Building institutional trust through e‐government trustworthiness cues
Bibliographic record
Abstract
Purpose Most theory and empirical research on the impact of e‐government on citizens' trust in government remains at the macro‐level and misses out on the complexities of the interaction between e‐services and citizens' trust in government. The purpose of this paper is to provide a deeper understanding of this complexity. Design/methodology/approach The research strategy is a comparative case study of two e‐services in Chile: a tax administration and an e‐procurement system. Data were collected from a variety of users (citizens and business owners) and public sector employees in the Araucania Region in Chile. Findings Within e‐services, the most easily perceived and influential trustworthiness cues are those outcomes that directly impact the citizen. These cues shape citizens' resultant interpretations of and trust in the public sector agency. Furthermore, the direction of this influence is mediated by individuals' particular circumstances and value positions. Key to understanding the process of building and destroying trust is the identification of the value conflicts that can emerge from e‐service implementations and how they align with citizens' values. Research limitations/implications The research conclusions are potentially an artefact of the financial nature of the e‐service transactions and the cultural uniqueness of Chileans. Originality/value The paper presents an original integrated conception of trust and institutional trust as well as a comparative analysis of citizens' perceptions and interpretations of “successful” e‐services.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".