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The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.

This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.

The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.

The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

In this study, a different approach is introduced to generate the kinetic sub-model for the modeling of solid-state pyrolysis reactions based on the thermogravimetric (TG) experimental data over a specified range of heating rates. Gene Expression Programming (GEP) is used to produce a correlation for the single-step global reaction rate as a function of determining kinetic variables, namely conversion, temperature, and heating rate.

For a case study on the coal pyrolysis, a coefficient of determination (R²) of 0.99 was obtained using the generated model according to the experimental benchmark data. Comparison of the model results with the experimental data proves the applicability, reliability, and convenience of GEP as a powerful tool for modeling purposes in the solid-state pyrolysis reactions.

The resulting kinetic sub-model takes advantage of particular characteristics, to be highly efficient, simple, accurate, and computationally attractive, which facilitates the CFD simulation of real pyrolizers under isothermal and non-isothermal conditions.

It should be emphasized that the above-mentioned manuscript is not under evaluation in any journals and submitted exclusively for consideration for possible publication in this journal. The generated kinetic model is in the final form of an algebraic correlation which, in comparison to the conventional kinetic models, suggests several advantages: to be relatively simpler, more accurate, and numerically efficient. These characteristics make the proposed model computationally attractive when used as a sub-model in CFD applications to simulate real pyrolizers under complex heating conditions.

This study investigates the effects of top management commitment (TMC) and Kaizen on quality management practices (QMP) and how the latter influence the performance of Jordanian public hospitals in the aftermath of COVID-19.

A survey-based questionnaire was employed to collect data from 222 practitioners and professionals working in public hospitals in Jordan. Partial least squares structural equation modeling (PLS-SEM) was undertaken to analyze data.

Significant and positive effects of TMC and Kaizen (continuous improvement) on QMP are highlighted by the results. The findings also show that QMP has a positive and significant impact on public hospitals' performance. Furthermore, the effects of TMC and Kaizen on performance were also found to be significant.

Practitioners and researchers will gain a greater understanding of how implementing QMP can enhance the performance of public hospitals in the context of the COVID-19 outbreak. The results underline the important role of Kaizen and TMC in the success of QMP and their effect on performance. This research is a cross-sectional study, and there is a need to conduct further empirical investigation based on secondary data or objective measurement of performance.

This research is one of the first studies to investigate the effects of QMP on public hospitals' performance following the COVID-19 outbreak. This study is one of the empirical examinations of QMP and Kaizen in developing countries by investigating Jordanian public hospitals.

The purpose of this study is to capture patients' and employees' perception of quality of care in the Indian private hospitals and to find the possible perceptual gaps between both the groups.

Authors have referred to the Victorian patient satisfaction monitoring (VPSM) scale and studied the responses of 327 patients and 327 employees collected from six private Indian tertiary care hospitals. SPSS v26 software was used to conduct the data reliability test, descriptive analysis and Mann–Whitney U test.

Authors have found significant differences in perceptions of quality of care between the patients and employees in the Indian hospitals. Employees have high positive perceptions towards the provided medical care whereas the patients have less favourable perceptions for many quality indicators.

This study findings help the healthcare managers, practitioners and healthcare workers of the Indian hospitals to understand the perceptions of both the employees and the patients towards healthcare quality elements and help to reduce the existing perceptual gap in the process of providing quality healthcare services.

To the best of authors knowledge, this is one of the pioneering studies conducted in Indian healthcare industry to capture and compare the perceptions of both the employees' and the patients' perceptions of various quality of care elements. This study highlighted the existing perceptual gap between the employees and the patients on various healthcare quality elements and indicated the critical areas for improvement to provide high quality healthcare services.

The primary objective of this study is to conduct a comprehensive literature review focused on the concept of service quality within the healthcare sector. This research aims to categorize and analyze the various dimensions associated with service quality.

The systematic literature review methodology was employed to accomplish current study goals, and specific criteria were utilized to select relevant research articles spanning the years from 2000 to 2023. Ultimately, 57 articles met the criteria and were included in the study.

Through a meticulous review of selected articles, our research identified and categorized 29 distinct measures for evaluating service quality in the healthcare sector. These measures were further grouped into five overarching categories, namely: overall service quality, personal characteristics, healthcare facility administration, patient attributes and the servicescape. Our investigation highlighted that the dimensions commonly utilized to assess service quality in healthcare primarily draw from the SERVQUAL framework. Additionally, there is significant emphasis on measurements related to the physical environment's quality, overall patient satisfaction and behavioral intention.

This review offers original value by synthesizing diverse literature on healthcare service quality, highlighting SEVQUAL dimensions and critical variables used for assessment. It provides insight into the subjective nature of service quality and varied sampling methods and contributes to enhancing service quality and patient satisfaction in healthcare settings.