发表机构
School of Technology, Woxsen University; Universitat de Girona; School of Sciences, Woxsen University（沃森大学技术学院; 格尔纳达大学; 沃森大学科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能发展下传统客户忠诚度范式受扰问题，提出动态可验证多智能体人类智能忠诚循环（DVM-HALL）模型，通过特定公式及机制确定品牌选择、校准信任等，还引入净人机分数（NHAS）并给出实证验证计划，为品牌应对转变提供理论。
AI 中文摘要
智能人工智能的迅速扩散从根本上扰乱了传统的客户忠诚度范式。随着人工智能从被动推荐算法演变为能够执行购买决策的自主、目标导向智能体，对消费者与品牌关系的传统理解需要进行结构性重新评估。通过综合人机协作、消费者决策和算法信任动态等现有文献，我们证明传统忠诚度模型未能考虑算法有限理性和构建的自主性。为解决此问题，我们引入了动态可验证多智能体人类智能忠诚循环（DVM-HALL）模型。我们通过softmax概率公式对品牌选择进行形式化，其中人类情感公平、智能机器体验效用、校准信任、委托权限和可验证执行共同决定选择。该模型具有递归更新机制，可在每次交互后动态校准信任和委托。至关重要的是该框架为去中心化金融（DeFi）和代币化忠诚度设置集成了一个可验证执行层，将执行风险，如汽油成本、滑点、MEV风险和智能合约漏洞，作为智能品牌偏好的核心预测因素。此外，我们引入了净人机分数（NHAS），这是一种可审计、风险加权的指标，旨在使用人类反馈、执行日志、基准比较和可验证收据来衡量人机一致性。最后，我们提出了一个全面的三阶段实证验证计划，涵盖受控购物实验、多智能体市场模拟和DeFi测试平台。这个框架为品牌应对即将到来的向机器客户的转变提供了所需的基础理论。
英文摘要
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.
