Bibliographic record
Abstract
ABSTRACT The accelerating growth in mobile internet communications is giving rise to a new form of interactive marketing. This research identifies the factors that affect youth consumer participation in a mobile-based word-of-mouth (WOM) campaign. The study used a “real” brand promotion—a new men9s hairstyling wax launched in the adolescent market—to stimulate interest and participation. Specifically, consumers were encouraged to spread the information via WOM and participate in a hairstyle photo contest. A core attitudinal model consisted of interpersonal connectivity, self-identification with the mobile device, affective commitment to the promoted brand, attitude toward the campaign, and willingness to make referrals. The data—based on the responses from 1,705 teenagers between the ages of 13 and 18 years—fit the model well and provided empirical support for all the hypothesized relationships. The model was further analyzed in terms of latent mean structures, which revealed that face-to-face WOM elicited stronger affective brand commitment and attitude toward the campaign than mobile-based WOM. This pattern is reversed, however, in the willingness to make referrals, suggesting that mobil-based WOM may be persuasive even when adolescents are less interested in the campaign content.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.273 | 0.090 |
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".