Shopping Motivation and the Influence of Perceived Product Quality and Relative Price in E-commerce
Bibliographic record
Abstract
Understanding a consumer's motivation to shop online with a vendor can help an e-business better understand the attitude of customers and what they look out for in their shopping decision-making process. Equally important in the shopping decision making process is the influence of the perceived quality of products and their price. Understanding how consumers are influenced by the perceived quality and price of products can help e-businesses to improve their customers' shopping experience. To contribute to ongoing research in this area, we investigate the influence of perceived product quality and price on the motivation of e-shoppers to shop online. In particular, we investigate which of perceived quality and price have a greater influence on the consumer's motivation to shop online. We also investigate the moderating effect of income and gender. Using a sample size of 241 e-commerce shoppers, we develop and test a global research model using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Our results suggest that balanced buyers (shoppers who are moderately motivated by convenience and variety seeking but do not plan ahead and are impulse buyers) are more influenced by the relative price of products compared to their quality. In addition, balanced buyers who earn over $30,000 are influenced by the quality of the product compared to those who earn less than $30,000. Furthermore, male shoppers who are motivated by the convenience of online shopping (convenience shoppers) are also influenced by the perceived quality of products compared to female shoppers who are not.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".