This study seeks to better understand the determinants of green building technology (GBT) adoption intention of construction developers in developing countries. In order to address these objectives, this study integrates the Diffusion of Innovation theory, the theory of Resource-based View, and the Resource Dependence Theory to analyze and construct the theoretical model of developers’ intentions to adopt GBTs from three perspectives, namely, technological, organizational, and environmental. The model was tested using survey data collected from 142 experienced managers in Vietnam. Data analysis was performed by SEM using the partial least squares (PLS) approach. The findings show that perceived GBT advantages, perceived GBT disadvantages, top management leadership, government support, project partners’ green building readiness, and social demand of green buildings are the significant factors that affect GBT adoption intention by developers. However, organizational GBT resource and GBT market readiness have no significant effect on developers’ GBT adoption intention. Theoretical and practical implications and limitations of the research are discussed, and suggestions for future research are also proposed.
In this paper, we study novel variants of the well‐known two‐echelon vehicle routing problem in which a truck works on the first echelon to transport parcels and a fleet of drones to intermediate depots while in the second echelon, the drones are used to deliver parcels from intermediate depots to customers. The objective is to minimize the completion time instead of the transportation cost as in classical two‐echelon vehicle routing problems. Depending on the context, a drone can be launched from the truck at an intermediate depot once (single‐trip drone) or several times (multiple‐trip drone). Mixed‐integer linear programming models are first proposed to formulate mathematically the problems and solve to optimality small‐sized instances. To handle larger instances, a metaheuristic based on the idea of greedy randomized adaptive search procedure is introduced. The main novel feature of our metaheuristic lies in the design of initial solution construction and local search operators, which can cover all the decision layers of the problems and run in scriptOfalse(1false)$\mathcal{O}(1)$ using additional data structures. Experimental results obtained on instances of different contexts are reported and analyzed.
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