How the combination of visual images and voice changes customer interest in online sales
Bibliographic record
Abstract
Since the outbreak of the COVID-19 pandemic, the widespread use of online tools has diversified business communication strategies. However, there are few studies that look at the unique characteristics of these digital environments, especially when cameras are turned off. In this study, how the combination of visual images and voice expressiveness influences customer interest in online sales situations was investigated. Using quantitative and qualitative data, the change in customer interest for six video stimuli that combine different nonverbal cues from salespeople was analyzed: visual images (name only, photo without facial expression, smiling photo) and voice (with expression vs. without expression). The McNemar test revealed significant changes in customer interest for all combinations, with the combinations of expressive voice and smiling photo proving to be the most effective. The qualitative analysis with KH Coder’s Co-occurrence network showed that the characteristics of the voice (e.g., brightness and intonation) and the consistency between the visual image and voice are decisive for the customer’s impression. In particular, customers who initially showed interest tended to lose interest owing to discomfort and mistrust caused by monotone voices and the inconsistency of nonverbal cues, which prevented them from engaging with the content. Conversely, customers who initially showed no interest were persuaded by expressive voices and the consistency of nonverbal cues to become enthusiastic about the content and engage with it. These findings offer practical insights into digital communication strategies in sales, education, and advertising, particularly for customizing approaches based on initial audience interest.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".