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Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

Our study aims to explore the interplay between dysfunctional cognitive schemas and rationality for decision comprehensiveness in organizational strategic decisions.

We used a cross-sectional design in which we evaluated individual decision rationality using an objective decision competence test and dysfunctional cognitive schemas in a sample of 270 managers (145 women with an average age of 41 years old). In addition, we asked managers to rate the decision comprehensiveness of their organization’s strategic decision processes.

Our findings support the detrimental impact of dysfunctional cognition in strategic decision-making in such a way that the association between individual managerial rationality and the comprehensiveness of organizational strategic decisions was positive only when managers reported low dysfunctional cognition, while when managers reported high levels of dysfunctional cognitive schemas, the association between rationality and comprehensiveness was negative.

Our study provides initial empirical evidence for the interplay between dysfunctional cognition and managerial rationality in strategic decision processes, and it opens venues for future research to explore the detrimental role of dysfunctional cognitive schemas in strategy processes.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

The purpose of this study is to identify organisational homeostasis factors in the context of healthcare organisations and to develop a conceptual model for green transformation.

The organisational homeostasis factors were determined by review of literature study and the opinions of healthcare experts. Scheduled interviews and closed-ended questionnaires are employed to collect data for this research. This study employed “TISM methodology” and “MICMAC analysis” to better comprehend how the components interact with one another and prioritise them based on their driving and dependence power.

This study identified 10 factors of organisational homeostasis in healthcare organisation. Recognition of interdependence, hormesis, strategic coalignment, consciousness on dependence of healthcare resources and cybernetic principle of regulations are the driving or key factors of this study.

The study's primary focus was on the organisational homeostasis factors in healthcare organisations. The methodological approach and structural model are used in a healthcare organisation; in the future, these approaches can be applied to other industries as well.

The key drivers of organisational homeostasis and the identified factors will be better comprehended and understood by academic and important stakeholders in healthcare organisations. Prioritizing the factors helps the policymakers to comprehend the organisational homeostasis for green transformation in healthcare.

In this study, the TISM and MICMAC analysis for healthcare is proposed as an innovative approach to address the organisational homeostasis concept in the context of green transformation in healthcare organisations.

This paper undertakes a structured literature review to analyse the literature on performance management and measurement (PMM) in universities over the last four decades. Over that time, PMM has emerged as an influential force in universities that impacts their operations and redefines their identity.

A structured literature review approach was used to analyse a sample of articles on PMM research from a broad range of disciplines over four decades. This was undertaken to understand the impacts of PMM practices on universities, highlight changes over time and point to avenues for future research.

The analysis highlights the fact that research on PMM in universities has grown significantly over the 40 years studied. We provide an overview of published articles over four decades regarding content, themes, theories, methods and impacts. We provide an empirical basis for discussing past, present and future university PMM research. The future research avenues offer multiple provocations for scholars and policymakers, for instance, PMM implementation strategies and relationships with various government programs and external evaluation and the role of different actors, particularly academics, in shaping PMM systems.

Unlike a traditional literature review, the structured literature review method can develop insights into how the field has changed over time and highlight possible future research. The sample for this literature review differs from previous reviews in covering a broad range of disciplines, including accounting.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

This study aims to analyze the effectiveness and efficiency of research-based community service programs in Indonesia that used government funds in 2017–2021.

The design of this research is a quantitative research method using a data envelopment analysis to evaluate 370 leading universities in Indonesia. Furthermore, six analytical models were considered to compare effectiveness and efficiency between universities. It involved two resource (budget and staff academic involved), three output (intellectual property, prototype and publication) and three outcome variables (economic impact, social impact and capacity building).

The findings showed that several universities are considered necessary, with great potential to increase output and outcome efficiency in community involvement. The study mapped and divided the position of 370 universities for additional information. The effectiveness aspect provides another perspective in assessing the performance of tertiary institutions in Indonesia and can be an option for evaluating research performance to improve the quality of output.

The authors use data from research and community service management information systems used, both the resources used and the results. Efficiency and effectiveness of 370 universities were compared in this study, including comparing their position on the previous assessment with the assessment of the results of this study. Approach to the concept of Mandl et al. (2008) regarding the relationship between input, output and outcome as the main component of the indicators, the authors apply to analyze efficiency and effectiveness.