The Shopper Schism™: Structural Disaggregation of Consumer and Shopper in AI-Mediated Commerce

Author: Paul F. Accornero

Affiliations: Founder, The AI Praxis

ORCID ID: https://orcid.org/0009-0009-2567-5155

SSRN Working Paper Series: https://ssrn.com/abstract=5753722

Date: November 2025 (Paper originally conceptualised in November 2024)

Contact: paul.accornero@gmail.com

WORKING PAPER

ABSTRACT

For over a century, commercial theory and practice have rested on a foundational assumption: the consumer who desires a product and the shopper who executes the purchase are the same human actor. This unity of intent and execution has shaped every framework from the marketing funnel to customer journey mapping, from brand-building theory to sales force management. That assumption is now breaking apart.

The emergence of autonomous AI purchasing agents-algorithms capable of independently evaluating, selecting, and transacting on behalf of human users-creates what we term the Shopper Schism: the permanent, structural disaggregation of consumer (the human who consumes) and shopper (the algorithm that purchases). This disaggregation is not a temporary technological curiosity but a fundamental restructuring of commercial relationships that invalidates core assumptions underlying modern marketing and strategic theory.

This paper applies established principal-agent theory to analyze this transformation. We demonstrate that the consumer-agent relationship exhibits classic agency problems-information asymmetry, goal misalignment, and moral hazard-but with distinctive characteristics arising from the non-human nature of the agent and the platform economics surrounding it. Through conceptual analysis grounded in emerging empirical research and illustrated with mini-case studies (Amazon Subscribe & Save, Tesla in-car purchasing), we develop five core propositions that explain how and why algorithmic intermediation fundamentally alters commercial dynamics.

We introduce the concept of the Gulf of Delegation-the expanding gap between human intent (what consumers want) and algorithmic execution (what agents purchase)-and demonstrate why this gulf cannot be eliminated through better technology but represents an inherent structural feature of delegated commerce. The paper concludes with implications for marketing theory, strategic management, and public policy, positioning the Shopper Schism as the defining challenge for commerce in the algorithmic age.

Keywords: Artificial Intelligence, Agentic Commerce, Principal-Agent Theory, Marketing Theory, Consumer Behavior, Algorithmic Decision-Making, Platform Economics, Brand Equity, Digital Commerce, AI Governance

JEL CLASSIFICATION: M31 (Marketing), M15 (IT Management), L81 (Retail and Wholesale Trade), D83 (Search, Learning, Information and Knowledge), 033 (Technological Change)

1.   INTRODUCTION

1.1  The End of a Century-Old Unity

Consider a simple act: a consumer runs out of coffee and purchases more. For the past hundred years, this scenario involved a single human actor performing two related but distinct functions. First, the consumer function: experiencing the need, forming preferences about brand, quality, and price, and deciding that a purchase is warranted. Second, the shopper function: traveling to a store or website, evaluating available options, comparing attributes and prices, and executing the transaction.

Marketing theory evolved to address this unified actor. The purchase funnel mapped the consumer's journey from awareness to consideration to purchase. Brand theory explained how emotional connections influenced both preference formation and selection behavior. Retail strategy optimized the shopping environment to influence the moment of choice. Customer relationship management sought to build loyalty that would drive repeat purchases. All of these frameworks assumed-so fundamentally that it was rarely even stated-that the person who would consume the product was the same person making the purchase decision.

This assumption is now obsolete.

Amazon's Alexa can independently reorder household staples when inventory runs low. Tesla vehicles can purchase software upgrades, connectivity packages, and even physical products without explicit human approval for each transaction. Google Assistant, Apple Siri, and a proliferation of specialized AI agents increasingly handle purchasing decisions across categories from groceries to travel to entertainment. Recent empirical research confirms that these agents make fundamentally different choices than humans would make-choices that concentrate markets, respond to non-obvious signals, and optimize along dimensions humans may not recognize or approve (Allouah et al., 2025).

We are witnessing the Shopper Schism: the permanent disaggregation of consumer and shopper, where humans retain the consumer function (defining needs, establishing preferences, experiencing consumption) but delegate the shopper function (evaluation, selection, transaction execution) to algorithmic intermediaries.

1.2  Why This Matters: Beyond Incremental Digitization

This transformation represents far more than another wave of digital disruption. It is not simply e-commerce 2.0, or mobile commerce 3.0, or voice commerce 4.0. Those were channel innovations that digitized the shopping experience but preserved the fundamental architecture: a human making purchase decisions, even if through a screen rather than in a physical store.

The Shopper Schism represents a paradigm shift from human decision-making aided by technology to algorithmic decision-making on behalf of humans. This is the difference between a calculator that helps you do math and an algorithm that does the math for you and simply tells you the answer. The former augments human capability; the latter replaces human judgment.

This shift has profound implications:

For marketing theory, it challenges the psychological foundations of persuasion-based competition. If the decision-maker is not human, why would emotional appeals, narrative branding, or sensory engagement matter? How do firms compete when the evaluator lacks human psychology?

For strategic management, it restructures the basis of competitive advantage. Traditional sources of differentiation-brand equity, sales force relationships, retail presence-may depreciate in value if the actual buyer (the algorithm) evaluates suppliers on entirely different criteria than humans would.

For consumer welfare, it raises urgent questions about agency, autonomy, and the potential for systemic manipulation. When purchasing decisions are delegated to intermediaries, who controls the intermediary? Whose interests does it serve? How can consumers verify that their agent is truly acting on their behalf?

For public policy, it creates new governance challenges around transparency, accountability, and market concentration. If a handful of platforms control the agents that make most purchases, market power shifts dramatically upstream from brands and retailers to platform providers.

1.3  Theoretical Lens: Principal-Agent Theory

This paper employs principal-agent theory as the primary analytical framework for understanding the Shopper Schism. This choice is deliberate and strategic. Principal-agent theory, grounded in economics and organizational theory (Eisenhardt, 1989; Jensen & Meckling, 1976; Ross, 1973), provides a rigorous, established lens for analyzing relationships where one party (the principal) delegates decision-making authority to another party (the agent) who acts on the principal's behalf.

The theory's core insight is that such delegation creates inherent problems:

•       Information asymmetry: The agent possesses information the principal lacks, particularly about the agent's own actions and the alternatives available

•       Goal misalignment: The agent's objectives may diverge from the principal's interests

•       Moral hazard: The agent may not exert appropriate effort or may act opportunistically when the principal cannot perfectly monitor behavior

While principal-agent theory traditionally addresses human-to-human delegation (shareholders to managers, patients to doctors, clients to lawyers), we argue it applies with distinctive force to consumer-algorithm relationships. The algorithmic agent exhibits information asymmetries far more severe than human agents (its decision logic is computationally opaque), operates within economic structures that create systematic goal misalignment (platform dual-role conflicts), and acts at scales and speeds that prevent meaningful human oversight (thousands of micro-decisions executed automatically).

Moreover, unlike human agents who can be socialized, incentivized, or legally constrained, algorithmic agents are designed, deployed, and monetized by platform providers whose interests may systematically diverge from consumer welfare. This creates what we term a three-party agency relationship-consumer (principal)----+ algorithm (agent)----+ platform (agent designer)-where the deepest agency conflicts may exist between consumer and platform rather than consumer and algorithm.

1.4  Contributions and Structure

This paper makes three primary contributions to management theory and practice.

First, we formally introduce and define the Shopper Schism as a foundational concept for understanding AI-mediated commerce. While fragmented discussions of AI shopping assistants exist across business press and technical literature, no systematic conceptual framework has addressed the fundamental restructuring of the consumer-shopper relationship. We provide that framework, positioning the phenomenon within established theory while identifying its distinctive characteristics.

Second, we apply principal-agent theory to this novel domain, extending agency theory to address autonomous, non-human commercial agents. We identify five core propositions that explain how algorithmic intermediation creates unique agency problems and fundamentally alters competitive dynamics. These propositions are grounded in theory, illustrated with emerging empirical evidence, and operationalized with testable hypotheses for future research.

Third, we introduce the concept of the Gulf of Delegation-the structural gap between human intent and algorithmic execution-and demonstrate why this gulf is inherent rather than solvable through better technology. This concept has direct strategic implications: it suggests firms must optimize not for human decision-makers but for algorithmic evaluators, requiring fundamental changes to product design, information architecture, and competitive strategy.

The paper proceeds as follows. Section 2 establishes the theoretical foundations by reviewing principal-agent theory and its application to AI systems. Section 3 formally defines the Shopper Schism and distinguishes it from related concepts. Section 4 develops five core propositions explaining the unique characteristics of algorithmic agency in commerce.

