The different roads not taken: Considering diverse foregone alternatives motivates future goal persistence
Bibliographic record
Abstract
Abstract Decisions are rarely made in isolation. Instead, deliberation often occurs in the context of prior related choices. This article finds that goal‐inconsistent foregone alternatives, options that were previously considered but not chosen, shape how consumers subsequently pursue their goals. Going beyond previous research on foregone alternatives and consumer satisfaction, the current research suggests that how consumers mentally construe foregone goal‐inconsistent alternatives impacts how they evaluate their prior goal‐consistent choices, which will, in turn, impact their motivation to continue making goal‐consistent choices. Specifically, we find the foregone alternative diversity effect: consumers who consider having previously foregone diverse (vs. similar) goal‐inconsistent alternatives in favor of a goal‐consistent action then believe that they have made a greater sacrifice, which had more of an impact on their focal goal. As a result, they are then more likely to subsequently make goal‐consistent choices. Our findings hold across different types of goals (exercise: Study 1, healthy eating: Studies 2, 3, and 5, weight loss: Study 4), and both real and hypothetical choices. We also identify theoretically motivated boundary conditions for the observed effect of considering foregone alternatives.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".