Which UGC features drive web purchase intent? A spike-and-slab Bayesian Variable Selection Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to identify user-generated content (UGC) features that determine web purchase decision making. Design/methodology/approach – The authors embed a spike-and-slab Bayesian variable selection mechanism into a logistic regression model to identify the UGC features that are critical to web purchase intent. This enables us to make a highly reliable analysis of survey data. Findings – The results indicate that the web purchase decision is driven by the relevance, up-to-dateness and credibility of the UGC information content. Research limitations/implications – The results show that the characteristics of UGC are seen as positive and the medium enables consumers to sort information and concentrate on aspects of the message that are similar to traditional word-of-mouth (WOM). One important implication is the relative importance of credibility which has been previously hypothesized to be lower in the electronic word-of-mouth (e-WOM) context. The results show that consumers consider credibility important as the improved technology provides more possibilities to find out about that factor. A limitation is that the data are not fully representative of the general population but our Bayesian method gives us high analytical quality. Practical implications – The study shows that UGC impacts consumer online purchase intentions. Marketers should understand the wide range of media that provide UGC and they should concentrate on the relevance, up-to-dateness and credibility of product information that they provide. Originality/value – The analytical quality of the spike- and- slab Bayesian method suggests a new way of understanding the impact of aspects of UGC on consumers.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it