Consumer self‐construal and trust as determinants of the reactance to a recommender advice
Bibliographic record
Abstract
Abstract Commercial recommendation agents (RAs) represent an important type of the decision support systems (DSSs) that are widely used by online retailers and firms. To date, little is known about the factors that shape the user's decision making and reactance toward the recommendations of these agents. Building on theories from psychology and information systems domains, this research proposes that a user's self‐construal and trust are two relevant factors that interact to shape the behavior toward the RA advice. Two studies, the first conducted using potential online customers and the second conducted at a behavioral laboratory, provided support to this proposition. The first study considered RA trust and showed that activating the interdependent self leads users with low (high) trust to exhibit high reactance behavior toward the RA advice. The second study variated trust using trust cues and corroborated the latter finding, while showing no important impact for the psychological reactance trait. As expected, in both studies the reactance behavior of independent users was not affected by trust. These results contribute by underscoring that social interdependence extends to RAs because the role of trust becomes salient when the interdependent self is activated for a user.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".