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Migration is on the rise due to globalization and human mobility. This has led to increased impacts that have affected many industries, including the construction industry. A large number of migrants are employed in the construction sector, and employers are challenged to make sure all employees are properly integrated to meet the demands needed for construction projects. This article addresses key differences between migrant and native workers to help hiring departments in the construction industry analyse workers' attitudes based on cultural and motivational factors to have the workforce they need to succeed.

The research used both quantitative and qualitative surveys. A two-part questionnaire, designed through a comprehensive literature review, was carried out to identify key differences between native and migrant workers. The data were obtained and then analysed using different statistical approaches, including factor analysis protocol, factor structure model, reliability analysis, relative importance index and nonparametric test analysis. A semi-structured interview was then conducted to discuss all the findings.

The study indicated that migrant workers, compared to natives, tend to give more importance to their working environment, particularly accommodation, work safety and relations with teammates. Also, migrants typically take a socialistic approach instead of an individual approach while at work and reveal an extensive range of behaviours based on a sense of belonging. It might be more important for migrants to have a place in society, to have a settled life and to be integrated into an established order than to improve their rights and benefits. On the other hand, the study argued that native workers tend to prioritize their benefits at work, such as regular payments for overtime and insurance premiums. Their behaviours might carry a more neutral and individual attitude as well as specific cultural traces.

The study is limited to a sample of participants in the Turkish construction sector. Further research based on more cultural models and motivational factors with a larger group of respondents from different countries could offer better results. The results of the study might not apply to a broad context due to many other factors that affect worker behaviours, such as geography, cultural structures and working conditions. Despite these drawbacks, the present paper may help employers and other stakeholders understand the best way to incorporate migrants into the construction industry.

This research is very important for the construction industry in various countries that are currently employing thousands of migrants. Being able to address the key differences between migrants and native workers based on cultural and motivational factors might help with engagement and create a level of harmony in the field for greater productivity.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.