Communicating online information via streaming video: the role of user goal
Bibliographic record
Abstract
Purpose The purpose of this paper, building on the media richness theory (MRT), is to propose that while communicating product information via streaming video should enhance outcome measures, such an enhancement will be evident mainly for users with equivocal, latent goals (i.e. recreational browsing) rather than for those with less equivocal, concrete goals (i.e. the search of a specific product). Design/methodology/approach The experiment involved 337 potential online consumers in Canada, and had full factorial design with four conditions (two methods to communicate product information: textual vs streaming video, and two goals: product searching vs recreational browsing). Analysis of covariance was used to test the hypotheses. Findings The results lent support to the hypotheses. The perceived information quality, trusting competence, and arousal for participants with recreational browsing goals were significantly affected when product information where communicated using streaming video. For participants with concrete goals (product searchers), the traditional textual method was as effective as the streaming video method. Practical implications The findings entice practitioners to use rich media such as the streaming video method to communicate online information predominantly for users with experiential browsing goals, and to use lean media for users with less equivocal, concrete goals. Originality/value The results contribute to the sparse literature that underscores the key role of user goal in shaping the effectiveness of online information. The results provide empirical support to the prediction of MRT that the use of rich media to communicate information is advantageous for users with latent, equivocal goals.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".