Token Theory of Marketing: Theorizing Value Exchange in an AI-Mediated and Tokenized Economy

International Journal of Management Science and Business Administration

Volume 12, Issue 4, July 2026, Pages 7-29

Token Theory of Marketing: Theorizing Value Exchange in an AI-Mediated and Tokenized Economy

^1,2 Suresh Sood

¹ Industry/Professional Fellow, Australian Artificial Intelligence Institute, University of Technology Sydney

² Adjunct Fellow, Frontier AI Research Centre, Macquarie University, Sydney

Abstract: Marketing is increasingly shaped by two related forms of tokenization: digital tokens representing and enabling programmable forms of value exchange, and computational tokens metering the artificial intelligence increasingly used to produce, personalize, and govern marketing activity. This article develops the Token Theory of Marketing (TTM) as a theoretical framework for explaining marketing exchange in the emerging environment. Rather than replacing established perspectives such as Service-Dominant Logic, Resource-Advantage Theory, and Dynamic Capabilities, TTM extends their explanatory reach by specifying mechanisms associated with programmable rights, conditional exchange, verifiable provenance, token-flow incentive design, and computational metering.

TTM distinguishes three interconnected domains of Tokenized Engagement, Tokenized Transactions, and Tokenized Business Models while introducing AI Token Economics as a cross-cutting computational layer rather than a separate pillar. In this formulation, AI functions operationally as an enabling capability, while computational token consumption becomes an integral mechanism for measuring and governing the resources used in AI-mediated marketing. The framework therefore connects the tokenization of market exchange with the token-metered infrastructure through which marketing is increasingly produced and evaluated.

The article develops ten testable propositions linking token architecture and token-mediated mechanisms to consumer, transactional, organizational, and marketing-performance outcomes. Importantly, TTM does not assume tokenization or blockchain inherently improves marketing outcomes. Predicted impacts are contingent on digital literacy, token utility, regulation, interoperability, trust, and market liquidity, together with appropriate privacy, governance, and technological design. TTM consequently provides a framework for investigating when, how, and under what conditions programmable exchange and token-metered intelligence reshape value creation and marketing performance.

Keywords: Token Theory of Marketing; tokenomics; AI tokens; token efficiency; blockchain marketing; tokenization; artificial intelligence; agentic AI; Era V; digital transformation; non-fungible tokens; carbon credits

Introduction

Marketing as a discipline always evolves in creative dialogue with the technological and institutional forces of the time. From the mass-media orientation characterizing early American marketing practice through the customer relationship management imperatives of the late twentieth century, each historical era demands new theoretical apparatus commensurate with the scale, complexity, and ethical demands of the moment (Clark et al., 2024; Hunt, 2020). Today, marketing stands at the threshold of the Shelby Hunt (2020) Era V, a period defined not merely by digital acceleration, but by the structural transformation of the economy’s underlying trust and value infrastructure. Three forces, individually powerful and collectively revolutionary, converge to define this moment. The emergence of blockchain technology as a decentralized ledger for immutable trust and programmable value (Iansiti & Lakhani, 2017; Swan, 2015; Tapscott & Tapscott, 2016); the pervasive deployment of AI as a personalization, prediction, and automation engine (Davenport et al., 2020; Huang & Rust, 2021); and the tokenization of virtually every class of asset from real estate and carbon credits to loyalty points and human attention enabling new forms of ownership, exchange, and participation prior economic architectures could not support (Catalini & Gans, 2020; Gleim & Stevens, 2021).

Against this backdrop, established marketing theories provide important but incomplete lenses for token-mediated exchange. Service-Dominant Logic (SDL) explains value co-creation and service ecosystems, but does not specifically model how programmable rights, conditional transfer, and smart-contract execution alter the architecture of exchange (Vargo & Lusch, 2004, 2008, 2016). Resource-Advantage (R-A) Theory explains competition through heterogeneous and imperfectly mobile resources (Hunt & Morgan, 1995; Hunt, 2000), but leaves open how tokenization changes resource divisibility, transferability, verifiability, and liquidity. Dynamic Capabilities theory explains how firms sense, seize, and transform (Teece et al., 1997; Teece, 2014), but is less specific about the exchange-level mechanisms through which token design, smart-contract governance, interoperability, and token-metered AI alter marketing outcomes. TTM therefore complements rather than displaces these theories by specifying mechanisms distinctive to token-mediated marketing: programmable rights, verifiable provenance, conditional execution, token-flow incentive design, and computational metering.

This article introduces the Token Theory of Marketing (TTM) as a direct response to the gap. TTM posits an increasing share of digital marketing is organized through token-mediated systems. Importantly, the term token now has two distinct but increasingly connected meanings. In blockchain and digital-asset systems, tokens can represent transferable or non-transferable rights, value, identity, consent, credentials, rewards, ownership, or environmental claims. In generative AI, tokens are computational units used by models to process and generate information. Gartner (Banerjee, 2026) describes these AI tokens as a common meter of AI consumption, with model context incorporating not only visible prompts and outputs but also instructions, history, retrieved content, tools, reasoning, and repeated agent calls. TTM does not collapse these meanings into one. Rather, it theorizes the convergence: marketing increasingly uses tokenized assets and relationships while simultaneously consuming AI tokens to create, personalize, automate, govern, and evaluate those relationships. This dual-token environment expands TTM from a theory of programmable exchange into a theory of programmable exchange plus computationally metered marketing intelligence.

TTM builds on SDL, R-A Theory, and Dynamic Capabilities while operating at a different explanatory level. SDL supplies the service-ecosystem and value-co-creation foundation; TTM specifies how programmable tokens can encode participation rights, rewards, consent conditions, and value transfers within those ecosystems (Lusch & Nambisan, 2015; Vargo & Lusch, 2016). R-A Theory supplies the resource-heterogeneity logic; TTM examines how tokenization can alter resource properties such as divisibility, provenance, portability, access, and liquidity, and how token-flow design may affect the realization of resource advantage (Hunt, 2000; Hunt & Morgan, 1995). Dynamic Capabilities supplies the adaptive logic of sensing, seizing, and transforming; TTM specifies token-ecosystem design and governance as possible microfoundations through which those capabilities are enacted in token-mediated markets (Teece, 2014; Teece et al., 1997). The claim is therefore one of theoretical specification and extension, not wholesale explanatory superiority.

This article proceeds as follows. The next section 2 establishes the theoretical background, reviewing the evolution toward Era V, the disruptive potential of blockchain and AI for marketing, and the limitations of existing frameworks. Section 3 presents the TTM conceptual architecture. Sections 4, 5, and 6 develop each of the TTM three pillars in detail. Section 7 grounds the framework in illustrative use cases. Section 8 advances ten research propositions. Sections 9 through 11 articulate theoretical contributions, managerial implications, and limitations before a concluding section positions TTM within the broader disciplinary horizon of Era V marketing.

Theoretical Background

Marketing in Era V: The Imperative for a New Paradigm

Hunt's (2020) call for renewal of marketing in Era V represents more than historiographical commentary. Constituting a theoretical mandate, Hunt propositions the discipline must develop frameworks commensurate with the complexity, dynamism, and ethical imperatives of a world wherein data is the primary productive resource, digital platforms supplant traditional distribution channels as the primary sites of consumer interaction, and sustainability moves from a peripheral consideration to a key dimension of brand value and legitimacy. Era V demands, as Hunt and colleagues (Clark et al., 2024; Hunt et al., 2021) articulate, adaptability at the system level, thinking crossing disciplinary and sectoral boundaries, and engagement with ethical challenges. This thinking includes privacy, equity, and environmental responsibility prior marketing theory largely treats as external constraints and not internal design principles.

The periodization of marketing theory through five eras traces a progressive expansion of the self-understanding and scope of the discipline (Clark et al., 2024). Era I, the simple trade orientation of early twentieth-century marketing, gave way to functional and managerial orientations (Eras II and III), subsequently a relationship and services emphasis (Era IV) culminating in the SDL revolutionary proposal all exchange is fundamentally service exchange (Vargo & Lusch, 2004). Era V marks a qualitative discontinuity from these prior transitions. Distinguished by the interpenetration of physical and digital realities in ways dissolving the boundaries between production and consumption, individual and community, and commercial and public value. The institutional infrastructure of Era V comprising blockchain networks, AI systems, token ecosystems, and decentralized autonomous organizations (DAOs^[1]) are not merely unavailable to prior marketing theorists but conceptually unimaginable within paradigmatic assumptions. TTM is designed specifically for this discontinuous moment of Era V.

Blockchain Technology and Disruptive Potential for Marketing

Blockchain technology, a distributed, cryptographically secure ledger recording transactions immutably across a network of nodes without central authority is among the most significant infrastructure innovations for market organization since the advent of the internet (Iansiti & Lakhani, 2017; Nakamoto, 2008; Tapscott & Tapscott, 2016). The defining blockchain characteristics, decentralization, immutability, programmability through smart contracts, and cryptographic verifiability address fundamental failures in conventional marketing exchange. The high cost of trust intermediation, the prevalence of fraud in digital advertising, the opacity of supply chains, and the asymmetric information structures that disadvantage consumers in data markets (Cong & He, 2019; Gleim & Stevens, 2021).

Gleim and Stevens (2021) document blockchain applications across the marketing value chain, including provenance, digital advertising, and smart-contract-enabled loyalty. Lumineau et al. (2021) similarly examine how distributed ledgers can support alternative governance arrangements, including token-governed communities. For TTM, blockchain is one possible trust and coordination infrastructure for token-mediated ecosystems. Its cryptographic verifiability, shared records, and programmable execution can reduce some forms of verification and intermediation cost, but these benefits are contingent on governance quality, oracle reliability, legal enforceability, interoperability, user capability, and the design of the underlying token system. TTM therefore treats blockchain affordances as conditional mechanisms rather than inherent advantages.

Figure 1

Artificial Intelligence as a Dynamic Marketing Capability

If blockchain provides the trust infrastructure of the digital marketing economy, AI provides the cognitive infrastructure. The deployment of machine learning, natural language processing, and predictive analytics is transforming marketing from an intuition-supplemented practice to a data-science-driven discipline capable of operating at individual scale across populations of millions (Davenport et al., 2020; Lamberton & Stephen, 2016; Wedel & Kannan, 2016). The Huang and Rust (2021) strategic framework for AI in marketing distinguishing mechanical AI (automating routine processes), thinking AI (generating analytical and strategic insights), and feeling AI (enabling emotional and social intelligence) illustrates the expanding scope of AI marketing applications and the progressive encroachment of algorithmic decision-making into domains previously reserved for human judgment.

From the perspective of Dynamic Capabilities (Teece et al., 1997), AI can function as an enabling capability that strengthens sensing, seizing, and transforming through pattern recognition, optimization, and system reconfiguration. Within TTM, however, AI has a more precise status. Operationally, AI is an enabling capability; theoretically, AI Token Economics is an integral cross-cutting component because computational tokens introduce a measurable resource-consumption and governance mechanism across all three TTM pillars. AI may also moderate particular relationships for example, the effect of tokenized data on personalization may depend on model quality and context design but moderation is proposition-specific rather than AI's primary theoretical role. TTM therefore embeds AI within token ecosystems, where it can act on governed data streams to personalize interactions, support consent workflows, anticipate redemption behavior, and adapt incentive structures, subject to privacy, quality, and governance constraints.

AI Tokenomics: The Computational Layer of TTM

Generative AI makes the relevance of the token to marketing unusually concrete. A language-model token is the basic unit through which a model processes and generates information. Gartner notes AI services meter inputs and outputs and may also account for cached and reasoning tokens; the context supplied to a model can include prompts, system instructions, conversation history, retrieved documents and tool definitions (Banerjee, 2026; Tung & Varma, 2026). Thus, every AI-mediated customer interaction has an underlying token footprint. Hyper-personalized copy, conversational commerce, service agents, recommendation explanations, synthetic research, campaign ideation and agentic workflow orchestration are not simply AI activities but they are token-consuming marketing activities.

