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.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".