Web site satisfaction and purchase intentions
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 A main focus in recent online consumer research has been on context specific trust, risk, and online buying experience. Despite the importance, their individual level “equivalents” – trust disposition, risk aversion, and technology readiness – have received limited attention. This research attempts to fill that gap by focussing on these crucial personality traits. Design/methodology/approach This research employs a survey‐based method to test a theoretically grounded set of hypotheses. The measurement model is tested using SEM and the hypotheses are tested using regression techniques. Findings The personality characteristics are found to have significant moderating effects on online purchase intentions. Interestingly, provided the consumers are satisfied, risk aversion is found to increase the likelihood of purchase. Moreover, while technology readiness increases the likelihood of online purchase, dispositional trust is found not to have a similar effect. Research limitations/implications Significant full and quasi moderator effects of three hitherto untested personality traits on online purchase behaviour are found. Results show that risk aversion, trust disposition, and technology readiness are fundamental to online consumer behaviour literature. Practical implications The results suggest that to be successful, relatively unknown web‐based service providers need to go beyond matching their large competitor and need to offer unique web sites to browsers. Results also indicate that personality traits pose both significant challenges as well as unexpected opportunities to online service providers in identifying inherently more loyal customers. Originality/value The paper identifies a set of hither to untested personality traits that have fundamental relevance to online consumer behaviour. It also offers practical recommendations to relatively unknown online service providers on how to compete with their better known competitors. Results are generalisable to online service providers in a number of industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it