A comparative analysis of dimensions of COO and animosity on industrial buyers’ attitudes and intentions
Bibliographic record
Abstract
Purpose Country of origin (COO) is well established as an extrinsic product cue that influences buyer behavior in the business-to-business (B2B) context. However, non-product-specific attitudes to a COO, including the notion of animosity, have received rather less attention. This paper aims to investigate COO as a multi-dimensional construct and animosity as a normative dimension of buyers’ attitudes and intentions. Design/methodology/approach The work is based on data collected from industrial buyers in Egypt and Canada to enable a comparative perspective between developing and developed countries. Structural equation modeling was used to test the study’s hypotheses. Findings Country of manufacture was an antecedent of perceived quality and a determinant of brand evaluation in both countries. Price was an antecedent of perceived risk and value in Egypt, while its impact on perceived risk was less pronounced in Canada. Perceived value was the strongest determinant of willingness to buy, while animosity played a significant role in this respect in Canada but not in Egypt. Research limitations/implications Country of brand was not included as a dimension to be investigated; industry type was not controlled and may confound the results; and generalization of the results is limited given the cross-sectional approach. Originality/value The study’s contribution lies in four main elements, viewed individually and in combination: investigating a large number of COO constructs that have not been studied within a single research context in B2B before; including the animosity construct in a B2B setting; contrasting “benefit received” and “sacrifice given” constructs that help to shape industrial buyers’ purchase decisions; and carrying out the research in two very different countries to help improve the generalizability of results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".