Which UGC features drive web purchase intent? A spike-and-slab Bayesian Variable Selection Approach
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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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 | 0.028 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".