Predicting Citizens Acceptance of Government-led e-Participation Initiatives through Social Media: A Theoretical Model

Date

2017-01-04

Contributor

Advisor

Department

Instructor

Depositor

Speaker

Researcher

Consultant

Interviewer

Narrator

Transcriber

Annotator

Journal Title

Journal ISSN

Volume Title

Publisher

Volume

Number/Issue

Starting Page

Ending Page

Alternative Title

Abstract

Whilst the idea of utilizing social media to advance government-led e-Participation initiatives has proliferated significantly in recent years, mostly such initiatives do not meet the intended expectations, as the majority of them fail to attract wider citizens’ audience. Overall, the key factors that could explain and predict citizens’ participation are not yet thoroughly identified. Therefore, the current study develops a theoretical citizen-centric model that seeks to explain and predict the intention of citizens’ behavior towards their involvement in government-led e-Participation initiatives through social media.The methodological approach is primarily based on utilizing and extending one of the well-known theories for describing a person acceptance behavior, namely the Theory of Planned Behavior. The model applies the main constructs of the Theory – attitude, subjective norms, and perceived behavioral control; and complements them with several constructs drawn from relevant literature. The paper contributes to understanding the reasons why citizens decide to engage or not in government-led e-Participation initiatives through social media.

Description

Keywords

Acceptance model, Electronic Government, Electronic Participation, Social media, Theory of planned behavior.

Citation

Extent

10 pages

Format

Geographic Location

Time Period

Related To

Proceedings of the 50th Hawaii International Conference on System Sciences

Related To (URI)

Table of Contents

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

Rights Holder

Local Contexts

Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.