Consumer Responses to Selected Activities: Price Increases, Lack of Product Information and Numerical Way of Expressing Product Prices
Bibliographic record
Abstract
The importance of constant consumer testing is emphasized in order for companies to deliver the highest value for the quality of products and services. To explain the psychological impact of price on product selection, and other factors that determine consumer behavior, a survey method was applied. When deciding to buy a product, the consumer’s perception of the value of selected re-search products (clothing, footwear, children’s equipment) is crucial and it can often differ from the value derived from the price set by the seller. The conducted research proved that sellers can really influence consumers’ decision to buy a product with their price, and that a large number of consumers perceive the price incorrectly and thus buy more than they planned. Having in mind the subject of this paper, the basic scientific goal was to define a consumer model that integrates factors (variables) influencing consumer behavior to answer the question of how and why con-sumers react to rising product prices, how much they use the importance of information about product quality as a parameter of the decision, and how much consumers when choosing a product notice the price ending in a different number from the number of zeros. As consumer behavior is strongly influenced by a number of factors, it can be defined that the consumer’s response to selected activities: price increases, lack of product information and numerical way of expressing product prices may not contain all factors and their relationships and simplifies the picture of the consumer model. In order to test hypotheses about the extent to which customers are sensitive and willing to replace a product with certain substitutes, i.e., how willing they are to conclude about a product they buy based on price if they do not have enough information about the product and how much zeros are favored by consumers when shopping, an empirical study was conducted on a sample of 214 respondents. The results of the research indicate that in moments when respondents do not have enough information about the product, they are not inclined to draw conclusions solely on the basis of price, and prices ending in odd numbers or non-zero are not more attractive than those ending in zero.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".