This introduces an economic mechanism in TTM formulation. The relevant managerial question is not whether token consumption should be minimized, because low consumption can also mean low capability or low value. Gartner defines token efficiency as quantifiable business value delivered per million tokens consumed and recommends linking token telemetry to workload-native outcomes rather than treating token counts as an end in themselves (Liu & Meinardi, 2026). For marketing, the analogous construct is marketing token efficiency: customer or business value attributable to an AI-mediated marketing activity relative to the computational tokens consumed. Depending on the use case, value can be operationalized as qualified leads, resolved service interactions, incremental conversion, retention, contribution margin, customer-lifetime-value uplift, or another outcome appropriate to the marketing task.

Agentic AI intensifies the importance of this mechanism. Multi-step agents repeatedly assemble context, retrieve information, invoke tools, reason, evaluate and retry. Gartner's 2026 research emphasizes context accumulation, reasoning, tool use and repeated calls can compound consumption, and effective governance therefore requires context engineering, model routing, caching, explicit stopping criteria, token-level visibility and cost-per-outcome measurement (Guseva & Tung, 2026; Tyagi & Pallin, 2026; Varma, 2026). TTM therefore treats AI token governance as a marketing capability. The ability to allocate computational attention to the customers, moments and tasks where it creates sufficient value, while constraining waste, latency, cost, privacy exposure and unnecessary energy use.

Figure 2

The sustainability implication is especially important for TTM. Gartner explicitly connects AI token consumption to energy use and recommends incorporating energy- and carbon-efficiency measures into AI budgeting (Guseva & Tung, 2026). This creates a productive tension within TTM. Tokenization may enable carbon credits and sustainability incentives, yet AI systems used to personalize, administer and verify tokenized ecosystems also consume energy. A credible Era V theory should therefore evaluate net value rather than celebrate tokenization technologically. The appropriate question becomes: what customer, economic, social and environmental value is created per unit of tokenized exchange and per unit of AI computation?

Gaps in Existing Theoretical Frameworks

Service-Dominant Logic (Vargo & Lusch, 2004, 2008, 2016) provides a strong foundation for understanding value as beneficiary-determined and co-created through service exchange. Its service-ecosystem concept is highly compatible with token networks. TTM adds specificity at the mechanism level: tokens can encode rights and obligations, make some forms of value transferable, condition access or rewards on verifiable events, and automate selected exchange rules through smart contracts. These mechanisms do not invalidate SDL; rather, they specify how resource integration and institutional arrangements may be technologically instantiated in token-mediated settings.

R-A Theory (Hunt & Morgan, 1995; Hunt, 2000) explains competitive advantage through heterogeneous, imperfectly mobile resources and superior resource deployment. TTM extends this logic by examining how tokenization can change the economic properties of resources for example, by making rights divisible, provenance verifiable, access programmable, or assets more transferable and by treating token-flow design as a mechanism that may influence resource deployment and appropriation. Tokenized resources can still be understood within broader informational, relational, legal, and organizational resource categories; TTM's contribution is therefore not to replace the R-A taxonomy, but to explain how token architecture can reconfigure resource properties and value flows in digital ecosystems.

Table 1.

Theoretical mechanisms distinguishing TTM from adjacent theories

Theory	Primary focus	What TTM adds	Distinctive TTM mechanism
Service-Dominant Logic	Value co-creation, resource integration, service ecosystems	How rights, rewards, access and exchange conditions can be encoded	Programmable rights and conditional value transfer
Resource-Advantage Theory	Resource heterogeneity, comparative advantage, competition	How tokenization changes divisibility, provenance, portability, access and liquidity	Tokenized resource properties and token-flow design
Dynamic Capabilities	Sensing, seizing and transforming	How token ecosystem design and governance can instantiate adaptive capability	Smart-contract governance, interoperability and ecosystem reconfiguration
Token Theory of Marketing	Token-mediated and token-metered marketing exchange	Integrates programmable exchange with AI computational metering	Programmability, verifiability, incentive/liquidity design and token-metered AI

The Token Theory of Marketing: Conceptual Architecture

TTM rests on a foundational ontological claim. In the digital economy, marketing is increasingly mediated by digitally measurable units that represent, transfer, compute, govern, or meter value. The theory therefore distinguishes two token families. Exchange tokens are programmable representations of rights or value, including loyalty tokens, data/consent tokens, carbon credits, fractional ownership tokens and NFTs. Computational tokens are the units through which generative and agentic AI process context and generate outputs. Exchange tokens can be owned, transferred, redeemed or governed according to their design; AI computational tokens generally are not customer assets and should not be represented as blockchain tokens. Their theoretical connection lies instead in function. Both types of tokens make previously opaque flows measurable and governable. One makes market value programmable; the other makes AI-mediated marketing consumption measurable. TTM focuses on the interaction between these layers.

Table 2.

The dual-token architecture of Token Theory of Marketing

Token form	Core characteristic	Marketing relevance
Exchange / blockchain token	Programmable representation of value, rights, identity, consent, ownership or verified claims; may be fungible or non-fungible.	Loyalty, provenance, fractional ownership, carbon credits, access, credentials, consent and incentive design.
AI computational token	Unit used by generative or multimodal models to process and generate information; consumption varies with context, model, reasoning and workflow design.	Meters the computational resource underlying personalization, content, service, research and agentic marketing workflows.
Token efficiency	Outcome-oriented relationship between value created and AI tokens consumed.	Links AI marketing cost to conversion, service resolution, retention, contribution, productivity or other use-case outcomes.
Token governance	Rules, telemetry, attribution, routing, budgets, consent and controls governing token creation or consumption.	Connects trust, privacy, cost discipline, sustainability and accountability to marketing execution.

Figure 3

The distinction between fungibility and non-fungibility is not merely technical but carries profound marketing implications. Fungible tokens enable scalable, liquid reward systems and data markets. Non-fungible tokens create the conditions for authentic scarcity, community identity, and provenance-verified brand storytelling phenomena conventional marketing theory has no structural means to theorize.

The TTM explanatory architecture retains three foundational market-facing pillars: Tokenized Engagement, Tokenized Transactions, and Tokenized Business Models. These describe what tokenization changes in markets. The Gartner evidence suggests an additional cross-cutting computational layer rather than a fourth exchange pillar: AI Token Economics. This layer explains how AI-mediated marketing intelligence is consumed, attributed, optimized and governed across all three pillars. Tokenized Engagement increasingly relies on AI tokens for personalization and conversational interaction; Tokenized Transactions may use AI for verification, fraud detection and service orchestration; and Tokenized Business Models increasingly embed agents whose economic viability depends on token efficiency. The architecture is therefore best represented as three market-facing pillars operating on a governed AI-token computational layer.

TTM makes four claims distinguishing it as a theoretical paradigm rather than a technology catalogue. First, it is explanatorily autonomous for marketing phenomena created by programmable token infrastructures. Second, it is predictively generative through testable propositions. Third, it is normatively oriented, placing consent, governance, privacy and sustainability inside the architecture. Fourth, it is economically measurable: the addition of AI tokenomics makes it possible to connect the computational intensity of AI-mediated marketing to customer and business outcomes. This fourth claim is important because Gartner cautions token volume alone is neither inherently good nor bad; value depends on whether consumption contributes to an appropriate outcome (Banerjee, 2026). TTM accordingly shifts attention from 'more AI' to value-producing token use.

Pillar I: Tokenized Engagement

The first pillar of TTM reconceptualizes customer engagement as a token-generating productive activity. In established marketing theory, engagement is understood as a multidimensional psychological and behavioral state characterized by cognitive absorption, emotional resonance, and behavioral activation generating value for the firm primarily through loyalty, advocacy, and repeated purchase (Brodie et al., 2011; Hollebeek et al., 2014; Kumar et al., 2019). TTM extends and deepens this understanding by arguing that in token-mediated environments, engagement is not merely a relational state but a productive economic act: customers who interact, share, review, create, or participate generate token-denominated value that flows through the ecosystem, rewarding creators, sustaining communities, enabling hyper-personalization, and creating new forms of accountability and reciprocity between brands and their most engaged stakeholders.

Such architectures may support consent traceability and data provenance, but they are not inherently GDPR- or CCPA-compliant. Blockchain immutability can conflict with data minimization, storage limitation, rectification, and erasure requirements. Privacy-preserving designs should therefore minimize personal data on-chain, keep identifiable data off-chain where practicable, use revocable access or cryptographic commitments appropriately, and conduct data-protection impact assessment where required. Tokenization should be treated as a design option whose compliance depends on architecture and governance, not as a legal advantage in itself.

The second mechanism is attention tokenization, the transformation of a consumer act of genuinely engaging with marketing content such as reading, watching, interacting into a blockchain-verified, financially rewarded event. Rather than relying on the probabilistic inferences underpinning conventional digital advertising measurement, blockchain-verified attention tokens create an auditable record of genuine engagement enabling dynamic pricing of advertising inventory, performance-based reward systems for consumers who invest attention deliberately, and new forms of decentralized influencer marketing where content quality and audience engagement are rewarded by verified token flows rather than opaque platform algorithms (Kannan, 2017; Lamberton & Stephen, 2016). The anti-fraud implications are significant; the endemic click fraud and impression inflation that costs the digital advertising industry tens of billions of dollars annually cannot survive in an ecosystem where every verified interaction is cryptographically attested.

The third mechanism is identity and credential tokenization through non-fungible tokens. NFTs enable marketers to create unique digital assets, limited-edition collectibles, exclusive access passes, verifiable membership credentials, achievement badges functioning simultaneously as engagement incentives, brand identity signals, and social currency within digital communities (Whitaker, 2019). Unlike conventional loyalty points, proprietary, non-transferable, and subject to unilateral devaluation by the issuing brand, NFT-based engagement assets carry verifiable scarcity and open-market tradeable value structurally aligning consumer and brand incentives over longer time horizons and at greater emotional intensity (Liu, 2007; Uncles et al., 2003). The consumer who holds a brand NFT is not merely a loyal customer but a verified community member with genuine financial skin in the game, a stakeholder relationship qualitatively different from anything that conventional loyalty program theory contemplates. Across all three mechanisms, Tokenized Engagement is theoretically grounded in the SDL principle of value co-creation and extension by specifying tokens as the programmable medium for structuring co-creation, compensation, and perpetuity.

Pillar II: Tokenized Transactions

The second pillar of TTM addresses the transformation of marketing exchange itself through blockchain-enabled tokenization of value flows. In traditional marketing theory, transactions are conceptualized as dyadic exchanges of money for goods or services, governed by contractual mechanisms depending on institutional intermediaries, banks, payment processors, escrow services and certification bodies to establish and maintain trust (Vargo & Lusch, 2004). The costs of these intermediaries are not trivial, they include monetary fees, processing delays, information asymmetries between transacting parties, and persistent vulnerability to fraud. Tokenized Transactions can reduce selected verification, reconciliation, and settlement costs through cryptographic proof and automated smart-contract execution, but can also introduce integration, governance, legal, cybersecurity, oracle, and interoperability costs. A smart contract is a self-executing program stored on a blockchain automatically enforcing the terms of an agreement when pre-specified conditions are met, without human intervention or third-party mediation (Cong & He, 2019; Lumineau et al., 2021). The marketing implications are profound and multidimensional.

In advertising and influencer marketing contexts, smart contracts enable payment architectures releasing compensation automatically when verifiable performance metrics are achieved inclusive of views, authenticated engagements and conversion events eliminating the audit disputes and payment delays that characterize conventional influencer contracting while providing brands with cryptographically verified performance data (Kannan, 2017). In supply chain marketing, authenticity tokens, cryptographically verified product certificates that travel with physical goods from manufacturer to consumer make product counterfeiting structurally impossible and enable real-time provenance communication transforming the conventional authentication claim from a brand assertion into a verifiable fact (Iansiti & Lakhani, 2017; Tapscott & Tapscott, 2016). This shift from asserted to verified authenticity represents a qualitative change in brand trust, one that incumbent marketing theory, developed in a world of asymmetric information, cannot fully capture.

