Trust intentions in readers of blogs
Bibliographic record
Abstract
Purpose The blogosphere is an active arena for the communication of topic‐area claims by marketer and non‐marketer sources. Determinants of influence in the blogosphere have not been well documented. The purpose of this paper is to investigate trust in bloggers, in a framework involving characteristics of bloggers and blogs and blog reading outcomes. Design/methodology/approach Blog‐reader perceptions of bloggers and blogs are derived and tested on a sample of blog readers for their effects on trust formation. Tests of mediation examine the role of perceived personal outcomes of blog reading in trust‐formation processes. Findings Trust formation is predicted by engagement knowledge of the blogger, unique reading experiences, and belief that the blog improved the marketspace. Blogger authoritative knowledge negatively impacted trust intentions. Positive experiences from blog reading mediate relationships between blog and blogger characteristics and intentions to trust. Research limitations/implications Blog readers examined in this initial investigation may not be totally representative of the general population of blog readers. Replications with other populations are needed. Practical implications The paper's findings suggest knowledge is an essential characteristic of a trustworthy blogger, but knowledge unrelated to everyday information needs holds little perceived value for readers. Firms operating blogs may wish to de‐emphasize their topic‐area authoritative knowledge and project a voice of topic‐area engagement. Originality/value The paper identifies salient trust‐related blogger and blog characteristics and provides an indication of a domain‐specific trust‐development process that is applicable to marketer and non‐marketer information sources.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".