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There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

The promulgation of group purchasing organizations (GPOs) into the healthcare (HC) sector is an invaluable procurement strategy to manage the suppliers effectively. This study aims to identify and prioritize the factors of integrating GPOs into the HC sector on the perspectives of the developing countries such as India.

The factors are identified from current literature exploration, experts’ support and experience surveys. The factors are scrutinized and shortlisted using the Delphi technique and analysed further using the best-worst model method.

The findings of the study highlight the cost reduction, fair distribution of savings and healthcare supply chain (HCSC) data standardization among others to be the most prioritized drivers. The consulting services provided by GPOs including training and development as a result of high competitiveness in the HC market has been prioritized the least.

The study bears some important implications for decision and policymakers. The managers should consider factors, namely, cost reduction, fair distribution of savings and HCSC data standardization on a priority basis that acts as motivation for the HC providers to join the GPOs.

The study provides valuable insights for HC providers to participate in the GPOs for cost savings and enhance the performances.

This study was intended to ensure learner-centred pedagogy in an open and distance learning environment by applying scaffolding and positive reinforcement techniques.

This study critically analysed the context and current instructional practice of Bangladesh Open University (BOU) via document analysis and literature review. The conceptual framework of this study was adapted from the ADDIE model, i.e. the analysis, design, development, implementation and evaluation model.

The study explored that the instructional practice of BOU was dominated by teacher-centred pedagogy. Hence, to ensure learner-centred pedagogy, the researchers developed three model lesson plans. These lesson plans infused the theoretical directives of scaffolding and positive reinforcement as well as several assessment tasks which can assess the learners’ lower-order and higher-order thinking skills. The researchers also presented possible challenges for the sound implementation of these model lesson plans and suggested pragmatic solutions accordingly.

This study recommended that the combined application of scaffolding and positive reinforcement would effectively ensure learner-centred pedagogy.

This paper aims to explore the drivers of sustainable healthcare supply chain (SHCSC) performance measurement through extensive literature review and experts' opinions. The drivers are then scrutinized and their priority vector is calculated to provide quality and cost-effective healthcare supply chain (HCSC) services.

The drivers of the SHCSC performance measurement are validated using the grey-Delphi technique. After validating the drivers, they are prioritized using the grey-analytic hierarchy process (G-AHP), a multi-criteria decision-making tool.

The findings of the study highlight the prioritized drivers based on the preferences given by the experts. The findings of the study highlight the most prioritized drivers of healthcare (HC) by-product management system, coordinating and facilitating green suppliers in the HCSC and green packaging of pharmaceutical as well as other essential items.

The HCSC managers should coordinate with all the stakeholders across the supply chain and involve them in the decision-making process to make products and services greener and become complicit in complying with the sustainable policy guidelines. The study highlights the strategic policy and managerial implications for implementing sustainability in the HCSC.

The validation and prioritization of the drivers of SHCSC in developing nations' contexts is the key contribution of the study. Grey-AHP enables a practical approach towards enhancing the sustainability of the HCSC and opening the doors for generalizing the study for future research works.

The study aims to envisage upon conceptualizing and developing the scales of smart health-care supply chain (HCSC) performance in the era of the fourth industrial revolution.

This study has implemented structural equation modelling to analyse the survey data. To analyse the collected data from the field investigation involving a sample size of 323, the IBM SPSS AMOS 26 software package is considered to implement exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in this study.

The measurement model of the study developed using EFA and CFA has resulted in validating 32 items out of the 42 items. Resultantly, the analysis using the above-mentioned tools and the parsimony of items to scale development makes it more susceptible to contributing significantly to the current HCSC literature.

The HC providers need to consider a holistic and systematic approach while taking into account the constructs of smart HCSC performance, specifically, the effect of HCSC responsiveness and industry 4.0 between the independent and dependent variables. The scales are validated from the perspectives of developing countries such as India, and hence, their generalizability with respect to first-world countries is practically limited.

The scales validated in this study would facilitate managers and key decision-makers to apply the various elements of HCSC practices, gauge the application of these scales and monitor the performance of health-care facilities.

The study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India.

The present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory.

The drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group.

The analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics.

