This paper develops a predictive conceptual framework for understanding trust evolution in AI-mediated commerce. Drawing on principal-agent theory and the historical precedent of Google Search, the paper presents a three-phase trust lifecycle model and introduces the concept of "computational trust".

This paper introduces 'algorithmic loyalty,' a conceptual framework examining how AI agents exhibit persistent brand preferences based on functional optimization, challenging traditional human-centric loyalty models.

This working paper tackles a fundamental question: How do B2B organizations compete when AI-powered procurement systems and autonomous agents replace human buyers?

Key insight: Relational capital (trust, networks, relationships) loses effectiveness when the buyer is a computational system.

This paper develops 'Algorithmic Readiness,' a new dynamic capabilities framework for how organizations must sense, seize, and transform to compete in AI-mediated markets

This paper introduces 'The Shopper Has No Eyes' framework, analyzing how traditional branding's reliance on sensory appeal and emotional resonance is challenged by autonomous AI purchasing agents. It proposes a shift to data transparency and the 'Great Value Sort.

This paper introduces Agent Intent Optimization (AIO), a novel conceptual framework addressing digital marketing challenges posed by autonomous AI agents, shifting focus from human psychology to machine-interpretable signals.

This paper introduces the 'Shopper Schism,' analyzing the divergence between human consumer intention and AI agent execution through the lens of principal-agent theory. It explores information asymmetry and moral hazard in delegated digital consumption.

The AIDA marketing funnel is obsolete. This paper introduces the 'API Call Model,' a new framework for when AI agents make purchases based on logic, not persuasion.