Section 5 illustrates these dynamics through two mini-case studies (Amazon Subscribe & Save, Tesla in-car purchasing). Section 6 discusses theoretical and managerial implications. Section 7 proposes a research agenda with testable hypotheses. Section 8 concludes.

2.   THEORETICAL FOUNDATIONS: PRINCIPAL-AGENT THEORY IN ALGORITHMIC CONTEXTS

2.1  Classical Principal-Agent Theory: Core Concepts

Principal-agent theory emerged in the 1970s to address a fundamental problem in economic organization: what happens when one party (the principal) must rely on another party (the agent) to act on their behalf, but the agent's interests and information diverge from the principal's? (Ross, 1973; Jensen & Meckling, 1976; Eisenhardt, 1989).

The canonical example is the shareholder-manager relationship. Shareholders own the firm and want to maximize its value, but they delegate operational control to professional managers. Managers, however, may have different objectives-maximizing their own compensation, pursuing growth over profitability for empire-building, or avoiding risk to protect their positions. Moreover, managers possess information about the firm's operations that shareholders lack, making it difficult for shareholders to monitor whether managers are truly serving shareholder interests.

The theory identifies three core problems in such relationships:

Information Asymmetry: The agent possesses information the principal lacks. This manifests in two forms:

•       Adverse selection (hidden information): The principal cannot fully observe the agent's capabilities, knowledge, or circumstances before delegation

•       Moral hazard (hidden action): The principal cannot perfectly monitor the agent's behavior after delegation

Goal Misalignment: The agent's utility function differs from the principal's. The agent may prioritize personal benefits over principal welfare, pursue objectives orthogonal to principal interests, or simply lack the capability to achieve what the principal desires.

Risk Preferences: The principal and agent may have different attitudes toward risk, with agents often more risk-averse because they cannot diversify their personal exposure as effectively as principals can.

Traditional solutions to agency problems include:

•       Monitoring: Direct observation of agent behavior (costly and often imperfect)

•       Incentive alignment: Compensation structures that align agent rewards with principal outcomes

•       Bonding: Agents make commitments or accept constraints that limit opportunistic behavior

•       Reputation mechanisms: Repeated interactions where agents build reputational capital

2.2  Principal-Agent Theory in Digital and AI Contexts

While principal-agent theory was developed for human-to-human relationships, recent scholarship has begun extending it to digital platforms and algorithmic systems (Bu9inca et al., 2021; Metcalf & Crawford, 2016).

Search engines present an early example: users (principals) delegate information discovery to search algorithms (agents), but search providers face conflicts between serving user interests and monetizing through advertising (Edelman, 2011). The famous Google Search case demonstrates how monetization pressures create systematic bias in algorithmic recommendations.

Social media algorithms exhibit similar dynamics: users want content that is personally relevant and valuable, but platforms want content that maximizes engagement metrics that drive advertising revenue (Tufekci, 2015). The agent (recommendation algorithm) serves two masters with potentially conflicting objectives.

Robo-advisors in finance delegate investment decisions to algorithms, creating agency relationships where the algorithm's recommendations may be influenced by factors beyond optimal client outcomes (Rossi & Utkus, 2020).

What makes AI-mediated commerce distinctive is the transactional nature of the delegation. Unlike search or content recommendation, purchasing agents execute financially binding decisions that directly transfer resources from consumers to vendors. The stakes are higher, the agency relationship is more complete, and the potential for systematic value extraction is greater.

2.3  Unique Characteristics of Algorithmic Agents

Algorithmic agents differ from human agents in ways that intensify agency problems:

Computational Opacity: While a human agent's thinking may be opaque, it operates through psychological processes we understand. Algorithmic decision-making, particularly in large language models and complex optimization systems, is computationally opaque-its reasoning cannot be fully explained even by its designers (Burrell, 2016). This creates information asymmetries that are structural rather than contingent.

Scale and Speed: Algorithmic agents can make thousands of decisions per second, making meaningful human oversight impossible. A consumer cannot review every micro-decision when their agent autonomously manages hundreds of transactions monthly.

Non-human Optimization: Algorithmic agents optimize along dimensions that may not align with human values or preferences. An agent tasked with "minimize cost" may achieve this goal technically while violating implicit constraints a human would understand (such as quality thresholds, ethical sourcing, or brand preferences that weren't explicitly specified).

Platform Embeddedness: Unlike human agents who operate relatively independently, algorithmic agents are typically embedded within platform ecosystems controlled by entities with distinct economic interests. Amazon's purchasing agent operates within Amazon's marketplace; Google's agent operates within Google's search ecosystem. The platform provider is both the agent's designer and a market participant with its own commercial agenda.

Dual-Role Conflicts: Many platform providers occupy dual roles-serving consumers while simultaneously monetizing vendor relationships. Google provides "helpful" search recommendations while selling advertising to appear in those results. Amazon offers "convenient" auto-purchasing while promoting its own private-label brands. This dual role creates systematic conflicts of interest that cannot be fully resolved through technical design.

2.4  From Augmentation to Delegation: A Critical Threshold

It is crucial to distinguish between algorithmic augmentation (Al that assists human decision-making) and algorithmic delegation (AI that makes decisions on behalf of humans). Much of the current AI systems literature addresses augmentation-how AI can enhance human judgment, improve decision quality, or increase efficiency while preserving human agency (Agrawal et al., 2019; Brynjolfsson & McAfee, 2017).

The Shopper Schism represents a shift from augmentation to delegation:

• Augmentation: "Alexa, show me coffee options under $15" -  Human reviews options and decides

• Delegation: "Alexa, keep me stocked with coffee" - Algorithm autonomously selects, purchases, and manages inventory

This threshold is not binary but represents a continuum of increasing autonomy. However, once an agent crosses into true autonomous purchasing-making transaction decisions without per-transaction human approval-the relationship fundamentally changes. The consumer is no longer the decision-maker but rather the rule-setter and outcome-evaluator. They define general preferences (constraints, objectives, priorities) but delegate the actual evaluation and selection.

This delegation transforms the nature of commercial competition. Firms no longer compete primarily to persuade human minds but to influence algorithmic selection. The battlefield shifts from psychology to engineering, from emotional resonance to computational optimization, from relationship-building to systemic reliability.

3.   DEFINING THE SHOPPER SCHISM

3.1  Formal Definition

The Shopper Schism is the structural disaggregation of the consumer function (need recognition, preference formation, consumption experience) and the shopper function (product evaluation, selection decision, transaction execution) into separate actors, where the consumer is a human and the shopper is an autonomous algorithmic agent.

This disaggregation creates a three-party commercial relationship:

1.     Human Consumer (Principal): The individual who will consume the product or service, establishes high-level preferences and constraints, and evaluates the consumption outcome

2.     Algorithmic Shopper (Agent): The AI system that performs product discovery, evaluates alternatives, makes selection decisions, and executes transactions on behalf of the consumer

3.     Brand/Retailer (Supplier): The entity providing products or services, now must influence both human preferences and algorithmic selection

The Schism is characterized by:

•       Permanence: This is not a temporary technological phase but a structural feature of digitally-mediated commerce

•       Incompleteness: The consumer cannot fully specify their preferences or constraints to the algorithmic agent

•       Opacity: The agent's decision logic and selection rationale are not fully transparent to the consumer

•       Economic Embeddedness: The agent operates within a platform ecosystem with independent economic interests

3.2  Distinguishing the Shopper Schism from Related Concepts

The Shopper Schism is distinct from several related phenomena:

Not simply "AI in retail": AI has been used in retail for decades-for inventory optimization, dynamic pricing, recommendation systems, demand forecasting. The Schism specifically refers to AI making autonomous purchasing decisions, not merely supporting business operations or suggesting options to humans.

Not equivalent to "e-commerce": Online shopping digitized the transaction but preserved human decision-making. A consumer comparing products on Amazon.corn is still the shopper. Only when that consumer delegates purchasing authority to an algorithm (e.g., Subscribe & Save, Alexa auto-ordering) does the Schism occur.

Not the same as "algorithmic recommendation": Netflix recommending rnovies, Spotify curating playlists, or Instagrarn showing content are augmentation-algorithms help humans choose. These do not create the Schism unless they move from recommendation to autonomous execution. (Example: if Spotify automatically purchased concert tickets based on listening habits, that would cross into Schism territory.)

Not synonymous with "voice commerce": Voice interfaces like Alexa can facilitate human-driven shopping ("Alexa, buy the coffee I ordered last time") without delegation, or they can enable true autonomous purchasing ("Alexa, keep me stocked with coffee"). Only the latter creates the Schism.