Due to the rapid surge in the number of COVID-19 cases in India, the health-care supply chain (HCSC) disruptions and uncertainties have increased manifold posing severe challenges to health-care facilities and significantly hampering the functioning of the health industry. This study aims to propose a hierarchical structural model of enablers of HCSC in the COVID-19 outbreak and identifies inter-relationships among them in the health-care market.

Enablers of emergency HCSC have been identified through extensive literature review and experts’ opinions. Subsequently, total interpretive structural modeling (TISM) and cross-impact matrix-multiplication (MICMAC) analysis have been implemented to determine the hierarchical inter-relationships among enablers and classify them according to their contribution to the overall system.

The research has identified and validated 15 enablers of the emergency supply chain in health-care businesses. The study resulted in a seven-level hierarchical structural model based on enabler’s driving and dependence powers. Further, the application of MICMAC analysis resulted in the classification of enablers into four groups, namely, autonomous, dependent, linkage and independent group.

This study would help health professionals, policymakers and academia to implement the theoretical model constructed to alleviate the effect of COVID-19 by improving the HCSC performances in pandemic situations. This study has social and economic implications in terms of cost-effective and efficient delivery of care services in health emergencies.

The proposed theoretical model constructed is a new effort addressing the issues of HCSC in the COVID-19 crisis. Procedural implementation of TISM and MICMAC analysis in this study would help researchers to grasp concepts in a very lucid manner. The present study is one of the very few studies analyzing enablers in pandemic situations by implementing the TISM approach.

With the rising needs of health-care (HC) services during the health outbreaks, it is essential to upgrade the existing HC delivery infrastructure. The study aims to prioritize and highlight the interrelationships among the key factors of the smart health-care supply chain (HCSC) by implementing the concept of Industry 4.0.

The key factors of implementing Industry 4.0 in HCSC have been identified through extensive literature review and stakeholders' opinions. To achieve the abovementioned objectives, the present study proposed hybrid multi-criteria decision-making (MCDM) tools, namely, the fuzzy-analytic hierarchy process (AHP) and fuzzy- decision-making trial and evaluation laboratory (DEMATEL). The Fuzzy-AHP prioritized the factors of implementing Industry 4.0 in HCSC, while the cause–effect relationships among the factors have been explored using fuzzy-DEMATEL.

The results of the study indicated that HC logistics management (HCLM) is the most prioritized factor of implementing Industry 4.0 in HCSC, followed by the integrated HCSC, then sustainable HCSC practices, HCSC innovation and technological aspects, HCSC institutional perspectives, HCSC competitiveness, social aspects and economic factors of HCSC. The cause–effect analysis has highlighted integrated HCSC, HCLM, HCSC competitiveness and social aspects as the cause group factors and they are the critical success parameters for implementing Industry 4.0 in the HCSC.

The results of the study can be useful for policymakers, humanitarian organizations, health administrators and other decision makers considering the smartening of HCSC to enhance the operational performance of health facilities.

This is one of the few studies to have been conducted so far in which the subfactors of HCSC implementing Industry 4.0 have been identified and analyzed using the fuzzy-AHP and fuzzy-DEMATEL hybrid approach.

The purpose of this paper is to analyse the legendary decision of the Appellate Division of the Supreme Court of Bangladesh. Apart from this, the author’s aim is to analyse the constitutional interpretation done by the Higher Judiciary of the Country. Another aim of this note is to simplify the decision of this case for the understanding of the mass people and law students.

To conclude this paper, both qualitative and analytical methods have been used.

In this paper, a summarized fact of the case has been presented. In addition to that, decision of the High Court Division in the Writ petition has been simplified with six simple points. Moreover, major arguments submitted by both the Appellant’s and Respondent’s Lawyers have been summarized. Decision of the Appellate Division has been simplified with 12 simple points along with the reasoning behind the judgement.

In this paper, only the case of Secretary, Ministry of Finance vs Md. Masdar Hossain and Others has been studied and analysed in depth.

This paper will mainly help the Law and Social Science students for simply understanding the decision and the reasoning behind the decision of the case. Apart from that, this case noting will help both academics and legal professionals to study this case within a short period.

This paper will help mass people who want to study about this case to simply understand the case in depth. In addition to that, people will get known about the procedure hoe the subordinate judiciary of Bangladesh became independent.

This conceptual paper is the original piece of work and is not under review in any other journal. This paper has not been published previously in any other journal.