A Two-Process View of Trust and Distrust Building in Recommendation Agents: A Process-Tracing Study
Bibliographic record
Abstract
Prior literature focuses on trust, while largely ignoring distrust, partly because of the assumption that an Information Technology (IT) design that builds trust in the IT will also prevent distrust-building. However, this assumption may not be true if trust-building processes and distrust-building processes in the context of IT usage are different. This paper proposes a two-process view of trust and distrust building, i.e., that trust-building and distrust-building processes are distinct and separate. In the context of recommendation agent (RA) usage in electronic commerce, a trust (distrust) process is defined as a customer’s favorable (unfavorable) interpretation of his or her interactions with an RA, resulting in a positive (negative) expectation that the RA can be relied upon for his or her shopping decisions. This study empirically tests a process theory rather than a variance theory. Variance theory research relies on logical arguments to explain and test the causality relationships among variables. Process theory research complements variance theory research by revealing and testing the mechanisms that constitute the processes by which certain variables influence others. In this process-tracing study, we collected and analyzed the concurrent verbal protocols from 49 participants using two RAs. The results of our protocol analysis support the proposed two-process view. The pattern of trust-building processes in RA usage is systematically different from that of distrust-building processes, which may suggest that some RA features should be designed to increase trust, and others to decrease distrust. The findings also suggest that distrust deserves research attention on its own merit. In a complex relationship involving both trust building and distrust building, understanding both trust and distrust processes, rather than focusing on trust alone, can lead to a more accurate representation and improved management of that complex relationship.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".