The Effects of Presentation Formats and Task Complexity on Online Consumers’ Product Understanding1
Bibliographic record
Abstract
This study assesses and compares four product presentation formats currently used online: static pictures, videos without narration, videos with narration, and virtual product experience (VPE), where consumers are able to virtually feel, touch, and try products. The effects of the four presentation formats on consumers’ product understanding as well as the moderating role of the complexity of product understanding tasks were examined in a laboratory experiment. Two constructs used to measure product understanding performance are actual product knowledge and perceived website diagnosticity (i.e., the extent to which consumers believe a website is helpful for them to understand products). The experimental results show that (1) both videos and VPE lead to higher perceived website diagnosticity than static pictures; (2) under a moderate task complexity condition, VPE and videos lead to the same level of actual product knowledge, but all are more effective than static pictures; (3) under a high task complexity condition, all four presentation formats are equally effective in terms of actual product knowledge. Moreover, the results also indicate that it is perceived website diagnosticity, not actual product knowledge, that affects the perceived usefulness of websites, which further influences consumers’ intentions to revisit the websites.
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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 | 0.004 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".