Riding the Country, Buying the Brand: How Country-of-Origin Image Drives the Purchase Behavior of Big Motorcycle in Indonesia
Bibliographic record
Abstract
This paper examines the role of country-of-origin image (COO image) values in the process of purchase decision making of big motorcycle consumers in Indonesia. Referring to the COO image values such as Authenticity, Differentiation, Quality Standard and Expertise, as well as the elements of purchase decision making process such as Need Recognition, Information Search, Evaluation of Alternatives, Purchase Decision and Post Purchase Decision, researchers conducted in-depth interviews to five Triumph big motorcycle consumers. The results show that the authenticity of the British-made product is the main consideration of consumers both in searching for information and in recognizing the need of big motorcycles. The competitive advantages of product that make it different from other products is the consideration in evaluating the brands, while product quality has the role in stimulating the purchase decision and post purchase actions, in which also strengthened by the perception towards the British-expertise in producing big motorcycles. This research is very beneficial to big motorcycle brands in understanding the mindset of Indonesian consumers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".