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The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.

A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.

The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.

The study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.

This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.

This study aims to address the key issues concerning supplier selection in traditional construction procurement by proposing an innovative, novel, state-of-the-art prototype plugin building information modelling – supplier search and ranking (BIM-SSR) and an associated conceptual framework. It enhances building information modelling (BIM) capabilities through web crawling and analytical hierarchy processes (AHP). It uses the World Wide Web to procure construction material suppliers.

Prevalent issues in traditional procurement of material suppliers have been identified through a rigorous literature review. Field experts vetted these issues. A framework has been presented to address these issues based on integrated web crawling and AHP as a multi-criteria decision-making (MCDM) method. A BIM prototype (BIM-SSR) has been developed using Python and plugged into Autodesk Revit to automate the search and evaluation of material suppliers based on precise material specifications from the BIM design. The BIM-SSR prototype is tested through a case study and validated by field professionals for its efficiency in tackling the identified issues.

Thirteen key issues have been identified concerning traditional construction procurement pertinent to supplier selection. Best-value procurement was encouraged by identifying supplier selection criteria such as cost, delivery time, experience, compliance with quality management standards, warranties, and claim period. The presented BIM-SSR prototype has an efficiency of 80%–95% in addressing the issues identified in this study and 97.5% effectiveness in improving the overall procurement management process.

The BIM-SSR prototype developed in this study is a novel and innovative addition to the body of knowledge that has been integrated into Autodesk Revit as a Plugin. Automation of supplier search and selection through digital technologies, including web crawling and integration of traditionally accepted MCDM methods such as AHP in BIM, is another innovation in the current study. Overall, this study presents a holistic, innovative system, from conceptual design to practical implementation and demonstration. This is one of the steps to help the traditional construction procurement process evolve into a more modern and digital procurement.

The main purpose of this research study is to investigate and examine the factors that might influence the intention to adopt and use mobile payment and their relationships during the COVID-19 pandemic.

This research study used both mobile payment adoption literature, The Technology Adoption Model and Unified Theory of Acceptance and Use of Technology, to propose a conceptual framework for mobile payment adoption. Quantitative method is used in which 306 participants responded to an online survey to validate the proposed conceptual framework.

The introduced integrated model embraced perceived risk, transaction transparency, mobile payment usefulness, social influence, performance expectation as independent variables and usage continuation intention to adopt mobile payment as the dependent variable. The results from data analysis have statistically revealed significant relationships and a positive impact of perceived risk, mobile payment usefulness, social influence and performance expectation. Also, the results identified a negative impact for the transaction transparency factor. As this research study is conducted at a later stage of the COVID-19 pandemic, it adds value to the existing literature by providing insights to business managers on the factors influencing mobile payment usage and other implications related to increasing the market potential for businesses in the new normality of the coronavirus pandemic.

This paper offers a combination conceptual framework of mobile payment adoption based on a literature review on mobile payment adoption from information systems perspective. It adapts integrated model to establish a more comprehensive innovation adoption framework for mobile payment.