Demographics, attitudes, and technology readiness
Bibliographic record
Abstract
Purpose The purpose of this paper is to test the cross-cultural validity of the Technology Readiness Index (TRI) (Parasuraman, 2000) and explore how demographics and attitudinal variables may help to explain adoption and use of technology-based products and services. Design/methodology/approach The study is based on surveys conducted with probabilistic samples from two culturally distant countries, the USA and Chile. Findings Results support the TRI’s cross-cultural validity. They also suggest that demographic variables do matter when explaining people’s willingness to adopt new technology, with education being the most consistent predictor. Moreover, some of the findings seem to challenge the attitude-behavior consistency implied by conventional theory – while attitudinal variables are better predictors of pro-technological behavior in the USA, with technology-related insecurity being the most important of four attitudinal dimensions included in the analysis, demographic variables perform as better predictors in Chile, with educational level outperforming age and gender. Originality/value This is the first-ever cross-cultural test of the TRI using actual consumer samples from two culturally very different countries.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".