The Effect of Consumer-Activated Mind-Set and Product Involvement on the Compliance With Recommender System Advice
Bibliographic record
Abstract
Whereas the research gauging the effectiveness of e-commerce recommender systems (RS) has depended on their design factors, recent work proposes a key role for consumer’s psychological factors. Involvement should reduce the compliance with RS advice because a consumer highly involved with the product perceives high choice risk and assigns low value to the advice. However, a consumer’s activated mind-set captured by implicit theory (fixed vs. growth mind-set) should also shape compliance. It is hypothesized that the two factors interact to jointly mitigate advice taking. Specifically, consumers whose fixed mind-set is primed comply with the RS advice less often when involvement is high. This and other anticipated effects (i.e., consumer’s importance of social approval, positive affect, and need for cognition) on advice compliance are tested in an experiment on 251 Canadian adults. In the experiment, compliance occurred when the participant follows the RS advice, and product involvement was initially measured. The results show that priming a fixed mind-set, which orients shoppers toward a performance goal, motivates them to comply with the RS advice when involvement is low. Priming a growth mind-set, which orients shoppers toward a learning goal, nullifies such effect. Positive affect and the importance of social approval had no significant impact on advice taking. Therefore, the effect of involvement on RS effectiveness is contingent on the shopper’s accessible mind-set.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".