Bibliographic record
Abstract
This article outlines our collaboration with Tireland, in which we conducted research regarding the potential success of selling tire online. We looked into aspects of an online presence that would be positively received by consumers in the tire industry. Our research has included an interview with the decision maker, five in-depth interviews, and an online questionnaire. The aim with these methods was to gain a further understanding of the current online tire industry and the constraints that may be present when implementing an ecommerce sales strategy. The target market we were focused on includes males and females between the ages of twenty to thirty. We received 114 responses to our online survey, of which 103 were part of our target market. The data we aimed to collect helped us compare variables such as level of education, online features, and annual household income with the individual’s likelihood to purchase tires online. It is through our SPSS analysis that we were able to gather a better understanding on the statistics surrounding the problem at hand.Our studies offered an insight into the necessary elements to create a successful ecommerce platform. Based on our five research questions, we determined that while variables such as income and education have a minor impact on the likelihood to purchase tires online, other variables such as available online features, tire characteristics, and history of online shopping are significant. Our analyses have led us to recommend that the company should target individuals who already typically shop online. As well, characteristics such as availability, customer service, and price are important to consumers when making a purchase. Finally, consumers are more willing to shop on a website that offers reviews, product information, reminders and an online question service. If these variables are focused upon that will be the greatest way for a company to implement a successful ecommerce platform.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".