Trust building in wine blogs: a content analysis
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify trust‐building signals and signaling patterns of commercial and non‐commercial wine bloggers within a trustworthiness framework and assess prominence of balanced versus unbalanced resource‐based or compensatory approaches for the management of consumer trust beliefs and the facilitation of positive trust intentions. Design/methodology/approach Development and validation of theory‐based signal‐classification scheme and two‐stage content analysis of trust‐building signals embedded in wine blogs. Findings It is found that wine bloggers manage consumer trust beliefs using an unbalanced signaling approach emphasizing ability over character. Ability sub‐dimension signals vary by commercial orientation. Also, character signaling varies with commercial orientation. Research limitations/implications Only English‐language wine blogs were studied. Limitations of content analysis procedures preclude direct evaluation of signal efficacy in absolute or contextualized terms. Practical implications Bloggers must secure reader trustworthiness to be effective communicators. Readers are likely to possess latent concerns about the bias of commercial bloggers and abilities of non‐commercial ones. Bloggers recognize the importance of ability signaling but may not be fully exploiting their positions of perceived advantage nor fully compensating for their distinctive inherent perceived weaknesses. Social implications Trustworthiness signaling in wine blogs has implications for bloggers in other contexts, including consumer and non‐consumer information environments and not‐for‐profit and governmental communicators. Blog and blogger trustworthiness must be addressed by these communicators to effect audience persuasion. Originality/value The paper discusses deductive development and validation of a novel signal classification scheme applied to trust building by bloggers that, through analysis of signal content, sheds light on behavior of commercial and non‐commercial information sources in emerging product information environments.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".