Analysis of the influence of perceived value on browsing behavior in C2C E-Commerce with depth of review as antecedent
Bibliographic record
Abstract
Online shopping has developed rapidly in Indonesia since the Covid-19 outbreak, making potential customers frequently browse e-commerce. In e-commerce there is a social commerce construct which is a construction originating from social commerce such as online reviews. Depth from online reviews on a product cannot necessarily be trusted and prospective customers also cannot use other people's experiences as an assessment of product quality. Customer review also considers perceived value from a utilitarian and hedonic perspective. Therefore, this study analyzes the effect of perceived value on browsing behavior in C2C e-commerce with the antecedent depth of review in Indonesia. This study uses SOR (stimulus-organism-response) theory. The population of this study are individuals who live in Indonesia and have done shopping online in one commerce which has facilities online review such as Tokopedia, Blibli, Amazon, Alibaba, and JD.ID. Sampling technique nonprobability sampling by using techniques convenience sampling total 137 samples. The data collection method uses the survey method, while the data analysis method used is PLS-SEM. The results of the study show that depth review affects perceived utilitarian and hedonic values, and perceived utilitarian and hedonic values also affect browsing. Thus, all hypotheses are accepted, which means that there is an influence of perceived value on browsing behavior in C2C e-commerce with an antecedent of review depth. This research can be used as a reference for further studies by digging deeper into the effect of the depth of review on other variables that can have an impact on the viability of a seller's business in e-commerce. This research can be used as a reference for sellers to evaluate and create strategies to encourage customers to give positive reviews so that they can influence other readers when browsing e-commerce where it is hoped that purchases will occur. This research pioneered the study of perceived value of browsing behavior in C2C e-commerce with antecedents of depth of review in Indonesia.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".