Not identical to "subscription services": Traditional subscriptions (magazines, meal kits) transfer purchasing decisions from the consumer to a vendor, but through explicit pre-commitment with limited vendor discretion. Algorithmic agents possess far greater autonomy to make real-time decisions across categories with minimal human oversight.

3.3  The Gulf of Delegation

Central to the Shopper Schism is what we term the Gulf of Delegation-the gap between what the human consumer intends and what the algorithmic shopper executes.

This gulf has multiple sources:

Preference Incompleteness: Humans cannot fully articulate all relevant constraints, priorities, and preferences. A consumer who says "I want good coffee" has implicit meanings for "good" (quality standards, taste preferences, ethical sourcing expectations) that are difficult to specify comprehensively to an algorithm.

Contextual Dynamism: Human preferences shift with context, mood, circumstances, and information that algorithms may not capture. What constitutes "good coffee" might differ when hosting guests versus personal consumption, or when budget is tight versus relaxed.

Computational Constraints: Even if perfectly specified, algorithmic agents face computational limits in evaluating all alternatives across all relevant dimensions. They employ simplifying heuristics that may not align with human judgment about acceptable trade-offs.

Platform Influence: The platform providing the agent has commercial interests that may shape how the agent is designed, trained, and operated. An Amazon-embedded agent may be subtly biased toward Amazon's private-label products, even if technically optimizing for stated consumer preferences.

Opacity and Inscrutability: The agent's decision logic, particularly in systems using large language models or complex machine learning, may be opaque even to its designers.

Consumers cannot audit whether the agent truly optimized for their interests or was influenced by hidden factors.

The Gulf of Delegation is not a bug to be fixed through better AI-it is an inherent feature of delegated decision-making by non-human agents operating within platform-controlled environments. Technology can narrow the gulf but cannot eliminate it. This structural gap creates permanent space for agency problems to manifest.

3.4  When Does the Schism Occur? A Spectrum of Autonomy

The Shopper Schism is not binary but exists on a spectrum of algorithmic autonomy:

Level O - No Algorithm: Pure human decision-making (shopping in physical store, browsing website and manually selecting)

Level 1 - Algorithmic Assistance: Algorithms provide information but humans make all decisions (product search results, comparison tools, reviews aggregation)

Level 2 - Algorithmic Recommendation: Algorithms suggest options but humans must explicitly approve each transaction ("Customers who bought X also bought Y"-human clicks to purchase)

Level 3 - Algorithmic Default with Human Override: Algorithms make purchase decisions but humans can easily intervene (Subscribe & Save that notifies before shipment)

Level 4 - Algorithmic Autonomy with Periodic Review: Algorithms make autonomous purchasing decisions within pre-defined parameters, humans review outcomes periodically (weekly groceries delivered without per-item approval)

Level 5 - Full Algorithmic Autonomy: Algorithms make purchasing decisions without human interaction (in-car automatic feature purchases, IoT device self-provisioning)

The Shopper Schism becomes operationally significant at Level 3 and fully manifests at Levels 4-5. At these levels, the algorithm is genuinely making selection decisions-not just suggesting or facilitating-and the consumer's role shifts from decision-maker to rule-setter and outcome-evaluator.

4.   FIVE CORE PROPOSITIONS OF THE SHOPPER SCHISM

We now develop five core propositions that explain how and why algorithmic intermediation fundamentally alters commercial dynamics. Each proposition is grounded in principal-agent theory, supported by emerging evidence, and generates testable hypotheses for future empirical research.

PROPOSITION 1: Divergent Optimization Functions (Consumer#- Agent)

Statement: The consumer's utility function (multi-dimensional, context-dependent, psychologically complex) fundamentally diverges from the algorithmic agent's optimization function (computationally specified, rigidly defined, platform-influenced), creating systematic goal misalignment.

Theoretical Foundation: Principal-agent theory posits that agency problems intensify when principal and agent have divergent objectives (Eisenhardt, 1989). In algorithmic commerce, this divergence is structural. Consumers optimize holistically across multiple dimensions-price, quality, brand affinity, ethical considerations, experiential factors-with weightings that shift contextually and include dimensions difficult to quantify. Algorithmic agents optimize according to explicitly defined objective functions that, by necessity, simplify or omit factors that cannot be computationally represented.

Mechanism: Consider a consumer who instructs their agent to "buy household staples when we're running low." The consumer's implicit optimization includes:

•       Price consciousness (within reason-don't buy the absolute cheapest if quality suffers)

•       Brand loyalty (prefer brands we know and trust)

•       Quality standards (products that actually work well, even if more expensive)

•       Ethical considerations (fair labor practices, environmental sustainability)

•       Convenience (reliable delivery, easy returns)

•       Variety (occasionally try something new to avoid monotony)

The agent, however, must translate these fuzzy preferences into a computable objective function. Most likely: "minimize cost while meeting explicit constraints (dietary restrictions, quantity requirements)." The result: the agent reliably purchases the cheapest available option that meets technical specifications, potentially violating implicit consumer preferences about quality, brand, ethics, or variety.

Empirical Support: Allouah et al. (2025) demonstrate that different AI shopping agents (using different large language models) make systematically different purchase decisions for identical consumer requests. This is evidence that agent optimization functions diverge both from human decision-making and from each other. Specifically, their research shows AI agents concentrating purchases among fewer brands than humans would choose, responding to structured data over qualitative information, and exhibiting "herd behavior" where multiple agents converge on similar choices regardless of whether those choices align with stated consumer preferences.

Strategic Implication: Firms cannot assume algorithmic agents will replicate human purchase behavior even when notionally optimizing for human preferences. This requires fundamental changes to competitive strategy-optimizing not for psychological appeal but for algorithmic evaluation criteria.

Testable Hypothesis: Hla: Purchase decisions made by algorithmic agents will diverge systematically from decisions made by human consumers given identical preference statements and product sets.

Hl b: The degree of divergence will be proportional to the complexity and implicitness of consumer preferences.

PROPOSITION 2: Radical Information Asymmetry Reversal

Statement: Traditional commerce featured information asymmetry favoring sellers (who knew more about products than consumers). Algorithmic commerce creates reversed information asymmetry, where agents access data about products, prices, availability, and alternatives far exceeding both consumer knowledge and seller visibility into agent decision logic.

Theoretical Foundation: Information asymmetry is central to agency problems (Ak:erlof, 1970; Spence, 1973). Traditionally, sellers possessed superior information about product quality, cost structures, and competitive offerings. Consumers faced the "lemons problem"-inability to distinguish high-quality from low-quality products before purchase. Brand reputation emerged partially as a mechanism to overcome this asymmetry (Klein & Leffler, 1981).

Algorithmic agents invert this dynamic. Modem AI agents can instantly access and process:

• Real-time pricing across all vendors

• Complete product specifications, reviews, and ratings

• Inventory availability and delivery options

• Historical performance data on vendor reliability

• Competitive alternatives the consumer may never have considered

Simultaneously, sellers face new information asymmetry: they cannot observe how the agent weights different factors, what trade-offs it makes, or why it selects one product over another. The agent's decision logic is a black box to both consumers and vendors.

Mechanism: Consider automotive in-car purchasing (Tesla, GM). The agent has comprehensive data about:

• Current vehicle usage patterns (energy consumption, maintenance schedules)

• Available upgrades and features

• Market pricing for add-ons

• User's financial account information

• Predicted utility from various purchases

Meanwhile, the consumer may not know what alternatives exist, what the agent considered, or why it recommended a specific purchase. The vendor (Tesla) knows even less about how external agents (not embedded in Tesla's system) might evaluate their products versus competitors'.

This creates a dual asymmetry:

•       Agents know more than consumers about available options and optimal choices

•       Neither consumers nor vendors fully understand agent decision processes

Empirical Support: Research on algorithmic pricing (Chen et al., 2021) demonstrates that algorithmic agents can detect and exploit pricing patterns invisible to humans. Work on recommendation systems shows algorithms accessing and processing volumes of comparative information that would overwhelm human cognitive capacity.

Strategic Implication: Competitive advantage shifts from information control (keeping superior knowledge about products/markets) to information provision-making comprehensive, structured, machine-readable data available for algorithmic evaluation. Firms that treat product information as proprietary or present it in human-friendly but computationally awkward formats become invisible to algorithmic shoppers.

Testable Hypothesis: H2a: Algorithmic agents will select products with superior structured data availability over products with superior human-facing marketing materials, controlling for objective quality.

H2b: The volume and granularity of machine-readable product data will be a stronger predictor of agent selection than brand recognition or consumer reviews.

