Bibliographic record
Abstract
Purpose The purpose of this paper is to test inferred bundle saving versus loss aggregation as explanations of consumer evaluations of bundles. Inferred bundle saving posits that consumer impressions of bundles are anchored in the daily economic reality that collections of goods as bundles are usually marketed at a discount to the same collection not bundled. Loss aggregation theory posits that consumers perceive an aggregation of prices as being less than the sum of its parts because they perceive prices as losses, and losses have a concave value perception; that is a small loss is perceived as large relative to its physical amount. Design/methodology/approach Previous research has shown that inferred bundle saving is a plausible alternative to loss aggregation. This research tests the two theories against each other in three experimental studies where they make opposite predictions. A meta‐analysis of the first two studies provides added evidence. Findings The predictions of inferred bundle saving were supported over the predictions of the loss aggregation prediction. Research limitations/implications Additional experimental studies are recommended to further test the proposed theory and its boundaries. Practical implications The presentation of a bundle to consumers sends a powerful message that “here lies a bargain.” In the absence of other information, consumers will form a favorable impression of the offering just because it is a bundle (and therefore must be a good buy). If the bundle is known to be undiscounted, then consumer reaction to the bundle is negative. Firms that offer bundles should ensure that their total message is consistent with savings of cash, or add consumer value through convenience/time saving. Originality/value The everyday observation that consumers expect a bundle to equal a saving has been ignored in favor of more complex theories of consumer behavior in many previous studies. The study presents results that favor the simpler theory.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".