A Fresh Look on Determinants of Online Repurchase Intention
Bibliographic record
Abstract
The e-commerce industry is continuously evolving and attained exponential growth as a result of the COVID 19-pandemic and the consequent lockdown. This has also generated possible changes in online consumer preferences. Although several studies have identified factors affecting online repurchase intention (ORI), a fresh look is required to identify key determinants of ORI. Thus, the current study, through extensive literature review, proposes and investigates such ORI determinants. Explanatory design with quantitative research method is used, and empirical data is collected using a self-administered e-questionnaire. A sample of 243 responses were collected from online consumers using snowball sampling. The data were then analyzed using AMOS 24 through series of statistical analysis. The findings show that web quality, service quality, credibility are key determinants of online repurchase intension. Electronic word of mouth (E-WoM) and customer relationship management (CRM) demonstrated insignificant impact on ORI. The results also identify perceived web quality as a factor with strongest impact on ORI.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".