A Study of Young Consumers’ In-Store Food Shopping Behaviour For Developing Smart Mobile Devices
Bibliographic record
Abstract
The purpose of this study was to explore in-store food purchasing behaviour of young adults with particular reference to the five-stage consumer purchase decisionmaking process, and to explore how current mobile technologies can aid consumers' instore food shopping experience.The researcher hypothesizes that consumers will have better food shopping experiences through customization and personalization of mobile applications to create personal value during in-store food shopping situations.A study was conducted where the researcher observed and shadowed a group of young adults to help the researcher learn about what information shoppers look for, and what their food choices are based on.The study discovered that there are a number of influential factors that contributed to a purchase decisions that participants assessed simultaneously.Results showed that key design functions for mobile tools and applications using existing technologies such as Mobile Recommendation Agents (MRA's) can enhance the usefulness of such tools and applications in order to create more personalized food-shopping experiences and to help consumers make the most informed purchase decisions by seeking information at the point-of-purchase, thereby alleviating any post-purchase psychological tension or anxiety.The results of this research provide valuable insights and recommendations for designers to develop mobile tools and applications for food shopping situations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".