Relational Outcomes of Multicommunicating: Integrating Incivility and Social Exchange Perspectives
Bibliographic record
Abstract
New communication technologies, increased virtual communication, and the intense pressure for managers and employees to be continually available and “online” are giving rise to a new and emerging workplace behavior: multicommunicating (MC), or the managing of multiple conversations at the same time. Whereas researchers in psychology and management have studied the phenomenon of multitasking, few have examined multitasking where one juggles not just multiple tasks but multiple people and often multiple media at the same time. We use the spiral theory of incivility to investigate the relational outcomes of MC from the perspective of the communication partners being juggled. Our research extends this theory by further exploring the starting point of the spiral and—through the application of social exchange theory—suggesting several antecedents to incivility that are important in the context of MC. Employing a survey methodology, both qualitative and quantitative data were collected to test the theory (n = 324) and were analyzed using qualitative thematic analysis and structural equation modeling. The results suggest several factors influencing the partner's perceptions of focal individual incivility during MC, including who initiates the conversation, whether one of the conversations being juggled is useful to the other conversation, the focal individual's performance during the conversation, whether the focal individual is more accessible to the partner, and whether the partner is certain of or only suspects the existence of the other conversation. Further, partners' perceptions of these factors are influenced by their individual orientations toward MC. Finally, the partners' perceptions of the focal individual's incivility influence their interpersonal trust in the focal individual.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| 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".