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Audience Response to Data-Driven Digital Out-of-Home Advertising: A Quantitative PLS-SEM Study of Perceived Relevance, Engagement, and Purchase Intention
Abstract
This study develops and tests a parsimonious quantitative model explaining how audiences respond to data-driven Digital Out-of-Home (DOOH) advertising. Drawing on the Stimulus-Organism-Response (S-O-R) framework, the Elaboration Likelihood Model (ELM), and the Technology Acceptance Model (TAM), four targeting stimuli (demographic targeting precision, psychographic fit, behavioral alignment, and interactive content dynamism) are positioned as antecedents of perceived ad relevance, which in turn drives audience engagement and purchase intention. A cross-sectional intercept survey of 800 respondents was conducted at five protocol-road DOOH locations in Jakarta, Indonesia, spanning two campaign conditions: conventional placement and data-driven placement informed by anonymized telecommunications audience profiles. Partial Least Squares Structural Equation Modeling with 5,000 bias-corrected and accelerated bootstrap subsamples supported all six hypotheses. Psychographic fit (β = 0.392) and demographic targeting precision (β = 0.365) were the strongest predictors of perceived ad relevance (R² = 0.528); relevance strongly predicted engagement (β = 0.678, R² = 0.459), and engagement predicted purchase intention (β = 0.717, R² = 0.513). All indirect effects were significant, measurement invariance and common method bias checks supported the model, and respondents in the data-driven condition scored higher on every construct (Cohen's d = 0.75–3.06). The findings indicate that audience-content congruence, rather than exposure volume or display interactivity, is the primary lever of DOOH effectiveness, and they offer advertisers an evidence-based ordering of targeting investments.
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DOI: https://doi.org/10.47738/jads.v7i4.1517
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