A unified theoretical framework explaining how AI-driven digital twin systems recursively shape and are shaped by consumer identity, trust, and societal values in marketing ecosystems.
The Reflexive Digital Twin Marketing Theory (RDTMT) emerges from a comprehensive PRISMA-ScR scoping review of 742 academic records (2015–2025), from which 55 core studies were synthesised. It addresses a critical gap: while digital twin technology has matured in engineering and healthcare, its application to consumer marketing ecosystems — and the ethical, identity, and value implications thereof — remained theoretically fragmented.
RDTMT bridges marketing theory, AI ethics, systems science, and consumer psychology to propose that digital twin marketing systems are not merely predictive tools but reflexive co-creators of consumer identity, trust, and societal norms.
The theory is grounded in Social Cognitive Theory, Service-Dominant Logic, and Responsible AI governance frameworks, while extending them to account for the recursive, bidirectional feedback loops unique to digital twin environments.
RDTMT is anchored in three interlocking constructs, each capturing a distinct dimension of the human–AI twin relationship.
The recursive, bidirectional feedback process by which AI-driven digital twins both model and modify consumer identity, preferences, and behaviours — creating a dynamic loop between representation and reality.
The degree to which marketing systems responsively and transparently incorporate consumer ethical concerns — measuring the system's capacity to bend toward moral accountability without breaking commercial viability.
A composite measure of alignment between AI-generated personalised value propositions and consumers' authentic personal, social, and cultural values — determining long-term brand loyalty and co-regulatory willingness.
Thematic synthesis of 55 core studies revealed four empirical clusters that map the operational landscape of RDTMT.
Real-time data ingestion loops that keep the digital twin aligned with evolving consumer states — encompassing behavioural, transactional, and contextual data streams. This cluster focuses on the technical infrastructure of reflexivity and its downstream effects on personalisation precision.
The machine-learning and generative AI mechanisms that translate twin-state data into personalised marketing decisions. Covers predictive modelling, dynamic pricing, recommendation elasticity, and the emergence of hyper-personalised consumer journeys.
Ethical, regulatory, and organisational mechanisms that govern digital twin marketing systems — spanning GDPR/AI Act compliance, algorithmic transparency, ethics boards, and the emerging concept of consumer-directed governance participation.
Macro-level dynamics where widespread digital twin adoption reshapes collective consumer identity, social norms, and cultural values — and in turn, how those shifts feed back into the design priorities of AI marketing systems.
The survey operationalises all nine propositions. Each is empirically testable through consumer response data.
| ID | Construct | Proposition |
|---|---|---|
| P1 | DR | Higher digital twin predictive accuracy positively amplifies consumer engagement while simultaneously increasing the risk of perceived autonomy erosion. |
| P2 | DR | Greater digital reflexivity intensity amplifies ambiguity in consumer perception between being "served" and being "surveilled." |
| P3 | DR | Increased digital reflexivity intensity positively predicts consumer demand for algorithmic governance transparency. |
| P4 | EFE | Firms that responsively adjust their digital twin systems in response to consumer ethical feedback demonstrate significantly higher consumer trust scores. |
| P5 | EFE | Commercial pressure from competitive markets negatively moderates firms' ethical feedback elasticity responsiveness. |
| P6 | EFE | Firms with high ethical feedback elasticity proactively establish consumer ethics boards before regulatory mandates require them. |
| P7 | VCI | High alignment between AI-generated value propositions and consumers' societal values positively predicts brand preference beyond functional utility. |
| P8 | VCI | High value coherence index scores are positively correlated with increased brand loyalty and consumer willingness to pay premium prices. |
| P9 | VCI | Higher value coherence between marketing systems and consumer values increases voluntary consumer co-operation with regulatory compliance mechanisms. |
RDTMT is a living framework. Your real-world consumer experience enriches its propositions with empirical grounding. Take the survey to add your voice to the academic record.
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