Factors Influencing the Selection of Rental Warehouses by Logistics Operators in the Bangkok Metropolitan Area
Keywords:
Warehouse Selection, Rental Warehouse, Logistics Operators, Bangkok Metropolitan Area, Third-Party LogisticsAbstract
Background and Aims: The Thai logistics industry has experienced continuous growth, particularly in the Bangkok Metropolitan Area, which serves as the country's primary transportation and distribution hub. The selection of rental warehouses or third-party logistics (3PL) warehouses has become a critical strategic decision for logistics operators. This study aimed to investigate the factors influencing the selection of rental warehouses by logistics operators in the Bangkok Metropolitan Area and to analyze the level of importance and relationships among these factors.
Methodology: This quantitative research employed a survey research design targeting logistics manager, sourcing managers, and middle-level executives in medium and large transportation and logistics companies registered with the Department of Business Development, with at least two years of experience using rental warehouses. The sample consisted of 200 respondents, determined using Yamane's (1967) formula at a 95% confidence level with a 7% margin of error. Data were collected through online questionnaires and on-site interviews, with support from the Thai Transportation and Logistics Association network. Descriptive statistics (mean, standard deviation, frequency) and inferential statistics, specifically multiple linear regression analysis, were used to analyze the data.
Results: The findings identified six key factors influencing rental warehouse selection: (1) cost and rental fee, (2) location, (3) security and accessibility, (4) warehouse technology, (5) contract flexibility, and (6) value-added services. Together, these factors explained 58.2% of the variance in warehouse selection decisions (R² = .582, F(6, 193) = 44.821, p < .001). Cost and rental fee (β = .312, p < .001) and location (β = .298, p < .001) exerted the strongest positive influence, followed by security and accessibility (β = .198, p = .001) and warehouse technology (β = .155, p = .015). Contract flexibility (β = .105, p = .115) and value-added services (β = .082, p = .242) did not significantly predict warehouse selection.
Conclusion: The findings provide empirical evidence and scientific insights to help logistics operators improve their procurement strategy and strategic planning. Warehouse owners and real estate developers can better understand market demands and priorities, enabling them to develop services aligned with market needs. Government agencies such as the Department of Business Development and the Digital Economy Center can utilize the results for resource allocation and policy formulation. This research aligns with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), contributing to an efficient, modern, and sustainable logistics industry.
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