The effect of intangible service quality on retailing during the COVID-19 pandemic in Saudi Arabia
Bibliographic record
Abstract
The purpose of this study is to investigate how online to offline service quality influences the customer’s perceived risk and trust towards the retailer and how these factors impact customer satisfaction and intention to revisit. The present study incorporates intangible service quality offline aspects, such as empathy, and online aspects, such as mobility. The objective of the research is to examine the integration of online to offline service quality models in Saudi Arabia during the COVID-19 pandemic, using key aspects of offline, online and mobile service quality. The data was collected using an online survey of 289 respondents from Saudi Arabia. The analysis was conducted using partial least square and structural equation modelling. This study finds that the intangibility of service quality has a positive impact on perceived trust; however, the direct relationship between the intangibility of service quality and perceived risk is not supported. The study’s results support the hypothesis that customer satisfaction has a positive impact on the intention to revisit and that received trust positively affects satisfaction. The results have implications for service managers in the retailing and e-commerce sectors and offer a better understanding of how different channels of service affect customers’ perceptions and intentions to revisit.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".