The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Bibliographic record
Abstract
In the context of personalization technologies, such as Web-based product-brokering recommendation agents (RAs) in electronic commerce, existing technology acceptance theories need to be expanded to take into account not only the cognitive beliefs leading to adoption behavior, but also the affect elicited by the personalized nature of the technology. This study takes a trust-centered, cognitive and emotional balanced perspective to study RA adoption. Grounded on the theory of reasoned action, the IT adoption literature, and the trust literature, this study theoretically articulates and empirically examines the effects of perceived personalization and familiarity on cognitive trust and emotional trust in an RA, and the impact of cognitive trust and emotional trust on the intention to adopt the RA either as a decision aid or as a delegated agent. An experiment was conducted using two commercial RAs. PLS analysis results provide empirical support for the proposed theoretical perspective. Perceived personalization significantly increases customers’ intention to adopt by increasing cognitive trust and emotional trust. Emotional trust plays an important role beyond cognitive trust in determining customers’ intention to adopt. Emotional trust fully mediates the impact of cognitive trust on the intention to adopt the RA as a delegated agent, while it only partially mediates the impact of cognitive trust on the intention to adopt the RA as a decision aid. Familiarity increases the intention to adopt through cognitive trust and emotional trust.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".