Too Busy to Be Manipulated: How Multitasking with Technology Improves Deception Detection in Collaborative Teamwork
Bibliographic record
Abstract
Deception is an unfortunate staple in group work. Guarding against team members’ deceptive tactics and alternative agendas is difficult and may seem even more difficult in technology-driven business environments that have made multitasking during teamwork increasingly commonplace. This research develops a foundation for a nuanced theoretical understanding of deception detection under these conditions. The intersection of information technology multitasking and deception detection theories is shown to produce various and sometimes competing ideas about how this type of multitasking might affect truthfulness assessments in real-time teamwork. A laboratory study involving a collaborative game helped evaluate the different ideas using manipulated deception and multitasking behaviors in a real-time, virtual group environment. The results provide evidence that information multitasking can actually improve deception detection, likely because multitaskers engage less in the team conversation, making themselves less manipulable. As understanding of multitasking benefits increases, managers and designers can incorporate effective multitasking into collaborative processes.
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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".