PROPOSITION 3: Temporal Separation of Preference Formation and Purchase Evaluation

Statement: The Shopper Schism temporally separates preference formation (occurring when consumers initially define parameters) from purchase evaluation (occurring when agents execute transactions), creating opportunities for preference drift, context loss, and systematic mismatch between intent and execution.

Theoretical Foundation: In traditional commerce, preference formation and evaluation occur proximally or simultaneously. A consumer enters a store needing coffee, evaluates options at that moment based on current preferences and contextual factors, and makes a purchase decision. Preference and evaluation are temporally coupled.

Algorithmic delegation decouples them. The consumer establishes preferences at Time 0 ("keep me stocked with coffee-I prefer organic, medium roast, around $15-20 per pound").

The agent executes purchases at Times 1, 2, 3... weeks or months later. Several problems emerge:

Preference Drift: Consumer preferences evolve. Perhaps at Time 0 organic certification was highly valued, but by Time 3 the consumer cares more about fair trade certification. Yet the agent continues optimizing for the original specification.

Context Loss: The consumer's original instructions may have assumed contexts that no longer apply. "Medium roast coffee around $15-20" made sense when working from home (high coffee consumption), but post-return to office that's wasteful overstocking. The agent lacks contextual awareness to recognize this change.

Specification Staleness: Market conditions change. New products enter. Prices shift. Competitors improve. The agent's decision at Time 3 optimizes against stale parameters that don't reflect current market realities or evolved consumer priorities.

Mechanism: Consider a consumer who sets up Amazon Subscribe & Save for multiple products-coffee, pet food, cleaning supplies. Initial setup reflects current preferences and circumstances. Three months later:

•       Consumer's favorite coffee brand changed its sourcing (no longer organic)

•       Pet's dietary needs shifted (new food required)

•       Consumer moved to a smaller apartment (needs less of cleaning product X)

The agent, however, continues executing purchases based on Time 0 specifications. It may faithfully optimize for "organic coffee" even when the original brand no longer qualifies, or continue delivering quantities appropriate for the old apartment, not the new one.

Unlike human shopping, where each purchase occasion allows contextual recalibration, delegated purchasing locks in preferences and executes repeatedly without situational adjustment.

Empirical Support: Research on subscription services (McCarthy et al., 2017) shows high cancellation rates driven by "preference mismatch"-services that were appropriate at subscription time become inappropriate but continue executing. Studies of algorithmic traders in financial markets (Kirilenko et al., 2014) demonstrate how algorithms executing historical instructions can generate outcomes misaligned with current principal interests when market conditions change.

Strategic Implication: Firms must design for "preference refresh"-mechanisms that periodically prompt consumers to update agent instructions, or algorithmic systems that detect when specifications may be stale and trigger human review. Additionally, competitive strategy must account for the fact that agents are optimizing against potentially outdated criteria, creating opportunities to win consideration by aligning with original specifications even when current consumer preferences have evolved.

Testable Hypothesis: H3a: The length of time between preference specification and purchase execution will be positively associated with consumer dissatisfaction with agent-mediated purchases.

H3b: Categories with higher preference volatility (fashion, technology) will show greater consumer-agent misalignment than categories with stable preferences (staple goods).

PROPOSITION 4: Three-Party Value Extraction Dynamics

Statement: The Shopper Schism creates a three-party relationship (Consumer +-+ Agent +-+ Brand/Retailer) where the platform controlling the agent possesses dual roles-serving consumer interests while monetizing vendor relationships-enabling systematic value extraction that operates invisibly to consumers.

Theoretical Foundation: Multi-sided platform economics (Rochet & Tirole, 2003; Parker et al., 2016) demonstrates that platforms serving multiple customer groups face inherent tension between value creation and value capture. When a platform serves both end-users (consumers) and business customers (vendors), it must balance their potentially conflicting interests.

In algorithmic commerce, this tension intensifies because:

1.     Consumers cannot directly observe how platform monetization influences agent recommendations

2.     Agents appear to be serving consumer interests (providing convenience, finding good deals) while potentially serving platform revenue objectives

3.     The platform controls algorithm design, training data, ranking criteria, and incentive structures-all opaque to consumers

This creates what we term hidden triangular extraction: value flows from consumers to platforms and from vendors to platforms through mechanisms that are not transparent to any party except the platform.

Mechanism: Consider Amazon's shopping agent within Alexa:

• Consumer perspective: "Alexa is helping me find products that meet my needs efficiently"

• Vendor perspective: "We pay Amazon for sponsored product placements and advertising"

• Reality: Amazon designs the agent to balance consumer satisfaction (good enough recommendations to maintain trust) with revenue maximization (prioritize products where Amazon captures higher margin-sponsored items, private-label brands, items with favorable commission structures)

The consumer believes the agent is optimizing for their preferences. The vendor believes they're paying for visibility in a fair marketplace. Neither fully recognizes that the agent's true objective function includes Amazon's own revenue maximization as a weighted factor alongside consumer utility.

Similar dynamics appear in:

• Google Shopping agents that may prioritize advertisers

• Tesla in-car purchasing favoring Tesla's own products

• Smart home devices that recommend the platform provider's ecosystem products

Empirical Support: Research on search engine monetization (Edelman & Lockwood, 2011) documents how commercial incentives systematically bias algorithmic recommendations, with users unable to distinguish paid placements from organic results. Analysis of Amazon's recommendation algorithms (Zhu & Liu, 2018) found systematic bias toward Amazon's private-label brands and higher-margin products. Studies of platform markets (Hagiu & Wright, 2015) demonstrate how platforms extract value from both sides of markets through mechanisms hidden from end-users.

Strategic Implication: Vendors face a new form of gatekeeper power-the platform controlling the purchasing agent can systematically influence recommendations and extract rent through multiple mechanisms (advertising, placement fees, margin structures, data access fees). This creates winner-take-most dynamics favoring platforms that control popular agents. For brands, the imperative becomes ensuring visibility and favorable positioning within platform-controlled agent ecosystems, potentially shifting marketing budgets from consumer-facing branding to platform-facing optimization.

Testable Hypothesis: H4a: Algorithmic agents will systematically favor products where the platform controlling the agent captures higher economic value, controlling for objective product quality and consumer preferences.

H4b: The magnitude of platform bias will be inversely related to competitive pressure (platforms with less competition will exhibit stronger bias).

H4c: Consumer awareness of platform conflicts of interest will be systematically lower for agent-mediated purchases than for human-mediated purchases.

PROPOSITION 5: Erosion of Brand-Consumer Relationships

Statement: The Shopper Schism degrades direct brand-consumer relationships because brands must influence algorithmic intermediaries rather than consumers directly, shifting competitive basis from emotional engagement to technical optimization and potentially commodifying products that previously competed on brand equity.

Theoretical Foundation: Brand theory (Keller, 1993; Aaker, 1991) positions brands as psychological constructs that create value through associations, emotional connections, and perceived differentiation. Brand equity translates into willingness to pay premiums, repeat purchases, and resilience against competitive threats. This value creation depends on direct engagement between brand and consumer-through advertising, experiences, personal interactions, and product use.

The Shopper Schism interposes an algorithmic intermediary between brand and consumer. Brands must now influence agents, not humans. But agents don't experience emotional connections, don't respond to storytelling, and don't value heritage or lifestyle associations.

They evaluate based on computable attributes-specifications, price, availability, reviews expressed as numerical ratings, structured data completeness.

This creates what we term brand disintermediation-the brand's relationship with its customer is mediated by an algorithm that filters out the very factors that traditionally created brand value.

Mechanism: Consider premium coffee brands:

•       Traditional model: Brand invests in storytelling (origin narratives, ethical sourcing), packaging design, experiential marketing, creating emotional connection that justifies

•       Agent-mediated model: Agent evaluates "medium roast organic coffee $15-20" by parsing structured data-price, certifications, ratings, delivery time-without processing brand narrative or emotional positioning

If two coffees are identically specified (both certified organic, medium roast, similar ratings), the agent will likely default to lower price, ignoring brand equity that justified the premium in human decision-making.

The result: brand investment in psychological positioning delivers diminishing returns. Competitive advantage shifts to:

• Operational excellence (delivery speed, reliability)

• Data infrastructure (complete, accurate, structured product information)

• Platform relationships (favorable positioning in agent ecosystems)

• Price competitiveness (agents weight price heavily) Premium brands that built equity through emotional engagement face brand equity erosion as purchasing shifts to agents that don't value emotional factors.

