Relationship between Information Richness and Exchange Outcomes
Bibliographic record
Abstract
The present study identifies the richness levels of various Internet media and empirically examines the moderating effects of Internet media richness (rich and lean media) and Internet communication governance mechanisms (legal contracts and relational norms) on the relationships between rich and lean information communication, and exchange outcomes. This study uses regression analysis to analyze data collected from 284 Chinese companies. The analysis reveals that: (1) Rich information exchange is effective when rich Internet media is frequently used. Conversely, the effectiveness of lean information exchange is not significantly affected by the frequent use of lean Internet media; (2) While lean information exchange is effective when legal contracts are extensively utilized as a governance mechanism, rich information exchange is effective when high levels of relational norms exist; and (3) Lean information exchange is effective when a high level of plural form governance (i.e., a combination of relational norms and legal contracts) exists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".