Among the most significant applications of Tokenized Transactions is the tokenization of carbon credits for sustainability marketing. Voluntary carbon credit markets where organizations purchase verified emissions reduction certificates to offset their carbon footprints have historically been characterized by opacity, high intermediary costs, and well-documented integrity failures undermining both environmental effectiveness and marketing value (Stern, 2007). Tokenized carbon credit systems, exemplified by platforms such as KlimaDAO, address each of these failures. Blockchain registration creates transparent, auditable records of each credit's provenance and retirement; smart contracts automate the matching of buyers and sellers without intermediary rent extraction; and the resulting market generates price signals that are more accurate and more responsive to supply and demand conditions than conventional equivalents (Ballesteros-Rodríguez et al., 2024). For marketers, the significance extends beyond cost efficiency where tokenized carbon credits transform sustainability commitments from asserted claims vulnerable to greenwashing accusations into cryptographically verifiable facts, creating a new class of marketing asset with genuine integrity and consumer trust value.

Real asset tokenization represents a further dimension of Pillar II with profound marketing implications. The landmark partnership between BlackRock and Securitize representing an ambition to tokenize $10 trillion of assets on blockchain infrastructure signals unambiguously that institutional-grade tokenized transaction systems have crossed the threshold from experimental to mainstream (Karayaneva, 2024). For financial services marketers, tokenized assets create new customer segments previously excluded from high-value investment products by minimum investment thresholds, generate differentiated positioning narratives around democratization and transparency, and enable product communication strategies built on verifiable rather than merely claimed attributes. Tokenized Transactions find theoretical grounding in R-A Theory through the proposition blockchain infrastructure constitutes a novel form of relational and informational resource. One that enables superior exchange efficiency and trust at scale and in dynamic capabilities theory through identification of smart contract governance and blockchain deployment as specific sensing-seizing-transforming capabilities generating durable marketing advantage.

Pillar III: Tokenized Business Models

The third pillar of TTM addresses perhaps the most radical dimension of tokenization. A capacity to enable entirely new architectures of value creation and commercial exchange with no precedent in pre-digital marketing theory. Tokenized Business Models emerge when tokenization moves beyond augmenting existing marketing activities to become the structural basis for new ways of organizing production, distribution, and value sharing across networks of participants. Networks where the boundaries between producer, consumer, investor, and community member dissolve into new, token-governed forms of stakeholder participation.

Fractional ownership (Figure 4) represents the foundational innovation of Tokenized Business Models. By encoding ownership rights as tokens on a blockchain, assets previously illiquid or accessible only to capital-wealthy investors of real estate, fine art, private equity stakes, premium consumer goods, and even time-denominated service experiences become divisible, tradeable, and marketable to audiences orders of magnitude larger than conventional ownership structures permit (Catalini & Gans, 2020). The marketing implications are transformative. A luxury automobile brand can tokenize vehicles, enabling consumers to acquire fractions of a fleet generating income through shared mobility services reconstituting the conventional purchase transaction as an investment-consumption hybrid generating ongoing engagement. A premium restaurant can tokenize tables, selling fractional seasonal reservations as membership tokens carrying priority access rights, social status signals, and secondary market tradability. A fitness centre can tokenize workout equipment, enabling fractional ownership generating token-denominated returns from energy production. These business models can be partially interpreted through existing theories, but TTM provides a more specific vocabulary for examining how programmable ownership, token-flow incentives, transferability, and governance interact within the marketing system.

Figure 4

Decentralized Finance (DeFi) integration represents a further dimension of Tokenized Business Models, enabling firms to offer asset-backed lending, yield-generating loyalty programs, and token-denominated investment participation directly to consumers without financial intermediaries (Cong & He, 2019). The marketing significance of DeFi lies in the capacity to transform brand loyalty from a cost centre where conventional loyalty programs represent pure operational expense into a value-generating ecosystem of token holders participating in the economic upside of brand growth and community expansion. This structural alignment of consumer and brand financial interests creates a form of stakeholder marketing SDL value co-creation principle anticipates conceptually but does not mechanically specify. The consumer is not merely a co-creator of experiential value but a co-owner of the economic infrastructure through which value is produced and distributed.

Decentralized affiliate and content ecosystems constitute a third category of Tokenized Business Models with immediate marketing relevance. In conventional affiliate marketing, platform intermediaries control traffic distribution, extract significant commission fees, and create information asymmetries between brands and creators that systematically undercompensate high-quality content at the margins (Lamberton & Stephen, 2016). Tokenized affiliate systems replace platform intermediaries with smart contracts automatically allocating token rewards to content creators, community referrers, and engaged participants based on verifiable, blockchain-recorded performance metrics. The result is a more efficient, transparent, and equitable distribution of marketing value. One that aligns the incentives of all ecosystem participants and enables the construction of creator communities of a scale, stability, and authenticity that conventionally intermediated systems cannot sustain. Theoretically, Tokenized Business Models extend Lusch and Nambisan (2015) service-dominant theory of innovation by specifying tokens as the medium of organising service ecosystems and value is allocated across networks of participants at scale.

5. Illustrative Applications: TTM in Practice

Table 3

Use Cases of Tokens in Marketing Organized by TTM Pillar

Pillar	Use Case / Mechanism	Marketing Application
I – Tokenized Engagement	Customer data tokens	Privacy-compliant hyper-personalization; GDPR/CCPA alignment
I – Tokenized Engagement	Tokenized loyalty programs	Flexible, blockchain-verified reward ecosystems; secondary-market tradability
I – Tokenized Engagement	NFT brand credentials	Unique digital collectibles; exclusive access; brand community membership
I – Tokenized Engagement	Attention tokens	Verified ad exposure; performance-based creator rewards; anti-fraud advertising
II – Tokenized Transactions	Smart contract settlements	Automated, trustless payment release upon verified performance metrics
II – Tokenized Transactions	Tokenized carbon credits	Transparent emissions trading; eco-conscious consumer marketing; green branding
II – Tokenized Transactions	Real estate / asset tokens	Frictionless digital-deed exchange; provenance marketing; democratized access
II – Tokenized Transactions	NFT marketplaces	Digital asset sales; brand collaborations; authenticated product drops
III – Tokenized Business Models	Fractional ownership	Revenue from partial asset rights; new market segments; democratized investing
III – Tokenized Business Models	DeFi-integrated programs	Yield-generating loyalty; asset-backed lending; token staking for brand equity
III – Tokenized Business Models	Decentralized affiliate marketing	Smart-contract reward automation; transparent revenue-sharing; creator incentives
III – Tokenized Business Models	Tokenized carbon-fitness ecosystems	Fitness-to-energy credits; health incentives; sustainability micro-revenue streams

Application I: Carbon Credit Generation in Fitness Ecosystems

A useful illustration of TTM's cross-domain logic is a fitness-to-energy environmental-attribute ecosystem combining energy-harvesting equipment, IoT measurement, and tokenized records. Exercise-generated electricity can be measured and recorded, but electricity output does not automatically become a carbon credit. Renewable energy certificates (RECs) and carbon offsets are distinct instruments: a REC represents the environmental attributes of one megawatt-hour of renewable electricity, whereas a carbon offset typically represents one metric tonne of CO2-equivalent emissions reduced, avoided, or removed. Any conversion from measured electricity or behavior into a tradable environmental instrument therefore requires an applicable registry, methodology, eligibility rules, verification, and controls against double counting. TTM's relevant mechanism is the tokenization and traceability of a validated environmental claim, not automatic credit creation.

Consider a representative scenario. Jane spends 30 minutes on energy-generating fitness equipment. IoT sensors measure the electricity produced and transmit a signed record to the platform. That record may support a reward token immediately, but it should not be labelled a carbon credit or REC unless the output satisfies the rules of an applicable environmental-attribute or carbon-crediting scheme. Where eligible, verified generation can be aggregated until it reaches the relevant issuance threshold; where a carbon methodology applies, emissions reductions must be quantified in CO2-equivalent terms and independently validated as required. The token can then represent a claim to a verified environmental attribute, reward, or share of program value. This distinction preserves the TTM logic while avoiding a technically incorrect one-to-one conversion between kilowatt-hours and carbon credits.

The benefits of this model extend well beyond individual incentives. At the population level, token-incentivized exercise produces measurable public health benefits alongside carbon offset outcomes, a dual social value proposition enabling participating brands to communicate authentic sustainability narratives supported by verifiable blockchain records. The challenges are correspondingly real. Technical integration across heterogeneous IoT, blockchain, and energy network systems requires significant infrastructure investment, regulatory compliance under GDPR, CCPA, and evolving carbon market governance frameworks demands ongoing legal expertise and consumer education regarding the translation of physical activity into digital environmental and financial value is essential for achieving meaningful adoption. The TTM analytical architecture helps marketers understand and address each of these challenges systematically.

Application II: Institutional Financial Asset Tokenization

The BlackRock partnership with Securitize representing an ambition to tokenize $10 trillion of financial assets on blockchain infrastructure marks a threshold moment for Pillar II of TTM (Karayaneva, 2024). The marketing implications of institutional-grade tokenized financial products are substantial and multidimensional. A new customer segments previously excluded from high-yield investment products by minimum investment thresholds become accessible through fractional token ownership. A differentiated competitive positioning becomes available to early-adopter firms through democratization, transparency, and technological leadership. The product marketing conversation shifts from asserted to verifiable attributes. This is a structural change in financial brand trust that no conventional marketing framework adequately theorizes. The competitive advantage in this context belongs to firms that can authentically align their token infrastructure with the values of access, fairness and transparency defining the emerging expectations of digitally sophisticated investor-consumers.

Application III: NFT-Driven Brand Communities

Non-fungible tokens have emerged as a powerful mechanism for constructing brand communities that integrate social identity, financial participation, and exclusive access within a single tokenized asset. Brands deploying NFT-based engagement programs create community structures in which ownership of a brand NFT confers verifiable membership rights, governance participation through token-weighted voting on brand decisions, and secondary market value that makes brand loyalty a financially meaningful rather than merely attitudinal commitment. This transforms brand loyalty from a behavioural tendency measured in purchase recency, frequency, and monetary value into a verifiable ownership stake, aligning consumer and brand interests in ways that SDL value co-creation principle anticipates theoretically but that conventional loyalty program design has never been able to structurally realize (Liu, 2007; Uncles et al., 2003; Whitaker, 2019). TTM Pillar I mechanism is especially evident in this application with NFTs creating persistent, high-intensity engagement because holders have genuine financial and communal skin in the game of brand success.

Application IV: Decentralized Loyalty and Reward Ecosystems

Traditional loyalty programs are structurally characterized by proprietary point systems, non-transferable rewards, restrictive redemption conditions, and unilateral devaluation risks that consistently limit their perceived value to consumers and their analytical value to marketers (Liu, 2007; Uncles et al., 2003). Tokenized loyalty programs address each of these structural deficiencies simultaneously. Loyalty tokens are interoperable across participating brand partners through shared smart contract infrastructure, tradeable on secondary markets at prices that reflect genuine supply and demand conditions, redeemable for a diverse portfolio of value forms including cryptocurrency, energy credits, charitable donations, premium experiences, and additional NFT-based credentials and analytically richer than conventional equivalents because every token transaction generates a verifiable, blockchain-recorded behavioural data point that can inform marketing strategy while also creating privacy and data-protection risks that require data minimization, appropriate off-chain storage, access controls, and clear governance. The resulting ecosystem is simultaneously more engaging for consumers, more cost-efficient for brands, and more productive of the high-quality behavioural insight adaptive marketing strategy requires (Hunt & Madhavaram, 2020; Wedel & Kannan, 2016).

