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https://hdl.handle.net/1959.11/61422
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DC Field | Value | Language |
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dc.contributor.author | Talukder, Shamim | en |
dc.contributor.author | Chiong, Raymond | en |
dc.contributor.author | Dhakal, Sandeep | en |
dc.contributor.author | Sorwar, Golam | en |
dc.contributor.author | Bao, Yukun | en |
dc.date.accessioned | 2024-07-10T01:02:53Z | - |
dc.date.available | 2024-07-10T01:02:53Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Journal of Systems and Information Technology, 21(4), p. 419-438 | en |
dc.identifier.issn | 1758-8847 | en |
dc.identifier.issn | 1328-7265 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/61422 | - |
dc.description.abstract | <p>Purpose</p> <p>Despite the widespread use of mobile government (m-government) services in developed countries, the adoption and acceptance of m-government services among citizens in developing countries is relatively low. The purpose of this study is to explore the most critical determinants of acceptance and use of m-government services in a developing country context.</p> <p>Design/methodology/approach</p> <p>The unified theory of acceptance and use of technology (UTAUT) extended with perceived mobility and mobile communication services (MCS) was used as the theoretical framework. Data was collected from 216 m-government users across Bangladesh and analyzed in two stages. First, structural equation modeling (SEM) was used to identify significant determinants affecting users' acceptance of m-government services. In the second stage, a neural network model was used to validate SEM results and determine the relative importance of the determinants of acceptance of m-government services.</p> <p>Findings</p> <p>The results show that facilitating conditions and performance expectancy are the two important precedents of behavioral intention to use m-government services, and performance expectancy mediates the relationship between MCS, mobility and the intention to use m-government services.</p> <p>Research limitations/implications</p> <p>Academically, this study extended and validated the underlying concept of UTAUT to capture the adoption behavior of individuals in a different cultural context. In particular, MCS might be the most critical antecedent towards mobile application studies. From a practical perspective, this study may provide valuable guidelines to government policymakers and system developers towards the development and effective implementation of m-government systems.</p> <p>Originality/value</p> <p>This study has contributed to the existing, but limited, literature on m-government service adoption in the context of a developing country. The predictive modeling approach is an innovative approach in the field of technology adoption.</p> | en |
dc.language | en | en |
dc.publisher | Emerald Publishing Limited | en |
dc.relation.ispartof | Journal of Systems and Information Technology | en |
dc.title | A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1108/JSIT-10-2017-0096 | en |
local.contributor.firstname | Shamim | en |
local.contributor.firstname | Raymond | en |
local.contributor.firstname | Sandeep | en |
local.contributor.firstname | Golam | en |
local.contributor.firstname | Yukun | en |
local.profile.school | School of Science & Technology | en |
local.profile.email | rchiong@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | United Kingdom | en |
local.format.startpage | 419 | en |
local.format.endpage | 438 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 21 | en |
local.identifier.issue | 4 | en |
local.contributor.lastname | Talukder | en |
local.contributor.lastname | Chiong | en |
local.contributor.lastname | Dhakal | en |
local.contributor.lastname | Sorwar | en |
local.contributor.lastname | Bao | en |
dc.identifier.staff | une-id:rchiong | en |
local.profile.orcid | 0000-0002-8285-1903 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/61422 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Talukder, Shamim | en |
local.search.author | Chiong, Raymond | en |
local.search.author | Dhakal, Sandeep | en |
local.search.author | Sorwar, Golam | en |
local.search.author | Bao, Yukun | en |
local.uneassociation | No | en |
dc.date.presented | 2019 | - |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.published | 2019 | en |
local.year.presented | 2019 | en |
local.fileurl.closedpublished | https://rune.une.edu.au/web/retrieve/af6598e1-a640-40c8-b85e-97292b4c436d | en |
local.subject.for2020 | 4602 Artificial intelligence | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.date.moved | 2024-07-23 | en |
Appears in Collections: | Journal Article School of Science and Technology |
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