The impact of social influence on perceived usefulness and behavioral intentions in the usage of non-pharmaceutical interventions (NPIs)
Bibliographic record
Abstract
Purpose: Against the backdrop of the technology acceptance model (TAM), theory of reasoned action, and social impact theory the purpose of this research is to examine the validity of the TAM and assess the impact of social influence on the usage of NPIs in order to determine how best to encourage people to engage in the use of NPIs.Design/methodology/approach: A survey instrument was used to gather data with a snowball sampling method from Canadian respondents. The survey questionnaire items were adapted from existing literature. Data analysis was done using PLS-SEM.Findings: The results indicate that the TAM framework is applicable in the context of the use of NPIs with the COVID-19 outbreak as all TAM relationships were positive and significant. In addition, the results show a positive and significant impact of social influence on perceived usefulness, attitudes, and behavioral intentions towards the usage of NPIs. Thus, social forces can be considered relevant when understanding the adoption of technology.Originality/value: This research gives a better understanding of how social influence impacts adoption of behavior, such as the use of NPIs, and can be used to support the use of NPIs to decrease the spreading of viruses.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".