The Effect of Brand Perceptions on Repurchase When Using the E-Commerce Website for Shopping
Bibliographic record
Abstract
This study aims to investigate the influence of some critical factors (store/brand perceptions, and trust in the webstore) on online repurchase intention. A pre-validated questionnaire was distributed to a convenience sample with response rate of 95.2% (n = 684) web-store buyers that were examined for assessing the research model. Primary data were collected during the period between December 2019 and February 2020, from respondents in Amman the capital of Jordan. Using AMOS 22.0 software, the collected data were analysed with structural equation modelling (SEM). Confirmatory factory analysis (CFA) was used to estimate the measurement model with respect to convergent and discriminant validities. This was followed by testing the structural model framework and research hypotheses. The results showed that retailers are able to increase trust in the web-store by carrying strong brands, and reap a number of benefits including image enhancement and pre-established demand, it has been found that the store as a brand could turn out to be as important that make it easier for customers to build trust, constituting a strong antecedent of behavioural intentions where behavioural intentions can lead to repurchase patterns. Unlike extant research, this study makes a novel contribution—to adduce evidence as per which both trust and intentions can have impact repurchase as far as web-stores are concerned. The relevancy can be explained with the fact that prior models were only found to have purchase intentions as the final dependent variable.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".