Boundary Conditions of TTM

TTM is not expected to generate uniform effects across markets. The mechanisms are contingent on at least six boundary conditions. First, digital literacy affects whether consumers can understand wallets, keys, token rights, and verification processes; low literacy can increase friction and weaken trust. Second, token utility matters: tokens with clear, recurring use value should produce different engagement effects from speculative or weakly useful tokens. Third, regulation shapes feasibility, particularly where tokens implicate securities, payments, consumer protection, privacy, or carbon-market rules. Fourth, interoperability determines whether tokens can move or retain utility across platforms and partners; closed or technically fragmented ecosystems may reduce network effects. Fifth, trust remains necessary even when verification is cryptographic because users must still trust issuers, interfaces, smart-contract code, oracles, governance processes, and legal remedies. Sixth, market liquidity conditions the realizable value of transferable tokens; thin or volatile markets may undermine redemption value and participation. These conditions define when TTM mechanisms are more or less likely to produce the predicted outcomes and should be incorporated into empirical tests.

Research Propositions

Ten research propositions follow from the TTM architecture. They retain the theory's three market-facing pillars while adding AI token economics as a cross-cutting mechanism. Together they define a research program spanning marketing, information systems, sustainability, AI economics and governance.

Engagement Propositions

Proposition 1 (P₁): Compared with equivalent non-tokenized engagement systems, consent-governed token mechanisms that make permissions and rewards transparent will increase perceived consumer control, which in turn will increase trust and sustained engagement. This indirect effect will weaken when digital literacy is low or token utility is unclear.

Proposition 2 (P₂): Compared with conventional tier-based loyalty credentials, tokenized community credentials that provide meaningful access, participation, or ownership rights will increase brand-community identification, which will increase advocacy and retention. The effect will be stronger when token utility is high and weaker when secondary-market liquidity is low or price volatility dominates use value.

Transaction Propositions

Proposition 3 (P₃): In transactions where performance conditions can be objectively verified, smart-contract automation will reduce verification and settlement effort, thereby increasing exchange efficiency relative to functionally equivalent intermediated processes. The effect will be weaker where oracle reliability, legal enforceability, interoperability, or regulatory clarity is low.

Proposition 4 (P₄): When environmental rewards are based on independently verified and methodologically valid environmental claims, tokenized sustainability programs will increase sustained pro-environmental behavior relative to information-only sustainability communications by increasing reward salience and feedback immediacy. The effect will weaken when token utility, claim credibility, or market liquidity is low.

Proposition 5 (P₅): Providing consumers with accessible, independently verifiable provenance information through tokenized authentication will increase perceived transparency, which will increase brand credibility relative to otherwise equivalent unverifiable claims. The effect will be stronger among consumers with sufficient digital literacy and weaker where verification interfaces are difficult to use or trusted institutions already provide equivalent assurance.

Business Model Propositions

Proposition 6 (P₆): Tokenization that lowers minimum participation thresholds and makes rights divisible will increase access to previously high-threshold offerings, thereby expanding addressable customer segments relative to equivalent non-tokenized structures. This effect will depend on regulatory permissibility, transaction costs, interoperability, and sufficient market liquidity.

Proposition 7 (P₇): Token programs with clear functional utility and multiple credible redemption pathways will produce higher repeat participation and lower churn than token programs whose value is primarily speculative or cost-saving for the issuer. Interoperability and stable liquidity will strengthen the relationship, while excessive volatility or opaque governance will weaken it.

Adaptive and Systemic Propositions

Proposition 8 (P₈): Where consumers can selectively authorize access to accurate token-linked data and update or revoke that access, higher data provenance and permission quality will improve the predictive performance of adaptive marketing models relative to comparable models trained on lower-provenance data. This effect depends on adequate sample coverage, a privacy-preserving architecture, and model quality. Unlike P₁, which predicts consumer trust and engagement through perceived control, P₈ predicts analytical performance through data provenance and permission quality.

Proposition 9 (P₉): Greater integration of verified token-event data across customer-journey stages will improve the timeliness of marketing feedback, increasing campaign adaptation speed and journey-level personalization relative to fragmented data architectures. The effect will weaken when ecosystem interoperability is low, data rights restrict reuse, or firms lack the dynamic capability to act on the feedback.

Proposition 10 (P₁₀): For AI-mediated marketing tasks of comparable quality, higher marketing token efficiency—greater customer or business value per unit of AI token consumption—will reduce cost per outcome and computational intensity. The relationship will be strengthened by token-level attribution, context engineering, appropriate model routing, caching, and bounded agentic workflows, and weakened by unnecessary context, repeated retries, excessive autonomy, or unobserved token consumption.

Theoretical Contributions

TTM makes four categories of theoretical contribution to marketing scholarship. The first is paradigmatic. TTM provides a framework for understanding marketing exchange when value, identity, consent, access and ownership can be represented programmatically. The revised theory sharpens rather than dilutes this claim by distinguishing exchange tokens from AI computational tokens. The token is therefore not asserted to be one homogeneous object. Instead, TTM identifies a broader organizing logic of digitally measurable units: some tokens represent market value and rights; others meter the computational work through which AI creates and manages marketing interactions.

The second contribution is explanatory. TTM accounts for phenomena such as fitness-to-energy carbon-credit ecosystems, NFT-enabled communities, fractional ownership, decentralized loyalty, and consent-governed data exchange. The AI-token extension adds a second class of emerging phenomena: why two apparently similar AI marketing journeys can have radically different economics because of context length, reasoning intensity, model choice, retries and agentic orchestration; why personalization at scale can create a new variable cost of customer interaction; and why the economic value of an AI campaign or service agent should be assessed against its token consumption rather than adoption alone. Gartner's distinction between productive and wasteful token consumption provides an operational bridge from AI infrastructure economics to marketing outcome measurement (Banerjee, 2026).

The third contribution is normative. TTM builds ethics, privacy, and sustainability into the theoretical core rather than treating them as external constraints on an otherwise amoral theory of competitive exchange. By designing consumer consent into the data token mechanism, embedding environmental value into the carbon credit architecture, and structuring economic equity into fractional ownership models, TTM offers a theoretical vision of marketing that is not merely efficient and effective but inherently aligns with the ethical and sustainability imperatives Hunt (2020) identifies as the defining characteristics of Era V. This normative integration distinguishes TTM from prior marketing theories in a way that matters profoundly at the current historical moment. As regulatory pressure on digital marketing intensifies, consumer expectations of corporate ethical responsibility escalate, and the climate crisis demands structural rather than voluntary corporate responses, a marketing theory treating ethics and sustainability as design principles rather than afterthoughts is not merely academically superior but is strategically indispensable.

The fourth contribution is metric and governance oriented. TTM introduces marketing token efficiency as a theoretically meaningful link between AI resources and marketing outcomes. This extends conventional measures such as cost per acquisition, cost per interaction and customer lifetime value into token-metered AI environments. Gartner's Token Efficiency Ratio provides the managerial antecedent for this move, but TTM relocates the logic into marketing theory by asking which forms of AI-mediated engagement create the greatest customer and firm value per computational unit while respecting quality, privacy and sustainability constraints (Liu & Meinardi, 2026). The result is a theory capable of examining both the creation of tokenized value and the cost of the intelligence used to create it.

Managerial Implications

The practical implications of TTM are substantial and immediately actionable for marketing leaders navigating the transition to Era V. At the data strategy level, the shift from surveillance-based to consent-based data collection structured through data token mechanisms compensating consumers directly for verified data sharing requires strategic investment in blockchain-compatible data infrastructure and privacy-preserving personalization systems. Firms delaying this transition face compounding regulatory liability under GDPR and CCPA and progressive competitive disadvantage as token-native competitors capture the trust premiums that consent-governed data systems generate among increasingly privacy-conscious consumer segments (Martin et al., 2017). The investment required is significant, but the alternative continued dependence on third-party cookie data in a post-cookie regulatory environment is not a sustainable competitive strategy.

For chief marketing officers, AI tokenomics adds a new layer to marketing accountability. As generative and agentic AI becomes embedded in content production, conversational commerce, service, research and personalization, token consumption becomes a variable input to marketing performance. Gartner recommends visibility and attribution by use case, workflow and outcome, and emphasizes value per token rather than token reduction alone (Anderson, 2026; Guseva & Tung, 2026; Liu & Meinardi, 2026). A TTM-informed marketing dashboard should therefore connect input, output, cached and reasoning-token consumption to marketing-native outcomes such as qualified leads, conversions, service resolution, retention, contribution margin and customer value. This makes AI consumption governable in the same managerial language as other marketing investments.

For loyalty program architects and customer experience leaders, TTM counsels a fundamental reconsideration of the logic and architecture of reward systems. The evidence and theoretical analysis presented here strongly suggest that tokenized loyalty ecosystems characterized by secondary market tradability, cross-platform redemption, DeFi integration, and NFT-based community credentials will generate higher engagement intensity, stronger retention outcomes, and richer behavioral analytics than conventional proprietary point systems. The transition requires cross-functional investment in token infrastructure, smart contract development, regulatory compliance expertise, and sustained consumer education but the competitive position available to early movers in tokenized loyalty is correspondingly durable, because token communities generate switching costs through accumulated financial stake and social identity conventional loyalty programs cannot replicate.

For sustainability officers and brand strategists, the TTM carbon credit tokenization framework offers a compelling mechanism for converting corporate environmental commitments from asserted claims vulnerable to greenwashing criticism into cryptographically verifiable marketing assets with genuine consumer trust value. The fitness-to-energy ecosystem case illustrates the potential to align health promotion, environmental sustainability, and brand differentiation within a single token-governed program, a combination of social value propositions structurally unavailable without token infrastructure. These programs require cross-sector ecosystem partnerships among fitness equipment manufacturers, energy providers, financial institutions, and tokenization platform developers but the resulting competitive position creates barriers to imitation that conventional sustainability marketing cannot approach.

Limitations and Future Research

As with any theoretical contribution, TTM carries limitations that define the current boundaries of explanatory scope and the most productive directions for future empirical and theoretical development. TTM is, at this stage of its development, a conceptual framework rather than a fully empirically validated theory. While the ten propositions are grounded in established theoretical traditions and illustrated by real-world use cases, systematic empirical testing across diverse industry contexts, consumer populations, regulatory environments, and cultural settings is required to establish TTM predictive validity and boundary conditions. Future research should prioritize the development of experimental designs, psychometrically validated survey instruments, and archival data strategies capable of testing TTM's propositions particularly those relating to the engagement advantages of NFT communities (P2), the efficiency advantages of smart-contract transactions (P3), and the behavioral sustainability effects of carbon credit token programs (P4).

A further boundary condition follows from the dual meaning of token. Blockchain tokens and AI computational tokens are technically and economically different objects. TTM's contribution depends on preserving this distinction rather than using 'token' as a loose metaphor. Future research should test whether the proposed convergence has explanatory power beyond linguistic similarity. In particular, studies should examine when token-level AI telemetry predicts marketing outcomes, how marketing token efficiency should be operationalized across tasks, whether optimizing value per token changes campaign or service design, and how computational token use translates into energy and carbon impacts. Gartner's practitioner metrics provide useful constructs for operationalization, but independent academic validation is required.

TTM's current treatment of regulation is necessarily high-level. The legal landscape governing token securities, payments, consumer protection, carbon markets, smart contracts, and personal data varies substantially across jurisdictions. In particular, blockchain should not be described as inherently privacy compliant. The European Data Protection Board's Guidelines 02/2025 emphasize that immutable ledgers can create difficulties for storage limitation, rectification, erasure, and the allocation of controller responsibilities. TTM therefore treats privacy-preserving architecture as a boundary condition: identifiable personal data should generally be minimized on-chain, off-chain storage and revocable access should be considered where appropriate, and data-protection-by-design and impact assessment should precede deployment. Regulatory uncertainty and compliance costs are expected to moderate token adoption, ecosystem participation, and realized advantage.

