DOI: 10.4121/89d32145-80de-4689-853e-9df32481cd5c.v1
title: Survey Data on BEV Adoption of Transport Cooperatives in the Philippines

description: The dataset titled <em>BEV Adoption Among Transport Cooperatives in the Philippines</em> contains 83 observations and 42 variables collected from medium to large transport cooperatives operating across Luzon, Visayas, and Mindanao. These cooperatives are part of the government’s Public Utility Vehicle Modernization Program (PUVMP) and were selected based on asset classification and franchise eligibility. The primary objective of the dataset is to examine the factors that influence the adoption of Battery Electric Vehicles (BEVs) within the public transport sector.The dataset includes demographic variables that describe the profile of each cooperative. These variables include the geographical area categorized into Area:Luzon (1)Visayas (2), and Mindanao (3), <br>Net worth classification:Up to Php 3,000,000 (1)Php 3,000,001 – 15,000,000 (2) Php 15,000,001 – 100,000,000 (3)More than Php Php100,000,000 (4) <br>Years of operation:Up to 3 years (1)More than 3 to 6 years (2)More than 6 to 9 years (3)More than 9 years (4)<br>, and <br>the percentage of Battery Electric Vehicles (BEVs) in the cooperative's fleet:<br>Between 1% - 25% (2)Between 26% - 50% (3)Between 26% - 50% (4)<br>These serve as important contextual indicators that help frame the cooperative’s capacity and readiness to adopt electric vehicles.<br>The core of the dataset is organized into three thematic blocks: opportunity factors, challenge factors, and BEV adoption indicators. The opportunity factors assess drivers that could encourage the adoption of BEVs. These are divided into three constructs—profit potential, environmental awareness, and technological competitiveness—each measured by several Likert-scaled items. Composite scores for each construct are computed as the mean of their respective indicators. A combined score, labeled “Opportunities,” represents the average perception of the cooperative regarding these enabling factors.Challenge factors, on the other hand, capture perceived barriers to BEV adoption. These are grouped into three constructs as well: startup investment, lack of government support, and infrastructure requirements. Each is again assessed using multiple items on a 4-point Likert scale, with composite scores computed for each construct. The overall “Challenges” score represents the perceived difficulty faced by the cooperative in transitioning to BEVs.<br>The final section of the dataset measures BEV adoption intentions through four indicators that gauge willingness, planning, and commitment to adopt. The responses are again on a 4-point Likert scale, and the average of these items forms the “Adoption” score. This variable acts as the primary outcome variable, allowing the analysis of how perceived opportunities and challenges relate to the cooperatives’ readiness to adopt BEVs.<br>Overall, the dataset offers a structured, multidimensional view of BEV adoption from the lens of Philippine transport cooperatives. With clearly defined constructs and validated measures, it is suitable for regression modeling and comparative analysis. The data suggest that while environmental and technological considerations are strong enablers, financial and infrastructural barriers continue to hinder broader adoption.