Interactions with e-government, new digital media and traditional channel choices: citizen-initiated factors
Bibliographic record
Abstract
Purpose – This paper aims to examine the factors that can predict citizen-initiated contact with e-government as an attempt to identify important differences between service channel selections. Although more than two decades have passed since the initiation of e-government, digital channel choice is still being questioned, compared to traditional channels, and the level of selection with channels is being investigated. Design/methodology/approach – This study states three research questions that are answered through a literature review and statistical analysis of a survey in a developed country. More specifically, it identifies the factors that impact channel choice and validates them with survey results. To this end, this paper utilizes data from a national Canadian survey, where citizens empirically evaluated their channel choice – e-government, new digital media and traditional service channels – for government contacts. Findings – Statistical analysis over this data return valuable findings such as that the e-government channel is more appropriate for information collection, whereas traditional service channels are more likely to establish individual problem solving. Moreover, the digital divide appears to have an impact on citizen channel choice. Furthermore, digitally literate citizens who are aware of privacy issues are more likely to use new digital media. Finally, citizens are quite satisfied from their new digital media experience, but are not as satisfied with their traditional contact experience. Originality/value – These outcomes show that e-government obstacles regarding digital divide, trust and efficiency remain active and have to be addressed more carefully by governments. This study shows that e-government and new digital media are not simple channel choices, but are complex in public service delivery. These outcomes confirm the significance of channel choice for transforming government, as e-government appears to be a part of a broader channel choice agenda.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".