The role of consumer and market heterogeneity is a central boundary condition rather than a peripheral limitation. TTM effects should vary with digital literacy, token utility, regulation, interoperability, trust, and market liquidity. Low digital literacy can raise participation costs; weak utility can turn engagement tokens into short-lived incentives; restrictive or uncertain regulation can limit feasible designs; poor interoperability can fragment value; low trust in issuers, code, oracles, or governance can offset cryptographic verifiability; and thin liquidity can make transferable tokens difficult to value or redeem. Future studies should model these conditions explicitly as moderators rather than assume tokenization produces uniform benefits. The macromarketing implications of widespread token adoption including market concentration, data sovereignty, exclusion, and the distribution of value between issuers and token holders remain a critical research agenda (Hunt et al., 2021).

10. Conclusion

This article introduces the Token Theory of Marketing as a theory of value exchange and AI-mediated marketing in Marketing Era V. Its three market-facing pillars of Tokenized Engagement, Tokenized Transactions, and Tokenized Business Models explain how programmable representations of value, rights, identity, consent and ownership reshape marketing ecosystems. The framework includes AI Token Economics as a cross-cutting computational layer, recognizing that generative and agentic AI now meter the production of marketing intelligence in tokens. This distinction allows TTM to connect two transformations that are usually studied separately: the tokenization of market exchange and the tokenization of AI consumption.

The integration is theoretically important. AI tokens are not blockchain assets, and blockchain tokens are not units of LLM computation. Yet both make flows previously difficult to observe increasingly measurable, attributable and governable. In TTM, the central managerial and research question therefore shifts from whether organizations use tokens or AI to how tokenized value and token-metered intelligence interact to create outcomes for customers, firms and society. Marketing token efficiency extends this logic by linking computational consumption to marketing value while retaining quality, trust, privacy and environmental sustainability as boundary conditions.

TTM consequently offers an Era V proposition for empirical evaluation rather than a claim of technological inevitability. Marketing environments are increasingly shaped by programmable digital rights, tokenized exchange, and token-metered AI, but whether these mechanisms create value depends on design and context. TTM therefore seeks to explain when token-mediated systems improve exchange, when their economics or governance become problematic, and how digital literacy, token utility, regulation, interoperability, trust, liquidity, privacy, and environmental constraints condition their effects.

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Volume X, Issue X, Month Year, Pages xx-xx (if it`s 1^st article, pagination starts with 7^th page not 1st)

DOI: 10.18775/ijmsba.1849-5664-5419.2014.XX.100X

URL: http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.XX.100X

Token Theory of Marketing:

Theorizing Value Exchange in an AI-Mediated and Tokenized Economy

¹ ² Suresh Sood,

¹ Industry/Professional Fellow, Australian Artificial Intelligence Institute, University of Technology Sydney

²Adjunct Fellow, Frontier AI Research Centre, Macquarie University, Sydney

Abstract: Marketing is increasingly shaped by two related forms of tokenization: digital tokens representing and enabling programmable forms of value exchange, and computational tokens metering the artificial intelligence increasingly used to produce, personalize, and govern marketing activity. This article develops the Token Theory of Marketing (TTM) as a theoretical framework for explaining marketing exchange in the emerging environment. Rather than replacing established perspectives such as Service-Dominant Logic, Resource-Advantage Theory, and Dynamic Capabilities, TTM extends their explanatory reach by specifying mechanisms associated with programmable rights, conditional exchange, verifiable provenance, token-flow incentive design, and computational metering.

TTM distinguishes three interconnected domains of Tokenized Engagement, Tokenized Transactions, and Tokenized Business Models while introducing AI Token Economics as a cross-cutting computational layer rather than a separate pillar. In this formulation, AI functions operationally as an enabling capability, while computational token consumption becomes an integral mechanism for measuring and governing the resources used in AI-mediated marketing. The framework therefore connects the tokenization of market exchange with the token-metered infrastructure through which marketing is increasingly produced and evaluated.

The article develops ten testable propositions linking token architecture and token-mediated mechanisms to consumer, transactional, organizational, and marketing-performance outcomes. Importantly, TTM does not assume tokenization or blockchain inherently improves marketing outcomes. Predicted impacts are contingent on digital literacy, token utility, regulation, interoperability, trust, and market liquidity, together with appropriate privacy, governance, and technological design. TTM consequently provides a framework for investigating when, how, and under what conditions programmable exchange and token-metered intelligence reshape value creation and marketing performance.

Keywords: Token Theory of Marketing; tokenomics; AI tokens; token efficiency; blockchain marketing; tokenization; artificial intelligence; agentic AI; Era V; digital transformation; non-fungible tokens; carbon credits

Introduction

Marketing as a discipline always evolves in creative dialogue with the technological and institutional forces of the time. From the mass-media orientation characterizing early American marketing practice through the customer relationship management imperatives of the late twentieth century, each historical era demands new theoretical apparatus commensurate with the scale, complexity, and ethical demands of the moment (Clark et al., 2024; Hunt, 2020). Today, marketing stands at the threshold of the Shelby Hunt (2020) Era V, a period defined not merely by digital acceleration, but by the structural transformation of the economy’s underlying trust and value infrastructure. Three forces, individually powerful and collectively revolutionary, converge to define this moment. The emergence of blockchain technology as a decentralized ledger for immutable trust and programmable value (Iansiti & Lakhani, 2017; Swan, 2015; Tapscott & Tapscott, 2016); the pervasive deployment of AI as a personalization, prediction, and automation engine (Davenport et al., 2020; Huang & Rust, 2021); and the tokenization of virtually every class of asset from real estate and carbon credits to loyalty points and human attention enabling new forms of ownership, exchange, and participation prior economic architectures could not support (Catalini & Gans, 2020; Gleim & Stevens, 2021).

Against this backdrop, established marketing theories provide important but incomplete lenses for token-mediated exchange. Service-Dominant Logic (SDL) explains value co-creation and service ecosystems, but does not specifically model how programmable rights, conditional transfer, and smart-contract execution alter the architecture of exchange (Vargo & Lusch, 2004, 2008, 2016). Resource-Advantage (R-A) Theory explains competition through heterogeneous and imperfectly mobile resources (Hunt & Morgan, 1995; Hunt, 2000), but leaves open how tokenization changes resource divisibility, transferability, verifiability, and liquidity. Dynamic Capabilities theory explains how firms sense, seize, and transform (Teece et al., 1997; Teece, 2014), but is less specific about the exchange-level mechanisms through which token design, smart-contract governance, interoperability, and token-metered AI alter marketing outcomes. TTM therefore complements rather than displaces these theories by specifying mechanisms distinctive to token-mediated marketing: programmable rights, verifiable provenance, conditional execution, token-flow incentive design, and computational metering.

This article introduces the Token Theory of Marketing (TTM) as a direct response to the gap. TTM posits an increasing share of digital marketing is organized through token-mediated systems. Importantly, the term token now has two distinct but increasingly connected meanings. In blockchain and digital-asset systems, tokens can represent transferable or non-transferable rights, value, identity, consent, credentials, rewards, ownership, or environmental claims. In generative AI, tokens are computational units used by models to process and generate information. Gartner (Banerjee, 2026) describes these AI tokens as a common meter of AI consumption, with model context incorporating not only visible prompts and outputs but also instructions, history, retrieved content, tools, reasoning, and repeated agent calls. TTM does not collapse these meanings into one. Rather, it theorizes the convergence: marketing increasingly uses tokenized assets and relationships while simultaneously consuming AI tokens to create, personalize, automate, govern, and evaluate those relationships. This dual-token environment expands TTM from a theory of programmable exchange into a theory of programmable exchange plus computationally metered marketing intelligence.

TTM builds on SDL, R-A Theory, and Dynamic Capabilities while operating at a different explanatory level. SDL supplies the service-ecosystem and value-co-creation foundation; TTM specifies how programmable tokens can encode participation rights, rewards, consent conditions, and value transfers within those ecosystems (Lusch & Nambisan, 2015; Vargo & Lusch, 2016). R-A Theory supplies the resource-heterogeneity logic; TTM examines how tokenization can alter resource properties such as divisibility, provenance, portability, access, and liquidity, and how token-flow design may affect the realization of resource advantage (Hunt, 2000; Hunt & Morgan, 1995). Dynamic Capabilities supplies the adaptive logic of sensing, seizing, and transforming; TTM specifies token-ecosystem design and governance as possible microfoundations through which those capabilities are enacted in token-mediated markets (Teece, 2014; Teece et al., 1997). The claim is therefore one of theoretical specification and extension, not wholesale explanatory superiority.

This article proceeds as follows. The next section 2 establishes the theoretical background, reviewing the evolution toward Era V, the disruptive potential of blockchain and AI for marketing, and the limitations of existing frameworks. Section 3 presents the TTM conceptual architecture. Sections 4, 5, and 6 develop each of the TTM three pillars in detail. Section 7 grounds the framework in illustrative use cases. Section 8 advances ten research propositions. Sections 9 through 11 articulate theoretical contributions, managerial implications, and limitations before a concluding section positions TTM within the broader disciplinary horizon of Era V marketing.

Theoretical Background

Marketing in Era V: The Imperative for a New Paradigm

Hunt's (2020) call for renewal of marketing in Era V represents more than historiographical commentary. Constituting a theoretical mandate, Hunt propositions the discipline must develop frameworks commensurate with the complexity, dynamism, and ethical imperatives of a world wherein data is the primary productive resource, digital platforms supplant traditional distribution channels as the primary sites of consumer interaction, and sustainability moves from a peripheral consideration to a key dimension of brand value and legitimacy. Era V demands, as Hunt and colleagues (Clark et al., 2024; Hunt et al., 2021) articulate, adaptability at the system level, thinking crossing disciplinary and sectoral boundaries, and engagement with ethical challenges. This thinking includes privacy, equity, and environmental responsibility prior marketing theory largely treats as external constraints and not internal design principles.

The periodization of marketing theory through five eras traces a progressive expansion of the self-understanding and scope of the discipline (Clark et al., 2024). Era I, the simple trade orientation of early twentieth-century marketing, gave way to functional and managerial orientations (Eras II and III), subsequently a relationship and services emphasis (Era IV) culminating in the SDL revolutionary proposal all exchange is fundamentally service exchange (Vargo & Lusch, 2004). Era V marks a qualitative discontinuity from these prior transitions. Distinguished by the interpenetration of physical and digital realities in ways dissolving the boundaries between production and consumption, individual and community, and commercial and public value. The institutional infrastructure of Era V comprising blockchain networks, AI systems, token ecosystems, and decentralized autonomous organizations (DAOs^[1]) are not merely unavailable to prior marketing theorists but conceptually unimaginable within paradigmatic assumptions. TTM is designed specifically for this discontinuous moment of Era V.

Blockchain Technology and Disruptive Potential for Marketing

Blockchain technology, a distributed, cryptographically secure ledger recording transactions immutably across a network of nodes without central authority is among the most significant infrastructure innovations for market organization since the advent of the internet (Iansiti & Lakhani, 2017; Nakamoto, 2008; Tapscott & Tapscott, 2016). The defining blockchain characteristics, decentralization, immutability, programmability through smart contracts, and cryptographic verifiability address fundamental failures in conventional marketing exchange. The high cost of trust intermediation, the prevalence of fraud in digital advertising, the opacity of supply chains, and the asymmetric information structures that disadvantage consumers in data markets (Cong & He, 2019; Gleim & Stevens, 2021).

Gleim and Stevens (2021) document blockchain applications across the marketing value chain, including provenance, digital advertising, and smart-contract-enabled loyalty. Lumineau et al. (2021) similarly examine how distributed ledgers can support alternative governance arrangements, including token-governed communities. For TTM, blockchain is one possible trust and coordination infrastructure for token-mediated ecosystems. Its cryptographic verifiability, shared records, and programmable execution can reduce some forms of verification and intermediation cost, but these benefits are contingent on governance quality, oracle reliability, legal enforceability, interoperability, user capability, and the design of the underlying token system. TTM therefore treats blockchain affordances as conditional mechanisms rather than inherent advantages.

