Country-of-origin image effects on satisfaction and purchase intention in the industrial market for seafood products : a study of Norwegian, Chilean and Canadian salmon buyers
Bibliographic record
Abstract
Despite the growing importance of country-of-origin (COO) effects in industrial markets, \nmost prior research has been concentrated on these effects only in consumer environments. \nIn addition, existing studies on country-of-origin image (COI) have mainly targeted the \nrelationship between COI and perceived product quality for durable goods. This study \nexamines the influence of country-of-origin image effects of three different countries on \nperceived quality, buyer satisfaction and purchase intention among industrial buyers of \nseafood products in the USA. Perceived supplier reliability, a new construct in COO \nresearch, is presented and linked with COI and buyer satisfaction in the conceptual model \ndeveloped by the researcher. This research uses a mixed methods approach, utilizing both \nsurveys and in-depth interviews, to gather relevant B2B data for identifying the main \ninfluencing factors of COI. Structural Equations Modeling (SEM) and multiple regression \nanalyses are employed in order to test the relationships proposed in the model. The \nanalyses show that COI impacts overall buyer satisfaction and purchase intentions \nindirectly and that its influence is mediated by perceived product quality and perceived \nsupplier reliability. Consistent with previous studies, perceived product quality is strongly \ninfluenced by the favorability of COI. COI is also found to strongly influence perceived \nsupplier reliability, although certain differences are visible between the various countries. \nFollowing the interviews, several new relationships, such as the one between COO, \nsustainability and CSR are also found.
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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 | 0.002 |
| 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) | 0.004 | 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".