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The purpose of this paper is to identify the most important factors for strategic purchasing of health services in the health sector by the Iran Health Insurance Company and to provide an applicable model for other similar organizations in developing countries.

The present qualitative study was conducted in 2017 to identify the factors affecting the implementation of strategic purchasing of health services in the health sector by the Iran Health Insurance Company and to provide an indigenous and practical model through two phases: semi-structured interview followed by a Delphi process.

According to the findings of this study, Ministry of Welfare, Labor and Social Security plays a central role in the strategic purchasing. In addition, this was also approved by the representatives of citizens in communities concerning professional associations, insurance agencies and representative of the Council of Ministers. Model development explored 9 themes and 54 sub-themes.

Based on the model, most attention has been paid to consumer role and inter-sector leadership of the company with other relevant organizations and systems including other insurance organizations and the welfare ministry. More importantly, the health insurance company should be able to communicate with providers and to choose the best providers, receiving price information through competition in the most appropriate mechanism. Guided by this model, it can strategically buy the best and the most effective services for its insured population.

It might help developing societies to promote their health systems based on targeting the health budgeting and financial constraints so that it is prioritized according to the strategic purchasing criteria and consequently, economic evaluation.

The linchpins of the present study are as follow: first, the pragmatic model presented in the paper could help developing health systems to overcome the impediment in the implementation progress of strategic purchasing. Second, the model satisfies the need of enough knowledge to apply strategic purchasing in the health system. Third, the indigents have long been given special protection and consideration in the model that has continued to capture the attention of every policy-maker, in particular, developing countries, the portion of which is significant. Fourth, based on this model, attention has been paid to consumer role and inter-sector leadership of organization with other relevant organizations and systems. Fifth, this model could be correspondent for every insurance company in countries with similar developing conditions.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.

Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.

The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.

Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.

The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

The numerical solution of third-order boundary value problems (BVPs) has a great importance because of their applications in fluid dynamics, aerodynamics, astrophysics, nuclear reactions, rocket science etc. The purpose of this paper is to develop two computational methods based on Hermite wavelet and Bernoulli wavelet for the solution of third-order initial/BVPs.

Because of the presence of singularity and the strong nonlinear nature, most of third-order BVPs do not occupy exact solution. Therefore, numerical techniques play an important role for the solution of such type of third-order BVPs. The proposed methods convert third-order BVPs into a system of algebraic equations, and on solving them, approximate solution is obtained. Finally, the numerical simulation has been done to validate the reliability and accuracy of developed methods.

This paper discussed the solution of linear, nonlinear, nonlinear singular (Emden–Fowler type) and self-adjoint singularly perturbed singular (generalized Emden–Fowler type) third-order BVPs using wavelets. A comparison of the results of proposed methods with the results of existing methods has been given. The proposed methods give the accuracy up to 19 decimal places as the resolution level is increased.

This paper is one of the first in the literature that investigates the solution of third-order Emden–Fowler-type equations using Bernoulli and Hermite wavelets. This paper also discusses the error bounds of the proposed methods for the stability of approximate solutions.

The purpose of this paper is to evaluate the capabilities of 5S program to meliorate the different competitive dimensions of manufacturing organizations and extracts the various significant factors which influence its successful implementation.

The paper involves empirical study of responses collected from various manufacturing organizations to investigate the inter-relationships between various 5S implementation success factors and competitive dimensions parameters through different statistical techniques. The study deploys Bonferroni post hoc test to establish the statistical significance of competitive dimensions improvements, progressively accrued over a reasonable period of time, through holistic 5S implementation.

The study validates the contributions of 5S program toward realization of significant improvements of various competitive dimensions such as overall organizational achievements, production achievements, quality and continuous improvement achievements, cost optimization achievements, employee-related achievements, effective workplace utilization and safety enhancement achievements accrued by Indian manufacturing organizations. Finally the discriminant validity test has been used in the study for classifying highly successful and moderately successful organizations.

The research aims to empirically test the relationships among various 5S implementation constructs and competitive dimensions parameters constructs in manufacturing organizations. The research work establishes that 5S initiatives have effectively contributed for realization of significant competitive dimensions, progressively from introduction to maturity phases. The research highlights the key significant implications for manufacturing managers.

The purpose of this study is to evaluate the role of 5S initiatives as a facilitator of accruing business excellence in the manufacturing organizations.

The study involves empirical investigation of 92 manufacturing organizations to evaluate the contributions of 5S initiatives toward accruing business performance excellence in the respondent organizations. The study involves nine significant stimulants for 5S implementation and seven crucial performance parameters leading to realization of business excellence in manufacturing organizations.

The results clearly indicate close association between the significant nine stimulants for 5S implementation and seven crucial business excellence performance parameters. The study also establishes that 5S initiatives can significantly facilitate achievement of business excellence in the manufacturing organizations.

