IT value creation in public sector: how IT-enabled capabilities mitigate tradeoffs in public organisations
Bibliographic record
Abstract
Governments today are striving to improve services in the public sector through digital transformation programs but face tremendous pressures from multiple fronts (economy, national security, healthcare, education, etc.). Even when worldwide enterprise IT spending for the government and education markets has been increasing and is expected to surpass $652 billion in 2023 to cater to such transformation programs, 80% of the government transformation efforts failed to achieve expected results. A plausible reason for this lacklustre performance could be the presence of tradeoffs or conflicts that is particularly salient in public organisations. To better understand the mechanisms by which IT enables or inhibits capabilities of the public organisations in attaining public value, we adopt a conflict resolution lens to study how information technology (IT) enabled capabilities to mitigate these tradeoffs. Using a dataset collected from public organisations in a European country unreeling from a financial crisis, we examine the processes by which IT enables public organisations to manage the tradeoffs arising from conflicting value-based goals. We identify three mitigation strategies facilitated via IT-enabled organisational capabilities – bias, tunnelling and hybridisation. This paper contributes to the understanding of how IT mitigates value-based tradeoffs in public organisations to achieve public value.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".