Where is the line between benign and invasive? An examination of psychological barriers to the acceptance of awareness monitoring systems
Bibliographic record
Abstract
Abstract As employees find themselves in geographically separated teams, the loss of face‐to‐face interaction has led to the development of new monitoring technologies that provide availability information for enhancing collaboration. Drawing on diverse literatures in electronic performance monitoring, computer supported cooperative work, privacy, and fairness, a comprehensive theoretical model of monitoring acceptance was developed to examine the effects of being monitored for availability. In the first study, over 600 employees from a large number of organizations responded to one of a variety of monitoring system characteristics. Although the model found strong support overall, results suggest that technical solutions, such as manipulating the characteristics of the awareness system, are not sufficient to ensure fairness and privacy. A second, focus group study, adds support for the theoretical model and provides an explanation for these quantitative results concerning system characteristics. Specifically, the qualitative evidence suggests that these systems can invade employees' psychological barriers ‐ and thus manipulating the technology will only have small effects on fairness and privacy because the technology has already crossed the line from benign to invasive. The paper concludes by presenting theoretical and practical implications for the consideration of psychological boundaries in the design and use of ubiquitous monitoring and communication technologies. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".