Figure 1

Artificial Intelligence as a Dynamic Marketing Capability

If blockchain provides the trust infrastructure of the digital marketing economy, AI provides the cognitive infrastructure. The deployment of machine learning, natural language processing, and predictive analytics is transforming marketing from an intuition-supplemented practice to a data-science-driven discipline capable of operating at individual scale across populations of millions (Davenport et al., 2020; Lamberton & Stephen, 2016; Wedel & Kannan, 2016). The Huang and Rust (2021) strategic framework for AI in marketing distinguishing mechanical AI (automating routine processes), thinking AI (generating analytical and strategic insights), and feeling AI (enabling emotional and social intelligence) illustrates the expanding scope of AI marketing applications and the progressive encroachment of algorithmic decision-making into domains previously reserved for human judgment.

From the perspective of Dynamic Capabilities (Teece et al., 1997), AI can function as an enabling capability that strengthens sensing, seizing, and transforming through pattern recognition, optimization, and system reconfiguration. Within TTM, however, AI has a more precise status. Operationally, AI is an enabling capability; theoretically, AI Token Economics is an integral cross-cutting component because computational tokens introduce a measurable resource-consumption and governance mechanism across all three TTM pillars. AI may also moderate particular relationships for example, the effect of tokenized data on personalization may depend on model quality and context design but moderation is proposition-specific rather than AI's primary theoretical role. TTM therefore embeds AI within token ecosystems, where it can act on governed data streams to personalize interactions, support consent workflows, anticipate redemption behavior, and adapt incentive structures, subject to privacy, quality, and governance constraints.

AI Tokenomics: The Computational Layer of TTM

Generative AI makes the relevance of the token to marketing unusually concrete. A language-model token is the basic unit through which a model processes and generates information. Gartner notes AI services meter inputs and outputs and may also account for cached and reasoning tokens; the context supplied to a model can include prompts, system instructions, conversation history, retrieved documents and tool definitions (Banerjee, 2026; Tung & Varma, 2026). Thus, every AI-mediated customer interaction has an underlying token footprint. Hyper-personalized copy, conversational commerce, service agents, recommendation explanations, synthetic research, campaign ideation and agentic workflow orchestration are not simply AI activities but they are token-consuming marketing activities.

This introduces an economic mechanism in TTM formulation. The relevant managerial question is not whether token consumption should be minimized, because low consumption can also mean low capability or low value. Gartner defines token efficiency as quantifiable business value delivered per million tokens consumed and recommends linking token telemetry to workload-native outcomes rather than treating token counts as an end in themselves (Liu & Meinardi, 2026). For marketing, the analogous construct is marketing token efficiency: customer or business value attributable to an AI-mediated marketing activity relative to the computational tokens consumed. Depending on the use case, value can be operationalized as qualified leads, resolved service interactions, incremental conversion, retention, contribution margin, customer-lifetime-value uplift, or another outcome appropriate to the marketing task.

Agentic AI intensifies the importance of this mechanism. Multi-step agents repeatedly assemble context, retrieve information, invoke tools, reason, evaluate and retry. Gartner's 2026 research emphasizes context accumulation, reasoning, tool use and repeated calls can compound consumption, and effective governance therefore requires context engineering, model routing, caching, explicit stopping criteria, token-level visibility and cost-per-outcome measurement (Guseva & Tung, 2026; Tyagi & Pallin, 2026; Varma, 2026). TTM therefore treats AI token governance as a marketing capability. The ability to allocate computational attention to the customers, moments and tasks where it creates sufficient value, while constraining waste, latency, cost, privacy exposure and unnecessary energy use.

Figure 2

The sustainability implication is especially important for TTM. Gartner explicitly connects AI token consumption to energy use and recommends incorporating energy- and carbon-efficiency measures into AI budgeting (Guseva & Tung, 2026). This creates a productive tension within TTM. Tokenization may enable carbon credits and sustainability incentives, yet AI systems used to personalize, administer and verify tokenized ecosystems also consume energy. A credible Era V theory should therefore evaluate net value rather than celebrate tokenization technologically. The appropriate question becomes: what customer, economic, social and environmental value is created per unit of tokenized exchange and per unit of AI computation?

Gaps in Existing Theoretical Frameworks

Service-Dominant Logic (Vargo & Lusch, 2004, 2008, 2016) provides a strong foundation for understanding value as beneficiary-determined and co-created through service exchange. Its service-ecosystem concept is highly compatible with token networks. TTM adds specificity at the mechanism level: tokens can encode rights and obligations, make some forms of value transferable, condition access or rewards on verifiable events, and automate selected exchange rules through smart contracts. These mechanisms do not invalidate SDL; rather, they specify how resource integration and institutional arrangements may be technologically instantiated in token-mediated settings.

R-A Theory (Hunt & Morgan, 1995; Hunt, 2000) explains competitive advantage through heterogeneous, imperfectly mobile resources and superior resource deployment. TTM extends this logic by examining how tokenization can change the economic properties of resources for example, by making rights divisible, provenance verifiable, access programmable, or assets more transferable and by treating token-flow design as a mechanism that may influence resource deployment and appropriation. Tokenized resources can still be understood within broader informational, relational, legal, and organizational resource categories; TTM's contribution is therefore not to replace the R-A taxonomy, but to explain how token architecture can reconfigure resource properties and value flows in digital ecosystems.

Table 1.

Theoretical mechanisms distinguishing TTM from adjacent theories

Theory	Primary focus	What TTM adds	Distinctive TTM mechanism
Service-Dominant Logic	Value co-creation, resource integration, service ecosystems	How rights, rewards, access and exchange conditions can be encoded	Programmable rights and conditional value transfer
Resource-Advantage Theory	Resource heterogeneity, comparative advantage, competition	How tokenization changes divisibility, provenance, portability, access and liquidity	Tokenized resource properties and token-flow design
Dynamic Capabilities	Sensing, seizing and transforming	How token ecosystem design and governance can instantiate adaptive capability	Smart-contract governance, interoperability and ecosystem reconfiguration
Token Theory of Marketing	Token-mediated and token-metered marketing exchange	Integrates programmable exchange with AI computational metering	Programmability, verifiability, incentive/liquidity design and token-metered AI

The Token Theory of Marketing: Conceptual Architecture

TTM rests on a foundational ontological claim. In the digital economy, marketing is increasingly mediated by digitally measurable units that represent, transfer, compute, govern, or meter value. The theory therefore distinguishes two token families. Exchange tokens are programmable representations of rights or value, including loyalty tokens, data/consent tokens, carbon credits, fractional ownership tokens and NFTs. Computational tokens are the units through which generative and agentic AI process context and generate outputs. Exchange tokens can be owned, transferred, redeemed or governed according to their design; AI computational tokens generally are not customer assets and should not be represented as blockchain tokens. Their theoretical connection lies instead in function. Both types of tokens make previously opaque flows measurable and governable. One makes market value programmable; the other makes AI-mediated marketing consumption measurable. TTM focuses on the interaction between these layers.

Table 2.

The dual-token architecture of Token Theory of Marketing

Token form	Core characteristic	Marketing relevance
Exchange / blockchain token	Programmable representation of value, rights, identity, consent, ownership or verified claims; may be fungible or non-fungible.	Loyalty, provenance, fractional ownership, carbon credits, access, credentials, consent and incentive design.
AI computational token	Unit used by generative or multimodal models to process and generate information; consumption varies with context, model, reasoning and workflow design.	Meters the computational resource underlying personalization, content, service, research and agentic marketing workflows.
Token efficiency	Outcome-oriented relationship between value created and AI tokens consumed.	Links AI marketing cost to conversion, service resolution, retention, contribution, productivity or other use-case outcomes.
Token governance	Rules, telemetry, attribution, routing, budgets, consent and controls governing token creation or consumption.	Connects trust, privacy, cost discipline, sustainability and accountability to marketing execution.

Figure 3

The distinction between fungibility and non-fungibility is not merely technical but carries profound marketing implications. Fungible tokens enable scalable, liquid reward systems and data markets. Non-fungible tokens create the conditions for authentic scarcity, community identity, and provenance-verified brand storytelling phenomena conventional marketing theory has no structural means to theorize.

The TTM explanatory architecture retains three foundational market-facing pillars: Tokenized Engagement, Tokenized Transactions, and Tokenized Business Models. These describe what tokenization changes in markets. The Gartner evidence suggests an additional cross-cutting computational layer rather than a fourth exchange pillar: AI Token Economics. This layer explains how AI-mediated marketing intelligence is consumed, attributed, optimized and governed across all three pillars. Tokenized Engagement increasingly relies on AI tokens for personalization and conversational interaction; Tokenized Transactions may use AI for verification, fraud detection and service orchestration; and Tokenized Business Models increasingly embed agents whose economic viability depends on token efficiency. The architecture is therefore best represented as three market-facing pillars operating on a governed AI-token computational layer.

TTM makes four claims distinguishing it as a theoretical paradigm rather than a technology catalogue. First, it is explanatorily autonomous for marketing phenomena created by programmable token infrastructures. Second, it is predictively generative through testable propositions. Third, it is normatively oriented, placing consent, governance, privacy and sustainability inside the architecture. Fourth, it is economically measurable: the addition of AI tokenomics makes it possible to connect the computational intensity of AI-mediated marketing to customer and business outcomes. This fourth claim is important because Gartner cautions token volume alone is neither inherently good nor bad; value depends on whether consumption contributes to an appropriate outcome (Banerjee, 2026). TTM accordingly shifts attention from 'more AI' to value-producing token use.

Pillar I: Tokenized Engagement

The first pillar of TTM reconceptualizes customer engagement as a token-generating productive activity. In established marketing theory, engagement is understood as a multidimensional psychological and behavioral state characterized by cognitive absorption, emotional resonance, and behavioral activation generating value for the firm primarily through loyalty, advocacy, and repeated purchase (Brodie et al., 2011; Hollebeek et al., 2014; Kumar et al., 2019). TTM extends and deepens this understanding by arguing that in token-mediated environments, engagement is not merely a relational state but a productive economic act: customers who interact, share, review, create, or participate generate token-denominated value that flows through the ecosystem, rewarding creators, sustaining communities, enabling hyper-personalization, and creating new forms of accountability and reciprocity between brands and their most engaged stakeholders.

Such architectures may support consent traceability and data provenance, but they are not inherently GDPR- or CCPA-compliant. Blockchain immutability can conflict with data minimization, storage limitation, rectification, and erasure requirements. Privacy-preserving designs should therefore minimize personal data on-chain, keep identifiable data off-chain where practicable, use revocable access or cryptographic commitments appropriately, and conduct data-protection impact assessment where required. Tokenization should be treated as a design option whose compliance depends on architecture and governance, not as a legal advantage in itself.

The second mechanism is attention tokenization, the transformation of a consumer act of genuinely engaging with marketing content such as reading, watching, interacting into a blockchain-verified, financially rewarded event. Rather than relying on the probabilistic inferences underpinning conventional digital advertising measurement, blockchain-verified attention tokens create an auditable record of genuine engagement enabling dynamic pricing of advertising inventory, performance-based reward systems for consumers who invest attention deliberately, and new forms of decentralized influencer marketing where content quality and audience engagement are rewarded by verified token flows rather than opaque platform algorithms (Kannan, 2017; Lamberton & Stephen, 2016). The anti-fraud implications are significant; the endemic click fraud and impression inflation that costs the digital advertising industry tens of billions of dollars annually cannot survive in an ecosystem where every verified interaction is cryptographically attested.

