Consumers’ Purchase Intentions of Shoes: Theory of Planned Behavior and Desired Attributes
Bibliographic record
Abstract
Nowadays, females viewed shoes are not considered just footwear to protect and comfort foot, but a fashionproduct for decoration and express self-image. The total annual sale of shoes is NT$ 60 billion in which femaleshoes accounted for about NT$36 billionin in Taiwan.The purpose of this research was to investigate whatfactors make the difference in female shoes purchase intentions? Factors included (1) shoes attributes; (2)attitude, subjective norm, and perceived behavioral control based on Theory of Planned Behavior; and (3)demographic and shopping behavior variables. A total of 450 convenience questionnaires were distributedoutside department stores using Mall-intercept method in Kaohsiung, Taiwan. The results indicated femaleconsumers who have higher purchase intentions of shoes have significant higher appraisal of shoes attributes instyle, colour, collocability, materials and brand name compare to those who have lower purchase intentions ofshoes. In addition, consumers who have higher purchase intentions of shoes have better attitude, subjective norm,and behavior control compare to those who have lower purchase intentions of shoes. Overall, youth femaleconsumers age between 18-35 years old have significant higher purchase intentions than the elder consumers.Additionally, the more shoes quantity and higher shoes purchase frequency consumers have, the higher purchaseintentions of shoes would perceived.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".