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A SEM-neural network approach for predicting antecedents of m-commerce acceptance · Study

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A SEM-neural network approach for predicting antecedents of m-commerce acceptance

Francisco José Liébana-Cabanillas, Veljko Marinković, Zoran Kalinić

2016-12-15articleInternational Journal of Information Management583 citationsclosed

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Technology Adoption and User Behaviour Consumer Behavior and Marketing Influence Health, Technology, Consumer Behavior

Abstract

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Citations in this library

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

2003User Acceptance of Information Technology: Toward A Unified View1

1975Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research

1988On the Evaluation of Structural Equation Models

2003Trust and TAM in Online Shopping: An Integrated Model1

1995PRODUCT DEVELOPMENT: PAST RESEARCH, PRESENT FINDINGS, AND FUTURE DIRECTIONS

2004What drives mobile commerce?

Cited by

2019Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence

2018Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model

2019Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach

2017Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach

2020Shopping intention at AI-powered automated retail stores (AIPARS)

2019How perceived trust mediates merchant's intention to use a mobile wallet technology

2019A meta-analysis of antecedents and consequences of trust in mobile commerce

2020Online food delivery services and behavioural intention – a test of an integrated TAM and TPB framework

Details

Year2016

Typearticle

VenueInternational Journal of Information Management

Languageen

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Identifiers

doi10.1016/j.ijinfomgt.2016.10.008

openalex_workW2561131998

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