The study deploys various statistical and validation techniques to evaluate the contributions of effective 5S approach as a facilitator for accruing business excellence in manufacturing organizations.

The purpose of this paper is to deploy structural equation modeling (SEM) technique to empirically validate the interrelationships amongst significant variables of 5S implementation and business excellence performance parameters (BEPP) in SEM_5S model.

The confirmatory factor analysis approach is utilized to generate the effective SEM_5S model by using AMOS 20.0 (analysis of moment structures) software. The data have been collected from different manufacturing organizations that have successfully deployed the 5S program by using well-designed questionnaire for the evaluation of SEM_5S model.

SEM of 5S various parameters has established that attributes like top management involvement initiatives, employee involvement initiatives, basic 5S initiatives (BFSI) and fifth S initiatives (Shitsuke) should be holistically emphasized during the implementation of 5S program, leading to attainment of high level of melioration in the BEPP. SEM has been deployed to evaluate the original and modification indices of the model, which further establishes the improvement in SEM’s effectiveness. The model establishes the significant impact of 5S implementation on business excellence of manufacturing organizations.

The outcomes of the study will help the organizational managers, HR executives and practitioners from manufacturing organizations to know about the significant factors which should be followed holistically to achieve overall organizational business excellence through strategic 5S initiatives.

To deal with the present situation and recover after the COVID-19 pandemic, construction firms are required to recognise the trends in construction supply chain management (CSCM) for the upcoming years and determine the appropriate practices towards the trends for the improvement of construction activities in terms of strategy, tactic and operations. This paper aims to recognise key trends in CSCM and uses these trends as strategic criteria for the evaluation and prioritisation of lean construction (LC) tools at different project phases including design and architectural engineering, planning and control, on-site construction and safety management.

The integrated analytic hierarchy process–Delphi method is used to collect and analyse the data from construction experts to evaluate the importance levels of the CSCM trends and recommend the appropriate tools for LC practices to improve project performances.

Seven key CSCM trends are identified: lean supply chain management (SCM), supply chain (SC) integration, SC standardisation, SC problem-solving, SC information-sharing, SC flexibility and SC sustainability. Based on these trends, a set of prioritised lean tools are suggested for LC practices, in which “virtual design construction” (VDC) and “last planner system” are considered as the central tools. These two LC practices can be integrated with other effective tools to support the strategic, tactical and operational targets in construction supply chain (CSC) projects.

This study gives the managerial implications by developing an application framework of LC practices for CSC projects. The framework promotes “VDC” as a strategic tool for the phase of design and architectural engineering and considers “last planner system” as the central LC practice for the phase of project planning and control. The framework also focuses on the improvement of efficiency in construction operations by taking into account the aspects of on-site collaboration, problem-solving, improvement and safety.

Up to date, there is still a lack of researches in classifying and prioritising the significant LC tools for each project phase to deal with CSC issues in both breadth and depth. Thus, this study is performed to provide construction managers with the awareness of CSCM trends on which they can focus to have strategic criteria for selecting LC practices to improve CSC performances.

This study aims to investigate the relationship between blockchain technology adoption and firm competitiveness through Halal supply chain performance as a mediating variable.

This paper has used the explanatory approach and multivariate data analysis using partial least squares with structural equation modelling. The data were collected from 178 Indonesian manufacturing firms producing Halal-certified foods and beverages.

The findings reveal that adopting blockchain technology positively and significantly affects Halal supply chain performance and firm competitiveness. The total indirect effect shows that the availability of blockchain technology indirectly affects the firm’s competitiveness through Halal supply chain performance.

This study has provided a novel theoretical framework showing that adopting blockchain technology can improve the Halal supply chain performance and the firm’s competitiveness. The transparency and integrity features of blockchain technology have strengthened the consumers’ confidence in the reliability of the Halal-certified food and beverage products.

In the present work, lean and resilient practices applied to supply chains are studied in order to evaluate their impact on the three dimensions of sustainability. Additionally, the mutual impact of lean and resilient supply chain practices is investigated. The paper aims to discuss these issues.

The aerospace sector and its supply chain are chosen, since lean and resilient practices have been proven relevant in the sector. A methodology based on Interpretive Structural Modeling approach is applied in order to identify the existing relationships between lean and resilient supply chain practices and their impact on the three different dimensions of sustainability.

The results reveal synergetic effects between lean and resilient practices. The former practices act as drivers of the latter practices. Hence, lean practices lead to direct and indirect effects in achieving supply chain sustainability.

The relationship between lean and resilient practices has been studied for the aerospace sector. Different sectors may lead to different results as the practices considered important in each sector may differ as well as the way in which each practice is implemented.

This study highlights the relationship existing between lean and resilient supply chain practices and their impact on sustainability. Additionally, several managerial implications are drawn out to help managers make better decisions.