Factors Influencing Facebook Users’ Political Participation: Investigating the Cambodian Case
Bibliographic record
Abstract
As social networking sites (SNS) have been actively used as a platform for the political participation, this study investigates factors influencing SNS users’ political participation intention and behavior in developing countries. More specifically, based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT) and Civic Voluntarism Model (CVM), we develop a research model on how technological factors (e.g., performance expectancy, effort expectancy, social influence, and facilitating conditions) as well as social factors (e.g., political interests and experience) influence Facebook users’ political participation intention, which lead to actual political participation behavior, focusing on the Cambodian context. Our research model will be empirically tested with survey samples gathered from Cambodian Facebook users and their actual political behaviors, measured by counting actual comments of each survey respondent one month after the survey. While prior studies have only focused on either technological or social influencing factors on online political participation, this paper is among the first attempts to investigate them from integrative and comparative perspectives. By highlighting the relative impacts of each factor in the context of developing countries, where direct and public challenging or criticizing on the government is still a fear for most citizens, this paper would provide an important lesson for other developing countries with a similar political environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".