Article: International Marketing Forum MEASURING MODERATIONS: A CROSS CULTURAL AND COMPARATIVE SERVICES CONSUMPTION STUDY BETWEEN BRAZILIANS AND CANADIANS
Bibliographic record
Abstract
Abstract: This study´s purpose is to examine relational benefits and consequent variables on Brazilian and Canadian consumers by moderating cultural idiosyncrasies. Comprised by a quantitative nature descriptive research 297 Brazilians and 207 Canadians were surveyed. Data was submitted to statistical tests via MANOVA comparisons, canonical correlation and regressive model moderations so as to verify the herein proposed technique. The study also gave rise to methodological contributions given the development of computational scripts employed to support the identification of each regression´s construct´s strength, orientation and path. Comparative analyses substantiate that when assessing services, Brazilians are more demanding than Canadians. Given that culturally, the former still feature greater power distance, there is ground for recommending that offering special treatment to Brazilians is an important satisfaction with employees leveraging factor. On the other hand, when unlike other fellow countrymen, Canadians receive such benefits, a general feeling arises that benefits and knacks of the kind should not be meant for but a few, shaping the service offer into one that is deemed unequal, within a society that is clearly more egalitarian than that of Brazil. Outcomes might further prove to be of use to the narrowing of Brazil-Canada business ties.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".