Putting the I in I-voting: An examination of internet voting adoption factors on the individual level
Bibliographic record
Abstract
Internet voting (i-voting) has been researched since countries started trialing it two decades ago. Although several countries have abandoned their trials, some implemented i-voting in national elections. I-voting research discusses successful implementations of i-voting in countries such as Estonia, Switzerland, and Canada, which has generated many different factors for successful adoption. However, no systematic literature review (SLR) on i-voting adoption factors has been identified. The problem that this thesis addresses is the lack of a comprehensive overview on reasons why an individual decides to adopt an i-voting solution. Thus, the purpose of this thesis is “to identify i-voting adoption factors on the individual level”. This study aims to answer the following research question: “How can TAM be adapted to explain an individual’s intention to adopt i-voting?” A semi systematic literature review of 117 articles is used that contains articles spanning two decades of i-voting research. The scope is narrowed down to adoption factors on the individual level and include the non-technical factors: “Voter experiences and perceptions”, “Trust”, and “Education”, and the technical factors: “User experience”, and “Performance”. The technology acceptance model (TAM) is used to explain how the factors relate to Perceived Ease of Use (PEOU) and Perceived Usability (PU) within TAM. A suggestion of an extended model is also made that includes other factors which were identified to explain individual adoption. Thus, the conclusion of this thesis is that TAM can in part explain an individual’s intention to adopt i-voting, but that it should be adapted to include the following additional factors: “Trust”, “Demographics”, “Education”, and “Voter experiences and perceptions”. Recommendations for future research on i-voting, limitations, and ethical and societal consequences are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".