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In today’s global competition, supply chain quality management is the key to a firms’ competitiveness. However, managers find that making sound quality and pricing decisions under a complex multi-echelon in the current competitive electronic commerce environment is daunting and challenging. The purpose of this paper is to examine the optimum quality strategies under different cooperative mechanisms and investigate its effects on channel members’ profits.

This paper is a result of a China-UK collaborative research effort, involving researchers with expertise in information systems, quality management, supply chain management, pricing, and game theory models. The authors consider the quality decisions of a single product in a supply chain system that consists of a supplier and two competing manufacturers. The authors examine the optimum quality strategies under different cooperative mechanisms and investigate its effects on channel members’ profits. A modified Nerlove-Arrow model is employed to investigate the quality levels on goodwill and product sales.

The results reveal that the traditional cooperative program is not very effective in the horizontal competitive market; and each channel member may have a profit improvement when the supplier integrates with a manufacturer.

The authors believe that this paper will contribute to the existing body of knowledge. Moreover, the paper provides insights for managers to better manage their supply chain quality management in an information-centric context.

The COVID-19 pandemic is certain to have an unprecedented impact on the global population, but marginalized and vulnerable groups in low-income countries (LICs) are predicted to carry the largest burden. This study focuses on the implications of COVID-19-related measures on three population groups in India, including (1) migrant laborers (of which a majority come from Scheduled Castes (SCs) and Scheduled Tribes (STs), as well as Other Backward Classes (OBCs)), (2) children from low-income families and, (3) refugees and internally displaced persons (IDPs).

This study adopts a sequential mixed-method research design. A desk-based study of a selection of government reports was undertaken on the COVID-19-related mitigation measures. The desk study was followed by in-depth interviews with purposively recruited high-ranking experts in specific sectors of policy implementation and service delivery across the country.

The outcomes of this study shed light on (1) the most urgent needs that need to be addressed per population group, (2) the variety of state-level responses as well as best practices observed to deal with mitigation issues and (3) opportunities for quick relief as well as more long-term solutions.

The COVID-19 pandemic has not only reduced people's means of maintaining a livelihood but has simultaneously revealed some of India's long-standing problems with infrastructure and resource distribution in a range of sectors, including nutrition and health, education, etc. There is an urgent need to construct effective pathways to trace and respond to those people who are desolate, and to learn from – and support – good practices at the grassroot level.

The current study contributes to the discussion on how inclusive public health might be reached.

After being forced to flee their respective home countries, Sri Lankan Tamils and Rohingya refugees resettled in the Indian state of Tamil Nadu. This study attempts to explore the extent to which the state has provided means for integration in the absence of refugee protection laws and citizenship.

A qualitative research approach was used, including in-depth interviews (IDIs) and focus group discussions (FGDs) with participants from both refugee groups between 2019 and early 2020. A representative sample of male and female Sri Lankan Tamils, living in or outside government camps, in urban and rural areas, was included (total number = 75). Similarly, a representative sample of the Rohingya refugee community was included for this study (n = 44).

Despite constraints imposed by inadequate infrastructure, the study finds that Sri Lankan Tamils and Rohingyas both show to be progressively integrated in local society and have been capable of fulfilling some important basic livelihood needs, especially with regards to education. Some areas for improvement are identified as well, most urgently in terms of health and accommodation.

Other states in India, as well as in similar low-income countries (LICs), could learn from the current case study with regards to administering workable policies for small groups of refugees.

With minimal state facilitation and within the context of limited legal backing, refugee groups have somewhat managed to re-built their lives. This study identifies the threshold of requirements that make this achievement possible and suggests what more could be done to further advance the current state.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

The purpose of this paper is to investigate risk propagation and resilience in the agribusiness supply chain (ASC).

The paper undertakes a systematic literature review (SLR). Overall, 94 articles from six databases published between 2000 and 2022 underwent descriptive and thematic analysis.

There is a lack of research on risk propagation and resilience in the ASC for more than two decades. Accordingly, this research fills the gap in the extant literature by advancing the construct of risk propagation and resilience in the ASC and developing a framework proposing directions in risk propagation and resilience in ASC research.

