Understanding Institutional Pressures of Artificial Intelligence Adoption in Indonesia Government: Study Case on Jabar Digital Services
Bibliographic record
Abstract
The adoption of artificial intelligence (AI) in the public sector is catalyzing global digital transformation. In Indonesia, despite the policy push for digitization, AI implementation faces unique challenges influenced by the dynamics of institutional pressures. This study aims to uncover the influence of coercive, mimetic, and normative pressures on AI adoption in Jabar Digital Services (JDS) and identify barriers and opportunities that arise in the local context. Using a qualitative approach with a case study design, data were obtained through semi-structured interviews, focus group discussions, and analysis of official documents. Thematic analysis was conducted to identify key patterns that reflect institutional dynamics in AI implementation. The findings of this study reveal that the adoption of artificial intelligence (AI) in Jabar Digital Services (JDS) is driven by a complex synergy between three types of institutional pressures. Coercive pressure, reflected through political pressures and leadership role, is considered to strongly encourage AI adoption. On the other hand, mimetic pressure encourages JDS to adopt best practices from developed countries from the perspective of frameworks, case studies, and international AI maturity models. Meanwhile, normative pressure arising from the expectations of the five pillars of pentahelix stakeholders including academia, private sector, government, community, and media increases the urgency of AI adoption. The synergy between coercive, mimetic, and normative pressures not only drives digital transformation and contextual approaches in JDS. This research extends the understanding of institutional isomorphism theory in developing countries and provides strategic recommendations for policymakers to integrate global practices with contextual customization to achieve effective digital transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".