How identity related goals moderate the role of attributes in product evaluation
Bibliographic record
Abstract
Abstract This research examines how identity related goals influence product evaluation. It is proposed that products are evaluated based on an attribute level to fulfill an identity related goal. Further, the positive relationship between an attribute level and product evaluation is strengthened by goal activation (the degree to which a goal occupies a consumer's thinking) and goal‐product fit (the extent to which consumers think a product is related to a particular goal). Results of three experimental studies support the above propositions. The research makes contributions in that it identifies two moderators, that is, goal activation and goal‐product fit, in the relationship between attributes ability and product evaluation. First, identity‐related goals are higher order and likely to have higher priority for consumers. However, results in this paper show that it still needs to be activated before it can exert an influence on attribute importance. Specifically, when an identity related goal (e.g., one supportive of the fair trade goal) is activated, it takes over a lower‐order goal (e.g., seek for a good taste or a good priced coffee in this case). As goal activation increases, relevant attributes become more important, and the positive relationship between an attribute level and product evaluation strengthens. Second, this paper introduces a new construct of goal‐product fit into identity‐related goals and product evaluation literature. The results of study three suggest that when goal‐product fit becomes stronger, the relevant attribute that can fulfill the goal becomes more important in the overall product evaluation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".