Journal ArticleDOI
The power of a thumbs-up: Will e-commerce switch to social commerce?
Chia-Ying Li,Yi-Cheng Ku +1 more
TLDR
The findings revealed that push effect, in terms of low transaction efficiency, drives customers away from e-commerce sites, whereas the pull effects, including social presence, social support, social benefit, and self-presentation, attract customers to social commerce sites.About:
This article is published in Information & Management.The article was published on 2017-09-01. It has received 134 citations till now. The article focuses on the topics: Conformity & E-commerce.read more
Citations
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Analyzing the effect of social support and community factors on customer engagement and its impact on loyalty behaviors toward social commerce websites
TL;DR: The findings show that customer engagement is a key predictor of the four dimensions of customer loyalty toward social commerce websites and indicates that social support and two community factors significantly affect customer engagement.
Journal ArticleDOI
Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues
TL;DR: This study proposes a new research model with these considerations to thoroughly examine the determinants of online impulse buying, and shows an important link for the three defined issues ofOnline impulse buying.
Journal ArticleDOI
Understanding the interplay of social commerce affordances and swift guanxi: An empirical study
TL;DR: The results indicate that interactivity, stickiness, and word of mouth exert positive effects on mutual understanding, reciprocal favor, and relationship harmony, to various degrees, and swift guanxi dimensions are determinants of consumers’ purchase intention in social commerce.
Journal ArticleDOI
Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications
TL;DR: This study investigates the key factors influencing the switching behaviors of mobile payment application through the perspective of the push–pull–mooring framework to inform service providers to retain existing users as well as attract potential users.
Journal ArticleDOI
Utilizing the Push-Pull-Mooring-Habit framework to explore users’ intention to switch from offline to online real-person English learning platform
Yu-Hsin Chen,Ching-Jui Keng +1 more
TL;DR: The results show that push effects, pull effects, mooring effects, and habit effects all significantly influence users’ switching intentions from offline to an online real-person English learning platform.
References
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Journal ArticleDOI
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Claes Fornell,David F. Larcker +1 more
TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Perceived Usefulness, Perceived Ease of Use, and User
TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.
Journal ArticleDOI
Perceived usefulness, perceived ease of use, and user acceptance of information technology
TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.
Book
Multiple Regression: Testing and Interpreting Interactions
Leona S. Aiken,Stephen G. West +1 more
TL;DR: In this article, the effects of predictor scaling on the coefficients of regression equations are investigated. But, they focus mainly on the effect of predictors scaling on coefficients of regressions.
Journal ArticleDOI
User acceptance of information technology: toward a unified view
TL;DR: The Unified Theory of Acceptance and Use of Technology (UTAUT) as mentioned in this paper is a unified model that integrates elements across the eight models, and empirically validate the unified model.
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Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
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