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Record W2901406351 · doi:10.4018/ijissc.2019010101

The Effect of Service Innovation on E-government Performance

2018· article· en· W2901406351 on OpenAlexaff
Seyed Hossein Iranmanesh, Abbas Keramati, Iman Behmanesh

Bibliographic record

VenueInternational Journal of Information Systems and Social Change · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsService innovationGovernment (linguistics)StakeholderBusinessService (business)MarketingValue (mathematics)Knowledge managementPublic relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Research on public sector innovation has gained momentum recently as electronic government performance has been met with criticism. The ambiguity comes from the lack of deep understanding of the intervening variables through which service innovation affects e-government performance. Therefore, this article presents a conceptual framework to better understand the impact of service innovation on e-government performance. The role of stakeholder involvement and stakeholder perceived value of innovation in the relationship between service innovation and e-government performance is examined. The data came from a survey of 120 Iranian government organizations. Reliability and validity of the measurement instrument were confirmed and statistical analysis was performed to test the framework. The results confirm the moderating effect of stakeholder involvement and the mediating role of stakeholder perceived value of innovation. It was also revealed that citizens are not actively engaged in the innovation process and their perceived value of innovative e-government services remains low.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.303
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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