The Effects of Price Variation in Luxury vs. Non-Luxury Products on Consumer Decisions
Bibliographic record
Abstract
Companies rely on their knowledge of how their clients and customers will react in response to actions taken to run their businesses. What drives consumers to make their choices is a question that revolves around knowing the behavior of consumers and how their behaviors can be utilized to work best with the goals of the company. Price is one of the many factors that play a key role in the purchase decision. This study is designed to determine price sensitivity regarding luxury vs non-luxury products. For this study, shoes and water were used as product lines and subtle vs obvious price increases as the variable. Fielding two questionnaires to obtain both non-comparative and relative data, the objective was to perform research to see what causes consumers to buy the more expensive brand for identical or similar versions of the same product. This study provides insights into consumer behavior and price sensitivity in the presence and absence of a luxury/non-luxury competitor and across high and low involvement categories. It demonstrated that luxury brands can retain consumer willingness to buy with price increases. Price sensitivity and the threshold at which consumers will switch from a non-luxury product to a luxury product could be affected by the price point of the product category. This study strives to understand the effects of price variation in luxury versus non-luxury products on consumer decision making. It is important to know the threshold of price that drives consumers to make the purchase decisions that they do.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".