Multicommunicating: Juggling Multiple Conversations in the Workplace
Bibliographic record
Abstract
As a result of newer communication technologies and an increase in virtual communication, employees often find themselves multicommunicating, or participating in multiple conversations at the same time. This research seeks to explore multicommunicating from the perspective of the person juggling multiple conversations at the same time—the focal individual. To better understand this phenomenon, we extend previous theorizing by including the concepts of the episode initiator (whether the second conversation was focal or partner initiated), the fit of the set of media used in the episode, one process gain (conversation leveraging), and process losses. Employing a series of pilot studies and a main study, the resulting model was analyzed using structural equation modeling, finding overall support for the model. Findings suggest that experienced intensity is an important factor influencing process losses experienced during multicommunicating, whereas episode initiator influences process losses and the process gain. Further, media fit moderates the relationship between intensity and process losses. The importance of multicommunicating in the workplace is discussed, the theoretical and practical contributions of this research are described, and limitations and suggestions for future research are outlined.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".