The role of E-Service Quality to create Consumer Trust in Shopping on the Shopee Marketplace during the COVID-19 Pandemic
Bibliographic record
Abstract
The Covid-19 pandemic has caused changes in consumer behaviour. Especially when it comes to shopping for everyday necessities. The limitations of doing transactions offline make people shop more for their daily needs online. This phenomenon makes e-commerce business increase sharply. Not only that, the e-commerce business competition map is increasingly competitive. The tight competition is reflected in the number of website visits and e-commerce application users in Indonesia. The coronavirus pandemic has not shown signs of ending until the third quarter of 2020. During the period of emigrants, e-commerce players are increasingly aggressively presenting online shopping as an alternative to fulfilling daily needs without leaving the house. MarkPlus Inc. Release the results of the latest research related to e-commerce. The survey results of The Asian Parent show that almost all respondents who are mothers in Indonesia use online shopping applications. As many as 80% of them shop at Shopee the most. This shows that Shopee is a marketplace that is favored by consumers. Electronic Service Quality or also known as E-ServQual is a new version of Service Quality (ServQual). E-ServQual was developed with the aim of evaluating the services provided online or over the internet. E-Service Quality is a service from the site to facilitate shopping, purchasing, and distribution activities effectively and efficiently (Chase, Jacobs, &Aquilano, 2006). According to Tjiptono (2011) there are seven dimensions of e-service quality, namely efficiency, reliability, fulfillment, privacy, responsiveness, compensation, contact (contact). One of the disadvantages of shopping online is the doubt and lack of consumer confidence in the product to be purchased, so online marketers must provide quality services so that consumers believe. According to Mowen and Minor (2012), consumer trust is the belief that consumers have about objects, attributes, and benefits. Objects can be products, people, companies, and anything that has beliefs and attitudes. Based on these backgrounds and explanations, this study aims to determine the partial and simultaneous influence of e-service quality variables on consumer confidence in shopping at shopee. Keywords: E-Service Quality, Trust, Shopee
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".