Empirical Support: Research on private-label growth (Lamey et al., 2018) shows premium brands lose share to lower-priced alternatives in contexts where emotional engagement is reduced. Studies of online marketplaces (Brynjolfsson et al., 2003) found price sensitivity increases with reduced brand visibility. Analysis of voice commerce (Dawar, 2018) suggests brand recall diminishes in voice-only interfaces where visual brand cues are absent.

Strategic Implication: Brands face a strategic dilemma: continue investing in human-facing emotional branding (declining ROI as agent-mediated commerce grows) or pivot to agent-facing technical optimization (surrendering differentiation and risking commodification). Some brands may pursue dual strategies-maintaining emotional positioning for direct-to-consumer channels while developing parallel "agent-optimized" product lines. Others may accept cornrnodification while achieving efficiencies through operational excellence.

Testable Hypothesis: H5a: Brand equity will have weaker predictive power for purchase outcomes in agent-mediated transactions compared to human-mediated transactions.

H5b: Premium brands will experience greater market share erosion in categories with high agent-mediation rates compared to categories with low agent-mediation rates.

H5c: The relationship between emotional brand positioning and purchase probability will approach zero as agent autonomy increases from Level 2 to Level 5.

5.   ILLUSTRATIVE CASE STUDIES

To ground these theoretical propositions in concrete examples, we examine two proto-agentic systems that illustrate the Shopper Schism dynamics: Amazon's Subscribe & Save and Tesla's in-car purchasing capabilities. These represent different autonomy levels and commercial contexts but both demonstrate the fundamental shift from human decision-making to algorithmic execution.

5.1  Case Study: Amazon Subscribe & Save

Context: Amazon's Subscribe & Save (S&S) program, launched in 2007, allows consumers to set up automatic recurring deliveries of frequently purchased items (household staples, groceries, personal care products). Consumers specify which products they want, delivery frequency, and receive a price discount (typically 5-15%) for committing to subscription.

Level of Autonomy: Level 3-4 (Algorithmic Default with Override--------------------------------------------------------------------------------- + Algorithmic

Autonomy with Periodic Review). Consumers receive notification before each shipment and can skip or modify, but the default is autonomous execution. Many consumers set and forget, allowing months of automatic purchasing without active intervention.

Manifestation of the Shopper Schism:

Proposition 1 (Divergent Optimization): The consumer's intent in setting up S&S is typically convenience-"! always need coffee; I don't want to think about reordering." The implicit optimization includes maintaining adequate inventory without excess, preserving preferred brands, and getting reasonable prices. Amazon's agent, however, executes a simpler function: "deliver [specified product] on [specified schedule]." This creates misalignments:

• Consumer consumption rate changes (uses more/less coffee), but agent maintains original schedule  + stockpiling or stockouts

• Consumer budget tightens + agent continues purchasing at original price point without adjustment

• Product quality degrades or pricing becomes uncompetitive + agent doesn't substitute even when better options exist

Proposition 2 (Information Asymmetry Reversal): Amazon's system possesses comprehensive data about pricing, availability, competitive products, and delivery logistics. It knows when the subscribed item goes out of stock, when alternatives are available at lower prices, when delivery routes are optimized. Yet this information rarely translates into consumer benefit. The consumer doesn't know if a better deal exists, doesn't know if their subscribed brand changed formulation, doesn't know if substitutes would better serve their needs. Meanwhile, brands selling through S&S have limited visibility into why consumers churn or what factors Amazon's system uses to recommend or substitute products.

Proposition 3 (Temporal Separation): S&S is explicitly designed to separate preference formation (initial setup) from purchase execution (ongoing automatic orders). A consumer sets up subscriptions based on Time O preferences and circumstances. Months later, circumstances change but purchases continue. Common scenarios:

• Dietary changes (switched to decaf coffee)- + agent delivers regular coffee

• Household changes (roommate moved out) + agent delivers quantities for original household size

• Product dissatisfaction (coffee brand changed roast profile) + agent maintains

• original subscription

The agent faithfully executes Time O instructions even when Time N circumstances make them suboptimal.

Proposition 4 (Three-Party Value Extraction): Amazon controls the S&S agent and operates the marketplace. This creates dual-role conflicts:

•       Amazon can subtly prioritize its private-label products in S&S recommendations

•       When a subscribed brand becomes unavailable, Amazon's substitution algorithm can favor higher-margin alternatives

•       Amazon captures data about consumption patterns and uses it to develop competing products

•       Brands pay fees for visibility but compete against Amazon's own brands that get preferential treatment Consumers believe the system serves their convenience. Brands believe they're participating in a fair marketplace. Amazon optimizes for its own platform economics.

Proposition 5 (Brand Erosion): S&S commodities products by reducing them to SKUs on a schedule. Brand differentiation that depends on packaging, shelf presence, impulse appeal, or emotional connection becomes irrelevant. A consumer who would pay a premium for Method cleaning products in-store may subscribe to Amazon Basics alternatives through S&S because the emotional components of brand preference don't activate in the automated reordering context. S&S incentivizes price sensitivity (through subscription discounts) and de-emphasizes brand loyalty.

Outcome: S&S represents successful algorithmic commerce from Amazon's perspective (generates predictable revenue, increases customer lock-in, provides data for product development) but exhibits clear Shopper Schism problems from consumer and brand perspectives. Consumer advocates document complaints about unwanted deliveries, inability to cancel, and poor substitutions. Brands report margin pressure and loss of customer relationships. The success of S&S demonstrates that modest algorithmic delegation (Level 3-4) can scale successfully despite these friction points-suggesting fully autonomous agents (Level 5) will create even more pronounced effects.

5.2  Case Study: Tesla In-Car Purchasing

Context: Tesla vehicles function as connected computers on wheels, with extensive software-defined features and over-the-air update capabilities. Tesla pioneered in-car purchasing, allowing vehicles to autonomously buy software features, performance upgrades, connectivity packages, and even physical products (merchandise, services) without requiring the driver to separately interact with external purchasing systems.

Level of Autonomy: Level 3-5 (varies by purchase type). Software feature purchases may require explicit driver approval (Level 3), while some automated subscriptions and micro-transactions can execute autonomously (Level 4-5). Tesla continues expanding autonomous purchasing capabilities with each software update.

Manifestation of the Shopper Schism:

Proposition 1 (Divergent Optimization): Tesla owners report friction between their intended vehicle usage and the vehicle's purchasing behavior. Examples:

• Owner thinks "I want faster acceleration occasionally" - Vehicle presents "Acceleration Boost" upgrade for $2,000

• Owner intent: temporary performance enhancement for specific situations

• Vehicle execution: permanent purchase of expensive feature

The vehicle's optimization ("maximize revenue per vehicle through feature upsells") diverges from owner's nuanced preference ("pay for performance when needed without permanent commitment").

Proposition 2 (Information Asymmetry Reversal): The Tesla vehicle possesses comprehensive information:

• Real-time usage patterns (when owner drives, how they drive, what features they use)

• Feature availability and pricing

• Owner's payment information

  Predictive analytics about which features owner might value The owner has far less information:

•       What features exist that they haven't discovered

•       How feature pricing compares to competitive offerings

•       Whether features represent genuine value or margin optimization

•       What data Tesla collects and how it influences purchase recommendations

Third-party vendors (aftermarket products, services) have even less information-they don't know how Tesla's in-car systems evaluate or present their offerings, if at all.

Proposition 3 (Temporal Separation): In-car purchasing blurs temporal boundaries between preference formation and execution in ways that create pressure for impulse purchases:

• Owner sets general preference at Time 0: "I'm interested in performance features"

•   Vehicle presents purchase opportunity at Time N: during a drive, when owner is experiencing sub-optimal acceleration

• Psychological state at Time N (momentary frustration) differs from rational decision-making at Time 0

• Purchase executes in-moment, potentially violating owner's broader financial constraints or priorities

This temporal compression can work against the consumer-encouraging purchases during sub-optimal decision-making states (driving, emotionally engaged, time-constrained).

Proposition 4 (Three-Party Value Extraction): Tesla occupies a position of extraordinary power:

•       Tesla designs the vehicle and software

•       Tesla operates the purchasing system

•       Tesla provides the products being purchased

•       Tesla sets the prices

•       Tesla controls what alternatives (if any) are presented

This vertical integration eliminates multi-sided platform tensions (Tesla doesn't mediate between consumers and third-party vendors-it is the vendor) but creates extreme principal-agent conflicts. The agent (Tesla's in-car system) is designed by, controlled by, and economically benefits the vendor. Consumer interests are secondary.

