Should Institutional Trust Matt er in Information Systems Research
Bibliographic record
Abstract
Given the importance of trust in online environments, information systems research has recently embraced trust research (e.g., Ba and Pavlou 2002; Gefen et al. 2003, Jarvenpaa et al. 1999; McKnight et al. 2002; Stewart 2003). However, despite the enormous interest in the topic of trust by IS researchers (with 129 published papers listed in ABI/INFORM), most of this research seems more appropriate for marketing or management journals, as interpersonal and interfirm trust have little to do with the information technology artifact. In contrast, with few exceptions (McKnight et al. 1998; Pavlou 2002; Pavlou and Gefen 2004), IS research on institutional trust is still sparse. Institutional trust is defined as the trustor’s belief that effective third-party guarantees are in place to assure the trustee’s behavior will be consistent with the trustor’s confident expectations. Institutional trust is perhaps more appropriate for IT-enabled environments where there is often minimal prior interaction and people mainly interact with new and unknown entities under the aegis of third parties who provide an institutional context. Also, there is evidence that IT can build effective institutional structures that engender trust in impersonal contexts (Pavlou 2002; Pavlou and Gefen 2004).
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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 | 0.070 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.019 | 0.054 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".