The third mechanism is identity and credential tokenization through non-fungible tokens. NFTs enable marketers to create unique digital assets, limited-edition collectibles, exclusive access passes, verifiable membership credentials, achievement badges functioning simultaneously as engagement incentives, brand identity signals, and social currency within digital communities (Whitaker, 2019). Unlike conventional loyalty points, proprietary, non-transferable, and subject to unilateral devaluation by the issuing brand, NFT-based engagement assets carry verifiable scarcity and open-market tradeable value structurally aligning consumer and brand incentives over longer time horizons and at greater emotional intensity (Liu, 2007; Uncles et al., 2003). The consumer who holds a brand NFT is not merely a loyal customer but a verified community member with genuine financial skin in the game, a stakeholder relationship qualitatively different from anything that conventional loyalty program theory contemplates. Across all three mechanisms, Tokenized Engagement is theoretically grounded in the SDL principle of value co-creation and extension by specifying tokens as the programmable medium for structuring co-creation, compensation, and perpetuity.

Pillar II: Tokenized Transactions

The second pillar of TTM addresses the transformation of marketing exchange itself through blockchain-enabled tokenization of value flows. In traditional marketing theory, transactions are conceptualized as dyadic exchanges of money for goods or services, governed by contractual mechanisms depending on institutional intermediaries, banks, payment processors, escrow services and certification bodies to establish and maintain trust (Vargo & Lusch, 2004). The costs of these intermediaries are not trivial, they include monetary fees, processing delays, information asymmetries between transacting parties, and persistent vulnerability to fraud. Tokenized Transactions can reduce selected verification, reconciliation, and settlement costs through cryptographic proof and automated smart-contract execution, but can also introduce integration, governance, legal, cybersecurity, oracle, and interoperability costs. A smart contract is a self-executing program stored on a blockchain automatically enforcing the terms of an agreement when pre-specified conditions are met, without human intervention or third-party mediation (Cong & He, 2019; Lumineau et al., 2021). The marketing implications are profound and multidimensional.

In advertising and influencer marketing contexts, smart contracts enable payment architectures releasing compensation automatically when verifiable performance metrics are achieved inclusive of views, authenticated engagements and conversion events eliminating the audit disputes and payment delays that characterize conventional influencer contracting while providing brands with cryptographically verified performance data (Kannan, 2017). In supply chain marketing, authenticity tokens, cryptographically verified product certificates that travel with physical goods from manufacturer to consumer make product counterfeiting structurally impossible and enable real-time provenance communication transforming the conventional authentication claim from a brand assertion into a verifiable fact (Iansiti & Lakhani, 2017; Tapscott & Tapscott, 2016). This shift from asserted to verified authenticity represents a qualitative change in brand trust, one that incumbent marketing theory, developed in a world of asymmetric information, cannot fully capture.

Among the most significant applications of Tokenized Transactions is the tokenization of carbon credits for sustainability marketing. Voluntary carbon credit markets where organizations purchase verified emissions reduction certificates to offset their carbon footprints have historically been characterized by opacity, high intermediary costs, and well-documented integrity failures undermining both environmental effectiveness and marketing value (Stern, 2007). Tokenized carbon credit systems, exemplified by platforms such as KlimaDAO, address each of these failures. Blockchain registration creates transparent, auditable records of each credit's provenance and retirement; smart contracts automate the matching of buyers and sellers without intermediary rent extraction; and the resulting market generates price signals that are more accurate and more responsive to supply and demand conditions than conventional equivalents (Ballesteros-Rodríguez et al., 2024). For marketers, the significance extends beyond cost efficiency where tokenized carbon credits transform sustainability commitments from asserted claims vulnerable to greenwashing accusations into cryptographically verifiable facts, creating a new class of marketing asset with genuine integrity and consumer trust value.

Real asset tokenization represents a further dimension of Pillar II with profound marketing implications. The landmark partnership between BlackRock and Securitize representing an ambition to tokenize $10 trillion of assets on blockchain infrastructure signals unambiguously that institutional-grade tokenized transaction systems have crossed the threshold from experimental to mainstream (Karayaneva, 2024). For financial services marketers, tokenized assets create new customer segments previously excluded from high-value investment products by minimum investment thresholds, generate differentiated positioning narratives around democratization and transparency, and enable product communication strategies built on verifiable rather than merely claimed attributes. Tokenized Transactions find theoretical grounding in R-A Theory through the proposition blockchain infrastructure constitutes a novel form of relational and informational resource. One that enables superior exchange efficiency and trust at scale and in dynamic capabilities theory through identification of smart contract governance and blockchain deployment as specific sensing-seizing-transforming capabilities generating durable marketing advantage.

Pillar III: Tokenized Business Models

The third pillar of TTM addresses perhaps the most radical dimension of tokenization. A capacity to enable entirely new architectures of value creation and commercial exchange with no precedent in pre-digital marketing theory. Tokenized Business Models emerge when tokenization moves beyond augmenting existing marketing activities to become the structural basis for new ways of organizing production, distribution, and value sharing across networks of participants. Networks where the boundaries between producer, consumer, investor, and community member dissolve into new, token-governed forms of stakeholder participation.

Fractional ownership (Figure 4) represents the foundational innovation of Tokenized Business Models. By encoding ownership rights as tokens on a blockchain, assets previously illiquid or accessible only to capital-wealthy investors of real estate, fine art, private equity stakes, premium consumer goods, and even time-denominated service experiences become divisible, tradeable, and marketable to audiences orders of magnitude larger than conventional ownership structures permit (Catalini & Gans, 2020). The marketing implications are transformative. A luxury automobile brand can tokenize vehicles, enabling consumers to acquire fractions of a fleet generating income through shared mobility services reconstituting the conventional purchase transaction as an investment-consumption hybrid generating ongoing engagement. A premium restaurant can tokenize tables, selling fractional seasonal reservations as membership tokens carrying priority access rights, social status signals, and secondary market tradability. A fitness centre can tokenize workout equipment, enabling fractional ownership generating token-denominated returns from energy production. These business models can be partially interpreted through existing theories, but TTM provides a more specific vocabulary for examining how programmable ownership, token-flow incentives, transferability, and governance interact within the marketing system.

Figure 4

Decentralized Finance (DeFi) integration represents a further dimension of Tokenized Business Models, enabling firms to offer asset-backed lending, yield-generating loyalty programs, and token-denominated investment participation directly to consumers without financial intermediaries (Cong & He, 2019). The marketing significance of DeFi lies in the capacity to transform brand loyalty from a cost centre where conventional loyalty programs represent pure operational expense into a value-generating ecosystem of token holders participating in the economic upside of brand growth and community expansion. This structural alignment of consumer and brand financial interests creates a form of stakeholder marketing SDL value co-creation principle anticipates conceptually but does not mechanically specify. The consumer is not merely a co-creator of experiential value but a co-owner of the economic infrastructure through which value is produced and distributed.

Decentralized affiliate and content ecosystems constitute a third category of Tokenized Business Models with immediate marketing relevance. In conventional affiliate marketing, platform intermediaries control traffic distribution, extract significant commission fees, and create information asymmetries between brands and creators that systematically undercompensate high-quality content at the margins (Lamberton & Stephen, 2016). Tokenized affiliate systems replace platform intermediaries with smart contracts automatically allocating token rewards to content creators, community referrers, and engaged participants based on verifiable, blockchain-recorded performance metrics. The result is a more efficient, transparent, and equitable distribution of marketing value. One that aligns the incentives of all ecosystem participants and enables the construction of creator communities of a scale, stability, and authenticity that conventionally intermediated systems cannot sustain. Theoretically, Tokenized Business Models extend Lusch and Nambisan (2015) service-dominant theory of innovation by specifying tokens as the medium of organising service ecosystems and value is allocated across networks of participants at scale.

5. Illustrative Applications: TTM in Practice

Table 3

Use Cases of Tokens in Marketing Organized by TTM Pillar

Pillar	Use Case / Mechanism	Marketing Application
I – Tokenized Engagement	Customer data tokens	Privacy-compliant hyper-personalization; GDPR/CCPA alignment
I – Tokenized Engagement	Tokenized loyalty programs	Flexible, blockchain-verified reward ecosystems; secondary-market tradability
I – Tokenized Engagement	NFT brand credentials	Unique digital collectibles; exclusive access; brand community membership
I – Tokenized Engagement	Attention tokens	Verified ad exposure; performance-based creator rewards; anti-fraud advertising
II – Tokenized Transactions	Smart contract settlements	Automated, trustless payment release upon verified performance metrics
II – Tokenized Transactions	Tokenized carbon credits	Transparent emissions trading; eco-conscious consumer marketing; green branding
II – Tokenized Transactions	Real estate / asset tokens	Frictionless digital-deed exchange; provenance marketing; democratized access
II – Tokenized Transactions	NFT marketplaces	Digital asset sales; brand collaborations; authenticated product drops
III – Tokenized Business Models	Fractional ownership	Revenue from partial asset rights; new market segments; democratized investing
III – Tokenized Business Models	DeFi-integrated programs	Yield-generating loyalty; asset-backed lending; token staking for brand equity
III – Tokenized Business Models	Decentralized affiliate marketing	Smart-contract reward automation; transparent revenue-sharing; creator incentives
III – Tokenized Business Models	Tokenized carbon-fitness ecosystems	Fitness-to-energy credits; health incentives; sustainability micro-revenue streams

Application I: Carbon Credit Generation in Fitness Ecosystems

A useful illustration of TTM's cross-domain logic is a fitness-to-energy environmental-attribute ecosystem combining energy-harvesting equipment, IoT measurement, and tokenized records. Exercise-generated electricity can be measured and recorded, but electricity output does not automatically become a carbon credit. Renewable energy certificates (RECs) and carbon offsets are distinct instruments: a REC represents the environmental attributes of one megawatt-hour of renewable electricity, whereas a carbon offset typically represents one metric tonne of CO2-equivalent emissions reduced, avoided, or removed. Any conversion from measured electricity or behavior into a tradable environmental instrument therefore requires an applicable registry, methodology, eligibility rules, verification, and controls against double counting. TTM's relevant mechanism is the tokenization and traceability of a validated environmental claim, not automatic credit creation.

Consider a representative scenario. Jane spends 30 minutes on energy-generating fitness equipment. IoT sensors measure the electricity produced and transmit a signed record to the platform. That record may support a reward token immediately, but it should not be labelled a carbon credit or REC unless the output satisfies the rules of an applicable environmental-attribute or carbon-crediting scheme. Where eligible, verified generation can be aggregated until it reaches the relevant issuance threshold; where a carbon methodology applies, emissions reductions must be quantified in CO2-equivalent terms and independently validated as required. The token can then represent a claim to a verified environmental attribute, reward, or share of program value. This distinction preserves the TTM logic while avoiding a technically incorrect one-to-one conversion between kilowatt-hours and carbon credits.

The benefits of this model extend well beyond individual incentives. At the population level, token-incentivized exercise produces measurable public health benefits alongside carbon offset outcomes, a dual social value proposition enabling participating brands to communicate authentic sustainability narratives supported by verifiable blockchain records. The challenges are correspondingly real. Technical integration across heterogeneous IoT, blockchain, and energy network systems requires significant infrastructure investment, regulatory compliance under GDPR, CCPA, and evolving carbon market governance frameworks demands ongoing legal expertise and consumer education regarding the translation of physical activity into digital environmental and financial value is essential for achieving meaningful adoption. The TTM analytical architecture helps marketers understand and address each of these challenges systematically.

Application II: Institutional Financial Asset Tokenization

The BlackRock partnership with Securitize representing an ambition to tokenize $10 trillion of financial assets on blockchain infrastructure marks a threshold moment for Pillar II of TTM (Karayaneva, 2024). The marketing implications of institutional-grade tokenized financial products are substantial and multidimensional. A new customer segments previously excluded from high-yield investment products by minimum investment thresholds become accessible through fractional token ownership. A differentiated competitive positioning becomes available to early-adopter firms through democratization, transparency, and technological leadership. The product marketing conversation shifts from asserted to verifiable attributes. This is a structural change in financial brand trust that no conventional marketing framework adequately theorizes. The competitive advantage in this context belongs to firms that can authentically align their token infrastructure with the values of access, fairness and transparency defining the emerging expectations of digitally sophisticated investor-consumers.