Tesla's system can:

• Present purchase opportunities when owner is psychologically vulnerable

• Price features arbitrarily (software features have near-zero marginal cost but Tesla charges hundreds or thousands)

• Restrict functionality already present in hardware behind payment walls

• Modify or remove purchased features through software updates

Proposition 5 (Brand Erosion): In the automotive context, Proposition 5 manifests differently. Rather than brands competing for attention from a purchasing agent, Tesla's brand relationship with owners becomes mediated by the in-car system. The human-Tesla emotional connection (brand loyalty from ownership experience) gets exploited by the algorithmic-Tesla commercial relationship (aggressive monetization through in-car purchasing).

Owners report feeling that Tesla's brand promise ("premium electric vehicle experience") is undermined by the in-car purchasing system's relentless upselling. Features that owners expected to be included (heated seats, full self-driving) become a la carte purchases. The brand relationship shifts from "Tesla enables my lifestyle" to "Tesla extracts ongoing revenue through my vehicle."

Outcome: Tesla's in-car purchasing is financially successful (generates substantial high-margin software revenue) but controversial among owners and regulators. Consumer advocates question the ethics of disabling hardware features unless users pay software unlock fees. Regulators examine whether in-car purchase prompts create driver distraction safety risks. The case illustrates how algorithmic purchasing, when fully controlled by the vendor, can operate more as value extraction than value creation-a form of principal-agent relationship where the agent unambiguously serves vendor interests over consumer welfare.

5.3  Cross-Case Insights

Both cases demonstrate the Shopper Schism at work, but with different characteristics:

Platform Control: Amazon (third-party marketplace platform) versus Tesla (vertically integrated vendor). Amazon faces tension between consumer satisfaction and vendor monetization. Tesla maximizes direct revenue extraction without multi-sided platform constraints.

Autonomy Levels: S&S operates primarily at Level 3-4 (default execution with override), while Tesla spans Level 3-5 (some purchases require approval, others execute automatically). Higher autonomy creates stronger manifestation of Schism dynamics.

Consumer Perception: S&S is generally perceived as convenience-enhancing despite friction points. Tesla in-car purchasing generates more controversy because value extraction is more obvious (disabling existing hardware, aggressive upselling).

Brand Implications: S&S commodities third-party brands. Tesla monetizes its own brand equity through algorithmic intermediation.

Both cases suggest the Shopper Schism creates commercial opportunities (for platforms/vendors) and risks (for consumers/brands) that will intensify as algorithmic autonomy increases and adoption scales. Neither system currently represents "full autonomy" purchasing agents, yet both already exhibit the theoretical dynamics our propositions predict.

6.   IMPLICATIONS FOR THEORY, PRACTICE, AND POLICY

6.1  Theoretical Implications

Extending Principal-Agent Theory to Algorithmic Contexts

This paper demonstrates that principal-agent theory provides valuable analytical leverage for understanding AI-mediated commerce, but the algorithmic context creates distinctive agency problems that existing theory doesn't fully address:

1.       Computational opacity vs. behavioral opacity: Traditional agents' thinking is opaque but operates through comprehensible human psychology. Algorithmic agents' decision-making is computationally opaque-its reasoning cannot be fully explained

even by designers. This creates information asymmetries that cannot be resolved through traditional monitoring mechanisms.

2.       Platform-embedded agency: Human agents typically operate independently; algorithmic agents are embedded within platform ecosystems with distinct economic interests. This creates nested agency problems (consumer + agent + platform) where the deepest conflicts may be structural rather than contingent.

3.       Scale and irreversibility: Human agents make decisions at human scale, allowing meaningful oversight. Algorithmic agents operate at speeds and scales preventing effective human monitoring. When problems arise, they may have affected thousands of transactions before detection.

These distinctive characteristics suggest algorithmic agency represents not merely a new application of existing theory but a novel theoretical territory requiring extension of principal-agent frameworks.

Challenging Marketing's Psychological Foundations

Marketing theory is fundamentally grounded in psychology-understanding human cognition, emotion, motivation, and behavior (Kotler & Keller, 2016). The Shopper Schism challenges this foundation by inserting a non-psychological decision-maker into the purchase process.

Core marketing concepts require reconceptualization:

•       Brand equity: If the purchaser doesn't experience psychological associations, what is brand equity worth? Does it matter at all, or only to the extent it shapes initial preference formation when consumers set agent parameters?

•       Customer journey: Traditional journey maps trace awareness ----+ consideration------------------------------------------------------------------------ +

purchase ----+ advocacy. What does this journey look like when an agent collapses consideration and purchase into an algorithmic instant, without the consumer consciously participating?

•       Persuasion: Marketing persuades humans through emotional appeals, social proof, narrative, sensory engagement. How does persuasion operate when the decision-maker is an algorithm?

•       Relationship marketing: Building long-term customer relationships depends on repeated interactions, trust development, and emotional connection (Gronroos, 1994). What does relationship-building mean when the customer delegates relationship management to an intermediary?

These questions suggest marketing theory may require fundamental reconstruction for algorithmic contexts, not merely incremental adaptation.

Reconceptualizing Competitive Advantage

Strategic management theory identifies various sources of competitive advantage-brand, scale economies, network effects, capabilities, resources (Barney, 1991; Porter, 1985). The Shopper Schism potentially reorders the value of these sources.

Sources that may decline in value:

•       Brand equity built on emotional positioning

•       Sales force relationships

•       Retail shelf presence

•       Advertising reach and frequency

Packaging and visual design Sources that may increase in value:

• Data infrastructure and API capabilities

• Operational excellence (delivery speed, reliability, accuracy)

• Platform relationships and ecosystem positioning

• Structured data completeness and accuracy

• Price competitiveness (algorithms weight price heavily)

This suggests competitive advantage is shifting from psychology-based differentiation to engineering-based enablement. Firms that built advantage through "being chosen by humans" must adapt to "being selected by algorithms."

6.2  Managerial Implications

For Brand Managers: From Persuasion to Optimization

Brand management historically focused on creating emotional connections, shaping perceptions, and building loyalty through psychological engagement. Algorithmic intermediation requires adding a parallel discipline: algorithmic brand management-ensuring the brand is discoverable, evaluable, and selectable by AI agents.

Key shifts:

1.       Invest in structured data: Make comprehensive product information available in machine-readable formats (Schema.org, JSON-LD, structured attributes)

2.       Optimize for algorithmic evaluation: Understand what factors agents weight heavily (certifications, specifications, ratings, operational metrics) and ensure strong performance on those dimensions

3.       Monitor algorithmic visibility: Track how agents discover, evaluate, and select products-not just human search behavior

4.       Develop platform relationships: Build technical and commercial relationships with platforms controlling major purchasing agents

5. Maintain dual positioning: Continue human-facing emotional branding for direct channels while developing agent-facing technical optimization for algorithmic channels

For Retailers: Platform Power and Disintermediation Risk

Traditional retailers face existential threat from algorithmic commerce. If purchasing agents bypass retail websites and physical stores, selecting products directly from manufacturer or platform inventories, retailers become structurally redundant.

Strategic responses:

1. Become the platform: Develop proprietary purchasing agents that consumers trust and use (Walmart, Target attempting this)

2. Specialize in algorithmic infrastructure: Provide superior data, fulfillment, and integration capabilities that make it easier for external agents to transact through your platform

3. Focus on experience categories: Emphasize categories where human shopping experience remains valuable (fashion, luxury, complex products) and algorithmic delegation is less suitable

4. Build agent relationships: Establish technical and commercial relationships with popular agent platforms, ensuring products are discoverable and attractively presented

For Platform Providers: The Trust Imperative

Platforms controlling purchasing agents possess extraordinary market power but face significant trust risks. If consumers perceive that agents serve platform revenue interests over consumer welfare, trust erosion could trigger user abandonment, regulatory intervention, or competitive disruption.

Strategic imperatives:

1.     Transparency: Disclose when and how commercial incentives influence agent recommendations

2.     Optionality: Allow consumers to adjust how agents weight factors (prefer lower price vs. faster delivery vs. ethical sourcing)

3.     Auditability: Provide mechanisms for consumers to understand why agents made specific selections

4.     Fiduciary framing: Position agents as fiduciaries legally obligated to serve consumer interests, not platform revenue

5.     Ethical restraint: Resist maximizing short-term monetization in ways that undermine long-term trust

Platforms that prioritize consumer trust may achieve sustainable competitive advantage. Those that aggressively extract value risk trust collapse similar to what occurred with Google Search.

For Product Designers: Agent-Centric Design

Product design has historically optimized for human users-aesthetic appeal, ergonomic functionality, emotional resonance, intuitive interfaces. Algorithmic commerce requires parallel optimization for algorithmic evaluation.

