An integrated model of the younger generation's online shopping behavior based on empirical evidence gathered from an emerging economy

The younger generation is the largest Internet user group in China. They are the first generation to grow up with computers, the Internet, smartphones, online social media, and online shopping. The individuals that belong to this generational cohort have one thing in common-their online shopping beh...

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Bibliographic Details
Published in:PloS one 2020-05, Vol.15 (5), p.e0232213-e0232213
Main Authors: Dang, Van Thac, Wang, Jianming, Vu, Thinh Truong
Format: Article
Language:English
Subjects:
Adult
Biology and Life Sciences
Cohort Studies
Computer and Information Sciences
Computer simulation
Computers
Consumer behavior
Consumer Behavior - statistics & numerical data
Consumer research
Decision making
Demographic aspects
Digital media
E-commerce
Electronic commerce
Emerging markets
Empirical equations
Female
Forecasts and trends
Habits
Humans
Internet
Male
Models, Economic
Multivariate statistical analysis
Online shopping
People and Places
Retail trade
Risk perception
Risk taking
Shopping
Smart phones
Smartphones
Social media
Social Sciences
Studies
Switching
User groups
Young Adult
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Summary:The younger generation is the largest Internet user group in China. They are the first generation to grow up with computers, the Internet, smartphones, online social media, and online shopping. The individuals that belong to this generational cohort have one thing in common-their online shopping behavior. To understand the shopping behavior of the younger Chinese generation, this study draws on the theoretical foundation of generational cohort theory. This study proposes an integrated model that examines the effects of information adoption, personalized service, perceived switching risk, and habitual behavior on purchase intention in the online shopping environment. Survey data have been collected from 407 Chinese people that belong to the post-90s generation. Structural equation modeling is used to analyze the data. Empirical findings show that information adoption, personalized service, and perceived switching risk are the most important predictors of online purchase intention. However, habitual behavior is negatively related to online purchase intention.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0232213