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The purpose of this study is to explore the effect of operations strategy (cost, quality, flexibility and delivery) and supply chain integration on innovation performance under influence of learning orientation.

Taking a quantitative and deductive approach, a conceptual framework was developed and tested by analyzing data gathered through survey questionnaire from 243 UK manufacturing firms using structural equation modeling.

The findings show that learning orientation influences operations strategy and supply chain integration, but it does not have a direct impact on innovation performance. Additionally, quality and flexibility strategies affect innovation performance and supply chain integration positively, while cost and delivery strategies do not have a significant effect on these variables.

Operations strategy types (cost, quality, flexibility and delivery) were studied as distinct variables, whereas supply chain integration also has several dimensions but that has not been investigated separately in the present research. The findings are also based on limited 243 responses from UK manufacturing firms.

Innovation performance of manufacturing firms can be improved through a more integrated supply chain if managers embody flexibility and quality capabilities in their operations and become learning oriented.

The effect of supply chain integration on innovation performance and learning orientation on supply chain integration and operations strategy types have not been fully explored in literature. Also, having all four operations strategy types in a direct relation to supply chain integration and innovation performance is another original aspect of the current study.

This paper aims to present a closed-loop supply chain (CLSC) optimization problem for a perishable agricultural product to achieve three pillars of sustainability, including minimizing total network costs and carbon dioxide emissions from different network activities and maximizing responsiveness to demands simultaneously.

The research problem is formulated as a multi-objective mixed-integer linear programming model, and classical approaches, including the LP-Metric and weighted Tchebycheff method, have been applied to solve the optimization model. A set of test problems has been proposed to validate the model, and the results are presented.

Computational time to find Pareto optimal solutions by using the weighted Tchebycheff method was twice as much as that of the LP-Metric method. Also, the result of the study is a mathematical model that can be applied to other products that are close to the fruit, such as vegetables.

The present study is limited to fruits supply chains and the inventory is considered at the distribution centers only. The study also considers only one type of transport.

The paper can assist supply chain managers to define strategies to achieve a sustainable CLSC network configuration for the fruits.

This study is one of the early studies to consider environmental indicators in fruits supply chain design along with two other indicators of sustainability, namely, economic and social indicators. Therefore, this can help supply chain managers to achieve sustainability by optimizing location decisions, inventory quantities and flow between facilities.

Green construction has begun implementing sustainable and environmentally friendly practices, but there has not yet been an assessment for green construction supply chain risks in the literature. Identification and assessment of potential risks will result in more appropriate risk mitigation strategies to overcome disruptions affecting higher performance. Thus, this study aims to identify green construction supply chain risks of residential mega-projects.

Interpretive structural modeling (ISM) provided a hierarchical model composed of seven layers that elucidated the driving influences between the elements. Matrice d’impacts croises-multiplication appliqúe an classement (MICMAC) analysis classified the elements into the driver, linkage and dependent variables based on their dependence and driving powers, providing a clearer understanding of risk factors and their influential characteristics. Using experts' knowledge and experience is compatible with the subjective nature of ‘supply chain risks’ and is more suitable while collecting pertinent quantitative data which is far more challenging.

Tenable output, using an international expert group, addressed key risk factors. Technical expertise and skilled labor, key customers, and corporate culture are found as elements with most driving power, and the final product and logistics coordination and supply chain configuration found as the most dependent risk factors. Managerial implications addressed the most fundamental risk sources and suggested practical proactive risk management approaches to maximize green supply chain performance.

Identified supply chain oriented key risk factors of the residential green mega projects add novelty to the context of green construction projects' supply chain management. And eliciting the influential relations of the key risk factors provide a bigger picture of key risks in green residential mega projects that can be extended by sub-risks related to process activities. Assessing supply chain risks' interactions in the context of green residential mega projects is a novel contribution to mega construction-project management's body of knowledge. Also, the key risk factors were categorized based on the characteristics known as driving power and dependence.