Factors affecting customer retention of e-marketplace industries through Stimulus-Organism-Response (SOR) model and mediating effect
Bibliographic record
Abstract
This research aims to study the factors influencing customer retention of E-Marketplace businesses by applying the SOR theory and the mediating effect by examining the relationship between value perception, customer engagement, brand loyalty, and customer retention variables by creating a model based on the SOR theory to study the influence of environmental factors that will lead to customer retention mechanisms. The study focused on a sample group of 426 people who have used E-Marketplace services using an online questionnaire as a research tool. The model was analyzed using the CFA and the hypothesis was tested using the SEM analysis. The results showed that stimuli (S), such as customer value perception, had a strong positive influence and a crucial role in the development and creation of a mechanism for assessing individuals' internal feelings (O) on two variables, customer engagement, brand loyalty, and directly influence customer retention. Furthermore, significant evidence also showed that brand loyalty has an important role as a mediator between customer value perception and customer retention, which is the outcome factor (R) of this study. This structural model can explain customer engagement and retention up to 86.50 percent. Therefore, this research has value as a theory and guideline in formulating an operational strategy for E-Marketplace businesses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".