Investigating Heterogeneous Media Multitasking Behavior: A Latent Class Analysis Approach
Bibliographic record
Abstract
This study focuses on media multitasking (MM) tendency while accounting for the heterogeneity of the store visits and purchase behaviors of media multitaskers. We employed a latent class model to identify several consumer segments and investigate the effect of behavioral traits on segment membership. Based on the results, we identified three segments and labeled them Apathetic, E-shopper, and E-buyer segments. Apathetic consumers show no interest in MM, store visits, and online purchases. The E-shopper segment records the highest MM, store visit probability, and low transaction rate, while the E-buyers exhibit the opposite behavior pattern. Furthermore, the results revealed that people who frequently use the Internet or television and watch more news programs are more likely to belong to the “E-shopper” segment. We also observed that people in the “E-buyer” segment are less probable to zap and watch variety shows more frequently. These findings are helpful for marketers to understand their customers better and devise more efficient marketing strategies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".