2026年5月团队成果被管理类期刊Production and Operations Management录用；

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基础信息

• 标题：Deriving Competitive Intelligence from Multifaceted User Behavior Data: An Interpretable Machine Learning Framework

• 作者：钱洋*，Hai Che, 刘业政，姜元春， Jennifer Shang

• 发表期刊/来源：Production and Operations Management

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摘要

Competitive intelligence is essential for operations management decision-making. Beyond traditional offline information channels, firms increasingly gather online data and resources to generate comprehensive competitive intelligence. This study derives competitive intelligence in large markets by developing an interpretable machine learning framework that integrates multifaceted user behavior data, including user favorites, user-commented products, and user textual comments. Considering the complementary nature of these data sources, we first combine latent features derived from user favorites and user-commented products to improve submarket inference. Using these inferred submarkets as supervised signals, we connect user-commented products and associated textual comments to uncover consumer perceptions. We estimate the model using multifaceted data on online user behavior in the automotive domain. The results demonstrate that our model effectively improves submarket identification, captures consumer perceptions, and predicts competitive positions for new entrants. The derived competitive intelligence helps managers make more informed decisions in product operations and marketing strategies.

中文翻译：

竞争情报是运营管理决策的核心依据。企业不再局限于传统线下信息渠道，愈发依托网络数据与资源构建全方位竞争情报体系。本文构建可解释机器学习研究框架，融合用户收藏行为、用户评价商品、用户文本评论等多维度用户行为数据，实现大市场环境下竞争情报的挖掘提取。鉴于各类数据源具备信息互补特性，研究首先融合用户收藏行为与商品评价行为挖掘得到的隐层特征，优化细分市场推演效果；再以推演所得细分市场为监督标签，关联用户评价商品及对应文本评论，深度挖掘消费者认知偏好。本文选取汽车行业线上多维度用户行为数据完成模型实证测算。实证结果表明，该框架可精准提升细分市场识别精度、有效捕捉消费者主观认知，并实现新入局企业市场竞争态势预判。研究所得竞争情报成果，能够助力企业管理者在产品运营与营销策略制定中做出更具科学性的决策。