Perception–intention misalignment in electric vehicle adoption

A cross-source analysis of survey and social media data in Thailand

Bibliographic Data

ID22355607
AuthorsKunyanuth Kularbphettong (0000-0003-2796-0514, Suan Sunandha Rajabhat University, corresponding author), Pattarapan Roonrakwit (Silpakorn University)
Year2026
Volume21
Pages100430
Publication date2026-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCleaner and Responsible Consumption (JOURNAL)
Journal identifiersISSN: 2666-7843
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.clrc.2026.100430
OpenAlexW7154332648
LanguageEN
References cited35

Electric vehicles (EVs) have experienced rapid growth in emerging markets; however, aggregate expansion does not necessarily reflect stable adoption intention. This study examines a “perception–intention gap” in Thailand by integrating structured survey data (n = 426) with a large-scale corpus of social media data (over 130,000 posts), which was preprocessed and filtered to 69,132 relevant observations for analysis. A mixed-methods approach was utilized, integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the explanatory sufficiency of theory-driven constructs alongside sentiment analysis and LDA-based topic modeling to identify trends in online speech and results indicate that, despite strong measurement reliability and validity, the TPB/DOI-based structural model exhibits very limited explanatory power (R 2 ≈ 0.018), suggesting weak explanatory adequacy of conventional intention constructs in this context. Conversely, social media analysis demonstrates significant discursive enthusiasm, especially about marketing-related content (perceived marketing effort; PME), indicating elevated visibility and engagement levels. A comparative analysis reveals a distinct disparity between survey-derived intention frameworks and online discourse prominence, suggesting that favorable digital opinion may not align with consistent adoption intent. These findings delineate the boundary conditions of intention-based behavioral models in nascent electric vehicle marketplaces and propose a cross-source diagnostic methodology for identifying discrepancies between discourse and expressed intention and the study offers a methodological perspective that digital sentiment should be viewed as a measure of discourse visibility rather than a direct sign of adoption readiness

Automatic vehicle location · Data collection · Electric vehicle · Social media · Economic and Environmental Valuation · Electric Vehicles and Infrastructure · Energy, Environment, and Transportation Policies

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