The Pursuit of Meaning and the Preference for Less Expensive Options
Bibliographic record
Abstract
Abstract Finding meaning in life is a fundamental human motivation. Along with pleasure, meaning is a pillar of happiness and well-being. Yet, despite the centrality of this motive, and despite firms’ attempts to appeal to this motive, scant research has investigated how the pursuit of meaning influences consumer choice, especially in comparison to the study of pleasure. While previous perspectives would suggest that the pursuit of meaning tilts consumers toward high-quality products, we predicted and found the opposite. As compared to a pleasure or (no goal) baseline condition, six studies demonstrate that the pursuit of meaning causes people to consider how they can otherwise use their money (opportunity costs) which in turn leads to a preference for less expensive goods. This effect is robust across multiple product categories and usage situations, including both experiential and material purchases, and is obtained even when the more expensive product is perceived to deliver greater meaning. For participants pursuing meaning, making opportunity costs salient has no effect on their choices, and encouraging opportunity cost neglect increases their willingness to pay for a more expensive item. This research thus provides an initial answer as to how the pursuit of meaning shapes consumer choice processes and preferences.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".