Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach

Zhu Zhang, Xuan Wei, Xiaolong Zheng, Daniel Dajun Zeng

Research output: Contribution to journalArticlepeer-review

Abstract

Mining product adoption intentions from social media could provide insights for many business practices, such as social media marketing. Existing methods mainly focus on text information but overlook other types of data. In light of the Integrated Behavioral Model (IBM), in this study, we argue that it is valuable to consider users’ social connections in addition to postings for identifying product adoption intentions. Based on this rationale, we propose a novel multiview deep learning framework to identify product adoption intentions. Extensive experiments show our proposed approach is effective, and demonstrate the benefit of incorporating social network information for intention identification.

Original languageEnglish (US)
Article number103484
JournalInformation and Management
Volume58
Issue number7
DOIs
StatePublished - Nov 2021
Externally publishedYes

Keywords

  • Deep learning
  • Multi-view learning
  • Product adoption intention
  • Text mining

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management

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