Mental Simulation and Product Evaluation: The Affective and Cognitive Dimensions of Process versus Outcome Simulation
Bibliographic record
Abstract
In this research, the authors examine the role of process versus outcome simulation in product evaluation and demonstrate how manipulating the type of information-processing mode (cognitive vs. affective) leads to unique effects in process and outcome simulation. The article begins with the premise that when consumers do not have well-formed preferences for a product, they tend to focus on the usage process. The authors predict and find that outcome simulation is more effective than process simulation in increasing product evaluation under a cognitive mode, whereas process simulation is more effective than outcome simulation under an affective mode. Establishing boundary conditions, the authors further show the effect of two important moderators that alter consumers' focus on/away from the product's usage process. Specifically, they show a reversal of the effect for each type of mental simulation for hedonic products, for which product benefits are the more salient aspect (vs. the usage process). Furthermore, a distant-future (vs. near-future) evaluation frame shifts people's focus away from the usage process toward product benefits and reverses the effect of each type of simulation. The authors conclude with a discussion of theoretical and managerial implications.
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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".