Cultural Effects on Technology Performance and Utilization
Bibliographic record
Abstract
Research to date on information technology (IT) adoption has focused primarily on homogeneous single country samples. This study integrates the Theory of Reasoned Action (TRA) and the Technology Acceptance Model (TAM) with Hofstede’s (1980, 1983) Masculinity/Femininity (MAS-FEM) work value dimension to focus instead on post adoption attitudes and behaviors among a mixed gender sample of 366 United States and Canadian users of a specialized supply chain IT. We test 11 hypotheses about attitudes towards IT within and between subgroups of users classified by nationality and gender. Consistent with the national MAS-FEM scores and contrary to the conventional consideration of the U.S. and Canada as a unitary homogenous cultural unit, we found significant differences between U.S. men and women, but not between Canadian men and women. These results support the importance of the MAS-FEM dimension—independent of gender—on user attitudes and help to clarify the relationship between culture and gender effects. Implications for managers responsible for technology implementation and management are discussed and directions for future research are offered.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".