The DECODE Framework
Cultural Determinants of Consumer Engagement in Digital Environments
A consumer-psychology framework examining how historical institutional distrust and cultural mistrust sensitivity shape trust in algorithmic marketing — and how transparency and personalization change that relationship.
From Institutional Experience to Consumer Response
The current model centers a serial pathway from historical institutional distrust to cultural mistrust sensitivity to post-exposure algorithmic trust, followed by downstream consumer outcomes.
Historical Institutional Distrust
Prior and inherited experiences with institutions shape how algorithmic marketing systems are interpreted.
Cultural Mistrust Sensitivity
Heightened vigilance toward culturally meaningful cues influences how consumers evaluate system intent and legitimacy.
Algorithmic Trust
Post-exposure trust in the marketing system becomes the central psychological mechanism connecting experience to response.
Behavioral Outcomes
Ad avoidance · Purchase intention · Brand trust
Transparency and Personalization as Assigned Conditions
DECODE separates the experimental manipulations from participants’ perceptions of those manipulations, preserving the causal interpretation of the moderation tests.
Randomized Moderator
Algorithmic Transparency
Participants are randomly assigned to high- or low-transparency conditions. Assigned condition functions as the causal moderator; perceived transparency is collected only as a manipulation check and for secondary analyses.
Randomized Moderator
Personalization Intensity
Participants are randomly assigned to high- or low-personalization conditions. Assigned condition is tested as a moderator of the DECODE pathway; perceived personalization is treated as a manipulation check rather than the causal moderator.
Question · Model · Contribution · Trajectory
The Question
When Does Personalization Build Trust — and When Does It Trigger Resistance?
Behavioral marketing increasingly depends on algorithmic systems that decide which consumers see which messages, how personalized those messages become, and how much explanation consumers receive about the process. These systems can create relevance, but they can also activate questions about surveillance, fairness, intent, and control.
DECODE asks how culturally grounded consumer experience shapes that response, with the current empirical program centering Black consumers and the relationship between historical institutional distrust, cultural mistrust sensitivity, and trust in algorithmic marketing systems.
The Model
A Moderated Serial Mediation Model of Algorithmic Trust
The current DECODE model proposes that historical institutional distrust predicts cultural mistrust sensitivity, which in turn predicts lower post-exposure algorithmic trust. Algorithmic trust then carries downstream consequences for ad avoidance, purchase intention, and brand trust.
Randomly assigned transparency and personalization conditions test when this pathway becomes stronger or weaker. This design keeps the causal claims tied to experimental assignment rather than to participants’ subjective ratings of the manipulation.
The Contribution
Behavioral Marketing Through a Cultural Psychology Lens
DECODE contributes to behavioral marketing by connecting consumer psychology, cultural mistrust, algorithmic trust, and experimental marketing design within one research program. It treats cultural experience as theoretically meaningful to consumer decision-making rather than as a demographic control variable.
The framework is designed to clarify why identical marketing technologies can produce different trust judgments and behavioral responses across consumers, and how marketers can distinguish useful personalization from experiences that feel intrusive, illegitimate, or unsafe.
The Larger Agenda
From One Framework to a Broader Behavioral Marketing Program
DECODE is a major research program within a broader scholarly agenda focused on the psychology behind consumer decisions. Across projects, Dr. Pugh examines trust, decision-making, culture, technology, fairness, and marketplace participation through a behavioral marketing lens.
Future extensions can test the framework across additional populations, platforms, and marketplace contexts, but those extensions are distinct from the current Black-consumer empirical model and should be treated as future research rather than as established components of the present framework.
Behavioral Marketing Research & Collaboration
Dr. Pugh welcomes scholarly collaboration around consumer psychology, algorithmic marketing, consumer trust, cultural mistrust, personalization, and experimental research design.