A construal level account of when consumers prefer to spend loyalty points over money
Bibliographic record
Abstract
Abstract The central questions answered in this research are, “For what, and when, do consumers prefer to spend loyalty points over money?” We use construal level theory (CLT) to theorize that loyalty (or reward) points are perceived abstractly while money is perceived concretely, and this impacts spending preferences. We find that consumers prefer to spend loyalty points (vs. money) on high desirability‐low feasibility (vs. low desirability‐high feasibility) consumption items. The same pattern also persists when the items that vary on desirability and feasibility are equivalently priced. Second, we show that the construal‐level matching phenomenon influences temporal decisions such that consumers prefer to spend points (vs. money) for items that are available later (vs. now). Third, the moderating effect of category type (experiential vs. material) is reported. Finally, we demonstrate two managerial applications that are reported in the Web Appendix. We show that managers may influence how consumers spend loyalty points (a) by altering the concreteness of the decision context, and (b) by manipulating the nature of loyalty points.
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 | 0.008 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".