Mobile Advertising in Distracted Environments: Exploring the Impact of Distractions on Dual-Task Interference
Bibliographic record
Abstract
It is increasingly common for consumers to engage with various tasks on their personal devices amid other distractions such as watching television at home, shopping at malls, or attending concerts. While this split in attention poses challenges, it also opens valuable opportunities for advertisers to strategically push targeted advertisements based on information about the user’s environment. Across a series of controlled lab experiments using a custom app developed for this study, we demonstrate how marketers can optimize pop-up advertising on consumers’ personal devices within distraction-filled environments. In doing so, we extend traditional insights from dual-task interference studies that have previously focused on corresponding tasks in isolation, without considering any stimuli from the environment. Our results indicate that, in the presence of additional stimuli from the environment, a facilitating relationship exists between the attention paid to a task and the effectiveness of pop-up advertisements interrupting the task. However, this relationship is moderated by the extent of attention diffusion from the environment. As the distance between the task and the environment increases, consumer attention to the task is more diffused, resulting in poorer encoding of the pop-up advertisements. Critically, optimizing the content and timing of pop-up advertisements to the environmental content can significantly improve their effectiveness. Our results have important implications for helping marketers develop actionable strategies for mobile advertising in distraction-filled environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".