Agent-Centric Design principles:

1. Radical verifiability: Make product claims verifiable through third-party data and certifications

2. Structural simplicity: Reduce unnecessary complexity that creates computational evaluation friction

3. Systemic interoperability: Ensure products work seamlessly with platform ecosystems agents interact with

4. Dynamic accessibility: Provide real-time data on availability, pricing, and delivery

5. Ethical transparency: Make supply chain, sourcing, and manufacturing practices auditable

Products designed primarily for human appeal may become invisible to algorithmic shoppers. Successful products will optimize for both human consumption and algorithmic evaluation.

6.3  Policy Implications

Consumer Protection in Algorithmic Commerce

Existing consumer protection law assumes human decision-makers who can make informed choices when provided adequate information. Algorithmic delegation challenges this model-consumers cannot directly observe agent behavior, don't understand agent decision logic, and may not recognize when agents act against their interests.

Policy considerations:

1.       Fiduciary standards: Require agents to operate as fiduciaries with legal duties to serve consumer interests

2.       Disclosure requirements: Mandate clear disclosure of conflicts of interest, platform commercial relationships, and how monetization influences agent behavior

3.       Opt-out rights: Ensure consumers can disable algorithmic purchasing without losing other platform benefits

4.       Recourse mechanisms: Create pathways for consumers to dispute purchases made by agents without explicit approval

5.       Audit rights: Allow consumers (or their representatives) to audit how agents make decisions

Antitrust and Market Concentration

If most consumers delegate purchasing to agents controlled by a few major platforms (Amazon, Google, Apple), market power concentrates dramatically. These platforms become obligate intermediaries-brands must participate in platform ecosystems or become invisible to consumers.

Antitrust considerations:

1.       Platform dominance: Assess whether platforms controlling popular agents possess monopoly power over market access

2.       Self-preferencing: Examine whether platforms systematically favor their own products or higher-margin offerings in agent recommendations

3.       Data advantages: Evaluate whether platforms' comprehensive data about consumer behavior creates insurmountable barriers to competition

4.       Interoperability requirements: Consider mandating that agents can access multiple platforms, preventing lock-in

5.       Vertical integration restrictions: Assess whether platforms should be prohibited from competing in markets where their agents make purchasing decisions

Algorithmic Transparency and Accountability

Algorithmic decision-making in high-stakes contexts (credit, employment, criminal justice) has generated calls for transparency and accountability. Commercial algorithms warrant similar scrutiny.

Policy considerations:

1. Explainability requirements: Require agents to explain why they selected specific products

2. Testing and auditing: Mandate independent testing of agents for bias, manipulation, and conflicts of interest

3. Performance standards: Establish minimum standards for agent accuracy, reliability, and alignment with consumer interests

4. Liability frameworks: Clarify legal responsibility when agent decisions cause consumer harm

5. Right to human decision-making: Allow consumers to require human review of consequential purchases

Data Privacy and Surveillance

Effective purchasing agents require comprehensive data about consumer preferences, behaviors, finances, and contexts. This creates significant privacy risks.

Policy considerations:

1. Data minimization: Limit the types of personal data agents can access

2. Purpose limitation: Restrict use of consumer data to serving consumer interests, not platform monetization

3. Consent requirements: Require explicit consent for data collection and sharing

4. Deletion rights: Allow consumers to delete historical data agents use for decision-making

5. Third-party access restrictions: Prohibit platforms from sharing consumer data with vendors or advertisers

7.   RESEARCH AGENDA: TESTABLE PROPOSITIONS AND EMPIRICAL DIRECTIONS

The Shopper Schism framework generates numerous testable propositions for empirical research. We organize these into six research streams.

7.1  Agent Decision-Making Processes

RQl: How do algorithmic agents' purchase decisions differ from human decisions given identical constraints and information?

Testable Hypotheses:

•       Hl .1: Agent decisions will exhibit lower variance across individuals than human decisions (agents from same system make similar choices; humans show individual differences)

•       Hl .2: Agents will weight structured, quantitative attributes more heavily than unstructured, qualitative information compared to human decision-makers

•       Hl.3: Agents will demonstrate stronger price sensitivity than humans for equivalent products

•       Hl .4: Agents will exhibit "herd behavior"-converging on popular or highly-rated options even when alternatives better match stated consumer preferences

Methodology: Experimental design where human subjects and multiple AI agents (GPT-4, Claude, domain-specific agents) receive identical purchase scenarios and constraints, comparing decision outcomes.

7.2  Consumer Preference Specification and Agent Alignment

RQ2: To what extent can consumers successfully specify preferences to agents, and how does specification completeness affect decision alignment?

•       H2.1: More detailed consumer preference specifications will be associated with higher consumer satisfaction with agent decisions

•       H2.2: There exists an "uncanny valley" of specification-medium-detail specifications perform worse than both simple and comprehensive specifications (simple defaults to safe choices; comprehensive enables good matches; medium creates false confidence)

•       H2.3: Consumers systematically underestimate the number and complexity of preferences relevant to purchase decisions

•       H2.4: Categories with higher experiential or contextual complexity (fashion, food) will show worse consumer-agent alignment than functional categories (batteries, paper towels)

Methodology: Longitudinal study tracking consumer satisfaction with agent-mediated purchases across different specification approaches and product categories.

7.3  Platform Conflicts of Interest and Value Extraction

RQ3: Do platforms controlling purchasing agents systematically bias recommendations toward higher-revenue products, and can consumers detect such bias?

Testable Hypotheses:

•       H3.l: Agent recommendations will be positively correlated with platform revenue capture (advertising, commissions, private-label margins), controlling for objective product quality

•       H3.2: Bias magnitude will be inversely related to competitive intensity in agent market (dominant platforms exhibit stronger bias)

•       H3.3: Consumer ability to detect bias will be significantly lower for agent-mediated recommendations than for human-facing advertising or search results

•       H3.4: Disclosure of conflicts of interest will reduce consumer trust but only modestly affect purchase behavior (cognitive dissonance: consumers acknowledge conflict but continue delegating)

Methodology: Audit studies comparing agent recommendations across platforms with different revenue models; consumer surveys measuring bias detection; experimental manipulation of conflict disclosure.

7.4  Brand Equity Depreciation in Algorithmic Markets

RQ4: How does brand equity affect purchase probability in agent-mediated versus human-mediated contexts?

Testable Hypotheses:

•       H4.1: Brand equity's effect on purchase probability will be significantly weaker in agent-mediated transactions compared to human-mediated transactions

•       H4.2: The depreciation effect will be strongest for brands built primarily on emotional positioning versus functional performance

•       H4.3: Premium brands will lose market share to value alternatives faster in agent-mediated categories than in human-mediated categories

•       H4.4: Brand equity will retain influence on initial preference formation (when consumers set agent parameters) but not on final selection (when agents execute purchases)

Methodology: Market analysis comparing brand performance in high-agent-mediation categories versus low-mediation categories; experimental studies manipulating mediation type.

7.5  Trust Formation and Erosion in Agent Relationships

RQS: What factors drive consumer trust in purchasing agents, and how does trust evolve over time?

Testable Hypotheses:

•       H5.l: Trust in agents will be positively associated with perceived alignment (agent choices match consumer preferences) and negatively associated with perceived platform conflicts

•       H5.2: Trust erosion will follow a three-phase pattern: initial utility-based trust+ gradual misalignment discovery-- + trust collapse or recalibration

•       H5.3: Single high-salience failures (agent makes obviously wrong choice) will damage trust more than multiple low-salience failures (agent makes suboptimal but acceptable choices)

•       H5.4: Transparency interventions (explaining why agent chose specific products) will have non-monotonic effects on trust-increasing trust when choices are good, decreasing trust when choices reveal conflicts

Methodology: Longitudinal surveys tracking trust over time; experimental manipulation of transparency and choice quality.

7.6  Competitive Dynamics in Agent-Mediated Markets

RQ6: How does algorithmic intermediation alter competitive dynamics, market concentration, and firm strategy?

Testable Hypotheses:

•       H6.l: Market concentration (HHI) will increase in categories with high agent-mediation rates compared to low-mediation categories

•       H6.2: Competitive advantage will shift from brand-based differentiation to operational excellence (measured by share gains of operationally superior firms in agent-mediated markets)

•       H6.3: Advertising effectiveness will decline as agent mediation increases (measured by ROI on brand advertising)

•       H6.4: Firms investing in algorithmic optimization (structured data, API access, platform relationships) will outperform firms investing in traditional marketing in agent-mediated categories

Methodology: Longitudinal market analysis tracking concentration, advertising ROI, and firm performance across categories with varying agent adoption rates.

