IJEEEE 2018 Vol.8(2): 66-73 ISSN: 2010-3654
doi: 10.17706/ijeeee.2018.8.2.66-73
doi: 10.17706/ijeeee.2018.8.2.66-73
Online Purchase Intention Based on TAM and IAM: A Literature Review
Yu Wang
Abstract—With the popularity of Internet and the appearance of e-commerce, the issue about how to affect
the purchase intentions of Internet users becomes one of the most popular topics in the research field of
consumer behavior. Obviously, understanding the relevant mechanism contributes greatly to utilizing the
influential factors to affect consumers. Among relevant researches, the researches based on the Technology
Acceptance Model (TAM) and Information Adoption Model (IAM) are representative, valuable and practical.
Hence, this article analyzes and summarizes the existing studies using TAM and IAM for studying online
purchase intention respectively by dividing those studies into different categories: 1) using original model,
2) adding variables into the model and 3) building up complex models based on the model. Finally,
implications are provided for future research and applications.
Index Terms—Technology acceptance model (TAM), information adoption model (IAM), purchase intention.
The author is with Department of Economic and Management, Tohoku University, 2 Chome-1-1 Katahira, Aoba Ward, Sendai City, Miyagi Prefecture, Japan (email: whc631211@vip.163.com).
Index Terms—Technology acceptance model (TAM), information adoption model (IAM), purchase intention.
The author is with Department of Economic and Management, Tohoku University, 2 Chome-1-1 Katahira, Aoba Ward, Sendai City, Miyagi Prefecture, Japan (email: whc631211@vip.163.com).
Cite: Yu Wang, "Online Purchase Intention Based on TAM and IAM: A Literature Review," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 8, no. 2, pp. 66-73, 2018.
General Information
ISSN: 2010-3654 (Online)
Abbreviated Title: Int. J. e-Educ. e-Bus. e-Manag. e-Learn.
Frequency: Quarterly
DOI: 10.17706/IJEEEE
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, INSPEC (IET)
E-mail: ijeeee@iap.org
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