Consumers’ Readiness to Accept Technology-Based Products and Services in Developing Countries: the Chilean Experience.
Bibliographic record
Abstract
The objective of this study was to test the external validity of the Technology Readiness Index (TRI) in a develop- ing country. A hypothesis was formulated in order to test predictability of demographics and attitudinal variables with intention to embrace and use technology-based products and services. The TRI taxonomy of five-segments was also tested with a hypothesis. The survey was conducted in Spanish using a professionally translated version of the 36-item TRI, with the same 5-point scale format as the original TRI study (Parasuraman, 2000). Results indicate that demographic variables still matter when explaining people’s willingness to adopt new technology, age being the most consistent predictor. Results also provide evidence that attitude gets more importance than demographics when the potential adoption of a new technology may carry some potential risks of being affected either economically or phys- ically. The cluster analysis procedure indicated that a four-cluster solution provided the best grouping of respondents into meaningful segments. Only 13% of Chileans can be classified as explorers (compared to around 15-20% in the U.S.). The explorers combined with 27% of Chileans classified as pioneers, constitute 40% of the Chilean population that is likely to be ready to immediately accept new technologies. Also, a discussion of the potential effect of specific national cultural dimensions is provided.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".