The Model of Relationship between the Perceived Values and the Purchase Behaviors toward Innovative Products
Bibliographic record
Abstract
As technologies have become ever innovative and mobile devices have become ever popular in recent years, most enterprises have been seeking innovation in their products to enhance the purchase intention of consumers and thus maintain their competitiveness. APPLE WATCH is one of such products. This study adopted the literature analysis and the empirical research to discuss the relevance among consumers’ perceived values (functional value, emotional value, epistemic value, social value, and conditional value), attitudes, purchase intention and actual purchase behavior and analyzed the differences in the relations among all the perspectives between those who had other APPLE products and those who did not. Through a questionnaire, this study adopted the convenience sampling in an investigation into those aging 20 or more in Taiwan and collected 760 valid copies. According to the results, there were significant differences in the intensity of five relation paths between those who had other APPLE products and those who did not. Specifically, (1) functional value had more significant positive effects on the path of consumer behaviors among those who did not have other APPLE products than among those who did; (2) epistemic value had more significant positive effects on the path of consumer attitudes among those who had other APPLE products than among those who did not; (3) conditional value had more significant positive effects on the path of consumer attitudes among those who did not have other APPLE products than among those who did; (4) emotional value had more significant positive effects on the path of consumers’ purchase intention among those who had other APPLE products than among those who did not; (5) epistemic value had more significant negative effects on the path of consumers’ purchase intention among those who had other APPLE products than among those who did not. The research results showed that there might be some differences in behavior model between the two groups. These results can serve as reference for practitioners in their making decisions on marketing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".