Firstly, only the Web of Science and Scopus databases were mostly used as primary sources while other databases were used as secondary sources to validate search results. Secondly, SLR is based on the peer-reviewed articles, books and conference papers; other non-academic sources relevant to the topic were not included in this paper.

The paper offers a set of constructs for understanding risk propagation and resilience in the ASC, develops a framework proposing directions in risk propagation and resilience in the ASC research and recommends three key themes for future research directions, namely, keep updated with recent constructs of risk propagation and resilience in the ASC, conduct case studies based on empirical studies to determine the current risk dependency and propagation in the ASC and conduct case studies based on empirical studies to determine resilience and sustainability in the ASC.

The rise of recent product recalls reveals that manufacturing firms are particularly vulnerable to product quality and safety where goods and materials have been sourced globally. The purpose of this paper is to explore the issues of quality and safety problems in global supply networks, and introduce a supply chain risk management (SCRM) framework to reduce the quality risk.

A conceptual SCRM framework for mitigating quality risk is developed. In addition, four SCRM treatment practices are proposed by consolidating the empirical literature in the operations management and supply chain management areas. The general feasibility was discussed based on literature.

The research has identified the root causes of the recent product recalls and a series of product harm scandals ranging from automobiles to unsafe toys. Supply chains are extended by outsourcing and stretched by globalization, which greatly increase the complexity of supply networks and decrease the visibility in risk and operation processes.

The paper identifies four SCRM practices, and proposes two distinct antecedents that can prompt the effectiveness of SCRM.

Despite its significance, research on how attribute framing affects ordering decisions in dual sourcing remains insufficient. Hence, this study investigated the effects of attribute framing in a sourcing task involving certain and uncertain qualities of two suppliers and analysed the role of attention with respect to suppliers' information in framing effects.

The impacts of attribute framing on sourcing decisions were demonstrated in two online between-subject (2 × 2 factorial) experimental studies involving professional samples. Study 2 was an eye-tracking experiment.

In Study 1 (N = 251), participants presented with a “high-quality” rather than a “low-quality” frame made different sourcing decisions, opting for larger percentage of order(s) from a supplier under the “high-quality” frame. This pattern holds true for suppliers who differ in risk. This finding was replicated in Study 2 (N = 129). Attention asymmetry related to the information on supplier quality contributes to this effect. Attention directed towards information regarding the supplier's quality under a positive frame mediated the relationship between attribute framing and sourcing decisions.

Highlighting the positive attributes of a risky supplier is essential when ordering from the risky supplier is an optimal decision. It is advantageous for suppliers to highlight positive rather than negative attributes when describing the quality of their components against others.

This is the first study to examine the effect of attention on the relationship between attribute framing and dual sourcing. This presents a new behavioural perspective wherein managers' attention to information plays a vital role.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

The paper reports case studies conducted in services organizations that apply Six Sigma in an emerging economy. Aspects of human resources and full-time specialist, infra-structure, top management leadership, customer requirements, focus on financial and non-financial results, structured method and strategic project selection are investigated. The paper aims to discuss these issues.

Case-based research is employed as methodological approach. Data were mostly gathered through semi-structured interviews with managers involved with Six Sigma implementation. When available, archival data were also used to achieve more evidence towards triangulation. Then, the aggregation of the results was done a posteriori.

It was verified that Six Sigma was successfully implemented in those companies, however with some drawbacks. The studied companies adopt Six Sigma in association with other quality management approaches, mainly ISO 9000: 2008 and lean production.

The study was limited to three service companies. Therefore, the study findings are not subject to generalisation for other similar firms.

The research shows the importance of Six Sigma implementation in service companies in addition to its contribution to practical application by identifying the relevant aspects of its introduction. It enables practitioners to use the findings on factors that should be taken into consideration when applying Six Sigma.

It is one of the few studies on Six Sigma analysis in emerging economies. In addition, the empirical results found support in existing theory to some extent.