Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach
Zhang, Zhu1,2; Wei, Xuan3; Zheng, Xiaolong1,4; Zeng, Daniel Dajun1,2,4
Source PublicationINFORMATION & MANAGEMENT
ISSN0378-7206
2021-11-01
Volume58Issue:7Pages:12
Corresponding AuthorZheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
AbstractMining 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.
KeywordText mining Deep learning Product adoption intention Multi-view learning
DOI10.1016/j.im.2021.103484
WOS KeywordNEURAL-NETWORKS ; SYSTEMS
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2020AAA0108401] ; Ministry of Science and Technology of China[2019QY (Y) 0101] ; Ministry of Science and Technology of China[2020AAA0103405] ; National Natural Science Foundation of China[71974187] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[71902179] ; National Natural Science Foundation of China[72074209] ; Longhua District Science and Technology Innovation Fund[10162a20200617b70da63] ; National Science Foundation[1228509]
Funding OrganizationMinistry of Science and Technology of China ; National Natural Science Foundation of China ; Longhua District Science and Technology Innovation Fund ; National Science Foundation
WOS Research AreaComputer Science ; Information Science & Library Science ; Business & Economics
WOS SubjectComputer Science, Information Systems ; Information Science & Library Science ; Management
WOS IDWOS:000697698400001
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46056
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorZheng, Xiaolong
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Shenzhen Artificial Intelligence & Data Sci Res I, Shenzhen 518129, Peoples R China
3.Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Dept Informat Technol & Innovat, Shanghai 200030, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Zhu,Wei, Xuan,Zheng, Xiaolong,et al. Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach[J]. INFORMATION & MANAGEMENT,2021,58(7):12.
APA Zhang, Zhu,Wei, Xuan,Zheng, Xiaolong,&Zeng, Daniel Dajun.(2021).Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach.INFORMATION & MANAGEMENT,58(7),12.
MLA Zhang, Zhu,et al."Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach".INFORMATION & MANAGEMENT 58.7(2021):12.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Zhu]'s Articles
[Wei, Xuan]'s Articles
[Zheng, Xiaolong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Zhu]'s Articles
[Wei, Xuan]'s Articles
[Zheng, Xiaolong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Zhu]'s Articles
[Wei, Xuan]'s Articles
[Zheng, Xiaolong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.