Bundles = discount? Revisiting complex theories of bundle effects
Bibliographic record
Abstract
Abstract Purpose – The paper seeks to propose and test a theory of the psychological impact of price bundling that is derived from bundling's economic impact. It is called the inferred bundle saving hypothesis. In the absence of explicit information about bundle savings, consumers infer a bundle saving when presented with a bundle offer. It is suggested that inferred bundle saving provides a simple, parsimonious explanation for pre‐ and post‐purchase bundle effects. Design/methodology/approach – The theory is tested in two laboratory studies that employ partial replications of two prior price bundle studies. Findings – The results show that the inferred bundle saving effect is robust in both product and service contexts, and can potentially explain the bundle effects found in these two studies. Research limitations/implications – Additional experimental studies are recommended to further test the proposed theory. Practical implications – First, contrary to convention, it is not always optimal for firms to integrate price information in a single bundle price. Second, firms may sometimes use the price‐bundling format to signal a bundle saving without actually offering one. Third, firms can manage consumption and expected refund of bundles by manipulating consumer perception of bundle saving. Originality/value – It is intuitive that consumers expect a bundle saving. However, this paper is the first to establish empirically the existence of this inferred bundle saving and demonstrate its potential as a theoretical explanation for various bundle effects. The research challenges the extant view that price bundling per se always enhances consumer pre‐purchase evaluation. Moreover, it connects economic and psychological research, as well as pre‐ and post‐purchase analysis, of bundle effects.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.001 |
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".