The purpose of this research is to identify factors affecting consumer’s brand preference toward environment-friendly products like electric vehicles in Bangkok, Thailand. The researcher conducted the study based on a quantitative approach and adapted a nonprobability sampling as a convenience sampling method. The data were collected from 400 respondents living in Bangkok, who are 18 years old and above, with significant knowledge of electric vehicles. This study adapted the Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) to examine the model accuracy, reliability and verification influence of various variables. The results revealed that social influence has significant effect on environment concern as well as a positive effect on attitude. The initial significance of environment concern leads to a positive effect on fuel efficiency, followed by brand preference. Lastly, attitude has a significant effect on brand preference as attitude of consumers toward environment-friendly products affects the encouragement of brand preference, which largely depends on individual opinion. From an environmental concern, the researchers identified fuel efficiency and attitude having a positive and significant effect on brand preference toward environment-friendly products for electric vehicles. The authors also found that environmental concern and social influences on green purchasing behavior were significantly interrelated.

Purpose – The purpose of this paper is to partition a last-mile delivery network into zones and to determine locations of last mile delivery centers (LMDCs) in Bangkok, Thailand. Research design, data, and methodology – As online shopping has become popular, parcel companies need to improve their delivery services as fast as possible. A network partition has been applied to evaluate suitable service areas by using METIS algorithm to solve this scenario and a facility location problem is used to address LMDC in a partitioned area. Research design, data, and methodology – Clustering and mixed integer programming algorithms are applied to partition the network and to locate facilities in the network. Results – Network partition improves last mile delivery service. METIS algorithm divided the area into 25 partitions by minimizing the inter-network links. To serve short-haul deliveries, this paper located 96 LMDCs in compact partitioning to satisfy customer demands. Conclusions –The computational results from the case study showed that the proposed two-phase algorithm with network partitioning and facility location can efficiently design a last-mile delivery network. It improves parcel delivery services when sending parcels to customers and reduces the overall delivery time. It is expected that the proposed two-phase approach can help parcel delivery companies minimize investment while providing faster delivery services.