-care services, as it decides the sustainability and growth of health-care service providers. This study aims to deliver a quantitative framework for service quality assessment in the health-care industry by classifying the health-care service quality parameters into four balanced scorecard (BSC) perspectives.Design/methodology/approachTo determine the service quality for the Indian health-care system, decision-making trial and evaluation laboratory and analytical network process are integrated in a fuzzy environment to contemplate the interaction among BSC perspectives and respective performance measures.FindingsThe results indicate "internal processes” perspective assumes the key role within BSC perspectives, while performance measures "nursing staff turnover” and "staff training” play the key roles. The results also signify that "patient satisfaction” is the most vital issue and can be strongly influenced by measures belonging to the "learning and growth” perspective. In "learning and growth” perspective, "staff training” is the most decisive criteria, very highly influencing "patient satisfaction”, highly influencing "profitability,” "change of cost per patient (both in and out patients)” and "outpatient waiting time” while moderately influencing "staff satisfaction,” "bed occupancy” and "nursing staff turnover”. Moreover, "staff training” criteria have a positive influence on "nursing staff turnover.”Originality/valueThe contributions of this study are in two folds in the domain of quantification of service quality for the health-care system. First, it delivers an assessment framework for Indian health-care service quality. Second, it demonstrates an application of the framework for a case situation and validates the proposed framework.
ABSTRACT
Education has undergone a paradigm shift due to the COVID-19 outbreak declared as a pandemic by the WHO in 2020. The current research attempts to identify the key factors which enable students' readiness for online learning (SROL). The Interpretive Structural Modelling (ISM) approach is used to draw contextual relationships among the enablers of SROL and these are further clustered using Matrix of Cross-impact Multiplications (MICMAC) analysis. Personality traits like 'Open to experience', 'Agreeableness' and 'Extraversion' emerged as driving enablers while;several factors like academic performance, prior exposure to online classes, self-efficacy in online settings, learner control, self-directed learning and motivation for learning emerged as linkage enablers which would ultimately affect the 'willingness for future exposure to online classes'. The understanding of these enablers can help instructors to customize their online course delivery and counsel students based on their levels of readiness for online learning.