Application III: NFT-Driven Brand Communities

Non-fungible tokens have emerged as a powerful mechanism for constructing brand communities that integrate social identity, financial participation, and exclusive access within a single tokenized asset. Brands deploying NFT-based engagement programs create community structures in which ownership of a brand NFT confers verifiable membership rights, governance participation through token-weighted voting on brand decisions, and secondary market value that makes brand loyalty a financially meaningful rather than merely attitudinal commitment. This transforms brand loyalty from a behavioural tendency measured in purchase recency, frequency, and monetary value into a verifiable ownership stake, aligning consumer and brand interests in ways that SDL value co-creation principle anticipates theoretically but that conventional loyalty program design has never been able to structurally realize (Liu, 2007; Uncles et al., 2003; Whitaker, 2019). TTM Pillar I mechanism is especially evident in this application with NFTs creating persistent, high-intensity engagement because holders have genuine financial and communal skin in the game of brand success.

Application IV: Decentralized Loyalty and Reward Ecosystems

Traditional loyalty programs are structurally characterized by proprietary point systems, non-transferable rewards, restrictive redemption conditions, and unilateral devaluation risks that consistently limit their perceived value to consumers and their analytical value to marketers (Liu, 2007; Uncles et al., 2003). Tokenized loyalty programs address each of these structural deficiencies simultaneously. Loyalty tokens are interoperable across participating brand partners through shared smart contract infrastructure, tradeable on secondary markets at prices that reflect genuine supply and demand conditions, redeemable for a diverse portfolio of value forms including cryptocurrency, energy credits, charitable donations, premium experiences, and additional NFT-based credentials and analytically richer than conventional equivalents because every token transaction generates a verifiable, blockchain-recorded behavioural data point that can inform marketing strategy while also creating privacy and data-protection risks that require data minimization, appropriate off-chain storage, access controls, and clear governance. The resulting ecosystem is simultaneously more engaging for consumers, more cost-efficient for brands, and more productive of the high-quality behavioural insight adaptive marketing strategy requires (Hunt & Madhavaram, 2020; Wedel & Kannan, 2016).

Boundary Conditions of TTM

TTM is not expected to generate uniform effects across markets. The mechanisms are contingent on at least six boundary conditions. First, digital literacy affects whether consumers can understand wallets, keys, token rights, and verification processes; low literacy can increase friction and weaken trust. Second, token utility matters: tokens with clear, recurring use value should produce different engagement effects from speculative or weakly useful tokens. Third, regulation shapes feasibility, particularly where tokens implicate securities, payments, consumer protection, privacy, or carbon-market rules. Fourth, interoperability determines whether tokens can move or retain utility across platforms and partners; closed or technically fragmented ecosystems may reduce network effects. Fifth, trust remains necessary even when verification is cryptographic because users must still trust issuers, interfaces, smart-contract code, oracles, governance processes, and legal remedies. Sixth, market liquidity conditions the realizable value of transferable tokens; thin or volatile markets may undermine redemption value and participation. These conditions define when TTM mechanisms are more or less likely to produce the predicted outcomes and should be incorporated into empirical tests.

Research Propositions

Ten research propositions follow from the TTM architecture. They retain the theory's three market-facing pillars while adding AI token economics as a cross-cutting mechanism. Together they define a research program spanning marketing, information systems, sustainability, AI economics and governance.

Engagement Propositions

Proposition 1 (P₁): Compared with equivalent non-tokenized engagement systems, consent-governed token mechanisms that make permissions and rewards transparent will increase perceived consumer control, which in turn will increase trust and sustained engagement. This indirect effect will weaken when digital literacy is low or token utility is unclear.

Proposition 2 (P₂): Compared with conventional tier-based loyalty credentials, tokenized community credentials that provide meaningful access, participation, or ownership rights will increase brand-community identification, which will increase advocacy and retention. The effect will be stronger when token utility is high and weaker when secondary-market liquidity is low or price volatility dominates use value.

Transaction Propositions

Proposition 3 (P₃): In transactions where performance conditions can be objectively verified, smart-contract automation will reduce verification and settlement effort, thereby increasing exchange efficiency relative to functionally equivalent intermediated processes. The effect will be weaker where oracle reliability, legal enforceability, interoperability, or regulatory clarity is low.

Proposition 4 (P₄): When environmental rewards are based on independently verified and methodologically valid environmental claims, tokenized sustainability programs will increase sustained pro-environmental behavior relative to information-only sustainability communications by increasing reward salience and feedback immediacy. The effect will weaken when token utility, claim credibility, or market liquidity is low.

Proposition 5 (P₅): Providing consumers with accessible, independently verifiable provenance information through tokenized authentication will increase perceived transparency, which will increase brand credibility relative to otherwise equivalent unverifiable claims. The effect will be stronger among consumers with sufficient digital literacy and weaker where verification interfaces are difficult to use or trusted institutions already provide equivalent assurance.

Business Model Propositions

Proposition 6 (P₆): Tokenization that lowers minimum participation thresholds and makes rights divisible will increase access to previously high-threshold offerings, thereby expanding addressable customer segments relative to equivalent non-tokenized structures. This effect will depend on regulatory permissibility, transaction costs, interoperability, and sufficient market liquidity.

Proposition 7 (P₇): Token programs with clear functional utility and multiple credible redemption pathways will produce higher repeat participation and lower churn than token programs whose value is primarily speculative or cost-saving for the issuer. Interoperability and stable liquidity will strengthen the relationship, while excessive volatility or opaque governance will weaken it.

Adaptive and Systemic Propositions

Proposition 8 (P₈): Where consumers can selectively authorize access to accurate token-linked data and update or revoke that access, higher data provenance and permission quality will improve the predictive performance of adaptive marketing models relative to comparable models trained on lower-provenance data. This effect depends on adequate sample coverage, a privacy-preserving architecture, and model quality. Unlike P₁, which predicts consumer trust and engagement through perceived control, P₈ predicts analytical performance through data provenance and permission quality.

Proposition 9 (P₉): Greater integration of verified token-event data across customer-journey stages will improve the timeliness of marketing feedback, increasing campaign adaptation speed and journey-level personalization relative to fragmented data architectures. The effect will weaken when ecosystem interoperability is low, data rights restrict reuse, or firms lack the dynamic capability to act on the feedback.

Proposition 10 (P₁₀): For AI-mediated marketing tasks of comparable quality, higher marketing token efficiency—greater customer or business value per unit of AI token consumption—will reduce cost per outcome and computational intensity. The relationship will be strengthened by token-level attribution, context engineering, appropriate model routing, caching, and bounded agentic workflows, and weakened by unnecessary context, repeated retries, excessive autonomy, or unobserved token consumption.

Theoretical Contributions

TTM makes four categories of theoretical contribution to marketing scholarship. The first is paradigmatic. TTM provides a framework for understanding marketing exchange when value, identity, consent, access and ownership can be represented programmatically. The revised theory sharpens rather than dilutes this claim by distinguishing exchange tokens from AI computational tokens. The token is therefore not asserted to be one homogeneous object. Instead, TTM identifies a broader organizing logic of digitally measurable units: some tokens represent market value and rights; others meter the computational work through which AI creates and manages marketing interactions.

The second contribution is explanatory. TTM accounts for phenomena such as fitness-to-energy carbon-credit ecosystems, NFT-enabled communities, fractional ownership, decentralized loyalty, and consent-governed data exchange. The AI-token extension adds a second class of emerging phenomena: why two apparently similar AI marketing journeys can have radically different economics because of context length, reasoning intensity, model choice, retries and agentic orchestration; why personalization at scale can create a new variable cost of customer interaction; and why the economic value of an AI campaign or service agent should be assessed against its token consumption rather than adoption alone. Gartner's distinction between productive and wasteful token consumption provides an operational bridge from AI infrastructure economics to marketing outcome measurement (Banerjee, 2026).

The third contribution is normative. TTM builds ethics, privacy, and sustainability into the theoretical core rather than treating them as external constraints on an otherwise amoral theory of competitive exchange. By designing consumer consent into the data token mechanism, embedding environmental value into the carbon credit architecture, and structuring economic equity into fractional ownership models, TTM offers a theoretical vision of marketing that is not merely efficient and effective but inherently aligns with the ethical and sustainability imperatives Hunt (2020) identifies as the defining characteristics of Era V. This normative integration distinguishes TTM from prior marketing theories in a way that matters profoundly at the current historical moment. As regulatory pressure on digital marketing intensifies, consumer expectations of corporate ethical responsibility escalate, and the climate crisis demands structural rather than voluntary corporate responses, a marketing theory treating ethics and sustainability as design principles rather than afterthoughts is not merely academically superior but is strategically indispensable.

The fourth contribution is metric and governance oriented. TTM introduces marketing token efficiency as a theoretically meaningful link between AI resources and marketing outcomes. This extends conventional measures such as cost per acquisition, cost per interaction and customer lifetime value into token-metered AI environments. Gartner's Token Efficiency Ratio provides the managerial antecedent for this move, but TTM relocates the logic into marketing theory by asking which forms of AI-mediated engagement create the greatest customer and firm value per computational unit while respecting quality, privacy and sustainability constraints (Liu & Meinardi, 2026). The result is a theory capable of examining both the creation of tokenized value and the cost of the intelligence used to create it.

Managerial Implications

The practical implications of TTM are substantial and immediately actionable for marketing leaders navigating the transition to Era V. At the data strategy level, the shift from surveillance-based to consent-based data collection structured through data token mechanisms compensating consumers directly for verified data sharing requires strategic investment in blockchain-compatible data infrastructure and privacy-preserving personalization systems. Firms delaying this transition face compounding regulatory liability under GDPR and CCPA and progressive competitive disadvantage as token-native competitors capture the trust premiums that consent-governed data systems generate among increasingly privacy-conscious consumer segments (Martin et al., 2017). The investment required is significant, but the alternative continued dependence on third-party cookie data in a post-cookie regulatory environment is not a sustainable competitive strategy.

For chief marketing officers, AI tokenomics adds a new layer to marketing accountability. As generative and agentic AI becomes embedded in content production, conversational commerce, service, research and personalization, token consumption becomes a variable input to marketing performance. Gartner recommends visibility and attribution by use case, workflow and outcome, and emphasizes value per token rather than token reduction alone (Anderson, 2026; Guseva & Tung, 2026; Liu & Meinardi, 2026). A TTM-informed marketing dashboard should therefore connect input, output, cached and reasoning-token consumption to marketing-native outcomes such as qualified leads, conversions, service resolution, retention, contribution margin and customer value. This makes AI consumption governable in the same managerial language as other marketing investments.

For loyalty program architects and customer experience leaders, TTM counsels a fundamental reconsideration of the logic and architecture of reward systems. The evidence and theoretical analysis presented here strongly suggest that tokenized loyalty ecosystems characterized by secondary market tradability, cross-platform redemption, DeFi integration, and NFT-based community credentials will generate higher engagement intensity, stronger retention outcomes, and richer behavioral analytics than conventional proprietary point systems. The transition requires cross-functional investment in token infrastructure, smart contract development, regulatory compliance expertise, and sustained consumer education but the competitive position available to early movers in tokenized loyalty is correspondingly durable, because token communities generate switching costs through accumulated financial stake and social identity conventional loyalty programs cannot replicate.

For sustainability officers and brand strategists, the TTM carbon credit tokenization framework offers a compelling mechanism for converting corporate environmental commitments from asserted claims vulnerable to greenwashing criticism into cryptographically verifiable marketing assets with genuine consumer trust value. The fitness-to-energy ecosystem case illustrates the potential to align health promotion, environmental sustainability, and brand differentiation within a single token-governed program, a combination of social value propositions structurally unavailable without token infrastructure. These programs require cross-sector ecosystem partnerships among fitness equipment manufacturers, energy providers, financial institutions, and tokenization platform developers but the resulting competitive position creates barriers to imitation that conventional sustainability marketing cannot approach.

Limitations and Future Research