The dynamics of trust development in the life cycle of information systems’ use: a case of online dating services
Bibliographic record
Abstract
This study takes a life cycle approach to investigate the process of trust development. The life cycle of user trust is based on the life cycle of information systems (IS) usage, including the pre-usage, usage, and post-usage stages. In this study, we conceptualise trust development in a three-stage model, and identify the driving forces for trust development and its impact on user behaviour at each stage. Using online dating services (ODS) as our research context, we collected survey data in the pre-usage stage (N = 129), usage stage (N = 241), and post-usage stage (N = 266). The results indicated that (1) initial trust in the pre-usage stage was based on second-hand information, (2) experiential trust in the usage stage was based on the integration of first-hand experience and initial trust, and (3) mature trust in the post-usage stage was based on the attribution of the final usage outcome. Our study contributes to the trust literature by providing a fuller picture of trust development from the perspective of the IS life cycle.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".