Managing Stakeholder Interests in e-Government Implementation
Bibliographic record
Abstract
As e-government plays an increasingly dominant role in modern public administrative management, its pervasive influence on organizations and individuals is apparent. It is, therefore, timely and relevant to examine e-governance—the fundamental mission of e-government. By adopting a stakeholder perspective, this study approaches the topic of e-governance in e-government from the three critical aspects of stakeholder management: (1) identification of stakeholders; (2) recognition of differing interests among stakeholders; and (3) how an organization caters to and furthers these interests. Findings from the case study point to the importance of (1) discarding the traditional preference for controls to develop instead a proactive attitude towards the identification of all relevant collaborators; (2) conducting cautious assessments of the technological restrictions underlying IT-transformed public services to map out the boundary for devising and implementing control and collaboration mechanisms in the system; and (3) developing strategies to align stakeholder interests so that participation in e-government can be self-governing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".