Cultural Differences and their Influence onTechnology Acceptance: An EmpiricalStudy of Taiwanese andCanadian Users
Bibliographic record
Abstract
This thesis examines the cultural differences between Canada and Taiwan and their influence on technology acceptance. A considerable amount of research has been conducted on the topic of technology acceptance. This study aims to incorporate the element of national culture in the UTAUT model and draws a comparison between Taiwan and Canada. Hofstede’s five cultural dimensions scores, identified as Power Distance, Uncertainty Avoidance, Individualism/Collectivism, Masculinity/Femininity and Time Orientation, are compared to scores obtained by this study. Furthermore, the moderating effect that national culture has between Performance Expectancy, Effort Expectancy and Behavioral Intention is assessed. This study attempts to gain insight on how two different cultures may accept new technologies differently. The results obtained show that there is an observable difference between Taiwan and Canada when it comes to technology acceptance. Furthermore, it is acknowledged that the cultural dimension scores obtained in this study are significantly different from those given by Hofstede’s study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".