Driving and Restraining Forces in the Implementation of Information Systems in the Public Sector: Scoping Review
Bibliographic record
Abstract
Background: Public sector organizations increasingly adopt information systems (ISs) to improve economic efficiency, service quality and overall adaptability. These projects represent substantial financial investments, making their success critical for organizational performance and societal impact. Objective: This scoping review aimed to identify the driving and restraining forces influencing IS implementation in public sector organizations and to explore strategies that support successful change processes. Methods: A total of 25 peer-reviewed articles were analyzed using Lewin's change theory to categorize and interpret driving and restraining forces. In addition, the narrative emerging from previous research on IS implementation was examined to explore how previous research portrays the success of IS implementation processes. Results: The findings highlight that IS implementation is influenced by 6 domains: organizational practices and challenges, technological factors and barriers, management practices and issues, change project factors and challenges, end user factors and concerns, as well as institutional factors and barriers. Key driving forces include leadership support, stakeholder involvement and system usability, while restraining forces encompass user resistance, technical challenges, and organizational silos. Conclusions: Despite the challenges, IS implementation offers significant opportunities for improving public sector operations and societal outcomes. Addressing restraining forces and leveraging driving forces is essential for achieving sustainable digital transformation. This study provides actionable insights for future IS implementation in the public sector.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".