7.7  Methodological Considerations for Future Research

Researching the Shopper Schism presents methodological challenges:

Measurement challenges: Agent-mediated purchases may be invisible to researchers (occur within closed systems, logged but not publicly observable). Creative measurement approaches needed: consumer surveys, platform data partnerships, experimental simulations.

Temporal dynamics: The phenomenon is rapidly evolving. Research findings may quickly become outdated as agent capabilities advance and adoption scales. Longitudinal designs essential.

Causal identification: Separating agent effects from correlated factors (tech-savvy consumers adopt agents and differ in other ways; categories suitable for agents have other characteristics). Natural experiments and quasi-experimental designs needed.

Ethical considerations: Research examining platform conflicts must avoid creating consumer panic while documenting legitimate concerns. Research involving consumer purchase data requires robust privacy protections.

8.   CONCLUSION

8.1  Summary of Key Insights

This paper has introduced and analyzed the Shopper Schism-the structural disaggregation of consumer and shopper functions as autonomous AI agents increasingly mediate purchasing decisions. Drawing on principal-agent theory, we have demonstrated that this transformation represents not incremental digitization but fundamental restructuring of commercial

relationships with profound implications for marketing theory, strategic management, consumer welfare, and public policy.

Our analysis generated five core propositions:

1. Divergent Optimization Functions: Algorithmic agents optimize according to computationally specified objectives that systematically diverge from consumers' multi-dimensional, context-dependent preferences, creating goal misalignment inherent to delegation.

2. Radical Information Asymmetry Reversal: Agents possess comprehensive information about products, markets, and alternatives far exceeding consumer knowledge, while agent decision logic remains opaque to both consumers and vendors-creating dual asymmetries favoring platforms.

3. Temporal Separation of Preference and Evaluation: The disaggregation of preference formation (when consumers set parameters) from purchase execution (when agents transact) creates opportunities for preference drift, context loss, and specification staleness.

4. Three-Party Value Extraction: Platform providers controlling agents occupy dual roles-serving consumers while monetizing vendors-enabling hidden value extraction operating invisibly to both consumers and brands.

5. Erosion of Brand-Consumer Relationships: Algorithmic intermediation degrades direct brand relationships by interposing agents that evaluate based on computational attributes rather than psychological associations, potentially commodifying products that previously competed on brand equity.

8.2  The Gulf of Delegation: An Inherent Feature

Central to understanding the Shopper Schism is recognizing that the Gulf of Delegation-the gap between human intent and algorithmic execution-is not a temporary technology problem to be solved through better AI. It is a structural feature of delegated decision-making by non-human agents embedded within commercial ecosystems.

Three factors make the Gulf inherent rather than solvable:

Preference incompleteness: Humans cannot exhaustively specify all relevant constraints, priorities, trade-offs, and contextual factors that should inform purchase decisions. Preferences are tacit, contextual, emergent, and partially unknowable even to the consumer.

Computational constraints: Even perfectly specified preferences would require infeasible computation to optimize globally across all alternatives and all dimensions. Agents must employ simplifying heuristics that introduce systematic divergence from ideal optimization.

Platform embeddedness: Agents operate within ecosystems controlled by entities with independent economic interests. These interests shape agent design, training, evaluation criteria, and recommendation logic in ways that cannot be fully aligned with consumer welfare due to fundamental conflicts in objective functions.

Technology can narrow the Gulf but cannot eliminate it. This insight has strategic importance: it suggests firms should not wait for "better" agents that perfectly serve consumer interests but should adapt now to the reality of persistent misalignment.

8.3  A Paradigm Shift, Not a Channel Innovation

It is critical to understand the Shopper Schism as a paradigm shift, not merely another channel in the evolution of commerce. The progression from physical retail -                       catalog -   e-commerce -                       mobile -    voice were all channel innovations that preserved the fundamental architecture: human decision-making mediated by technology.

Algorithmic commerce represents a qualitative leap: replacing human judgment with computational optimization. This is analogous to the difference between:

•       Spreadsheets (augment human calculation) - Algorithmic trading (replace human decision-making)

•       GPS navigation (assist human driving) -  Autonomous vehicles (replace human driving)

•       Spell-check (augment human writing) -  AI content generation (replace human writing)

In each case, the transition from augmentation to autonomy fundamentally changes relationships, capabilities, risks, and value creation dynamics. Commercial institutions, regulations, strategies, and theories built for the augmentation paradigm become inadequate for the autonomy paradigm.

8.4  Urgency of Response

While fully autonomous purchasing agents remain nascent (most consumers have not yet delegated significant purchasing authority to algorithms), the trajectory is clear and the transformation will accelerate rapidly. Several factors drive urgency:

Technological capability: Large language models and multimodal AI systems now possess the capability to understand natural language instructions, navigate complex information environments, and execute purchases across diverse categories. The technical barrier to autonomous purchasing has largely fallen.

Economic incentives: Platforms face powerful incentives to promote agent adoption-agents increase platform lock-in, generate valuable data, create monetization opportunities, and shift market power upstream to platforms. Platforms will invest heavily in making agents valuable to consumers.

Consumer convenience: Genuine consumer benefit exists in delegating routine, low-stakes purchasing. Once consumers experience the convenience of "never thinking about household staples again," they may accept agents even with imperfections.

Network effects: As more consumers adopt agents, vendors must optimize for algorithmic evaluation to maintain visibility. This creates competitive pressure driving further agent adoption and optimization, generating a self-reinforcing cycle.

Generational shift: Younger consumers exhibit greater comfort with AI delegation and less attachment to traditional shopping experiences. As they gain purchasing power, agent adoption will accelerate.

Given these forces, firms, researchers, and policymakers should act now to understand and shape algorithmic commerce rather than waiting for widespread adoption to create fait accompli.

8.5  A Research Agenda for the Algorithmic Age

This paper has outlined six research streams with specific testable hypotheses, but the Shopper Schism opens broader questions requiring sustained scholarly attention:

Normative questions: Should societies permit or encourage autonomous purchasing? What rights should consumers retain? What obligations should platforms bear? When does algorithmic delegation serve welfare versus harm autonomy?

Equity questions: Will algorithmic commerce exacerbate or reduce inequality? Will sophisticated consumers configure agents that optimize effectively while less-sophisticated consumers accept suboptimal defaults? Will agents discriminate based on race, income, or other protected characteristics?

Societal impact questions: What happens to employment in retail, sales, marketing, and brand management as human persuasion becomes less relevant? How do communities change when shopping is no longer a social activity but an invisible algorithmic process?

Innovation questions: How does algorithmic intermediation affect innovation incentives? Do agents favor established products with extensive data over innovative newcomers with limited information? Does computational evaluation bias toward incremental improvement over radical innovation?

Governance questions: Who should regulate algorithmic commerce? What institutional mechanisms can ensure agents serve consumer interests? How can societies balance innovation benefits against consumer protection?

These questions require interdisciplinary collaboration spanning marketing, strategy, economics, computer science, law, philosophy, and public policy.

8.6  Final Reflection

The Shopper Schism is not dystopian inevitability nor utopian progress-it is transformation that creates both opportunities and risks, benefits and costs, winners and losers. How this transformation unfolds depends on choices firms make, regulations governments enact, technologies developers build, and delegations consumers accept or resist.

Understanding the Shopper Schism as a fundamental restructuring of commercial relationships-rather than merely a new technology or channel-is the first step toward shaping algorithmic commerce in ways that serve human flourishing rather than merely computational efficiency. This paper has provided the conceptual foundation and theoretical framework. The challenge now is to build institutions, strategies, technologies, and governance structures worthy of that foundation.

The age of the Algorithmic Shopper has begun. How we respond will define commerce for generations.

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AI USAGE DISCLOSURE

Generative AI tools were used in the preparation of this manuscript to assist with literature review, drafting, editing, and text refinement. The author takes full responsibility for all content, claims, and conclusions presented in this work. All interpretations and theoretical contributions represent the author's original scholarly analysis. AI tools were used as writing assistants only, not as sources of original research.

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© 2025 Paul F. Accornero

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You are free to share this work for non-commercial purposes with appropriate attribution, but may not create derivative works without permission. Nor commercial use or permissions beyond the scope of this license, please contact: paul.accornero@gmail.com

This working paper is part of a research program examining the strategic and organizational implications of AI-mediated commerce. Comments and feedback welcome. Please cite as: Accornero, P. F. (2025). The Shopper Schism: Structural Disaggregation of Consumer and Shopper in AI-Mediated Commerce. SSRN Working Paper.