Mental Simulation and Product Evaluation: The Affective and Cognitive Dimensions of Process Versus Outcome Simulation
Bibliographic record
Abstract
Purchasing products that consumers have not used before (e.g., baby stroller for first time parents or a new product like an iPad) can be a challenge for consumers. In this article, the authors examine how mentally simulating two specific aspects of a product - the product usage process vs. the product benefits - might impact product evaluation, and how the effectiveness of each aspect might depend on whether consumers rely on their thinking or feelings. Based on the premise that when consumers don’t have well-formed preferences for a product they tend to focus on the usage process, the authors predict that when consumers rely on their thinking, prompting them to think about the product benefit (i.e., outcome simulation) leads to higher evaluation than prompting them to think about the usage process (i.e., process simulation) because the former highlights the naturally ignored product aspect whereas the latter is redundant. However, when consumers rely on their feelings, focusing on the usage process is more effective than focusing on the product benefit, because the detailed usage steps related with process simulation evoke a higher level of affective immersion in using the product. A first experiment based on the Tablet PC confirmed this prediction. Further, the authors demonstrated a reversal of the effect when consumers’ focus is naturally shifted from the usage process toward the product benefit. This reversal occurred for hedonic product such as the Apple iPad where people’s primary focus is the affective enjoyment (vs. how to use it), or for the same functional product when consumers are asked to evaluate it for a purchase decision in two months (distant future) rather than for in two days (near future), because distant future naturally directs people’s focus to the product benefit. These findings have important implications for marketing managers of products that are new to their consumers. They suggest that under a cognitive focus, simulating about the naturally ignored aspects should lead to higher evaluations; whereas under an affective focus, simulating about the naturally more salient product aspects should result in enhanced evaluation due to the saliency and vividness of those product aspects being more easily transformed into positive affective responses. The findings further suggest that while incorporating this strategy in the marketing practice, marketers should adjust their strategies based on whether the product serves predominantly a functional purpose or is positioned more on affective dimensions, and whether consumers evaluate the product for more immediate purchase or for the more distant future.
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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 | 0.004 | 0.036 |
| 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".