Factors affecting customers' satisfaction in e-commerce marketplace during COVID-19 pandemic: developing market context
Bibliographic record
Abstract
COVID-19 pandemic is forcing consumers from developing countries along with their peers from developed markets to opt for online shopping and undertake many more activities feasible through the help of information and communication technology (ICT). This is a relatively new phenomenon for some developing countries' markets. Therefore, we aim to examine the factors that affect customers' satisfaction on using the e-commerce system in culturally diverse developing countries. A survey questionnaire was designed and randomly distributed to 260 respondents in order to find out how customer satisfaction depend largely on factors such as service quality, information quality, and system quality as well as perceived usefulness and self-efficacy. The study found that IQ, SYSQ, PU, and SE have significant positive relationship with customers' satisfaction on e-commerce. However, SEVQ did not have any significant relationship with customer' satisfaction towards e-commerce. This study addresses important evidence on how e-commerce can respond to COVID-19 transition and receive numerous benefits from online marketplace. The findings can help managers in e-commerce sector to contextualise while formulating their business policies and develop marketing strategies in developing market context in order to improve the willingness of customers to engage in online purchasing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".