Online Visual Merchandising Cues Impacting Consumer Pleasure and Arousal: An Empirical Study
Bibliographic record
Abstract
The visual merchandising is a crucial aspect of physical and online retail establishments, as it significantly impacts consumer behavior.In an online retail setting, visual merchandising takes the form of online visual merchandising cues (OVMC), such as product images, videos, product descriptions, and website design.These elements are critical in attracting customers, increasing engagement, and driving sales.This study aims to focus on the significant OVMC influence on consumer pleasure and arousal.The study's sample population comprised respondents who shop for electronics products online in the Vijayawada city of Andhra Pradesh, India.The study used the probability sampling design known as systematic random sampling and comprised 385 respondents.This study suggests that OVMC, including website aesthetic appeal, website ads, homepage landing, product reviews, and product feature explanation videos, are essential in persuading visitors to go from casual online browsing to purchasing electronic products.This study contributes to the existing body of literature by providing a comprehensive overview of the critical factors that impact consumer behavior in an online retail setting, specifically concerning electronic products.It provides insights into how marketers can use online visual cues to influence consumer behavior and drive sales in the e-commerce market.It offers insight into the OVMC and the key factors influencing consumer pleasure and arousal in online purchases so that online marketers can adapt their marketing strategies to reach and induce online purchasers effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".