.png){kind=icon}
返回
Deriving competitive intelligence from multifaceted user behavior data: An interpretable machine learning framework
.png){kind=icon}
PRE
AI
DOI：10.1177/10591478261457235.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
期刊
P
IF:
5.1
论文数:
237
被引数:
0
机构
引用论文
.png){kind=icon}
Identifying Influential Users by Topic in Unstructured User-Generated Content通过主题识别非结构化用户生成内容中的有影响力用户
PREAI
分享收藏Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data使用大搜索数据可视化1,000多种产品之间的不对称竞争
PREAI
分享收藏
查看更多内容