Management Science · 2026 · Sebastian Krakowski、Darek Haftor、Johannes Luger、Natallia Pashkevich、Sebastian Raisch
中文摘要
人类与人工智能（AI）算法在非结构化的管理任务上日益互动。我们提出，将这种人机互动调整至与个体的认知偏好相一致，对于提升绩效至关重要。本文通过在一家跨国制药企业开展的田野实验检验这一假说。在实验中，我们操控了人机互动的四个情境参数——工作流程、决策权限、培训和激励——使其与销售专家的认知风格（划分为适应型或创新型）相匹配。结果表明，定制化的互动显著提升了销售绩效，而未定制化的互动相对于定制化条件和控制条件均产生了负向处理效应。定性证据提示，这一负向结果源于未定制化互动中的角色冲突与角色模糊。为进一步探究上述结果背后的机制，对AI登录数据的中介分析显示，人机互动定制化促使销售专家调整其AI使用方式，进而促成了所观察到的绩效结果。这些发现支持一种以人为本的AI方法，即优先考虑个体的信息处理需求，并据此定制其与AI的互动。
Abstract
Humans and artificial intelligence (AI) algorithms increasingly interact on unstructured managerial tasks. We propose that tailoring this human-AI interaction to align with individuals’ cognitive preferences is essential for enhancing performance. This hypothesis is examined through a field experiment in a multinational pharmaceutical firm. In the experiment, we manipulated four contextual parameters of human-AI interaction—work procedures, decision-making authority, training, and incentives—to align with sales experts’ cognitive styles, categorized as either adaptors or innovators. Our results show that tailored interaction significantly improves sales performance, whereas untailored interaction results in negative treatment effects compared with both the tailored and control conditions. Qualitative evidence suggests that this negative outcome arises from role conflicts and ambiguities in untailored interaction. Exploring the mechanisms underlying these outcomes further, a mediation analysis of AI login data reveals that human-AI interaction tailoring leads sales experts to adjust their AI utilization, which contributes to the observed performance outcomes. These findings support a human-centered approach to AI that prioritizes individuals’ information-processing needs and tailors their interaction with AI accordingly.